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EPA Document #740R18008
December 20, 2019
Office of Chemical Safety and
Pollution Prevention

&EPA

United States
Environmental Protection Agency

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Risk Evaluation for
Trichloroethylene
CASRN: 79-01-6

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Cl

H

Cl

Cl

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TABLE OF CONTENTS

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ACKN"OWLEDGEMENTS ············•- ·••···.... ........................................
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ABBREVIATIONS ............................................
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EXECUTIVESUMMA.RY....................................................................................................................................
.2S

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Physical and ChemicalProperties...............................................................................................
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1.2 Uses and Production Volume......................................................................................................
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1.2.1 Data and InformationSources ..............................................................................................
1.2.2 Domestic Manufactureof Trichloroethylene.........................................................................
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1.3 Regulatory and AssessmentHistory ...........................................................................................
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1.4
Scope of the Evaluation...............................................................................................................
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1.4.1 Conditions of Use Included in the Risk EvaJuation...............................................................
1.4.2 ConceptualModels ................................................................................................................
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1.5
SystematicReview ......................................................................................................................
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1.5.1 Data and InformationCollection...........................................................................................
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1.5.2 Data Evaluation .....................................................................................................................
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1.5.3 Data Integration.....................................................................................................................63

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IN'TRODUCTION.............
·-·····--···-- ..................................................
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EXPOSU"RES
......................................................................................................................................
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2.1
Fate and Transport.......................................................................................................................
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2.1.1 Fate and Transport Approachand Methodology...................................................................
2.1.2 Summary of Fate and Transport............................................................................................
65
2.1.3 Assumptions and Key Sources of Uncertaintyfor Fate and Transport.................................67
2.2
EnvironmentalExposures ...........................................................................................................
67

2.2.1 EnvironmentalExposuresOverview.....................................................................................
67
2.2.2 EnvironmentalReleasesto Water...............................................................
...........................68
2.2.2.1 Results for Daily Release Estimate ................................................................................
68
2.2.2.2 Approach and Methodology...........................................................................................
69
2.2.2.2.1 Water Release Estimates.........................................................................................
69
2.2.2.2.2 Estimates ofNumber of Facilities...........................................................................
70
Estimates of Release Days ......................................................................................
72
2.2.2.3 Assumptionsand Key Sources of Uncertainty for EnvironmentalReleases ................. 72
2.2.2.3.1 Summary of Overall Confidencein Release Estimates..........................................73
2.2.3 Aquatic Exposure ModelingApproach................................................................................. 80
2.2.3.J Exposure and Fate AssessmentScreening(E-FAST)Tool 2014 Inputs ........................ 80
2.2.3.2 E-FAST 2014 Equations.................................................................................................
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2.2.3.3 E-FAST 2014 Outputs....................................................................................................
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2.2.4 Surface Water MonitoringData GatheringApproach...........................................................
2.2.4.1 Method for SystematicReview of Surface Water MonitoringData ..............................83
2.2.4.2 Method for Obtaining Surface Water Monitoring Data from WQX/WQP.................... 83
2.2.5 Geospatial Analysis Approach ..............................................................................................
85
2.2.6 EnvironmentalExposureResults...........................................................................................
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2.2.6. l Terrestrial EnvironmentalExposures............................................................................. 86
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2.2.6.2 Aquatic EnvironmentalExposures.................................................................................
2.2.2.2.3

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Predicted Surface Water Concentrations:E-FAST 2014 Modeling .......................86

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2.2.6.2.l

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2.2.6.2.2 Monitored Surface Water Concentrations...............................................................89
2.2.6.2.3 GeospatialAnalysis ComparingPredictedand Measured Surface Water
Concentrations..........................................................................................................................
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Assumptionsand Key Sources of Uncertaintyfor EnvironmentalExposures...............93
2.2.6.4 Confidencein Aquatic Exposure Scenarios...................................................................94
2.3 Human Exposures.......................................................................................................................
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2.3.1 OccupationalExposures ........................................................................................................
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2.3.1. l Results for OccupationalAssessment............................................................................
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2.3.1.2 Approachand Methodology................................................
.........................................l 04
2.3.1.2.1 General...............................................................................
................................... 104
2.2.6.3

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2.3.1.2.2 Inhalation Exposure MonitoringData.......................................................
............ 105
2.3.1.2.3 Inhalation Exposure Modeling..............................................................................
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2.3.1.2.4 Acute and Chronic Inhalation ExposureEstimates............................................... 107
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2.3.1.2.5 Dermal Exposure Modeling ..................................................................................

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2.3.1.2.6 Considerationof EngineeringControlsand Personal Protective Equipment ....... 115
2.3.1.2.7 Number of Workers and OccupationalNon-Users Exposed ................................118

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2.3.1.3 Assumptionsand Key Sources ofUncertainty for OccupationalExposures ............... 122
2.3.1.3.1 Number of Workers...............................................................................................
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2.3.1.3.2 Analysis of Exposure MonitoringData....................................................
............. 123

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2.3.1.3.3 Near-Field/Far-FieldModel Framework...............................................................123
2.3.1.3.4 Modeled Dermal Exposures....................................................
.............................. 125

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2.3.1.3.5 Summary of Overall Confidencein Inhalation Exposure Estimates.....................125
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2.3.2 Consumer Exposures ................................................................................
2.3.2.1 ConsumerConditions of Use Evaluated....................................................................... 132
2.3.2.2 ConsumerExposure Routes Evaluated.................................................................
....... 133
2.3.2.2.1 Inh.alatlon
.....................................
............................................................
...............134
2.3.2.2.2 Dermal..............................................................................................................
..... 134
2.3.2.3 Potentially Exposed or SusceptibleSubpopulations....................................................134
2.3.2.4 ConsumerExposures Approach and Methodology......................................................135
2.3.2.4.1 Modeling Approach...............................................................................................135

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2.3.2.5 Conswner Exposure Scenariosand Modeling Inputs................................................... 139
2.3.2.5.1 Consumer Exposure Model Inputs........................................................................140

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ConsumerExposure Results......................................................................................
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Characterizationof Exposure Results ...................................................................148

2.3.2.6.1

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2.3.2.6.2 Consumer Exposure Estimates..............................................................................
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Summary of ConsumerExposure Assessment......................................................173
2.3.2.7 Assumptions and Key Sources of Uncertaintyfor Conswner Exposures....................175
2.3.2.7.1 Modeling ApproachUncertainties........................................................................175

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2.3.2.7.2 Data Uncertainties.....................................................................................
............ 177

2.3.2.6.3

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2.3.2.8 Confidencein Consumer Exposure Scenarios ............................................................. 178
2.3.3 Potentially Exposed or SusceptibleSubpopulations............................................................183

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HAZARDS ......................... - ............................................................................. . ............................... 187

3.1 EnvironmentalHazards .............................................................................................................
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3.1.1 Approach and Methodology................................................................................................
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3.1.2 Hazard Identification...........................................................................................................
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3.1.3 Species Sensitivity Distributions(SSDs).............................................................................192
3. 1.4 Weight of Evidence .............................................................................................................
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3.1.5 Concentrationsof Concern ..................................................................................................
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3.1.6 Summary of EnvironmentalHazard ....................................................................................
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3.1.7 Assumptions and Key Uncertainitiesfor EnvironmentalHazard Data ...............................197
3.2 Human Health Hazards .............................................................................................................
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3.2.1 Approach and Methodology................................................................................................
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3.2.2 Toxicokinetics......................................................................................................................
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3.2.2.1 Physiologically-BasedPharmacokinetic(PBPK) Modeling Approach .......................203
3.2.3 Hazard Identification...........................................................................................................
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3 .2.3.1 Non-Cancer Hazards ....................................................................................................
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3.2.3.1. l Liver toxicity .........................................................................................................
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3.2.3.1.2 Kidney toxicity......................................................................................................
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3.2.3.1.3 Neurotoxicity.........................................................................................................
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3.2.3.1.4 Irnmunotoxicity(including sensitization).............................................................209
3.2.3.1.5 Reproductivetoxicity ............................................................................................
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3.2.3.1.6 DevelopmentalToxicity........................................................................................
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3.2.3.1.7 Overt Toxicity Following Acute/ShortTenn Exposure........................................214
3.2.3.2 Genotoxicityand Cancer Hazards ................................................................................
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3.2.3.2.l Kidney cancer..............................................................................
..........................215
3.2.3.2.2 Liver cancer...........................................................................................................
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3.2.3.2.3 Cancer of the immune system ...............................................................................
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3.2.3.2.4 Other cancers.........................................................................................................
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3.2.4 Weight of Scientific·Evidence.............................................................................................
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3.2.4.1 Non-CancerHazards ....................................................................................................
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3.2.4.1.1 Livertoxicity ........................................................................................
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3.2.4.1.2 '.Kidneytoxicity......................................................................................................
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3.2.4.1.3 Neurotoxicity.........................................................................................................
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3.2.4.1.4 Irnmunotoxicity..................................................................................................... 217
3.2.4.1.5 Reproductivetoxicity ............................................................................................
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3.2.4.1.6 DevelopmentalToxicity........................................................................................
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3.2.4. I .7 Overt Toxicity Following Acute/ShortTenn Exposure........................................221
3.2.4.2 Cancer Hazards.............................................................................................................
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3.2.4.2.1 Meta-AnalysisResults...........................................................................................
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3.2.4.2.2 Mode of Action .....................................................................................................
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3.2.4.3 Summaryof Human Health HazardsUsed to Evaluate Acute and Chronic Exposures
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3.2.5 Dose-ResponseAssessment.................................................................................................
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3.2.5.l Selectionof Studies for Dose-ResponseAssessment...................................................225
3.2.5.1.1 Liver toxicity..................................................
.......................................................226

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3.2.5.1.2 Kidney toxicity......................................................................................................
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3.2.5.1.3 Neurotoxicity...............................................................
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3.2.5.1.4 Immunotoxicity.....................................................................................................227

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3.2.5.1.5 Reproductivetoxicity .........................................................................................
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3.2.5.l.6 Developmentaltoxicity .........................................................................................
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3.2.5.1.7 Cancer....................................................................................................................
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3.2.5.2 Potentially Exposed and SusceptibleSubpopulations(PESS) .....................................229
3.2.5.3 Derivationof Points of Departure (PODs)...................................................................
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3.2.5.3.1 BMDModeling and UncertaintyFactors (UFs) ...................................................231
3.2.5.3.2 Non-CancerPODs for Acute Exposure ................................................................
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3.2.5.3.3 Non-Cancer PODs for Chronic Exposures............................................................237
3.2.5.3.4 Cancer POD for Lifetime Exposures ....................................................................
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3.2.5.4 SelectedPODs for Human Health Hazard Domains....................................................249
3.2.6 Assumptionsand Key Sources of Uncertaintyfor Human Health Haz.ard......................... 251
3.2.6.1 Haz.ardIdentificationand Weight of Evidence ............................................................251
3.2.6.2 Derivation of PODs, UFs, and PBPK Results..............................................................251
3.2.6.3 Cancer Dose Response .................................................
................................................252
3.2.6.4 Confidencein Human Health Haz.ardData Integration...............................................253

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.RISKC.HA.RACTERIZA.
TION ···-··· ..................._..........................................................................
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4.1 EnvironmentalRisk ..............................................................................
.....................................255
4. I .1 Risk Estimation Approach............................................
.......................................................256
4.1.2 Risk Estimation for Aquatic .....................................................
...........................................256
4.1.3 Risk Estimation for Sediment..........................................................
....................................271
4.1.4 Risk Estimation for Terrestrial ............................................................................
................ 271
4.2
Human Health Risk ...................................................................................................................
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4.2.1 Risk Estimation Approach...................................................................................................
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4.2. l .1 RepresentativePoints of Departurefor Use in Risk Estimation .................................. 276
4.2.2 Risk E.stimation for OccupationalExposuresby Exposure Scenario..................................276
4.2.3 Risk Estimation for Consumer Exposuresby Exposure Scenario.......................................318
4.3
Assumptions and Key Sources of Uncertaintyfor Risk Characterization................................344
4.3. l EnvironmentalRisk Characterization..................................................................
................344
4.3.2 Human Health Risk Characterization..................................................................................344
4.3.2.1 OccupationalExposure Considerations........................................................................
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4.3.2.2 Consumer/BystanderExposure Considerations...........................................................
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4.3.2.3 Dermal Absorption Considerations..............................................................................
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4.3.2.4 Confidence in Risk Estimates.......................................................................................
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4.4 Other Risk Related Considerations...........................................................................................
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4.4. l Potentially Exposed or Susceptible Populations .................................................................. 348
4.4.2 Aggregate and Sentinel Exposures ........................................... ................................. .......... 348
4.5
Risk Conclusions ....................................................................................................................... 350
4.5.1 Environmental Risk Conclusions ........................................................................................ 350
4.5.1 Human Health Risk Conclusions ......................................................................................... 353
4.5.1.1 Summary of Risk Estimates for Workers and ONUs .................. ................................. 353
4.5 .1.2 Summary of Risk Estimates for Consumers and Bystanders ....................................... 367

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RISK DETERM."INA
TION............ ..................................................................................
- .............371
Unreasonable Risk.....................................................................................................................
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5.1.1 Overview ...................................................... ...................................................................... .. 371
5.1.l RiskstoHumanHealth ........................................................................................................ 372
5. l . l .l Determining Non-Cancer Risks ................................................................................... 372
5.1.1.2 DeterminingCancerRisks .................................................. .......................................... 373
5 .1.2 Determining Environmental Risk .............................................................................. .......... 373
5.2 Risk Determinations for TCE ............................................................................ ........................ 374
5.3
Detailed Risk Determinations by Condition ofUse ................. .............................................. ....379
5.3.l Manufacture - Domestic manufacture .............................................. ................................... 379
5.3.2 Manufacture-Import {includes repackaging and loading/unloading) ................................ 381
5 .3.3 Processing - Processing as a reactant/intennediate in industrial gas manufacturing {e.g.,,
manufacture of fluorinated gases used as refrigerants, foam blowing agents and solvents)382
5.3.4 Processing- Incorporation into formulation, mixture or reaction product- Solvents (for
cleaning or degreasing); adhesives and sealant chemicals; solvents (which become part of
product formulation or mixture) (e.g., lubricants and greases, paints and coatings, other
uses) ........................................................................................................... .......................... 383
5.3.5 Processing- Incorporation into articles - Solvents (becomes an integral components of
articles) ............................................................................................... ................................. 384
5.3.6 Processing-Repackaging- Solvents {for cleaning or degreasing) .................................... 385
5.3.7 Processing - Recycling ................................................................................................. ....... 386
5.3.8 Distribution in Commerce ................................................................................................... 387
5.3.9 Industrial/Commercial Use- Solvents {for cleaning or degreasing)- Batch vapor degreaser
{open-top) ........................................................... ................................................................. 388
5.3.10 Industrial/Commercial Use- Solvents {for cleaning or degreasing)-Batch vapor degreaser
(closed-loop) .................................................................................................. ...................... 389
5.3. 11 Industrial/Commercial Use- Solvents {for cleaning or degreasing) - In-line vapor degreaser
(conveyorized) ..................................................................................................................... 390
5.3.12 Industrial/Commercial Use- Solvents (for cleaning or degreasing)- In-line vapor degreaser
{web cleaner) ....................................................................................................................... 392
5.3.13 Industrial/Commercial Use- Solvents (for cleaning or degreasing)- Cold cleaner .......... 393
5.3.14 Industrial/Commercial Use - Solvents (for cleaning or degreasing)- Aerosol spray
degreaser/cleaner; mold release ............................................................................... ............ 394
5.3.15 Industrial/Commercial Use-Lubricants and greases/lubricants and lubricant additives -Tap
and die fluid ......................................................................................................................... 396
5.3.16 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant additives Penetrating lubricant............................................................................................................
397
5.3.17 Industrial/Commercial Use - Adhesives and sealants - Solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant ....................................................... 398
5.3.18 Industrial/Commercial Use-Functional fluids {closed systems)-Heat exchange fluid ... 400

5.1

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5.3.19 Industrial/Commercial Use-Paints and coatings-Diluent in solvent-based paints and
coatings................................................................................................................................401
5.3.20 Industrial/Commercial Use - Cleaning and furniture care products - Carpet cleaner; wipe

cleani.ng
..................................................................................
~ ....................
.............................402
5.3.21 Industrial/Commercial Use - Laundry and dishwashing products - Spot remover ............ 404
5.3.22 Industrial/Commercial Use-Arts, crafts and hobby materials- Fixatives and finishing
spray coatings ...................................................................................................................... 405
5.3.23 Industrial/Commercial Use-Corrosion inhibitors and anti-scaling agents -Corrosion
inhibitors and anti-scaling agents ........................................................................................ 406
5.3.24 Industrial/Commercial Use - Processing aids - Process solvent used in battery manufacture;
process solvent used in polymer fiber spinning, fluoroelastomer manufacture, and Alcantara
manufacture;-extraction solvent used in caprolactam manufacture; precipitant used in betacyclodextrin manufacture .................................................................................................... 408
5.3.25 Industrial/Commercial Use- Ink, toner, and colorant products - Toner aid ...................... 409
5.3.26 Industrial/Commercial Use - Automotive care products- Brake and parts cleaners ......... 410
5.3.27 Industrial/Commercial Use-Apparel and footwear care products- Shoe polish .............. 412
5.3.28 Industrial/Commercial Use - Hoof polishes; gun scrubber; lace wig and hair extension
glues; pepper spray; other miscellaneous industrial and commercial uses ......................... 413
5.3.29 Disposal ............................................................................................................................... 415
5.3.30 Consumer Use - Solvents (for cleaning or degreasing) - Brake and parts cleaner ............. 416
5.3.31 Conswner Use - Solvents (for cleaning or degreasing)-Aerosol electronic
degreaser/cleaner ................................................................................................................. 417
5.3.32 Conswner Use - Solvents (for cleaning or degreasing)- Liquid electronic degreaser/cleaner
..............................................
.......................
.............................................................................
418
5.3.33 Consumer Use- Solvents (for cleaning or degreasing)-Aerosol spray degreaser/cleaner418
5.3.34 Consumer Use - Solvents (for cleaning or degreasing)-Liquid degreaser/cleaner ........... 419
5.3.35 Conswner Use - Solvents (for cleaning or degreasing)-Aerosol gun scrubber ................ 420
5.3.36 Conswner Use - Solvents (for cleaning or degreasing)- Liquid gun scrubber .................. 421
5.3.37 Consumer Use - Solvents (for cleaning or degreasing)- Mold release .............................. 422
5.3.38 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire cleaner ................... 423
5.3.39 Consumer Use - Solvents (for cleaning or degreasing)- Liquid tire cleaner ..................... 423
5.3.40 Consumer Use- Lubricants and greases- Tap and die fluid.............................................. 424
5.3.41 Consumer Use- Lubricants and greases -Penetrating lubricant........................................ 425
5.3.42 Consumer Use - Adhesives and sealants - Solvent-based adhesive and sealant ................ 426
5.3.43 Consumer Use - Adhesives and sealants - Mirror edge sealant ......................................... 427
5.3.44 Consumer Use-Adhesives and sealants -Tire repair cement/sealer................................. 428
5.3.45 Consumer Use- Cleaning and furniture care products-Carpet cleaner ............................ 428
5.3.46 Consumer Use- Cleaning and furniture care products-Aerosol spot remover ................ 429
5.3.47 Consumer Use- Cleaning and furniture care products -Liquid spot remover .................. 430
5.3.48 Consumer Use-Arts, crafts, and hobby materials- Fixatives and finishing spray coatings
............................................................................................................................................. 431
5.3.49 Consumer Use- Apparel and footwear care products - Shoe polish .................................. 432
5.3.50 Consumer Use - Other consumer uses- Fabric spray ........................................................ 433
5.3.51 Consumer Use- Other consumer uses- Film cleaner ........................................................ 433
5.3.52 Consumer Use-Othe r consumeruses-Hoofpolish ......................................................... 434
5.3.53 Conswner Use- Other consumer uses- Pepper spray ....................................................... 435
5.3.54 Consumer Use-Other consumer uses- Toner aid...............................................
.............. 436
5.3.55 Consumer Use - Other consumer uses - Lace wig and hair extension glues ...................... 436
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REFERENCES ............................................................ ................. - ......................................................... 438

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APPE.NDICES .................................................... .................................................................................... 459

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Appendix A REG"'ULATORY
HISTORY··-··················..................................................
. .................. 459

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A. I
A.2
A.3

Federal Laws and Regulations ...................................................................... ...................... ..... .459
State Laws and Regulations ......................................................................................................465
International Laws and Regulation.~....................... ........................... ............ ............................ 466

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Appendix B LIST OF SUPPLEMENTAL DOCUMENTS ............................................................ 468

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Ap·pendix C ENvm.ONMENT AL "EXPOSURES........................................................................... 470

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Appendix D CONSUMER E.xi>OSURES........................................................................................ 512

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D. I Model Sensitivity .......................................................... ............................................... ............. 5 I 2
D.1.1 Continuous Variables ................ ........................................ .................................................... 512
D.1 .2 Categorical V ariables ................. ........................................................................ ................... 515
D.2 Monitoring Data .................... ................................................................................... - ......••. SI 5
D.2.1 Indoor Air Monitoring .............................................................. ...................................... ......515
D.2.2 Personal breathing Zone Monitoring Data ....................................
. ......................................517

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Appendix. E ENVIRONMENT AL HAZARDS ............................................................................... . 519

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....

E. l

Species Sensitivity Distribution (SSD} Methodology .................................................... :.......... 519
E.2
Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as
Modeled in E-FAST ............................ ....................... .................... ........................ .............. ................ 526
Appendix F

BENCHMARK DOSE ANALYSIS FOR (Selgrade and Gilmour, 2010) ................ 586

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F.1
BMDS Wizard Output Report - Monality .................. ........................................................ ...... 586
F .1.1 BMDS Summary of Mortality - BMR 10% ......................................................................... 586
F .1.2 BMDS Summary of Mortality - BMR: 5% .............. ............................ .......... ............. ......... 589
F.1.3 BMDS Summary ofMortality - BMR: 1%..................... ............................... ................. ..... 591
F.2 BMDS Wizard Output Report - Numberof Mice Infected.......................................................594
F .2.1 BMDS Summary of Infected at 72 hours - BMR - 10% ......................................... ........... .594

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Appendix G WEIGHT OF EVIDENCE FOR CONGENITAL HEART DEFECTS .................. 596

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G.1 EPA Review of the Charles River (2019) Study .................................................................... ... 596
G.1.1 Study Methodology and Re sults ............................. ........................ ............................... .......596
G.1.2 EPA Review ...................................................................................... ............ ........................ 597
G .1.2.1 Comparing Results Between Charles River and Johnson Studies ..................... ............ 597
G.1.2.2 Differences in Types of Malformations Observed ....................................... .................599
G.1.2.3 Methodology Differences .............................................................................................. 603
G.1.2 .4 Adversity of Small VSDs ..................................................... ............................. ............ 605
G.2 WOE Analy sis for Congenital Cardiac Defects ....................................................... ................. 606
G.2.1 Methodology ............................
....................................
....................
...............
'!'•
•····..·····.........
.......
....................
606
G.2.2 WOE Results By Study Type ··········-············· ...................................................................... 610

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Appendix H MET A-ANALYSIS FOR CANCER ........................................................................... 618

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314
315
316

H. l Study Screening and Selection .................................................................................................. 618
HJ .1 Data Quality and Inclusion/Exclusion Criteria Screening .................................................... 618
H.1 .2 Screening results ............... .......................................................... .......................................... 619
H.1.3 Pooled Cohorts ...................................................................................................... ................ 620
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H.2 Meta-Analysis Methods and Results .................................................................... ..................... 621
H.2.1 Methods ................................................................................................................................ 621
H.2.2 Results ................................................................................................................................... 623
H.2.2.1 Initial Meta-Analyses .................................................................................................... 623
H.2.2.2 Sensitivity analyses ....................................................................................................... 629
H.2.3 Selected RR estimates and confidence intervals by study and cancer type .......................... 637
H.2.4 Sample Stata commands for meta-analysis .......................................................................... 643

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Appendix I APPROACH FOR ESTIMATING WATER RELEASES FROM
MANUFACTURING SITES USING EFFLUENT GUIDELINES ................................................. 644

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Appendix J SAMPLE CALCULATIONS FOR CALCULATING ACUTE AND CHRONIC
(NON-CANCER AND CANCER) INHALATION EXPOSURES ................................................... 648

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329

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333
334

J.l
J.2

Example High-EndAC, ADC, and LADC ...............................................................................
648
Example Central Tendency ABC, ADC, and LADC ................................................................ 649

Appendix K VAPOR DEGREASING AND COLD CLEANING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODELS APPROACH AND PARAMETERS...............................650

K. 1
K.2

Model Design Equations ...........................................................................................................
65l
Model Parameters......................................................................................................................
655
K.2.1 Far-Field Volume ................................................................................... ..................... ..........660

335

K.2.2 Air Excha:rlge
Rate.........................................................................................................................
66-0

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338

K.2.3 Near-Field Indoor Air Speed ................................................................................................ 660
K.2.4 Near-Field Volume ...............................................................................................................661
K.2.5 Exposure Duration ................................................................................................................ 661

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K.2.6 Averaging Time ............................................................
........................................................661
K.2.7 Vapor Generation Rate ..................................................................................................
.......661
664
K.2.8 Operating Hours....................................................................................................................

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Appendix L BRAKE SERVICING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
MODEL APPROACH AND PARAMETERS ..................................................................................... . 666

340

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L.1 Model Design Equations ........................................................................................................... 666
L.2 Model Parameters ................................................................................................................... ...67I
L.2.1 Far-Field Volum.e..................................................................................................................
674
L.2.2 ·Air Exchange Rate .................................................... ............................................................ 674
L.2.3 Near-Field Indoor Air Speed ................................................................................................674
L.2.4 Near-Field Volume ............................................................................................................... 675
L.2.5 Application Time .................................................................................................................. 675
L.2.6 Averaging Time .................................................................................................................... 675
L.2. 7 Trichloroethylene Weight Fraction.......................................................................................
675
L.2.8 Volume of Degreaser Used per Brake Job ........................................................................... 676
L.2.9 Number of Applications per Brake Job ................................................................................ 676
L.2.10 Amount ofTrichloroethylene Used per Application ............................................................ 677
L.2.11 Operating Hours per Week ................................................................................................... 677
L.2.12 Number of Brake Jobs per Work Shift ................................................................................. 677

358

Appendix M SPOT CLEANING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE

359

MODEL APPROACHAND PAR.A.METERS
...................................................................................678

360

M.1

Model Design Equations ........................................................................................................ ...678

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M.2 Model Parameters ............ ........................................................................................................ ..682
M.2.1 Far-FieldVolume .................................................................................................................. 686
M.2.2 Near-Field Volume ............................................................................................................... 686
M.2.3 Air Exchange Rate ...................................................... .......................................................... 686
M.2.4 Near-Field Indoor Wind Speed ............................................................................................. 686
M.2.5 Averaging Time ............................................ ........................................... ............. ................ 687
M.2.6 Use Rate ................ ............ ......................................................................................... ...........687
M.2.7 Vapor Generation Rate ......................................................................................................... 687
M.2.8 Operating Hours .................................................................................................................... 687
M.2.9 Operating Days ........... .......................................................................................................... 688
M.2.lOFractional Number of Operating Days that a Worker Works ............................................... 688

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Appendix N BENCHMARK DOSE MODELINGUPDATE FOR NESTED FETAL DATA
FROM (Johnson et al., 2003) ...............................................................................................................
690

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LIST OF TABLES
Table 1-1 Physical and Chemical Properties ofTCE ............................................................................... 37

Table 1-2 Production Volume ofTCE in CDR Reporting Period (2012 to 2015) a .................................
38
Table 1-3. Assessment History ofTCE ....................................................................................................41
Table 1-4. Categories and Subcategories of Occupational Conditions of Use and Corresponding
Occupational Exposure Scenario...................................................................................... 43
Table 1-5. Categories and Subcategories of Consumer Conditions of Use .............................................. 49
Table 2-1 Environmental Fate Characteristic ofTCE .............................................................................. 64
Table 2-2: Summary ofEPA's daily water release estimates for each OES and also EPA's Overall
Confidence in these estimates. ......................................................................................... 69
Table 2-3: Summary ofEPA's estimates for the number of facilities for each OES ... ........................... 71
Table 2-4: Summary of EPA's estimates for release days expected for each OES ................................. 72
Table 2-5: Summary of Overall Confidencein Release Estimates by OES............................................ 73
Table 2-6 Industry Sector Modeled for Facilities without Site-Specific Flow Data in E-FAST 2014 ..... 81
Table 2-7. Summary of Swface Water Concentrations by OES for Maximum Days of Release Scenario
...................................................................................
........................................................ 86
Table 2-8. Summary of Surface Water Concentrations by OES for 20 Days ofRe]ease Scenario .......... 87
Table 2-9. Summary ofSwface Water Concentrations by OES for 20 Days of Release Scenario for
Indirect Releases to a non-POTW WWTP ....................................................................... 87
Table 2-10. Measured Concentrations ofT.CE in Surface Water Obtained from the Water Quality Portal:
2013-20171........

... .... ....................

........ ..... ..... ........... .... ... ..... ..... .......... ................

......... ....

89

Table 2-11. Ambient Levels ofTCE in U.S. Surface Water from Published Literature .......................... 91
Table 2-12: A summary for each of the 18 occupational exposure scenarios (OESs). ........................... 99
Table 2-13: Summary of inhalation exposure results for Workers based on monitoring data and
exposure modeling for each OES................................................................................... 100
Table 2-14: Summary of inhalation exposure results for ONUs based on monitoring data and exposure
modeling for each OES................................................................................................... 101
Table 2-15: A summary of dermal retained dose for Workers based on exposure modeling for each OES
............................................
.....................................
.·····················.................................. 102
Table 2-16: Summary of the iotal number of workers and ONUs potentially exposed to TCE for each
OES ................................................................................................................................. 103
Table 2-17: Parameter Values for Calculating Inhalation Exposure Estimates..................................... 108
Table 2-18: Overview of Average Worker Tenure from U.S. Census SIPP (Age Group So+)............. 110
Table 2-19: Median Year of Tenure with Current Employer by Age Group...... .................................. 111
Table 2-20: Glove Protection Factors for Different Denn.alProtection Strategies. .............................. 113
Table 2-21: EPA grouped dermal exposures associated with the various OESs into four bins ............ 114
Table 2-22: Assigned Protection Factors for Respirators in OSHA Standard 29 CFR § 1910.134. ..... 116
Table 2-23. Number and Percent of Establishments and Employees Using Respirators Within 12 Months
Prior to Survey ....................................................................................... ......................... 117
Table 2-24: SOCs with Worker and ONU Designations for All Conditions of Use Except.. ............... 119
Table 2-25: SOCs with Worker and ONU Designations for Dry Cleaning Facilities ........................... 119
Table 2-26: Estimated Number of Potentially Exposed Workers and ONUs underNAICS 812320.... 120
Table 2-27: Summary of overall confidence in inhalation exposure estimates by OES. ...................... 125
Table 2-28. Evaluated Consumer Conditions of Use and Products for TCE.......................................... 132
Table 2-29. Default Modeling Input Parameters .................................................................................... 140
Table 2-30. Consumer Product Modeling Scenarios and Varied Input Parameters............................... 142
Table 2-31. Consumer Product Modeling Scenarios and Additional Scenario-SpecificInput Parameters
.........................................................................................................·······················......... 146
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Table 2-32. Acute Inhalation Exposure Summary: Brake & Parts Cleaner ........................................... 149
Table 2-33. Acute Dermal Exposure Summary: Brake & Parts Cleaner ................................................ 150
Table 2-34. Acute Inhalation Exposure Summary: Aerosol Electronic Degreaser/Cleaner ................... 151
Table 2-35. Acute Inhalation Exposure Summary: Liquid Electronic Degreaser/Cleaner ..................... 151
Table 2-36. Acute Dermal Exposure Summary: Liquid Electronic Degreaser/Cleaner ......................... 152
Table 2-37. Acute Inhalation Exposure Summary: Aerosol Spray Degreaser/Cleaner .......................... 152
Table 2-38. Acute Dermal Exposure Summary: Aerosol Spray Degreaser/Cleaner .............................. 153
Table 2-39. Acute Inhalation Exposure Summary: Liquid Degreaser/Cleaner ...................................... 154
Table 2-40. Acute Dermal Exposure Summary: Liquid Degreaser/Cleaner .......................................... 154
Table 2-41. Acute Inhalation Exposure Summary: Aerosol Gun Scrubber ............................................ 155
Table 2-42. Acute Dermal Exposure Summary: Aerosol Gun Scrubber ................................................ 155
Table 2-43. Acute Inhalation Exposure Summary: Liquid Gun Scrubber .............................................. 156
Table 2-44. Acute Dermal Exposure Summary: Liquid Gun Scrubber .................................................. 157
Table 2-45. Acute Inhalation Exposure Summary: Mold Release ......................................................... 157
Table 2-46. Acute Inhalation Exposure Summary: Aerosol Tire Cleaner ......................................... ..... 158
Table 2-47. Acute Dermal Exposure Summary: Aerosol Tire Cleaner .................................................. 159
Table 2-48. Acute Inhalation Exposure Summary: Liquid Tire Cleaner ................................................ 159
Table 2-49. Acute Dermal Exposure Summary: Liquid Tire Cleaner .................................................... 160
Table 2-50. Acute Inhalation Exposure Summary: Tap & Die Fluid ..................................................... 160
Table 2-51. Acute Inhalation Exposure Summary: Penetrating Lubricant ................................. ............ 161
Table 2-52. Acute Inhalation Exposure Summary: Solvent-based Adhesive & Sealant ..................... ... 162
Table 2-53. Acute Inhalation Exposure Summary: Mirror-Edge Sealant. .............................................. 162
Table 2-54. Acute Inhalation Exposure Summary: Tire Repair cement/Sealer ...................................... 163
Table 2-55. Acute Inhalation Exposure Summary: Carpet Cleaner ....................................................... 164
Table 2-56. Acute Dermal Exposure Summary: Carpet Cleaner ........................................................... . 164
Table 2-57. Acute Inhalation Exposure Summary: Aerosol Spot Remover ........................................... 165
Table 2-58. Acute Dermal Exposure Summary: Aerosol Spot Remover ............................................... 165
Table 2-59. Acute Inhalation Exposure Summary: Liquid Spot Remover ............................................. 166
Table 2-60. Acute Dermal Exposure Summary: Liquid Spot Remover ................................................. 167
Table 2-61. Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings ................... 167
Table 2-62. Acute Inhalation Exposure Summary: Shoe Polish ............................................................. 168
Table 2-63. Acute Dermal Exposure Summary: Shoe Polish ............ .............................................. ....... 169
Table 2-64. Acute Inhalation Exposure Summary: Fabric Spray ........................................................... 169
Table 2-65. Acute Inhalation Exposure Summary: Film Cleaner ......................................... .................. 170
Table 2-66. Acute Inhalation Exposure Summary: Hoof Polish ............................................................ 171
Table 2-67. Acute Inhalation Exposure Summary: Pepper Spray .......................................................... 171
Table 2-68. Acute Inhalation Exposure Summary: Toner Aide ............................................................. 172
Table 2-69. Acute Inhalation Exposure Summary: Lace Wig and Hair Extension Glues, as Predicted
with Solvent-Based Adhesive & Sealant Scenario ......................................................... 173
Table 2-70. Evaluated Pathways for Consumer Conditions of Use ........................................................ 173
Table 2-71. Summary of Consumer Exposure Levels by Category ....................................................... 174
Table 2-72. Confidence Ratings for Acute Inhalation Consumer Exposure Modeling Scenarios ......... 180
Table 2-73. Confidence Ratings for Acute Dermal Consumer Exposure Modeling Scenarios .............. 182
Table 2-74. Percentage of Employed Persons by Age, Sex, and Industry Sector ............. ..................... 185
Table 2-75. Percentage of Employed Adolescent by Detailed Industry Sector ...................................... 186
Table 3-1 Ecological Haz.ard Charactemation ofTCE for Aquatic Organisms ...................... .............. 190
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity ........................................... 197
Table 3-3 TCE Metabolites Identified by Pathway ..................................... ........................................... 202
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Table 3-4 Common Metabolites ofTCE and Related Compounds........................................................202
Table 3-5 List of All of the PBPK-ModeledDose Metrics Used in the TCE IRIS Assessment ............ 204
Tal;,le3-6. Overall Summary Scores by Line of Evidence for Cardiac Defects from TCE .................... 220
Table 3-7: Dose-response analysis of selected studies considered for acute exposure scenarios .......... 236
Table 3-8: Dose-response analysis of selected studies considered for evaluation of liver toxicity ........ 238
Table 3-9: Dose-response analysis of selected studies considered for evaluation of kidney toxicity .... 239
Table 3-10: Dose-response analysis of selected studies considered for evaluation of neurological effects

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·························
································································································
················241
Table 3-11: Dose-response analysis of selected studies considered for evaluation of immune effects . 243
Table 3-12: Dose-response analysis of selected studies considered for evaluation of reproductive effects
......................................................................................................................................... 245
Table 3-13: Dose-response analysis of selected studies considered for acute exposure scenarios ........ 249
Table 3-14: Dose-response analysis of selected studies considered for chronic exposure scenarios ..... 250
Table 3-15: Cancer Points of Departure for Lifetime Exposure Scenarios ............................................ 251
Table 4-1. Environmental Risk Quotients for Facilities Releasing TCE to Surface Water as Modeled in
E-FAST (RQs ~ 1 in bold)..............................................................................................260
Table 4-2. RQs Calculated using Monitored EnvironmentalConcentrationsfrom Water Quality Portal

488

................................................................ ································· ········· ··················· ............ 264

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Table 4-3. RQs Calculated using Monitored EnvironmentalConcentrationsfrom Published Literatme
.........................................................................................................................................
265
Table 4-4. Use Scenarios, Populations of Interest and ToxicologicalEndpoints Used for Acute and
Chronic Exposures.......................................................................................................... 273
Table 4-5: Most Sensitive Endpoints from Each Health Domain for Risk Estimation........................... 276
Table 4-6. Occupation,alRisk Estimation - Manufacturing.;................. ........................................ ......... 278
Table 4-7. Occupational Risk Estimation - Processing as a Reactant .................................................... 280
Table 4-8. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Monitoring
Data .................................................................................................................................282
Table 4-9. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Modeling
Data ................................................................................................................................. 283
Table 4-10. Occupational Risk Estimation-Batch Closed-Loop Vapor Degreasing ............................ 285
Table 4-11. Occupational Risk Estimation - ConveyorizedVapor Degreasing - Inhalation Monitoring
Data ................................................................................................................................. 287
Table 4-12. Occupational Risk Estimation- ConveyorizedVapor Degreasing- Inhalation Modeling
Data................................................................................................................................. 288
Table 4-13. Occupational Risk Estimation - Web Vapor Degreasing.................................................... 290
Table 4-14. Occupational Risk Estimation - Cold Cleaning...................................................................292
Table 4-15. Occupational Risk Estimation - Aerosol Applications........................................................294
Table 4-16. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Monitoring Data ................................................. ............................... 296
Table 4-17. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Modeling Data................................................................................... 297
Table 4-18. Occupational Risk Estimation-Formulation of Aerosol and Non-Aerosol Products ........ 299
Table 4-19. Occupational Risk Estimation- Repackaging..................................................................... 301
Table 4-20. Occupational Risk Estimation- MetalworkingFluids- InhalationMonitoring Data ......... 303
Table 4-21. Occupational Risk Estimation - MetalworkingFluids - InhalationModeling Data ........... 304
Table 4-22. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Industrial
Setting)............................................................................................................................ 306
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Table 4-23. Occupational Risk Estimation -Adhesives, Sealants, Paints, and Coatings (Commercial
Setting) ............................................................................................................................ 308
Table 4-24. Occupational Risk Estimation - Industrial Processing Aid (12 hr) .................................. ... 310
Table 4-25. Occupational Risk Estimation - Commercial Printing and Copying................................... 312
Table 4-26. Occupational Risk Estimation - Other Industrial Uses ........................................................ 314
Table 4-27. Occupational Risk Estimation - Process Solvent Recycling and Worker Handling of Wastes
......................................................................................................................................... 316
Table 4-28. Consumer Risk Estimation - Solvents for Cleaning and Degreasing- Brake and Parts
Cleaner ............................................................................................................................ 319
Table 4-29. Consumer Risk Estimation - Solvents for Cleaning and Degreasing- Aerosol Electronic
Degreaser/Cleaner ........................................................................................................... 320
Table 4-30. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
Degreaser/Cleaner ........................................................................................................... 321
Table 4-31. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
Degreaser/Cleaner ........................................................................................................... 322
Table 4-32. Consumer Risk Estimation- Solvents for Cleaning and Degreasing- Liquid
Degreaser/Cleaner ........................................................................................................... 323
Table 4-33. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Gun Scrubber
......................................................................................................................................... 324
Table 4-34. Consumer Risk Estimation - Solvents for Cleaning and Degreasing- Liquid Gun Scrubber
......................................................................................................................................... 325
Table 4-35. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Mold Release ........ 326
Table 4-36. Consumer Risk Estimation- Solvents for Cleaning and Degreasing- Aerosol Tire Cleaner
......................................................................................................................................... 327
Table 4-37. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Tire Cleaner328
Table 4-38. Consumer Risk Estimation - Lubricants and Greases - Tap and Die Fluid ......................... 329
Table 4-39. Consumer Risk Estimation - Lubricants and Greases - Penetrating Lubricant ................... 330
Table 4-40. Consumer Risk Estimation - Adhesives and Sealants - Solvent-Based Adhesive and Sealant
......................................................................................................................................... 331
Table 4-41. Consumer Risk Estimation-Adhesives and Sealants -Mirror Edge Sealant ..................... 332
Table 4-42. Consumer Risk Estimation -Adhesives and Sealants - Tire Repair Cement/ Sealer ......... 333
Table 4-43. Consumer Risk Estimation - Cleaning and Furniture Care Products - Carpet Cleaner ....... 334
Table 4-44. Consumer Risk Estimation - Cleaning and Furniture Care Products - Aerosol Spot Remover
......................................................................................................................................... 335
Table 4-45. Consumer Risk Estimation - Cleaning and Furniture Care Products - Liquid Spot Remover
......................................................................................................................................... 336
Table 4-46. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and Finishing
Spray Coatings ................................................................................................................ 337
Table 4-47. Consumer Risk Estimation-Apparel and Footwear Care Products- Shoe Polish ............. 338
Table 4-48. Consumer Risk Estimation - Other Consumer Uses - Fabric Spray ................................... 339
Table 4-49. Consumer Risk Estimation - Other Consumer Uses - Film Cleaner ................................... 340
Table 4-50. Consumer Risk Estimation- Other Consumer Uses - Hoof Polish ..................................... 341
Table 4-51. Consumer Risk Estimation - Other Consumer Uses - Pepper Spray................................... 342
Table 4-52. Consumer Risk Estimation- Other Consumer Uses - Toner Aid ....................................... 342
Table 4-53. Consumer Risk Estimation - Other Consumer Uses - Lace Wig and Hair Extension Glues
......................................................................................................................................... 343
Table 4-54. Facilities with Acute or Chronic Risk Identified for Aquatic Organisms (RQs ~ 1 in bold)
.........................................................................................................................................
352
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Table 4-55. Occupational Risk Summary Table ............................. .................. ................... ............. ...... 355
Table 4-56. Conswner Risk Sununary Table .......................................................................................... 367
Table 5-1. Summary of Unreasonable Risk Determinations by Condition of Use ................................. 376

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LIST OF FIGURES

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Figure 1-1. TCE Life Cycle Diagra.tn .......................... .................... .................. .... ...........................•....... 51
Figure 1-2. TCE Conceptual Model for Industrial and Commercial Activities and Uses: Potential
Exposures and Hazards .................................................. ................................................... 53
Figure 1-3. TCE Conceptual Model for Conswner Activities and Uses: Potential Exposures and Hazards
......................................................................................................................................................
54
Figure 1-4. TCE Conceptual Model for Environmental Releases and Wastes: Potential Exposures and
Hazards .......... .... .. .. ........ .......... .. .......... .................................... .... .. ..... ..•....... .. ....... .. .. ....... 55
Figure 1-5. Literature Flow Diagram for Environmental Fate and Transport .......................................... 58
Figure 1-6. Literature Flow Diagram for Engineering Releases and Occupational Exposure ................. 59
Figure 1-7. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources ........... 60
Figure 1-8. Literature Flow Diagram for Environmental Hazard.............................................................61
Figure 1-9. Literature Flow Diagram for Human Health Hazard ............................................................. 62
Figure 2-1: An overview of how EPA estimated daily water releases for each OES .............................. 68
Figure 2-2. WQP Search Option. Surface water data were obtained from the WQP by querying the
Sampling Parameters search option for the characteristic(STORET data), Parameter
Code (NWIS data), and date range parameter.................................................................. 84
Figure 2-3 . Distribution of Active Facility Releases Modeled ...................... ......................... ............ ...... 88
Figure 2-4. Modeled Release Characteristics (Percent Occurrence) ........................................................ 89
Figure 2-5. Temporal WQX Sampling and Surface Water Concentration Trends: 2013 - 2017 ............ . 90
Figure 2-6: Components of an occupational assessment for each OBS; please refer to Section 2.2.2.2.2
for additional details on the approach and methodology for estimating number of
facilities ..... ...... .................................................................... ................................ .............. 96
Figure 2-7: Illustrative applications of the NF/FF model to various exposure scenarios ...................... 106
Figure 3-1. Species Sensitivity Distribution (SSD) for Algae Species Using ECsos (Etterson, 2019) ... 192
Figure 3-2. Species Sensitivity Distributions (SSDs) for Acute Hazard Data Using LCsos or ECsos
(Etterson, 2019) ............................. ......................................... ............. ............................ 194
Figure 3-3. EPA Approach to Hazard Identification, Data Integration, and Dose-Response Analysis for
TCE ............. .............. ..................... ............................................... ................ .................. 199
Figure 3-4 Dose-Response Analyses of Rodent Non-Cancer Effects Using the Rodent and Human PBPK
Models .............................................................................................................................
205
Figure 3-5 Example ofHEC99 Estimation through Interpecies, lntraspecies and Route-to - Route
Extrapolation from a Rodent Study LOAEL/NOAEL. ................................................... 206
Figure 4-1. Concentrations ofTrichloroethylene from Releasing Facilities (Higher Release Frequency
Scenarios) and WQX Monitoring Stations: Year 2016, East US ................................... 266
Figure 4-2. Concentrations ofTrichloroethylene from Releasing Facilities (Higher Release Frequency
Scenarios) and WQX Monitoring Stations: Year 2016, West US .......................... ........ 267
Figure 4-3. Concentrations ofTrichloroethylene from Releasing Facilities (20 Days of Release Scenario)
and WQX Monitoring Stations: Year 2016, East US ............. ........................................ 268
Figure 4-4. Concentrations ofTrichloroethylene Releasing Facilities (20 Days of Release Scenario) and
WQX Monitoring Stations: Year 2016, West US .................... ............. .......................... 269
Figure 4-5. Co-location ofTrichloroethylene-Releasing Facilities and WQX Monitoring Stations at the
HUC 8 Level in NC ............. ................... .......................... ................... ..... ............. ......... 270

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Figure 4-6. Co-location ofTrichloroethylene-Releasing Facilities and WQX Monitoring Stations at the
271
HUC 8 Level in NM .......................................................................................................

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LIST OF APPENDIX TABLES

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629
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659

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Table_Apx
Table_Apx
Table_Apx
Table_Apx
Table_Apx
Table_ Apx

A-1. Federal Laws and Regulations ..................................................................................... 459
A-2. State Laws and Regulations ......................................................................................... 465
A-3. Regulatory Actions by Other Governments and Tribes .............................................. 466
C-1. Facility-Specific Aquatic Exposure Modeling Results ................................................ 470
D-1. TCE Residential Indoor Air Concentrations (µg/m 3) in the United States and Canada
............................................................................................................................ ............. 516
D-2. Personal Breathing Zone Concentrations (µg/m3) for TCE in the United States
(General/R.esidential) ...................................................................................................... 518
E-1. Standard Error for all dsitributions and fitting methods from the SSD Toolbox using
TCE's algae hazard data (Etterson. 2019) ...................................................................... 520
E-2. Standard Error for all distributions and fitting methods from the SSD Toolbox using
TCE's acute hazard data (Etterson, 2019) ...................................................................... 523
E-3. Environmental RQs by Facility (with RQs 2: 1 in bold) .............................................. 526
F-1. Summary of BMD Modeling Results for Martality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra

Risk .................................................................................................................................
586
Table_Apx F-2. Summary ofBMD Modeling Results for Mortality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra

Risk.................................................................................................................................
589
Table_Apx F-3. Summary ofBMD Modeling Results for Mortality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 201 O);BMR = 1% Extra
Risk.................................................................................................................................
591
Table_Apx F-4. Summary ofBMD Modeling Results for Number of Mice Infected at 72 Hours after
Infection Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR =
10% Extra Risk ............................................................................................................... 594
Table_Apx F-5. Plot of Incidence by Dose (ppm) with Fitted Curve for Probit Model for Number of
Mice Infected at 72 Hours after Infection Following Inhalation Exposure to TCE
(Selgrade and Gilmour 201 O);BMR = 10% Extra Risk ................................................. 595
Table_ Apx G-1. Experimental Design ................................................................................................... 596
Table_Apx G-2. Summary of Observed Interventricular Defects ............................ .............................. 597
Table_Apx G-3. Incidence of total heart malformations in Johnson and Charles River studies ............ 597
Table_ Apx G-4. Incidence of VSDs in Johnson and Charles River studies ........................................... 598
Table_ Apx G-5. Heart and Cardiovascular Defects Observed in Oral TCE studies .............................. 599
Table_Apx G-6. Cardiac Defects Observed in Literature ...................................................................... 601
Table_ Apx G-7. Cardiac Defects Observed After Exposure to RA or TCE .......................................... 602
Table_Apx G-8. Weight-of-Evidence Table for Epidemiology Studies ................................................ 610
Table_Apx G-9. Weight-of-Evidence Table for In V'zvoAnimal Toxicity Studies ................................ 612
Table_Apx G-10. Weight-of-Evidence Table for Mechanistic Studies .................................................. 615
Table_Apx G-11. Overall Weight-of-Evidence Table and Summary Scores ......................................... 617
Table_Apx H-1. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies Identified
in EPA's Literature Search ............................................................................................. 618
Table_Apx H-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based on
Inclusion/Exclusion Criteria ........................................................................................... 619
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Table_Apx H-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the 2011
IRIS Assessment or EPA Literature Search ......................................................... ........... 620
Table_ Apx H-4. Analysis of influential studies: NHL ........................................................................... 629
Table_Apx H-5. Analysis of influential studies: Kidney cancer ................................. ........................... 629
Table_Apx H-6. Analysis of influential studies: Liver cancer .................... .......... ............... .................. 630
Table_Apx H-7. Selected RR estimates for NHL associated.with TCE exposure (overall effect) from
cohort studies published after U.S. EPA (2011) ......................................................... .... 63 7
Table_Apx H-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
case-control studies .................................................... .................................... ................. 638
Table_Apx H-9. Selected RR estimates for NHL associated with TCE exposure (effect in the highe~
exposure group) studies ........................................................... ;...................................... 638
Table_Apx H-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from cohort studies .............................................................................................. 63 9
Table_Apx H-11. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from case-control studies published after U.S . EPA (2011) ............................... 640
Table_Apx H-12. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from cohort studies ......................................................................................................... 641
Table_Apx H-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from case-control studies published after U.S. EPA (2011) ....................... .................... 642
Table_Apx 1-1. Summary of OCPSF Effluent Guidelines for Trichloroethylene ..................... ............. 644
Table_Apx I-2. Default Parameters for Estimating Water Releases ofTrichloroethylene from
Manufacturing Sites .......................... .............................................................................. 64 5
Table_Apx I-3. Summary of Facility Trichloroethylene Production Volumes and Wastewater Flow
Rates ..........................................................................................•...................•.....•...•...
•... 646
Table_Apx K-1. Summary of Parameter Values and Distributions Used in the Open-Top Vapor
Degreasing Near-Field/Far-Field Inhalation Exposure Model ...................... ................. 656
Table_Apx K-2. Summary of Parameter Values and Distributions Used in the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model ....................... .............. ..................... 657
Table_Apx K-3. Summary of Parameter Values and Distributions Used in the Web Degreasing NearField/Far-Field Inhalation Exposure Model.. .................................................................. 658
Table_Apx K-4. Summary of Parameter Values and Distributions Used in the Cold Cleaning NearField/Far-Field Inhalation Exposure Model.. ................................... ............................... 659
Table_Apx K-5. Summary of Trichloroethylene Vapor Degreasing and Cold Cleaning Data from the
2014 NEI..............................................................
............................................
...............661
Table_Apx K-6. Distribution of Trichloroethylene Open-Top Vapor Degreasing Unit Emissions ....... 662
TabJe_Apx K-7. Distribution of Trichloroethylene Conveyorized Degreasing Unit Emissions ............ 663
Table_Apx K-8. Distribution ofTrichloroethylene Web Degreasing Unit Emissions ........................... 664
Table_Apx K-9. Distribution ofTrichloroethylene Cold Cleaning Unit Emissions .............................. 664
Table_Apx K-10. Distribution ofTrichloroethylene Open-Top Vapor Degreasing Operating Hours ... 664
Tab le_Apx K-11. Distribution ofTrichloroethylene Conveyorized Degreasing Operating Hours ........ 664
Table_Apx K-12. Distribution ofTrichloroethylene Web Degreasing Operating Hours ...................... 665
Table_Apx K-13. Distribution of Trichloroethylene Cold Cleaning Operating Hours .......................... 665
Table~Apx L-1. Summaryof Parameter Values and Distributions Used in the Brake Servicing Near................................ 672
Field/Far-Field Inhalation Exposure Model....................................
Table_Apx L-2. Summary ofTrichloroethylene-Based Aerosol Degreaser Formulations .................... 676
Table_Apx M-1. Summary of Parameter Values and Distributions Used in the Spot Cleaning NearField/Far-Field Inhalation Exposure Model.. .................................................................. 683
Table_Apx M-2. Composite Distribution of Dry Cleaning Facility Floor Areas ....................................686
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Table_Apx N-1. Results for Best-Fitting Model in Comparison to Results ........................................... 691

no

LIST OF APPENDIXFIGURES

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754

Figure_Apx
Figure_ Apx
Figure_ Apx
Figure_Apx

755

D-1. Elasticities (2::0.05) fur Panunelc:rs Applic:u in El ..................................................... 513
D-2. Elasticities ~ 0.05) for Parameters Applied in E3 ..................................................... 514
D-3. Elasticities (2: 0.05) for Parameters Applied inP _DER2b ......................................... 515
E-1. SSD Toolbox interface and list ofHCoss for each distribution and fitting method using
TCE's algae hazard data (Etterson, 2019) ............ ................................. ......... ................ 519
Figure_Apx E-2. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard data
(Etterson, 2019) ............................................................................................... ................ 520
Figure_Apx E-3. TCE algae data fit with triangular distribution and graphical methods fitting method
(Etterson, 2019) ............................................................................................................... 521
Figure_Apx E-4. SSD Toolbox interface showing HCoss and P values for each distribution and fitting
method using TCE's acute hazard data (Etterson, 2019) ................................................ 522
Figure_Apx E-5. AICc for the four distribution options in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2019) ............................................................................................................... 523
Figure_Apx E-6. All distributions and fitting methods in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2019) ................................................................................................................ 524
Figure_ Apx E- 7. TCE' s acute hazard data fit with the normal, logistic, triangular, and gumbel
distributions using maximum likelihood fitting method in the SSD Toolbox for (Etterson,
2019) ········"·'········"·""' ''·· ............................................................................................... 525
Figure _Apx F-1. Plot oflncidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010); BMR = 10% Extra Risk ................................................... .................... . 587
Figure_Apx F-2. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010) ; BMR = 5% Extra Risk...........................................
...............................589
Figure_Apx F-3. Plot ofincidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010); BMR = 1% Extra Risk .......................................................................... 592
Figure_Apx H-1. Fixed-effects model, overall association ofNHL and exposure to TCE .................... 623
Figure_Apx H-2. Random-effects model, overall association of NHL and exposure to TCE ............... 624
Figure_Apx H-3. Fixed-effects model, association of NHL and high exposure to TCE ........................ 624
Figure_Apx H-4. Random-effects model, association ofNHL and high exposure to TCE ................... 625
Figure_Apx H-5. Fixed-effects model, overall association of kidney cancer and ............... ................ ... 626
Figure_Apx H-6. Random-effects model, overall association of kidney cancer and ............................. 626
Figure_Apx H-7. Fixed-effects model, overall association of liver cancer and ..................................... 627
Figure_Apx H-8. Random-effects model, overall association of liver cancer and .............................. ... 628
Figure_Apx H-9. Fixed-effects model, overall association of NHL and exposure to TCE, study of
Vlaanderen et al. (2013) omitted .................................................................................... 631
Figure_Apx H-10. Fixed-effects model, association ofNHL and high exposure to TCE, study of
Vlaanderen et al. (2013) omitted .............. ................................ ............. ........ .......... ....... 631
Figure_Apx H-11. Fixed-effects model, overall association of kidney cancer and ............ .................... 632
Figure_Apx H-12. Fixed-effects model, overall association of liver cancer and ................................... 632
Figure_Apx H-13. Fixed-effects model, overall association of NHL and ................ ...................... ........ 633
Figure_Apx H-14. Fixed-effects model, overall association of kidney cancer and ........................... ..... 634
Figure_Apx H-15. Fixed-effects model, overall association of liver cancer and ................................... 634
Figure_Apx H-16. Funnel plots for publication bias ........... .................................................... .......... ..... 635
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Figure_Apx K-1. The Near-Field/Far-Field Model as Applied to the Open-Top Vapor Degreasing NearField/Far-FieldInhalationExposure Model and the Cold Cleaning Near-Field/Far-Field
InhalationExposure Model............................................................................................. 651
Figure_Apx K-2. The Near-Field/Far-Field Model as Applied to the Conveyorized Degreasing NearField/Far-FieldInhalation Exposure Model.................................................................... 652
Figure_Apx K-3. The Near-Field/Far-Field Model as Applied to the Web Degreasing Near-Field/FarField InhalationExposure Model. ..........................................................
......................... 652
Figure L-1. The Near-Field/Far-FieldModel as Applied to the Brake Servicing Near-Field/Far-Field
InhalationExposure Model............................................................................................. 667
Figure_Apx M-1. The Near-Field/Far-FieldModel as Applied to the Spot Cleaning Near-Field/Far-Field
Inhalation Exposure Model ............................................................................................. 679

767

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ACKNOWLEDGEMENTS

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This report was developed by the United States Environmental Protection Agency (EPA), Office of
Chemical Safety and Pollution Prevention (OCSPP), Office of Pollution Prevention and Toxics (OPPT).

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792

Acknowledgements
The EPA Assessment Team gratefully acknowledges participation and/or input from Intra-agency
reviewers that included multiple offices within EPA, Inter-agency reviewers that included multiple
Federal agencies, and assistance from EPA contractors: GDIT (Contract No. CIO-SP3,
HHSN316201200013W), ERG (Contract No. EP-W-12-006),Versar (Contract No. EP-W-17-006), ICF
(Contract No. EPC14001 and 68HERC19D0003), SRC (Contract No . EP-W-12-003 and
68HERH19D0022), and Abt Associates (Contract No. EPW-16-009).
EPA also acknowledges the contributions ofMasashi Ando from the National Institute of Technology
and Evaluation (NITE) in Japan for his contribution to the systematic review of environmental exposure
data.
Docket
Supporting information can be found in public docket (Docket: EPA-HQ-OPPT-2019-0500) .
Disclaimer
Reference herein to any specific commercial products, process or service by trade name, trademark,
manufacturer or otherwise does not constitute or imply its endorsement, recommendation or favoring by
the United States Government.

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ABBREVIATIONS

794
795
796.
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825

Degrees Celsius
Vacuum Pennittivity
ACGlli
American Conference of Governmental Industrial Hygienists
AEGL
Acute Exposure Guideline Level
ADD
Average Daily Dose
AF
Assessment Factor
AQS
Air Quality System
ATCM
Airborne Toxic Control Measure
atrn
Atmosphere(s)
ATSDR
Agency for Toxic Substances and Disease Registries
BAF
Bioaccumulation Factor
BCF
Bioconcentration Factor
BIOWIN
The EPI Suite™ module that predicts biodegradation rates
body weight314
BW 314
CAA
Clean Air Act
CARB
California Air Resources Board
CASRN
Chemical Abstracts Service Registry Number
CBI
Confidential Business Information
CCR
California Code of Regulations
CDC
Centers for Disease Control and Prevention
CDR
Chemical Data Reporting
CEHD
Chemical Exposure Health Data
CEM
Consumer Exposure Model
CEP A
Canadian Environmental Protection Act
CERCLA Comprehensive Environmental Response, Compensation, and Liability Act
CFC
Chlorofluorocarbon
CFR
Code of Federal Regulations
CH
Chloral Hydrate
ChemSTEER Chemical Screening Tool for Exposure and EnvironmentalReleases
CHIRP
Chemical Risk Information Platform
Ch V
Chronic Value
Cubic Centimeter(s)
cm 3
CNS
Central Nervous System
COC
Concentration of Concern
COU
Conditions of Use
CPCat
Chemical and Product Categories
CSCL
Chemical Substances Control Law
CW A
Clean Water Act
CYP
Cytochrome P450
DCA
Dichloroacetic acid
DCVC
S-dichlorovinyl-L-cysteine
DCVG
S-dichlorovinyl -glutathione
DEVL
Dermal Exposure to Volatile Liquids
DIY
Do-It-Yourself
DMR
Discharge Monitoring Report
ECso
Effect concentration at which 50% oftest organisms exhibit an effect
ECCC
Environment and Climate Change Canada
Page 21 of 691

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827
828
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832
833
834
835
836
837
838
839
840

°C

c0

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842
843
844

845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868

869
870
871

872
873
874
875
876
877
878
879
880
881
882

883
884
885

886
887
888

ECHA
European ChemicalsAgency
EDC
Ethylene Dichloride
E-FAST
Exposure and Fate Assessment ScreeningTool
EG
Effluent Guidelines
EPA
EnvironmentalProtection Agency
EPCRA
Emergency Planning and CommunityRight-to-KnowAct
EPI Suite™ Estimation Program Interface Suite™
Emission Scenario Document
ESD
European Union
EU
Food and Drug Administration
FDA
Federal Food, Drug, and Cosmetic Act
FFDCA
Federal Insecticide,Fungicide,and RodenticideAct
FIFRA
Federal Register
FR
Gram(s)
g
GenerallyAvailable Control Technology
GACT
Generic Scenario
GS
Glutathione
GSH
Glutathione-S-transferase
GST
HazardousAir Pollutant
HAP
Hydrochlorofluorocarbon
HCFC
HydrochloricAcid
HCl
Hazardous Concentrationthresholdfor 5% of species in a Species SensitivityDistribution
HCos
HEC
Human Equivalent Concentration
HED
Human Equivalent Dose
HFC
Hydrofluorocarbon
Health Hazard Evaluation
HHE
HPV
High ProductjonVolmne
Hr
Hour
InternationalAgency for Researchon Cancer
IARC
Integrated Compliance InformationSystem
ICIS
ImmediatelyDangerous to Life and Health
IDLH
Integrated ManagementInformationSystem
IMIS
Integrated Risk InformationSystem
IRIS
Industrial Safety and Health Act
ISHA
Initial Statement of Reasons
ISOR
Inhalation Unit Risk
IUR
Soil Organic Carbon-WaterPartitioningCoefficient
Koe
Octanol/WaterPartition Coefficient
Kow
Kilogram(s)
kg
Liter(s)
L
Pound(s)
lb
Lethal Concentrationat which 50% of test organisms die
LCso
Lowest-observed-adverse-effect-level
LOAEL
Lowest-observable-effectConcentration
LOEC
m3
Cubic Meter(s)
Maximum Achievable Control Technology
MACT
Maximum AcceptableToxicant Concentration
MATC
Multi-Chamber
Concentrationand Exposure Model
MCCEM
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890
891

892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928

929
930
931
932
933
934
935
936

MCL
MCLG
mg

Maximum ContaminantLevel
Maximum ContaminantLevel Goal
Milligram(s)
mmHg
Millimeter(s) of Mercury
MOA
Mode of Action
mPa ·s
Millipascal(s)-Second
MSDS
Material Safety Data Sheet
MSW
Municipal Solid Waste
NAICS
North American Industry ClassificationSystem
NATA
National Scale Air-ToxicsAssessment
NCEA
National Center for EnvironmentalAssessment
NICNAS Australia National Industrial ChemicalsNotification and Assessment Scheme
NCP
National ContingencyPlan
NEI
National Emissions Inventory
NESHAP National Emission Standardsfor Hazardous Air Pollutants
NHANES National Health and Nutrition Examination Survey
NICNAS National Industrial ChemicalsNotification and Assessment Scheme
NIH
National Institute of Health
NICNAS National Industrial ChemicalsNotification and Assessment Scheme
NIOSH
National Institute for OccupationalSafety and Health
NITE
National Institute of Technologyand Evaluation
NOAEL
No-Observed-Adverse-Effect-Level
NOEC
No-observable-effectConcentration
NPDES
National Pollutant Discharge Elimination System
NPDWR National Primary Drinking Water Regulation
National Research Council
NRC
National Toxicology Program
N71>
NWIS
National Water InformationSystem
OCPSF
Organic Chemicals, Plastics and SyntheticFibers
Office of Chemical Safety and Pollution Prevention
OCSPP
Organization for EconomicCo-operationand Development
OECD
Office of EnvironmentalHealth Hazard Assessment
OEHHA
OES
OccupationalExposure Scenario
OccupationalExposure Limits
OEL
OccupationalNon-User
ONU
Office of Pollution Prevention and Toxics
OPPT
OR
Odds Ratio
OccupationalSafety and Health Administration
OSHA
Oral Slope Factor
OSF
Office of Science and Technology
OST
OTVD
Open-Top Vapor Degreaser
ow
Office of Water
Physiologically-BasedPhannacokinetic
PBPK
PBZ
Personal Breathing Zone
Tetracbloroethylene
PCE
PECO
Population, Exposure, Comparator,and Outcome
Permissible Exposure Limit
PEL
PESS
Potentially Exposed or SusceptibleSubpopulations
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938

939
940
941
942
943
944

945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979

Point of Departure
Publicly Owned Treatment Works
Part(s) per Billion
Personal Protective Equipment
Part(s) per Million
Particle Size Distribution
Production Volume
Quality Control
Quantitative Structure Activity Relationship
Resource Conservation and Recovery Act
Registration, Evaluation, Authorisation and Restriction of Chemicals
Relative Exposure Limit
Relative Risk
RR
Risk and Technology Review
RTR
Safety Data Sheet
SDS
Safe Drinking Water Act
SDWA
Screening Information Dataset
SIDS
Significant New Use Notice
SNUN
Significant New Use Rule
SNUR
Synthetic Organic Chemical Manufacturing Industry
SOCMI
SPARC Performs Automated Reasoning in Chemistry
SPARC
Specific Environmental Release Categories
SpERC
Short-Term Exposure Limit
STEL
STP model Sewage Treatment Plant model
STORET STOrage and RETrieval
Species Sensitivity Distribution
SSD
Transparent, clear, consistent, and reasonable
TCCR
TCA
Trichloroacetic acid
Trichloroethylene
TCE
Trichloroethanol
TCOH
Trichloroethanol, gluuronide conjugate
TCOO
Threshold Limit Value
TLV
Toxics Release Inventory
TRI
Toxic Substances Control Act
TSCA
Time Weighted Average
TWA
Underground Injection Control
UIC
United States
U.S.
Ultraviolet
UV
United States Geological Survey
usos
Volatile Organic Compound
voe
Vapor Pressure
VP
Year(s)
Yr
POD
POTW
ppb
PPE
ppm
PSD
PV
QC
QSAR
RCRA
REACH
REL

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981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
000
001
002
003
004
005
006
007
008
009
010
011
012
013
014
015
016
017
018
019
020
021
022
023
024
025
026
027

EXECUTIVE SUMMARY
This draft risk evaluation for trichloroethylene was performed in accordance with th~ Frank R.
Lautenberg Chemical Safety for the 21st Century Act and is being disseminated for public comment
and peer review. The Frank R. Lautenberg Chemical Safety for the 21st Century Act amended the
Toxic Substances Control Act (TSCA), the Nation's primary chemicals management law, in June
2016. As per EPA's fmal rule, Procedures [or Chemical Risk Evaluation Under the Amended Toxic
Substances Control Act (82 FR 33726), EPA is talcing comment on this draft, and will also obtain peer
review on this draft risk evaluation for trichloroethylene. All conclusions, findings, and determinations
in this document are preliminary and subject to comment The final risk evaluation may change in
response to public comments received on the draft risk evaluation and/or in response to peer review,
which itself may be informed by public comments. The preliminary conclusions, findings, and
determinations in this draft risk evaluation are for the purpose of identifying whether the chemical
substance presents unreasonable risk or no unreasonable risk under the conditions of use, in
accordance with TSCA section 6, and are not intended to represent any findings under TSCA
section 7.
TSCA § 26(h) and (i) require EPA to use scientific infonnation, technical procedures, measures,
methods, protocols, methodologies and models consistent with the best availaj,le science and to base
its decisions on the weight of the scientific evidence. To meet these TSCA § 26 science standards,
EPA used the TSCA systematic review process described in the Application of Systematic Review in
TSCA Risk Evaluations document (U.S. EPA. 2018b). The data collection, evaluation, and integration
stages of the syst~matic review process are used to develop the exposure, fate, and hazard assessments
for risk evaluations.
Trichloroethylene has a wide-range of uses in consumer and commercial products and in industry. An
estimated 83.6% ofTCE's annual production volume is used as an intennediate in the manufacture of
the hydrofluorocarbon, HFC-134a, an alternative to the refrigerant chlorofluorocarbon, CFC-12.
Another 14.7% ofTCE production volume is used as a degreasing solvent, leaving approximately 1.7%
for other uses. The total aggregate production volume decreased from 220.5 to 171.9million pounds
between 2012 and 2015.
EPA evaluated TCE's conditions of use (COUs), including the following categories of use: solvent for
cleaning and degreasing, lubricants and greases, adhesives and sealants, functional fluids _in a closed
system, paints and coatings laundry and dishwashing products and arts, crafts and hobby materials.
Trichloroethylene is subject to federal and state regulations and reporting requirements.
Trichloroethylene has been a reportable Toxics Release Inventory (TRI) chemical under Section 313 of
the Emergency Planning and Community Right-to-KnowAct (EPCRA) since 1987. It is designated as a
Hazardous Air Pollutant (HAP) under the Clean Air Act (CAA), and is a hazardous substance under the
Comprehensive Environmental Response, Compensation and Liability Act (CERCLA). It is subject to
National Primary Drinking Water Regulations (NPDWR) under the Safe Drinking Water Act (SOWA)
and designated as a toxic pollutant under the Clean Water Act (CWA) and as such is subject to effluent
limitations. Under TSCA, EPA previously assessed risks from use of trichloroethylene in commercial
solvent degreasing (aerosol and vapor), consumer use as a spray applied protective coating for arts and
crafts and commercial use as a spot remover at dry cleaning facilities (U.S. EPA. 2014b).
Approach

EPA used reasonably available information (defined in 40 Code of Federal Regulations (CFR) 702.33
as "information that EPA possesses, or can reasonably obtain and synthesize for use in risk
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evaluations, considering the deadlines for completing the evaluation"), in a fit-for-purpose approach,
to develop a risk evaluation that relies on the best available science and is based on the weight of the

scientific evidence. EPA used previous analyses as a starting point for identifying key and supporting
studies to inform the exposure, fate, and hazard assessments. EPA also evaluated other studies
published since the publication of previous analyses. EPA reviewed the information and evaluated the
quality of the methods and reporting of results of the individual studies using the evaluation strategies
described in Application a/Systematic Review in TSCA RiskEvaluations (!,!.S. EPA. 2018b).

In the scope document and problem formulatio~ EPA identified the conditions of use and presented
three conceptual models and an analysis plan for this draft risk evaluation. These have been carried into
the draft risk evaluation where EPA has evaluated the risk to the environment and human health, using
both monitoring data and modeling approaches, for the conditions of use (identified in Section 1.4.1 of
this draft risk evaluation). EPA quantitatively evaluated the risk to aquatic species from exposure to
surface water. EPA evaluated the risk to workers, from inhalation and dermal exposures, and
occupational non-users (ONUs)1,from inhalation exposures, by comparing the estimated exposures to
acute and chronic human health hazards. EPA also evaluated the risk to consumers, from inhalation and
dermal exposures, and bystanders, from inhalation exposures, by comparing the estimated exposures to
acute human health hazards.
EPA used environmental fate parameters, physical-chemicalproperties, modeling, and monitoring data
to assess ambient water exposure to aquatic organisms. While trichloroethylene is present in various
environmental media, such as groundwater, surface water, and air, EPA determined during problem
formulation that no further analysis beyond what was presented in the problem formulation document
would be done for environmental exposure pathways for land application of biosolids and sediment,
and water or soil pathways for terrestrial organisms, in this draft risk evaluation. However, exposures
to aquatic organisms from ambient surface water, are assessed and presented in this draft risk
evaluation. These analyses are described in Sections 2.1 and 2.2.
EPA reviewed the environmental hazard data using the data quality review evaluation metrics and the
rating criteria described in the Application of Systematic Review in TSCA Risk Evaluations (U.S. EPA.
2018b). As stated in Section 3.1, the available environmental hazard data indicate that TCE presents
hazard to aquatic organisms. For acute exposures, toxicity values are as low as 7.8 mg/L to 33.85
mg/L(resulting in a geometric mean of 16 mg/L) for invertebrates. For chronic exposures, toxicity
values for fish and aquatic invertebrates are as low as 7.88 mg/Land 9.2 mg/L, respectively. The data
also indicated that TCE presents hazard for aquatic plants, with toxicity values in algae as low as 0.03
mg/L, and a wide range in toxicity between algae species. TCE is not expected to accumulate in aquatic
organisms.
EPA evaluated exposures to trichloroethylene in occupational and consumer settings for the conditions
of use included in the scope of the risk evaluatio~ listed in Section 1.4. In occupational settings, EPA
evaluated acute and chronic inhalation exposures to workers and ONUs, and acute and chronic dermal
exposures to workers. EPA used inhalation monitoring data from literature sources, where reasonably
available and that met data evaluation criteria, as well as, modeling approaches, where reasonably
available, to estimate potential inhalation exposures. Dermal doses for workers were estimated in these
scenarios since dermal monitoring data was not reasonably available. In consumer settings, EPA
evaluated acute inhalation exposures to both consumers and bystanders, and acute dermal exposures to
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ONUs are workers who do not directly handle trichloroethylenebut perfonn work in an area where trichloroethyleneis
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consumers. Inhalation exposures and dennal doses for consumers and bystanders in these scenarios were
estimated since inhalation and dermal monitoring data were not reasonably available. These analyses are
described in Section 2.3 of this draft risk evaluation .
EPA evaluated reasonably available information for human health hazards and identified hazard
endpoints including acute and chronic toxicity for non-cancer effects and cancer, as described in Section
3.2. EPA used the Frameworkfor Human HealthRisk Assessmentto Inform Decision Making (!,I.S.
EPA. 2014a ) to evaluate, extract, and integrate trichloroethylene ' s human health hazard and dosen:.sponse information . EPA reviewed key and supporting information from previous hazard assessments
[TSCA Work Plan Chemical Risk Assessment Trichloroeth , lene: Deureasin g. Spot Cleanin g and Arts &
Crafts Use (U.S. EPA. 2014b), Toxicolo gical Review ofTrichloroeth , lene (U.S. EPA . 2O1le ), and other
national and international assessments listed in Table 1-3]. EPA also screened and evaluated studies that
were published since these reviews (i.e., from 2010 - 2017, in addition to select studies published after
completion of the literature search).
EPA developed a hazard and dose-response analysis using endpoints observed in inhalation and oral
hazard studies, evaluated the weight of the scientific evidence considering EPA and National Research
Council (NRC) risk assessment guidance, and selected the points of departure (POD) for acute, chronic
and non-cancer endpoints, and inhalation unit risk (TUR)and cancer slope factors (CSF) for cancer risk
estimates. Health haz.ardsof TCE described and reviewed in this risk evaluation include: acute overt
toxicity, liver toxicity, kidney toxicity, neurotoxicity, immunotoxicity (mcluding sensitization) ,
reproductive toxicity, developmental toxicity, and cancer. Following dose-response analysis,
representative PODs were identified for multiple non-cancer endpoints within the domains of liver
toxicity, kidney toxicity, neurotoxicity, immunotoxicity, reproductive toxicity, and developmental
toxicity .

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For cancer, EPA performed meta-analyses in order to statistically evaluate the epidemiological data for
non-Hodgkin Lymphoma (NHL), kidney cancer, and liver cancer. EPA utilized similar methodology as
was employed in the 2011 EPA TCE IRIS Assessment (U.S. EPA , 201 le) and included sensitivity
analyses, as needed, to partition the results based on both heterogene ity and study quality . See Appendix
H for full details and results . The 2019 meta-analysis of all relevant studies examining kidney cancer,
liver cancer , or NHL (Appendix H) concluded that there is a statistical significant association between
TCE exposure and increased incidence of all three cancers, Thls was the same conclusion as the
previous EPA meta-analysis in the 2011 IRIS Assessment (!J.S. EPA. 2011e). Therefore, EPA utilized
the same inhalation unit risk and oral slope factor estimates as were derived in (1!.S. EPA . 201 le ) and
cited in the 2014 TSCA Work Plan Chemical Risk Assessment (U.S. EPA , 2014b). A linear nonthreshold assumption was applied to the TCE cancer dose-response analysis because there is sufficient
evidence that TCE-i.nduced kidney cancer operates primarily through a mutagenic mode of action while it
cannot be ruled out for the other two cancer types.

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Risk Characterization

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Environmental Risk: For environmental risk, EPA utilized a risk quotient (RQ) to compare the
environmental concentration to the effect level to characteriz.e the risk to aquatic organisms . EPA
included a qualitive assessment describing trichloroethylene exposure from sediments for aquatic
organisms organisms, and land-applied biosolids, water, and soil for terrestrial organisms.
Trichloroethylene is not expected to accumulate in sediments, and is expected to be mobile in soil, and
migrate to water or volatilize to air. The results of the risk characterization are in Section 4. 1, including
a table (fable 4-1). that summarizes the RQs for acute and chronic risks. Surfacewater concentrations

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ofTCE were modeled for 214 releases.
EPA identified the expected environmental exposures for aquatic species under the conditions of use in
the scope of the risk evaluation. Estimated releases from specific facilities result in modeled surface
water concentrations that exceed the aquatic benchmark (RQ > 1) for either chronic, acute, and/ or
algae concentrations of concern for the following conditions of use in various locations (see Table
4-1 ): processing as a reactant; open top vapor degreasing; repackaging; adhesives; sealants; paints and
coatings; industrial processing aid; other industrial uses; other commercial uses; process solvent
recycling and worker handling of wastes; and waste water treatment plants. Details of these estimates
are in Section 4.1.2.

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Qualitative consideration of the physical-chemical and fate characteristics, as well as consideration of
the conditions of use for TCE indicated limited presence in terrestrial environments and aquatic
sediments (Section 4.1.3 and 4.1.4). Therefore EPA did not find risks for sediment or terrestrial
organisms.
Human Health Risks: Risks were estimated following both acute and chronic exposure for
representative endpoints from every hazard domain.

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For workers and ONUs, EPA estimated potential cancer risk from chronic exposures to
trichloroethylene using inhalation unit risk or dermal cancer slope factor values multiplied by the
chronic exposure for each COU. For workers and ONUs, EPA also estimated potential non-cancer
risks resulting from acute and chronic inhalation and dermal exposures using a Margin of Exposure
(MOE) approach. For workers, EPA estimated risks using several occupational exposure scenarios,
with scenario-specific assumptions regarding the expected use of personal protective equipment (PPE)
for respiratory and dermal exposures for workers directly handling trichloroethylene. More
information on respiratory and dermal protection, including EPA's approach regarding the
occupational exposure scenarios for trichloroethylene, is in Section 2.3.1.
For workers, acute and chronic non-cancer and cancer risks were indicated for all exposure scenarios
and occupational conditions of use wider high-end2 inhalation exposure levels. Non-cancer risks were
also identified for all exposure scenarios with expected use of respiratory protection up to APF = 50.
When only considering the central tendency3inhalation exposure level, risks were not identified for
three out of 18 occupational exposure scenarios. Acute and chronic non-cancer and cancer risks were
indicated for all exposure scenarios and occupational conditions of use under both high-end and central
tendency dermal exposure levels. Risks are still indicated for all exposure scenarios when gloves are
worn even when assuming the maximum applicable glove protection (either PF 10 or 20). Risks were
also identified for multiple endpoints under both acute and chronic exposure scenarios.
ONUs are expected to have lower exposure levels than workers in most instances but exposures could
not always be quantified and risk estimates for ONUs may be similar to workers in some settings.
Therefore, when separate ONU exposure estimates were not available, EPA provided risk estimates for
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A high-end is assumed to be representativeof occupationalexposuresthat occur at probabilitiesabove the 90th percentile
but below the exposure of the individualwith the highest exposure.EPA providedresults at the 95th percentile when
available.
3 A central tendency is assumedto be representative occupationalexposuresin the center of the distributionfor a given
conditionof use. For risk evaluation,EPA used the 50th percentile(median),mean (arithmeticor geometric),mode, or
midpointvalues of a distributionas representativeof the centraltendency scenario.

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ONUs based on worker values assuming that ONU exposure may be as high as workers in various
circumstances. Acute and chronic non-cancer risks to ONUs were indicated for all exposure scenarios.
ONUs are not expected to be using PPE to reduce exposures to trichloroethylene used in their vicinity.
ONUs are not expected to be dermally exposed to trichloroethylene and therefore dermal risks to ONUs
were not assessed. EPA' s estimates for ONU risks for each occupational exposure scenario are
presented alongside worker risk estimates in Section 4.2.2 and Table 4-55 in Section 4.5.1.0.

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For consumer.sand bystanders for consumer use, EPA estimated non-cancer risks resulting from acute
inhalation or dermal exposures (applicable to consumers only) that were modeled with a range of user
intensities, described in detail in Section 2.3.2. Bystanders are assumed to not have direct dermal
contact with TCE. Based on reasonably available information, EPA determined that consumers or
bystanders would not use PPE and that all exposures would be acute, rather than chronic.
For consumers, acute risks were indicated for all exposure scenarios and consumer conditions of use at
both medium and high-intensity acute inhalati~n and dermal consumer exposure scenarios. Acute risks
were also indicated for bystanders at both medium and high-intensity acute inhalation levels. Risks
were identified for multiple endpoints. EPA's estimates for consumer and bystander risks for each
consumer use exposure scenario are presented in Section 4.2.3 and summarized in Table 4-56 in
Section 4.5.1.2.
Uncertainties: Key assumptions and uncertainties in ~e environmental risk estimation include
uncertainties regarding the hazard data for aquatic species andsurface water concentrations.
Additionally the available environmental monitoring data was limited temporally and geographically.
For the human health risk estimation, key assumptions and uncertainties are related to data on
exposures, exposure model input parameters, and the estimates for ONU inhalation exposures for COUs
in which monitoring data or probabilistic modeling data was not reasonably available. Additional
sources of uncertainty related to human health hazard include selection of the appropriate
Physiologically-Based Phannacokinetic (PBPK) dose-metric for each endpoint, the dose-response for
the cardiac toxicity endpoint, and the adjustment of the cancer PODs to account for cancer at multiple
sites. Assumptions and key sources of uncertainty in-the risk characterization are detailed in Section
4.3.

Potentially Exposed or Susceptible Subpopulations (PESS): TSCA § 6(bX4) requires that EPA conduct
a risk evaluation to "determine whethera chemicalsubstancepresents an unreasonablerisk of injury to
health or the environment,without considerationof cost or other non-riskfactors, includingan
unreasonablerisk to a potentially exposedor susceptiblesubpopulationidentifiedas relevant to the risk
evaluation by theAdministrator, under the conditionsof use." TSCA § 3(12) states that "the term
'potentiallyexposed or susceptiblesubpopulation' meansa group of individualswithin the general
population identifiedby the Administratorwho, due to eithergreater susceptibilityor greater exposure,
may be at greater risk than the generalpopulation of adversehealth effectsfrom exposureto a chemical
substance or mixture,such as infants, children,pregnant women,workers, or the elderly."

In developing the risk evaluation, EPA analyzed the reasonably available information to ascertain
whether some human receptor groups may have greater exposure or greater susceptibility than the
general population to the hazard posed by a chemical. For consideration of the potentially exposed
groups, EPA considered trichloroethylene exposures to be higher among workers using
trichloroethylene and ONUs in the vicinity of trichloroethyleneuse than the exposures experienced by
the general population. Risk estimates were also provided separately for ONUs when sufficient data
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were reasonably available. EPA was unable to provide separate risk estimates when insufficient
information was available for quantifying ONU exposure. EPA considered the central tendency risk
estimate when determining ONU risk for those conditions of use for which ONU exposures were not
separately estimated. Consumer risk estimates were provided for low, medium, and high intensities of
use, accounting for differences in duration, weight fraction, and mass used. Dermal risk estimates were
calculated for both average adult workers and women of childbearing age. The use of the 99th
percentile Human Equivalent Concentration/Dose(HEC/HED)99PODvalues derived from relevant
(PBPK) dose metrics also account for the vast majority oftoxicokinetic variation across the
population. By relying on the 99th percentile output of the PBPK model, these values are expected to
be protective of particularly susceptible subpopulations, including those with genetic polymorphisms
resulting in increased activity ofbioactivating enzymes. While there may not be a risk for all endpoints
to ail individuals or to an individual at all times, assessment of risks for all relevant endpoints using
toxicokinetic values for the most sensitive 1% of the population is expected to sufficiently cover any
particularly susceptible subpopulations.
Aggregate and Sentinel Exposures Section 2605(b)(4)(F)(ii) of TSCA requires the EPA, as a part of the
risk evaluation, to describe whether aggregate or sentinel exposures under the conditions of use were
considered and the basis for their consideration. The EPA has defined aggregate exposure as "the
combinedexposures to an individualfrom a single chemicalsubstanceacross multiple routes and
across multiplepathways (40 CFR § 702.33) ." Exposures to trichloroethylene were evaluated by
inhalation and dermal routes separately. Inhalation and dermal exposures are assumed to occur
simultaneously for workers and conswners. EPA chose not to employ simple additivity of exposure
pathways at this time within a condition of use because of the uncertainties present in the current
exposure estimation procedures, which may may lead to an underestimate or overestimate of the actual
total exposure.
The EPA defines sentinel exposure as "the exposureto a single chemicalsubstancethat representsthe
plausible upper bound of exposurerelative to all other exposureswithin a broad categoryof similar or
related exposures(40 CFR § 702.33)." In this risk evaluation, the EPA considered sentinel exposure the
highest exposure given the details of the conditions of use and the potentia1exposure scenarios. EPA
considered sentinel exposures by considering risks to populations who may have upper bound (e.g.,
high-end, high intensities of use) exposures.

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Risk Determination
In each risk evaluation under TSCA section 6(b), EPA determines whether a chemical substance
presents an unreasonable risk of injury to health or the environment, under the conditions of use. The
determination does not consider costs or other non-risk factors. In making this determination, EPA
considers relevant risk-related factors, including, but not limited to: the effects of the chemical substance
on health and human exposure to such substance under the conditions of use (including cancer and noncancer risks); the effects of the chemical substance on the environment and environmental exposure
under the conditions of use; the population exposed (including any potentially exposed or susceptible
subpopulations); the severity ofhaz.ard (including the nature of the hazard, the irreversibility of the
haz.ard); and uncertainties. EPA also talcesinto consideration the Agency's confidence in the data used
in the risk estimate. This includes an eva1uationof the strengths, limitations, and uncertainties associated
with the information used to inform the risk estimate and the risk characterization. The rationale for the
risk determination is discussed in section 5.1.

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Environmental Risks: EPA identified risks from acute and chronic exposures for aquatic organisms (e.g.,
aquatic invertebrates and fish) neartwo facilities releasing TCE to surface water. One facility had an
acute RQ ~ 1 (RQ = 3.11), exceeding the acute COC of3,200 ppb and indicating risk to aquatic
organisms from acute exposures. This facility is one of 59 facilities modeled by EPA that use TCE for
open-top vapor degreasing (see Section 4.4.1). This facility and one other facility (one of 11 facilities
that process TCE as a reactant) had chronic RQs ~ 1, exceeding the chronic COC of788 ppb for 20 days
(see Section 4.4.1). Monitored data from the Water Quality Portal and grey literature show no
exceedances of the acute COC and the chronic COC in ambient water. Given the uncertainties in the
modeling data and exceedance of RQ for only two data points out of 70, and no exceedances of RQ from
monitoring data, EPA does not consider these risks unreasonable (see Section 5.2).
Risks of Injury to Health: EPA's determination of unreasonable risk for specific conditions of use of
TCE listed below are based on health risks to workers, occupationalnon-users, consumers, or bystanders
from consumer use. As described below, risks to general population either were not relevant for these
conditions of use or were evaluated and not found to be unreasonable. TCE has a large database of
human health toxicity data. For each hazard domain there are several endpoints, and often a single
endpoint was examined by multiple studies. Risks from acute exposures include developmental toxicity
and pulmonary immunotoxicity. For chronic exposures, EPA identified risks of non-cancer effects (liver
toxicity, kidney toxicity, neurotoxicity, immunotoxicity,reproductive toxicity, and developmental
toxicity) as well as cancers of liver, kidney, and non-Hodgkin Lymphoma.
Risk to the General Population: General population exposures to TCE may occur from industrial and/ or
commercial uses; industrial releases to air, water or land; and other conditions of use. As part of the
problem formulation for TCE, EPA found those exposure pathways are covered under the jurisdiction of
other environmental statutes, administered by EPA, which adequately assess and effectively manage
those exposures, i.e., CAA, SDWA, CWA, and RCRA. EPA believes this TSCA risk evaluation should
focus on those exposure pathways associated with TSCA conditions of use that are not subject to the
regulatory regimes discussed above because those pathways are likely to represent the greatest areas of
concern to EPA. Therefore, EPA did not evaluate hazardsor exposures to the general population in this
risk evaluation, and there is no risk determination for the general population (!J.S. EPA. 2018d).
Risk to Workers: EPA evaluated workers' acute and chronic inhalation and dermal occupational
exposures for cancer and non-cancer risks and determined whether any risks are unreasonable. The
drivers for EPA' s detennination of unreasonable risk for workers are developmental cardiac toxjcity
resulting from acute and chronic inhalation and dermal exposures, and, for most conditions of use,
cancer resulting from chronic inhalation and dermal exposures. For workers, EPA determined that all
applicable conditions of use for TCE presented unreasonable risks. The detenninations reflect the
severity of the effects associated with the occupational exposuresto TCE and incorporate consideration
of expected PPE (frequently estimated to be a respirator of APF 25 or 50 and gloves with PF 5 - 20). A
full description ofEPA's determination for each condition of use is in Section 5.2.
Risk to Occupational Non-Users (ONUs): EPA evaluated ONU acute and chronic inhalation
occupational exposures for cancer and non-cancer risks and determined whether any risks are
unreasonable. The drivers for EPA's determination of unreasonable risks to ONUs are developmental
cardiac toxicity resulting from acute and chronic inhalation and cancer resulting from chronic inhalation.
The detenninations reflect the severity of the effects associated with the occupational exposures to TCE
and the expected absence of PPE for ONUs. For dermal exposures, because ONUs are not expected to
be dermally exposed to TCE, dermal risks to ONUs generally were not evaluated. For inhalation
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exposures, EPA, where possible, used monitoring or modeling information to estimate ONU exposures
and to describe the risks separately from directly exposed workers. For some conditions of use, EPA did
not separately calculate risk estimates for ONUs and workers. For these conditions of use, there is
uncertainty in the ONU risk estimates since the data or modeling did not distinguish between worker and
ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower than inhalation
exposures for workers directly handling the chemical substance; however, the relative exposure of
ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA considered
the central tendency risk estimate when determining ONU risk for those conditions of use for which
ONU exposures were not separately estimated, and determined that most of applicable conditions of use
present unreasonable risks. Estimated numbers of occupationalnon-users are in section 2.3 .1.2.7.
Risk to Consumers:EPA evaluated conswner acute inhalation and dermal exposures for non-cancer
risks and determined whether any risks are unreasonable. The driver for EPA's determination of
unreasonable risk is developmental cardiac toxicity from acute inhalation, and, usually, from dermal
exposure as well. Generally, risks for consumers were indicated by acute inhalation and dermal exposure
at low, medium, and high intensity use. For consumers, EPA determined that all consumer conditions of
use present unreasonable risks. A full description of EPA's determination for each condition of use is in
section 5.2.
Risk to Bystanders {from consumer uses): EPA evaluated bystander acute inhalation exposures for noncancer risks and determined whether any risks are unreasonable. The driver for EPA' s determination of
unreasonable risk is developmental cardiac toxicity from acute inhalation exposure. Generally, risks for
bystanders were indicated by acute inhalation exposure scenarios at low, medium, and high intensity
use. Because bystanders are not expected to be dennally exposed to TCE, dermal non-cancer risks to
bystanders were not identified. For bystanders, EPA determined that all but one applicable condition of
use presents unreasonable risk. A full description ofEPA's determination for each condition of use is in
Section 5.2.

Summaryof risk determinations:
EPA has preliminarily determined that the following conditions of use ofTCE do not present an
unreasonable risk of injury under any scenarios. The details of these determinations are presented in
table 5-1 in section 5.2. EPA' s preliminary determination regarding environmental risks are summarized
above and presented in more detail in section 5.2.
Conditions of Use that Do Not Present an Unreasonable Risk

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EPA has preliminarily determined that the following conditions of use ofTCE present an unreasonable
risk of injury to health to workers (including, in some cases, occupational non-users) or to conswners
(including, in some cases, bystanders). The details of these determinations are presented in table 5-1 in
section 5.2.
Manufacturing that Presents an Unreasonable Risk
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Import (includingrepackagingand loading/unloading)

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Processing as a reactant/intennediate
Incorporationinto a fonnulation, mixture or reaction product (solventsfor cleaning or degreasing)
Incorporation into a fonnulation, mixture or reaction product (adhesivesand sealant chemicals)
Incorporation into a fonnulation, mixture or reactionproduct (solventswhich become part of product
formulationor mixture)
Incorporation into articles
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As a solvent for batch vapor degreasing(open-top)
As a solvent for batch vapor degreasing(closed-loop)
As a solvent for in-line vapor degreasing(conveyorized)
As a solvent for in-line vapor degreasing(we~leaner)
As a solvent for eold cleaning
As a solvent for aerosol spray degreaser/cleaner
As a solvent for mold release
As a lubricantand grease in tap and die fluid
As a lubricantand grease in penetratinglubricant
As an adhesive and sealant in solvent-basedadhesives and sealants
As an adhesive and sealant in solvent in tire repair cement/sealer
As an adhesive and sealant in solvent in mirror edge sealant
As a functional fluid in heat exchange fluid
In paints and coatings as a diluent in solvent-basedpaints and coatings
In cleaning and furniture care productsas carpet cleaner
In cleaning and furniture care products as wipe cleaning
In laundry and dishwashingproducts as spot remover
In arts, crafts, and hobby materials as fixatives and finishing spray coatings
As corrosion inhibitors and anti-scaling agents
As processing aids in process solvent use in battery manufacture
As processing aids in process solvent used in polymer fiber spinning,fluoroelastomermanufactureand
Alcantara manufacture
As processing aids in extractionsolvent used in caprolactammanufacture
As processing aids in precipitant used in beta-cyclodextrinmanufacture

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Industrial/Commercial Uses that Present an Unreasonable Risk
•
•

•
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•
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As ink, toner and colorant products in toner aid
In automotive care products as brake parts cleaner
In apparel and footwear care products as shoe polish
As hoof polish
As gun scrubber
As lace wig and hair extensions glues
As pepper spray
Other miscellaneous industrial and commercial uses

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Consumer Uses that Present ao Unreasonable Risk
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•

As a solvent in brake and parts cleaner
As a solvent in aerosol electronic degreaser/cleaner
As a solvent in liquid electronic degreaser/cleaner
As a solvent in aerosol spray degreaser/cleaner
As a solvent in liquid degreaser/cleaner
As a solvent in aerosol gun scrubber
As a solvent in liquid gun scrubber
As a solvent in mold release
As a solvent in aerosol tire cleaner
As a solvent in liquid tire cleaner
As a lubricant and grease (tap and die fluid)
As a lubricant and grease (penetrating lubricant)
As an adhesive and sealant (solvent-based adhesive and sealant)
As an adhesive and sealant (mirror edge sealant)
As an adhesive and sealant (tire repair cement/sealer)
As a cleaning and furniture care product (carpet cleaner)
As a cleaning and furniture care product (aerosol spot remover)
As a cleaning and furniture care product (liquid spot remover)
In arts, crafts, and hobby materials as fixative and :finishingspray coating
In apparel and footwear products as shoe polish
As fabric spray
As film cleaner
As pepper spray
As hoof polish
As toner aid
As lace wig and hair extension glues

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Disposal that Presents an Unreasonable Risk
•

Disposal

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1 INTRODUCTION
This document presents the draft risk evaluation for trichloroethylene(ICE) under the Frank R.
Lautenberg Chemical Safety for the 21st Century Act which amended the Toxic Substances Control Act,
the Nation's primary chemicals management law, in June 2016.
The EPA published the scope of the risk evaluation forTCE (U.S. EPA 2017i) in June 2017, and the
problem formulation in May, 2018 (U.S. EPA 2018d). which represented the analytical phase of risk
evaluation in which ''the purpose for the assessment is articulated,the problem is defined , and a plan for
analyzing and characterizing risk is determined" as described in Section 2.2 of the Framework/or
Human Health Risk Assessmentto ln(orm DecisionMaking. The problem formulation (U.S. EPA,
2018d) presented three conceptual models and an analysis plan. Based on EPA's analysis of the
conditions of use, physical-chemicaland fate properties, environmentalreleases, and exposure
pathways, the problem formulation preliminarily concluded that further analysis was necessary for
exposure pathways to ecological receptors exposed via surface water, workers, and consumers. The
conclusions of the problem formulationwere that no further analysis was necessary in the risk
evaluation for sediment, soil and land-applied biosolid pathways leading to exposure to terrestrial and
aquatic organisms and for water pathways leading to exposure to terrestrial organisms. Further analysis
was not conducted for biosolid, soil and sediment pathways, and for water pathways of exposure to
terrestrial organisms, based on a qualitative assessment of the physical-chemicalproperties and fate of
trichloroethylene in the environment and a quantitative comparison of hazards and exposures for aquatic
and terrestrial organisms. The qualitative assessment for trichloroethyleneis presented in Appendix H.
EPA also excluded from risk evaluation ambient air, drinking water, land disposal, ambient water, and
waste incineration pathways leading to exposures to the general population and terrestrial organisms
since those pathways are regulated under other environmental statutes administered by EPA which
adequately assess and effectively manage exposures. EPA received comments on the published problem
formulation for tricbloroethylene and has considered the comments specific to trichloroethylene, as well
as more general comments regarding EPA's chemical risk evaluation approach for developing the draft
risk evaluations for the first IO chemicals EPA is evaluating.
The EPA indicated in the analysis plan of the problem formulation that it would review the full study
reports obtained for physical and chemical properties, environmentalfate properties, environmental
hazard and human health hazard studies. For human exposure pathways, the EPA further analyzed
inhalation exposures to vapors and mists for workers, occupational non-users consumers, and
bystanders. Dermal exposures were analyzed for skin contact with liquids for workers and consumers.
For environmental release pathways, the EPA further analyzed surface water exposure to aquatic
vertebrates, invertebrates, and plants.
In this draft risk evaluation, Section 1.1 presents the basic physical-chemicalcharacteristics of
trichloroethylene, as well as a background on regulatory history, conditions of use, and conceptual
models, with particular emphasis on any changes since the publication of the problem formulation. This
section also includes a discussion of.the systematic review process utilized in this draft risk evaluation.
Section 1 provides a discussion and analysis of the exposures,both health and environmental, that can
be expected based on the conditions of use for trichloroethylene. Section 3 discusses environmental and
health hazards oftrichloroethylene. Section 4 presents the risk characterization,where EPA integrates
and assesses reasonably available information on health and environmentalhazards and exposures, as
required by TSCA (15 U.S.C. 2605(b)(4)(F)).This section also includes a discussion of any
uncertainties and how they impact the draft risk evaluation. Section 5 presents EPA' s proposed
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determinationof whether the chemical presents an unreasonablerisk under the conditionsof use, as
required under TSCA (15 U.S.C. 2605(b)(4)).

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As per EPA's final rule, Proceduresfor ChemicalRisk EvaluationUnder the AmendedToxic
SubstancesControlAct (82 FR 33726 (July 20, 2017)), this draft risk evaluation will be subject to both
public comment and peer review, which are distinct but rdated processes. EPA is providing 60 days for
public comment on any and all aspects of this draft risk evaluation,including the submissionof any
additional information that might be relevant to the science underlying the risk evaluationand the
outcome of the systematicreview associated with trichloroethylene.This satisfies TSCA (15 U.S.C.
2605(b)(4)(H)), which requires EPA to provide public notice and an opportunity for comment on a draft
risk evaluation prior to publishing a final risk evaluation.

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Peer review will be conducted jn accordance with EPA 's regulatory procedures for chemical risk
evaluations, including using the EPA Peer Review Handbookand other methods consistent with section
26 ofTSCA (See 40 CFR 702.45). As explainedin the Risk EvaluationRule (82 FR 33726 (July 20,
2017) ), the purpose of peer review is for the independentreview of the science underlyingthe risk
assessment.Peer review will therefore address aspects of the underlying science as outlined in the
charge to the peer review panel such as hazard assessment,assessment of dose-response,exposure
assessment,and risk characterization.

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As EPA explained in the Risk EvaluationRule (82 FR 33726 (July 20, 2017)), it is important for peer
reviewers to consider how the underlying risk evaluationanalyses fit together to produce an integrated
risk characterization,which forms the basis of an unreasonablerisk determination.EPA believes peer
reviewers will be most effective in this role if they receive the benefit of public comments on draft risk
evaluationsprior to peer review. For this reason, and consistent with standard Agency practice, the
public comment period will precede peer review on this draft risk evaluation. The final risk evaluation
may change in response to public commentsreceivedon the draft risk evaluation and/or in response to
peer review, which itself may be informed by public comments.BPA will respond to public and peer
review comments received on the draft risk evaluationand will explain changes made to the draft risk
evaluationfor trichloroethylenein response to those comments in the final risk evaluation.

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EPA solicited input on the first 10 chemicals as it developeduse documents, scope documents, and
problem formulations.At each step, BPA has received informationand comments specificto individual
chemicals and of a more general nature relating to various aspects of the risk evaluationprocess,
technical issues, and the regulatory and statutoryrequirements.EPA has considered comments and
information received at each step in the process and factored in the information and comments as the
Agency deemed appropriate and relevant including commentson the published problem formulation of
trichloroethylene.Thus, in addition to any new commentson the draft risk evaluation,the public should
re-submit or clearly identify at this point any previouslyfiled comments, modified as appropriate, that
are relevant to this risk evaluation and that the submitterfeels have not been addressed.EPA does not
intend to further respond to comments submittedprior to the publication of this draft risk evaluation
unless they are clearly identified in commentson this draft risk evaluation.
EPA continuesto review the recent court decision in Safer Chemicals Healthy Families v. EPA, Nos.
17-72260 et al. (9th Cir. 2019). This draft risk evaluationdoes not reflect any changes that may occur as
a result of that decision. EPA is still seeking public comment on and peer review of this version.
however. EPA will communicatethe Agency's plans, including the possibility of supplementalversions,
in response to the court decision as appropriate.
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1.1 Physical and ChemicalProperties

----Physical-chemicalproperties influence the environmental behavior and the toxic properties of a
chemical, thereby informingthe potential conditionsof use, exposurepathways and routes and hazards
that EPA considered. For scope developmen~EPA consideredthe measured or estimated physicalchemical properties set forth in Table 1-1and found no additional information duringproblem
formulation or the draft risk evaluationthat would change these values.
TCE is a colorless liquid with a pleasant, sweet odor resemblingthat of chloroform. It is considered a
volatile organic compound (VOC) because of its moderate boiling point, 87.2°C, and high vapor
pressure, 73.46 mm Hg at 25°C. TCE is moderatelywater soluble (1.280 g/L at 25°C) and has a.log
octanol/water partition coefficient(Kow) of2.42. The density ofTCE, 1.46 g/m3 at 20°C, is greater than
that of water.

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Table 1- 1 PhlYSICft
. I an d Chem1ca
. I Prooerties ofTCE
Value
Propertv

Molecular Formula
Molecular Wei2bt

References

a

Boiling Point
Density

CiHCh
131.39g/mole
Colorless,liquid, sweet,
pleasant odor, resembles
chloroform
-84.7°C
s1.2°c
1.46 g/cm3 at 20°C

Vapor Pressure

73.72 mmHg at 25°Cb

(Daubert and Danner.
1995)

Vapor Density

4.53

(O'Neil et al.. 2006 )

Water Solubility

1,280mg/Lat 25°C

(Horvath et al.. 1999)

Octanol/WaterPartition
Coefficient (Lo g Kow l

2.42

Henry's Law Constant

9.85E-03atm·m3/mole

Flash Point

90°C (closed CUD)

Auto Flammabili ty

Viscosity
Refractive Index

410°c (Estimated)
0.545 mPa·s at 25°C
1.4775at 20°C

Dielectric Constant

3.4 eo at 16°C

Physical Form
Meltin g Point

(O'Neil et al.. 2006 )

(Lide. 2007 )
(Lide, 2007)
(ECB. 2000 )

(Baner jee et al.. 1980)

(Leiuhton and Calo.
1981_)

<ECB. 2000 )
(WHO. 1985)
(Lide. 2007)
(O'Neil et al .. 2001 )
(Weast and Selbv.
196~)

a Measuredunlessotherwisenoted
b This value was updatedbased on systematicreview re,.analysisof originalvalues. The originalvalue of73.46
mmHg, from (Daubert and Danner. 1989), was used for occupationaland consumermodelingof inhalation
exnosures:The effect of this small differenceis exoectedto be ne~l~ible for associatedexoosureestimates.

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1.2 Uses and Production Volume
This section contains use and productionvolume information for TCE.
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1.2.1

Data and Information Sources

The summary of use and production volume information for TCE that is presented below is based on
research conducted for the Problem FormulationDocument Trichloroethylene(EPA-740-Rl -7014) and
any additional information that was learned since the publication of that document. The previous
research was based on reasonably available information, including the Use and MarketProfilefor
Trichloroethylene,(EPA-HO-OPPT-2016-0737-0056)1 public meetings, and meetings with companies,
industry groups, chemical users and other stakeholders to aid in identifying conditions of use and
verifying conditions of use identified by the EPA. The information and input received from the public,
stakeholder meetings and the additional contacts was incorporated into this section to the extent
appropriate. Thus, EPA believes the manufacture, processing, distribution, use and disposal activities
constitute the conditions of use within the scope of the risk evaluation for trichloroethylene, based on
reasonably available information.
1.2.2

Domestic Manufacture ofTricbloroethylene

A life cycle diagram is provided (
Figure 1-1) depicting the conditions of use that are within the scope of the risk evaluation during various
life cycle stages including manufacturing, processing, use (industrial, commercial, consumer; when
distinguishable), distribution and disposal. The information is grouped according to Chemical Data
Reporting (CDR) processing codes and use categories (including functional use codes for industrial uses
and product categories for industrial, commercial and consumer uses), in combination with other data
sources (e.g.,, published literature and consultation with stakeholders),to provide an overview of
conditions of use. The EPA notes that some subcategories of use may be grouped under multiple CDR
categories.

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For the purposes of this risk evaluation, CDR definitions were used. CDR use categories include the
following: "industrial use" means use at a site at which one or more chemicals or mixtures are
manufactured (including imported) or processed. "Commercial use" means the use of a chemical or a
mixture containing a chemical (including as part of an article) in a commercial enterprise providing
saleable goods or services. "Consumer use" means the use of a chemical or a mixture containing a
chemical (including as part of an article, such as furniture or clothing) when sold to or made available to
consumers for their use (U.S. EPA. 2016d).
To understand conditions of use relative to one another and associated potential exposures under those
conditions of use, the life cycle diagram includes the production volume associated with each stage of
the life cycle, as reported in the 2016 CDR reporting (U.S. EPA, 2016d) when the volume was not
claimed confidential business infonnation (CBI). The 2016 CDR reporting data for TCE are provided in
Table 1-2 for TCE from the EPA's CDR database (U.S. EPA, 2016d). For the 2016 CDR reporting
period, non-confidential data indicate a 1otalof 13 manufacturers and importers of TCE in the United
States.
Table 1-2 Production Volume ofTCE in CDR Reportina Period (2012 to 2015) •
Reporting Y ear

2012

2013

2014

2015

Total Aggregate
Production Volume (lbs)

220,536,812

198,987,532

191,996,578

171,929,400

• The CDR datafor the 2016 reporting
period is available
via ChemView<http
s:/Lj
ava.epa,goy/chemview).
Becauseof an
ongoingCBIsubstantiation
process requiredby amendedTSCA,theCDRdataavailable
in therisk evaluation
is more
specificthancurrentlyin ChemView.

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As reported in the Use Document [EPA-HO-OPPT-2016-0737-0003(U.S. EPA . 2017c)], as well as in
the 2014 TCE risk assessment (U.S. EPA. 2014b), an estimated 83.6% ofTCE's annual production
volume is used as an intermediate in the manufacture of the hydrofluorocarbon, HFC-134a, an
alternative to the refrigerant chlorofluorocarbon,CFC-12. Another 14.7%ofTCE production volume is
used as a degreasing solvent, leaving approximately 1.7% for other uses. Also reflected in the life cycle
diagram is the fact that TCE, as a widely used solvent, has numerous applications across industrial,
commercial and consumer settings.
Descriptions of the industrial, commercial and consumer use categories identified from the 2016 CDR
and included in the life cycle diagram (
Figure 1-1.) are summarized below (U.S. EPA. 2016d). The descriptions provide a brief overview of the
use category; the [EnvironmentalReleasesand OccupationalExposureAssessment. Docket: EPA-HQOPPT-2019-0500)]contains more detailed descriptions (e.g.,, process descriptions, worker activities,
process flow diagrams, equipment illustrations)for each manufacture, processing, use and disposal
category. The descriptions provided below are primarily based on the corresponding industrial function
category and/or commercial and consumer product category descriptions from the 2016 CDR and can be
found in the EPA's Instructions for Reporting 2016 TSCA Chemical Data Reporting (U.S. EPA. 2016b).
The following describes several categories where TCE has been used; the [EnvironmentalReleases and
OccupationalExposure Assessment. Docket:EPA-HQ-OPPT-2019-0500)]provides additional processrelated information on the remaining categories and life cycle stages.
The "Solvents for Cleaning and Degreasing" category encompasses chemical substances used to
dissolve oils, greases and similar materials from a variety ofsubstrates including metal smfaces,
glassware and textiles. This category includes the use ofTCE in vapor degreasing, cold cleaning and in
industrial and commercial aerosol degreasing products.
The "Lubricants and Greases" category encompasses chemical substances contained in products used
to reduce friction, heat generation and wear between solid surfaces. 1bis category includes the use of
TCE in penetrating lubricants, and tap and die fluids for industrial, commercial and consumer uses.
The "Adhesives and Sealants" category encompasses chemical substances contained in adhesive and
sealant products used to fasten other materials together. This category includes the use ofTCE in mirroredge sealants, lace wig and hair extension glues and other adhesive products.
The "Functional Fluids (closed system)" category encompasses liquid or gaseous chemical substances
used for one or more operational properties in a closed system. Examples are heat transfer agents (e.g.,,
coolants and refrigerants).
The "Paints and Coatings" category encompasses chemical substances contained in paints, lacquers,
varnishes and other coating products that are applied as a thin continuous layer to a surface. Coating
may provide protection to surfaces from a variety of effects such as corrosion and ultraviolet (UV)
degradation; may be purely decorative; or may provide other functions. The EPA anticipates that the
primary subcategory to be the use ofTCE in solvent-based coatings. This category covers industrial,
commercial and conswn.eruses of paints and coatings.
The "Cleaning and Furniture Care Products" category encompasses chemical substances contained
in products that are used to remove dirt, grease, stains and foreign matter from :furnitureand :furnishings,
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or to cleanse, sanitize, bleach, scour, polish, protect or improve the appearance of surfaces. This
category includes the use ofTCE for spot cleaning and carpet cleaning.
The "Laundry and Dishwashing Products" category encompasses chemical substances contained in
laundry and dishwashing products and aids formulated as a liquid, granular, powder, gel, cakes, and
flakes that are intended for consumer or commercial use.

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The "Arts, Crafts and Hobby Materials" category encompasses chemical substances contained in arts,
crafts, and hobby materials that are intended for consumer or commercial use.

1.3 Regulatory and Assessment History
The EPA conducted a search of existing domestic and international laws, regulations and assessments
pertaining to TCE. The EPA compiled this summary from data available from federal, state,
international and other government sources, as cited in Appendix A.

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Federal Laws and Regulations
TCE is subject to federal statutes or regulations, other than TSCA, that are implemented by other offices
within the EPA and/or other federal agencies/departments. A summary of federal laws, regulations and
implementing authorities is provided in Appendix A.l.
State Laws and Regulations
TCE is subject to state statutes or regulations implemented by state agencies or departments. A summary
of state laws, regulations and implementing authorities is provided in Appendix A.2
Laws and Regulations in Other Countries and International Treades or Agreements
TCE is subject to statutes or regulations in countries other than the United States and/or international
treaties and/or agreements. A summary of these laws, regulations, treaties and/or agreements is provided
in Appendix A.3
The EPA has identified assessments conducted by other agency programs and organizations (see Table
1-3).Depending on the source, these assessments may include information on conditions of use,
hazards, exposures, and potentially exposed or susceptible subpopulations (PESS)-infonnation useful
to the EPA in preparing this risk evaluation. Table 1-3 shows the assessments th.at have been conducted.
In addition to using this information, EPA conducted a full review of the data collected [see
Trichloroethylene (CASRN 79-01-6) Bibliography: Supplemental File for the TSCA Scope Document,
EP A-HO-OPPT-2016-073 7) using the literature search strategy (see Strategy for Conducting Literature
Searches/or Trich/oroethylcne: Supplemental File for the TSCA Scope Document, EPA-HO-OPPT2016-073 7] to ensure that the EPA is considering information that has been made available since these
assessments were conducted.

In its previous TCE Risk Assessment (U.S. EPA. 2014b ), risks from use ofTCE in commercial and
consumer solvent degreasing (aerosol and vapor), consumer use as a spray-applied protective coating for
arts and crafts and commercial use as a spot remover at dry-cleaning facilities were assessed. The TCE
Risk Assessment wa,; used to support two proposed rules under TSCA section 6 (81 FR 91592 ;
December 12, 2016; 82 FR 7432 ; January 19,2017) to address risks from use ofTCE. Along with other
reasonably available information, the EPA used the existing TSCA risk assessments to inform its
development of the TCE risk evaluation.

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600 Table 1-3. Assessment History of TCE
Authoring Organization

Assessment

EPA Assessments
Office of Chemical Safety
and Pollution Prevention
(OCSPP)/ Office of
Pollution Prevention and
Toxics (OPPT)

TSCA Work Plan Chemical Risk Assessment Trichloroeth~lene: De~sin
and Arts & Crafts Use (U.S. EPA. 2014b)

OCSPP/OPPT

Su12gl
emental Occu~ tional Ex12Q
sure and Risk Reduction Technical Report in Sup12Qrt
of
Risk Management O9tions for Trichloroeili:tlene (TCE} Use in Aerosol Degreasing (~
EPA20 16t)

OCSPP/OPPT

Sum1Iemental Eximsure and Risk Reduction Technical Reimrt in Sug(!ort of Risk
Management ORtions for Trichloroeth)'.lene {TCE) Use in Consumer Aerosol De~asin g
(U.S. EPA. 2016e)

OCSPP/OPPT

Suimlemental OccuW!tional Ex12Q
sure and Risk Reduction Technical Rei;iort in Support of
Risk Management Ogtions for Trichloroethvlene {TCE}Use in Spot Cleaning (U.S. EPA,
2016g)

OCSPP/OPPT

Sugplemental Occueational ExRQsure and Risk Reduction Technical Re12Q
rt in Su1,m
ort of
Risk Management Options for Trichloroeth)'.lcne {TCE) Use in Va12Qr
Degreasing [RlN
2070-AKl 11(U .S. EPA, 2016h)

g, SRQtCleaning

Integrated Risk Infonnation Toxicological Review of Trichloroethylene (U.S. EPA, 201 le )
System (IRIS)
for Trichloroeth):'.
lene (TCE) and Related Chemicals (U.S.
Sources, Emission and EXJ;!QSure
National Center for
Environmental Assessment. EPA. 2001)
(NCEA)
Office of Water (OW)/
Office of Science and
Technology (OST)

Undate of Human Healfu Ambient Wj!terQual ity Criteria: Trichloroeth):'.
lene (TCE} 79-01-6
(U .S. EPA, 20 15b)

Other U.S.-Based Organizations
Agency for Toxic

Draft Toxicologic-alProfile for Trichloroethylene (ATSDR. 2014a)

Substances and Disease

Registries (ATSDR)

Final Toxicological Profile for Trichloroeth):'.
lene
(ATSDR. 2019)

National Research Council Assessing the Human Health Risks of Trichloroeth)'.lene: Ke):'.Scientific Issues (NRC, 2006)
(NRC)

Public Heath Goal for Trichloroethylene in Drinking Water (Ca!EPA, 2009)
Office ofEnviromnental
Health Hazard Assessment
(OEHHA), Pesticide and
Environmental Toxicology
Section

International
Institute for Healfu and
Consumer Protection,

European UniQ
n Risk Assessment R~RQrt. I richloroethy(ene ffiCB, 2Q04}

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Authoring Organization

Assessment

European Chemicals

Bureau
Australia National
Industrial Chemicals
Notification and
Assessment Scheme

Trichloroeth, Jene: Priorit, Existing Chemical Assessment Re~ort No. 8 (NICNAS. 2000)

(NICNAS)
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602
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1.4 Scope of the Evaluation

----------------------

---

1.4.1 Conditions of Use Includedin the Risk Evaluation
TSCA § 3(4) defines the conditionsof use (COUs) as "the circumstances,as determinedby the

Administrator,under which a chemical substance is intended, known, or reasonably foreseen to be
manufactured , processed, distributed in commerce,used, or disposed of." The conditions of use are
described below in Table 1-4 and Table 1-5. No additional information was received by the EPA
following the publication of the problem formulation(U. S. EPA. 2018d) that would update or otherwise
require changes to the conditionsof use or the life cycle diagram (
Figure 1-1) as presented in the problem formulation(U.S. EPA. 2018d). Nonetheless,EPA decided to
reorganizethe conditions of use for this risk evaluationbased on how are they assessed by exposure
scenario. In this risk evaluation,the COUs as described in (U.S. EPA. 2018d ) were evaluated for
occupational scenarios based on correspondingoccupationalexposure scenarios (OES) (Table 1-4). The
occupationalCOUs are also applicableto environmentalreceptors based on water releases from these
activities. Consumer scenarioswere evaluatedseparatelyfrom occupationalscenarios, and EPA recategorized certain COUs based on product function.None of these changes resulted in any difference in
how these products are or would have been assessed, they simply reflect a recategorizationin order to
improve clarity. Additionally,subcategorieswere added based on availabilityof differing forms of a
product (e.g., aerosol vs liquid). The updated consumer conditionsof use and explanationsfor the
changes are presented in Table 1-5.

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.

Table 1-4 Catee:onesandS ubeatee:onesoro ccu ,atlonaIC ondi'tions ofU sean dC orresnondin2OccuoationalExoosure Scenario
OccupationalExposure
b
Subcategory
Category•
Scenario(OES)
References
Life Cycle Stage
Manufacture
Processing

Domesticmanufacture Domesticmanufacture

Manufacturing

(U.S. EPA, 2016d}

Import

Import

Repackaging

!U.S. EPA. 2016d}

Processing as a
reactant/
intermediate

Intermediatein industrial gas
Processing as a reactant
manufacturing(e.g.,,
manufactureoffluorinated
gases used as refrigerants, foam
blowing agents and solvents)

Processing Incorporationinto
formulation,mixture
or reaction product

Solvents (for cleaning or
degreasing)

Adhesivesand sealant
chemicals

Formulation of Aerosol
and Non-Aerosol
Products

!U.S. EPA, 2016d}; EPA-

HO-OPPT-2016-07370013: EPA-HO-OPPT2016-0737-0013: EPAHO-OPPT-2016-07370026; EPA-HO-OPPT2016-0737-0027
(U.S. EPA . 2016d}
(U.S. EPA , 2016d)
(U.S. 1~r A , 2016d}; EPA-

Solvents (which become part of
product formulationor mixture)
(e.g.,. lubricants and greases,
paints and coatings, other uses)

HQ-OPPT-2016-07370003; EPA-HQ-OPPT2016-0737-0056
! U.S. EPA . 2016d}

Processingincorporatedinto
articles

Solvents (becomes an integral
componentsof articles)

Repackaging

Solvents (for cleaning or
degreasing)

Repackaging

{U.S. EPA, 2016d}

Recycling

Recycling

Process Solvent
Recycling and Worker
Handling of Wastes

(U.S. EPA, 20171)

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Life Cycle Stage
Distribution in commerce

Category•
Distribution

Industrial/commercial use Solvents (for cleaning
or degreasing)

Subcategory

b

Occupational Exposure
Scenario (OES)

References

Not assessed as a
separate operation;
exposures/releases from
distribution are
considered within each
condition of use.

EPA-HQ-OPPT-2016-

Batch vapor degreaser (e.g.,,
open-top, closed-loop) c

Batch Open-Top Vapor
Degreasing;
Batch Closed-Loop
Vapor Degreasing

EPA-HO-OPPT-20160737-0003 , {U.S. EPA.
2014b) . (U.S. EPA ,
2016h) , EPA-HO-OPPT2016-0737-0056

In-line vapor degreaser (e.g.,,
conveyorized, web cleaner) c

Conveyorized Vapor
Degreasing;
Web Vapor Degreasing

EPA-HQ-OPPT-20160737-0003 . {U.S. EPA .
2014b) . (U.S. EPA .
2016h) , EPA-HO-OPPT2016-073 7-0056

Cold cleaner

Cold Cleaning

EPA-HO-OPPT-20160737 -0003 ; !U.S. EPA .
2017h) ; EPA-HO-OPPT2016-073 7-0056

Aerosol spray degreaser/
cleaner c

Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases

EPA-HO-OPPT-20160737-0003 . {U .S. EPA .
2014b) , <U.S. EPA ,
2016f) , (U.S. EPA .
2016e) , EPA-HO-OPPT2016-0737-0056

Distribution

Mold release

Page 44 of 691

0737-0003

EPA-HO-OPPT-20160737-0003 ; EPA-HQOPPT-2016-073 7-0056

 INTERAGENCYDRAFT - DO NO 1 Cllf OR QUOTE

Life Cycle Stage

Category•

Subcategory

b

Tap and die fluid
Lubricantsand
greases/lubricantsand
lubricant additives

Penetrating lubricant

Adhesivesand sealants Solvent-basedadhesives and
sealants

Functional fluids
(closed systems)

Occupational Exposure
Scenario (OES)

MetalworkingFluids

References

{U.S.EPA , 2016d }; FPAHO-OPPT-2016-07370003 ; EPA-H O-OPPT2016-0737-0028 , EPAHO-OPPT-2016-07370056

Aerosol Applications:
Spray
Degreasing/Cleaning,
AutomotiveBrake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases;
MetalworkingFluids

{U.S. EPA. 2016d) , EPAHO-OPPT-2016-07370056 ; EPA-HO-OPPT2016-0737-0003 ; EPAHO-OPPT-2016-07370028

Adhesives, Sealants,
Paints, and Coatings

(U.S. EPA . 2016d }, EPAHO -OPPT-2016-07370056 ; EPA-HQ-OPPT2016-073 7-0003

Tire repair cement/sealer

{U.S. EPA . 2016d ), EPAHO-OPPT-2016-07370056 ; EPA-H Q-OPPT2016-073 7-0003

Mirror edge sealant

EPA-H O-OPPT-20 I 60737-0003 : (U.S. EPA ,
2014b) , EPA-H O-OPPT2016-073 7-00 56

Heat exchange fluid

Page 45 of 691

Other Industrial Uses

{U.S. EPA , 2017h }

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

Li.fe Cycle Stage

Category a
Paints and coatings

Subcategoryb
Diluent in solvent-basedpaints
and coatings

Cleaning and furniture Carpet cleaner
care products

Lawidry and
dishwashingproducts

OccupationalExposure
Scenario (OES)

Re.ferences

Adhesives, Sealants,
Paints, and Coatings

{U.S. EPA, 2016d}, EPAHO-OPPT-2016-07370056; EPA-HQ-OPPT2016-0737-0003; EPAHO-OPPT-2016-07370010: EPA-HO-OPPT2016-0737-0015; EPAHQ-OPPT-2016-07370027;

Spot Cleaning, Wipe
Cleaning and Carpet
Cleaning

EPA-HO-OPPT-20160737-0056; EPA-HOOPPT-2016-0737-0003

Wipe cleaning d

EPA-HO-OPPT-20160737-0056; EPJ\-HOOPPT-2016-0737-0003

Spot remover c

EPA-HO-OPPT-20160737-0003, !U.S. EPA.
2014b). {U.S. EPA,
2016g), EPA-HQ-Ol'PT2016-0737-0056
(U.S. EPA. 2014b}

Arts, crafts and hobby Fixatives and finishing spray
coatings c
materials

Adhesives, Sealants,
Paints, and Coatings

Corrosion inhibitors
Corrosion inhibitors and antiand anti-scaling agents scaling agents

Industrial Processing Aid (U.S. EPA, 2016d}

Processingaids

Process solvent used in battery
manufacture

{U.S. EPA. 2017h}

Process solvent used in polymer
fiber spinning, tluoroelastomer
manufacture and Alcantara
manufacture

(U.S. EPA, 2017h}

Page 46 of 691

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Life Cycle Stage

Subcategoryb

Category 8

OccupationalExposure
Scenario (OES)

References

Extraction solvent used in
caprolactam manufacture

{U.S. EPA , 2017h}

Precipitant used in betacyclodextrin manufacture

(U.S. EPA . 2017hl

Ink, toner and colorant Toner aid
products

Commercial Printing and
Copying

EPA-HQ-OPPT-20160737-0056 ; EPA-HQOPPT-2016-073 7-0003

Automotive care
products

Brake and parts cleaner

Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases

EPA-HQ-OPPT-20 160737-0056 ; EPA-HQOPPT-2016-0737-0003

Apparel and footwear
care products

Shoe polish

Other Commercial Uses

{U.S. EPA , 2017h}

Other uses

Hoof polishes

EPA-HQ-OPPT-20160737-0056 ; EPA-HQOPPT-2016-0737-0003

Pepper spray

EPA-H Q-OPPT- 20160737-0056 ; EPA-HO0PPT-2016-073 7-0003

Lacewig and hair extension

EPA-HQ-OPPT-20160737-0056 ; EPA-HOOPPT-2016-0737-0003

'

glues

Gun scrubber

Page 47 of 691

EPA-H D-OPPT -20160737-0056 ; EPA-HOOPPT-2016-0737-0003

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

Life Cycle Stage

Disposal

Categorya

Disposal

Subcategoryb

OccupationalExposure
Scenario (OES)

References

Other miscellaneousindustrial
and commercialuses

{U.S. EPA . 2017h}

Industrialpre-treatment

(U.S. EPA. 20171)

Process Solvent
Industrialwastewatertreatment Recyclingand Worker
Handling of Wastes
Publicly owned treatment works
(POTW)

•These categoriesof conditionsof use appear in the Life Cycle Diagram, reflect CDR codes, and broadly represent conditionsof use ofTCE in industrialand/or
commercialsettings.
bThese subcategoriesreflect more specific uses of TCE.
e This includes uses assessed in the (U.S. EPA, 2014b),risk assessment.
d This condition of use involves wipe cleaning. Note that the problem formulationdescribed "cleaningwipes" as a condition of use. This referred to the application of a
product that is then wiped off, rather than a pre-wet towelette.

1621
1622

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.

T abl e 15
- Catee:ones an dS u bcatee:ones ore onsumer C ondi"tions ofU se

623
Life
Cycle

Subcategory

Category

Stage
Use

Brake & Parts Cleaner2
AerosolElectronicDegreaser/Cleaner1
Liquid ElectronicDegreaser/Cleaner1
Aerosol Spray Degreaser/Cleaner1
Liquid Degreaser/Clean:er1
AerosolGun Scrubber1•3
Liquid Gun Scrubber1•3
Mold Release
AerosolTire Cleaner1•4
Liquid Tire Cleaner1•4
Tap & Die Fluid
Lubricantsand Greases
PenetratingLubricant5
Solvent-basedAdhesive& Sealant
Adhesivesand Sealants
Mirror-edgeSealant
Tire Repair Cement/Sealer
Carpet
Cleaner
Cleaningand FurnitureCare
Products10
Aerosol Spot Remover1•6
6
Liquid Spot Remover1•
Arts, Crafts, and Hobby Materials
Fixatives& FinishingSpray Coatings7
Apparel and FootwearCare Products Shoe Polish
Fabric Spray8
Other ConsumerUses
Film Cleaner
Hoof Polish
Pepper Spray
Solventsfor Cleaningand
Degreasing

Toner Aid9
Lace Wig and Hair ExtensionGlues

Page 49 of 691

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Life
Cycle
Stage

Category

Subcategory

1 Fonn

was determined based on the specific products identified as representative of the associated product
subcategories.Distinct subcategories based on differing fonns (aerosol and liquid) were not specifically
defined in the Problem Formulation. They were added due to product availabilitybased on additional
research that helped to differentiate specific product fonns (i.e., liquid or aerosol) and types .
2 The brake cleaner subcategory was listed in Table 2-3 of the Problem Formulation as being associated
with the automotive care products category; however,the same brake cleaning conditions of use are now
associated with the broader solvents for cleaning and degreasing category. This change does not impact
evaluated conditions of use, as the evaluatedproduct scenarios are based on the brake cleaner product(s)
and not a broader category of use.
3 The gun scrubber subcategory was listed in Table 2-3 of the Problem Fonnulation as being associated
with the other consumer uses category; however, the same gun scrubber conditions of use are now
associated with the broader solvents for cleaning and degreasing category. This change does not impact
evaluated conditions of use, as the evaluatedproduct scenariosare based on the gun scrubberproduct(s)
and not a broader category of use.
4
Tire cleaner products / subcategories of use were not specifically called out in the Problem Formulation;
however, such products were identified in the 2017 Use and Market Report and Preliminary Infonnation
on Manufacturing,Processing, Distribution,Use, and Disposal:TCE (l l.S. EPA. 2017c) and fit within the
broader Solvents for Cleaning and Degreasingcategory.
5 Based on additional research into the specific product(s) associated with the broader lubricants and
greases category, the subcategoryname was updated from penetrating lubricant to lubricant.
6
The spot remover subcategorywas listed in Table 2-3 of the Problem Formulation as being associated
with the laundry and dishwashing products category;however, the same spot remover conditions of use are
now associated with the cleaning and furniture care products category. This change does not impact
evaluated conditions ofuse, as the evaluated product scenariosare based on the spot removerproduct(s)
and not a broader category of use.
7
Note that this subcategory is referred to as "clear protective coating spray" in U.S. EPA(2014b) and as
"spray fixative" in the TCE Significant New Use Rule (80 FR 47441).
8 Fabric spray (specifically an anti-fray spray) was added following Problem Formulationbased on
identification in the fmal 2014 TCE Work Plan ChemicalRisk Assessment (U.S. EPA. 2014b).
9 The toner aid subcategory was listed in Table 2-3 of the Problem Fonnulation as being associated with
the Ink, toner, and colorant products category;however, the toner aid use is not like use of a toner or
pigment; therefore, the same toner aid condition of use is now associated with the other consumeruse
category. This change does not impact evaluatedconditions of use, as the evaluated product scenarios are
based on the toner aid product(s) and not a broadercategory of use.
H>
Note that the problem formulation described"cleaning wipes" as a condition of use for this category.
However, that referred to the applicationof a product that is then wiped off, rather than a pre-wet
towelette. A number of consumer conditionsof use involve wipe cleaning and are described in detail in
section 2.3.2.62 as leading to dermal contactwith impeded evaporation.

624

Page SOof 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

·~

PROCESSING

MFG/IMPORT

l

INDUSTRIAL,
COMMERCIAL
, CONSUMERUSES
SolvellllfarO.RnC ... .,........
(VolumeOM)
i!,8., VilPO"~ cold deanin&
Mlt'osol~ moldretease

Processinl
asa
Manufacture

rl

(lndudes Import)

Reactant/Intermediate

~

-

(VolumeCSI)
I.'.g., intermedi.itefOf

(17L9 mlilionlbs.)

WASTEDISPOSAL

t.ullrlcaalland~
(18S,0001b$.I

refrigerantmanllfxture

e g., lub!ical'lt, tap and die fluid

-

--

--

Incorporated into
Formulation,Mhlture,
or ReactionProducts

.

Furu:tlanal
FSulds
(doled

I
-

--

...

,,,_.ml

{VolumPCBI)

Disposal

e.g.. refriserillt

(VolumeCBI)

Repadatgina
(wlunle C81)

,.

AdhM!velllld SNlafltl
(Volumf, CBI)
e,s., mirror «ige seal.Hit

-

I

I

PaintsIncl c.oatlnp

(Volume, at)

a...lns end Fumltllnl CentProduetl

I

(Volume CBI)

e.g., carpet clean«

\

,

'

l

I.Mmdlylnll Dilllwellq

Ml, oafb, and~
Mlteriab
e.g., spray.applied protec~ c0.1tin1

,,I

I
I

D

Recycling

I
Manufacture {Includes lmpo,t)

D

Processlns

l625
1626
l627 Figure 1-1. TCE Life Cycle Diagram
1628

l629
l630
l631

.

ProclUCII

e.g., spot retnowf

Apparatand faoawear care ProdtKU
e.g•• shoe polish

I
I

Other Use,, Ind . (.offOIIDft lnhlllttors and
An!J.Sallnl
A&enlS
{Vol-CBI);
PrownlntAlds ; Ink, T_, Colorant
Pnleluas;Autumotlwcare l'Rldudl;
M~
(e,8.,hoot polish. peppe,
spray)

p

1·

D

..

..

Category of Cond1t1oosof Use. The maJOOly
of conditions of use were evaluated lo< both occupational and

consumerscenarios,howeverthereare somedifferencesbasedon re-categorizat
ion of consumeruses.

The life cycle diagram depicts the conditions of use that are within the scope of the risk evaluation during various life cycle stages including
manufacturing, processing, use (industrial, commercial, consumer), distribution and disposal. The production volumes shown are for
reporting year 2015 from the 2016 CDR reporting period (U.S. EPA, 2016d). Activities related to distribution (e.g.,, loading and unloading)
will be considered throughout the TCE life cycle, rather than using a single distribution scenario.
Page 51 of 691

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632
633

634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651

652
653
654
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659
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661
662
663
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665
666

1.4.2 Conceptual Models
The conceptual models for this draft risk evaluationare shown in Figure 1-2., Figure 1-3. and Figure
1-4.. The EPA considered the potential for hazards to hwnan health and the environmentresulting from
exposure pathways outlined in the preliminaryconceptual models of the TCE scope document (U.S.
EPA 2017 d). These conceptual models consideredpotential exposures resulting from consumer
activities and uses, industrial/ commercialactivities,and environmentalreleases and wastes. The
problem formulation documents refinedthe initial conceptualmodels and analysis plans that were
provided in the scope documents (U.S. EPA. 2017d).

For the purpose of this evaluation,EPA consideredworkers and occupationalnon-users,which includes
men and women of reproductiveage (Figure 1-2). Consumer exposure was assessedfor various
pathways for users age 16 and older along with bystandersof all ages (Figure 1-3).
The potential pathways that were determinedto be included in the risk evaluation but not to warrant
further analysis in this draft risk evaluationwere: exposureto both humans and ecological organisms
due to land application of biosolids followingwastewatertreatment, exposure to organisms through the
sediment compartment,and exposure to terrestrial organisms.In the problem formulation,the EPA
determinedthat no further evaluationof these pathways is needed due to the physical/chemical
properties associated with TCE (high vapor pressure)and its rapid volatilizationto air from soil and
water or rapid migration through soil into groundwater.Due to TCE's fate properties, a significant
portion ofTCE would not be available to enter the sedimentcompartment.
The potential pathways that were determinedto be incJudedin the risk evaluation and further analyzed
include:
• Exposure to aquatic species (i.e. aquatic plants) via contaminatedsurface water.
• Inhalation and dermal exposuresto workers and consumers, and inhalation exposures to ONUs
and bystanders, from industrial/commercialactivities and consumer activities.
• Inhalation and dermal exposuresto workers and inhalation exposuresto ONUs from waste
handling, treatment and disposal.
Review and evaluation of reasonably availableinformationon TCE confirmedthe preliminary
conclusions in the problem formulationand as a result, the EPA confirms further analysis of the
pathways outlined in the conceptualmodels. The conceptualmodels from the problem formulation are
shown below in Figure 1-2., Figure 1-3. and Figure 1-4..

Page 52 of 691

 INOUS'r'RlAL
ANDCOMMEROAl
EXPOSURE
PATHWAY

ACTIVITIES
/ USES

EXPOSURE
ROUTE

RECEPTORS
•

HAZARDS

Manufacturing
Processing:

• Processing as ,1

reatt.int/interrnedi~t,
• Incorporated into

formular100s,
miirtures,or
reaction products

liquid cont.Jct

Dermal

Workers•

Vapor/ Mist

Inhalation

Occupational
Non-Users

• Reflaclcasins

HazardsPotffldalty At5odated
WfttlAcuteand]ot ctwonu:
Exposures

• Non-incofporatille

activities

Sees.ctlonZ.4.2

Fugitive

Recycling

Emissions'

Solventsfor Cleanin&and

· Degre~ing
Lubricantsand Greases

Adhesivesand Sealants
Functional Fluids
Paintsand Coatings

Cleaning and FurnitureCare
Products

KEY:

Other Industrial or
CornmercialUses•

--+

Laundryand 0ishwashing
Products

Pathwaysand receptors that were not
further analyzed
Pathwaysthat were not further analyzed.
Pathwaysthat were not further analyzed.

1~,L..

wasteHandllng.

Treatment and 0isposal

Wastt>waterond
LiquidWastes
{SeeFfgurt 2-4)

1667
1668 Figure 1-2. TCE ConceptualModel for Industrialand CommercialActivitiesand Uses: PotentialExposuresand Hazards
The conceptualmodel presents the exposure pathways, exposure routes and hazardsto human receptors from industrial and commercial
1670 activities and uses ofTCE.
1671 a Some products are used in both commercialand consumer applications.Additional uses ofTCE are included in Table 1-4.
1672 b Fugitive air emissionsare those that are not stack emissions, and include fugitive equipment leaks from valves, pump seals, flanges,
1673 compressors,sampling connectionsand open-ended lines; evaporativelosses from surface impoundmentand spills; and releases from
1674 building ventilation systems.
l675 c Receptorsinclude Potentially Exposed or Susceptible Subpopulations(PESS) includingwomen of childbearing age and their children and
1676 geneticallysusceptiblepopulations.
1677 d When data and information are availableto support the analysis, EPA also considers the effect that engineeringcontrols and/or personal
1678 protective equipment have on occupationalexposure levels.
1669

Page 53 of 691

 1679
SOiventsfor Cleaningand
Degreasing
Lubriunts and Greases
Adhesivesand sealants

Liquid Contact

HazardsPotentlally

Vapor/Mist

Associated
with Acute and/or
Chronk
Expo"'res

Cleaningand Furniture Care
Products
Inhalation

Arts, Crafts, and Hobby

Materials
Apparel and Footwear Care
Products
Other ConsumerUses•

KEY:

--+
l680
1681
l682
1683
1684
1685
1686
1687
1688
l689

Pathwaysand receptors that were not
furth er analyzed
Pathwaysthat were not further analyzed.

Pathwaysthat were not further analyzed.

Figure 1-3. TCE Conceptual Model for Consumer Activities and Uses: Potential Exposures and Hazards

The conceptual model presents the exposure pathways, exposure routes and hazards to human receptors from consumer activities and uses of
TCE.
a Some products are used in both commercial and consumer applications. Additional uses of TCE are included in Table 1-4.
b Exposure may occur through mists that deposit in the upper respiratory tract however, based on physical chemical properties, mists of TCE
will likely be rapidly absorbed in the respiratory tract or evaporate and not result in an oral exposure. Although less likely given the physicalchemical properties, oral exposure may also occur from incidental ingestion of residue on hand/body.
c Receptors include Potentially Exposed or Susceptible Subpopulations (PESS).

1690

Page 54 of 691

 lndl.lStrial
PreTreatment or
lndustrl■l

wwr

Water

Aquatic

Sediment

Species

Blosolids

Terrestrial
Species

Ha1ardsPotentially Assodateclwith Acute
and/or ChranlcExposures:
See Sedlon 2..4.l

Wastewatllr«
Liquidwastfl •
POlW

Soil

KEY
:

--+
1691

1692
L693
1694
1695

l696
l697

Pathw.iys and receptors that were not
further analyml
Pathwaysthat were not further analyzed.
Pathwaysthat were not further analyzed.

Figure 1-4. TCE ConceptualModel for EnvironmentalReleases and Wastes:Potential Exposuresand Hazards
The conceptual model presents the exposure pathways,exposure routes and hazards to human and environmentalreceptors from
environmentalreleases and wastes ofTCE.
a Industrial wastewater or liquid wastes may be treated on-site and then released to surface water (direct discharge), or pre-treated and released
to POTW (indirect discharge).

Page 55 of 691

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1.5 Systematic Review
TSCA requires the EPA to use scientific information, technical procedures, measwes, methods,

protocols, methodologies and models consistent with the best available science and base decisions under
section 6 on the weight of scientific evidence. Within the TSCA risk evaluation context, the weight of
the scientific evidence is defined as "a systematic review method,applied in a manner suited to the
nature of the evidence or decision, that uses a pre-establishedprotocol to comprehensively,objectively,
transparently, and consistentlyidentify and evaluateeach stream of evidence, including strengths,
limitations, and relevance of each study and to integrateevidence as necessary and appropriate based
upon strengths, limitations, and relevance". (40 CFR 702.33).
To meet the TSCA § 26(h) science standards, EPA used the TSCA systematic review process described
in the Application of SystematicReview in TSCA Risk Evaluationsdocument (1!.S. EPA, 2018b). The
process complements the risk evaluation process in that the data collection, data evaluation and data
integration stages of the systematic review process are used to develop the exposure and hazard
assessments based on reasonably available information. EPA defines "reasonably available
information" to mean information that EPA possesses, or can reasonably obtain and synthesize for use in
risk evaluations, considering the deadlines for completing the evaluation (40 CFR 702.33).
EPA is implementing systematic review methods and approaches within the regulatory context of the
amended TSCA. Although EPA will make an effort to adopt as many best practices as practicable from
the systematic review community, EPA expects modifications to the process to ensure that the
identification, screening, evaluation and integration of data and information can support timely
regulatory decision making under the aggressive timelines of the statute.
1.5.1 Data and Information Collection
EPA planned and conducted a comprehensive literature search based on key words related to the
different discipline-specific evidence supporting the risk evaluation (e.g.,, environmental fate and
transport; engineering releases and occupational exposure; consumers and environmental exposure; and
environmental and human health hazard) . EPA then developed and applied inclusion and exclusion
criteria during the title and abstract screening to identify information potentially relevant for the risk
evaluation process. The literature and screening strategy as specifically applied to TCE is described in
the Strategyfor ConductingLiterature Searchesfor Trichloroethy/ene(ICE): SupplementalFilefor the
TSCA Scope Document(ll .S. EPA 2017e) and the results of1he title and abstract screening process
were published in the [Trichloroethylene(CASRN 79-01-6)Bibliography:SupplementalFilefor the
TSCA Scope Document; (U.S.-EPA. 2017i)].

For studies determined to be on-topic (or relevant) after title and abstract screening, EPA conducted a
full text screening to further exclude references that were not relevant to the risk evaluation. Screening
decisions were made based on eligibility criteria documented in the form of the populations, exposures,
comparators, and outcomes (PECO) framework or a modified framework4 • Data sources that met the
criteria were carried forward to the data evaluation stage. The inclusion and exclusion criteria for full
text screening for TCE are available in Appendix F of the Problem Formulationof the Risk Evaluation
for Trichloroethylene(U.S. EPA. 2018d)
4

A PESO statement was used during the full text screeningof environmentalfate and transport data sources. PESO stands
for Pathways and Processes,Exposure,Settingor Scenario,and Outcomes. A R.ESOstatementwas used during the full text
screeningof the engineeringand occupationalexposureliterature. RESO stands for Receptors,Exposure, Setting or
Scenario,and Outcomes.

Page 56 of 691

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779
780

Although EPA conducted a comprehensive search and screeningprocess as described above, EPA made
the decision to leverage the literature published in previous assessments5 when identifying relevant key
and supporting data6 and information for developingthe TCE risk evaluation. This is discussed in the
Strategyfor ConductingLiterature Searchesfor Trichloroethylene:Supplemental Document to the
TSCA Scope Document (U.S. EPA. 201?e). In general, many of the key and supporting data sources
were identified in the comprehensive Trichloroethylene(CASRN 79-01-6) Bibliography: Supplemental
Filefor the TSCA Scope Document;(U.S. EPA. 2017i). However, there were instances that EPA missed
relevant references that were not captured in the initial categorizationof the on-topic references. EPA
found additional relevant data and information using backward reference searching, which was a
technique that will be included in future search strategies. This issue was discussed in Section 4 of the
Application o/ S vstematic Review for TSCA Risk Evaluations (U.S. EPA . 2018b ). Other relevant key
and supporting references were identified through targeted supplemental searches to support the
analytical approaches and methods in the trichloroethylene risk evaluation (e.g.,, to locate specific
information for exposure modeling) or to identify new data and information published after the date
limits of the initial search.

EPA used previous chemical assessments to quickly identify relevant key and supporting information as
a pragmatic approach to expedite the quality evaluation of the data sources, but many of those data
sources were already captured in the comprehensive literature as explained above. EPA also considered
newer information not taken into account by previous chemical assessments as described in the Strategy
for ConductingLiterature Searchesfor Trichloroethylene:SupplementalDocument to the TSCA Scope
Document(U.S. EPA 2017e). EPA then evaluated the confidence of the key and supporting data
sources as well as newer information instead of evaluating the confidence of all the underlying evidence
ever published on a chemical substance's fate and transport, environmental releases, environmental and
human exposure and hazards. All other literature from previous authoritative assessments were
considered as supplemental information and were cited in the context of the previous assessment. A
comprehensive evaluation of all of the data and information ever published for a chemical substance
would be extremely labor intensive and could not be achieved considering the deadlines specified in
TSCA section 6(b)(4)(G) for completing such evaluation for most chemical substances especially those
that have a data rich database such as TCE. Furthermore, EPA evaluated how EPA' s evaluation of the
key and supporting data and information and newer information would change the previous conclusions
presented in the previous assessments.
This pragmatic approach allowed EPA to maximize the scientific and analytical efforts of other

regulatory and non-regulatory agencies by accepting for the most part the relevant scientific knowledge
gathered and analyzed by others except for influential information sow-cesthat may have an impact on
the weight of the scientific evidence and ultimately the risk findings. The influential information (i.e.,
key/supporting) came from a smaller pool of sources subject to the rigor of the TSCA systematic review
process to ensure that the risk evaluation uses the best available science and the weight of the scientific
evidence.
s Examples of existingassessmentsare EPA's chemicalassessments(e.g.,previouswork plan risk assessments,problem
formulationdocuments),ATSDR's ToxicologicalProfiles,EPA's IRIS assessmentsand ECHA's dossiers.This is described
in more detail in the Strategy for Conduct ing LiteratureSearchesfor Trich/oroeihylene:SupplementalDocumentto the
TSCA Scope Document(U.S . EPA . 2017e ).
6 Key and supportingdata and infonnation are those that supportkey analyses,arguments,and/orconclusionsin the risk
evaluation.

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782
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784
785
786
787
788
789
790
791
792
793
794
795

Figures 1-5 to 1-9 below depict the literature flow diagrams illustrating the results of this process for
each scientific discipline-specific evidence supporting the draft risk evaluation. Each diagram provides
the total number of references at the start of each systematic review stage (i.e., data search, data
screening, data evaluation, data extraction/data integration) and those excluded based on criteria guiding
the screening and data quality evaluation decisions.
EPA made the decision to bypass the data screening step for data sources that were highly relevant to the
draft risk evaluation as described above. These data sources are depicted as "key/supporting data
sources" in the literature flow diagrams. Note that the number of"key/supporting data sources" were
excluded from the total count during the data screening stage and added, for the most part, to the data
evaluation stage depending on the discipline-specific evidence. The exception was the engineering
environmental releases and occupational exposure data sources that were subject to a combined data
extraction and evaluation step (Figure 1-6.).

*Key/Supporting
Data Sources(n=l)

Data Screening (n:10,039)

Data Evaluation (n:61)

I
796

797

1~~
800
801
802
803
804
805
806
807
808
809

ExcludedReferences
{n=9,979)

Excluded:Refthat are
unacceptable based on the
evaluation criteria (n=9)

Data Extraction/Data Integration (n=S2)
___,

*This is a keyand supporting source from ·existing assessments, the EPISuite TM set of models, that was highly relevant
for the TSCArisk evaluation. This source bypassedthe data screeningstep and moved directly to the data evaluation
step.

Figure 1-5. Literature Flow Diagram for Environmental Fate and Transport
Note: Literature search results for the environmental fate and transport ofTCE yielded 10,040studies. During problem
formulation,following data screening, most environmentalexposure pathwayswere removed from the conceptual models.
As a result, 9,979 studies were deemed off-topic and excluded. One key source(U.S. EPA. 2012b ) and the remaining 61
studies related to environmentalexposure pathways retained in the conceptualmodels entered data evaluation, where 9
studies were deemed unacceptableand 52 moved into data extraction and integration.Note: Data sources identifiedrelevant
to physical-chemicalproperties were not included in this literature flow diagram. The data quality evaluation of physicalchemical properties studies can be found in the supplementaldocument, [Data Quality Evaluation of Physical-Chemical
Properties Studies. Docket: EPA-HQ-OPPT-2019-0500]and the extracted data are presented in Table 1-1.

Page 58 of 691

 (

oataSearchResults (n•10, 132)

[
Key/supporting
<Jata~urces
(11::8}

Data Screening(n-10, 132)

]1--~•.,l

ExcludedRef&rences(n::9,973)

•

-

+

-

Data .Extraction/Data~aluation (rt-1671

Data lnt1!11Jatton
(n•32)

810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
83-1
832
833

)

~

I

Excluded:J;ef tl1atare

unacceptable
baSed on
evafualioncrrteria(n=43J

-

"Data Sourcestnatwere not
integrated (n=92)

Figure 1-6. Literature Flow Diagram for Engineering Releases and Occupational Exposure
Note: Literature search results for environmentalrelease and occupationalexposure yielded l 0, 132 data sources. Of these data
sources, 159 were detennined to be relevant for the risk evaluation through the data screening process. These relevant data
sources were entered into the data extraction/evaluationphase. After data extraction/evaluation,EPA identified several data
gaps and performed a supplemental,targeted search to fill these gaps (e.g., to locate informationneeded forexposuremodeling).
The supplemental search yielded 8 relevant data sources that bypassed the data screening step [List of Key and Supporting
Studies for EnvironmentalReleases and OccupationalExposure. Docket: EPA-HQ-OPPT-2019-0500)]and were evaluated
and extracted in accordance with Appendix D: Data Quality Criteriafor OccupationalExposure and Release Data of the
Application of SystematicReviewfor 1SCA Risk Evalualions document (U.S. EPA. 2018b), Of the 167 sources from which
data were extracted and evaluated, 43 sources only contained data that were rated as unacceptable based on serious flaws
detected during the evaluation. Of the 124 sources forwarded for data integration, data from 32 sources were integrated, and
92 sources contained data that were not integrated (e.g.,,lower quality data that werenot needed due to the existence of higher
quality data, data for release media that were removed from scope after data collection).
*The quality of data in these sources (n=92) were acceptable for risk assessmentpurposes, but they were ultimately excluded
froni further consideration based on EPA's integration approach for environmental reJease and occupational exposure
data/infonnation. EPA' s approach uses a hierarchy of preferences that guide decisions about what types of data/information
are included for further analysis, synthesis and integration into the environmental release and occupational exposure
assessments. EPA prefers using data with the highest rated quality among those in the higher level of the hierarchy of
preferences (i.e., data> modeling> occupationalexposure limits or release limits). If warranted,EPA may use data/infonnation
of lower rated quality as supportive evidence in the environmentalrelease and occupationalexposure assessments.

Page 59 of 691

 ~ Search Results(n • 1128)

C

Data Screening (n =112~

----1111,11

Excluded References{n• 997)

!

______
...,
______
1 J

(

OataEvalualion(n=131)

) ----

,

•excludedReferences (n • 62)

unacceptablebasedon data eveluationcriteria (n .. 15}
Not primarysource,notextractable
or
not most relevant(n • 47)

'-Data ExtractiontOata lnte11ration (n ; 69)

834
835
836
837
838
839
840
841
842
843
844

845
846

Figure 1-7. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources
Note: EPA conducted a literaturesearch to determinerelevantdata sources for assessing exposuresfor trichloroethylene
within the scope of the risk evaluation.This search identified 1128 data sourcesincludingrelevant supplementaldocuments.
Of these, 997 were excludedduring the screeningof the title, abstract,and/or full text and 131 data sources were
recommendedfor data evaluationacross up to five major study types in accordancewithAppendix E:Data Quality Criteria
for Studies on Consumer,General Population and EnvironmentalExposure of the Application of Systematic Review for
TSCA Risk Evaluations document(U.S. EPA. 2018b). Followingthe evaluationprocess, 69 referenceswere forwarded for
further extraction and data integration.EPA has not developeddata quality criteria for all types of exposure information,
some of which may be relevant when estimating consumerexposures.This is the case for absorptionand permeabilitydata
and some product-specificdata such as density and weight fractionoften reported in Safety Data Sheets. As appropriate,EPA
evaluated and summarizedthese data to determinetheir utility with supportingthe risk evaluation.

847

848
849
850
851

Page 60 of 691

 Oilt a Searcn Result s (n

~

8555)

q,

-

Tille /Abstract Screening (n • 8563)

ECOTOXQllena
111::8144)

.i

I

-

Full Tf'XI Screening tn • 41~1

,'
KtylSUppoltinO

Studies
(n

EXQJl1e4
Referencesw. lo

=2l

-

+

Exduc!e.tRUtnctS dUeto
ECOTOXOlteoa
(n.,3501

Ex<.Weo Reteren<:esthat are

unacceptable
oaseo

Data E~alllilllOll
(n 71}

one~lua1IOII
enttnaand/erare
out of scope

&

1n s 46)

,,
00111ExlJac,on I Oato Integration (n - 25)

852
853
854
855
856
857
858
859
860

)

Figure 1-8. Literature Flow Diagram for Environmental Hazard
Note: The environmentalhazard data sourceswere identifiedthrough literaturesearchesand screening strategies using the
ECOTOXicologyKnowledgebaseSystem(ECOTOX)StandingOperatingPr-0cedures.For studies detennined to be on-topic
after title and abstract screening,EPA conducteda full text screeningto furtherexcludereferences that were not relevant to
the risk evaluation.Screening decisionswere made based on eligibilitycriteria as documentedin the ECOTOXUser Guide
(U.S. EPA, 2018c). Additional details canbe fomd in the Strategyfor ConductingLiteratureSearchesfor Trichloroethylene
Supplemental Document to the TSCAScopeDocument (!l..S. EPA . 2017e).

861
862
863
864

865
866
867
868
869

870
871
872
873

874
875
876
877
878
879

The "Key/SupportingStudies" box representsdata sourcescited in an existing assessment(EnvironmentCanada and Health
Canada, 1993) that were considered highly relevant for the TSCA risk evaluationbecausethey were used as key and
supporting informationby another regulatoryorganizationto support their chemicalhazard and risk assessment These
citations were found independentlyfrom the ECOTOXprocess. These studies bypassedthe data screening step and moved
directly to the data evaluation step. These two studieswere ultimately excludedbecause they examinedhazard to terrestrial
species and the relevant exposurepathwayof air releases has since been determinedto be out of scope.
The literature search process for environmentalhazard data found 8,565 citationsfor TCE. At the title and abstract screening
phase, 8,144 citationswere excluded as off-topicusing ECOTOXicologyknowledgebasecriteria The remaining 419
citations underwent a more thorough full text screeningusing the same criteriato determinewhich citations should undergo
data evaluation. For data evaluation,EPA developed data quality evaluation (DQE) criteriato evaluatethe data under TSCA,
based on a combination of EPA' s ECOTOXicologyknowledgebase(ECOTOX)criteria and the Criteria for Reporting and
Evaluating ecotoxicity Data (CRED).There were 71 citations that went to data evaluationfor TCE, which included the
above-mentionedtwo additionalcitations gathered from (EnvironmentCanadaand HealthCanada. 1993) that were later
excluded as out of scope. EPA analyz.edeach of these studies using the DQE results to determineoverall study quality.
Twenty-five studies were considered acceptableand were rated high, medium,or low quality during this analysis. The
extracted data from these 25 studies were used during data integrationfor TCE.

880

Page 61 of 691

 T

Data Search Res ult s (n : 6,049)

l

+

Key13upportiflg

data sources
(n =95>

)

Data Screenin g (n - 5954 )

:I

ExclUdedReferences(n = 5869 )

I

I

n= 85

...

+

Data Evaluati on {n

~

180}

~

+

--

Excluded: Refttlat are

unacceptable
basedon

evaluation criteria(n = 10)

Data Extractloni'OataIntegration (n .. 170)

881
882

Figure 1-9. Literature Flow Diagram for Human Health Hazard

883
884

885
886
887
888
889

890
891
892
893

894
895

Note: The literature search results for human health hazard ofTCE yielded 6,049 studies. This included 95 key and
supporting studies identifiedfrom previous EPA assessments.Of the 5,954 new studies screened for relevance, 5,869 were
excluded as off topic. The remaining 85 new studies together with the 95 key and supportingstuctiesentered data evaluation.
Ten studies were deemed m1acceptablebased on the evaluationcriteria for human health hazard data sources and the
remaining 170 studies were carried forward to data extraction/dataintegration. Additional details can be found in the .~trategy
for ConductingLiterature Searchesfor TrichloroethyleneSupplementalDocument to the TSCAScope Document (U.S. EPA,
2017e).
The "Key/SupportingStucties"box represents data sources cited in an existing assessment (U.S. EPA. 201le) that were
considered highly relevant for the TSCA risk evaluationbecausethey were used as key and supporting informationby
another regulatory organizationto support their chemicalhazard and risk assessment. For a list of the key and supporting
studies, see [List of Key and SupportingStudiesfor HumanHealth Hazard. Docket# EPA-HQ-OPPT-2019-0500]
.

896
897
898
899
900
901
902
903
904
905
906
907
908
909

1.S.2 Data Evaluation
During the data evaluation stage, the EPA assesses the quality of the methods and reporting of results of
the individuaJ studies identified during problem formulation using the evaJuation strategies described in
Application of SystematicReview in TSCA Risk Evaluations(U.S. EPA. 2018b ). The EPA evaluated the
quality of the on-topic TCE study reports identified in [Trichloroethylene (C.ASRN 79-01-6)
Bibliography: SupplementalFilefor the TSCA Scope Document; (U.S. EPA 2017i)], and gave all
studies an overall high , medium, low or unacceptable confidence rating during data evaluation.
The results of the data quality evaluations for key studies are summarized in Section 2.1 (Fate and
Transport), Section 2.2.2 (Releases to the Environment), Section 2.2.6 (Environmental Exposures),
Section 2.3 (Human Exposures), Section 3.1 (Environmental Hazards) and Section 3.2 (Hwnan Health
Hazards). Supplemental files 7 also provide details of the data evaluations including individual metric
scores and the overall study score for each data source (Docket: EPA-HQ-OPPT-2019-0500).

7

See Appendix B for the list of all supplementalfiles.

Page 62 of 691

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916

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919

920
921
922
923
924
925

926

1.5.3 Data Integration
Data integration includes analysis, synthesis and integration of information for the risk evaluation.
During data integration, the EPA considers quality, consistency, relevancy, coherence and biological
plausibility to make final conclusions regarding the weight of the scientific evidence. As stated in
Application of SystematicReview in TSCARisk Evaluations(U.S. EPA. 2018b), data integration
involves transparently discussing the significant issues, strengths, and limitations ·as well as the
uncertainties of the reasonably available information and the major points of interpretation (U.S. EPA,
2018e). EPA defines "reasonably available information" to mean information that EPA possesses, or can
reasonably obtain and synthesize for use in risk evaluations, considering the deadlines for completing
the evaluation (Proceduresfor ChemicalRisk Evaluation Underthe Amended Toxic Substances Control
Act (82 FR 33726).

EPA used previous assessments (see Table 1-3) to identify key and supporting information and then
analyzed and synthesized available evidence regarding TCE's chemical properties, environmental fate
and transport properties and its potential for exposure and hazard EPA's analysis also considered recent
data sources that were not considered in the previous assessments (Section 1.5.1) as well as reasonably
available information on potentially exposed or susceptible subpopulations.

927

928
929

930
931
932
933
934

The exposures and hazardssections describe EPA's analysis of the influential information (i.e., key and
supporting data) that were found acceptable based on the data quality reviews as well as discussion of
other scientific knowledge using the approach described in Section 1.5.1. The exposure section also
describes whether aggregate or sentinel exposures to a chemical substance were considered under the
conditions of use within the scope of the risk evaluation, and the basis for that consideration.

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1

2 EXPOSURES

2
3
4
5

For TSCA exposure as sessments , EPA evaluated exposures and releases to the environment resulting
from the conditions of use applicable to TCE. Post -release pathways and routes were described to
characterize the relationship or connection between the conditions of use for TCE (Section 1.4.1) and
the .exposure to human receptors, including potentially exposed or susceptible subpopulations (PESS)
and ecological receptors. EPA considered, where relevant, the duration, intensity (concentration),
frequency and number of exposures in characterizing exposures to TCE .

6
7

8
9
10
11
12
13
14
15
16
17
18

2.1 Fate and Transport

---------

Environmental fate includes both transport and transformation processes . Environmental transport is the
movement of the chemical within and between environmental media. Tran sformation occurs through the
degradation or reaction of the chemical with other species in the environment. Hence , knowledge of the
environmental fate of the chemical informs the determination of the specific exposure pathways and
potential human and environmental receptors EPA expects to consider in the risk evaluation. Table 2-1
presents environmental fate data that EPA identified and considered in the Scoping and Problem
Formulation documents as well as additional data extracted form the systematic review process.

Table 2-1 Environmental Fate Characteristic ofTCE
Property or
Endpoint

Value a

References

Data Quality
Rating

Indirect
photodegradation

1-11 days (atmospheric oxidation based on
measured hydroxyl radical oxidation)

Hydrolysis half-life

10.7 months (average; decomposition in aerated
water in the dark; part of the reaction may have
occurred in the vapor phase)

(Dillin !! et al .. 1975)

38.9%after 28 days (aerobic OCED 302B

(Toba jas et al., 2012)

High

100% degradation after 60 days (anaerobic
serum bottle test)

(Lom1.et al.. 1993)

High

100% degradation after 40 days (anaerobic
groundwater microcosms with added
hydrogen/acetate)

(Schmidt and Tiehm .

High

Biodegradation

(11.S. EPA. 2014b )

High

High

Inherent biodegradability test)

TC E removed slowly with a reduction of 40%
after 8 weeks (TCE (200 µg/L) incubated with
batch bacterial cultures under methanogenic
conditions)

Page 64 of 691

2008)

(Bouwer and
Mccart ,, 1983)

High

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Property or

Value a

Endpoint

References

Data Quality
Rating

99.98% degradation after 2 or 4 days (anaerobic
continuous flow)

(Vogel and
McCart y. 1985)

High

100% degradation after 20 days (aerobic with
Methane culture, aerobic with phenol culture)

(Lon g et al.. 1993)

High

17 (Bluegill)

(Barrows. 1980)

High

Bfoaccumulation
factor (BAF)

24 (estimated)

(U.S. EPA . 2012b )

High

Organic
carbon:water
partition coefficient
(Log~c)

1.8 (estimated)

(U.S. EPA. 2014b )

High

Bioconcentration
factor (BCF)

• Measuredunless otherwise noted

19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44

2.1.1 Fate and Transport Approach and Methodology
EPA gathered and evaluated environmental fate information according to the process described in the
Application of SystematicReview in TSCARisk Evaluations(11.S. EPA . 2018b ). Reasonable available
environmental fate data, including biotic and abiotic degradation rates, removal during wastewater
treatment, volatilization from lakes and rivers, and organic carbon:water partition coefficient (Koc) were
selected for use in this assessment document.
Other fate estimates were based on modeling results from EPI (Estimation Programs Interface) Suite™
(U.S. EPA . 2012b ), a predictive tool for physical/chemical and environmental fate properties
(https://www.e pa. gov/tsca-screenin g-tools/e pi-suitetm-estirnation- prouram-interface ). EPI SuiteTM was
reviewed by the EPA Science Advisory Board
(ht tp://yosemite.e pa. gov/sab/sab product.nsf/02ad90b 136fc2 l ef85256eba00436459/CCF982BA9816
F9CFCFA8525735200739805/ $File/sab-07-0l l. p@ and the individual models have been peer
reviewed in numerous articles published in technical journals. Citations for such articles are available in
the BPI Suite™ help files. Table 2-1 provides environmental fate data that EPA considered while
assessing the fate ofTCE. The data in Table 2-1 were updated after problem formulation with
information identified through systematic review .

2.1.2 Summary of Fate and Transport
The EPI Suite™ (U.S. EPA . 2012b ) STP model was run using default settings (set biodegradation halflife to 10,000 hours) to evaluate the potential for TCE to volatilize to air or adsorb to sludge during
wastewater treatment. In order to improve the accuracy of the EPI Suite™ estimations, physical and
chemical properties (Log Kow, Boiling point, Melting point, Vapor Pressure, Water solubility, Henry's
Law Constant) from Table 1-1 were entered into EPI Suite along with TCE's SMILES notation entry
(C(=CCL)(CL)CL) before running the module.

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45
46
47

IfTCE is released to the air, TCE does not absorb radiation well at wavelengths that are present in the
lower atmosphere (>290 run) so direct photolysis is not a main degradation process. Degradation by
reactants in the atmosphere has a half-life of several days meaning that long range transport is possible.

48
49
50
51
52
53
54

55
56
57
58

59
60
61
62
63
64
65
66
67
68
69

70
71
72
73
74
75
76
77
78
79
80
81
82

IfTCE is released to water, sediment or soil, the fate ofTCE is influenced by volatilization from the
water surface or from soil as indicated by its physical chemical properties (e.g., Henry's law constant)
and by microbial biodegradation under some conditions. The EPI Suite™ model that estimates
volatilization from lakes and rivers ("Volatilization" model) was run using default settings to evaluate
the volatilization half-life ofTCE in surface water. The volatilization model estimates that the half-life
of TCE in a model river is 1.2 hours and the half-life in a model lake is 110 hours. Therefore, the
volatilization is likely to be a significant removal process.
If TCE is released to wastewater treatment, the removal percentage of TCE is eatimted by using the STP
model in EPI SuiteTMas 81%, including 80% removal via volatilization and 1% removal via adsorption.
1bis value (81 %) is used for the calculation of exposure assement in this document.
The biodegradation of TCE in the environment is dependent on a variety of factors and thus, a wide
range of degradation rates have been reported (ranging from days to years). The BIO WIN module in the
BPI Suite™ was run using default settings to estimate biodegradation rates ofTCE in soil and sediment.
Three out of the four models built in the BIOWIN module (BIOWlN 1, 2, and 5) estimate that TCE will
not rapidly biodegrade in aerobic environments, while a fourth (BIOWIN 6) estimates that TCE will
rapidly biodegrade in aerobic environments. The weight-of-evidence from these estimates suggests that
TCE does not biodegradate fast under aerobic condition. This conclusion is supported by test results in a
frequently cited publication (Rott et al.. 1982) which indicates 19% aerobic biodegradation in 28 days
(OECD 30 ID) and 2.4% aerobic biodegradation in 14 days (OECD 301C), respectively. The data was
also cited in the 2004 EU TCE Risk Assessment (ECB. 2004 ).
During systematic review process, a high-quality aerobic serum bottle biodegradation study, in which
100% degradation in 20 days was reported in Methane and phenol cultures. The result indicates that the
aerobic degradation rate with either Methane or Phenol culture is "fast",is different from the BIOWIN
precictions. However, the "fast" aerobic biodegradation with special cultures cannot represent general
environmental conditions, so the "slow aerobic biodegradation" considered in the scoping and problem
formulation documents was not changed in this risk evaluation document.
During the systematic review for fate endpoints, several high-quality anaerobic biodegradation test data
were identified and inserted into the original fate table summarized in the Problem Formulation
document (U.S. EPA . 2018c ). The added anaerobic biodegradation data confirms that TCE anaerobic
biodegradation rate is "fast".

83
84
85
86
87
88

The systematic review did not identify any additional studies for sorption coefficient to soil and
sediments, therefore, the log Koc value was estimated with EPI Suite™ as 1.8, which is close to the
measured values ranged from 1.86 to 2.17 with different soils in the previous TCE assessments. These
log Kocvalues (1.8-2.17) suggest that the sorption of TCE to soil and sediment is low and TCE is
mobile in soil and sediment.

89
90
91
92

The systematic review identified a high quality bioconcentration data with low BCF ( BCF=l 7;
Barrows . 1980). The BAF ofTCE is also low (BAF=24) based on BPI Suite™ estimation. Therefore,
TCE is not expected to accumulate in aquatic organisms due to low BCF and BAF.

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93
94

95
96
97

98
99

I 00
101
102
103

2.1.3 Assumptions and Key So~!es ~f Uncertainty for Fate and Transport
A range of biodegradationrates have been reported for TCE. The range of degradation rates reported
were measured in laboratorystudies for biodegradationin water, soil and sediment. These studies are
subject to several sources of variabilityincluding variabilityinherent in the methodology,
interlaboratoryvariability and variabilitydue to factors such as the specificmicrobial populations used,
water, soil and sediment chemistry,oxygen concentration/redoxpotential, of the collected samples used
in the study, temperature and test substanceconcentration.No single value is universally applicable as it
is influencedby these variables and possibly others. However,the weight of evidence shows the aerobic
biodegradation ofTCE is slow and the anerobic biodegradationin anaerobic condition is fast.

That range of Log Kocvalues (1.8-2.17) is supportedby the basic principles of environmentalchemistry
104 which states that the Koc is typically within one order of magnitude(one log unit) of the octanol:water
IiOS partition coefficient (Koc) .
106
107
108
109
110
111
112
113
114
115
116

117
118
119
120
121
122
123
124
125
126
127
128
129
130

131
132
133
134
135
136
137

2.2 Environmental Exposures
2.2.1

Environmental Exposures Overview

In this section, EPA presents environrilentalexposuresto trichloroethylenefor aquatic and terrestrial
organisms. The aquatic exposure assessmentofTCE focuses on releases ofTCE to surface water from
facilities that manufacture,process, or use TCE under industrialand/or commercial conditions of use,
categorized into occupationalexposure scenarios(OES). The aquatic exposure assessment also includes
an analysis ofcollected surface water monitoring data from EPA's Water Quality Exchange (WQX)
using the online Water Quality Portal (WQP)tool and published literature obtained and evaluated
through a systematicreview process. Exposureto terrestrialorganismsis expected to be low since
physical chemical properties do not support an exposurepathway through water and soil pathways to
these organisms.
Aquatic exposures associated with the industrial and commercialconditions of use evaluated were
predicted through modeling. Predicted surface water concentrationsresulting from facility releases in
the EPA Lifecycle Release Analysis conductedfor reportingyear 2016. Release estimates were based on
loading and/or production volume informationobtainedfrom TRI, DMR, and CDR (See Section 2.2.2).
The surface water modeling was conductedwith EPA's Exposure and Fate Assessment ScreeningTool,
version 2014 (E-FAST 2014 ), using reported annual release/loadingamounts (kg/yr ) and estimates of
the number of days per year that the annual load is released. The ProbabilisticDilution Model (PDM), a
module ofE-FAST 2014, was run to predict the number of days per year predicted stream
concentrationsare expected to exceed the designatedchronic aquatic concentrationof concern (COC)
value.
WQX is the nation's largest source of water quality monitoring data and includes results from EPA's
STORage and RETrieval (STORET)Data Warehouse, the United States Geological Service (USGS)
National Water Information System (NWIS),and other federal, state, and tribal sources. A literature
search was also conducted to identify other peer-reviewedor gray sources of measured surface water
concentrationsin the US. The measured concentrationsreflect ambient surface water concentrationsat
the monitoring sites but cannot be directly attributedto specificindustrial or commercialconditions of
use. A geospatial analysis at the watershed level was conductedto compare the measured and predicted
surface water concentrationsand investigatewhether modeled facility releases may be located within the
same watershed as observed concentrationsin surface water.

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2.2.2 EnvironmentalReleases to Water
EPA categorized the conditions of use (COUs) listed in Table 1-4 into 18 Occupational Exposure
Scenarios (OES). For each OES, a daily water release was estimated based on annual releases, release
days, and the number of facilities (Figure 2-1 ). In this section, EPA describes its approach and
methodology for estimating daily water releases, and for each OES, provides a summary of release days,
number of facilities, and daily water releases. For detailed facility level results, see the "Water Release
Assessment" section for each OES in [EnvironmentalReleasesand OccupationalExposureAssessment.
Docket: EPA-HQ-OPPT-2019-0500)].

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147

Figure 2-1: An overview of how EPA estimated daily water releases for each OES8•
OES

Daily Release
Estimate

Annual
Releases

Release

Days

Number of
Facilities

TRI,COR,DMR,
TRt, DMR,ELG

ESD,Assumptions

NEI, Census,
Market Reports

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149
150
151
152
153
154
155
156
157
158
159

2.2.2.1
Results for Daily ReleaseEstimate
EPA combined its estimates for annual releases, release days, and number of facilities to estimate a
range for daily water releases for each OES. A summary of these ranges across facilities is presented in
Table 2-2. See Table 2-5 for more details. For some OES, EPA was not able to estimate or did not
expect water releases. For example:
•
•

OES Aerosol Application: Waterreleases were not expected due to the volatilenature of TCE; releases
from this OES are expectedto be to air.
OES Formulation of Aerosol and Non-Aerosol Products: All releases reportedin TRI were to off-site
land, incineration, or recycling.

160
161
162

163
164

165
166
167

a TRI = Toxics Release Inventory; DMR = Discharge Monitoring Report; NEI = National Emissions Inventory; CDR =
Chemical Data Reporting; ELG = Effluent Limitation Guidelines;ESD = Emission Scenario Document

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Table 2-2: Summary of EPA's daily water release estimates for each OES and also EPA's Overall
Confidence in these estimates.
Estimated Daily
Release Range
Across Sites
fblsite-dav)

Occupational Exposure
Scenario (OES)

Overall
Confidence

Notes

Minimum Maximum
Manufacturing
Processing as a Reactant
Formulation of Aerosol and
Non-AerosolProducts

0
l.7E-03

1.27
0.02

M
M

-

-

-

Reoackasdn11:
Batch Open-TopVapor
Degreasing
Batch Closed-LoopVapor
Dem-easing
Conveyorized Vapor
Des.rreasin
e:
Web Vapor Degreasing

6.8E-06
2.53E-07

1.1
1.96

M
M

2.53E-07

1.96

M

2.53E-07

1.96

M

2.53E-07

1.96

M

Cold Cleaning

2.53E-07

1.96

M

-

-

H

Aerosol Applications:Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
MetalworkingFluids

2.53E-07

1.96

M

Adhesives, Sealants, Paints,

3.68E-06

0.30

M

9.2E-06
2.9E-05

1.6
8.0E-05

M

5.SE-04
2.0E-04

0.4
2.0E-04

M

1.9E-06
l.6E-06

0.013
24.1

M
M

No information
identified to estimate
water releases

Same as Batch OpenTop Vanor De£reasin2
Same as Batch OpenTop Vaoor Degreasin2
Same as Batch OpenTop Vaoor De11:Teasing
Same as Batch OpenToo Vapor Dem-easing
EPA expects releases of
TCE to be to air for this
OES

Same as Batch OpenTop Vaoor Degreasing

and Coatine.s

Other Industrial Uses
Spot Cleaning and Wipe
Cleanimi
Industrial Processin~ Aid
CommercialPrinting and
Coovinjt
Other Commercial Uses
Process Solvent Recycling and
Worker Handling of Wastes

M

-

Based on only one
reported release in D:MR

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171
172
173
174
175
176

2.2.2.2
2.2.2.2.1

Approach and Methodology

Water Release Estimates

Where available, EPA used 2016 TRI (U.S. EPA. 2017g) and 2016 DMR( U.S. EPA 2016a) data to
provide a basis for estimatingreleases. Facilities are only required to report to TRI if the facility has I 0
or more full-time employees, is included in an applicable NAICS code, and manufactures, processes, or
uses the chemical in quantities greater than a certain threshold (25,000 pounds for manufacturers and

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processors of TCE and 10,000 pounds for users of TCE). Due to these limitations, .some sites that
manufacture, process, or use TCE may not report to TRI and are therefore not included in these datasets.
For the 2016 DMR( U.S. EPA , 2016a ), EPA used the Water Pollutant Loading Tool withinEPA's
Enforcement and Compliance History Online (ECHO) to query all TCE point source water discharges in
2016. DMR data are submitted by National Pollutant Discharge Elimination System (NPDES) permit
holders to states or directly to the EPA according to the monitoring requirements of the facility's permit.
States are only required to load major discharger data into DMR and may or may not load minor
discharger data. The definition of major vs. minor discharger is set by each state and could be based on
discharge volume or facility size. Due to these limitations, some sites that discharge TCE may not be
included in the DMR dataset.

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190
191
192
193
194
195

Where releases are expected but TRI and DMR data were not available or where EPA determined TRI
and DMR data did not sufficiently represent releases ofTCE to water for a condition of use, releases
were estimated using data from literature, relevant Emission Scenario Documents (ESDs) or Generic
Scenarios (GSs), existing EPA models (e.g.,, EPA Water Saturation Loss Model), and/or relevant
Effluent Limitation Guidelines (ELG). ELG are national regulatory standards set forth by EPA for
wastewater discharges to surface water and municipal sewage treatment plants. For more details, please
refer to Appendix I.

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198
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200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219

2.2.2.2.2 Estimates of Number of Facilities
Where available, EPA used 2016 CDR (U.S . EPA . 2016c), 2016 TRI (U.S. EPA . 2017 !!.), 2016
Discharge Monitoring Report (DMR) (U .S. EPA . 20 16a) and 2014 National Emissions Inventory (NEI)
(U.S. EPA. 2018a ) data to provide a basis to estimate the number of sites using TCE within a condition
of use. Generally, information for reporting sites in CDR and NEI was sufficient to accurately
characterize each reporting sites condition of use. However, information for determining the condition
of use for reporting sites in TRI and DMR is typically more limited.

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222
223

In DMR, the only information reported on condition of use is each site's Standard Industrial
Classification (SIC) code. EPA could not determine each reporting site's condition of use based on SIC
code alone; therefore, EPA supplemented the SIC code information with the same supplementary
information used for the TRI sites (market data, public comments, and industry meetings).

In TRI, sites submitting a Form R indicate whether they perform a variety of activities related to the
chemical including, but not limited to: produce the chemical; import the chemical; use the chemical as a
reactant; use the chemical as a chemical processing aid; and ancillary or other use. In TRI, sites
submitting Form A are not required to designate an activity. For both Form Rand Form A, TRI sites are
also required to report the primary North American Industry Classification System (NAICS) code for
their site. For each TRI site, EPA used the reported primary NAICS code and activity indicators to
determine the condition of use at the site. For instances where EPA could not definitively determine the
condition of use because: 1) the reported NAICS codes could include multiple conditions of use; 2) the
site reported multiple activities; and/or 3) the site did not report activities due to submitting a Form A,
EPA had to make an assumption on the condition of use to avoid double counting the site. For these
sites, EPA supplemented the NAICS code and activity information with the following information to
determine a "most likely" or "primary" condition of use:
• Information on known uses of the chemical and market data identifying the most prevalent
conditions of use of the chemical.
• Information obtained from public comments and/or industry meetings with EPA that provided
specific information on the site.

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Where the number of sites could not be determined using CDR/fRI/DMR/NEI or where these data
sources were determined to insufficiently capture the number of sites within a condition of use, EPA
supplemented the available data with U.S. economic data using the following method:
• Identify the North American Industry Classification System (NAICS) codes for the industry
sectors associated with these uses.
• Estimate total number of sites using the U.S. Census' Statistics of US Businesses (SUSB) (U.S.
Census Bureau, 2015) data on total establishments by 6-digit NAICS.
• Use market penetration data to estimate the percentage of establishments likely to be using TCE
instead of other chemicals.
• Combine the ~ generated in Steps I through 3 to produce an estimate of the number of sites
using TCE in each 6-digit NAICS code, and sum across all applicable NAICS codes for the
condition of use to arrive at a total estimate of the number of sites within the condition of use.

..

Table 2 3 : Summaryo fEPA' s estima tes fior ·thenum be ro f f:aciliti es floreac hOES
Occupational Exposure
Numberof
Notes
Scenario (OES)
Facilities
Manufacturing
Processing as a Reactant
Formulation of Aerosol and
Non-Aerosol Products
Reoackruzintt
Batch Open-Top Vapor
Degreasing
Batch Closed-Loop Vapor
DeRreasing
Conveyorized Vapor
Deirreasin2.
Web Vaoor Degreasing
Cold Cleaning
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
Metalworking Fluids
Adhesives, Sealants,Paints, and
Coatings
Other Industrial Uses
Spot Cleaning and Wipe
Cleanine
Industrial Processing Aid
Commercial Printing and
Coo-ving
Other Commercial Uses
Process Solvent Recycling and
Worker Handling of Wastes

5

5 to 440
19

22
194

.

Based on CDR rePorting
Based on TRI and DMR reporting, and Census data for
NAICS 325120 (Industrial Gas Manufacturinsz)
Based on TRI reporting
Based on TRI and DMR reporting
Based on NEI and TR1reporting

4

Based on NEI reporting

8

Based on NEI reporting

I
13
4,366

Based on NEI reoorting
Based on NEI reporting
Based on Census data and market penetration estimates
based on California Air Resources Board (CARB) survey
of automotivemaintenanceand repair facilities

-

No information identifiedto estimate number of facilities
Based on NEI, TRI, and DMR reporting

70

49
63,748
18

30

Based on TRI and DMR reporting
Based on Censusdata for NAICS codes 812300, 812320,
561740~assumed 100%market oenetrationfor TCE.
Based on TRI and DMR.reporting
No informationidentified to estimate number of facilities
No informationidentifiedto estimate number of facilities
Based on TRI and DMR reporting

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2.2.2.2.3 Estimates of Release Days

241

EPA referenced Emission Scenario Documents (ESDs) or needed to make assumptions when estimating
release days for each OES. A summary along with a brief explanation is presented in Table 2-4 below.

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243
244

..

Table 2-4 Summaryo f EPA' s est'1mat es fior release d ays exoec t ed fioreac hOES
.Release
Occupational Exposure
Notes
Days
Scenario (OES)
Manufacturing

350

Proces sing as a Reactant

350

Formulation of Aero sol and
Non-Aerosol Products
Reoackagin~
Batch Open-Top Vapor
Degreasing
Batch Closed-Loop Vapor
DeQ'.reasin2
Conveyorized Vapor
De2reasing
Web Vapor Degreasin g
Cold Cleanin2.
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
Metalworkin~ Fluids
Adhesives , Sealants , Paints, and
Coating s
Other Industrial Uses
Spot Cleaning and Wipe
Cleaning
Industrial Process in~ Aid
Commercial Printing and
Coov in2
Other Commer cial Uses
Proces s Solvent Recycling and
Worker Handlin,z of Wastes

-

.

Assumed seven days per week and 50 weeks per year with
two weeks oer year for shutdown activitie s.
Assumed seven days per week and 50 weeks per year with
two weeks oer year for shutdown activities.
Water releases not estimated for this OES.

250
260

Assumed 5 days per week and 50 weeks oer vear .
2017 ESD on Use of Vapor Degreasing

260

2017 ESD on Use of Vapor Degreasing

260

2017 ESD on Use of Vapor Degreasing

260
260

2017 ESD on Use ofVanor De2reasing
2017 ESD on Use ofVaoor De2reasin2.
Water releases not expected from this OES.

-

260
250

250
300

2017 ESD on Use ofVaoor Degreasin~
2011 ESD on the Application of Radiation Curable
Coatings, Inks, and Adhesives via Spray, Vacuum , Roll
and Curtain Coatinp;
Assumed 5 days per week and 50 weeks per vear .
Assumed 6 days per week and 50 weeks per year .

300
250

Assumed 6 days per week and 50 weeks oer vear.
Assumed 5 days per week and 50 weeks per year .

250
250

Assumed 5 days per week and 50 weeks oer year.
Assumed 5 days per week and 50 weeks per year .

245
246

2.2.2.3

247

Releases

248
249
250
251
252

253
254

Assumptions and Key Sources of Uncertainty for Environmental

EPA estimated water releases using reported discharges from the 2016 TRI and the 2016 DMR. TRI and
DMR data were determined to have a "medium" confidencerating through EPA' s systematic review
process. Due to reporting requirementsfor TRI and DMR, the number of sites for a given OES may be
underestimated. It is uncertain, the extend to which, sites not captured in these databases discharge
wastewater containing TCE and whether any such dischargeswould be to surface water, POTW, or nonPOTW WWT.
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257
258
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260
261
262
263
264
265
266

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268

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270
271
272
273
274
275
276
277
278
279
280
281
282
283
284

285
286
287
288
289
290

291
292

293
294

295
296
297

In addition, information on the use ofTCE at facilities in TRI and DMRis limited; therefore, there is
some uncertainty as to whether the number of facilities estimated for a given OES do in fact represent
that specific OES. If sites were categorized under a different OES, the annual wastewater discharges for
each site would remain unchanged; however, average daily discharges may change depending on the
release days expected for the different OES.
Facilities reporting to TRI and DMR only report annual discharges;to assess daily discharges, EPA
estimated the release days and averaged the annual releases.over these days. There is some uncertainty
that all sites for a given OES operate for the assumed duration; therefore, the average daily discharges
may be higher if sites have fewer release days or lower if they have greater release days. TRI-reporting
facilities are required to submit their ''best available data" to EPA for TRI reporting purposes. Some
facilities are required to measure or monitor emission or other waste management quantities due to
regulations unrelated to the TRI Program (e.g., permitting requirements),or due to company policies.
These existing, readily available dataare often used by facilities for TRI reporting purposes, as they
represent the best available data. When monitoring or direct measurement data are not readily available,
or are known to be non-representativefor TRI reporting purposes, the TRI regulations require that
facilities determine release and other waste management quantities of TRI-listed chemicals by making
reasonable estimates. These reasonable estimates may be obtained through various Release Estimation
Techniques, including mass-balancecalculations, the use of emission factors, and engineering
calculations. There may be greater uncertainty in dataresulting from estimates compared to monitoring
measurements.
Furthermore, TCE concentrationsin wastewater discharges at each site may vary from day-to-day such
that on any given day the actual daily discharges may be higher or lower than the estimated average
daily discharge.
In some cases, the number of facilities for a given OES was estimated using data from the U.S. Census.
In such cases, the average daily release caJculated from sites reporting to TRI or DMR was applied to
the total number of sites reported in (!J:.S. Census Bureau. 2015). It is uncertain how accurate this
average release is to actual releases at these sites; therefore, releases may be higher or lower than the
calculated amount.

The 2014 NEI was also used to estimate the number of facilities for various OES. NEI does not report
water release information, therefore, an average release was calculated from the sites reporting water
releases to TRI and DMR and applied to sites reported in NEI. It is uncertainhow accurate this average
release is to actual releases at these sites; therefore, releases may be higher or lower than the calculated
amount.
2.2.2.3.1 Summary of Overall Confidence in Release Estimates
Table 2-5 provides a summary ofEPA's overall confidence in its release estimates for each of the

Occupational Exposure Scenarios assessed.

-

Table 2 5: Summarv ofO verail C0 nfidencem. R e1ease Es.t1mate s b1y OES.
Occupational Exposure
Scenario (OES)

Overall Confidence in Release Estimates

Manufacturing

Wastewaterdischarges are assessed using reporteddischarges from the 2016
TRI for three sites. TRI data were determinedto have a ''medium" confidence

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OccupationalExposure
Scenario (OES)

Overall Confidencein Release Estimates
rating through EPA' s systematic review process. Facilities reporting to TRI
only report annual discharges; to assess daily discharges, EPA assumed 350
days/yr of operation and averaged the annual discharges over the operating
days. There is some uncertaintythat all sites manufacturing TCE will operate
for this duration; therefore, the average daily discharges may be higher if sites
operate for fewer than 350 days/yr or lower if they operate for greater than 350
days/yr. Furthermore,TCE concentrations in wastewater discharges at each
site may vary from day-to-daysuch that on any given day the actual daily
discharges may be higher or lower than the estimated average daily discharge.
One of the three sites reporting to TRI also reported to DMR. This information
was also assessed. The same uncertainties discussed above for TRI releases
also apply to the DMR data. Based on this infonnation, EPA has a medium
confidence in the wastewaterdischarge estimates for the four sites in the 2016
TRI and 2016 DMR.
Water discharges from the remaining two sites were estimated using the
maximum daily and monthly discharge limits in the OCPSF EG and the
estimated volume of wastewaterproduced per pound ofTCE production from
the Specific EnvironmentalRelease Category (SpERC) developed by the
European Solvent Industry Group for the manufacture of a substance. The
estimates assume the sites operate at the limits set by the EG; actual releases
may be lower for sites operating below the limits or higher for sites not in
compliance with the OCPSF EG. Based on this informationEPA has a
medium confidence in the wastewater discharge estimates for these two sites.

Processing as ,a Reactant

Wastewaterdischarges are assessed using reported discharges from the 2016
1RI and the 2016 DMR. 1RI and DMR data were determined to have a
"medium" confidencerating through EPA's systematic review process. Due to
reporting requirements for TRI and DMR, the number of sites in this OES may
be underestimated.It is uncertain the extent that sites not captured in these
databases discharge wastewatercontaining TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, infonnation on the conditions of use ofTCE at facilities in TRI
and DMR is limited;therefore, there is some uncertainty as to whether all the
sites assessed in this section are processing TCE as a reactant rather than a
different OES. If the sites were categorized under a different OES. the annual
wastewater discharges for each site would remain unchanged; however,
average daily discharges may change depending on the number of operating
days expected for the OES.
Facilities reportingto TRI and DMR only report annual discharges; to assess
daily discharges, EPA assumed 350 days/yr of operation and averaged the
annual discharges over the operating days. There is some uncertainty that all
sites processing TCE as a reactant will operate for this duration; therefore, the
average daily discharges may be higher if sites operate for fewer than 350
days/yr or lower if they operate for greater than 350 days/yr. Furthermore,
TCE concentrationsin wastewater discharges at each site may vary from dayto-day such that on any given day the actual daily discharges may be higher or
lower than the estimatedaverage daily discharge. Based on this information,
EPA has a medium confidence in the wastewater discharge estimates.

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Occupational Exposure
Scenario (OES)

Overall Confidencein Release Estimates

Formulation of Aerosol and
Non-Aerosol Products

All sites reporting in TRI show zero water releases and EPA did not have
enough informationto estimate water releases from this OES.

Repac~ging

Wastewaterdischarges are assessed usirigreported discharges from the 2016
TRI and the 2016 DMR. TRI and DMR data were determined to have a
''medium" confidencerating through EPA's systematic review process. Due to
reportingrequirementsfor TRI and D~ the number of sites in this OES may
be underestimated. It is uncertainthe extent that sites not captured in these
databases discharge wastewatercontaining TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally,informationon the conditionsof use ofTCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as to whether all the
sites assessed in this section are performingrepackaging activities rather than
a different OES. If the sites were categorizedunder a different OES, the
annual wastewaterdischarges for each site would remain unchanged; however,
average daily discharges may change dependingon the number of operating
days expected for the OES.
Facilities reporting to TRI and DMR only report annual discharges; to assess
daily discharges,EPA assumed 250 days/yr of operation and averaged the
annual discharges over the operating days. There is some uncertainty that all
sites repackagingTCE will operate for this duration; therefore, the average
daily dischargesmay be higher if sites operate for fewer than 250 days/yr or
lower if they operate for greater than 250 days/yr. Furthermore, TCE
concentrationsin wastewaterdischarges at each site may vary from day-to-day
such that on any given day the actual daily discharges may be higher or lower
than the estimated average daily discharge. Based on this infonnation, EPA
has a medium confidence in the wastewaterdischarge estimates.

Batch Open-TopVapor
Degreasing

Wastewaterdischarges are assessed using reported discharges from the 2016
TRI and the 2016 DMR. TRI and DMR data were determined to have a
''medium" confidencerating through EPA' s systematicreview process. Due to
reporting requirementsfor TRI and D~ EPA does not expect all sites using
TCE in OTVD to be captured in the databases. It is uncertain the extent that
sites not captured in these databases discharge wastewatercontaining TCE and
whether any such discharges would be to surface water, POTW, or non-POTW
WWf; however, the sites may be requiredto comply with an EG depending
on the industry in which the OTVD is being used. Additionally, information
on the conditionsof use ofTCE at facilities in TRI and DMR is limited;
therefore,there is some uncertaintyas to whether all of the sites assessed in
this section are using TCE in OTVD rather than a different OES (including
other vapor degreasing and cold cleaning operationsand use of TCE in
metalworkingfluids). If the sites were categorizedunder a different OES, the
annual wastewater discharges for each site would remain unchanged; however,
average daily discharges may change dependingon the number of operating
days expected for the OES.
Facilities reporting to TRI and DMR only report annualdischarges; to assess
daily discharges, EPA assumed 260 days/yr of operationand averaged the
annual discharges over the operating days. There is some uncertainty that all
sites using TCE in OTVDs will operate for this duration;therefore, the

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OccupationalExposure
Scenario (OES)

Overall Confidencein Release Estimates
average daily dischargesmay be higher if sites operate for fewer than 260
days/yr or lower if they operate for greater than 260 days/yr. Furthermore,
TCE concentrationsin wastewaterdischarges at each site may vary from dayto-day such that on any given day the actual daily discharges may be higher or
lower than the estimated average daily discharge. Based on this information,
EPA has a medium confidencein the wastewater discharge estimates.

Batch Closed-Loop Vapor
Degreasing

Same as the Open-TopVapor Degreasing(OTVD) OES.

Conveyorized Vapor
Degreasing

Same as the Open-TopVapor Degreasing (OTVD) OES.

Web Vapor Degreasing

Same as the Open-TopVapor Degreasing (OTVD) OES.

Cold Cleaning

Same as the Open-TopVapor Degreasing (OTVD) OES.

Aerosol Applications:
Spray Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners, Penetrating
Lubricants, and Mold
Releases

EPA assessed no wastewaterdischarges for this OES. There is some
uncertaintyas to whether and how much TCE may deposit on shop floors.
However, due to the volatility of TCE, EPA expects TCE to evaporate from
any such deposit prior to it being discharged; thus, limiting any potential
discharges to surfacewater, POTW, or non-POTW WWT from this source.
Based on this information,EPA has a high confidence in the release
assessment

MetalworkingFluids

Same as the Open-TopVapor Degreasing(OTVD) OES.

Adhesives, Sealants, Paints,
and Coatings

Wastewaterdischarges are assessed using reported dischargesfrom the 2016
TRI and the 2016 DMR TRI and DMR data were detennined to have a
"medium" confidencerating through EPA's systematicreview process. Due to
reporting requirementsfor TRI and DMR, the number of sites in this OES may
be underestimated.It is uncertainthe extent that sites not captured in these
databases discharge wastewatercontaining TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, informationon the conditions of use ofTCE at facilities in TRI
and DMR is limited;therefore, there is some uncertaintyas to whether all the
sites assessed in this section are performing adhesive, sealant, paint or coating
activities rather than a different OES. If the sites were categorized under a
different OES, the annual wastewater discharges for each site would remain
unchanged;however, average daily discharges may change depending on the
number of operatingdays expected for the OES.
Facilitiesreportingto TRI and DMR only report annual discharges; to assess
daily discharges,EPA assumed 250 days/yr of operationand·averaged the
annual discharges over the operating days. There is some uncertaintythat all
sites using TCE in adhesives,sealants, paints and coatings will operate for this
duration; therefore,the averagedaily discharges may be higher if sites operate
for fewer than 250 days/yr or lower if they operate for greater than 250
days/yr. Furthermore,TCE concentrationsin wastewaterdischarges at each

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OccupationalExposure

Overall Confidencein ReleaseEstimates

Scenario (OES)
site may vary from day-to-day such that on any given day the actual daily
dischargesmay be higher or lower than the estimated average daily discharge.
There is further uncertaintythat the number of sites obtained from the 2014
NEI represent the total number of sites using adhesives, sealants, paintsor
coatings containingTCE. NEI data only covers specific industries which may
not capture the entirety of industries using these products and NEI does not
include operationsthat are classified as area sources because area sources are
reported at the county level and do not include site-specific information. It is
uncertainthe extent that sites not capturedin this assessment discharge
wastewater containingTCE and whether any such discharges would be to
surface water, POTW, or non-POTWWWT. Also, NEI do not report water
release information,therefore, an average release was calculated from the sites
reportingwater releases to TRI and DMR and applied to sites reported in NEI.
It is uncertainhow accurate this average release is to actual releases as these
sites; therefore, releases may be higher or lower than the calculated amount.
Based on this information, EPA has a medium confidence in the wastewater
discharge estimates.
Other Ind,nstrial Uses

Wastewaterdischarges are assessed using reported c;l.ischarges
from the 2016
TRI and the 2016 DMR. TRI and DMR data were determined to have a
"medium" confidencerating throughEPA's systematic review process. Due to
reporting requirementsfor TRI and DMR, the number of sites in this OBS may
be underestimated.It is uncertain the extent that sites not captured in these
' ,databases discharge wastewatercontainingTCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, informationon the conditionsof use ofTCE at facilities in TRI
and DMR is limited;therefore, there is some uncertaintyas to whether all the
sites assessed in ~s section are performing other industrial uses rather than a
different OES. If the sites were categorizedunder a different OBS, the annual
wastewater discharges for each site would remain unchanged; however,
average daily dischargesmay change dependingon the number of operating
days expected for the OES.
Facilities reporting to TRI and DMR.only report annual discharges; to assess
daily discharges,BPA assumed 250 days/yr of operationand averaged the
annual dischargesover the operating days. There is some uncertainty that all
sites using TCE for other industrial uses will operate for this duration;
therefore, the average daily discharges may be higher if sites operate for fewer
than 250 days/yr or lower if they operate for greater than 250 days/yr.
Furthermore,TCE concentrations in wastewaterdischarges at each site may
vazy from day-to-daysuch that on any given day the actual daily discharges
may be higher or lower than the estimated average daily discharge. Based on
this information,EPA bas a medium confidencein the wastewater discharge
estimates.

Spot Cleaning and Wipe
Cleaning

Wastewaterdischarges from spot cleaning facilities at industrial launderers are
assessed using reported discharges from the 2016 DMR. DMR data were
determinedto have a "medium" confidencerating through EPA' s systematic
review process. DMR only contains infonnation for 2 sites. Additional sites

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Overall Confidence in Release Estimates
may not be in DMR because they may have no water discharges or because
they discharge to sewer rather than surface water (sewer discharges not
reported in DMR). Facilities reporting to DMR only report annual discharges;
to assess daily discharges,BPA assumed annual days of operation and
averaged the annual discharges over the operating days. There is some
uncertaintythat all industrial launderers using TCE will operate for this
duration; therefore, the average daily discharges may be higher if sites operate
for fewer than the operating days or lower if they operate for greater than the
operating days. Furthennore, TCE concentrations in wastewater discharges at
each site may vary from day-to-day such that on any given day the actual daily
discharges may be higher or lower than the estimated average daily discharge.
Based on this information,EPA has a medium confidence in the wastewater
discharge estimates at industrial launderers.
There is further uncertaintythat the releases estimated for the total number of
sites obtained from the U.S. Census' Bureau for spot, carpet and wipe cleaning
accurately reflect releases from these sites. An average release was calculated
from the sites reporting water releases to DMR and applied to the total number
of sites reported in (U.S. Census Bureau . 2015). It is uncertain how accurate
this average release is to actual releases as these sites; therefore, releases may
be higher or lower than the calculated amount. It is also uncertain the extent
that sites not captured in this assessment discharge wastewater containing TCE
and whether any such discharges would be to surface water, POTW, or nonPOTWWWT. Based on this infonnation, EPA has a medium confidence in
the wastewaterdischarge estimates.

Industrial Processing Aid

Wastewaterdischarges are assessed using reported discharges from the 2016
TRI and the 2016 DMR. TRI and DMR data were determined to have a
"medium" confidence rating through EPA's systematic review process. Due to
reporting requirements for TRI and DMR, the number of sites in this OES may
be underestimated.It is uncertain the extent that sites not captured in these
databasesdischarge wastewatercontaining TCE and whether any such
discharges would be to swface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use ofTCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as to whether all the
sites assessed in this section are using TCE as an industrial processing aid
rather than a different OES. If the sites were categorized under a different
OBS, the annual wastewater discharges for each site would remain unchanged;
however, average daily discharges may change depending on the number of
operating days expected for the OES.
Facilities reporting to TRI and DMR only report annual discharges; to assess
daily discharges,EPA assumed 300 days/yr of operation and averaged the
annual discharges over the operating days. There is some uncertainty that all
sites using TCE as an industrial processing aid will operate for this duration;
therefore, the average daily discharges may be higher if sites operate for fewer
than 300 days/yr or lower if they operate for greater than 300 days/yr.
Furthermore,TCE concentrations in wastewater discharges at each site may
vary from day-to-day such that on any given day the actual daily discharges

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may be higher or lower than the estimated average daily discharge. Based on
this information,EPA has a medium confidencein the wastewaterdischarge
estimates.

Commercial Printing and
Copying

Wastewaterdischargesfrom one commercialprinting and copying site was
found in the 2016 DMR. DMR data were determined to have a ''medium"
confidencerating through EPA's systematicreview process. However, EPA
acknowledgesthis site does not represent the entirety of commercialprinting
and copying sites using TCE; data was not available to estimate water releases
from additionalsites.

Other Commercial Uses

Wastewaterdischarges are assessed using reported discharges from the 201,6
O:MR.DMR data were determinedto have a ''medium" confidencerating
through EPA's systematicreview process.Due to reporting requirementsfor
DMR, these sites are not expectedto capture the entirety of water releases
from this OES. It is uncertainthe extent that sites not captured in DMR
discharge wastewatercontainingTCE and whether any such dischargeswould
be to surface water, POTW,or non-POTW WWT. Additionally, infonnation
on the conditionsof use ofTCE at facilities in DMR is limited; therefore, there
is some uncertaintyas to whether all the sites assessed in this section are
performingother commercialuses rather than a different OBS. If the sites
were categorizedunder a differentOES, the annual wastewaterdischarges for
each site would remain unchanged;however, average daily dischargesmay
change dependingon the number of operatingdays expectedfor the OES.
Facilities reporting to DMR only report annual discharges; to assess daily
discharges, EPA assumed 250 days/yr of operationand averaged the annual
discharges over the operatingdays. There is some uncertaintythat all sites
using TCE in other commercialuses will operate for this duration; therefore,
the average daily dischargesmay be higher if sites operate for fewer than 250
days/yr or lower if they operate for greater than 250 days/yr. Furthermore,
TCE concentrations in wastewaterdischarges at each site may vary from dayto-day such that on any given day the actual daily dischargesmay be higher or
lower than the estimatedaverage daily discharge. Based on this information,
EPA has a medium confidencein the wastewaterdischarge estimates.

Process Solvent Recycling
and Worker Handling of
Wastes

Wastewaterdischarges are assessed using reported discharges from the 2016
TRI and the 2016 DMR. TRI and DMR data were detennined to have a
"medium" confidencerating through EPA's systematicreview process. Due to
reporting requirementsfor TRI and DMR. the number of sites in this OES may
be underestimated.It is uncertainthe extent that sites not captured in these
databasesdischargewastewatercontainingTCE and whether any such
dischargeswould be to surfacewater, POTW, or non-POTWWWT.
Additionally,informationon the conditionsof use ofTCE at facilities in TRI
and DMR is limited;therefore,there is some uncertaintyas to whether all the
sites assessedin this section are recycling/disposingof TCE rather than a
different OES. If the sites were categorizedunder a different OES, the annual
wastewaterdischargesfor each site would remain unchanged;however,
average daily dischargesmay change dependingon the number of operating
days expectedfor the OBS.

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Scenario (OES)

Overall Confidence in Release Estimates

Facilities reporting to TRI and DMR only reportannualdischarges; to assess
daily discharges, EPA asswned 250 days/yr of operation and averaged the
annuaJdischarges over the operating days. There is some uncertainty that all
sites recycling/disposingofTCE will operate for this duration; therefore, the
average daily discharges may be higher if sites operate for fewer than 250
days/yr or lower if they operate for greater than 250 days/yr. Furthennore,
TCE concentrations in wastewater discharges at each site may vary from dayto-day such that on any given day the actual daily discharges may be higher or
lower than the estimated average daily discharge. Based on this information,
EPA has a medium conf"Kience
in the wastewater discharge estimates.

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2.2.3 Aquatic Exposure~odeling Approach
Surface water concentrationsresulting from wastewater releases of TCE from facilities that use,
manufacture, or process TCE related to the evaluated industrialand commercial conditions of use were
modeled using EPA's Exposure and Fate Assessment Screening Tool, Version 2014 (!,!.S.EPA. 2014c).
E-FAST 2014 estimates chemical concentrationsin surface water resulting from releases to surface
water, resulting in exposure estimates at the point of release. Advantagesto this model are that it
requires minimal input parametersand it has undergone extensivepeer review by experts outside of
EPA. A brief description of the calculationsperformed within the tool, as well as a description of
required inputs and the methodologyto obtaining and using inputs specific to this assessment is
described below. To obtain more detailed information on the E-F AST 2014 tool from the model
documentation (U.S. EPA. 2007), as well as to download the tool, visit this web address:

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https://www .epagov/tsca-screening-tools/e-fast-exposure-and-fate-asscssment-screening-tool-version-

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2014/.

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2.2.3.1
Exposure and Fate Assessment Screening (E-FAST) Tool 2014 Inputs
The required modeling inputs are discussedbelow.

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304

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308

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315
316
317

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321

Chemicalrekase to wastewater(WWR)
Annualwastewaterloadingestimates(kg/site/yearor lb/site/year)werepredictedin Section2.2.2 and based on
reportedproductionloadingor productionvohnneestimates.To modeltheserel~ withinExposure and Fate
Assessment Screening Tool 2014,the annualrelease is convertedto a dailyrelease using an estimateddays of
releaseper year. Belowis an ex.amplecalculation:
WWR (kw'site/day)= Annualloading(kg/site/year)/ Daysreleasedper year (days/year)

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In caseswherethe total annualreleaseamountfromone facilityis dischargedvia multiplemechanisms(i.e.,
directto surfacewater and/orindirectlythroughone or more WWTPs), the annualreleaseamo\llltwas divided
accordingly based on reportedinformationin TRI (Form R).

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ReleaseDays (days/year)
The numberof daysper year that the chemicalis dischargedis used to calculatea dailyreleaseamountfrom
annualloadingestimates(see Eq. 3). Currentregulationsdo not requirefacilitiesto reportthe mnnber of days
associatedwi1hreportedreleases.Therefore,tworeleasescenariosweremodeledfor directdischargingfacilities
to providea rangeof surface waterconcentrations
predicted by E-FAST2014.Thetwo scenariosmodeledare a

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higherreleasefrequency(200 to 365 .days)basedon releaseestimatesin Section2.2.2 and a low-endrelease
frequencyof 20 days of releaseperyear as an estimateof releasesthat couldlead to chronicrisk for aquatic
organisms.For dischargesfiom watertreatmentfacilities(e.g.,,POTWs,STPs,WWTPs),onlythehigher
releasefrequencywasmodeledbecausesuch treatmentsitesare anticipatedto discharge more frequentlythan
non-treatmentfacilities.

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Removal from wastewatertreatment(WWR¾)
The WWR% is the percentage of the chemical removed from wastewater during treatment before
discharge to a body of water. As discussed in Section 2.1.1, the WWR% for TCE is estimated as 81%.
The WWR % of 81 % was applied, when appropriate, to volumes characterized as being transferred offsite for treatment at a water treatment facility prior to discharge to surface water. A WWR% of zero was
used for direct releases to surface water because the release estimates are based on estimated release
(post-treatment). In cases where it wasn't clear whether the release was direct or indirect, both possible

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348

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350

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356

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scenarios were modeled.
Facility or Industry Sector

.

The required site-specific stream flow or dilution factor information is contained in the E-FAST2014
database,which is accessedby querying a facility National Pollutant Discharge Elimination System
(NPDES) number, facilityname, or reach code. For facilitiesthat directlydischargeto surfacewater(i.e.,
"directdischargers"),the NPDES of the directdischargeris selectedfrom the database.For facilitiesthat
indirectlydischargeto surfacewater (i.e.,''indirectdischargers"because
the releaseis sent to a water treatment
facilityprior to dischargeto surfacewater), the NPDESof the receivingtreatment facilityis selected.The
receiving facilityname and locationwas obtainedfrom the 1RI database(Fonn R), if available.As TRI does not
containthe NPDES of receivingfacilities,the NPDESwasobtainedusing EPA' s Envirofactssearchtool.If a
facilityNPDES was not availablein the E-FAST-2014database,the releasewas modeledusing water body data
for a surrogateNPDES (preferred)or anindustrysector,as descnl,edbelow.

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Swrogate NPDES:In cases where the site-specific NPDES was not available in the E-FAST 2014
database, the preferred alternative was to select the NPDES for a nearby facility that discharges to the

same waterbody. Nearby facilities were identified using the Chemical Safety Mapper within IGEMS
and/or search of the E-FAST 2014 by reach code.

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368

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Indust;cySector(SIC Code Option): If the NPDES is unknown, no close analog could be identified,
or the exact location of a chemical loading is unknown, surface water concentrations were modeled
using the "SIC Code Option" within E-FAST 2014. This option uses the 10th and 50th percentile
receiving stream flows for dischargers in a given industry sector, as defined by the Standard Industrial
Classification (SIC) codes of the industry. Table 2-6 below provides the industrial sectors that were
applied as needed for each condition of use category.
. wit
. hout s·1te,.SipecI"fi C Fl ow Datam
. E -FAST 2014
Table2- 6Ind ustry sector M o deled f;or F acii'1ties
IndustrySectorin E-FAST2014 for
StreamFlow Data1

Conditionof Use
OBS: Adhesives,Sealants,Paints, and Coatings

Adhesivesand SealantsManufacture

OBS: CommercialPrinting and Copying

Printing

OBS: IndustrialProcessingAid

POTW2(Industrial)

OBS: Manufacturing

OrganicChemicalsManufacture

OBS:NIA Water TreatmentFacility

POTW2 (Industrial)

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Conditionof Use

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IndustrySector in E-FASTl014 for
StreamFlow Data 1

OES: Other Commercial Uses

POTW2(Industrial)

OES: Other Industrial Uses

POTW2(Industrial)

OES: OTVD (Includes releases for Closed-Loop Degreasing,
Conveyorized Degreasing, Web Degreasing, Cold Cleaning, and
Metalworking Fluids)

PrimacyMetal Forming Manufacture

OES: Process Solvent Recycling and Worker Handling of Wastes

POTW2(Industrial)

OES: Processing as a Reactant

Organic Chemicals Manufacture

OES: Repackaging

n/a

n/a
OES: Spot Cleaning and Carpet Cleaning
= Not applicable because a NPDES or surrogate NPDES ~as available in E-FAST 2014 to obtain a site-specific stream
flow for all facilities within the OES.
2 POTW = Publicly Owned Treatment Works
1 n/a

Concentration of Concern
Concentrations of Concern (COCs) are threshold concentrationsbelow which adverse effects on aquatic
life are expected to be minimal. See Section 3.1.5 for a full discussion of acute and chronic COCs for
TCE. For E-FAST modeling, only the chronic COCs are entered for use in PDM runs, which compare
estimated stream concentrations calculated based on an annual stream flow distribution to the chronic
COCs and return the number of days per year the selected COCs are exceeded. The COCs used in the
PDM module ofE-FAST 2014 forTCE were 3, 788, and 52,000 ppb.

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2.2.3.2
E-FAST 2014 Equations
Surface Water Concentrations
E-FAST 2014 estimates site-specific surface water concentrationsfor discharges to both free-flowing
water bodies (i.e., rivers and streams) and for still water bodies (i.e., bays, lakes, and estuaries).

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For free-flowing water body assessments, E-FAST 2014cancalculatesurface water concentrations for
four stream.flowconditions (7Ql 0, harmonic mean, 30Q5, and 1QlO flows) using the following equation:

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_ WWRxCFtx( 1- ~)
SFxCF2

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396
397

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SWC -

(Eq. 1)

where:

swc
SF

Surface water concentration (parts per billion (ppb) or µg/L)
Chemicalrelease to wastewater (kg/day)
Removal from wastewater treatment(%)
Estimated flow of the receiving stream (MLD)

CPI

Conversion factor (10 µg/kg)

CF2

Conversionfactor (10 Uday/MLD)

WWR
WWT

•

9

6

399

400
401
402
403
404
405

406

For still water body assessments, no simple streamflowvalue represents dilution in these types of water
bodies. As such, E-FAST 2014 accountsfor dilution by incorporatingan acute or chronic dilution factor
for the water body of interest instead of streamflows. Dilution factors in E-FAST 2014 are typically 1
(representing no dilution) to 200. The following equation is used to calculate surface water
concentrations in still water bodies:

_ WWRx
(17oj xCP1

SWC -

PFxCF2xDF

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where:

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WWR

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swc

CFl

Surfacewater concentration(ppb or µg/L)
Chemic~lreleaseto wastewater(kg/day)
Removalfromwastewatertreatment(%)
Effluentflow of the dischargingfacility(MLD)
Acuteor chronicdilution factorused for the water body (typically between I and 200)
9
Conversionfactor(lO µg/kg)

CF2

Conversionfactor(IO Uday/MLD)

410

WWT

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412

DF

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414

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416

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418
419
420
421
422

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424
425
426

427
428
429
430
431
432
433
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436

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438

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444

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PF

6

Days of COC Exceedance

The ProbabilisticDilution Model (PDM) portion ofE-FAST 2014 was also run for free-flowing water
bodies, which predicts the number of days per year a chemical's concentration of concern (COC) in an
ambient water body will be exceeded. The model is based on a simple mass balance approach presented
by (Di Toro. 1984) that uses probabilitydistributionsas inputs to reflect that streams follow a highly
variable seasonal flow pattern and there are numerous variablesin a manufacturingprocess that can
affect the chemical concentrationand flow rate of the effluent PDM does not estimate exceedances for
chemicals discharged to still waters, such as lakes, bays, or estuaries.For these water bodies, the days of
exceedance is asswned be zero unless 1hepredicted surface water concentration exceeds the COC. In
these cases, the days of exceedanceis set to the number of release days per year (see required inputs
below).
2.2.3.3
E-FAST 2014 Outputs
E-FAST 2014providesesitmatesof surfacewater concentrationfor multiplestream flow parameters.The
concentrationsreflectpredictedlevelsof TCE in the receivingwater bodyat the point of releaseand do not
incorporatedownstreamtransportor post-releasechemicalfateprocesses.For this aquaticexposure
assessment,sitt>-specific
surfacewater concentrationestimatesfor free-flowingwater bodies are reportedfor
both the 7Ql0 and harmonicmean streamflows.The 7Q10 stream flow is the lowest consecutive 7-day
average flow during any 10-yearperiod. The harmonic mean stream flow is the inverse mean of
reciprocal daily arithmetic mean flow values. Site-specificsurfacewater concentrationestimates for still
water bodies are reported for calculationsusing the acute dilution factors. In cases where site-specific
flow/dilution data were not available,the releases were modeled using stream flows of a representative
industry sector, as calculated from all facilities assigned to the industry sector in the E-FAST database.
Estimatesfrom this calculationmethodare reportedfor the 10th Percentileharmonic mean and 10th
Percentile 7Q 10 stream flows.

2.2.4 Surface Water Monitoring Data Gathering Approach
2.2.4.1 .
Method for Systematic Review of Surface Water Monitoring Data
EPA conducteda full systematicreview of published literatureto identify studies reporting
concentrationsof TCE in surface water in the United States. Studies clearly associatedwith releases
from Superfund sites, improper disposal methods, and landfills were considered not to meet the PECO
statement and excluded from data evaluation and extraction.The systematicreview process is described
in detail in Section 1.5. A total of28 surface water studies were extractedand the results are summarized
in Section 2.2.6.2.2. No concentrationdata from the US were identifiedprior to 2000.
2.2.4.2
Method for Obtaining Surface Water Monitoring Data from
WQX/WQP
For this aquatic exposure assessment,the primary source for the occurrenceof TCE in surface water is
monitoring data retrieved from the Water Quality Portal (WQP), which integratespublicly available US
water quality data from multiple databases: I) the United States GeologicalSurveyNational Water

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Information System (USGS NWIS); 2) EPA' s STOrage and RETrieval (STORET); and 3) the United
States Department of Agriculture Agricultural Researc h Service (USDA ARS) Sustaining The Earth's
Watersheds - Agricultural Research Database System (STEW ARDS). NWIS is the Nation's principal
repository of water resources data USGS collects from over 1.5 million sites, including sites from the
National Water-Quality Assessment (NA WQA). STORET refers to an electronic data system originally
created by EPA in the 1960' s to compile water quality monitoring data. NWIS and STORET now use
common web services , allowing data to be published through the WQP tool. The WQP tool and User
Guide is accessed from the following website: (http:/ /www.waterqual it vdata.u s/portal.j sp).

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Data Retrieval from WQP

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Surface water data for TCE were downloaded from the WQP on October 3, 2018. The WQP can be
searched through three different search options: Location Parameters, Site Parameters, and Sampling
Parameters. Three queries were performed using the Sampling Parameters search, as shown in Figure
2-2.. One query obtained STORET data using the Characteristics parameter (selected "Trichlorethylene
(STORET) " and two queries obtained NWIS data using the Parameter Codes (34485 for
''Trichloroethene, water, filtered , recoverable, micrograms per liter" and 39180 for "Trichloroethene,
water, unfi]tered , recoverable , micrograms per liter"). Parameters codes were obtained from the USGS
website http s://nwis.waterd ata.usu s.gov/usa/nwis /pmcode s using the chemical CASRN. All queries were
performed using a Date Range of 01-01-2013 to 12-31-2017. Both the "Site data only" and "Sample
results (physical/chemical metadata)" were selected for download in "MS Excel 2007+ " format. The
"Site data only " file contains monitoring site information (i.e., location in hydrologic cycle, HUC and
geographic coordinates); whereas the "Sample result'' file contains the sample collection data and
analytical results for individual samples.
SAMPLINGPARAMETERS
SAMF'UNGPARAMEreRS

SAMF'LINGF'ARAMETERS

S. mpi. Mtdi4:

SampleMtdl.t:

Chu•ctffl1tlc

o,oup,

Cl\n•ccen.ijc
Group:
Cft.aractttf ttrea:

Pro,!Kt10:

Mintmum NSUltl

~· Iii.:
M,ninwm nw fts
/lltf Iii.!

to:

MinlM\IMrttuftl
pe.rsiw:

to:

to:
81ologl"'I s•mpllng p,r•mtttrs:

7

B1ologlc~I S.3.ITtplirig
p.1t~nttttrs : ?

Blologlc>Isompll•O p•r•mottrs : 7

Aasemblap :
Tno,,omio NU\t :

THortomlc NJrnt :

Tllx.onomtO
Namt;

47 5
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477
478
479
480
481
482

Figure 2-2. WQP Search Option. Surface water data were obtained from the WQP by querying
the Sampling Parameters search option for the characteristic (STORET data), Parameter Code
(NWIS data), and date range parameter
Data Filtering and Cleansing ·
The ••site data only'' and "Sample resu lts (physical/c hemical metadata)" files were linked together using
the common field "Monitoring Locati on Identifier " and then filtered and cleansed. Specifi cally,
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cleansing focused on obtaining samples were only for the media of interest (i.e., surface water), were not
quality control samples (i.e., field blanks), were of high analytical quality (i.e., no quality control issues,
sample contamination, or estimated values), and were not associated with contaminated sites (i.e.,
Superfund).

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490
491

Following filtering to obtain the final dataset, the domains ..ResultDetectionConditionText,"
"ResultCommentText," and "MeasureQualifierCode" were examined to identify samples with nondetect concentrations. All non-detect samples were tagged and the concentrations were converted to ½
the reported detection limit for summary calculation purposes. If a detection limit was not provided,
calculations were performed using the average of the reporteddetection limits in all samples (calculated
as 0.3 µg/L).

492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
S 10
511
512

2.2.5 Geospatial Analysis Approach
Using 2016 data, the measured surface water concentratiorus from the WQP and predicted concentrations
from the modeled facility releases were mapped in ArcGIS to conduct a watershed analysis at the
Hydrologic Unit Code (HUC) 8 and HUC 12 level. The purpose of the analysis is to identify if any the
observed surface water concentrations could be associated with the modeled facility releases. In
addition, the analysis included a search for Superfund sites within 1 to 5 miles of the surface water
monitoring stations to possible exclude these monitoring sites from the analysis. A U.S. map was
developed to provide a spatial representation of the measured and predicted concentrations. HUCs with
co-located monitoring stations and facility releases were identified and examined further. Maps were
developed on a U.S. scale to provide a spatial display of the concentrations, as well as at the HUC scale
to focus on co-located monitoring stations and facility releases.

Geographic Coordinates
The location of the monitoring stations was determined from the geographic coordinates (latitude and
longitude) provided in WQP. Releases from facilities were located based on the geographic coordinates
for the NPDES, TRI, and/or FRS of the mapped facility, as provided by FRS. For indirect dischargers,
the location of the receiving facility was mapped if known. If not known, the location of the indirect
discharger was mapped. Superfund sites in 2016 were identified and mapped using geographic
coordinates of the "front door," as reported in the Superfund Enterprise Management S vstem <SEMS l
database in Envirofacts.

513
514
515
516

Surface Water Concentrations
The surface water concentrations associated with the monitoring stations and facility releases are
denoted on the maps using COCs to determine the concentration thresholds:

517
~52,000 µ/L (exceeds all COC for algae, aquatic invertebrate, and fish

-

orange 788-51,999 µ/L (exceeds the COC for algae and aquatic invertebrate, but not for fish)

green
-

3-787 µ/L (exceeds the COC for algae, but not for aquatic invertebrate or fish)
Detec~

purple

but less than 3 µ/L (less than all COC)

Not Detected (applies only to measured concentrations; detection limits vary)

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518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539

For the predicted concentrations, the concentrations represent conditions under low flow conditions (i.e.,
7Q10 flows). The harmonic mean concentrations were not mapped, but are presented in the detailed
swnmary tables.
Symbols and Layering
Due to the scale of the maps, some symbols may overlap each other if the monitoring stations and
facilities are near each other or there are multiple releases modeled for the same facility (i.e., one facility
is both a direct discharger and a receiving facility). As such, the maps are layered to make sure that the
most important information is always be visible. The following rules were applied:
monitoring stations (small circles) are always on top of indirect discharge releases (medium triangles),
which are always on top of direct discharge releases (large squares), and
within each symbol type (monitoring station, direct release, and indirect release), a higher concentration
level is always on top of a lower concentration level (i.e., from top to bottom: ~52,000 µ/L (red), 78851,999 µ!I, (orange), 3-787 µ/L (green), <3µ/L (blue), and not detected (purple).

2.2.6

2.2.6.1
Terrestrial Environmental Exposures
Exposure to terrestrial organisms is expected to be low since physical chemical properties do not support
an exposure pathway through water and soil pathways to these organisms. The partition of TCE into
sediments is very low. Furthermore, the primary fate ofTCE released to surface waters or surface soils
is volatilization.
2.2.6.2

540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555

556
557

Environmental Exposure Results

Aquatic Environmental Exposures

2.2.6.2.1 Predicted Surface Water Concentrations: E-FAST 2014 Modeling
A summary of the surface water concentration estimates modeled using £-FAST 2014, based on the
lifecycle release analysis for the year 2016, is summarized by OES category in Table 2-7 through Table
2-9. A break-out of facility-specific modeling results organized per OES, with predicted surface water
concentrations and associated days of COC exceedance, are included in Appendix C. These facilityspecific modeling results are utilized and discussed in environmental risk characterization presented in
Section 4.1.2.
For the higher release frequency scenarios (250-365 days of release/year), predicted surface water
concentrations under 7Ql0 flow conditions ranged from 1.27£-5 to 765.63 ppb (Table 2-7). For the 20
days of release/year scenario for direct dischargers, predicted surface water concentrations under 7Q10
flow conditions ranged from 0.00019 to 9,937.5 ppb (Table 2-8). For comparison purposes, indirect
releases to non-POTW wwrPs were also modeled for the 20 days of release/year scenario, resulting in
surface water concentrations of 0.2 to 339.11 ppb (Table 2-9).

Table 2-7. Summary of Surface Water Concentrations by OES for Maximum Days of Release
Scenario
No. of Releases
Modeled

OES

6

Manufacturing

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SurfaceWaterConcentration
(7Q10) (ppb)
Min

Max

0.00514

2.77

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Processing as a Reactant (low-end # of sites)
Processing as a Reactant
Repackaging
OTVD
Adhesives, Sealants, Paints, and Coatings
Other Industrial Uses
Spot Cleaning and Carpet Cleaning
Industrial Processing Aid
Commercial Printing and Copying
Other Commercial Uses
Process Solvent Recycling and Worker Handlingof Wastes
N/A(WWTP)

Grand Total

3

0.0000518

l69

4

0.18

0.92

4

0.0000189

27.18

51

0.0000822

765.63

104

0.000818

10.83

16

0.0000941

9.5

1

0.00388

0.00388

6

0.000419

9.3

1

0.00292

0.00292

5
4

0.00564

9

0.98

11.76

9

0.0000127

214

1.27E-5

0.7
765.63

558
559
560
561

Table 2-8. Summary of Surface Water Concentrations by OES for 20 Days of Release Scenario
For Direct Releases
No. of Releases

OES

Modeled

Manufacturing
Processing as a Reactant (low-end# of sites)
Processing as a Reactant
Repackaging
OTVD
Adhesives, Sealants, Paints, and Coatings
Other Industrial Uses
Spot Cleaning and Carpet Cleaning
Industrial Processing Aid
Commercial Printing and Copying
Other Commercial Uses
Process Solvent Recycling and Worker Handling of Wastes

Surface Water Concentration
(7Q10) (ppb)
Min

Max

3

0.0897

49.91

3

0.000907

3000

2

16.45

16.45

3

0.000235

89.13

51

0 .00103

9937.5

52

0.0101

133.33

16

0.00154

200

I

0.0485

0.0485

3

0.00335

2.2

1
5
1

0.0365

0.0365

0.0658

110

13824

138.24

N/A(WWTP)

9

0.00019

12.79

Grand Total

150

0.00019

9,937.5

562
563

564
565

Table 2-9. Summary of Surface Water Concentrations by OES for 20 Days of Release Scenario for
Indirect Releases to a non-POTW WWTP
No. of Releases
Modeled

OES

Surface Water Concentration
(7Ql0) (ppb)
Min

Max

Manufacturing

3

9.48

42 .14

Processingas a Reactant
Repackaging

1

3.13

3.13

1

339.11

339.11

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IndustrialProcessing Aid
Process Solvent Recycling and Worker Handlin2 of Wastes
Grand Total

566
567
568
569
570
571
572
573

514
575
576
577
578
579
580
581
582

3

138.34

3

0.2
11.26

106.75

11

0.2

339.11

On a site-specific basis, the predicted surface water concentrations did not exceed the highest COC
(52,000 ppb) for any facility and only exceeded the COC of788 ppb for two releasing facilities (US
Nasa Michoud Assembly Facility in New Orleans, LA and Praxair Technology Center in Tonawand~
NY). These release scenarios were 20-day scenarios involving release to a still water body, which
applied no additional dilution . There were 102 modeled releases that exceeded the lowest COC of 3 ppb.
A detailed summary table by facility is provided in Appendix C .
Characterization of Modeled Releases
As discussed in Section 2.2.2, releases ofTCE were estimated based on data from TRI, DMRs, and
CDR (primarily TRl and DMR) for the 2016 calendar year. Release estimates were generally facilityspecific and releasing facilities were assigned to one of 13 occupational exposure scenarios (OES).
Overall, modeling was conducted on 157 unique active releasing facilities plus one OES with sites
nationwide (440 unknown sites in OES Processing as a Reactant). As shown in Figure 2-3., the releases
occurred in 39 states. With respect to watersheds, the releases occurred across 122 HUC-8 areas and
144-HUC 12 areas.
NV

NC
TX
OH
IL. Ml, PA

S(
KS, KY. LA, MN. TN

CA. FL. IN, WV

MA.NE.WA

AL.AR.CO. CT MD NJ, NM. RI.WI

t,

0£, ID,ME. MO. MS, NH.OK.OR, VA

0

.2

8

10

12

14

16

18

Number of Uniqut' fac ilities Releasing TRICHLOROHHYLENE Per State

583
584
585
586
587
588
589
590
591
592
593

Figure 2-3. Distribution of Active Facility Releases Modeled
As shown in Figure 24., direct and indirect dischargers accounted for 70% and 30% of the total releases
modeled, respectively. Site-specific waterbody flow/dilution data (identified via NPDES) were available
in E-FAST 2014 for the majority of the releases (58%); surrogate waterbody flow/dilution data were
used in only 15% of the cases, with the remaining cases (26%) run using a representative industry sector
SIC code. For releases modeled with a NPDES (including a surrogate NPDES), surface water
concentrations were calculated for free-flowing water bodies in 86% of the cases, and still water bodies
for the remaining cases (14%).

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Oirec:t Rrfeas•ir
tndirf'CI Rolraser

Industry Sector

-

Site-Specific NPOf:S
Su1rogat11NPD[S

Free-Flowing
Still Water

594
595
596
597
598

599
600
601
602

603
604

605
606
607

Figure 2-4. Modeled Release Characteristics (Percent Occurrence)
2.2.6.2.2 Monitored Surface Water Concentrations
Measured Concentrations of TCE/rom WQXIWQP
A summaryof the WQX data obtained from the WQP is provided in Table 2-10 below for years 20132017. Per year, the cleansed datasets evaluated contained between 46 and 793 surface water samples
collected from 89 to 193 unique monitoring stations. Detection frequencies were low, ranging from 0 to
8.7%. Concentrationsranged from not detected (ND; <0.022-5) to 0.11 µg/L in 2013, ND (<0 .022-5) to
1.86 µg/L in 2014, ND (<0.025-2.4)to 0.011 µg/L in 2015, all ND (<0.025-5) in 2016, and ND (<0.0255) to 2.0 µg/L in 2017. The temporal trend based on the average and maximum concentrations of all
samples is graphically presented in Figure 2-5 .. Peaks were observed in 2014 and 2017; however,
caution should be used in interpretingtrends with these data due to the small number of samples and the

lack of samples collected from the same sites over multiple years.

608

609
610

Table 2-10. Measured Concentrations ofTCE in Surface Water Obtained from the Water Quality
Portal: 2013-2017 1
Concentration (µg/L) in all samples

Year

611

612
613

Detection

Frequency

No.of
Samples
(No. of
Unique
Stations)

2013

4.67%

793 (164)

2014

3.78%-

609 (155}

2015

1.42%

352 (91)

2016

0.0%

473 (109)

2017

8.70%

46 (25}

Concentrations(µg/L) in only samples above the
detection limit

Average

No. of Samples
(No. ofUnique
Deviation)3
Stations)

Range2

(Standard

ND (<0.022-<5) to
0.11
ND (<0.022-<5)to
1.86
ND (<0.025-<2.4)
to0 .011
ND (<0.025-<5)
ND (<0.025-<5)to
2.0
ND (<0.022-<5)to

Average

Range

(Standard
Deviation)3

0.21 (0,26)

37 (22)

0.008 to 0.11

0.051 (0.016)

0.33 (0.31)

23 (13)

0.0055 to 1.86

0.17 (0.41)

0.42 (0.16)

5 (2)

0.0075 to 0.011

0.009 (0.001)

0.44 (0.27)

0 (0}

NA

NA

0.47 (0.53)

4 (I)

1.0 to 2.0

1.5 (0.71)

All
0.33 (0.29)
69 (39)
3.04%
2273 (384)
0.0055 to 2.0
0.13 (0.35)
2.0
Years
1Data were downloaded from the Water Quality Portal( www.watergualit, data.us) on 10/3/2018. STORET surface water data
was ,obtained by selecting "TCE (STORET)" for the Characteristic. NWIS surface water data were obtained by selecting
"34485; 39180" for the Parameter Codes. Samples were filtered for surface water media and locations only. Results were

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616
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619

reviewedand cleansed(i.e., samples/siteswere eliminatedif identifiedas estimated,qualitycontrol, media type other than
surface water, Superfund,Jandfill, failed laboratoryqualitycontrol, etc.).
2ND= Not Detected.Reporteddetectionlimitsin all samplesranged from 0.022 to S µg/L .
3
Calcu1ationswere performedusing ½ the reporteddetectionlimit when results were reported as not detected.If a detection
limit was not provided, calculationswere performedusing the averageof the reported detectionlimits in all samples(0.65
µg/L).

620
900

2.2

800

2
1.8 ::,

700

1,6

~ 600

~ S00

...

1

:i 400
I.I

u

0

l

1.4 ~
l.l .Q

~

0.8 ~
0.6 g
0
0.4 u

00
200

1Ck
l

0.2
0

11

2013

2014

2015

2016

r-iumber of Samples

Number of Sites

Number of Detections

2017

-

Maximum Concentration (ug/l)

621
622
623
624
625
626
627
628
629
630
631
632
633
634
635

Characterization of WOX Data
The original dataset downloaded contained 31,456 samples for years 2013 through 2017. Following the
filtering and cleansing procedure, only 7% of the samples remained (2;2.73samples). The majority of the
samples were excluded because they were an off-topic media (i.e., groundwater, artificial, bulk
deposition, leachate, municipal waste, or stormwater) or location type (i.e., landfill, spring, or well). A
smaller number of samples were excluded because they were quality control samples, estimated values,
or had other quality control issues. Samples associated with one Superfu.ndsite (Palermo Wellfield
Superfund Site) were also excluded.

636
637
638

For the 2016 cleansed dataset (473 samples), observations were made in 10 states/territories (AZ, KS,
MN, MO, NJ, NM, NC, PA, 1N, and TX) at 109 unique monitoring sites, with 1 to 13 samples collected
per sampling site.

639
640
641
642
643
644
645
646

MeasuredConcentratwnsof TCE/rom PublishedLiterature

_.._ Average Concentration (ug/l)

Figure 2-5. Temporal WQX Sampling and Surface Water Concentration Trends: 2013 - 2017

The quantitative ecological assessment used the 2016 data set only. For the 2016 data, concentrations in
all samples were non-detect. No samples in the 2013-2017 dataset had concentrations exceeding the
lowest COC of 3 µg/L .

Systematic review of published literature yielded only a minimal amount of surface water monitoring
data for TCE; a summary of the individual studies is provided in Table 2-11. In six U.S. studies
encompassing 1,177 surface water samples collected from river and oceans throughout the nation
between 1979 and 2001, reported concentrations of TCE ranged from below the detection limit (0.0001
to 0.08) to 17.3 µg/L, with reported central tendency values ranging from 0.0002 to 1.17 µg/L. The
maximum concentration was collected from the Charles River in Boston, Massachusetts (an urban area)
between 1998 and 2000 (Robinson et al.. 2004). The next highest TCE concentration was 2.0 µg/L,
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662

collected during a large nationwide survey of surface water for drinking water sources (rivers and
reservoirs) between 1999 and 2000 (USGS. 2003). Robinson et al. (2004) reported the results of
sampling conducted between 1996 and 2000 from 26 urban sites nationwide (n=71 l samples), as part of
the National Water-Quality Assessment (NAWQA) Program; the median TCE concentration was only
0.09 µg/L (detection frequency of 41%). One US study (U.S. EPA, 1977) reported much higher
concentrations ofTCE in surface water, up to 447 µg/L . These samples were collected in 1976/1977
from the vicinity of facilities producing and/or using methylchloroform, thus the concentrations reflect
historical levels of TCE and are not considered to be representative of current conditions. Not enough
information is available to provide a trend analysis of US surface water concentrations identified in
published literature.
Systematic review also identified data from various other countries and regions, including China, Korea,
United Kingdom, Russia, Portugal, Belgium, Greece, Japan, France, Italy, and Antarctica (see [Data
Extraction Tables/or EnvironmentalMonitoringData. Docket: EPA-HQ-OPPT-2019-0500]).
Table 2-11. Ambient Levels ofTCE in U.S. Surface Water from Published Literature
Concentration (µg/L)
Location
Type

Site Information

Dates
Sampled

N
(Det.Freq.)

Anchorage, AK;
Chester Creek (6 urban 1998-2001
11 (0)
sampling sites)
Nation-wide; Surface
water for drinking water
1999-2000 375 (0.008)
sources (rivers and
reservoirs)
Nation-wide; Urban
Rivers (26 sites, as part
1996-2000 711 (0.41)
oftheNAWQA

Range

Central
Tendency
(Standard
Deviation)

All samples ND
(<0.08)

Source

Data Quality
Score

(!l SGS,
2006)

Medium

ND
(<0.2)2.0

NR

(USGS,
2003)

Medium

NR

Median:
0.09

(Robinson
etal. , 2004)

Medium

Pro 2ram )

Ambient

Boston, MA; Charles
1998-2000
Rivers
Gulf of Mexico, near
mouthofthe
Mississippi River and
on the Louisiana Shelf
(11 stations in the open
1980
ocean and coast
representing both
unpolluted and
anthropogenic
influences)
Two Bridges, NJ;
1996-1998
Passaic River

29 (1)

NR- 17.3

Median:
1.17

(Robinson
et al., 2004)

Medium

11 (0.27)

ND0.05

NR

(Sauer,
198I)

Medium

~tern Pacific Ocean
(California,US to
Valparaiso, Chile)

1979-1981

30 (0.9)

1976

2(1.0)

Near
Facility

Baton Rouge,LA (Ethyl

(methylchlorofonn

samples (surface)
collected unstream and

Corporation); Streattt

10 (0.4)

Median: 0.1 (Robinson
etal.. 2004)
Mean:0.3
ND
(0.002);
(Singh et
(<0.0001)
Median:
al., 1983)
-0.0007
0.0002
NR

0.4 - 37

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NR

(U.S. EPA..,
1977)

Medium

Medium

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Concentration (µ.g/L)
Location
Type

Site Information

producer
or user)

downstream of the
outfall.

Dates
Sampled

N
(Det. Freq.)

Range

Central
Tendency
(Standard

Source

Data Quality
Score

Deviation )

Freeport, TX (Dow
Chemical Plant); Stream
samples (bottom and
surface) collected from
1976
6 (1.0)
0.9-126
the receiving stream at
the plant outfall and
upstream and
downstream of the
outfall.
Geismar,LA (Vulcan
Materials Plant); 3
surface water samples
collected from the
1976
3 (1.0)
S-74
receivingstreamat the
plant outfall and
upstream and
downstream of the
outfall.
Lake Charles, LA (PPG
Industries); Stream
samples (bottom and
surface) collected from
1976
S (1.0)
29-447
the receiving stream at
the plant outfall and
upstream and
downstream of the
outfall.
Auburn, WA (Boeing
Company); Stream
samples (surface)
collected from the
S (LO)
5 -30
1977
receivingstreamat
outfalls and/or upstream
and downstream of the
outfall.
NR = Not reported
ND = Not detected;detectionlimit reportedin parethesisif available

663
664
665
666
667
668
669
670

NR

NR

(U.S. EPA,
1977)

(U.S. EPA,
1977)

Mean:282
(156);
(U.S.EPA ,
Median:
1977)

High

High

High

353

NR

(U.S. EPA,
1977)

High

2.2.6.2.3 Geospatial Analysis Comparing Predicted and Measured Surface Water Concentrations
A geospatial analysis at the watershed level (HUC-8 and HUC-12) was conducted to compare the
measured and predicted surface water concentrations in 2016 and investigate if the facility releases may
be associated with the observed concentrations in surface water. Overall, there are 39 US
states/territories with either a measured concentration or a predicted concentration; at the watershed
level, there are 155 HUC-8 areas and 241 HUC-12 areas with either measured or predicted
concentrations.

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672
673
674
675
676
677
678
679
680
681
682

683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701

The monitoring stations co-located with facilities in the same HUC in the 2016 set were assessed for
proximity to Superfund sites to determine if the Superfund sites could be contributing to TCE releases,
and thus would not fall under the scope of this evaluation. No Superfund sites were identified·within 5
miles of these sites.
Co-location of releasing facilities and monitoring sampling locations was examined for presence in the
same watershed (HUC-8 and HUC-12). Co-location does not mean there is an upstream/downstream
connection between release and sampling sites.

2.2.6.3

Assumptions and Key Sources of Uncertainty for Environmental

Exposures

E-F AST 2014 estimates surface water concentrations at the point of release, without post-release
accounting for environmental fate or degradation such as volatiliz.atio~ biodegradatio~ photolysis,
hydrolysis, or partitioning. Additionally, E-FAST does not estimate stream concentrations based on the
potential for downstream transport and dilution. These considerations tend to lead to higher predicted
surface water concentrations. Dilution is incorporated, but it is based on the stream flow applied.
Therefore, there is uncertainty regarding the level of TCE that would be predicted downstream of a
releasing facility or after accounting for potential volatilization from the water surface, which is
dependent on the degree of mixing in a receiving water body.

Releases modeled using E-FAST 2014 were predicted based on engineering site-specific estimates, as
based on DMR, 1RI, and/or CDR databases. These data that form the basis for engineering estimates are
self-reported by facilities subject to minimum reporting thresholds; therefore, they may not capture
releases from certain facilities not meeting reporting thresholds (i.e., environmental releases may be
underestimated).
The days of release applied in modeling have a direct impact on predicting surface water concentrations.
The greater the number ofrelease days assumed, the more the per-day release is diluted (asswning the
same overall annual loading estimate). Both the higher release frequency and lower release frequency
scenarios were based on estimates and were not based on actual facility reporting. Therefore, there is
uncertainty regarding which release scenario is more likely, although the determination was made to
consider only the higher release frequency for scenarios involving water treatment facilities.

702
703
704

705
706

707
708
109
710

711

712
713
714
715
716
717

718

Another key parameter in modeling is the applied stream flow distribution, which provides for the
immediate dilution of the release estimate. The flow distributions are applied by selecting a facilityspecific NPDES code in E-FAST. When site-specific or surrogate site-specific stream flow data were
not available, flow data based on a representative industry sector were used in the assessment. This
includes cases where a receiving facility for an indirect release could not be determined. In such cases, it
is likely that the stream concentration estimates are higher than they w~uld be if a facility-specific
NPDES code was able to be applied, except in certain cases (e.g.,, NODES associated with low-flow or
intermittent streams or bays). Additionally, 1he stream flow data currently available in E-FAST 2014 are
15 to 30 years old. More recent flow data are available through the National Hydrological Dataset
(NHD) but are not available within the E-FAST model.
With respect to the geospatial comparison of modeled estimates with ambient data obtained from WQX,
one limitation is the accuracy of the latitudes and longitudes. The geographic coordinates for facilities
were obtained from the FRS Interests geodatabase, which are assigned through various methods
including photo-interpretation, ~ddress matching, and GPS. These are considered "Best Pick"
coordinates. While EPA does assign accuracy values for each record based on the methodused, the true
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721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736

accuracy of any individual point is unknown. Also, in some cases the receiving facilities for indirect
releases could not be determined. In these cases, the location of the active releaser was mapped. As
such, the co-location of facilities and monitoring sites may have been missed. As the number of
unknown receiving facilities was small and most monitoring sites had samples with concentrations
below the detection limit, this would have minimal impact on the watershed analysis. It is also important
to note that only a few USGS-NWIS and STORET monitoring station locations aligned with the
watersheds of the TCE -releasing facilities identified under the scope of this assessment, and the two colocated monitoring stations had samples with concentrations below the detection limit; therefore, no
direct correlation can be made between them. While these data reflect low levels of trichlorethylene in
ambient surface water samples, they cannot be interpreted as reflecting concentrations downstream of
direct release sites, which could be higher than reported measured levels.
The WQP Tool contains data from USGS-NWIS and STORET databases, and is one of the largest
environmental monitoring databases in the US; however, comprehensive information needed for data
interpretation is not always readily available. In some instances, proprietary information may be
withheld, or specific details regarding analytical techniques may be unclear, or not reported at all. As a
result, there are uncertainties in the reported data that are difficult to quantify with regard to impacts on
exposure estimates.

737
738
739
740

741
742
743

744
745
746
747
748
749
750

751
752
753
754
755
756

757
758
759
760
761
762
763
764
765
766

The quality of the data provided in the USGS-NWIS and STORET datasets varies, and some of the
information provided is non-quantitative. While many individual sampling results were obtained from
these datasets, the monitoring studies used to collect the data were not specifically designed to evaluate
TCE distribution across the US. The available data represent a variety of discrete locations and time
periods; therefore, it is unclear whether the data are representative of other locations in the US. While
these data reflect low levels of trichlorethylene in ambient surface water samples, they directly reflect
sampling done in specific states.

2.2.6.4
Confidence in Aquatic Exposure Scenarios
Confidence ratings for aquatic exposure scenarios are informed by uncertainties surrounding inputs and
approaches used in modeling surface water concentrations. In Section 2.2.2.1, confidence ratings are
assigned to these estimated daily releases (kg/site-day) on a per occupational exposure scenario (OES)
basis and primarily reflect moderate confidence (one OES shows high confidence for this estimate). As
these release estimates serve as the key inputs into the exposure mode and are therefore a key
component of the overall aquatic exposure scenario confidence.
Other considerations that impact confidence in the aquatic exposure scenarios include the model used

(E-FAST 2014) and its associated default and user-selected values and related uncertainties. As
described in Section 2.2.6.3, there are uncertainties related to the ability ofE-FAST 2014 to incorporate
downstream fate and transport; the likely number of release days from given discharging facilities; and,
in some cases (i.e., when the NPDES for the discharging facility cannot be found within the E-F AST
database), the applied stream flow distribution.
There are monitoring data available in surface water that reflect both near-facility and ambient (i.e.,
background) exposure levels in this media in the United States (see Table 2-10 and Table 2-11).
Samples characterizing background levels in swface water ranged from non-detect (ND) to 17.3 µg/L,
from both literature and the Water Quality Portal database. However, based on the modeling approach
using site-specific releases and considering that the predicted concentrations reflect near-site
concentrations prior to any additional fate and transport processes, these background exposure levels are
not as useful in corroborating the modeling approach. Near-facility monitoring data collected between
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767
768
769
770
771
772
773
77 4

1976 and 1977 show levels ofTCE ranging from 0.4 to 447 µg/L, which encompasses the range of the
modeled estimates across all OES (with the exception of two sites, which are associated with releases
into a still water body) (see [AquaticExposureModeling Outputsfrom E-FAST. Docket: EPA-HQOPPT-2019-0500]). However, these data are not attributable to any of the specific sites modeled, nor are
they reflective of ongoing TCE use or release patterns.
Based on the above considerations, the aquatic exposure assessment scenarios have an overalJ moderate
confidence.

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2.3 Human Exposures

775
776

777
778

779
780
781
782
783
784

785
786

787

2.3.1 Occupational Exposures
_
_
EPA categorized the conditions of use (COUs) listed in Table 1-4 into 18 OccupationalExposure
Scenarios (OES). In this section, EPA describes its approachand methodology to estimating
occupational exposures and provides a summary of results by OES for inhalation and dermal exposure,
and also the number of workers and occupational non-users (ONUs) potentially exposed (Figure 2-6).
ONUs include employees that work at the site where TCE is manufactured,processed, used, recycled, or
disposed of, but these employees do not directly handle the chemical and are therefore expected to have
lower inhalation exposures and are not expected to have dermal exposures. For detailed occupational
exposure results, the reviewer is referred to the (i) "ExposureAssessment" section for each OES, and (ii)
"Dermal Exposure Assessment'' section in [Environmental Releases and Occupational Exposure
Assessment. Docket : EPA-HQ-OPPT-2019-0500 ]. An occupationalexposure assessment includes the
following components:

788

789
790
791

•
•

792

793
794

•

Inhalation Exposure: Central tendency and high-end estimates of inhalation exposure to

workers and occupationalnon-users by OES.
Dermal Exposure: Occupational exposure scenarioswere grouped into bins based on common
characteristics and dermal exposure was estimated for workers for each of these bins
Number of Workers and Occupational Non-Users: An estimate of the number of workers and
occupational non-users (ONUs) potentially exposed to the chemical for each OES.

795

796
797

Figure 2-6: Components of an occupational assessment for each OES9 ; please refer to Section 2.2.2.2.2

for additional details on the approach and methodologyfor estimating number of facilities.
OES
Occupational
Assessment

I
Inha lation
Exposure

Derm al
Exposure

# of Workers, :
I

lONUs ~xposedl

I
Monitoring
Data

Modeling

Modeling

NF/FF,ESD

or
ONUs per site

Number of
facilities

~L

,.._...

HSIA, Reports,
NIOSH,OSHA

l # Workers

I

DEVLmodel

BLS, Census,

ESO

Census,NEI,
TRI, DMR, CDR

798

TRI = Toxics Release Inventory; DMR.= Discharge MonitoringReport;NEI =National EmissionsInventory; CDR =
Chemical Data Reporting;ELG = EffluentLimitationGuidelines; ESD = Emission ScenarioDocument;BLS = Bureau of
Labor Statistics;NIOSH = National Instituteof OccupationalSafey and Health; OSHA = OccupationalSafety and Health
Administration;HSIA= HallogenatedSolventIndustryAlliance; NF/FF= Near-Field/Far-Field;DEVL = DermalExposure
to Volatile Liquids.
9

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800
80 l
802

2.3.l .1
Results for.OccupationalAssessment
In some cases, EPA identified relevant inhalation exposure monitoring data for a given OES. The
quality of this monitoring data was assessed and EPA established an overall confidence for the data
when integrated into the occupational exposure assessment.

803
804
805
806
807
808
809
810

811
812
813
814
815
816
817
818
819
820
821
822

Where monitoring data was available, EPA used this data to characterize central tendency and high end
inhalation exposures. Where no inhalation monitoring data was identified, but inhalation exposure
models were available, EPA estimated central tendency and high end exposures using only modeling
approaches. lfbo~ inhalation monitoring data and exposure models were available, where applicable,
EPA presented central tendency and high end exposures using both. EPA did not identify any measured
dermal exposure estimates. In all cases, the Dermal Exposure to Volatile Liquids (DEVL) model was
used to estimate high-end and central tendency dermal exposures for workers in each OES.
In Table 2-12, EPA provides a summary for each of the 18 occupational exposure scenarios (OESs) by
indicating whether monitoring data was available, how many data points were identified, the quality of
the data, EPA' s overall confidence in the data, whether the data was used to estimate inhalation
exposures for workers and ONUs, and also whether EPA used modeling to estimate inhalation and
dermal exposures for workers and ONUs.
In many cases, EPA did not have monitoring data to estimate inhalation exposure for ONUs. In some
cases, this was addressed with the use of exposure models. However, approximately 50% of OESs lack
inhalation exposure estimates for ONUs. In addition, EPA expects ONU exposures to be less than
worker exposures. Dermal exposure for ONUs was not estimated because these employees are not
expected to be in direct contac~with TCE.

823
824
825
826
827
828

829
830
831

832

A swnmary of inhalation exposure results based on monitoring data and exposure modeling for each
OES is presented for workers in Table 2-13 and ONUs in Table 2-14. These tables provide a summary
of time weighted average (TWA) inhalation exposure estimates as well as Acute Exposure
Concentrations (AC), Average Daily Concentrations (ADC), and Lifetime Average Daily
Concentrations (LADC). The ADC is used to characterize risks for chronic non-cancer health effects
whereas the LADC is used for chronic cancer healtheffects. Additional details regarding AC, ADC,
and LADC calculations are available in section 2.3.1.2.4, while EPA's approach and methodology for
modeling inhalation exposure using the Near-Field/Far-Field mass balance model can be found in
2.3.1.2.3.

833
834
835
836

837
838
839
840
841
842
843
844
845

Table 2-15 includes a summary of central tendency and high-end dermal exposure results based on
exposure modeling for workers in each OES. Occluded dermal exposures may occur when liquid
becomes trapped between the skin and protective clothing (e.g.., gloves). This may result in the liquid
being unable to evaporate from the skin surface which may increase the quantity ofliquid absorbed.
Where applicable, both non-occluded and occluded exposure scenarios are assessed and the impact of
various glove protection factors (PFs) are also estimated. EPA estimated the dermal retained dose for
workers for each OES. These dose estimates assume one exposure event (applied dose) per work day
and that approximately eight to thirteen percent10 of the applied dose is absorbed through the skin.
Central tendency and high-end dermal estimates also factor in ranged values for two variables, the
surface area of contact, and the quantity remaining on the skin. Additional information on these
variables can be found in section 2.3.1.2.5.

10 The

absorbed fraction is a function of indoor air speed, which differs for industrialand commercialsettings.

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846
847
848
849
850
851
852
853
854
855
856
857
858
859

860

EPA also estimated central tendency and high-end dermal retained doses for occluded scenarios for
OESs where occlusion was reasonably expected to occur. Occluded scenarios are generally expected
where workers come into contact with bulk liquid TCE during use in open systems (e.g.,, during solvent
changeout in vapor degreasing) and not expected in closed-typesystems (e.g.,, during connection/
disconnection of hoses used in loading of bulk containersin manufacturing).
Dermal exposure estimates are provided for each OES, where the OESs are "binned" based on the
maximum possible exposure concentration (Y derm),the likely level of exposure, and potential for
occlusion. The exposure concentration is determinedbased on EPA's review of currently available
products and formulations containing TCE. For example, EPA found that TCE concentrationin
degreasing formulations such as C-60 Solvent Degreasercan be as high as 100 percent. The calculated
absorbed dose is low for all non-occludedscenarios since TCE evaporates quickly after exposure.
Dermal exposure to liquid is not expected for occupationalnon-users, since they do not directly handle
TCE. Additional details on EPA's approach and methodologyfor estimating dermal exposures for
workers can be found in section 2.3.1.2.5.

861

862
863
864
865
866
867

Table 2-16 provides a summary ofEPA's estimates for the total exposed workers and ONUs for each
OES. In order to prepare these estimates, EPA first attemptedto identify North American Industrial
Classification (NAICS) codes associated with each OES. For these NAICS codes, EPA then reviewed
Standard OccupationalClassification(SOC) codes from the Bureau of Labor Statistics (BLS) and
classified relevant SOC codes as workers or ONUs. All other SOC codes were assumed to represent
occupations where exposure is unlikely.

868
869
870

871
872
873
874
875

876
877
878

879
880
881

Based on this combination ofNAICS and SOC codes, EPA estimated the total number of workers and
ONUs potentially exposed for the various OES. EPA also estimated the total number facilities
associated with the NAICS codes previously identified based on data from the U.S. Census Bureau.
EPA then estimated the average number of workers and ONUs potentially exposed per site by dividing
the total number of workers and ONUs by the total number of facilities. Finally, using EPA's estimates
for the number of facilities using TCE, EPA was able to estimate the total number of workers and ONUs
potentially exposed to TCE for reach OES.
Additional details on EPA's approach and methodologyfor estimating the number of facilities using
TCE and the number of workers and ONUs potentially exposed to TCE can be found in sections
2.2.2.2.2 and 2.3.1.2.7, respectively.

882

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883
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885

Table 2-12: A summary for each of the 18 occupational exposure scenarios (OESs).
[Note: for many cases EPA was not able to estimate inhalation exposure for ONUs, but EPA expects these to be lower than inhalation
exwsure for Workers; dennal exposure not estimated for ONUs since they are not expected to be in direct contact with TCE.l
I
Inhalation Exposure
1 Dermal Exposure
Occupational Exposure
Modelingd
Overall
Monitoring
Modeling
Scenario (OES)
I Monitoring #Data DataQuality Worker ONU Worker ONU Confidence I Worker
ONU
I
Data
Points
Ratio~

!
I

Manufacturing
Processing as a Reactant

'

i
I.

✓

X

X

X

X

X

X

✓

16

M

✓

✓

33

H

✓

X

X

X

Repackaging

~

✓

33
123

H
M

✓

X

X

X

✓

✓

✓

✓

I

i

Batch Closed-Loop Vapor
De2reasim!
Conveyorized Vapor Degreasing

I

Web Vapor Degreasing
1

Commercial Printingand Copying
Other Commercial Uses
Process Solvent Recycling and
Worker Handlingof Wastes
a
b.
c.
d.

LtoM

M
MtoH
M

19

H

✓

X

X

X

MtoH

✓

18

M

✓

X

✓

✓

LtoM

I.

X

-

-

X

X

✓

✓

LtoM

i

X

-

X

X

✓

✓

X

-

-

X

X

✓

✓

LtoM
M

✓

3

H

✓

X

✓

X

LtoM

':
!
•
I

I

✓

24

MtoH;M'>

✓

✓

X

X

LtoM

✓

16

✓

X

X

X

LtoM

!

✓

✓

X

✓

✓

I

✓

8
34

M
M

✓

20

i

Industrial Processing Aid

✓

MtoH

✓

I
Adhesives, Sealants,Paints, and
Coatint?:
s
~
Other Industrial Uses
Spot Cleaning and Wipe Cleaning

889

H

I

Aerosol Applications
Metalworking Fluids

888

16

I

Cold Cleaning

887

✓

Formulation of Aerosol and NonAerosol Products
Batch Open-Top Vapor Degreasing

886

!

I

!
I

I

I

I

I

✓
✓

8

33

H

✓

✓

X

X

M

✓

X

X

X

M

✓

.)C

✓

✓

H

✓

X

X

X

M
MtoH;LtoMc
LtoM

M

i

-

-

I.

✓

~

✓

-

~

✓

i

✓

-

•

I

-

✓

-

1
.I
1

✓

I

✓

-

I

✓

~

✓

i
!

✓

✓

I

I

✓

!

✓

I

✓

'

.

--.
I

---.

l
MtoH

✓

I

✓
✓

✓

-

-

-

-

Aerosol Applications:SprayDegreasing/Cleaning,AutomotiveBrake and Parts Cleaners,PenetratingLubricants,and Mold Releases
For Workers, data qualityis M to H; For ONUs, data quality is is M.
For Workers, overall confidenceis M to H; For ONUs, overall confidence is L to M.
EPA has a medium level of confidencein its dermal exposure estimateswhich are based on high-end/centraltendencyparametersand commercial/industrial settings.

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.

•
dataan d exposure mo e me or eachOES •
T a ble 2-13 Summaryo f.mh a Ia f10n exposure resu Its fior Wor kers based on mom'¢orme-

Occupational Exposure
Scenario (OES)

I

I

I
I

I
I

Inhalation Monitoring (Worker, ppm)

Inhalation Modeling (Worker, ppm)

:

:

TWA
iHE CT

AC
ADC
LADC
TWA
AC
ADC
LADC
HE CT HE CT HE CT i HE CT HE CT HE CT HE CT
Manufacturing
0.86
0.38
0.13 0.59 8.6E-02 0.30 3.4E-02! ! 2.6
- - Processing as a Reactant
i 2.6 0.38 0.86 0.13 0.59 8.6E-02 0.30 3.4E-02i - Formulation of Aerosol and Non11.14 4.9E-04 0.38 l.6E-0~ 0.26 1.lE-04 0.13 4.SE-051 I
I
Aerosol Products
Repackaging
! 1.14 4.9E-04 0.38 l.6E-04 0.26 1.lE-04 0.13 4.SE-05!
- Batch Open-Top Vaoor Degreasing
i77.8 13.8 25.9 4.6 17.8 3.2 9.1
1.3 i 388.0 34.8 129.3 11.6 88.5 8.0 35.3 3.0
Batch Closed-Loop Vapor Degreasing i 1.45 0.46 0.48 0.15 0.33 0.10 0.17 4.2E-021 - Convevorized Vapor DeS?:reasinS?: ,48.3 32.4 16.l 10.8 11.0 7.4
5.7
2.9 ~3043.0 40.8 1014.3 13.6 694.8 9.3 275.2 5.3
Web Vapor Degreasing
- : 14.1 5.9 4.7 2.0 3.2 1.4 1.3 0.51
Cold Cleaning
i 57.2 3.3 19.1 I.I 13.1 0.76 5.2 0.28
i
Aerosol Applicationsa
E - I 24.0 7.6 8.0 2.5 5.5 1.7 2.2 0.65
Metalworking Fluids
6.3 ! 0.26 0.07 0.09 0.02 0.06 0.02 0.03 0.01
!75.4 69.7 25.1 23.2 17.2 15.9 8.8
Adhesives, Sealants, Paints, and
4.6 13.2 1.5 9.0 1.1 4.6 0.42
i39.5
- Coatings
I
I
Other Industrial Uses
I 2.6 0.38 0.86 0.13 0.59 0.09 0.30 3.4E-021 Soot Cleanin2 and Wipe Cleaning
! 2.9 0.38 0.95 0.13 0.67 0.09 0.34 3.6E-02! 2.8 0.96 0.92 0.32 0.65 0.23 0.26 0.08
Industrial Processing Aidb
: 12.8 4.3
6.4 2.13 4.39 1.5 2.2 0.58 ; - Commercial Printing and Copying
8.5E-02
0.70
0.03
0.48
0.02
0.25
I 2.1
7.7E-031 - Other Commercial Uses
I 2.9 0.38 0.95 0.13 0.67 0.09 0.34 3.6E-021 2.8 0.96 0.92 0.32 0.65 0.23 0.26 8.4EI
I
02
Process Solvent Recycling and Worker 1.1 4.9E-04 0.38 1.6E-04 0.26 LlE-0-' 0.13 4.5E-05i Handling of Wastes
1
I

-

:-

-

-

-

-

-

-

-

-

-

i

-

.

.
i

891
892
893

-

a. Aerosol Applications:Spray Degreasing/Cleaning,Automotive Brake and Parts Cleaners, PenetratingLubricants, and Mold Releases
b. Exposure for this OESis based on a 12 hr TWA; all other exposuresbased on 8 hr TWAs

894
895
896
897
898
899
900

901
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902

903
904

Table 2-14: Summaryof inhalationexposureresults for ONUs based on monitoringdata and exposuremodelingfor each OES.
[Note: for many cases EPA was not able to estimate inhalationexposure for ONUs, but EPA expects these to be lower than inahation
exposure for Workers.l
I
I
InhalationMonitoring(ONU, ppm)
InhalationModeling(ONU, ppm)
I
I
OccupationalExposure
I
I
Scenario(OES)
I TWA
ADC
I TWA
AC
AC
LADC
ADC
LADC
!HECT BE CT HE er BE CT ~ BE CT HE CT HE CT HE CT
Manufacturinf?
i - - - - i - - Processing as a Reactant
I I
- I Formulation of Aerosol and Non- - - - - - - Aerosol Products
Repackruz.ing
Batch Ooen-Top Vapor Degreasing
Batch Closed-Loop Vapor Degreasing
Conveyorized Vapor Degreasing

I
•
•I I 9 .1
~ -

iI

-

-

-

-

1.1

3.0

0.37

2.1

-

-

-

-

-

-

-

-

-

-

-

0.25

1.06

-

-

-

-

- i -

-

0.10 1237 .0 18.l

-

~

-

i 1878.
0
i 9.6
I 34.7
~ 1.0

-

54.0

4.1

-

-

-

-

-

7.8

428.8

5.3

168.

3.6

-

-

79.0

6.0

-

23.3 626.0

:

p.o

-

-

-

911
912
Page 101 of 691

-

.

3.2
1.0
11.6
0.61
0.35 4.7E-02

-

21.1

1.5

3

I

Web Vapor De!!reasin2
3.1
i - - Cold Cleaning
I 1.8
- - Aerosol Aoolicationsa
0.14
!- - Metalworking Fluids
- - 0.94 0.33 0.31 0.23 0.21 0.12 8.5E-02i Adhesives, Sealants, Paints, and
Coatings
I
I
Other Industrial Uses
I - I - - .
0.16
Soot Cleaning and Wipe Cleanin11.
1.8
0.48
0.58
!
!IndustrialProcessingAidb
: 2.9 1.3 1.5 0.66 0.99 0.45 0.51
0.18:
.
.
.
Commercial Printing and Cooving
i - - i Other Commercial Uses
I - - - - - I 1.8 0.48 0.58 0.16
Process Solvent Recycling andWorker I - - - - II Handlinu of Wastes
~
a. AerosolApplications: SprayDegreasing/Cleaning,AutomotiveBrake and Parts Cleaners,PenetratingLubricants,and Mold Releases
b. Exposurefor this OES is basedon a 12hr TWA; all other exposuresbased on 8 hr TWAs

:

905
906
907
908
909
910

;.

2.2

0.71
0.87
0.27
7.9
0.42
0.15
3.1
0.24 ~.2E..o2 0.09 1.2E-02

-

-

-

0.11
-.

0.41

0.11

-

-

0.41

-

.

-

-

-

-

-

-

0.16 4.2E-02

-

-

0.16 4.2E-02

-

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 INTERAGENCYDRAFI - DO NOT CITE OR QUOTE
913

914
915
916

Table 2-15: A summary of dermal retained dose for Workers based on exposure modeling for each OES
[Note: an explanationof each Bin is provided in Table 2-21; where applicable,both non-occluded and occluded exposure scenarios are

assessed and the impact of various glove protection factors (PFs) are also estimated;estimates assume one exposure event per work day and
that aooroximatelyeiruit to thirteen percent of the applied dose is absorbedthrou2h.the skin (see Section 2.3.1.2.S for additional details).l
I

I
O«upational Exposure
Scenario (OES)

I
I .
I 8 ID
I

I
I
I

I

I

I

I
I
I

MaxTCE
Weight
No
I Fraction I Gloves
I
I
I (Max Yderm) I (!>F==1)
I
I HE
CT

I
I

:

I

Non-Occluded Worker Dermal Retained Dose
(mg/day)
Protective
Gloves

Protective
Gloves

(PF=S)

(PF= 10)

HE

CT

HE

CT

I Occluded Worker
I Dermal Retained
Protective I
Dose
Gloves
(PF= 20)

HE
9.22
9.22
9.22

:

:
:

917
918
919
920
921

-

:

:.

-

i

Page 102 of 691

!

CT I

Manufacturing
1.0
: 184.36 61.45 36.87 12.29 18.44 6.15
3.07
• 1
Processinit as a Reactant
l.O
i 184.36 61.45 36.87 12.29 18.44 6.15
3.07
i 1 i
Formulation of Aerosol and Non1.0
1184.36 61.45 36.87 12.29 18.44 6.15
3.07
D 1 I
I
Aerosol Products
I
I
a 1 !
Reoackalrin2
1.0
! 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Batch Open-Top Vapor Degreasing
2
1.0
: 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Batch Closed-Loop Vapor Degreasing
2 i
1.0
i 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Conveyorized Vaoor DeRreasing
I 2 I
1.0
1184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Web Vaoor Degreasing
2 !
1.0
! 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Cold Cleaning
2
1.0
: 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Aerosol Anolicationsa
1.0
i 184.36 61.45 36.87 12.29 18.44 6.15
I 3 i
Metalworking Fluids
0.8
1147.49 49.16 29.50 9.83
14.75 4.92
I 4 I
D 3 I
Adhesives, Sealants, I Industrial
1165.92 55.31 33.18 11.06 16.59 5.53
0.9
Paints, and Coatings I Commercial ! 3 !
0.9
!260.50 86.83 52.10 17.37 26.05 8.68 Other Industrial Uses
1
1.0
: 184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Spot Cleaning and Wipe Cleaning
1.0
i 289.44 96.48 57.89 19.30 28.94 9.65
i 4 i
Industrial Processing Aid
I 1 I
1.0
1184.36 61.45 36.87 12.29 18.44 6.15 . 9.22 3.07
Commercial Printing and Cooving
4
0.35
1 101.30 33.77 20.26 6.75
10.13 3.38
!
!
Other Commercial Uses
4
1.0
: 289.44 96.48 57.89 19.30 28.94 9.65
Process Solvent Recycling and Workeri 1 I
1.0
184.36 61.45 36.87 12.29 18.44 6.15 9.22 3.07
Handlin!!of Wastes
I
I
I
a. AerosolApplications:SprayDegreasing/Cleaning,AutomotiveBrake and Parts Cleaners,PenetratingLubricants,and MoldReleases

:

I

-

(mg/day)
HE

I

-

:

I
I
!

-

n
I

:

2247
2,247
2247
2247
2.247

:

i

i
I
!
:

-

-

:
o

I
I

I
I

11798

I

-

2247

I

.

i
I

2247

-

-

-

749
749
749
749
749

0 599
u

-

-

I

I

749

-

!

262
749

B

-

786

!

CT

.

a

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

922
923
924

Table 2-16: Summary of the total number of workers and ONUs potentially exposed to TCE for each OES
[Note: EPA' s approach and methodologyfor estimating the number of facilities using TCE and the number of workers and ONUs potentially
e osed to TCE can be found in sections 2.2.2.2.2 and 2.3.1.2.7, res ctivel .
I
Total
I Total I
I
I
OccupationalExposure
I Exposed I Exposed I Total I Number of I
Notes
Scenario (OES)
I Workers I ONUs I Exposed I Facilitiesb I
I
350
I
170
530
5
I
I
I
Processin as a Reactant
Formulation of Aerosol and NonAerosol Products

I

Con

I
I

Aerosol A lications"
I Metalworking fluids

Adhesives, Sealants, Paints, and
Coatin s
Other Industrial Uses
Spot Cleaning and Wipe Cleaning

I

36
4922
50
92

Bate
Web apor egreasmg

120to 6 100
306

660
14 00

I
I
I

400
1,690

I

I

I
I

Other Commercial Uses

I
I

925

926

I

1100
15 900

2,300
244,000

1,000
25,300

I
I
I

3 300
269,000

I

I EPA does not have data to estimate the total

13

I
I

I

I Based on ESD on the Useof Metalworking
I Fluids, EPA estimates 46 Workers and 2 ONUs
I per site; the number of sites that useTCE-based
I metalworkin fluids is unknown to EPA.
70
49
63,748

I

I Based on assumption of 100% market
I enetration.

18

I
I

I

I

I
I

I
I

I
I

I

I Based on NIOSH HHE, EPA estimates 44
I Workers and 74 ONUs per site; EPA does not
have data to estimate total number of sites
I EPA does not have data to estimate the total
I workers and ONUs ex osed to TCE

Process Solvent Recycling and
140
380
520
30
I
I
I
I
Worker Handlin of Wastes
a. AerosolApplication
s: SprayDegreasing/Cleaning
, AutomotiveBrakeandPartsCleaners,PenetratingLubricants,andMoldReleases
b. Pleasereferto Table2-3 for notesrelatedto estimatesfor Numberof FacilitiesusingTCE.

Page 103 of 691

osed to TCE.

4 366

I

I

I

I workers and ONUs ex

I

450

140

1

I
I
I
4,400

I

8

I

I

1,400

310

I

5 to440
19

22
194
4

I

I

3,000

I

48
7,810
68
130

I
I

I

I

180 to 9 000
405

I

I
Industrial Processin Aid
Commercial Printing and Copying

I

12
2,889
18
32

I

I
I
I
I
0

55 to 2 900
99

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

2.3.1.2

927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
94 7
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965

Approach and Methodology

2.3.t.2.1 General
EPA provided occupational exposure results representative of central tendency conditions and high-end
conditions. A central tendency is assumed to be representative of occupational exposures in the center of
the distribution for a given condition of use. For risk evaluation, EPA used the 50th percentile (median),
mean (arithmetic or geometric), mode, or midpoint values of a distribution as representative of the
central tendency scenario. EPA' s preference is to provide the 50th percentile of the distribution.
However, if the full distribution is not known, EPA may assume that the mean, mode, or midpoint of the
distribution represents the central tendency depending on the statistics available for the distribution.
A high-end is assumed to be representative of occupational exposures that occur at probabilities above
the 90th percentile but below the exposure of the individual with the highest exposure (U.S. EPA . 1992).
For risk evaluation, EPA provided high-end results at the 95th percentile. If the 95th percentile is not
available, EPA used a different percentile greater than or equal to the 90th percentile but less than or
equal to the 99 .9th percentile, depending on the statistics available for the distribution. If the full
distribution is not known and the preferred statistics are not available, EPA estimated a maximum or
bounding estimate in lieu of the high-end.

For occupational exposures, EPA used measured or estimated air concentrations to calculate exposure
concentration metrics required for risk assessment, such as average daily concentration (ADC) and
lifetime average daily concentration (LADC). These calculations require additional parameter inputs,
such as years of exposure, exposure duration and frequency, and lifetime years. EPA estimated exposure
concentrations from monitoring data, modeling, or occupational exposure limits.
For the final exposure result metrics, each of the input parameters (e.g.,, air concentrations, working
years, exposure frequency, lifetime years) may be a point estimate (i.e., a single descriptor or statistic,
such as central tendency or high-end) or a full distribution. EPA considered three general approaches for
estimating the final exposure result metrics:
•
•

•

Deterministic calculations:BPA used combinations of point estimates of each parameter to
estimate a central tendency and high-end for each final exposure metric result.
Probabilistic (stochastic)calculations:EPA used Monte Carlo simulations using the full
distribution of each parameter to calculate a full distribution of the final exposure metric results
and selecting the 50th and 95th percentiles of this resulting distribution as the central tendency
and high-end, respectively.
Combination of deterministicand probabilisticcalculations:EPA had full distributions for
some parameters but point estimates of the remaining parameters. For example, EPA used Monte
Carlo modeling to estimate exposure concentrations, but only had point estimates of exposure
duration and frequency, and lifetime years.

966
967
968
969
970
971
972

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973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
000

EPA follows the following hierarchy in selecting data and approachesfor assessing inhalation
exposures:
1. Monitoringdata:
a. Personal and directly applicable
b. Area and directly applicable

c. Personal and potentially applicableor similar
d. Area and potentially applicableor similar
2. Modeling approaches:
a. Surrogatemonitoring data
b. Fundamentalmodeling approaches
c. Statisticalregression modelingapproaches
3. Occupationalexposure limits:
a. Company-specificOELs (for site-specificexposure assessments,e.g.,, there is only one
manufacturerwho provides to EPA their internal OEL but does not provide monitoring data)
b. OSHA PEL
c. Voluntarylimits (ACGIH TLV, NIOSH REL, OccupationalAlliance for Risk Science
(OARS) workplace environmentalexposurelevel (WEEL) [formerly by AIHA])

EPA assessed TCE occupationalexposure of the followingtwo receptor categories: male or female
workers who are 2:16years or older; and, female workers of reproductiveage (2:16years to less than 50
years).
2.3.1.2.2 Inhalation Exposure Monitoring Data
EPA reviewed workplace inhalation monitoring data collectedby governmentagencies such as OSHA
and NIOSH, monitoring data found in published literature(i.e., personal exposure monitoring data and

area monitoring data), and monitoring data submittedvia public comments. Studies were evaluated
using the evaluation strategies laid out in the Applicationof Systema/i.c Review in TSCARisk
Evaluations( U.S. EPA. 2018b).

ooi
002
003
004

005
006

007
008
009
010
011

012
013
014

015
016
017
018

Exposures are calculated from the datasets provided in the sources depending on the size of the dataset.
For datasets with six or more data points, central tendency and high-end exposures were estimated using
the 50th percentile and 95th percentile. For datasets with three to five data points, central tendency
exposure was calculated using the 50th percentile and the maximum was presented as the high-end
exposure estimate. For datasets with two data points, the midpoint was presented as a midpoint value
and the higher of the two values was presented as a higher value. Finally, data sets with only one data
point presented the value as a what-if exposure.For datasets including exposure data that were reported
as below the limit of detection (LOD), EPA estimatedthe exposure concentrationsfor these data,
following EPA's Guidelines for Statistical Analysis of OccupationalExposure Data (U.S. EPA. 1994a)
which recommendsusing the LOD/✓2 if the geometricstandard deviation of the data is less than 3 .0
and LOD/2 if the geometric standard deviation is 3.0 or greater.

2.3.1.2.3 Inhalation Exposure Modeling
EPA's inhalation exposure modeling is based on a near-field/far-fieldapproach(NF/FF) ili:icas. 2009 ),
where a vapor generation source located inside the near-field diffuses into the surroundingenvironment.
Workers are assumed to be exposed to TCE vapor concentrationsin the near-field,while occupational
non-users are exposed at concentrationsin the far-field. Applicationsof the NF/FF model are illustrated
in Figure 2-7.

019
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Open -Top Vapor Degreas ing and Cold Cleaning

Brake Servicing

,-

+

..
◄

Converyorized Degreasing

Web Degreasing

►

Spot Cleaning
Far-Reid

020
021
022
023
024
025
026
027
028
029
030
031
032

Figure 2-7: Illustrative applications of the NF/FF model to various exposure scenarios.

As the figures show, volatile TCE becomes airborne in the near-field , resulting in worker exposures at a
TCE concentration CNF.The concentration is directly proportional to the evaporation rate of TCE, G,
into the near-field, whose volume is denoted by VNF. In the case of brake servicing , there is no
evaporation rate. Rather, the aerosol degreaser is assumed to immediately become airborne in the nearfield zone upon application, resulting in a sudden rise in the near-field concentration.
The ventilation rate for the near-field zone (QNF)determines how quickly TCE dissipates into the farfield, resulting in occupational non-user exposures to TCE at a concentration Cpp.V FF denotes the
volume of the far-field space into which the TCE dissipates out of the near-field. The ventilation rate for
the surroundings, denoted by QFF,determines how quickly TCE dissipates out of the surrounding space
and into the outside air. The NF/FF model design equations are presented below.
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033
034

Near-Field Mass Balance

035
036
037

Far-Field Mass Balance

038
039
040

Where:

042

Vpp

=
=

043

(}NF

=

044
045
046
047

Qpp
CNF
CFF
G
t

=

041

VNF

048
049
050
051
052
053
054
055

056.
057
058
059

=
=
=

=

near-fieldvolume;
far-field volume;
near-fieldventilation rate;
far-field ventilationrate;
average near-field concentration;
average far-field concentration;
average vapor generationrate; and
elapsed time.

For details on the modeling approach and model equations,please refer to Appendix K; Appendix L;
and Appendix M.
2.3.1.2.4 Acute and Chronic Inhalation Exposure Estimates
This report assesses TCE exposures to workers in occupationalsettings, presented as time weighted
average (TWA). The TWA exposures are then used to calculateacute exposure (AC), average daily
concentration (ADC) for chronic, non-cancerrisks, and lifetimeaverage daily concentration (LADC) for
chronic, cancer risks.

Acute workplace exposuresare assumed to be equal to the contaminantconcentration in air (TWA):
CxED

060

AC=---

ATacute

061
062

Where:
AC

063
064

C
ED

065

ATacute

066
067
068
069
070

= acute exposure concentration
= contaminantconcentrationin air (TWA)
= exposure duration (hr/day)
= acute averagingtime (24 hrs)

ADC and LADC are used to estimate workplace exposuresfor non-cancer and cancer risks, respectively.
These exposures are estimated as follows:

Cx EDx EFxWY
ADCor LADC= -----AT or ATc

071

072

day
AT=WYx365-x24yr

073
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hr
day

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
day
ATc = LTx 365-x
yr

074

hr
24-d
ay

075

076
077
078

079
080
081

082
083

084
085
086

087
088
089
090

091
092
093
094

095

Where:
ADC
LADC
ED
EF
WY
AT
ATc
AWD
f
LT

= Average daily concentration used for chronic non-cancer risk calculations
= Lifetime average daily concentration used for chronic cancer risk calculations

= Exposure duration (hr/day)
= Exposure frequency (day/yr)
= Working years per lifetime (yr)

= Averaging time (hr) for chronic, non-cancer risk
= Averaging time (hr) for cancer risk
= Annual working days (day/yr)
= Fractional working days with exposure (unitless)
= Lifetime years (yr) for cancer risk

The parameter values in Table 2-17 are used to calculate each of the above acute or chronic exposure
estimates. Where exposure is calculated using probabilistic modeling, the AC, ADC, and LADC
calculations are integrated into the Monte Carlo simulation. Where multiple values are provided for ED
and EF, it indicates that EPA may have used different values for different conditions of use. The
rationale for these differences are described below in this section (also see 5.3.55Appendix J for
example calculations).

..

Ta ble 2- 17 Parameter VIa ues ~or Cla cuI.atmg-lnhl.a at1onExoosure E.stimates
Symbol

Value

Unit

Exposure Duration

ED

8 or24

hr/day

Exposure Frequency

EF

250

days/yr

Working years

WY

31 (50th percentile)
40 (95th percentile)

years

Lifetime Years, cancer

LT

78

years

Averaging Time, noncancer

AT

271,560 (central tendency)8
350,400 (high-end)b

hr

Averaging Time, cancer

ATc

683,280

hr

Parameter Name

096
097

• Calculatedusing the sod>
percentilevalue for workingyears (WY)
Calculatedusing the 9S1hpercentilevalue for workingyears (WY)

1>

098

099
100
101

102
103
104

Exposure Duration (ED)

EPA generally uses an exposure duration of 8 hours per day for averaging full-shift exposures with an
exception of spot-cleaning. Operating hours for spot cleaning were assessed as 2 to 5 hours/day.
Exposure Frequency {EF)

105
106
107
108
109

EPA generally uses an exposure frequency of 250 days per year with the following exception: spot
cleaning. EPA assumed spot cleaners may operate between five and six days per week and 50 to 52
weeks per year resulting in a range of250 to 312 annual working days per year (AWD). Taking into
account fractional days exposed (t) resulted in an exposure frequency (EF) of249 at the 50th percentile
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110
111

and 313 at the 95th percentile.

112
114

BF is expressed as the number of days per year a:worker is exposedto the chemical being assessed. In
some cases, it may be reasonableto assume a worker is exposedto the chemical on each working day. In
other cases, it may be more appropriateto estimate a worker's exposure to the chemical occurs during a

115
116

subset of the worker's annualworking days. The relationshipbetween exposure :frequencyand annual
working days can be descnoed mathematicallyas follows:

113

117
118
119

EF =fxAWD
Where:

EF

120
121
122
123
124

= exposure frequency,the number of days per year a worker is exposed to the chemical

(day/yr)
= fractionalnumber of annualworking days during which a worker is exposed to the
chemical (unitless)
AWD = annual working days, the number of days per year a worker works (day/yr)
f

125

126
127
128
129
130
131

132
133
134
135
136
137
138

139
140
141
142
143
144
145
146

147
148

BLS (2016) provides data on the total number of hours worked and total number of employees by each
industry NAICS code. These data are available from the 3- to 6-digit NAICS level (where 3-digit
NAICS are less granular and 6-digit NAICS are the most granular).Dividing the total, annual hours
worked by the number of employees yields the average number of hours worked per employee per year
for each NAICS.
EPA has identified approximately140 NAICS codes applicableto the multiple conditions of use for the
ten chemicals undergoing risk evaluation.For each NAICS code of interest, EPA looked up the average
hours worked per employeeper year at the most granularNAICS level available (i.e., 4-digit, 5-digit, or
6-digit). EPA convertedthe working hours per employeeto working days per year per employee
assuming employees work an average of eight hours per day. The average number of days per year
worked, or AWD, ranges from 169 to 282 days per year, with a 50th percentile value of 250 days per
year. EPA repeated this analysis for all NAICS codes at the 4-digit level. The average A WD for all 4digit NAICS codes ranges from 111 to 282 days per year, with a 50th percentile value of 228 days per
year. 250 days per year is approximatelythe 75th percentile. In the absence of industry- and TCEspecific data, BPA assumes the parameter/is equal to one for all conditions of use.
Working Years (WY)
EPA has developed a triangular distribution for working years. EPA has defined the parameters of the

triangular distribution as follows:
•

149

150

•

151
152

•

153

Minimum value: BLS CPS tenure data with current employer as a low-~d estimate of the
number of lifetime.working years: l 0.4 years;
Mode value: The 50th percentile tenure data with all employersfrom SIPP as a mode value for
the number oflifetime working years: 36 years; and
Maximum value: The maximum average tenure data with all employersfrom SIPP as a high-end
estimate on the number oflifetime working years: 44 years.

155

This triangular distributionhas a 50th percentilevalue of 31 years and a 95th percentile value of 40 years.
EPA uses these values for central tendency and high-end ADC and LADC calculations,respectively.

156
157

The BLS (U.S. BLS. 2014) provides informationon employeetenure with currentemployerobtained

154

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158

159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188

189
190
191
192
193
194

from the Current Population Survey (CPS). CPS is a monthly sample survey of about 60,000 households
that provides information on the labor force status of the civilian non-institutional population age 16 and
over; CPS data are released every two years. The data are available by demographics and by generic
industry sectors but are not available by NA1CS codes.
The U.S. Census' (U.S. Census Bureau . 2019) Survey oflncome and Program Participation (SIPP)
provides information on lifetime tenure with all employers. SIPP is a household survey that collects data
on income, labor force participation, social program participation and eligibility, and general
demographic characteristics through a continuous series of national panel surveys of between 14,000
and 52,000 households (U.S. Census Bureau, 2019). EPA analyzed the 2008 SIPP Panel Wave 1, a panel
that began in 2008 and covers the interview months of September 2008 through December 2008 (U.S.
Census Bureau . 2019). For this panel, lifetime tenure data are available by Census Industry Codes,
which can be cross-walked with NAICS codes.
SIPP data include fields for the industry in which each surveyed, employed individual works
(TJBIND 1), worker age (TAGE), and years of work experience with all employers over the surveyed
individual's lifetime. 11 Census household surveys use different industry codes than the NAJCS codes
used in its firm surveys, so these were converted to NAICS using a published crosswalk (U.S. Census
Bureau, 2013). EPA calculated the average tenure for the following age groups: 1) workers age 50 and
older; 2) workers age 60 and older; and 3) workers of all ages employed at time of survey. EPA used
tenure data for age group "50 and older" to determine the high-end lifetime working years, because the
sample size in this age group is often substantially higher than the sample size for age group "60 and
older". For some industries, the number of workers surveyed, or the sample size, was too small to
provide a reliable representation of the worker tenure in that industry. Therefore, EPA excluded data
where the sample size is less than five from our analysis.
Table 2-18 summarizes the average tenure for workers age 50 and older from SIPP data. Although the
tenure may differ for any given industry sector, -there is no significant variability between the 50th and
95th percentile values of average tenure across manufacturing and non-manufacturing sectors.

Table 2-18: Overview of Averae:eWorker Tenure from U.S. Census SIPP (Ae:e Group So+)
Working Years
Industry Sectors
Average 50111Percentile 95th Percentile Maximum
All industry sectors relevant to the 10
chemicals undergoing risk evaluation

35.9

36

39

44

Manufacturing sectors (NAICS 31-33)

35.7

36

39

40

Non-manufacturing sectors (NAICS 42-81)

36.1

36

39

44

Source: (U.S. Census Bureau, 2019)
Note: Industries where sample size is less than five are excludedfrom this analysis.

BLS CPS data provides the median years of tenure that wage and salary workers had been with their
current employer. Table 2-19 presents CPS data for all demographics (men and women) by age group
from 2008 to 2012. To estimate the low-end value on number of working years, EPA uses the most
11 To calculate the number of years of work experienceEPA took the differencebetween the year first worked
(TMAK.MNYR)and the current data year (i.e., 2008). EPA then subtractedany interveningmonths when not working

(ETIMEOFF).

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19S
196

197
198
199

200

201

recent (2014) CPS data for workers age 55 to 64 years, which indicatesa median tenure of 10.4 years
with their current employer.The use of this low-endvalue represents a scenario where workers are only
exposed to the chemical of interest for a portion of their lifetime working years, as they may change jobs
or move from one industry to another throughouttheir career.

.

T abl e 2- 19 M edian Yearo fT enurewtt· hC urrentE mpoyer
I
bIY A,l[e Grouo.
January 2008
January 2010
January 2012
Age

January 2014

16 years and over

4.1

4.4

4.6

4.6

16 to 17 years

0.7

0.7

0.7

0.7

18to 19years

0.8

1.0

0.8

0.8

20 to 24 years

1.3

1.5

1.3

1.3

25 years and o\ler

5.1

5.2

5.4

5.5

25 to 34 years

2.7

3.1

3.2

3.0

35 to 44 years

4.9

5.1

5.3

5.2

45 to 54 years

7.8

7.8

7.9

55 to 64 years

7.6
9.9

10.0

10.3

10.4

6S years and over

10.2

9.9

10.3

10.3

Source: (U.S. BLS. 2014 ).

202

203

Lifetime Years (LT)

204

EPA assumes a lifetime of 78 years for all worker demographics.

205
206
207
208

2.3.1.2.5 Dermal Exposure Modeling
Denna! exposure data was not readily availablefor the OESs in the assessment.Because TCE is a
volatile -liquid that readily evaporates from the skin, EPA estimateddermal exposures using the Dermal
Exposure to Volatile Liquids (DEVL) Model. This model determines a dermal potential dose rate based
on an assumed amount of liquid on skin during one contact event per day and the steady-statefractional
absorption for TCE based on a theoretical:frameworkprovided by Kasting (Kasting and Miller, 2006).
The amount of liquid on the skin is adjusted by the weight fraction of TCE in the liquid to which the

209

210
211
212

worker is exposed.

213
214
215
216
217
218

The DEVL is used to assess occupationaldermal exposure scenarios because the exposureduration is
typically not known across a wide variety of worker activities,and the model's event-basedapproach
allows exposure estimation using the number of exposure events, rather than exposure duration. Further,
the model can account for the impact of glove use, which is common, and often expected,in
occupational settings.

219
220

221
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EPA estimated workers' dermal exposure to TCE for the industrial and commercialoccupational
exposure scenarios (OESs) consideringevaporationofliquid from the surfaceof the hands and use with
and without gloves. The OSHA recommendsemployers utilize the hierarchy of controls for reducing or
removing h.az.ardousexposures. The most effective controls are elimination, substitution,or engineering
controls. Gloves are the last course of worker protection in the hierarchy of controls and should only be
considered when process design and eng~eering controls cannot reduce workplaceexposure to an
acceptable level.
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Vapor absorption during dermal exposure requires that TCE be capable of achieving a sufficient
concentration in the media at the temperature and atmospheric pressure of the scenario under
evaluation to provide a significant driving force for skin penetration. Because TCE is a volatile liquid (VP
= 73 .46 mmHg and 25°C), the dennal absorption of TCE depends on the type and duration of exposure.
Where exposure is not occluded, only a fraction ofTCE that comes into contact with the skin will be
absorbed as the chemical readily evaporates from the skin. Dermal exposure may be significant in cases of
occluded exposure, repeated contacts, or dermal immersion. For example, work activities with a high degree
of splash potential may result in TCE liquids trapped inside the gloves, inhibiting the evaporation of TCE
and increasing the exposure duration. EPA collected and reviewed available SDSs (Safety Data Sheets)
to inform the evaluation of gloves used with TCE in liquid and aerosol form at varying concentrations.
Trichloroethylene in liquid form at 99-100% concentration is expected to be used in both industrial and
commercial settings. For industrial scenarios using this form ofTCE, the following OESs are expected;
Manufacture ofTCE, Processing as a Reactant, Industrial Processing Aid, Formulation of Aerosol and
Non Aerosol Products, Repackaging, Process Solvent Recycling, Batch Open Top Vapor Degreasing,
Batch Closed-Loop Vapor Degreasing, Conveyorized Vapor Degreasing, and Web Vapor Degreasmg.
For trichlorethylene in liquid form at 99-100% concentration an SDS from Mallinckrodt Baker Inc.
recommended neoprene gloves and an SDS from Solvents Australia PTY. LTD. recommended the use
of gloves made from rubber, PVC, or nitrile (LS. EPA 201 ?c).
Commercial OESs where TCE in liquid form at 99-100% concentration is expected includes Spot
Cleaning, Wipe Cleaning, and Carpet Cleaning. An SDS for an R.R. Street & Co. cleaning agent
recommended wearing Viton ® [Butyl-rubber], PVA, or Barrier TM gloves. Two gun wipe cleaning
agent manufacturers A.V.W. Inc. and G.B. Distributors recommend Viton or Neoprene gloves and
polyethylene, neoprene, or PV A gloves, respectively (U.S. EPA 2017 c ).
For Aerosol Degreasing and Aerosol Lubricants applications, TCE is used in a range of concentrations
in aerosol form. An SDS for a 90-100% TCE aerosol degreasing agent from Brownells, Inc.
recommended using PVA gloves and an SDS for a 45-55% TCE aerosol brake parts cleaner from Zep
Manufacturing Co. recommended using Viton® gloves (U.S. EPA . 2017c ).
Metalworking Fluids and Adhesives, Sealants, Paints, and Coatings typically contain a maximum TCE
concentration of 80-90%. An SDS from LPS Laboratories presented a tap and die fluid at 80-90% TCE
concentration and recommended using Viton® [Butyl-rubber] , Silver Shield®[PE and EVOH laminate)
and PV A gloves. An SDS for a 75-90% TCE adhesive from Rema Tip Top recommended using
Neoprene, Butyl-rubber, or nitrile rubber (U.S. EPA . 2017 c).
EPA did not find any SDSs with applicable use towards commercial printing and copying applications.
To assess exposure, EPA used the DermalExposure to VolatileLiquids Model to calculate the dermal
retained dose for both non-occluded and occluded scenarios. The equation modifies the EPA 2-Hand
Dermal Exposure to Liquids Model by incorporating a "fraction absorbed (fabs)"parame~r to account
for the evaporation of volatile chemicals and a "protection factor (PF)" to account for glove use. Default
PF values, which vary depending on the type of glove used and the presence of employee training
program , are shown in Table 2-20:

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D

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exp

==S

X (

Qu

X !abs)
PF

X

Y.

derm

X

FT

Where:
• S is the surface area of contact: 535 cm2 ( centraltendency)and 1,070 cm2 (highend),
representingthe total surface area of one and two hands, respectively.
• Qu is the quantity remaining on the skin: 1.4mg/cm 2-event (central tendency) and 2.1 mg/cm 2event (high-end).This is the high-end default value used in the EPA dermal models ((U .S. EPA.

282

283

•

284

•
•

2013a ).
Y<1em1is the weight fraction of the chemical of interest in the liquid (0 S Y denn $ 1)
FT is the frequencyof events (1 event per day)
fabsis the fraction of applied mass that is absorbed(Default for TCE: 0.08 for industrial facilities

•

and 0.13 for commercial facilities)
PF is the glove protection factor (Table 2-20)

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2%

297

The steady state fractional absorption(fabs)for TCE is estimatedto be 0.08 in industrial facilities with
higher indoorwindflows or 0.13 in commercialfacilitieswith lower indoor windspeeds based on a
theoreticalframeworkprovided by Kasting and Miller (2006) (Kastin g and Miller. 2006 ), meaning
approximately8 or 13 percent of the applied dose is absorbedthrough the skin following exposure, from
industrial and commercial settings, respectively.However,there is a large standard deviation in the
experimentalmeasurement, which is indicative of the difficulty in spreading a small, rapidly evaporating
dose of TCE evenly over the skin surface.

..

T abl e 2-20 GIove Pr0 tect·IOD F ac ton fior Diflieren t D erm al P ro tec ti on Strat emes.
.
Dermal Protection Characteristics

Setting

a No gloves used, or any glove / gauntlet without permeationdata

1

and without employee training
b. Gloves with available permeation data indicatingthat the

material of constructionoffers good protection for the substance

Industrial and
Commercial

298
299
300

301
302
303

5

Uses

c. Chemicallyresistant gloves (i.e., as b above) with ''basic"
employee training
d. Chemically resistant gloves in combinationwith specific
activity training (e.g.,, procedure for glove removal and disposal)
for tasks where dermal exposure can be expectedto occur

Protection
Factor,PF

10

Industrial
Uses Only

20

To streamlinethe dermal exposure assessment,EPA groupedthe various OESs based on characteristics
known to effect dermal exposure such as the maximum weight fraction of TCE could be present in that
scenario,open or closed system use of TCE, and large or small-scaleuse. Four different groups or
"bins" were created based on this analysis (Table 2-21).

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Table 2-21: EPA 2J'OUPeddermal exposures associated with the various OESs into four bins.
Bin # Description
1

Bin 1 covers industrial uses that generally occur in closed systems. For these uses, dermal exposure is
likely limited to chemical loading/unloadingactivities (e.g., connecting hoses) and taking quality control
samples. EPA assesses the following glove use scenarios for Bin 1 conditions of use:

No gloves used: Operators in these industrial uses, while working around closed-system equipment, may
not wear gloves or may wear gloves fer abrasion protection or gripping that are not chemical resistant.
Gloves used with a protection factor of 5, 10, and 20: Operators may wear chemical-resistant gloves
when taking quality control samples or when connecting and disconnecting hoses during
loading/unloading activities. EPA assumes gloves may offer a range of protection, depending on the
type of glove and employeetraining provided.
Scenarios not assessed: EPA does not assess occlusion as workers in these industries are not likely to
come into contact with bulk liquid TCE that could lead to chemical permeation under the cuff of the
glove or excessive liquid contact time leading to chemical penneation through the glove.

2

Bin 2 covers industrial degreasing uses, which are not closed systems. For these uses, there is greater
opportunity for dermal exposure during activities such as charging and draining degreasing equipment,
drumming waste solvent, and removing waste sludge. EPA assesses the following glove use scenarios
for Bin 2 conditions of use:
No gloves used: Due to the variety of shop types in these uses the actual use of gloves is uncertain. EPA
assumes workers may not wear gloves or may wear gloves for abrasion protection or gripping that are
not chemical resistant during routine operations such as adding and removing parts from degreasing
equipment.
Gloves used with a protection factor of 5, 10, and 20: Workers may wear chemical-resistant gloves when
charging and draining degreasing equipment, drumming waste solvent, and removing waste sludge. EPA
assumes gloves may offer a range of protection, depending on the type of glove and employee training
provided.
Occluded Exposure: Occlusion may occur when workers are handling bulk liquid TCE when charging
and draining degreasing equipment, drumming waste solvent, and removing waste sludge that could lead
to chemical penneation under the cuff of the glove or excessive liquid contact time leading to chemical
permeation through the glove.

3

Bin 3 covers aerosol uses, where workers are likely to have direct dennal contact with film applied to
substrate and incidental deposition of aerosol to skin. EPA assesses the following glove use scenarios for
Bin 3 conditions of use:
No gloves used: Actual use of gloves in this use is uncertain. EPA assumes workers may not wear
gloves or may wear gloves for abrasion protection or gripping that are not chemical resistant during
routine aerosol applications.
Gloves used with a protection factor of 5 and 10: Workers may wear chemical-resistant gloves when
applying aerosol products. EPA assumes the commercial facilities in Bin 3 do not offer activity-specific
training on donning and doffing gloves.
Scenarios not assessed: EPA does not assess glove use with protection factors of 20 as EPA assumes
chemical-resistant gloves used in these industries would either not be accompanied by trainingor be
accompanied by basic employee training, but not activity-specifictraining. EPA does not assess
occlusion for aerosol applications because TCE formulations are often supplied in an aerosol spray can
and contact with bulk liquid is unlikely. EPA also does not assess occlusion for non-aerosol niche uses
because the potential for occlusion is unknown

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Bin# Description
4

Bin 4 covers commercialactivities of similar maximum concentration.Most of these uses are uses as
spot cleaners or in wipe cleaning, and/or uses expected to have direct dermal contact with bulk liquids.
EPA assesses the following glove use scenariosfor Bin 4 conditions of use:
No gloves used: Actual use of gloves in this use is uncertain.EPA assumes workers may not wear
gloves during routine operations (e.g.,, spot cleaning).
Gloves used with a protection factor of 5 and 10: Workersmay wear chemical-resistant gloves when
charging and draining solvent to/from maclµnes, removingand disposing sludge, and maintaining
equipment EPA assumes the commercial facilities in Bin 4 do not offer activity-specific training on
donning and doffing gloves.
Occluded Exposure: Occlusion may occur when workers are handling bulk liquid TCE when charging
and draining solvent to/from machines, removingand disposing sludge, and maintaining equipment that
could lead to chemical permeation under the cuff of the glove or excessive liquid contact time leading to
chemical permeation through the glove.
Scenarios not assessed: EPA does not assess glove use with protection factors of 20 as EPA assumes
chemical-resistantgloves used in these industrieswould either not be accompanied by training or be
accompanied by basic employee training, but not activity-specifictraining.

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333

2.3.1.2.6 Considerationof EngineeringControls and Personal Protective Equipment
OSHA and NIOSH recommend that employers utilize the hierarchy of controls to address hazardous
exposures in the workplace. The hierarchy of controls strategy outlines, in descending order of priority,
the use of elimination, substitution, engineering controls, administrative controls, and lastly personal
protective equipment (PPE). The hierarchy of controls prioritizes the most effective measures first which
is to eliminate or substitute the harmful chemical (e.g.,, use a different process, substitute with a less
hazardous material), thereby preventing or reducing exposure potential. Following elimination and
substitution, the hierarchy recommends engineering controls to isolate employees from the hazard,
followed by administrative controls, or changes in work practices to reduce exposure potential (e.g.,,
source enclosure, local exhaust ventilation systems). Administrative controls are policies and procedures
instituted and overseen by the employer to protect worker exposures. As the last means of control, the
use of personal protective equipment (e.g.,, respirators, gloves) is recommended, when the other control
measures cannot reduce workplace exposure to an acceptable level.
Respiratory Protectwn

OSHA's Respiratory Protection Standard (29 CFR § 1910.134)requires employers in certain industries
to address workplace hazards by implementing engineering control measures and, if these are not
feasible, provide respirators that are applicable and suitable for the purpose intended. Respirator
selection provisions are provided in§ 1910.134(d)and require that appropriate respirators are selected
based on the respiratory haz.ard(s)to which the worker will be exposed and workplace and user factors
that affect respirator performance and reliability. Assigned protection factors (APFs) are provided in
Table 1 under§ 1910.134(d)(3)(i)(A)(see Table 2-22) and refer to the level of respiratory protection
that a respirator or class of respirators is expected to provide to employees when the employer
implements a continuing, effective respiratory protection program.
The United States has several regulatory and non-regulatory exposure limits for TCE: an OSHA PEL of
100 ppm 8-hour TWA, a NIOSH Recommended Exposure Limit (REL) of2 ppm as a 60-minute ceiling
and an American Conference of_Government Industrial Hygienists (ACGIH) 8-hour TWA of 50 ppm
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(ATSDR. 2014b). If respirators are necessary in atmospheres that are not immediately dangerous to life
or health, workers must use NIOSH-certified air-purifying respirators or NIOSH-approved supplied-air
respirators with the appropriate APF. Respirators that meet these criteria include air-purifying
respirators with organic vapor cartridges. Table 2-22 can be used as a guide to show the protectiveness
of each category of respirator. Based on the APF, inhalation exposures may be reduced by a factor of 5
to 10,000, assuming workers and occupational non-users are complying with their employer's
respiratory protection program.
The respirators should be used when effective engineering controls are not feasible as per OSHA's 29
CFR § 1910.132. The knowledge of the range of respirator APFs is intended to assist employers in
selecting the appropriate type of respirator that could provide a level of protection needed for a specific
exposure scenario. Table 2-22 lists the range of APFs for respirators. The complexity and burden of
wearing respirators increases with increasing APF. The APFs are not to be assumed to be
interchangeable for any conditions of use, any workplace, or any worker or ONU.
Table 2-22: Assi2ned Proteetion Factors for Respirators in OSHA Standard 29 CFR § 1910 .134.
Type of Respirator

Quarter
Mask

HaH
Mask

Full
Facepiece

5

10

50

50

1,000

1. Air-Purifying Respirator

2. Power Air-Purifying Respirator
(PAPR)

Helmet/
Hood

Loosefitting
Facepiece

25/1,000

25

25/1,000

25

I 3. Supplied-Air Respirator (SAR) or Airline Respirator

Demand mode

10

50

Continuous flow mode

50

1,000

50

1,000

10

50

50

10,000

10,000

Pressure-demand or other positive"
Ipressure mode
4. Self-Contained Breathing Apparatus (SCBA)
Demand mode

350
351
352
353
354
355
356
357
358
359
360
361

362

Pressure-demand or other positivepressure mode (e.g.,, open/closed
circuit)
Source: 29 CFR § 1910.134(d)(3)(i)(A)

The National Institute for Occupational Safety and Health (NIOSH) and the U.S. Department of Labor's
Bureau of Labor Statistics (BLS) conducted a voluntary survey of U.S. employers regarding the use of
respiratory protective devices between August 2001 and January 2002. The survey was sent to a sample
of 40,002 establishments designed to represent all private sector establishments. The survey had a 75.5%
response rate ili IOSH. 2001). A voluntary survey may not be representative of all private industry
respirator use patterns as some establishments with low or no respirator use may choose to not respond
to the survey. Therefore, results of the survey may potentially be biased towards higher respirator use.
NIOSH and BLS estimated about 619,400 establishments used respirators for voluntary or required
purposes (including emergency and non-emergency uses). About 281,800 establishments (45%) were
estimated to have had respirator use for required purposes in the 12 months prior to the survey. The
281,800 establishments estimated to have had respirator use for required purposes were estimated to be
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3 77
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384

approximately 4.5% of all private industry establishments in the U.S. at the time (NIOSH. 2001). The
survey found that the establishments that required respirator use had the following respirator program
characteristics (NIOSH. 200 1):
• 59%provided training to workers on respirator use.
,. 34% had a written respiratory protection program.
• 47% perfonned an assessment of the employees• medical fitness to wear respirators.
• 24% included air sampling to determine respirator selection.

The survey report does not provide a result for respirator fit testing or identify if fit testing was included
in one of the other program characteristics.Of the establishmentsthat had respirator use for a required
purpose within the 12 months prior to the survey, NIOSH and BLS found (NIOSH. 2001):
• Non-powered air purifying respirators are most commo~ 94% overall and varying from 89% to 100%
across industrysectors.
•

•

Of the establishments that used non-powered air-purifying respirators for a required purpose within the
12 months prior to the survey, NIOSH and BLS found (NIOSH. 2001):
• A high majority use dust masks, 76% overall and varying from 56% to 88% across industry sectors.
,. A varying fraction use half-mask respirators, 52% overall and varying from 26% to 66% across industry

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Powered air-purifyingrespirators represent a minority of respiratoruse, 15% overall and varying from 7%
to 22% across industry sectors.
Supplied air respirators represent a minority of respiratoruse, 17% overall and varying from 4% to 37%
across industrysectors.

•

sectors .
A varying fraction use full-facepiecerespirators, 23% overall and varying from 4% to 33% across

industry sectors.

Table 2-23 swnmarizes the number and percent of all private industry establishments and employees
that used respirators for a required purpose within the 12 months prior to the survey and includes a
breakdown by industry sector (NIOSH. 2001 ).

392
393

Table 2-23. Number and Percent of Establishments and Employees Using Respirators Within 12

394

Mo nths P nor
. t o S wvev

Establishments

Industry

Total Private Industry
Agriculture, forestry, and fishing
Mining
Construction
Manufacturing
Transportation and public utilities
Wholesale Trade
Retail Trade
Finance, Insurance, and Real
Estate
Services

Number

Percent of All
Establishments

281,776
13,186

4.5
9.4

Employees
Percent of
All
Number
Employees
3,303,414

16,948

1.3

101,778
53,984
590,987
882,475
189,867
182,922
118,200

4,202

0.7

22,911

89,629

4.0

3,493

64,172
48,556
10,351
3 1,238

395
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11.7
9.6
12.8
3.7

5.2

1,160,289

3.1
5.8
9.9
8.9
4.8

2.8
2.6
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414

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423
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443

The EPA recognizes that the statistics of this survey may not be representative of all industry sectors and
conditions of use for TCE. However, these results provide survey-based information for understanding
the potential uncertainty in broad application of respiratory protective equipment factors to modeled and
monitored occupational exposure values.
2.3.1.2.7 Number of Workers and Occupational Non-Users Exposed
This section summarizes the methods that EPA used to estimate the number of workers who are
potentially exposed to TCE in each of its conditions of use. The method consists of the following steps:
1. Identify the North American Industry Classification System (NAICS) codes for the industry
sectors associated with each condition of use.
2. Estimate total employment by industry/occupation combination using the Bureau of Labor
Statistics' Occupational Employment Statistics data (U.S. BLS . 2016 ).
3. Refine the estimates based on BLS Occupational Employment Statistics data where they are not
sufficiently granular by using the U.S. Census' (U.S. Census Bureau . 2015 ) Statistics of U.S.
Businesses (SUSB) data on total employment by 6-digit NAICS.
4. Estimate the percentage of employees likely to be using TCE instead of other chemicals (i.e., the
market penetration ofTCE in the condition of use).
5. Estimate the number of sites and number of potentially exposed employees per site.
6. Estimate the number of potentially exposed employees within the condition of use.

Step 1: Identifying Affected NAICS Codes

As a first step, EPA identified NAICS industry codes associated with each condition of use. EPA
generally identified NAICS industry codes for a condition of use by:
• Querying the U.S. Census Bureau's NAJCS Search tool using keywords associated with each
condition of use to identify NAICS codes with descriptions that match the condition of use.
• Referencing EPA Generic Scenarios (GS's) and Organisation for Economic Co-operation and
Development (OECD) Emission Scenario Documents (ESDs) for a condition of use to identify
NAICS codes cited by the GS or ESD.
• Reviewing Chemical Data Reporting (CDR) data for the chemical, identifying the industrial
sector codes reported for downstream industrial uses , and matching those industrial sector codes
to NAICS codes using Table D-2 provided in the CDR reportin g instructions .
Each condition of use section in the main body of this report identifies the NAICS codes EPA identified
for the respective condition of use.
Step 2: Estimating Total Employment by Industry and Occupation
BLS's (U.S. BLS . 2016 ) Occupational Employement Statistics data provide employment data for
workers in specific industries and occupations. The industries are classified by NAICS codes (identified
previously), and occupations are classified by Standard Occupational Classification (SOC) codes.

Among the relevant NAICS codes (identified previously), EPA reviewed the occupation description and
identified those occupations (SOC codes) where workers are potentially exposed to TCE. Table 2-24
shows the SOC codes EPA classified as occupations potentially exposed to TCE. These occupations are
classified into workers (W) and occupational non-users (0). All other SOC codes are assumed to
represent occupations where exposure is unlikely.
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Table 2..24: SOCs with Worker and ONU Designations for All Conditions of Use Except
Dry Clean1n2
'

soc
11-9020
17-2000
17-3000
19-2031
19-4000

47-1000
47-2000
49-1000

49-2000
49-3000

49-9010
49-9020
49-9040

49-9060
49-9070
49-9090
51-1000
51-2000
51-4020
51-6010
51-6020
51-6030
51-6040
51-6050
51-6090
51-8020
51-8090
51-9000

Octupation

Designation

ConstrUctionMana.f!ers
Engineers
Drafters,EngineeringTechnicians, and MappingTechnicians
Chemists
Life, Physical,and SocialScienceTechnicians
Supervisorsof Constructionand ExtractionWorkers
ConstructionTrades Workers
Sunervisorsof Installation,Maintenance,and Repair Workers
Electricaland ElectronicEquipmentMechanics,Installers, and
Reoairers
Vehicleand MobileEquipmentMechanics, Installers,and Repairers
Controland Valve Installersand Repairers
Heating, Air Conditioning, and RefrigerationMechanicsand Installers
IndustrialMachineryInstallation, Repair,and MaintenanceWorkers
PrecisionInstrumentand EquipmentRepairers
Maintenance and Repair Workers,General
MiscellaneousInstallation,Maintenance,and Repair Workers
Supervisorsof ProductionWorkers
Assemblersand Fabricators
FormingMachineSetters, Operators,and Tenders,Metal and Plastic
Laundryand Dry-CleaningWorkers
Pressers,Textile, Garment,and RelatedMaterials
SewingMachineOperators
Shoe and Leather Workers
Tailors,Dressmakers,and Sewers
MiscellaneousTextile,Annarel, and FurnishingsWorkers
StationaryEngineersand Boiler Operators
MiscellaneousPlant and System Operators
Other ProductionOccupations

0
0
0
0
0
0

w
0

w
w
w
w
w

w
w
w
0

w
w

w
w
0
0
0
0

w
w
w

447

W = worker designation
0 = ONU designation

450
451
452
453
454
455
456

For dry cleaning facilities, due to the unique nature of work expected at these facilities and that different
workers may be expected to share among activities with higher exposure potential (e .g.,, unloading the
dry cleaning machine, pressing/finishing a dry cleaned load), EPA made different SOC code worker and
ONU assignments for this condition of use. Table 2-25 summarizesthe SOC codes with worker and
ONU designations used for dry cleaning facilities.

448
449

.

.
tor D'l"VCleanm2 F ac1'Ii ties
·
Table 2-25 SOC s wit. h Wor k eran dONUD es1.!?Dations

soc

41-2000
49-9040
49-9070
49-9090
51-6010
51-6020
51-6030

Occupation

Designation

Retail Sales Workers
IndustrialMachinery Installation,Repair, and MaintenanceWorkers
Maintenanceand Repair Workers,General
MiscellaneousInstallation,Maintenance,and Repair Workers
Laund.rvand Dry-CleaningWorkers
Pressers,Textile, Garment,and RelatedMaterials
Sewing MachineOperators

0

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w
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soc

O«uoatfon

51-6040
51-6050

Shoe and Leather Workers
Tailors, Dressmakers, and Sewers
MiscellaneousTextile, Apparel, and FurnishingsWorkers

51-6090

457
458

lk3lgnation
0
0
0

W = worker designation
0 = ONU designation

459

460
461

462
463
464

465
466

467
468
469
470
471

472
473
474
475

476
477

478
479
480
481
482
483

484

After identifying relevant NAICS and SOC codes, EPA used BLS data to determine total employment
by industry and by occupation based on the NAICS and SOC combinations. For example, there are
110,640 employees associated with 4-digit NAICS 8123 (Drycleaning and Laundry Services) and SOC
51·6010 (Laundry and Dry-Cleaning Workers).
Using a combination ofNAICS and SOC codes to estimate total employment provides more accurate
estimates for the number of workers than using NAICS codes alone. Using only NAICS codes to
estimate number of workers typically result in an overestimate, because not all workers employed in that
industry sector will be exposed. How:ever,in some cases, BLS only provide employment data at the 4digit or 5.digit NAICS level; therefore, further refinement of this approach may be needed (see next
step).
Step 3: Refining Employment Estimates to Account for lack ofNAICS Granularity
The third step in EPA's methodology was to further refine the employment estimates by using total

employment data in the U.S. Census Bureau's (U.S. Census Bureau, 2015) SUSB. In some cases, BLS
OES's occupation-specific data are only available at the 4-digit or 5-digit NAICS level, whereas the
SUSB data are available at the 6-digit level (but are not occupation-specific). Identifying specific 6-digit
NAICS will ensure that only industries with potential TCE exposure are included. As an example, OES
data are available for the 4-digit NAICS 8123 Drycleaning and Laundry Services, which includes the
following 6•digit NAICS:
•
•
•
•

NAICS 812310 Coin-Operated Laundries and Drycleaners;
NAICS 812320 Drycleaning and Laundry Services (except Coin-Operated);
NAICS 812331 Linen Supply; and
NAICS 812332 Industrial Launderers.

485

486
487

In this exampJe, only NAICS 812320 is of interest. The Census data allow EPA to calculate employment
in the specific 6-digit NAICS of interest as a percentage of employment in the BLS 4-digit NAICS.

488
489
490
491
492

The 6-digit NAICS 812320 comprises 46 percent of total employment under the 4-digit NAICS 8123.
This percentage can be multiplied by the occupation-specific employment estimates given in the BLS
Occupational Employment Statistics data to further refine our estimates of the number of employees
with potential exposure.

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494
495
496

Table 2-26 illustrates this granularity adjustment for NAICS 812320.

..

Table 2-26 Estimate
.
d Num bero f Potenfall
1 lYExposedW orkers and ONUsun der NAICS 812320
NAICS

soc
CODE

SOC Description

Occupation
Designation

Employment
bySOCat4digitNAICS

o/eof Total
.Employment

level
8123

41-2000

Retail Sales Workers

0

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44,500

Estimated
Employment
by SOC at 6-digitNAICS

level
46.00/o

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Industrial Machinery
Installation, Repair, and
Maintenance Workers
Maintenanceand Repair
8123
49-9070
Workers, General
Miscellaneous Installation,
49-9090 Maintenance, and Repair
8123
Workers
Laundry and Dry-Cleaning
8123
51-6010
Workers
Pressers, Textile, Gannent,
8123
51-6020
and Related Materials
8123
51-6030 Sewing Machine Operators
51-6040
Shoe and Leather Workers
8123
Tailors, Dressmakers,and
8123
51-6050
Sewers
Miscellaneous Textile,
51-6090
Apparel, and Furnishings
8123
Worlcers
Total Potentially Exposed Employees
Total Workers
Total Occupational Non-Users
Note: numbers may not sum exactly due to rounding.
W = worker
8123

497
498
499
500
501
502
503
504
505
506

507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526

49-9040

w

1,790

46.0%

823

w

3,260

46.0%

1,499

w

1,080

46.0%

497

w

110,640

46.0%

50,867

w

40,250

46.0%

18,505

0
0

1,660
46.0%
Not Reported for this NAICS Code

763

0

2,890

46.0%

1,329

0

0

46.0%

0

206.070

94,740
72,190
22,551

0 ==occupational non-user

Source: (U.S. Census Bureau. 2015); (U.S. BLS. 2016)

Step 4: Estimating the Percentage of Workers Using TCE Instead of Other Chemicals
In the final step, EPA accounted for the market share by applying a factor to the number of workers
determined in Step 3. This accounts for the fact that TCE may be only one of multiple chemicals used
for the applications of interest. EPA did not identify market penetration data any conditions of use . In
the absence of market penetration data for a given condition of use, EPA assumed TCE may be used at
up to all sites and by up to all workers calculated in this method as a bounding estimate. This assumes a
market penetration of 100%. Market penetration is discussed for each condition of use in the main body
of this report.
Step S: Estimating the Number of Workers per Site
EPA calculated the number of workers and occupational non-users in each industry/occupation
combination using the formula below (granularity adjustment is only applicable where SOC data are not
available at the 6-digit NAICS level):
Number o/Workers or ONUs in NAICSISOC (Step 2) xGranularity Adjustment Percentage (Step 3)
Number of Workers or ONUs in the Industry/Occupation Combination

=

EPA then estimated the total number of estabHshmentsby obtaining the number of establishments
reported in the U.S. Census Bureau's SUSB (U.S. Census Bureau. 2015) data at the 6-digit NAICS
level.
EPA then summed the number of workers and occupational non-users over all occupations within a
NAICS code and divided these sums by the number of establishments in the NAICS code to calculate
the average number of workers and occupational non-users per site.

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554

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572

Step 6: Estimating the Number of Workers and Sites for a Condition of Use
EPA estimated the number of workers and occupational non-users potentially exposed to TCE and the
number of sites that use TCE in a given condition of use through the following steps:

1. Obtaining the total number of establishments by:
a. Obtaining the number of establishments from SUSB (U.S. Census Bureau,2015) at the 6-digit
NAICS level (Step 5) for each NAICS code in the condition of use and summing these
values; or
b. Obtaining the number of establishments from the Toxics Release Inventory (TRI), Discharge
Monitoring Report (DMR) data, National Emissions Inventory (NEI), or literature for the
condition of use.
2. Estimating the number of establishments that use TCE by taking the total number of
establishm~nts from Item 1 and multiplying it by the market penetration factor from Step 4.
3. Estimating the number of workers and occupational non-users potentially exposed to TCE by
taking the number of establishments calculated in Item 2 and multiplying it by the average
number of workers and occupational non-users per site from Step 5.

2.3.1.3
Exposures

Assumptions and Key Sources of Uncertainty for Occupational

2.3.1.3.1 Number of Workers
There are a number of uncertainties surrounding the estimated number of workers potentially exposed to
TCE, as outlined below. Most are unlikely to result in a systematic underestimate or overestimate, but
could result in an inaccurate estimate.
CDR data are used to estimate the number of workers associated with manufacturing. There are inherent
limitations to the use ofCDR data as they are reported by manufacturers and importers ofTCE.
Manufacturers and importers are only required to report if they manufactured or imported TCE in excess
of25,000 pounds at a single site during any calendar from 2012 to 2015; as such, CDR may not capture
all sites and workers associated with any given chemical.
There are also uncertainties with BLS data, which are used to estimate the number of workers for the
remaining conditions of use. First, BLS' OES employment data for each industry/occupation
combination are only available at the 3-, 4-, or 5-digit NAICS level, rather than the full 6-digit NAICS
level. This lack of granularity could result in an overestimate of the number of exposed workers if some
6-digit NAICS are included in the less granular BLS estimates but are not, in reality, likely to use TCE
for the assessed applications. EPA addressed this issue by refining the OES estimates using total
employment data from the U.S. Census' SUSB. However, this approach assumes that the distribution of
occupation types (SOC codes) in each 6-digit NAICS is equal to the distribution of occupation types at
the parent 5-digit NAICS level. If the distribution of workers in occupations with TCE exposure differs
from the overall distribution of workers in each NAICS, then this approach will result in inaccuracy .
Second, EPA' s judgments about which industries (represented by NAICS codes) and occupations
(represented by SOC codes) are associated with the uses assessed in this report are based on EPA's
understanding of how TCE is used in each industty. Designations of which industries and occupations
have potential exposures is nevertheless subjective, and some industries/occupations with few exposures
might erroneously be included, or some industries/occupations with exposures might erroneously be

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excluded. This would result in inaccuracy but would be unlikely to systematically either overestimate or
underestimate the count of exposed workers.

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612

2.3.1.3.2 Analysis of ExposureMonitoringData
This report uses existing worker exposure monitoring data to assess exposure to TCE during several
conditions of use. To analyze the exposure data, EPA categorized each PBZ data point as either
''worker" or "occupational non-user'\ The categorizationsare based on descriptions of worker job
activity as provided in literature and EPA'sjudgment. In general, samples for employees that are
expected to have the highest exposure from direct handling ofTCE are categorized as "worker" and
samples for employees that are expected to have the lower exposure and do not directly handle TCE are
categorized as "occupational non-user".

613

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615
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617
618
619

Exposures for occupational non-users can vary substantially. Most data sources do not sufficiently
descn'be the proximity of these employees to the TCE exposure source. As such, exposure levels for the
"occupational non-user" category will have high variability depending on the specific work activity
performed. It is possible that some employees categorizedas "occupational non-user" have exposures
similar to those in the ''worker" category depending on their specific work activity pattern.
Some data sources may be inherently biased. For example, bias may be present if exposure monitoring
was conducted to address concerns regarding adverse human health effects reported followingexposures
during use. Similarly, OSHA CEHD are obtained from OSHA inspections, which may be the result of
worker complaints, and may provide exposure results that may generally exceed the industry average.
Some scenarios have limited exposure monitoringdata in literature, if any. Where there are few data
points available, it is unlikely the results will be representative of worker exposure across the industry.
In cases where there was no exposure monitoring data, EPA may have used monitoring data from
similar conditions of use as surrogate. While these conditions of use have similar worker activities
contributing to exposures, it is unknown that the results will be fully representative of worker exposure
across different conditions of use.
Where sufficient data were available, the 95th and 50th percentile exposure concentrations were
calculated using available data. The 95th percentile exposure concentration is intended to represent a
high-end exposure level, while the 50th percentile exposure concentration represents typical exposure
level. The underlying distribution of the data, and the representativenessof the available data, are not
known. Where discrete data was not available, EPA used reported statistics (i.e., median, mean, 90th
percentile, etc.). Since EPA could not verify these values, there is an added level of uncertainty.
EPA calculated ADC and LADC values assuming workers and ONUs are regularly exposed during their
entire working lifetime, which likely results in an overestimate. Individualsmay change jobs during the
course of their career such that they are no longer exposed to TCE, and that actual ADC and LADC
values become lower than the estimates presented.
2.3.1.3.3 Near-Field/Far-Field Model Framework
The near-field/far-field approach is used as a framework to model inhalation exposure for many

conditions of use. The following describe uncertainties and simplifying assumptions generally
associated with this modeling approach:
•

There is some degree of uncertainty associated with each model input parameter. In general, the
model inputs were determined based on review of available literatme. Where the distribution of
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•
•

•

•

•

the input parameter is known, a distributionis assigned to capture uncertaintyin the Monte Carlo
analysis. Where the distribution is unknown, a uniform distribution is often used The use of a
uniform distribution will capture the low-end and high-end values but may not accurately reflect
actual distribution of the input parameters.
The model assumes the near-field and far-field are well mixed, such that each zone can be
approximatedby a single, averageconcentration.
All emissions from the facility are assumedto enter the near-field.This assumption will
overestimateexposures and risks in facilitieswhere some emissionsdo not enter the airspaces
relevant to worker exposure modeling.
The exposure models estimate airborne concentrations.Exposures are calculated by assuming
workers spend the entire activityduration in their respective exposurezones (i.e., the worker in
the near-field and the occupationalnon-user in the far-field). Since vapor degreasing and cold
cleaning involve automatedprocesses,a worker may actually walk away from the near-field
during part of the process and return when it is time to Wlloadthe degreaser. As such, assuming
the worker is exposed at the near-field concentrationfor the entire activity duration may
overestimateexposure.
For certain TCE applications (e.g., vapor degreasing and cold cleaning), TCE vapor is assumed
to emit continuously while the equipmentoperates (i.e. constant vapor generation rate). Actual
vapor generation rate may vary with time. However, small time variability in vapor generation is
unlikely to have a large impact in the exposure estimates as exposuresare calculated as a timeweighted average.
The exposure models representmodel workplacesettings for each TCE condition of use. The
models have not been regressedor fitted with monitoring data.

Each subsequent item below discussesuncertaintiesassociatedwith the individual model.
Vapor Degreasing and Cold Cleaning Models
The OTVD, conveyorizedvapor degreasing,and cold cleaning assessmentsuse a near-field/far-field

approachto model worker exposure. In additionto the uncertaintiesdescribedabove, the vapor
degreasing and cold cleaning models have the followinguncertainties:
• To estimate vapor generationrate for each equipmenttype, EPA used a distribution of the
emission rates reported in the 2014 NEI for each degreasing/coldcleaning equipment type. NEI
only contains information on major sourcesnot area sources. Therefore,the emission rate
distribution used in modeling may not be representativeof degreasing/coldcleaning equipment
emission rates at area sources.
• The emission rate for conveyorizedvapor degreasing is based on equipmentat eight sites. It is
uncertain how representativethese data are of a ''typical" site.
• EPA assumes workers and occupationalnon-users remove themselves from the contaminated
near- and far-field zones at the conclusionof the task, such that they are no longer exposed to
any residual TCE in air.
Brake Servicing Model

The aerosol degreasing assessmentalso uses a near-field/far-fieldapproachto model worker exposure.
Specific uncertainties associatedwith the aerosol degreasing scenario are presented below:
•

The model references a CA.RBstudy (CARB. 2000) on brake servicingto estimate use rate and
application frequency of the degreasingproduct. The brake servicingscenario may not be
representativeof the use rates for oilier aerosol degreasing applicationsinvolvingTCE.
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•

669

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671
672
673

674
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676

•

The TCE Use Dossier(U.S. EPA. 2017c) presented 16 different aerosol degreasing formulations
containingTCE. For each Monte Carlo iteration,the model determines the TCE concentration in
product by selecting one of 16 possible formulations,assuming the distribution for each
fonnulation is equal to that foWldin a survey of brakecleaning shops in California. It is
uncertain if this distribution is representativeof other geographiclocations within the U.S .
Some of the aerosol formulationspresentedin the TCE Use Dossier (U.S. EPA. 2017c)were
provided as ranges. For each Monte Carlo iterationthe model selects a TCE concentrationwithin
the range of concentrationsusing a uniform distribution.In reality, the TCE concentrationin the
formulationmay be more consistentthan the range provided.

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685
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687

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690

Spot Cleanin2 Model

The multi-zone spot cleaning model also uses a near-field/far-fieldapproach. Specific uncertainties
associated with the spot cleaning scenarioare presentedbelow:
• The model assumes a use rate based on estimatesof the amount of TCE-based spot cleaner sold
in California and the number of textile cleaningfacilities in California (IR.TA. 2007). It is
uncertain if this distribution is representativeof other geographiclocations in the U.S.
• The model assumes a facility floor area based on data from (CARB.2006) and King County
(Whittaker and Johanson
. 2011). It is unknown how representativethe area is of "typical" spot
cleaning facilities. Therefore, these assumptionsmay result in an overestimate or underestimate
of worker exposure during spot cleaning.
• Many of the model input parameters were obtained from( VonGrote et al.. 2003), which is a
German study. Aspects of the U.S. spot cleaningfacilities may differ from German facilities.
However, it is not known whether the use of German data will under- or over-estimateexposure.

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693

2.3.1.3.4 Modeled Dermal Exposures

701
702

The Dermal Exposure to VolatileLiquids Model is used to estimate dermal exposure·to TCE in
occupational settings. The model assumes a fixed fractionalabsorptionof the applied dose; however,
fractional absorptionmay be dependent on skin loading conditions.The model also assumes a single
exposure event per day based on existing frameworkof the EPAIOPPT2-Hand Dermal Exposureto
Liquids Model and does not address variabilityin exposureduration and frequency. Additionally,the
studies used to obtain the underlying values of the quantity remaing on the skin (Qu) did not takeinto
considerationthe fact that liquid retention on the skin may vary with individualsand techniquesof
application on and removal from the hands. Also the data used were developedfrom three kinds of oils;
therefore, the data may not be applicableto other liquids. Based on the uncertaintiesdescribed above,
EPA has a medium level of confidencein the assessed baseline exposure.

703
704

2.3. t .3.5 Summary of Overall Confidence in Inhalation Exposure Estimates
Table 2-27 provides a summary ofEPA's overall confidencein its inhalationexposure estimates for

705

each of the OccupationalExposure Scenariosassessed.

706
707

. m
. halation exoosure estimates b,y OES
I ence m
T abl e 2--27 Summaryo f ove rall con fid

694
695
696
697
698

699
700

.

.

Occupational Exposure
Scenario (OES)

Overall Confidence in Inhalation Exposure Estimates

Manufacturing

EPAconsideredthe assessmentapproach,the qualityof the data, and
uncertaintiesin assessmentresults to detennine a level of confidencefor the 8hr TWA data. For the inhalationair concentrationdata. the primary strengths

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OccupationalExposure
Scenario(OES)

OverallConfidencein InhalationExposureEstimates
include the assessment approach, which is the use of monitoring data, the
highest of the inhalation approach hierarchy.These monitoring data include 16
data points from I source, and the data quality ratings from systematic review
for these data were high. The primary limitationsof these data include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industries and sites covered by this
scenario. Based on these strengths and limitationsof the inhalation air
concentrationdata, the overall confidence for these 8-hr TWA data in this
scenario is medium to high.

Pr~essiog as a Reactant

EPA considered the assessment approach, the quality of the data, and
uncertaintiesin assessmentresults to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of surrogate monitoring data, in the
middle of the inhalation approach hierarchy.These monitoring data include 16
data points from 1 source, and the data quality ratings from systematic review
for these data were medium. 1he primary limitationsof these data include the
uncertainty of the representativenessof these surrogate data toward the true
distribution of inhalation concentrationsfor the industries and sites covered by
this scenario. Based on these strengths and limitations of the inhalation air
concentrationdata, the overall confidence for these 8-hr TWA data in this
scenario is medium to low.

Formulation of Aerosol and
Non-Aerosol Products

EPA considered the assessment approach,the quality of the data, and
uncertaintiesin assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of surrogate monitoring data, in the
middle of the inhalation approach hierarchy.These monitoring data include 33
data points from 1 source, and the data quality ratings from systematic review
for these data were high. The primary limitationsof these data include the
uncertainty of the representativenessof these surrogate data toward the true
distribution of inhalation concentrationsfor the industries and sites covered by
this scenario. Based on these strengths and limitations of the inhalation air
concentrationdata, the overall confidence for these 8-hr TWA data in this
scenario is medium.

Repackaging

EPA considered the assessment approach, the quality of the data, and
uncertaintiesin assessmentresults to determine a level of confidence for the 8hr TWA data. For the inhalation air concentrationdata, the primary strengths
include the assessment approach, which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 33
data points from 1 source, and the data quality ratings from systematic review
for these data were high. The primary limita1ionsof these data include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industries and sites covered by this
scenario. Based on these strengths and limitationsof the inhalation air
concentrationdata, the overall confidence for these 8-hr TWA data in this
scenario is medium to high.

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OccupationalExposure
Scenario (OES)

OverallConfidencein InhalationExposure Estimates

Batch Open-Top Vapor

EPA considered the assessmentapproach, the quality of the data, and
uncertainties in assessmentresults to determine a level of confidence for the 8hr TWA data. For the inhalation air concentration data, the primary strengths
include the assessment approach,which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include
123 data points from 16 sources, and the data quality ratings from systematic
review for these data were medium. The primary limi1ationsof these data
include the uncertainty of the representativenessof these data toward the true
distribution of inhalation concentrationsfor the industries and sites covered by
this scenario. Based on these strengthsand limitations of the inhalation air
concentration data, the overall confidence for these 8-hr TWA data in this
scenario is medium.

Degreasing

EPA also considered the use of modeling, which is in the middle of the
inhalation approachhierarchy. A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Vapor
generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National Emissions Inventory. The primary limitations of
the air concentrationoutputs from the model include the uncertainty of the
representativenessof these data toward the true distribution of inhalation
concentrations for the industries and sites covered by this scenario. Added
uncertainties includethat the underlying methodologies used to estimate these
emissions in the 2014 NEI are unknown. Based on these strengths and
limitations of the air concentrations,the overall confidence for these 8-hi
TWA data in this scenario is medium to low.

Batch Closed-LoopVapor
Degreasing

ConveyorizedVapor
Degreasing

EPA considered the assessmentapproach, the quality of the data, and
uncertainties in assessmentresults to determine a level of confidence for the 8hr TWA data. For the inhalation air concentration data, the primary strengths
include the assessment approach,which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 19
data points from 1 source, and the data quality ratings from systematic review
for these data were high. The primary limitations of these data include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industries and sites covered by this
scenario. Based on these strengths and limitationsof the inhalation air
concentration data, the overall confidence for these 8-hr TWA data in this
scenario is medium to high.
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA data. For the inhalation air concentration data, the primary strengths
include the assessment approach,which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 18
data points from 2 sources, and the data quality ratings from systematic review
for these data were medium. The primary limitationsof these data include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industries and sites covered by this
scenario. Based on these strengths and limitationsof the inhalation air

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Occupational Exposure
Scenario(OES)

Overall Confidence in Inhalation Exposure Estimates
concentrationdata, the overall confidencefor these 8-hr TWA data in this
scenario is medium to low.
EPA also consideredthe use of modeling,which is in the middle of the
inhalation approach hierarchy. A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Vapor
generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National EmissionsInventory. The primary limitations of
the air concentrationoutputs from the model include the uncertainty of the
representativenessof these data toward the true distribution of inhalation
concentrationsfor the industries and sites covered by this scenario. Added
uncertaintiesinclude that emissions data available in the 2014 NEI were only
found for three total units, and the underlying methodologiesused to estimate
these emissions are unknown. Based on these strengths and limitations of the
air concentrations,the overall confidencefor these 8-hr TWA data in this
scenario is medium to low.

Web Vapor Degreasing

EPA considered the assessmentapproach, the quality of the data, and
uncertaintiesin assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of modeling, in the middle of the
inhalation approachhierarchy. A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Vapor
generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National EmissionsInventory. The primary limitationsof
the air concentrationoutputs from the model include the uncertainty of the
representativenessof these data toward the true distribution of inhalation
concentrationsfor the industriesand sites covered by this scenario. Added
wicertaintiesinclude that emissionsdata available in the 2011 NEI were only
found for one unit, and the underlyingmethodologiesused to estimate the
emission is unknown. Based on these strengths and limitations of the air
concentrations,the overall confidence for these 8-hr TWA data in this scenario
is mediwn to low.

Cold Cleaning

EPA consideredthe assessment approach,the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of modeling, in the middle of the
inhalation approach hierarchy.A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Vapor
generationrates were derived from TCE-unit emissions and operating hours
reported in the 2014 National EmissionsInventory. The primary limitationsof
the air concentration outputs from the model include the uncertainty of the
representativenessof these data toward the true distribution of inhalation
concentrationsfor the industriesand sites covered by this scenario. Added
uncertainties include that emissions data available in the 2014 NEI were only
found for ten total units, and the underlying methodologiesused to estimate
these emissions are unknown.Based on these strengths and limitations of the
air concentrations,the overall confidence for these 8-hr TWA data in this
scenario is medium to low.

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Occupational Exposure
Scenario (OES)

Overall Confidence in Inhalation Exposure Estimates

Aerosol Applications:
Spray Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners, Penetrating
Lubricants, and Mold
Releases

EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of modeling, in the middle of the
inhalation approach hierarchy. A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Various
model parameters were derived from a CARB brake service study and TCE
concentration data for 16 products representative of the OES. The primary
limitations of the air concentration outputs from the model include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industries and sites covered by this
scenario. Based on these strengths and limitations of the air concentrations, the
overall confidence for these 8-hrTWAdata in this scenario is medium.

MetalworkingFluids

EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations.The primary strengths include the
assessment approach, which is the use of monitoring data, the highest of the
inhalation approach hierarchy. These monitoring data include 3 data points
from 1 source, and the data quality ratings from systematic review for these
data were high. The primary limitationsof these data include limited dataset (3
datapoints from 1 site), and the uncertainty of the representativeness of these
datatoward the true distribution of inhalation concentrations for the industries
and sites covered by this scenario. Based on these strengths and limitations of
the inhalation air concentration data, the overall confidence for these 8-hr
TWA data in this scenario is low.
EPA also considered the use of modeling, which is in the middle of the
inhalation approach hierarchy. Data from the 2011 Emission Scenario
Document on the Use of Metalworking Fluids was used to estimate inhalation
exposures. The primary limitations of the exposure outputs from this model
include the uncertainty of the representativenessof these data toward the true
distribution of inhalation for all TCE uses for the industries and sites covered
by this scenario, and the difference between the modeling data and monitoring
data. Added uncertainties include that the underlying TCE concentration used
in the metalworking fluid was assumed :fromone metalworking fluid product.
Based on these strengths and limitations of the air concentrations, the overall
confidence for these 8-hr TWA data in this scenario is medium.

Adhesives, Sealants,Paints,
and Coatings

EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA data. For the inhalation air concentration data, the primary strengths
include the assessment approach, which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 22
data points from 2 sources, and the data quality ratings from systematic review
for these data were medium to high. The primary limitations of these data
include the uncertainty of the representativenessof these data toward the true
distribution of inhalation concentrations for the industries and sites covered by
this scenario. Based on these strengths and limitations of the inhalation air

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OccupationalExposure
Scenario (OES)

Overall Confidencein InhalationExposureEstimates
concentration data, the overall confidence for these 8-hr1WA data in this
scenario is medium to medium to low.
For the ONU inhalation air concentration data, the primary strengths include
the assessment approach, which is the use of monitoring data, the highest of
the inhalation approach hierarchy. These monitoring data include 2 data points
from I source, and the data quality ratings from systematic review for the data
point was high. The primary limitations of this data is the limited dataset (two
data points from 1 site), and the uncertainty of the representativeness of this
data toward the true distribution of inhalation concentrations for the industries
and sites covered by this scenario. Based on these strengths and limitations of
the inhalation air concentration data, the overall confidence for these 8-br
TWA data in this scenario is medium to low.

Other Industrial Uses

EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA inhalation air concentrations. The primary strengths include the
assessment approach, which is the use of surrogate monitoring data, in the
middle of the inhalation approach hierarchy. These monitoring data include 16
data points from I source, and the data quality ratings from systematic review
for these data were medium. The primary limitations of these data include the
uncertainty of the representativenessof these surrogate data toward the true
distribution of inhalation concentrations for the industries and sites covered by
this scenario. Based on these strengths and limitations of the inhalation air
concentration data, the overall confidence for these 8-hr TWA data in this
scenario is medium to low.

Spot Cleaning and Wipe
Cleaning

EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8hr TWA data. For the inhalation air concentration data, the primary strengths
include the assessment approach, which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 8
data points :from2 sources, and the data quality ratings from systematic review
for these data were medium. The primary limitations of these data include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrations for the industries and sites covered by this
scenario. Based on these strengths and limitations of the inhalation air
concentration data, the overall confidence for these 8-hr TWA data in this
scenario is medium to low.
EPA also considered the use of modeling, which is in the middle of the
inhalation approach hierarchy. A Monte Carlo simulation with 100,000
iterations was used to capture the range of potential input parameters. Various
model parameters were derived from a CARB study. The primary limitations
of the air concentration outputs from the model include the uncertainty of the
representativeness of these data toward the true distribution of inhalation
concentrations for the industries and sites covered by this scenario. Added
uncertainties include that the underlying methodologies used to obtain the
values in the CARB study, as well as the assumed TCE concentration in the
spot cleaning product. Based on these strengths and limitations of the air

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Occupational Exposure
Scenario (OES)

Overall Confidence in Inhalation Exposure Estimates
concentrations,the overall confidencefor these 8-hr TWA data in this scenario
is medium to low.
Despite these limitations, the modeling and monitaing results match each
other very closely. Therefore,the overall confidence is medium.

Industrial Processing Aid

EPA consideredthe assessment approach,the quality of the data, and
uncertaintiesin assessmentresults to determine a level of confidence for the
12-hrTWA data. For the inhalationair concentration data, the primary
strengths include the assessment approach,which is the use of monitoring
data, the highest of the inhalationapproach hierarchy. These monitoring data
include30 data points from 1 source, and the data quality ratings from
systematicreview for these data were high. The primaty limitations of these
data include the uncertaintyof the representativenessof these data toward the
true distribution of inhalationconcentrationsfor the industries and sites
covered by this scenario.Based on these strengths and limitations of the
inhalation air concentrationdata, the overall confidence for these 12-hr TWA
data in this scenario is medium to high.
For the ONU inhalation air concentrationdata, the primary strengths include
the assessment approach, which is the use of monitoring data, the highest of
the inhalation approach hierarchy.These monitoring data include 4 datapoints
from 1 source, and the data quality ratings from·systematic review for the data
point was high. The primary limitationsof this single data point include the
uncertainty of the representativenessof these data toward the true distribution
of inhalation concentrationsfor the industriesand sites covered by this
scenario. Based on these strengths and limitationsof the inhalation air
concentrationdata, the overall confidence for these 12-hr TWA data in this
scenario is mediumto low.

Commercial Printing and
Copying

EPA consideredthe assessmentapproach,the quality of the data, and
uncertaintiesin assessment results to determine a level of confidence for the 8hr TWA data. For the inhalationair concentrationdata, the primary strengths
includethe assessment approach,which is the use of monitoring data, the
highest of the inhalation approach hierarchy. These monitoring data include 20
data points from 1 source, and the data quality ratings from systematic review
for these data were medium. The primary limitationsof these data include a
limited dataset, and the uncertaintyof the representativenessof these data
toward the true distribution of inhalationconcentrationsfor the industries and
sites covered by this scenario.Based on these strengths and limitationsof the
inhalation air concentrationdata, the overall confidence for these 8-hr TWA
data in this scenario is mediumto low.

Other Commercial Uses

EPA did not identify any inhalationexposure monitoring data related to this
OES. EPA assumes the exposure sources, routes, and exposure levels are
similar to those for the Spot Cleaning and Wipe Cleaning OES.

Process Solvent Recycling
and Worker Handling of
Wastes

EPA did not identify any inhalationexposure monitoring data related to waste
handling/recycling. EPA assumes the exposure sources,routes, and exposure
levels are similar to those for the RepackagingOES.

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2.3.2 Consumer Exposures
_ _
_
TCE can be found in consumer and commercial products that are available for purchase at common
retailers and can therefore result in exposures to household consumers (i.e., receptors who use a product

directly) and bystanders (i.e., receptors who are a non-product users that are incidentally exposed to the
product or article) (U.S. EPA. 2017c, h) .

2.3.2.1

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Consumer Conditions of Use Evaluated

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Conditions of use associated with consumer exposure were described in the Problem Formulation (U.S.
EPA. 2018d). The availability of TCE in consumer products was determined through the development of
EPA ' s 2017 Market and Use Report (U.S. EPA. 2017h ) and Preliminary Information on Manufacturing,
Processing, Distribution, Use, and Disposal: TCE (U.S. EPA . 2017c). Additional online research was
undertaken following Problem Fonnulation to confirm TCE concentrations and compile a
comprehensive list of products that may be available to consumers for household use. These resources
were used to select the most appropriate product-specific inputs (e.g.,, weight fraction and fonnulation
type) associated with each consumer condition of use.

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Table 2-28 lays out consumer condition of use categories and associated product subcategories
evaluated for TCE. Based on additional research, conditions of use may be described in more detail
(e.g.,, formulation type. specific product type) when compared to the tables presented in the Problem
Fonnulation (U.S . EPA. 2018d). Any differences between the displayed categories and those presented
in the Problem Formulation are described in the footnotes.

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Table 2-28. Evaluated Consumer Conditions of Use and Products for TCE

Life
Cycle

Category

1

Product Subcategory

Form

Brake& Parts Cleaner
Electronic
Degreaser
/Cleaner3
ElectronicDegreaser
/Cleaner3
AerosolSprayDegreaser
/Cleaner
Liquid Degreaser
/Cleaner3
Gun Scrubber"
Gun Scrubber"
MoldRelease
TireCleaner5
TireCleaner5
Tap& DieFluid
PenetratingLubricant6
Solvent-based
Adhesive
& Sealant
Mirror-edgeSealant
TireRepair Cement/Seal
er
CarpetCleaner
7
Spot Remover
7
SpotRemover

Aerosol
Aerosol

Stage
Use

SolventsforCleaningand
Degreasing

Lubricantsand Greases
AdhesivesandSealants

CleaningandFurniture
Care
Products11

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No. or
Products
Utilized in
Modeling•
4

Liquid

9
I

Aerosol

8

Liquid

2

Aerosol

2

Liquid

l

Aerosol
Aerosol

2

Liquid

Aerosol

I
1
5
3
1
5
1
1

Liquid

4

Aerosol
Aerosol
Liquid

Aerosol
Liquid
Liquid

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Life
Cycle
Stage

No.or
Category

Product Subcategory

Form1

Products

Utilized in
Modeling 1

Arts, Crafts, and Hobby Materials
Apparel and Footwear Care Products
Other Consumer Uses

Fixatives & Finishing Spray
Coatinas8
Shoe Polish
Fabric Spray9

Aerosol

Aerosol

I

Film Cleaner

Aerosol

2

Hoof Polish

Aerosol

1

Pepper Spray

Aerosol

2

Toner Aid10

Aerosol

1

Aerosol

I
1

Lace Wig and Hair Extension Glues
Unknown
o
Fonn was determined based on the specific products identified as representativeof the associated product
subcategories. Please see Supplemental File [ConsumerExposureAssessmentModelInput Parameters.Docket:
EPA-HQ-OPPT -2019-0500] for the full list of representative products.
2
The brake cleaner subcategorywas listed in Table 2-3 of the Problem Formulation as being associated with the
automotive care products category; however, the same brake cleaning conditions of use are now associated with the
broader solvents for cleaning and degreasing category. This change does not impact evaluated conditions of use, as
the evaluated product scenarios are based on the brake cleaner product(s) and not a broadet"category of use.
3
Liquid degreaser/cleaner and electronic degreaser/cleaner(aerosol and liquid}were not specifically named in the
Problem Formulation as a potential consumer subcategories. They were added due to product availability based on
the additional research noted above that helped to differentiate specific product forms (i.e., liquid or aerosol) and
types.
4
The gun scrubber subcategory was listed in Table 2-3 of the Problem Fonnulation as being associated with the
other consumer uses category; however, the same gun scrubber conditions of use are now associated with the
broader solvents for cleaning and degreasing category. This change does not impact evaluated conditions of use, as
the evaluated product scenarios.are based on the gun scrubber product(s) and not a broader category of use.
5 Tire cleaner products / subcategories of use were not specifically called out in the Problem Formulation; however,
such products were identified in the 2017 Use and Market Report and Preliminary lnfonnation on Manufacturing,
Processing, Distribution, Use, and Disposal: TCE (!J S. EPA. 2017c} and fit within the broader Solvents for
Cleaning and Degreasing category.
6
Based on additional research into the specific product(s) associated with the broader lubricants and greases
category, the subcategory name was updated from penetrating lubricant to lubricant.
7 The spot remover subcategory was listed in Table 2-3 of the Problem Formulation as being associated with the
laundry and dishwashing products category; however, the same spot remover conditions of use are now associated
with the cleaning and furniture care products category. This change does not impact evaluated conditions of use, as
the evaluated product scenarios are based on the spot remover product(s) ·and not a broader category of use.
8
Note that this subcategory is referred to as "clear protective coating spray" in U.S. EPA (2014b) and as "spray
fixative" in the TCE Significant New Use Rule (80 FR 47441).
9 Fabric spray (specifically an anti-fray spray) was added following Problem Fonnulation based on identification in
the final 2014 TCE Work Plan Chemical Risk Assessment (US. EPA, 2014b).
10 The toner aid subcategory was listed in Table 2-3 of the Problem Formulation as being associated with the Ink,
toner, and colorant products category; however, the toner aid use is not like use of a toner or pigment; therefore, the
same toner aid condition of use is now associated with the other consumer use category. This change does not
impact evaluated conditions of use, as the evaluated product scenarios are based on the toner aid product(s) and not
a broader category of use.
11 Note that the problem fonnulation described "cleaning wipes" as a condition of use for this category. However,
that referred to the application of a product that is then wiped off, rather than a pre-wet towelette. A number of
consumer conditions of use involve wipe cleaning and are described in detail in section 2.3.2.62 as leading to
dermal contact with imneded evaooration.
,
1

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2.3.2.2

Consumer Exposure Routes Evalua~

Inhalation and dermal exposures are evaluated for acute exposure scenarios, i.e., those resulting from
short-term or daily exposures. Chronic exposure scenarios resulting from long-term use of household

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consumer products were not evaluated. Although high-end frequencies of consumer use are up to 50
times per year, approximately one time per week, available toxicological data is based on either single or
continuous TCE exposure. There is uncertaintyregarding the extrapolation from continuous studies in
animals to the case of repeated, intennittent human exposures. Therefore, while consumers at the high-end
frequency of use may be at risk for chronic hazard effects, EPA is unable to develop risk estimates for
this population.

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2.3.2.2.1 Inhalation
The acute exposure via inhalation is the most significant route of exposure for consumer exposure
scenarios for users and bystanders. This is in line with EPA's 2014 TSCA Work Plan Chemical Risk
Assessment, which evaluated acute inhalation exposure to consumers and bystanders from degreasing
and arts & crafts uses (U.S. EPA 2014b ). EPA evaluated inhalation exposures for consumers and
bystanders for all consumer conditions of use.

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2.3.2.2.2 Dermal
EPA assessed dermal exposures to TCE from consumer uses. Instantaneous exposures to skin are
expected to evaporate before significant dermal absorption occurs based on TCE's physical chemical
properties which include the vapor pressure, water solubility and log Kow.
The log Kow
estimate is 0.8%
absorption and 99.2% volatilization and is derived from IHSkinPerm, a mathematical tool for estimating
dermal absorption. Exposure that occurs as a deposition over time or a repeated exposure that maintains
a thin layer of liquid TCE had greater absorption based on the estimate from IHSkinPenn for an 8-hr
exposure is 1.6% absorption and 98.4% volatilization. Dermal exposures to liquid TCE are expected to
be concurrent with inhalation exposures, which are anticipated to reflect the preponderance of overall
exposure from a use or activity for most consumer exposure scenarios. This agrees with the NIOSH skin
notation profile for TCE, which estimates a low hazard potential by dermal absorption for systemic
effects when inhalation and dermal exposures are concurrent (NIOSH, 2017). There may be certain
scenarios with higher dermal exposure potential - where liquid TCE is not able to evaporate readily and
volatilization is inhibited. An example of this is a user holding a rag soaked with TCE against their palm
during a cleaning activity. Therefore, dermal exposures are quantified and presented for consumer use
scenarios that may involve dermal contact with impeded evaporation.

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Background levels ofTCE in indoor and outdoor air are not assessed in this assessment; therefore, there
is a potential for underestimating consumer inhalation exposures, particularly for populations living near
a facility emitting TCE or living in a home with other sources of TCE, such as ICE-containing products
stored in the home. Similarly, inhalation exposures were evaluated on a product-specific basis and are
based on use of a single product type within a day, not multiple products.

Generally, individuals that have contact with liquid TCE would be users and not bystanders. Therefore,
dermal exposures to liquid TCE are not expected and inhalation is the primary route of exposure for
bystanders. There is potential for bystanders or users to have indirect dermal contact via contact with a
surface upon which TCE has been applied (e.g.,, counter, floor). Based on the expectation that TCE
would evaporate from the surface rapidly, with <1% dermal absorption predicted from instantaneous
contact, this route is unlikely to contribute significantly to overall exposure.
Potentially Exposed or Susceptible Subpopulations
As part of the Problem Formulation (U.S. EPA 2018d), EPA identified consumers and bystanders
associated with use ofTCE-containing consumer products as a potentially exposed and susceptible
subpopulation due to greater exposure. Additionally,higher-intensity users (i.e., those using consumer
products for longer durations and in greater amounts) were considered and evaluated. Exposures and
2.3.2.3

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risks for these subpopulationsare consideredand evaluatedherein. Consumers are considered to include
youth and adults over age 11, but bystandersin the home exposed via inhalation are considered to
include any age group, from infant to adult, includingpregnant women.Highly exposed (high-intensity
users) and potentially exposed or susceptiblesubpopulations(PESS) within this overall schema as
receptor categories overlap, as individualsmay belong to multiple receptor groups.

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2.3.2.4
ConsumerExposuresApproach and Methodology
Modeling was conducted to estimate exposurefrom the identifiedconswner conditions of use.
Exposures via inhalation and dermal contact to ICE-containing consumerproducts were estimated using
EPA's Consumer Exposure Model (CEM) Version 2.1 (1I.S. EPA. 2019a), along with consumer
behavioral pattern data (i.e., use patterns) and product-specificcharacteristics.

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Residential indoor air and personal breathingzone data were identified and evaluated during systematic
review. However, measured levels are not attributableto specific consumerproducts or conditions of use
and were therefore not comparedto modeledestimates. For a summaryof these data, see Appendix D.2.
2.3.2.4.1 Modeling Approach
Consumer Exposure Model (CEM) Version2.1 was selected for the consumer exposure modeling as the
most appropriate model to use based on the type of input data available for TCE-containing consumer
products. Moreover, EPA did not have the input parameter data (i.e., product-specificchamber emission
data) required to run higher-t1erindoor air models. The advantagesof using CEM to assess exposures to
consumers and bystanders are the following:
• CEM model bas been peer-reviewed;
• CEM accommodatesthe distinct inputs available for the products containing TCE; and
• CEM uses the same calculationengine to compute indoor air concentrationsfrom a source as the
higher-tier Multi-ChamberConcentrationand Exposure Model (MCCEM) but does not require
measured chamber emission values.
For a characterizationof model sensitivity,see Appendix D.1 .
Modeling Air Concentrations and Inhalation Exposure

CEM predicts indoor air concentrationsfrom consumer product use by implementinga deterministic,
mass-balance calculation utilizing an emissionprofile determinedby implementingappropriate emission
scenarios. The model uses a two-zone representationof the building of use (e.g.,, residence, school,
office), with Zone 1 representing the room where the consumer product is used (e.g.,, a utility room) and
zone 2 being the remainder of the building. The product user is placed within Zone 1 for the duration of
use, while a bystander is placed in Zone 2 during product use. Otherwise,product users and bystanders
follow prescribed activity patterns throughout the simulatedperiod. In some instancesof product use, a
higher concentration of product is expected very near the product user; CEM addresses this by further
dividing Zone 1 into near-field, with a default volume of lm 3, and far-field, which reflects the remainder
of Zone 1. Each zone is considered well-mixed.Product users are exposedto airborne concentrations
estimated within the near-field during the time of use and otherwise follow their prescribed activity
pattern. Bystanders follow their prescribedactivity pattern and are exposed to far-field concentrations
when they are in Zone 1. Background concentrationscan be set to a non-zero concentrationif desired.
For acute exposure scenarios, emissionsfrom each incidenceof product usage are estimated over a
period of 72 hours using the followingapproachthat account for how a product is used or applied, the
total applied mass of the product, the weight fraction of the chemical in the product, and the molecular
weight and vapor pressure of the chemical.
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The general steps of the calculation engine within the CEM model include:
• Introduction of the chemical (i.e., TCE) into the room of use (Zone 1) through two possible
pathways: (1) overspray of the product or (2) evaporation from a thin film;
• Transfer of the chemical to the rest of the house (Zone 2) due to exchange of air between the
different rooms;
• Exchange of the house air with outdoor air; and
• Compilation of estimated air concentrations in each zone as the modeled occupant (i.e., user or
bystander) moves about the house per prescribed activity patterns.
As receptors move between zones in the model, the associated zonal air concentrations at each 30second time step were compiled to reflect the air concentrations a user and bystander would be exposed
to throughout the simulation period. Time weighted averages (TWAs) were then computed based on
these user and bystander concentration time series per available human health hazard data. For TCE, 3and 24-hour TWAs were quantified for use in risk evaluation based on alignment relevant acute human
health hazard endpoints.
Emission Models
Based on the suite of product scenarios developed to evaluate the TCE consumer conditions of use, the
specific emission models applied for the purposes of modeling TCE products include: El: Emission
from Product Applied to a Surface Indoors Incremental Source Model and E3: Emission from Product
Sprayed.
El assumes a constant application rate over a user-specified duration of use and an emission rate that
declines exponentially over time, at a rate that depends on the chemical molecular weight and vapor
pressure. This emission model is generally applicable to liquid products applied to surfaces that
evaporate from those surfaces, such as cleaners. EI was applied for all liquid formulations in the
modeling ofTCE conswner inhalation exposures. E3 assumes a small percentage of product becomes
airborne rather than contacting the target surface and therefore immediately available for uptake via
inhalation. This is called "overspray" and is not well characterized, though default parameters ranging
from 4.5 to 6% overspray are based on a combination of modeled and empirical data from Jayjock
(2012) and are said to reflect reasonable worst-case overspray potential (11.S. EPA. 2017b ). The
remainder of chemical is assumed to contact the target surface and volatilize at a rnte I.hatdepends on the
chemical molecular weight and vapor pressure. The aerosolized portion is treated using a constant
emission rate model while the non-aerosolized mass is treated in the same manner as liquid products
applied to a surface, combining a constant application rate with an exponentially declining rate. In U.S.
EPA (2014b ), modeled scenarios were found not to be sensitive to this parameter, with overspray
fractions of 1 and 25% producing nearly identical peak concentrations for TCE. Both E 1 and E3 have a
near-field model option that is selected to capture the higher concentration in the breathing zone of a
product user during use.
For additional details on CEM 2.1 's underlying emission models, assumptions, and algorithms, please
see the User Guide Section 3: Detailed Descriptions of Models within CEM (11.S. EPA. 2019a). The
emission models used have been compared to other model results and measured data; see Appendix D:
Model Corroboration of the User Guide Appendices for the results of these analyses (U.S. EPA. 2019b).
Modeling Dermal Exposure
CEM also contains a dermal modeling component that estimates absorbed dermal doses resulting from
dermal contact with chemicals found in consumer products. Based on the described dermalexposure

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conditions (i.e., dermal contact with impeded evaporation)and the chemical-and scenario-specific input
parameters available for use in modeling (e.g.,, scenario-specificuse duration, measured dermal
permeability coefficient), "P_DER2b: Dermal Dose from Product Applied to Skin, Permeability Model"
was selected as the most appropriatemodel to estimate dermal exposures from consumer products
containing TCE. P_DER2b estimates dermal flux based on a permeabilitycoefficient (.Kp)and is based
on.the ability of a chemical to penetrate the skin layer once contact occurs. It assumes a constant supply
of chemical directly in contact with the skin throughoutthe exposure duration. The acute form of the
model is given below:
K-pX Dcu:X p X
ADR =

¥wFQcu: Dil
X

X

X

WF

X

EDac X CF1

ATacx CF2

Where:

ADR
Kp
Dao

= Potential acute dose rate (mg/kg-day)

= Permeabilitycoefficient(cm/hr)
= Duration of use (min/event)
p
= Density of fonnulation (g/cm3)
SA/BW = Surface area to body weight ratio (cm2/kg)
FQac =Frequency of use (events/day, 1 for acute exposurescenarios)
Oil
= Product dilution fraction (unitless, I [no dilution]for all TCE scenarios)
WF
= Weight fraction of chemicalin product (unitless)
EDao = Exposure duration (days)
= Conversion factor (1,000 mg/g)
CPI
CF2
= Conversion factor (60 min/hr)
ATac = Averagingtime (days, l for acute exposurescenarios)

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.Kpis a measure of the rate of chemical flux through the skin. The parameter can either be specified by
the user (if measured data are available) or be estimated within CEM using a chemical's molecular
weight and octanol-waterpartition coefficient (Kow).Note the permeability model does not inherently
account for evaporative losses (unless the availableflux or Kpvalues are based on non-occluded,
evaporative conditions), which can be considerablefor volatile chemicals in scenarios where
evaporation is not impeded. While the permeabilitymodel does not explicitly represent exposures
involving such impeded evaporation,the model assumptions make it the preferred model for an such a
scenario (e.g.,, a scenario wherein dermal contact involved impeded evaporation, or where there is
potential for dermal immersion): Furthermore,it incoiporates scenario-specificproduct use durations
and distinct surface area to body weight ratios for various user populations.For additional details on
P_DER2b, please see the CEM User Guide Section 3: Detailed Descriptionsof Models within CEM
(11.S. EPA. 2019a).
For TCE, a measured dermal permeability coefficient(kp 0.019cm/hr) is applied, based on findings
from Poet (2000), as summarized and presented in the 2017 NIOSH Skin Notation Profile for TCE
(Hudson and Dotson. 20 17) . The permeabilitycoefficient selected was based on a human water-patch
test and was within range of the estimated Kt>values presented in the 2017 NIOSH Skin Notation Profile
(0.01197 cm/hr) (Hudson and Dotson. 2017) and within the CEM model (0.028 cm/hr), both predicted
using chemical properties.
Dermal exposure estimates are only quantified and presented for consumer exposure scenarios that
could involve such dermal contact with impeded evaporation (e.g.,, applicationor cleaning with a rag
pressed against user's hand), per the focus described in Section 2.3.2.2.2.

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925
926
927
928
929
930
931
932
933
934
935
936
93 7
938
939
940
941
942
943

Varia.don
To capture a range of potential exposure levels associated with consumer conditions of use, three input
parameters were varied: mass of product used, weight fractio~ and duration of use. Aside from these
three parameters, model inputs were held constant across a specific scenario or across all product
scenarios. For example, certain inputs such as the room of use (and associated room/Zone 1 volume),
overspray fraction, and surface area to body weight ratio exposed in dennal exposure scenarios were
held constant across the multiple iterations of a single product scenario but differed across product
scenarios based on their scenario-specific nature. Other parameters such as chemical properties, building
volume, air exchange rate, and user and bystander activity patterns (i.e., movements around the home)
were held constant across all product scenarios and runs. The majority of the non-varied modeling
parameters reflect central tendency inputs (i.e., median or mean values; see Table 2-29); therefore, the
combination of high -end inputs for the three varied parameters do not reflect "worst-case" or bounding
estimates.

944

The low-, mid-, and/or high-end weight fractions were selected principally from MSDS/SDS fonns. For
subcategories where there was only one product with a weight fraction range, only one weight fraction
was used for modeling. If there were two or more products with weight fraction ranges, the low-end of
lowest non-zero range and high-end of highest range were the bounding weight fractions. For a central
tendency weight fraction, the mid-point between bounding weight fractions was calculated. In the case
of unknown weight fractions, values were selected from the range of related products. Further detail is
provided in the Supplemental File, [ConsumerExposureAssessmentModelInput ParametersDocket:

945
946
94 7
948
949
950

Varied Inputs:
Considering the model sensitivity analysis summarized in Appendix D. l and the availability of highquality use-pattern data, EPA varied three input parameters: chemical weight fraction (WF) in a
consumer product; mass of product used per use event; and duration of product use per event.

951
952

EPA-HQ-OPPT-2019-0500].

953
954

Mass of product used and duration of use selections define user characteristics (e.g.,, high-intensity user,
moderate-intensity user, low-intensity user) and are based on the national Household solvent products: A
national usage survey (U.S. EPA . 1987). Westat ( 1987) surveyed thousands of American households to
gather information on consumer behavior (i.e., use patterns) and product characteristics (e.g.,, product
formulation type) related to product categories that may contain halogenated solvents like TCE. The
survey was rated as having "high" quality during the data evaluation phase of systematic review. Weight
fraction (i.e., the percentage ofTCE in the product formulation) represents the true range in the market
based on manufacturer-developed Safety Data Sheets (SDSs).

955
956
957
958
959
960

961
962
963

964

965
966
967

For each parameter varied, up to three distinct inputs were modeled to address known variability across
these three parameters. While this approach resulted in up to 27 distinct exposure results for each
product scenario/condition of use, this was a deterministic assessment and results reflect a range based
on variation of three key parameters, not a distribution. Unlike inhalation modeling, for dermal
modeling, only the weight fraction and duration of product use were varied because mass used is not a
parameter in the dermal exposure model P_DER2b.

968
969
970

971
972
973

In the model sensitivity analysis, summarized in Appendix D.1 and shown· in the user guide appendices
(U.S. EPA, 2019b ), additional parameters are identified as highly sensitive, including the air exchange
rate and zone volume. However, the central tendency default modeling values were held constant for
these inputs. The inputs varied included those that characterize actual users and reflect levels of TCE in
actual products .
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974
975
976
977
978
979

980
981
982

2.3.2.~

ConsumerExposure Scenarios and Modeling Inputs

Exposure modeling scenarios comprise information that characterizes chemical properties, products, and
use patterns, including:
• Formulations (e.g.,, weight fraction, formulation type [aerosol, liquid]);
• Chemical or product-specific properties (e.g.,, product density, vapor pressure, molecular weight
diffusion coefficient, overspray fraction, transfer coefficients, dilution factor);
• Use patterns (e.g .., frequency, duration, and amount used) ;
• Human exposure factors (e.g.,, body weight, inhalation rate); and
• Environmental conditions (e.g.,, air exchange rates and room size).

983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
000
001
002
003
004
005
006
007
008
009
010
011
012
013
O14
015
O16
O17
018

Consumer exposure modeling scenarios based on the identified conditions of use were built based on
identified TCE products that may be available to consumers, including solvents for cleaning and
degreasing, lubricants and greases, adhesives and sealants , and other uses . The subcategories of use (i.e.,
consumer product types} cited in Table 2-28 were used to develop distinct consumer exposure modeling
scenarios for use in estimating inhalation and dermal exposure to consumers and bystanders . The
availability of TCE in consumer products was determined through the development ofEPA's 2017
Market and Use Report and Preliminary Information on Manufacturing, Processing, Distribution, Use,
and Disposal: TCE. Additional online research was undertaken following Problem Formulation to
confirm TCE concentrations and compile a comprehensive list of products that may be available to
consumers for household use. Specific product characteristics obtained from manufacturer websites
and/or Safety Data Sheets (SDSs) such as form/formulation type, weight fraction and density, were used
to select the most appropriate product-specific inputs (e.g.,, weight fraction and formulation type)
associated with each consumer condition of use . Please see Supplemental File [ConsumerExposure
AssessmentModel Input ParametersDocket: EPA-HQ-OPPT-2019-0S00]for
full product details,
including product-specific formulations , weight fractions, and densities.
CEM requires inputs governing chemical properties, product characteristics, use environment, and user
patterns (i.e., user behavior) . These include inputs such as physical chemical properties, weight fraction,
formulation type, duration of product use, mass of product used, and Zone 1 (room of use) volume. To
determine relevance and appropriateness of the consumer use pattern parameters, EPA reviewed the
consumer product categories available in Household Solvent Products: A National Usage Survey (U.S.
EPA . 1987), referred to as the "Westat survey" or "Westat" herein. Westat (1987} surveyed thousands of
American households to gather information on consumer behavior (i.e., use patterns} and product
characteristics (e.g.,, product formulation type) related to product categories that may contain
halogenated solvents like TCE. It forms the basis for relevant chapters ofEPA's Exposure Factors
Handbook and was used to derive certain default parameters in EPA 's CEM 2.1. Westat ( 1987) includes
survey response data on 30 distinct product categories and reports the following: numbers of
respondents; percentage of respondents reporting use; frequency of use; duration of use; time spent in
the room of use ; brand of product used; form of product used; amowit of product used; and room of use.

In evaluating Westat survey data for appropriateness, EPA considered the similarity of product category,
as well as the similarity of reported product formulation type (i.e., aerosol, liquid) . When a direct
alignment could not be found between the consumer product and Westat product category, EPA used
professional judgement in considering other Westat categories with reasonable ranges for use duration
and amount of product used.

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019

2.3.2.5.1 Consumer Exposure Model Inputs

020
021
022
023
024
025
026
027
028
029
030
031
032
033

Chemical-specificinputs required to model consumer inhalationand dennal exposure included physical
and chemical properties (Table 1-1), as well as a chemical-specificdermal permeabilitycoefficient
(0.019 cm/hr), which were held constant across all modeling scenariosand iterations.
The consumer exposure model requires product-specificdata based on product characteristics and use
patterns. It also requires fixed inputs to define the exposure zones (e.g.,, room and building volumes, air
exchange rates, interzonalventilationrates,); general use patternsdefining the amount of time a receptor
is likely to be in the home; receptor characteristics(e.g.,, age, surfacearea to body weight ratios); and
emission characteristics(e.g.,, backgroundair concentration,emissionfactor). These default inputs are
held constant for a given scenario but may vary across scenariosbased on scenario-specificexposure
factors or assumptions. As such, these inputs were not altered to capture within-scenariovariation. Table
2-29 shows these default parameters.
Table 2-29 Dea
f: ult Modelinl~ In1put p aramet ers
ParameterType
Building
Characteristic1

Modeling
Parameter

Default Value
Modeled

Value
Characterization

Reference

Central Tendency
(Mean)

(U.S. EPA. 20llc )

(ml)

Air Exchange Rate 0.452
(ln' I)

Central Tendency
(Median)

(U.S. EPA, 2011c)

NA

Default (U.S. EPA. 2019~ h)

Building Volume

Interzonal
Ventilation Rate
(m3Jbr)3

492

Garage: 109
All other rooms

modeled: 107
Emission
Characteristics

Background Air
Concentration
(mg/m3)

0

Minimum

Gas Phase Mass
Based on chemical properties and estimated
withinCEM
Transfer
Coefficient (m/br)
Emission Factor
(ug/m2/hr)
Saturation
Concentration in

5.18E+05

Based on chemical
properties and
estimated wi1hin
CEM

Aerosol Fraction
(Spray Scenarios
Only)

0.06

High-end

Product Dilution

I (no dilution)

NA

Based on formulation and
intended use

Stay at home4

NA

Default(U.S. EPA, 2019a, h)

Use Start Time

9.AM.5

NA

NA

Frequency of Use

1 event per day

NA

Default(U.S. EPA. 2019a, h)

Acute Averaging
Time

I day

NA

Air( mg/m3)

Fraction
Use Patterns and Receptor Activity
Exposure Factors Pattern

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ParameterType

Modeling
Parameter
SurfaceArea to
Body Weight Ratio

Default Value
Modeled

Value
Characterization

Reference

Inside of One Hand
Adult (21+): 3.10
Youth (16-20): 2.90

Centraltendency
(mean)

Youth (I 1-16):3.17
10%ofHands
Adult (21+}: 1.24
Youth (16-20): 1.16

Central tendency
(mean)

Youth (11-16): 1.27
1
An overall residentialbuilding volume of 492 m3 is used to calculateair concentrationsin Zone 2 and room volume is
used to calculate air concentrationsin Zone I. The volume of the near-fieldbubble in Zone 1 was assumed to be 1 m3 in
all cases, with the remaining volwne of Zone 1 comprisingthe far-field volume.
2
Air exchangerates differed for two scenarios:pepper spray and hoof polish (see
Table 2-31).
3
The default interzonal air flows are a function of the overall air exchangerate and volume of the building, as well as the
"openness" of the room itself. Kitchens, living rooms, garages,schools, and offices are considered more open to the rest
of the home or building of use; bedrooms, bathrooms, laundryrooms, and utility rooms are usually accessed through one
door and are consideredmore closed.
4
The activity pattern (i.e., zone location throughout the simulatedexposureperiod) for user and bystanderwas the
default "stay-at-home" resident,which assumes the receptors are primarilyin the home (in either Zone I or 2)
throughout the day. These activity patterns in CEM were developed based on ConsolidatedHuman Activity Database
(CHAD) data of activity patterns (Isaacs. 2014).
s Product use was assumed to start at 9 AM in the morning; as such, the user was asswned to be in the room of use {Zone
l) at that time, regardless of the default activity pattern placement at 9 AM.

034
035
036
037
038
039
040
041
042
043
044
045

Table 2-30 displays TCE consumer product modeling scenarios and associated product-specific inputs
that were varied to capture within-scenario variation. These varied inputs include: weight fraction,
duration of use, and mass of product used. Westat (1987) is the basis for duration of use and mass of
product used and product SDSs are the basis for weight fraction and formulation type.
Table 2-31 presents the conswner product modeling scenarios and associated scenario-specific inputs
that were not varied within product modeling scenarios but did vary across scenarios. In modeling
exposures within and across all scenarios, parameters displayed in both below tables were utilized, along
with the general chemical-specificcharacteristics and other model defaults. Please see Supplemental
File [ConsumerExposureAssessmentModel Input ParametersDocket: EPA-HQ-OPPT-2019-0500]for
a spreadsheet summarizing all of the model inputs a.Q.d
product information.

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W46

T abl e 2-30 Consumer Pro duc tM odelin12 Scenar1os an d Van'ed InIPU tP arame t ers
Consumer
Category

Solvents
for
Cleaning
and
Degreasing

Product
Sulr
Categories

Form
(No. of
Pdts)1

Brake&
Parts
Cleaner
Electronic
Degreaser/
Cleaner

Aerosol

Electronic
Degreaser/
Cleaner

Liquid

Spray
Degreaser/
Cleaner

Aerosol

Liquid

Liquid

Degreaser/
Cleaner

(2)

Gun

Aerosol

Scrubber

(2)

Range
of

Weight

Fraction

(%
TCE)
0-100

2

Weight Fractions
Selected for
Modeling
1% TCEt

Mln 2

Mid

Max

20

60

100

(4)

Aerosol

30-100

30

65

100

(9)

100

100

60-100

60

(1)

100

Liquid

Scrubber

(1)

Duration of Use
(min)
10th

50th

95th

%ile3

%Ile

%ile

1

15

120

Brake
Quieters/
Cleaners
Specialized
Electronics
Cleaners
(for TV,
VCR,
Razor etc.)
Specialized
Electronics
Cleaners
(for TV,
VCR,
Razor etc.)
Engine

0.17

2

30

90- 100

Solvent-

100

Range
of
Product
Density
(g/cm3)4
l.231.62

60- 1006

100 8

60

100

100

Page 142 of 691

10th
%ile
47.9

[l]

so•h
191.6
(4]

95t1i
%ile
766.5
[16]

/4He

0

1.25-

1.8

1.52

[0.04]

22.5
[0.5)

337.1
[7.5]

2

30

1.46

1.7
(0.04]

21.6
(0.5]

323.8
[7.5]

5

15

120

1.461.52

130.8
[2.91]

521.4
[11.6]

2157.4
(48]

2

15

120

1.456

24.1
[0.56]

139.9
(3.25]

1377.7

NA

0.7
[0.45

NA

Type

Cleaning
Fluids or
Degreasers
SolventType
Cleaning
Fluids or
Deweasers7
SolventType
Cleaning
Fluids or
7
Del!J'easers

Mass of Product Used
(g, [oz))

0.17

Degreasing5

(8)

Gun

Selected
Westat
Survey
Scenario

2

15

120

1.361.465

[32)

mL]8

2

15

120

1.36

NA

0.6
(0.45

mL]S

NA

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
,

Consumer
Category

Product
SubCategories
Mold
Release

Tire Cleaner
Tire Cleaner

Range

Form
(No. of
Pdts) 1

of
Weight
Fraction
(%

Weight Fractions
Selected for
Modeling
C¾ TCE>
Min 2

Mid

Mu

TCEl2
40-68.9

40

68.9

Aerosol
(2)

70-100

70

100

Liquid

80- 100

Aerosol
(2)

100

(1)

Lubricants
and
Greases

Adhesives
and
Sealants

98

Tap&Die
Flujd

Aerosol

Penetrating
Lubricant

Aerosol

Solvent-

Liquid
(3)

5 ->90

s

Mirror-edge

Aerosol

20-40

40

Sealant

(1)

Tire Repair
Cement/
Sealer

Liquid
(5)

based

98

{I)

5 - 50

5

27.5

50

(5)

41.5

90

Adhesive&
Sealant

65-95

65

80

95

Selected
Westat

Duration of Use
(min)

Survey
10th

Scenario

0.08

¾ile
2

95'1t
o/oile
30

5

15

60

%ile3

Other
Lubricants
(Excluding
Automotive)
Tire/
Hubcap
Cleaner
Tire/
Hubcap
Cleaner
Other
Lubricants
(Excluding
Automotive)
Other
Lubricants
(Excluding
Automotive)
Contact
Cement,
Super
Glues, and
Spray
Adhesives
Contact
Cement,
Super
Glues, and
Spray
Adhesives
Contact
Cement,
Super
Glues, and
Spray
Adhesives

Page 143 of 691

11
so•

5

0.08

0.08

15

2

2

60
30

30

Range
of
Product
Density

Mass of Product Used
(g, [oz])
10th
%Ue
4.3
[0.1]

S()lh

95th

o/oile
23.4
(0.55]

%He
212.9
[SJ

10.S
[0.53]

52.9

[2.67J

317.0
[16}

0.671.493

23.4
[0.53]

117.9
(2.67]

706.4

0.9

2.7
[0.1]

14.8

(0.55]

134.5
[5]

0.6361.42

4.2
[0.1]

23.1
[0.55]

209.9

(g/cm3)4
0.771.44
0.67

[16J

[5]

0.33

4.25

60

1.331.45

1.3
[0.03]

10.7
[0.25]

185.2
[4.32)

0.33

4.25

60

0.614

0.5
(0.03]

4.5
(0.25]

[4.32]

1.3
[0.03]

10.7
[0.2S]

185.2
(4.32]

0.33

4.25

60

1.45

78.4

 lNTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Range

Consumer
Category
Cleaning
and
Furniture
Care

Products
Arts,
Crafts, and
Hobby
Materials
Apparel
and
Footwear
Care
Products
Other
Consumer
Uses

Product
SubCategories

Form
(No. of
Pdts) 1

Carpet
Cleaner
Spot
Remover
Spot
Remover
Fixatives&

Liquid

Finishing

(I)

Spray
Coatin2s
Shoe Polish

of
Weight
Fraction
(%
TCE)2

Weight Fractions
Selected for
Modeling
!% TCE>
Min2

99

99

20- 30

30

Mid

Max

(1)

Aerosol
(1)

Liquid

<50-

(4)

>75

Aerosol

20 • 30

50
30

75

Selected

Duration of Use

Westat

(min)

Survey
Scenario

Range

of
Product
Density

}«)th

SOth

9sth

%ile3

¾ile

0.25

¾ile
5

30

1.6

0.25

5

30

1.562

0.25

5

30

1.251.45

0.25

5

60

0.704

Spot
Removers
Spot
Removers
Spot
Removers
Aerosol

(g/cm3)"

Rust

Mass of Product Used
(g, [oz])
10th

%lie
11.8
[0.25]
11.5
[0.25]

50111
¾ile

95111
¾ile

62.9
[1.33]
61.4

526.6
(11.13]
514.1

[1.33]

[11.13]

[0.45)

57.0
[1.33]
45.2
[2.17]

477 .2
[I 1.13]
306.0
[14.7]

10.7
[0.25]

9.4

Removers9
10-20

20

Spray Shoe
Polish

0.5

5

30

0.512

2.9
[0.19]

15.4
[1.02]

151.4
(10]

Fabric Spray Aerosol
(l)

20-40

40

1.4

10

60

0.614

11.4
[0.63]

49.9
[2.75]

326.8

Film
Cleaner

Aerosol

80 - 100

1.456

19.4
[0.45]

93.4
[2.17]

632.9
[14 .7]

Hoof Polish

Aerosol

Water
Repellents/
Protectors
(for Suede,
Leather, and
Cloth)
Aerosol
Rust
Removers9
Spray Shoe
Polish11

0.5120.704

4.0

21.2
[1.02]

208.2

[0.19]

1.25

NA

4.0
[0.108

NA

Aerosol
(1)

100

(2)
3010

30

(1)

Pepper
Spray

Aerosol

Toner Aid

Aerosol

91.5

91.5

NA 12

(2)

0.25

0.5
NA

5
5

o.os12

60

30

NA

1.45-

[18]

[IO]

112

Aerosol
0.25
60
1
13.3
64.2
434.7
5
(1)
[0.45]
[2.17]
[14.7]
Rust
Removers9
1
The number of products identifiedis based on the product lists in EPA's 2017 Market and Use Report and Preliminary Informationon Manufacturing,
Processing, Distribution,Use and Disposal: TCE, as well as the 2014 TSCA Work Plan Chemicalrusk Assessment for TCE (U.S. EPA. 2017c , h) . Please see
SUDolemental
File [ConsumerExoosure AssessmentModel lnout ParametersDocket: EPA-HO-OPPT-2019-05001for the full Droductlist utilized.
10- 20

20

Page 144 of 691

 INTERAGENCYDRAFT - DO NOT en E OR QUOTE
Range
of
Weight
Fraction

Weight Fractions
Selected for
Modeling

Range
Mass of ProductUsed
of
(g, (oz])
(No. of
I¾
TCE>
Survey
1----.--,
--.-,----,--1
Product
Categories
Pdts)1
('¾
I
Scenario
10"'
50th
95th
Density
i---l- O
_th___,1
....-s_O_
1 _9_5_th---1
th_ ...2
3
2
41
TC~)
Min
Mid Max
%Ile'
41/oile /oile
(g/cm )4 %ile
¾ile
•1one
2
Weight fractionswere primarily sourced from product Safety Data Sheets (SDSs) or Material Safety Data Sheets (MSDSs), unless otherwisenoted. Please
see SUpplementalFile [ConsumerExposureAssessmentModel Input ParametersDocket:EPA-HQ-OPPT-1019-0500]
for more detailed informationon
weight fraction sourcing and ranges. If a single weight fraction was used in modeling, it appears in the "Min" weight fraction column, but does not reflect a
Consumer
Category

Product
Sub-

Form

Selected
Westat

I

Duration of Use
(min)

I

I

minimum.
Low-end (10th percentile) durations reported by Westat that are less than 0.5 min (30 sec) are modeled as being equal to 0.5 min (smallest time-step modeled).
Product density ranges reflect identifiedproducts containingTCE and were sourced from product SDSs or MSDSs. The high end of the range identifiedwas
used to convert reported ounces of product used fran Westat ( 1987) to grams of product used, as required for model input.
5 Two Westat product categorieswere consideredfor use (engine degreasing and solvent-typecleaning fluids or degreasers);however, engine degreasingwas
selected to source duration of use, room of use, and amount used parameters due to the high percentage of respondents (78.9%) reporting aerosol use.
6
No weight fraction was readily available for the aerosol and liquid gun scrubber foimulations,so the weight fractionswere based on the ranges reflectedby
the aerosol and liquid degreasingproducts.
7 The solvent-typecleaning fluids or degreasers product category from Westat was used as a surrogatefor gun scrubbers for the selection of use durations.
Product-specificliterature was identifiedand applied for mass of product used.
8 Based on EPA/EPABresearch and the Eezox Premium Gun Care testing results (AS1M Bl 17-5 Salt Spray Fog Test), 0.42-0.45 mL of the product was used
to coat the fireann in a very thin film, which is in-line with use directions.
9
Three modeling scenarios(film cleaner, spray fixative/coating,and toner aid) had no directly-alignedWestat product categories. Therefore, a number of
Westat product categories and use pattern data were considered for appropriateness, with a focus on primary fonnulation type (aerosol or liquid), duration of
use, and amount used. The rust remover product categoryreflects 98% aerosol products and a lower use duration and amount usedthan many of the other
solvent degreasing-typeuses.
10 Weight fraction and density were not readily available, so were based on the ranges reflected by the spray fixative/coatingand aerosol shoe polish products.
11 There were no readily availabledata sources for aerosol hoof polish use patterns; the Westat spray shoe polish product category was usedfor selection of use
duration and amount used.
12 Based on EPA/EPABregearchthat fo1JDd
one spray from the most common civilian canister is estimatedto be approximately0.0216-0.108ounces (based on
a pepper sprav manufacturer's website). Sprayingoccurred between 3 and 5 seconds (convertedto minutes for use in modeling) before obtaining desired effect
(Bertilsson et al. 2017 ).
3

4

2047
W48

W49

wso
2051

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!052

o e
Table 2-31 ConsumerPdro uct MdlinS

ConsumerCategory

Solvents for Cleaning
and Degreasing

Lubricants and
Greases

Product SubCategories

Brake & Parts
Cleaner
Electronic Degreaser/
Cleaner
Electronic
De2reaser/Cleaner

~

. soec1.ti c In1put p arameters
cenanos and Add'.
1tion al ScenanoForm
(No.of
Pdts) 1

Aerosol (4)
Aerosol (9)
Liquid (I)

Spray

Aerosol (8)

Degreaser/Cleaner
Liquid
Degreaser/Cleaner
Gun Scrubber

Liquid (2)
Aerosol (2)

Gun Scrubber

Liquid(l)

Mold Release

Aerosol (2)

Tire Cleaner

Aerosol(2)

Tire Cleaner

Liquid (l)

Tap & Die Fluid

Aerosol (I)

Penetrating Lubricant

Aerosol (5)

Zonel
Room of Use
(Volumem3)2
Garage
(90}
Utility
(20)
Utility
(20)
Garage
(90)
Utility
(20}
Utility
(20)

CEM
Emission
Model
Applied3

Air Exchange
Rate
(br-1)

Interzonal
Ventilation
Rate
(m3/hr)

CEM
Dermal
Exposure
Model
AppJied4

Dermal
Surface Area
Exposed5

E3

0.45

109

P_DER2b

10%ofhands

E3

0.45

107

NA

10%ofhands

El

0.45

107

P_DER2b

E3

0.45

109

P_DER2b

Inside of one
hand
10%ofhands

El

0.45

107

P_DER2b

E3

0.45

107

NA

El

0.45

107

P DER2b

E3

0.45

107

NA

Inside of one
hand
10%ofhands

E3

0.45

109

P_DER2b

10%ofhands

El

0.45

109

P_DER2b

E3

0.45

107

NA

Inside of one
hand
10%ofhands

E3

0.45

107

NA

10%ofhands

Utility
(20)

El

0.45

107

NA

Bathroom

E3

0.45

107

· NA

Inside of one
hand
l0%ofhands

El

0.45

109

NA

El

0.45

107

P DER2b

Utility
(20)
Utility
(20)
Garage
(90)
Garage
(90)
Utility
(20)
Utility

Inside of one
hand
10%ofhands

(20)

Adhesives and
Sealants

Solvent-based
Adhesive & Sealant
Mirror-edge Sealant

Liquid (3)
Aerosol (I)

(15)
Tire Repair Cement/
Sealer
Carpet Cleaner

Liquid(5)
Liquid (1)

Garage
(90)
Bedroom
(36)

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hand
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ConsumerCategory

ProductSubCategories

Form
(No.of
Pdts)1

Zonel
RoomofUse
(Volume m3)2

CEM
Emission
Model
Applied3

Air Ex~bange

Rate
(hr 1)

Interzonal
Ventilation
Rate
· (m3/br)

CEM
Dermal
Expo,ure
Model
Applied4

Dermal
SurfaceArea
Exposed5

Cleaning and
Furniture Care

Spot Remover

Aerosol (1)

Utility
(20)

E3

0.45

107

P_DER2b

10% of hands

Products

Spot Remover

Liquid (4)

El

0.45

107

P_DER2b

Arts, Crafts, and
Hobbv Materials
Apparel and
Footwear care

Fixatives & Finishing
Sorav Coatiniz.
s
Shoe Polish

Aerosol (1)

E3

0.45

107

NA

Inside of one
hand
10%ofhands

Aerosol (1)

Utility
(20)
Utility
(20)
Utility
(20)

E3

0.45

107

NA

Inside of one
band

Fabric Spray

Aerosol (l)

E3

0.45

107

NA

lo<'/oof hands

Film Cleaner

Aerosol (2)

Utility
(20)
Utility
(20)

B3

0.45

107

NA

10%ofhand s

oroducts

Other Consumer Uses

46
E3
109
NA
10% of hands
E3
1007
0
10%ofhands
Outside7
NA
Utility
E3
0.45
10%ofhands
NA
107
(20)
1The nwnber of products identified is base.don the product lists in EPA's 2017 Market and Use Report and Preliminary Infonnation on Manufacturing, Processing,
Distribution,Use and Disposal: TCE (U.S. EPA. 2017c, h), as well as the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (U.S. EPA. 2014b). It is possible
that specific products and/or formulationsidentified in those reports and used herein to select appropriate weight fractions, formulationtypes, and formulationdensities
for use in modeling no longer containTCE or are no longerreadily availableto consumersfor purchase;however,they were still consideredfor sourcing such information
since they were identified as in these recent EPA publications and therefore represent reasonably-foreseenuses. Please see SupplementalFile for the full product list
utilized.
2
The use environment(room of use) was generally based on the Westsat(m.z) survey of consumerbehavior patterns, which reported the percentages for the location
of last use of product. In cases where the room was identifiedas "other inside room," the utility room was selected based on professionaljudgment. Additionally,
professionaljudgment was applied to certain uses, such as those that could reasonably be used in a garage setting..
3
Emission models used for TCE include El - Emission from Product Applied to a Surface Indoors IncrementalSource Model and E3 - Emission from Product Sprayed.
4
All scenarios utilized the P_DER2b model for dermal exposure- Dennal Dose from Product Applied to Skin, Penneability Model
5
Surface area exposed only applied in dermal scenarios. The indicated sw-faceareas are combinedwith mean receptor body weights to get surface area to body weight
ratios (SA:BW)that are used in estimatingdennal dose.
6
For the purposed of modeling typical aerosol hoof polish consumerexposure, a barn setting was approximatedby selecting the garage as the room of use and changing
the default air exchange rate from 0.45 to 4 lu' 1, which more closely aligns with recommendedventilation levels in a horse barn (Pennsylvania State LJniversit\, 2016)
7
The outdoor environmentwas approximatedby selecting the garage as the room of use and increasingthe air exchange rate from 0.45 to I 00. The "room of use" was
also edited to reflect a 16 m3 cloud around user (rou!!hlv 6.5-foot dome or cloud surrounding user).

Hoof Polish
Pepper Spray
Toner Aid

Aerosol Cl )
Aerosol (2)
Aerosol (l)

Bam 6

2053

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054
055
056
057
058
059
060
061
062
063
064
065
066
067
068
069
070
071
072
073
074
075
076
077
078
079
080
081
082
083
084
085
086
087
088
089
090
091
092
093
094

095
096
097
098
099

The 2014 TCE TSCA Work Plan Chemical Risk Assessment included two consumer conditions of use:
aerosol degreaser and clear protective coating spray (referred to as "spray fixative" 80 FR 47441) (U.S.
EPA . 20 14b) . The inputs included in the 2014 assessment differed from those used in this assessment for
similar conditions of use, either due to updated parameter data (e.g.,, Zone 2 volume), or professional
judgment. The most notable difference between the 2014 scenarios related to the single mass used
parameter selected. In the 2014 assessment, aerosol degreaser was modeled assuming 24 g (0.85 oz) and
clear protecting coating spray was modeled assuming 1lg (0.39 oz). These inputs were not based on user
survey data and were described in the 2014 assessment as "potentially on the low end" when compared
against the Westat survey data employed in this 2019 risk evaluation.

2.3.2.6

Consumer Exposure Results

Acute inhalation and dermal exposure results are presented below for each consumer condition of use.
Denna! exposure results are only presented for those scenarios deemed to have the potential for dermal
contact with impeded evaoporation per the scope presented in the May 2018 Problem F onnulation (U.S.
EPA. 2018d). These conditions of use are organized by product subcategories and are also referred to
herein as consumer modeling scenarios. Inhalation estimates are presented in terms of acute indoor air
concentrations (ppm) resulting from a single consumer use event within a one-day exposure period; they
are provided for users and bystanders. Acute dermal exposure estimates are presented as an acute dose
(mg/kg/day); they are provided for users only.

2.3.2.6.1 Characterization of Exposure Results
As described in Section 2.3.2.4.1, the consumer exposure modeling approach was deterministic, but a

range of exposure results were estimated based on varying three parameters: weight fraction, mass of
product use~ and duration of use/exposure duration. While the exposure results are not reflective of a
probabilistic distribution of all possible exposure levels, the exposure scenarios modeled incorporated
low-end (10th percentile), central tendency (50th percentile), and high -end (95th percentile) inputs from
Westat (1987) for two of the three varied parameters: mass of product used and exposure duration. Since
these inputs primarily reflect user characterization, results are presented for "high-intensity users,"
''moderate-intensity users," and "low-intensity users." For example, the exposure scenario combining
high-end inputs for these three parameters is referred to as a "high-intensity user" scenario. Weight
fraction inputs cannot be described in the same terms, as they reflect the range of actual product weight
fractions, per associated SDSs, and do not reflect a distribution of user survey data.
Other modeling parameters that were not varied (e.g.,, room volume, air exchange rate, building
volume) reflect central tendency inputs. Therefore, these exposure scenarios and results are not
bounding or "worst-case" and may not capture the maximum or minimum of all possible exposure
levels .
For TCE, 3- and 24-hr TWA air concentrations are provided for consumers and bystanders. These are
based on the relevant human health hazard metrics. The 3-hr TWA air concentrations are higher than the
24-hr air concentrations in all scenarios due to the shorter averaging time surrounding the use event.
Likewise, the air concentrations associated with the user are higher than those associated with the
bystander in all scenarios due to the higher concentration of chemical expected in the room of use (Zone
1) coupled with the greater amount of time a consumer is assumed to be in the room of use (during and
after use event) compared with the bystander. While it is assumed that a bystander of any age, including
pregnant women and children, could be exposed to the reported concentrations, the concentrations
themselves are not unique for specific subpopulations. The concentrations reported reflect the
concentration a consumer or bystander would be exposed to.

100
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101

102
103
104

105
106
107

108
109

Dermal exposur e scenarios and results are presented for youth and adult age groups, with the youth (age
11-15) resulting in the highest estimates dermal exposures due to differences in surface area to body
weight ratios in these groups. Results are not presented specifically for pregnant women or women of
reproductive age; however, the range of results presented for adults and youth age groups are expected
to cover dermal exposures for pregnant women as well, with the youth (11-15) providing the highest
surface area to body weight ratio, thereby providing the highest dermal exposure estimate (see below
table for rationale). All values below are sourced and/or derived from EPA ' s 2011 Exposure Factors
Handbook (U.S . EPA 201 l c).
Parameter

Adult

Youth Youth
(16-21) (11- 15)

Pregnant
Women

Women Women
(21+)

(16-21)

10%ofHands

Surface Area
(cm2)
Body Weight

('kg)

SA:BW

99

83

72

g91

g91

g32

80

7 1.6

56.8

75 3

744

65.95

1.24

1.16

I.27

1.19

1.20

l.26

1Surface area based on women 2 1+
2

Surface area based on combinedmale/female 16-21
Body weight for all pregnant women
4
Body weight for females 21+
5Body weight for females 16-21
3

110

2.3.2.6.2 Consumer Exposure Estimates

111

Solventsfor Cleaning and Degreasing

112
113
114

Brake & Parts Cleaner
Exposure to TCE in brake & parts cleaner products was evaluated based on four aerosol products with
weightfractions ranging from 0-20% to 90-100% TCE .

115
116

117
118
119

Westat Survey data on brake quieters and cleaners were used as the basis for duration of use and mass of
product used. Survey responses indicate that 2.6% of respondents have used products in this category;
65.6% reported use of aerosol formulations. The room of use (Zone 1) was set to the garage (90 m 3)
although the We stat survey data for this category indicate primarily outdoor use.

120
121

122
123
124
125
126
127

Inhalation expo sures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. SeeSupplemental Files [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
Dermal Exposures.Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based on all
iterations of this modeling scenario.

.

Table 2-32 Acuet lnh aIation Exposure summary: Bra ke &P artsCI eaner
Duration of
Use
(min)

Weight
Fractfoo1

High-Intensity User

95 th %ile
(120)

Max

Moderate-Intensity User

50 th %ile
(15)

Scenario Description

(%)
(100)

Mid
(60)

Mass Used
(g)

951h%ile
(766.5)
5011t%ile
(191.6)

Page 149 of 691

3-hrMax

24-hrMax

TWA
(ppm)

TWA
(ppm)

User

3.97E+o2

5.76E+ol

Bystander

l .00E+02
6.60E+Ol
l.48E+Ol

l.67E+ol

Product User
or Bystander

User
Bystander

9.06
2.26

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

Scenario Description

Weight

Use

Fraction•

(min)

(%)

Mass Used
(g)

ProductUser
or Bystander

3-brMax

24-brMax

TWA

TWA

(ppm)

(ppm)

5.16
7.09E-Ol
User
Min
10th %ile
(1)
(20)
(47.9)
Bystander
1.19
1.8lE-Ol
1
Actual product weight fractions were: 0-20%; 45-55%;97.5%; 90-100%. 600/ois a mathematically-derivedmid-point (i.e.,
mean) for use in modeling, based on the minimum and maximum inputs.

Low-IntensityUser

128
129
130
131
132
133
134

Duration of

10th %ile

135

Dermal exposures are also presented for this scenario,as it is assumed that the product could be applied
in a manner leading to dermal contact with impededevaporation,thereby increasingthe likelihood
and/or duration of dermal absorption.There is uncertaintysurroundingthe duration of dermal contact
with impeded evaporation.The exposuredurations modeled could exceed the duration of such dermal
contact; therefore, the higher-enddurations may result in an overestimationof dermal exposure.

136
137

e
Tabl e 2--33 A cuet Derma IE xposure summary: B rak&PrtCI
a s

.

Scenario
Description

Duration
ofUse
(min)

Weight
Fraction 1

High-Intensity
User

9S1"%ile
(120)

Max

Central
Tendency

50th %ile
(15)

Receptor

AcuteADR
(mg/kg/day)

Adult (~l years)
Youth (16-20 years)

7.63E+ol
7.14E+ol

Youth (11-15 years)

7.80E+ol

Adult (2:21years)
Youth (16-20 years)

5.72

(%)

(100)

Mid
(60)

eaner

5.35

138
139

Youth (11-15 years)
5.85
Adult (2:21years)
l.27E-01
Low-Intensity
10th %ile
Min
Youth (16-20years)
1.19E-01
(20)
(1)
User
Youth (11-15 years)
l.30E-0l
'Actual product weight fractions were: 0-20%; 45-55%; 97.5%; 90-100%. 600/ois a mathematically-derivedmid-point (i.e.,
mean) for use in modeling, based on the minimum and maximum inputs.

140
141
142
143

Aerosol Electronic Degreaser/Cleaner
Exposure to TCE in aerosol electronicdegreasing/cleaningproducts was evaluatedbased on nine
aerosol products with weight fractionsranging from 30-100% TCE.

144

145
146
147
148

149

150
151
152
153
154

155

Westat Survey data on specializedelectronicscleaners were used as the basis for duration of use and
mass of product used . Survey responses indicate 13.1% of respondentshave used products in this
category; 34% reported use of aerosol formulationsand 56% reported use of liquid formulations.
Therefore,these Westat data were applied to both aerosol and liquid product scenarios. The room of use
(Zone 1) was set to the utility room (20 m3) although the Westat survey data for this category indicate
living room and other inside room as the top two locations of reported use.
Inhalation exposures for users and bystandersare presented below reflectinghigh-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModelingResults and Risk Estimatesfor
for the full range of results based
ConsumerInhalationExposures.Docket: EPA-HQ-OPPT-2019-0500]
on all iterations of this modeling scenario.

156

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157

.

. D e2reaser:/Cl eaner
Table 2 -34 Acuet Inha lation EXP-OSUre
summarv: AerosoI Electr ODIC
Durationof
Scenario Description

160

161
162
163
164
165
166
167
168
169
170

171
172
173

174
175
176
177
178
179

180

181
182
183

Weight
Fraction 1
(%)

(g)

Product User
or Bystander

3-brMax
TWA
(ppm)

24-hrMax
TWA
(ppm)

95th %ile
(30)

Max

(100)

95th %ile
(337.l)

There are no dermal exposures quantifiedfor this scenario,as this use pattern is not expectedto involve
dermal contact with impeded evaporation.
Liquid ElectronicDegreaser/Cleaner
Exposure to TCE in liquid electronicdegreasing/cleaningproductswas evaluated based on one liquid
product with a weight fraction of l 00% TCE.
Westat Survey data on specializedelectronicscleanerswere used as the basis for duration of use and
mass of product used. Survey responsesindicate 13.1% of respondentshave used products in this
category; 34% reporteduse of aerosol formulationsand 56% reported use of liquid formulations.
Therefore, these Westat data were applied to both aerosoland liquid product scenarios. The room of use
(Zone 1) was set to the utility room (20 m3) althoughthe Westat survey data for this categoryindicate
living room and other inside room as the top two locationsof reported use.
Inhalation exposuresfor users and bystandersare presentedbelow reflectinghigh-, moderate-,and lowintensity user scenarios. See SupplementalFiles [ExposureModeling Results and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
Dermal Exposures.Docket: EPA-HQ-OPPT-2019-0500]
iterations of this modeling scenario.

.

. D'ef!reaser/Cleaner
T abl e 2 -3S Acue
t lnh alation Exposure summary: L.1qu1'd El ectron1c
Scenario Description

High-IntensityUser

Duration of

Weight

Use
(min)

Fraction 1

9Slh%ile
(30)

(%)
(100)

th

Moderate-IntensityUser

SO %ile
(2)
10th %ile

(100)

Product User
or Bystander

3-brMax

50th %ile

User
Bystander
User

(22.5)

Bystander

Mass Used
(g)

95th %ile
(337.1)

10th %ile

24-br Max

TWA

TWA

(ppm)

(ppm)

2.70E+02
4.83E+01
l.75E+Ol
2.90
1.30
2.27E-01

3.6IE+ol
7.26
2.33
4.36E-01

User
l.74E-OJ
(100)
( 1.8)
(0.5)2
Bvstander
3.41£-02
1
Single weight fractionof 1000/oavailable.
2The I 0th percentile dtrrarionfrom Westat was < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
ti.mestepin the model run.
Low-IntensityUser

184
185
186
187

Mass Used

User
2.8IE+o2
3.76E+OJ
Bystander
5.03E+ol
7.56
th
User
l.19E+ol
1.58
50 %ile
Mid
50th %ile
Moderate-IntensityUser
(2)
(65)
(22.5)
Bystander
1.96
2.95E-Ol
th
th
10 %ile
10 %ile
User
4.15E-Ol
Min
5.55E-02
Low-IntensityUser
(30)
(1.8)
(0.5)2
Bystander
7.21E-02
l.08E-02
1
Actual product weight fractions were: 30-50%; 30-60%; 97.2%; 98%; 60-100%; and 90-100%. 65% is a mathematicallyderived mid-point (i.e., mean)for use in modeling, based on the minimum and maximum inputs.
2
The J01hpercentile duration from Westat is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
High-Intensity User

158
159

Use
(min)

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188
189
190
191
192

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.

193
194

Table 2-3 6. Acute Dermal Exposure
Scenario
Description

High-Intensity
User

ModerateIntensity User

Low-Intensity
User

195

196
197

Duration
ofUse

Weight
Fraction 1

(min)

(%)

951a¾ile
(30)

(100)

50th %ile
(2)

10111¾ile
(0.5)2

( 100)

(100)

summary: L"IQUI'dEl ectromc· Dear easer/C leaner
Receptor

AcuteADR
(mg/kg/day)

Adult ~I . years)
Youth (16-20 years)

4.30E+Ol
4.03E+ol

Youth (11-15 years)

4.39E+ol

Adult (2::21 years)

2.88

Youth (16-20 years)

2.68

Youth (11-15 years)
Adult (2::21years)

2.92

Youth (16-20 years)
Youth (11-15 years)

7.ISE-01
6.70E-OI
7.318-01

1

Single weight fraction of 100% available.
The IOU•
percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model nm.

2

198
199

200
201

202
203
204
205
206

Aerosol Spray Degreaser/Cleaner
Exposure to TCE in aerosol spray degreaser/cleaner products was evaluated based on eight aerosol
products with weight fractions ranging from 60-100%TCE.
Westat Survey data on engine degreasing were used as the basis for duration of use and mass of product
used. Survey responses indicate that 17.2% of respondents have used products in this category; 78.9%
reported use of aerosol formulations. The room of use (Zone 1) was set to the garage (90 m3) although
the Westat survey data for this category indicate primarily outdoor use.

207
208

209
210
211
212
213

214

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalation Exposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
Dermal Exposures. Docket: EPA-HQ-OPPT-2019-0500]
iterations of this modeling scenario.

.

Ta ble 2-37 Acu te Inhalati on Exposure summary: A eroso 1S,pray Deiereaser/Cl eaner
Duration of
Scenario Descript ion

Use
(min)

215

High-Intensity User

95th ¾ile
(120)

Moderate-Intensity User

50th %ile
(15)

Weight
Fraction 1
(¾)

Max
( 100)

Max
( 100)

Mass Used
(g)
95th ¾ile
(2157.4)
50th ¾ile
(521.4)

10th ¾ile
Min
10th ¾ile
Low-Intensity User
(60)
(5)
(130.8)
1Actualproduct weight fractions were: 60-100% and 90-100%.

Page 152 of 691

Product

User or
Bystander
User
Bystander
User
Bystander
User
Bvstander

3-hrMa:t
TWA
(ppm)

24-hrMax
TWA
(ppm)

l.1 2E+03
2.82E+o2
2.99E+02

1.62£+02
4.71E+ol
4.1 IE+o l
l .02E+Ol

6.70E+ol
4.54E+Ol
9.83

6.20
1.50

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217
218
219
220

221
222

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225
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228
229

This condition of use was also assessed in the 2014 TSCA Work Plan Chemical Risk Assessment and
refined in the 2016 SupplementalExposure and Risk ReductionTechnical Report in Support of Risk
management Options for TCE (TCE) Use in ConsumerAerosol Degreasing. In these prior assessments,
different inputs were used for certain modelingparameters includingmass used and duration of use.
Please see the referenced documents for full details. The amount used (24 g TCE - roughly 27 g
product) in the 2014 assessment is much lower than the 10thpercentile input obtained from the Westat
survey engine degreasingscenario. The lower amount applied in 2014 more closely reflects an aerosol
electronic cleaning condition of use, which is characterizedby a median mass used of 0.5 oz, or 22.5 g.
It is therefore unlikely that the previous assessmentcaptured exposures for consumer involved in larger
degreasing efforts such as engine degreasingor brake cleaning.Theinputs and associated 24-hr acute air
concentrations for users and bystanders from the 2014assessmentare shown below.

s

2014 A cue
t lnhal atton
. E xposure ummary: AerosoIS ,pray Dieirreaser/Cl eaner
Scenario Description

230
231

232

233
234
235
236
237
238
239
240
241

242

Weight
Fraction

(min)

(%)

Mass Used
(g)

Product
User or
Bystander

24-hr
TWA
(ppm)

2014 Work Plan
User
2.92
{24)1
Chemical Risk
60
90
Bystander
0.8
Assessment
1Note that this conversion assumes a formulation density of 1. Actual product densities range from 1.46-1.52g/cm3• This
input is also provided in tenns of mass of TCE per use, rather than mass of product per use, which is the actual model input.
24 g ofTCE in this 90% fonnulation would equate to roughly 27 g of product per use.
2This user air concentration was shown in the 2014 assessment as 2 ppm; however, in the 20 16 supplementalreport, it was
corrected to 2.9 ppm due to an earlier rounding error or typo .

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dennal contact with impeded evaporation,thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertaintysurroundingthe duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimationof dermal exposure.
D,em-easer/Cleaner
Table 2-38. Aen t e Derma IE xposure summary: A erosoIS 1>ray
Scenarlo
Description

High-Intensity
User

Duration
ofUse
(min)

95th %ile
(120)

Weight
Fraction1
(%)

Max
(100)

Receptor

50th %ile
(15)

AcuteADR
(mg/kg/day)

Adult ~l years)
Youth (16-20 years)

7.16E+-01
6.70E+-Ol

Youth (11-15 years)

7.32E+-Ol

Adult (2:21years)
Youth
(16-20 years)
(100)
Youth (11-15 years)
Adult~21 years)
Min
10th %ite
Low-Intensity
Youth (16-20 years)
(60)
(S)
User
Youth (11-IS years)
1Actual product weight fractions were: 60-100% and 90-100%.
ModerateIntensity User

243
244
245
246
247

Duration of
Use

Max

8.94

8.37
9.15
1.79
1.67
1.83

Liguid Degreaser/Cleaner

Exposure to TCE in liquid degreasing/cleaningproducts was evaluated based on two aerosol products
with weight fractions ranging from 90-100% TCE.
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250
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253
254
255
256
257
258

259
260

Westat Survey data on solvent-typecleaning fluids or degreaserswere used as the basis for room of use.
duration of use, and mass of product used. Survey responses indicatethat 28.1% of respondents have
used products in this category;74.4%reported use of liquidformulations.The room of use (Zone 1) was
set to the utility room (20 m3).
Inhalation exposures for users and bystandersare presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
Dermal Exposures. Docket: EPA-HQ-OPPT-2019-0500
] for the full range of results based on all
iterations of this modeling scenario.
. Exoosore summarv: L.JQUI.dD en-easer'.
/Cl eaner
Table 2-39. Acute I nbaI ation
Scenario Description

Durationof
Use

Weight
Fraction1

(min)

(¾)

High-Intensity User
Moderate-IntensityUser
Low-IntensityUser

261

262
263
264

265
266
267
268
269

95th %ile
(120)
501h%ile
(15)
10th %ile

Mass Used
(g)

95th %ile

(100)

(1337.7)
50111%ile
(139.9)

(100)

(100)
(2)
1
Actual product weight fractions were: 90-100% and I 00%.

1Qlh%ile
(24.1)

Product User
or Bystander
User
Bystander
User
Bystander
User
Bystander

TWA

24-brMax
TWA

(ppm)

(ppm)

1.05E+03
2.28E+02
l.17E+o2
l.97E+01
l.95E+o1
3.24

1.46E+02
3.61E+Ol
l.56E+0l

3-hrM.ax

2.96

2.60
4.86E-01

Dermal exposures are also presented for this scenario,as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasingthe likelihood
and/or duration of dermal absorption.There is uncertainty surroundingthe duration of dermal contact
with iinpeded evaporation. The exposure durations modeledcould exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimationof dermal exposure.

.

/Cl ea ner
Table 2-40 Acuet DermaIE :s:posuresummary: I.:IQUl"dD e2:reaser:
Scenario
Description

Duration
ofUse
(min)

Weight
Fraction1
(%)

Receptor

AcuteADR
(mg/kg/day)

Adult (2:21years)
Youth (16-20 years)

l.71E+o2
l.60E+o2

Youth (11-15 years)

I.75E+o2
2.14E+0l
2.0IE+ol
2.19E+0l
2.85

270

Adult ~21 years)
(100)
Youth (16-20 years)
Youth (I 1-15years)
Adult (2:21years)
Low-Intensity
lOth ¾ile
(100)
Youth (16-20 years)
(2)
User
Youth (11-15 years)
1Actual product weight fractions were: 90-100% and l 00%.

271
272

Aerosol Gun Scrubber

High-Intensity
User

ModerateIntensity User

95th %ile
(120)

(100)

50th %ile
(15)

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273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288

289

Exposure to TCE in aerosol gun scrubber/cleanerproducts was evaluated based on two aerosol products.
Only one product had a reported weight fraction (97%), so modelingwas based on the full range of
aerosol degreasing formulation weight fractions (60-100%).
Westat Survey data on solvent-typecleaning fluids or degreaserswere used as the basis for room of use
and duration, while manufacturerdata on the amount of product required to coat a firearm in a very thin
film were used as the basis for the mass of product used. The Westat survey product category selected
was not aligned well with this specific use, but the duration data for the selected category was deemed
reasonable for use in modeling. The room of use (Zone 1) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
Dermal Exposures.Docket:EPA-HQ-OPPT-2019-0500]for the full range of results based on all
iterations of this modeling scenario.

.

T abl e 2-41 Acuet lnh alation Exposure summary: Aeroso JGun Scrubb er
Durationof
Use

Weight
Fraction1

(min)

(%)

High-Intensity User

9511t%ile
(120)

Max
(100)

Moderate-Intensity User

50th %ile
(15)

(100)

Scenario Des<:ription

294
295
296
297

298
299

(g)

(0.7)

Max

(0.7)

3-hrMax
TWA

24-brMax
TWA

(ppm)

(ppm)

7.44£ -02
l. 83E-02

User

5.35E-01
1.I6E-01
5.87£-01

7.83£ -02

Bystander

9.87E -02

1.48E-02

Product User
or Bystander
User
Bystand er

10111%ile

User
Min
3.41E-Ol
4.SSE-02
(0.7)
(2)
(60)
Bvstander
5.64E-02
8.47£-03
10nly one product had a reported weight fraction(97%), so modelmg was based on the full range of aerosol degreasing
formulation weight fractions(60-100%).
Low-Intensity User

290
291
292
293

MassUsed

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation,thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surroundingthe duration of dermal contact
with impeded evaporation.The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimationof dermal exposure.

.

T abl e 2-42 A cuet DermalE xposure summary: A erosoI Gun Scrubber
Scenario
Description
High -Intensity
User

ModerateIntensity User

Low-Intensity
User

Duration
ofUse
(min)

95dt%ile
(120)

50 th %ile
(15)

10th %ile
(2)

Weight
Fraction
(%)

Max
(100)

Max
(100)

Min
(60)

Receptor

AcuteADR
(mg/kg/day)

Adult ~ 1 years)
Youth (16-20 years) -

6.90E1-0l
6.45E+OI

Youth (11-15 years)

7.06E1-0l

Adult~l

years)

8.62

Youth (16-20 years)

8.07

Youth (11-15 years)

8.82

Adult ~21 years)

6.90E-Ol

Youth (16-20years)

6.48E-Ol

Youth (11-15 years)

7.08E-OI

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300

301
302
303

304
305
306
307
308
309
310
311

312
313
314
315
316
317
318
319

320

10nl y one product had a reported weight fraction (97%), so modelingwas based on the full range of aerosol degreasing
fonnulation weight fractions(60-100%,).

Liquid Gun Scrubber
Exposure to TCE in liquid gun scrubber/cleaner products was evaluated based on one liquid product
with an unreported weight fraction. Modeling was based on the upper-end of the narrow range of liquid
degreasing formulationweight fractions (90-100%).
Westat Survey data on solvent-typecleaning fluids or degreaserswere used as the basis for room of use
and duration, while manufacturerdata on the amount of product required to coat a fireann in a very thin
film were used as the basis for the mass of product used. The Westat survey product category selected
was not aligned well with this specific use, but tbe durationdata for the selected category was deemed
reasonable for use in modeling. The room of use (Zone 1) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presentedbelow reflecting high-, moderate-, and lowintensity user scenarios. SeeSupplementalFiles [ExposweModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
Dermal Exposures.Docket: EPA-HQ-OPPT-2019-0500]
iterations of this modeling scenario.

.

Ta ble 2-43 ACU te I D haIafton Exposure summary: L'lQUl'dG un Scru bber
Scenario Description

323

324
325
326

327
328

Weight
Fraction1

(min)

(%)

Mass Used
(g)

Product User
or Bystander

3-hrMax
TWA
(ppm)

24-hrMax

TWA
(ppm)

User
4.58E-0I
6.37E-02
Bystander
9.94E-02
1.57£ -02
User
5.03E-0l
6.71
E-02
5Qlh%ile
(0.7)
Moderate-Intensity User
(100)
(15)
Bystander
8.46E-02
1.27E-02
th
User
4.65E-0l
6.22E-02
10 %ile
(100)
(0.7)
Low-Intensity User
(2)
Bystander
8.09E-02
l.22E-02
1Modeling was based on 1heupper-end of the narrow range of liquid degreasingformulation weight fractions (90-100%).
High-Intensity User

321
322

Duration of
Use
95th %ile
(120)

(100)

(0.7)

Dermal exposures are also presented for this scenario,as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation,thereby increasing the likelihood
and/or duration of dermal absorption.There is uncertaintysurrowidingthe duration of dermal contact
with impeded evaporation.Toe exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimationof dermal exposure.

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329

.

Tabl e 2-44 Acuet Derm.aIE xposure summary: LiLQW'd Gun Scru.bber
Scenario
Description
High-Intensity
User

ModerateIntensity User
Low-Intensity
User

330
331
332
333
334

335
336
337
338
339
340

341
342
343
344

345
346
347
348
349

Duration
ofUse
(m in)

Weight
Fraction1
(%)

95 th %ile
(120)

(100)

50th %ile

(15)

(100)

l.63E+-02

Adult ~1 years)
Youth (16-20 years)
Youth (11-15 years)

2.00E+-01

l.87E+-Ol
2.04E+-01
2.68

Mold Release
Exposure to TCE in mold release products was evaluated based on two aerosol products with weight
fractions ranging from 40-68.9% TCE.
Westat Survey data on other lubricants (excluding automotive)were used as the basis for room of use,
duration of use, and mass of product used. For this product scenario,EPA believes that the selected
lubricant Westat scenario, although not a direct match with mold release products, better aligns with the
product use pattern when compared against other options, such as solvent-typecleaning fluid, which
conveys a much higher use duration and mass used. Survey responses indicate that 34.5% of
respondents have used products in this category; 32.5% reported use of aerosol formulations. The room
of use (Zone I) was set to the utility room (20 m3) .
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModelingResults and Risk Estimatesfor
for the full range of results based
ConsumerInhalationExposures.Docket: EPA-HQ-OPPT-2019-0500]
on all iterations of this modeling scenario.

.

• Exoosure summarv: M0 ldR eIease
Ta ble 2-45 A cute Inh aIation

Moderate-IntensityUser

Durationof
Use
(min)

Weight
Fraction1

95 th %ile

Max

(30)

(68.9)

95 th %ile
(212 .9)

(%)

Mass Used
{g)

50 %ile
(2)

Max

sQlh%ile

(68.9)

(23.4)

tOlh%ile

Min

10th %ile
(4.3)

ProductUser
or Bystander
User
Bystander
User
Bystander
User
Bystander

J..hrMax
TWA

24-brMax
TWA

(ppm)

(ppm)

1.22E+o2
2. l9E+ol

l.64E +01
3.29

l.31E+ol

1.75

2.16

3.25E-Ol

1.32
l.77E -01
(0.5)2
(40)
2.30E-01
3.45E-02
1Actual product weight fractions were: 40-50% and 68.9%.
2The JOlhpercentile duration from Westat is < 0.5 minutes. but 0.5 minutes was used in the model, as it reflects the smallest

Low-IntensityUser

355
356

Youth (11-15 years)

{lOO)

th

353
354

1.60E+02
1.50E+02

Youth (16-20 years)
2.50
Youth (11-15 years)
2.72
1
Modeling was based on the upper-end of the narrow range of liquid degreasingformulationweight fractions (90-1000/4).

High-IntensityUser

352

Adult ~21 years)
Youth (16-20 years)

Adult ~21 years)

10th %ile
(2)

Scenario Description

350
351

AcuteADR
(mg/kg/day)

Receptor

timestep in the model run.

There are no dermal exposures quantified for this scenario,as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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357
358
359
360
361
362
363
364

365
366
367
368
369
370
371

372
373
374

Aerosol Tire Cleaner
Exposure to TCE in aerosol tire cleaning products was evaluated based on two aerosol products with
weight fractions ranging from 70-100% TCE.
Westat Survey data on tire and hubcap cleaners were used as the basis for duration of use and mass of
product used. Survey responses indicate that 15.9% of respondents have used products in this category;
29.5% reported use of aerosol formulations and 70.5% reported use of liquid formulations. Therefore,
these Westat data were applied to both aerosol and liquid product scenarios. The room of use (Zone 1)
was set to the garage (90 m 3) although the Westat survey data for this category indicate primarily
outdoor use.
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See Supplemental Files [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposures and Risk ExposureModelingResults and Risk Estimatesfor Consumer
Dermal Exposures. Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based on all
iterations of this modeling scenario.

.

. E xoosure s ummarv:A eroso IT.ll'e Cleaner
Tabl e 2-46 Acute I nh aIation
Scenario Description

Duration of
Use

Weight
Fraction1
(%)

Mass Used
(g)

Product User
or Bystander

95111%ile

Max

95th %ile

(60)

(100)

(317)

50"'%ile

Max

(15)

(100)

50"'%ile
(52.9)

User
Bystander
User
Bystander
User
Bvstander

(min)

High-Intensity User
Moderate-IntensityUser

375
376
377
378
379
380
381

10th %ile

}()th%ile
Min
(70)
(10.5)
1
Actual product weight fractions were: 70-90% and 80- 100%.

Low-Intensity User

(5)

3-brMax
TWA

24-brMax
TWA

(ppm)

(ppm)

l.04E-+-02
4.39E-+-Ol
3.04E-+-OI
6.80
4.25
9.21E-Ol

l.57E-+-01
6.84
4.17
1.04
5.81E-01
1.40E-0l

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.

382

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383

.

Tabl e 2-47 Acuet Derm.aIE :x,posure
summarv:A erosoIT"ire Cleaner
Scenario
Description
High-Intensity
User

ModerateIntensity User

Weight
Fraction1

(min) .

AcuteADR
(mg/kg/day)

Receptor

(o/o)

951h%ile
(60)

Max
(100)

50th %ile

Max
(100)

(15)

Adult (2:21years)
Youth (16-20 years)

l.58E+ol
1.48E+Ol

Youth (11-15 years)

l.61E+ol

Adult~l years)
Youth (16-20 years)
Youth (11-15 years)

3.94
3.69
4.03
9.17E-Ol
8.6lE-Ol
9.38E-Ol

Adult ~1 years)
Youth (16-20 years)
Youth(ll-15 years)
1
Actual product weight fractions were: 70-90% and 80-100%.
Low-Intensity
User

384

Duration
ofUse

101h%ile
(5)

Min
(70)

385

386
387
388

389
390
391
392
393
394
395
396
397
398
399

400
401
402
403

Liquid Tire Cleaner
Exposure to TCE in liquid tire cleaningproducts was evaluated based on one liquid product with a
weight fractions ranging of 80-100%TCE.
Westat Survey data on tire and hubcap cleaners were used as the basis for duration of use and mass of
product used. Surveyresponses indicatethat 15.9% of respondentshave used products in this category;
29.5% reported use of aerosol formulationsand 70.5% reported use ofliquid fonnulations. Therefore,
these Westat data were applied to both aerosol and liquid product scenarios.The room of use (Zone 1)
was set to the garage (90 m3) although the Westat survey data for this category indicate primarily
outdoor use.
Inhalation exposures for users and bystandersare presented below reflectinghigh-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk F,xposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
DermalExposures. Docket: EPA-HQ-OPPT-2019-0500]
iterations of this modeling scenario.

.

' "dT"ire Cleaner
Table 2-48 ACUte I D haIation Exposure summary: LIQUI
Scenario Description
High-Intensity User
Moderate-IntensityUser

Weight
Fraction1

(min)

(%)

95th %ile
{60)

50111%ile
(15)
10th

%ile
(5)
1Single weight fraction of 80-1000/oavailable.
Low-IntensityUser

404

Duration or
Use

(100)
(100)

(100)

Mass Used
(g)

95th o/oile
(706.4)
50th %ile
(I 17.9)

}0th %ile
(23.4)

Product User
or Bystander

User
Bystander
User
Bystander
User
Bvstander

3-hrMax

24-hrMax

TWA
(ppm)

TWA
(ppm)

3.33E+o2
9.79E+Ol
6.77E+Ol
l.52E+ol
l .35E+ol
2.93

4.76E+ol
l .52E+ol
9.28
2.32
1.85
4.47E-Ol

405
406

407
408

Dermal exposures are also presented for this scenario, as it is assumedthat the product could be applied
in a manner leading to dermal contact with impeded evaporation,thereby increasingthe likelihood
and/or duration of dermal absorption.There.is uncertainty surroundingthe duration of dermal contact

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409

410

with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.

411

412

.

Table 2-49 Acuet Denna IE xposure summary: LilQU id Tire Cleaner
Scenario
Description

High-Intensity
User

ModerateIntensity User

Duration
of Use
(min)

95th %ile
(60)
50th %ile
(15)

Weight
Fraction'
(%)

(100)

(100)

AcuteADR
(mg/kg/day)

Receptor

Adult ~ ] years)
Youth (16-20 years)

8.78E+ol
8.23E+ol

Youth (11-15 years)

8.99E+ol

Adult (2::
21 years)

220E+ol

Youth (16-20 years)
Youth (11-15 years)

2.06E+ol
2.24E+Ol

Adult (2::21years)
Low-Intensity
User

10th %ile
(5)

413
414

1Single weight

415
416
417

Lubricants and Greases

418
419

(100)

Youth (16-20 years)

7.33
6.85

Youth (11-15 years)

7.49

fraction of80-100% available.

Tap & Die Fluid
Exposure to TCE in tap & die fluid was evaluated based on one aerosol product with a weight fraction
of98%TCE .

420
421
422

423
424
425
426
427
428
429
430

43l

Westat Survey data on other lubricants (excluding automotive) were used to select room of use, duration
of use, and mass of product used. Survey responses indicated that 34.5% of respondents have used
products in this category; 32.5% reported use of aerosol formulations. The room of use (Zone I) was set
to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModeling Results and Risk Estimatesfor
ConsumerlnJuilationExposures.Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based
on all iterations of this modeling scenario.

-

. Exoosure summarv: Tao & o·1e FlUl'd
Table 2 SO. Acute lnh aIation
Scenario Description

Duration of
Use
(min)

Weight
Fraction 1
(o/o)

Mass Used
(g)

Product User
or Bystander

High-Intensity User

9511t
%ile
(30)

(98)

95th %ile
(134.5)

User

1.IOE+o2

l.47E+ol

Bystander

l.97E+ol

2.95

l.ISE+ol
1.95

1.57
2.93E-Ol
2.78E-Ol

Moderate-IntensityUser
Low-Intensity User

432
433
434

50th %ile
(2)
I()lh %ile
2

(0.5)

3-hrMax
TWA
(ppm)

(98)

50 %ile
(14.8)

User
Bystander

(98)

10th %ile
(2.7)

User

2.03

Bystander

4.96E-01

th

24-hrMax
TWA
(ppm)

8.53E-02
available.
2The 10th percentile duration from Westat is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model nm .
1Single weightfraction of98%

Page 160 of691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
435

436
437

There are no dermal exposures quantifiedfor this scenario, as this use pattern is not expected to involve
dennal contact with impeded evaporation.

438

439
440
441

Penetrating Lubricant
Exposure to TCE in lubricant products was evaluated based on five aerosol products with weight
fractions ranging from 5-50 % TCE.

442

443
444

445
446

Westat Survey data on other lubricants (excluding automotive)were used as the basis for room of use,
duration of use, and mass of product used. Survey responses indicate that 34.5% of respondents have
used products in this category; 32.5%reported use of aerosol formulations.The room of use (Zone 1)
was set to the utility room (20 m3).

447

451

Inhalation exposures for users and bystandersare presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFile [Exposure ModelingResults and Risk Estimatesfor
ConsumerInhalation Exposures. Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based
on all iterations of this modeling scenario.

452
453

Table 2-51 Acute Inht·aation E;xnosure summary: p enetr atin2 L ub.ncant

448

449
450

.

Scenario Description

Weight
Fraction•
(•/4)

Mass Used
(g)

Product User
or Bystander

3-hrMax

TWA
(ppm)

24-brMax
TWA
(ppm)

User
8.74E +ol
1.17E+0l
Bystander
l. 56E+-01
2.35
5.16
6.88E--01
User
5()dl%ile
Mid
50th %ile
Moderate-IntensityUser
(2)
(27.5)
(23 .1)
Bystander
8.53E-0l
l.28E-0l
}Qlh%ile
User
lOd!%ile
Min
1.62E--O
l
2.16E-0 2
Low-IntensityUser
(5)
(4.2)
(0.5}2
Bvstander
2.80E-02
4.21E-03
1Actual product weight fractions were: 5-10%; 10-20%;30-40%;48.8%; and 30-50%.27.5% is a mathematically-derived
mid-point (i.e., mean) for use in modeling, based on the minimumand maximum inputs.
2The I 0th percentile duration from Westat is < 0.5 minutes,but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model nm.
High-IntensityUser

454
455
456
457

Duration of
Use
(min)

95th %ile
(30}

Max
(50}

95111¾ile
(209.9}

458

459
460
461

There are no dermal exposures quantifiedfor this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.

462

Adhesives and Sealants

463
464
465

466

Solvent-basedAdhesive & Sealant
Exposme to TCE in solvent-basedadhesive & sealant products was evaluated based on three liquid
products with weight fractions ranging from 5->90% TCE.

467

471

Westat Survey data on contact cement, superglue, and spray adhesive were used as the basis for room of
use, duration of use, and mass of product used. Survey responses indicate that 60.6% of respondents
have used products in this category; 97.1% reported use of liquid formulations.The room of use (Zone
1) was set to the utility room (20 m3).

472
473
474

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. SeeSupplementalFile [ExposureModelingResults and Risk Estimatesfor

468
469
470

Page 161 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

475
476
477
478

Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.

.

. Exposure summary: Solvent- base dAdh es1ve
' & Seala nt
Table 2-52 Acute Inh a Iation
ScenarioDescription

Durationof
Use

Weight
Fraction1

(min)

(%)

Mass Used
(g)

Product
User or
Bystander

.

3-hrMax
TWA

24-brMax
TWA

(ppm)

(ppm)

480
481
482

User
2.46E+02
3.22E+ol
Bystander
2.68E+Ol
4.06
th
th
User
7.76
1.00
SO %ile
50 %ile
Mid
Moderate-IntensityUser
(4.25)
(47.5)
(10.7)
Bystander
6.86E-01
I.03E-01
th
th
10 %ile
Min
10 %ile
User
6.72E-02
8.83E-03
Low-Intensity User
(5)
(1.3)
(0.5)2
Bystander
8.68£ -03
l .30E-03
1Actual product weight fractions were: 5-15%;40-60; and >90%. 47.5% is a mathematically-derived mid-point (i.e., mean)
for use in modeling, based on the minimum and maximum inputs.
2The 10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.

483
484
485

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with.impeded evaporation.

High-Intensity User

479

486
487
488
489

95th %ile
(60)

Max
(90)

951"%ile
(1852 )

Mirror-edge Sealant
Exposure to TCE in mirror-edge sealant products was evaluated based on one aerosol product with a
weight fraction of 20-40% TCE.

490
491
492
493

494
495
496
497
498

Westat Survey data on contact cement, superglue, and spray adhesive were used as the basis for duration
of use and mass of product used. While there was no Westat scenario that directly aligned with use as a
mirror-edge sealant, the selected category is believed to be the best fit based on the associated range of
use duration and mass used. Survey responses indicate that 60.6% of respondents have used products in
this category; 97.1% reported use ofliquid formulations. While the formulation type used by the
majority of respondents for this category does not reflect the modeled use, which is an aerosol, it
represents the best fit category available. The room of use (Zone 1)was set to the bathroom (15 m3)
based on the product' s apparent use on mirror edging.

499

500
501
502

503
504

505

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModelingResults and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based
on all iterations of this modeling scenario.

- .

Tabl e 2 53 Acu te In haIat·10n Exposure summary:

1rror-Ed12eSealant

Durationof
Use

Weight
Fraction1

(min)

(¾)

High-IntensityUser

95th %ile
(60)

(40)

Moderate-Intensity User

50th %ile
(4.25)

(40)

5Qlh%ile
(4.5)

(40)

10th %ile
(0.5)

Scenario Description

Low-Intensity User

10th %ile
(0.5)2

Mass Used
(g)

ProductUser
or Bystander

95th %ile

User
Bystander
User
Bystander
User
Bystander

(78.4)

Page 162 of691

3-hrMax
TWA

24-hrMax
TWA

(ppm)

(ppm)

2.45E+ol
5.21
8.31
1.34
l.68E-Ol
2.71E-02

7.84E-Ol
1.1 I
2.0l E-01
2.24E-02
4.078-03

3.33

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
506
507
508

1Single weight fraction of20-40% available.
2

The 10th percentile durationfrom Westat is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the modelnm.

509
510
511
512

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dennal contact with impeded evaporation.

513

Tire Repair Cement/Sealer
Exposure to TCE in tire repair products was evaluated based on five liquid products with weight
fractions ranging from 65-95% TCE.

514

515
516

517
518
519
520
521
522

W estat Survey data on contact cement, superglue, and spray adhesive were used as the basis for duration

of use and mass of product used. Survey responses indicate that 60.6% of respondents have used
products in this category; 97.1% reported use of liquid formulations. The room of use (Zone I) was set
to the garage (90 m3) based on the product's apparent use on tires.

525

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See Supplemental File [ExposureModeling Results and Risk Estimatesfor
ConsumerInhalation Exposures. Docket:EPA-HQ-OPPT-2019-0500]for the full range of results based
on all iterations of this modeling scenario.

526
527

Ta ble 2-54 Acu te lnh aIati on Exposure summary: T"J.l'eRepa1rcemen t/Seal er

523
524

.

.Scenario Description

531

Weight

Fraction•
(%)

Mass Used
(g)

Product
User or
Bystander

3-hrMax
TWA
(ppm)

24-hrMax
TWA
(ppm)

User
8.30E+ol
l.18E+ol
Bystander
2.44E+ol
3.80
User
4.85
6.64E-Ol
50th %ile
50th %ile
Mid
Moderate-IntensityUser
(10.7)
(4.25)
(80)
Bystander
1.07
l.6 3E-Ol
111
th
User
10 %ile
Min
10 %ile
4.32E-01
5.97E-02
Low-Intensity User
(65)
(1.3)
(0.5)2
Bvstander
l.OSE-01
1.59£-02
1Actual product weight fractions were: 65-80%; 70-85%;75-90%; and 80-95%. 80% is a mathematically-derivedmid-point
(i.e., mean) for use in modeling, based on the minimumand maximuminputs.
2The 10th percentileduration from Westat is < 0.5 minutes,but 0.5 minutes was used in the model, as it reflectsthe smallest
timestep in the model run.
High-IntensityUser

528
529
530

Duration of
Use
(min)

95th %ile
(60)

Max
(95)

95th %ile
(185.2)

532

533
534
535

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.

536

Cleaning and Furniture Care Products

537
538
539
540
541
542
543
544
545
546

Cawet Cleaner
Exposure to TCE in carpet cleaner was evaluated based on a single liquid formulation with a weight
fraction of >99% TCE.

Westat Survey data on spot removers were used to select the duration of use and mass of product used.
Survey responses indicate that 39 .1% of respondents have used products in this category; 43 .9%
reported use of a liquid formulation. The room of use (Zone 1) was set to the bedroom (36 m 3) based on
professional judgement. There are no data in the Westat Survey exactly matching a use as a carpet
cleaner; therefore, data reflecting spot cleaners were applied.
Page 163 of691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

547
548
549
550
551
552
553
554

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See Supplemental Files [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
Dermal Exposures.Docket: EPA-HQ-OPPT-2019-0500]
iterations of this modeling scenario.

.

. Exposure summarv: Caroet Cl eaner
Ta ble 2-55 Acute Inh alation
Scenario Description

High-Intensity User
Moderate-IntensityUser

Duration of
Use
(min)
95th %ile
(30)

5Qlh%ile
(5)
th

556
557
558
559
560
561
562
563
564

565

(99)

95th %iJe
(526.6)

(99)

50th %ile
(62.9)

Product
User or
Bystander

3-hrMax
TWA
(ppm)

24-:hrMax
TWA
(ppm)

User
Bystander
User
Bystander
User
Bvstander

3.90E+02
7.65E+Ol
4.75E+Ol
8.39
8.14

S.26E+Ol
1.ISE+Ol
6.36
1.26

10 %ile
1.10
(99)
(11.8)
(0.5)2
1.55
2.33E-Ol
1
Single weight fraction of>990/oavailable.
2
The 10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a ~er
leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dennal exposure.

.

Table 2-56 Acuet Derma IE xoosure summarv: C arpetCI eaner
Scenario
Description

566
567
568
569
570
571
572

(%)

Mass Used
(g)

10th %ile

Low-IntensityUser

555

Weight
Fraction1

Duration
ofUse

Weight
Fraction1

(min)

(%)

High-Intensity
User

95111%ile
(30)

CentralTendency

50th %ile

Low-Intensity
User

10th %ile
(0.5)2

(5)

(99)

(99)

(99)

Receptor

AcuteADR
(mg/kg/day)

Aduh ~21 years)
Youth (16-20 years)
Youth (11-15 years)

4.6SE+ol
4.36E+Ol
4.77E+Ol

Adult (~21 years)
Youth (16-20 years)
Youth (11-15 years)
Adult ~l years)
Youth (16-20 years)
Youth (11-15years)

7.77
7.28
7.93
3.89E-Ol
3.64E-OI
3.98E--Ol

1

Single weight fraction of>99% available.

2The10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.

Aerosol Spot Remover
Exposure to TCE in aerosol spot remover products was evaluated based on one aerosol product with a
weight fraction of 20-30%TCE.
Page 164 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

573
574
575

516
577
578
579
580
581
582
583
584

Westat Survey data on spot removers were used as the basis for room of use, duration of use, and mass
of product used. Survey responses indicate that 39.1% ofrespondents have used products in this
category; 43.9% reported use of a liquid formulationand 56.1% reported use of an aerosol formulation.
Therefore, these Westat data were applied to both aerosol and liquid product scenarios. The room of use
(Zone 1) was set to the utility room (20 m3) .
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModeling Results and Risk Estimatesfor
ConsumerInhalation Exposures and Risk Exposure ModelingRes.ultsand Risk Estimatesfor Consumer
Dermal Exposures. Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based on all
iterations of this modeling scenario.

585

586

.

Table 2 -57 Acu te lnh aIaf10nExposure summary: Aeroso IS•POtR emover
Scenario Description

Durationof
Use

Fraction 1

(min)

(%)

High-IntensityUser
Moderate-IntensityUser
Low-IntensityUser

95th o/oile
(30)
50th %ile
(5)

101ho/oile
(0.5) 2

Weight

(30)

(30)
(30)

3-hr Max

(g)

Product
User or

TWA

24-brMax
TWA

Bystander

(ppm)

(ppm)

9511>
o/oile
(514.1)

User
Bystander
User
Bystander
User
Bystander

2.50E+02
2.28E+01
2.93E+0l
2.49
4.34

3.24E+0l
3.43
3.78
3.75E-OI
5.65E-0l

4.59E -Ol

6.90E-02

Mass Used

5otti%ile
(61.4)

101b%ile
(11.15)

587
588
589
590

Single weight fraction of20-30% available.
2
The 1011i
percentile duration from Westat is < 0.5 minutes, but 0.5 minuteswas used in the model, as it reflects the smallest
timestep in the model nm.

591
592
593

595

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation,thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertaintysurroundingthe duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimationof dermal exposure.

596
597

Table 2 58 Acute DermalE xposure

594

1

- .

Scenario
Description

High-Intensity
User
ModerateIntensity User

Low-Intensity
User

598
599

600

Duration
ofUse
(min)

95th %He
(30)
50lh%ile
(5)

10th o/oile
(0.5)2

Weight
Fraction•
(¾)
(30)

(30)

(30)

summary: Aeroso IS
Receptor
Adult ~21 years)
Youth (16-20 years)
Youth(11-15 years)
Adult (?:21years)
Youth (16-20 years)
Youth (11-15 years)
Adult ~21 years)
Youth (16-20 years)
Youth (11-15 years)

~t :Remover
AcuteADR
(mg/kg/day)

5.52
5.16
5.64

9.18E-01
8.61E-Ol

9.42E-Ol
9.lSE-02
8.61E-02

9.42E-02
Single weight fraction of20-30% available.
2 The I 0th percentile duration from Westat is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
1

Page 165 of 691

 INTERAGENCY DRAFT ·- DO NOT CITE OR QUOTE

601
602
603

604
605
606

607
608
609
610
611

612
613

614
615
616
617
618

Liguid Spot Remover
Exposure to TCE in liquid spot remover products was evaluated based on four liquid products with
weight fractions ranging from 50-75%.
Westat Survey data on spot removers were used as the basis for room of use, duration of use, and mass
of product used. Survey responses indicate that 39.1% of respondents have used products in this
category; 43.9% reported use of a liquid formulation and 56.1% reported use of an aerosol formulation.
Therefore, these Westat data were applied to both aerosol and liquid product scenarios. The room of use
(Zone I) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. See Supplemental Files [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk ExposureModelingResults and Risk Estimatesfor Consumer
for the full range of results based on all
Dermal Exposures.Docket:EPA-HQ-OPPT-2019-0500]
iterations of thls modeling scenario.

.

Table 2-S9 Acutelnh alat'10n Ex:posure summary: L.IQUI'dS,potR emover
Scenario Description

Duration of
Use
(min)

High-IntensityUser
Moderate-IntensityUser

951h%iJe
(30)

Max

(5)

(75)

627
628

Mass Used
(g)

95lh%iJe
(477.2)
som%ile
(57)
IOth %ile

Product

3-br Max

24-brMax

User or

TWA
(ppm)

TWA
(ppm)

Bystander
User
Bystander
User
Bystander
User
Bvstander

2.98E+-02
5.34E+-OI
3.55E+Ol

3.99E+-Ol

5.80
4.09
7.14E-Ol

8.72E-Ol

8.02

4.73

Min
10 %ile
5.47E-Ol
(0.5)2
(50)
(10.7)
l.07E-01
1
Actual product weight fractions were: <50%; <75%; and >75%.
2The 10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
Low-IntensityUser

626

Max
(75)

500t%ile
th

619
620
621
622
623
624
625

Weight

Fraction•
(o/e)

Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.

Page 166 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
629

T a bl e 2-60.Acuet Derma IE xposure summary: L"1qu1'd Sipot R emover
Scenario
Description

High-Intensity
User
ModerateIntensity User

Duration
ofUse

Weight

(min)

Fraction
(%}

95th ¾ile
(30)

Max
(75)

50th ¾ile
(5)

Max
(75)

AcuteADR

Receptor

(mg/kg/day)

Adult~ 1 years)
Youth (16-20 years)
Youth (11-15 years)

630
631
632
633
634
635

636
637

10th %ile
(0.5)2

Min
(50)

328 E+ol

Adult (~21 years)

5.33

Youth (16-20 years)

4.99
5.45

Youth (II - 15 years)
Low-Intensity
User

3.2IE+ol
3.00E+Ol

Adult (~2 1 years)

3.55E-Ol

Youth (16-20 years)

3.33E-01

Youth (11-15 years)
3.63E-Ol
fractions were: <50%; <75%; and >75%.
2
The 10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
1Actual product weight

Arts, Crafts,and Hobby Materials
Fixatives & Finishing Spray Coating
Exposure to TCE in fixatives & finishing spray coating productswas evaluated based on one aerosol
product with a weight fraction of20-30% TCE.

638

639
640
641

642
643

644
645
646
647
648
649

650
651
652

Westat Survey data on aerosol rust removers were used as the basis for duration of use and mass of
product used. This Westat category was selected as a surrogate,as there were no well-aligned product
categories for this use. However, survey responses for the selected sUITOgate
category reported 98.3%
use of aerosol formulations,which is supportiveof its applicationto the modeled product scenario.
Duration of use and mass of product data were also reviewed for reasonablenessand were considered
more reasonable (i.e., lower) than the higher use patterns associatedwith most of the solvent degreasing
or cleaning categories. The room of use (Zone 1) was set to the utility room (20 m3) .
Inhalation exposures for users and bystanders are presentedbelow reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModelingResults and Risk Estimatesfor
Consumer InhalationExposures. Docket: EPA-HQ-OPPT-2019-0500]for the full range of results based
on all iterations of this modeling scenario.

.

. h.m2 S•l~ray Coatin
T able 2-61 A cute Inhalati on E xoosure summary: F"
o:atives & F.IDIS
Duration of

Scenario Description

653
654
655

Use

Weight
Fraction 1

(min)

(%)

Mass Used
(g)
th

High-Intensity User

95tb%ile
(60)

(30)

9S ¾ile
(306)

Moderate-IntensityUser

50th %iJe
(5)

(30)

50th %ile
(45.2)

~

Product User
or Bystander

3-hrMax
TWA
(ppm)

24-hr Max
TWA

User
Bystander
User

6.83E+ol
l.51E+01
l.l 3E+ol

9.31
2.28
l.50

(ppm)

Bystander
1.84
2.77E-Ol
User
2.17
2.90E-Ol
10th %ile
10th %ile
Low-Intensity User
(30)
(0.5)2
(9.4)
Bystander
3.76E-OI
5.66E-02
1Single product weight fraction of20-30% available.
2The 10th percentile duration from Westat is< 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.

656

Page 167 of 691

 INTERAGENCYDRAPT - DO NOT CITE OR QUOTE
657

658
659
660
661
662
663
664

This condition of use was also assessed in the 2014 TSCA Work Plan ChemicalRisk Assessment(U.S.
EPA. 2014b ). In the prior assessment,different inputs were used for certain modeling parameters
including mass used and duration of use. The amount ofTCE used (11 g- roughly 37 g of product) in
the 2014 assessment is roughly equivalent to the 50thpercentileinput obtained from the Westat survey
rust remover surrogate scenario applied in this latest evaluation.These inputs and associated 24-hr acute
air concentrationsfor users and bystandersareincludedbelow.
. h'm2 S,pray C oati n~
2014 Acuet I nh alaf10n Exposure summary: Fix af1ves& F'IBIS
Scenario Description

665
666
667

668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690

Weight
Fraction

(min)

(%)

Mass Used
(g)

Product
User or
Bystander

24-hr
TWA
(ppm)

User
0.4
2014 Chemical Work
11I
30
30
Plan Risk Assessment
Bystander
0.1
1Note that this conversion assumes a formulation density of I. Actual product densities range from 1.46-1.52g/cm 3 •
This input is also provided in terms of mass ofTCE per use, rather than mass of product per use, which is the actual
model input. 11 g ofTCE in this 30% formulation would equate to roughly 37 g of product per use, which is similar
to the central tendency input used in the current evaluation..

There are no dermal exposures quantified for this scenario,as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Apparel and Footwearcare Products
Shoe Polish
Exposure to TCE in shoe polish productswas evaluatedbased on one aerosol product with a weight
fraction of 10-20% TCE.
Westat Survey data on spray shoe polish were used as the basis for room of use, duration of use, and
mass of product used. Survey responses indicate that 11.7% of respondentshave used products in this
category; 97.7% reported use of aerosol formulations.The room of use (Zone 1) was set to the utility
room (20 m3).
Inhalation exposures for users and bystanders are presentedbelow reflecting high-, moderate-, and lowintensity user scenarios. See SupplementalFiles [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposuresand Risk "ExposureModelingResults and Risk Estimatesfor Consumer
Dermal Exposures.Docket: EPA-HQ-OPPT-2019-0500]
for the full range of results based on all
iterations of this modeling scenario.
. E xp-0suresummarv: Shoe P0 li Sh
Table 2-62 . Acute In hal at1on
Scenario Description

High-Intensity User
Moderate-IntensityUser

Duration of
Use

Weight
Fraction 1

(min)

(%)

9S1'1
%ile
(30)
50th %ile
(5)

10th %ile
(0.5)
'Single weight fraction of 10:.20%available.
Low-Intensity User

691
692

Duration of
Use

Mass Used
(g)

(20)

95th %ile
(151.4)

(20)

50th %ile
(15.4)

(20)

10th %ile
(2.9)

Page 168 of 691

Product User
or Bystander

3-br Max
TWA
(ppm)

24-brMax
TWA
(ppm)

User
Bystander
User
Bystander
User
Bvstander

2.52E+ol
4.52
2.56
4.18E-Ol
4.46E-01
7.74E-02

3.38
6.79E-01
3.41E-Ol
6.28E-02
5.96E-02
l.l6E-02

 INTERAGENCYDRAFT - DO NO I CITE OR QUOTE

693
694
695
696
697

Dermal exposures are also presentedfor this scenario,as it is assumed that the product could be applied
in a manner leading to dennal contact with impeded evaporation,thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertaintysurroundingthe duration of dermal contact
with impeded evaporation.The exposure durations modeledcould exceed the duration of such dermal
contact; therefore, the higher-enddurations may result in an overestimationof dermal exposure.

698
699

Table 2-63 Acuet Derm.aIE xposure summarv: Sh oe PI'
0 ISh

.

Scenario
Description

High-Intensity
User
ModerateIntensity User
Low-Intensity
User

700
701
702
703
704

705
706
707
708
709
710

711

712
713
714
715

Duration
ofUse
(min )

95th %ile
(30)

Weight
Fraction1
(%)

(20)

(20)

10th %ile
(0.5)

(20)

AcuteADR
(mg/kg/day)

Adult ~21 years)
Youth (16-20 years)

3.02
2.82

Youth (11-15 years)

3.08
5.00E-01
4.70E-01
5.14E-Ol
5.00E-02
4.70E-02
5.14E-02

Adult ~2 I years)

50th %ile
(5)

1Single weight fraction of

Receptor

Youth (16-20 years)
Youth (11-15 years)
Adult ~ 21 years)
Youth (16-20 years)
Youth (11-15years)

10-200/oavailable.

Other Consumer Uses
Fabric Spray
Exposure to TCE in fabric spray products was evaluated based on one aerosol product with a weight
fraction of20-40% TCE. Thisuse (i.e., no-fray fabricspray) was originallyidentified in the 2014TSCA
Work Plan ChemicalRiskAssessmentofTCE( U.S. EPA 2014b).
Westat Survey data on water repellents/protectorsfor suede, leather, and cloth were used as the basis for
room of use, duration of use, and mass of product used. Surveyresponses indicate that 35.5% of
respondents have used products in this category; 72.1 % reported use of aerosol formulations. The room
of use (Zone 1) was set to the utility room (20 m3).

716

Inhalation exposures for users and bystanders are presentedbelow reflectinghigh-, moderate-, and lowintensity user scenarios. See SupplementalFile [ExposureModeling.Resultsand Risk Estimatesfor
ConsumerInhalationExposures.Docket:EPA-HQ-OPPT-2019-0500]
for the full range of results based
on all iterations of this modeling scenario.

717
718

T a ble 2-64 Acute lnhl'a.ation Exposure summary: Fab.nc Sipra y

.

Scenario Description

High-IntensityUser
Moderate-IntensityUser
Low-IntensityUser

719

Duration or
Use
(min)

95th %ile
(60)

50th %ile
(10)
10th %ile
(1.4)

Weight
Fraction1

(%)
(40)
(40)
(40)

Mass Used
(g)

Product User
or Bystander

95th %ile
(326.8)

User
Bystander
User
Bystander
User
Bystander

50th %ile
(49.9)

IOth %ile
(I 1.4)

1

Single product weight fraction of 20-40% available.

720
Page 169 of691

3--brMax

24-hrMax

TWA
(ppm)

TWA
(ppm)

1.93E+02
2.IOE+Ol
324E+ol
2.75
5. 64
6.09E-Ol

2.53E+ol
3.18
4.18
4.l3E-Ol
7.35E-Ol
9.l5E-02

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to 'involve
dermal contact with impeded evaporation.
Film Cleaner
Exposure to TCE in film cleaner products was evaluated based on two aerosol products with weight
fractions ranging 80-100%TCE.
Westat Survey data on aerosol rust removers were used as the basis for duration of use and mass of
product used. This Westat category was selected as a surrogate, as there were no well-aligned product
categories for this use. However, survey responses for the selected surrogate category reported 98.3%
use of aerosol formulations, which is supportive of its application to the modeled product scenario.
Duration of use and mass of product data were also reviewed for reasonableness and were considered
more reasonable (i.e., lower) than the higher use patterns associated with most of the solvent degreasing
or cleaning categories. The room of use (Zone 1) was set to the utility room (20 m 3).
Inhalation exposures for users and bystanders are presented below reflecting high- , moderate-, and lowintensity user scenarios. See Supplemental File [ExposureModelingResults and Risk Estimatesfor
for the full range of results based
ConsumerInhalationExposures. Docket: EPA-HQ-OPPT-2019-0500]
on all iterations of this modeling scenario.

.

Table 2-65 Acuet InhalafIOD Exposure summary:Film Cleaner
Scenario Description

744

745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760

Weight
Fraction 1

(%)

Mass Used
(g)

High-Intensity User

95th ¾ile
(60)

(100)

9511,
%ile
(632.9)

Moderate-Intensity User

50th %ile
(5)

(100)

50th %ile
(93.4)

10th %ile

10th

Product

User or
Bystander

User
Bystander
User
Bystander

3-brMu
TWA
(ppm)

24-hrMax
TWA

(ppm)

4.71E+o2
1.04E+02
7.77E+Ol

6.42E+Ol
l.57E+OI
1.03E+Ol

1.27E+Ol
l. 49E+Ol
2.59

l.91
1.99
3.89E-Ol

%ile
User
(100)
(0.5)2
(19.4)
Bvst:ander
1Actual product weight fractions were: 80-100% and 95%.
2
Tbe 10th percentile duration from Westat is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
Low-Intensity User

742
743

Duration of
Use
(min)

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Hoof Polish
Exposure to TCE in hoof polish products was evaluated based on one aerosol product with an
unreported weight fraction. Modeling was based on the upper-end of the narrow range of shoe polish
and spray fixative/coating formulation weight fractions (20-30%).
Westat Survey data on spray shoe polish were used as the basis for duration of use and mass of product
used. This Westat category was selected as a surrogate, as there were no well-aligned product categories
for this use. Survey data indicate that 11.7% of respondents used products in this category; 97.7%
reported use of aerosol formulations. The room of use (Zone 1) was set to approximate a barn
environment. This was done by using a garage (90 m3) but increasing the default air exchange rate of a
residential room from 0.45 to 4 air exchanged per hour, which was based on recommended ventilation
rates for a horse stable (Penns\lvania State Universil\ . 2016).
Page 170 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

761
762
763
764

765
766

767

Inhalation expo sures for users and bystanders are presented below reflecting high- , moderate-, and lowintensity user scenarios. See Supplemental File [ExposureModelingResults and Risk Estimatesfor
ConsumerInhalationExposures.Docket: EPA-HQ-OPPT-2019
-0500]for the full range of results based
on all iterations of this modeling scenario.

.

Tabl e 2-6(; Acuet In haa
I tion Exoosure summarv: H00 fP 0 rIS h
Use

Weight
Fractton1

(min)

(%)

Durat ion of

ScenarioDescription

771

772

(g)

ProductUser
or Bystander

3-h r Max
TWA
(ppm)

24-brMai:
TWA

(ppm)

User
l.76E+0l
2.2 1
Bystander
8.83E-0 2
I.I0E-02
User
1.73
2.16E-0l
5()1h%ile
5()1h%ile
Moderate-IntensityUser
(30)
(5)
(212)
Bystander
3.8 1E-03
4 .76E-04
th
User
101h
%ile
10 %ile
2.46E-0l
3.0SE-02
Low-Intensity User
(30)
(0.5)
{4)
Bvstander
6.23E-04
7.79E-05
1Actual weight fraction is not reported;modeling was based on the upper-end of the narrow range of shoe polish and spray
fixative/coating formulation weight fractions(20-30%).
High-IntensityUser

768
769
770

Mass Used

95111%ile
(30)

(30)

95dl%ile
(2082)

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.

773

774
775

776

Pepper Spray
Exposure to TCE in pepper spray products was evaluated based on two aerosol products with a single
reported weight fraction of91.5% TCE.

777
778

779
780

781
782
783
784

785

Internal research was the basis for duration of use and mas s of product used . One spray from the most
common civilian canist er is estimated to be approximately 0.0216-0.108 ounces, based on information
on a pepper sprav manufacturer's website . Spraying occurred between 3 and 5 seconds (0.05-0.08 min)
before obtaining desired effect (Bertilsson et aL 2017) . The room of use (Zone 1) was set to
approximate a "cloud " around the user (16 m 3) in an outdoor environment. This was done by increasing
the default air exchange rate of a residential room from 0.45 to 100 air exchanges per hour. Since the
interzonal ventilation rate for this "outdoor" scenario is held at 0, there are no bystander exposures
estimated. Based on the limited parameter data for this scenario, no inputs were varied.

786

790

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios . See Supplemental File [ExposureModelingResults and Risk Estimatesfor
for the full range of results based
ConsumerInhalationExposures.Docket: EPA-HQ-OPPT-2019-0500]
on all iterations of this modeling scenario.

791
792

T abl e 2 -67 Acute Inhlti
a a on E xnosure summary: p epper s•1
>ray

787

788
789

.

ScenarioDescription
Single Scenario

793
794

795
796

Duration of
Use

Weight
Fraction•

(min)

(%)

(0.5)2

(91.S)

Mass Used
(g)

Product User
or Bystander

(4)

User
Bystander

3-brMax

(ppm)

24-hrMax
TWA
(ppm)

J.42E-Ol
1.42E-01

l.77E -02
l.77E -02

TWA

1
Single weight fraction of 91.5% available.
2The selected < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallesttimestep in the model run.
l"Bystanderin the home not modeled due to simulated outdoor scenario - can be considered equal to user.

Page 171 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
797

798

There are no dennal exposures quantified for this scenario, as this use pattern is not expected to involve
dennal contact with unpeeledevaporation.

799

800
801
802

Toner Aide
Exposure to TCE in toner aide products was evaluated based on one aerosol product with a weight
fraction of 10-20% TCE.

803
804

805
806
807
808
809

810

Westat Survey data on aerosol rust removers were used as the basis for duration of use and mass of
product used. This Westat category was selected as a surrogate, as there were no well-aligned product
categories for this use. However, survey responses for the selected surrogate category reported 98.3%
use of aerosol formulations, which is supportive of its application to the modeled product scenario.
Duration of use and mass of product data were also reviewed for reasonableness and were considered
more reasonable (i.e., lower) than the higher use patterns associated with most of the solvent degreasing
or cleaning categories. The room of use (Zone 1) was set to the utility room (20 m3) .

811

812
813

814

815
816

817

Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and lowintensity user scenarios. SeeSupplementalFile [ExposureModeling Results and Risk Estimatesfor
Consumer InhalationExposures. Docket: EPA-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.

.

Table 2-68 Acuet I nh alaf10n Exposure summary: Toner Aid e
Scenario Description

Duration of
Use
(min)

Weight
Fractlon 1
(o/o)

th

818
819

820
821
822
823
824
825
826

827
828

829
830
831
832

Mass Used
(g)
th

High-Intensity User

95 %ile
(60)

(20)

95 %ile
(434.7)

Moderate-Intensity User

50th %ile
(5)

(20)

50'11%ile
(64.2)

Product User
or Bystander
User
Bystander
User
Bystander

3-hrMax
TWA
(ppm)

6.47E+ol
l.43E+ol
l.07E+ol

24-hrMax
TWA
(ppm)
8.82
2.16

1.42

1.74
2.62E-0I
10th %ile
User
2.05
2.73E-01
10th %ile
(20)
Low-Intensity User
(0.5)2
(13.3)
Bystander
3.SSE-01
5.34E-02
1
Single weight fraction of 10-20% available.
2The selected < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest timestep in the model run.

There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Lace Wig and Hair Extension Glues
Exposure to TCE in lace wig and hairextension glue products was qualitatively evaluated based on
analogy to another condition of use- solvent-basedadhesives & sealants. Lace wig and hair extension
glues were not specifically modeled due to the lack of identified products on which to base weight
fraction, formulation type, etc. It is therefore compared to the solvent-based adhesive & sealant
modeling scenarios that combined low-end and central tendency mass of product used with central
tendency and high-end durations of use and the mid-point weight fraction. It is believed that these are
the closest surrogate scenarios.

Page 172 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
833

834

Table 2-69. Acute Inhalation Exposure Summary: Lace Wig and Hair Extension Glues, as
Predicted with Solvent-Based Adhesive & Sealant Scenario
Use

Weight
Fraction 1

(min)

(%)

Duration or
Scenario Description

835
836

Mass Used
(g)
th

Moderate to HighIntensity User

95th %ile
(60)

Mid

50 %ile

(47.5)

(10.7)

Low to ModerateIntensity User

S()tb%iJe
(5)

Mid
(47.5)

10111%ile
(13)

Product User
or Bystander
User
Bystander
User

3-hrMax
TWA
(ppm)

24-hr Mat:
TWA
(ppm)

7.5 1
8. l8E-0l

9.82E-0l
1.24E -Ol

9.43E-Ol

l.22E-0l

Bystander
8.33E-02
l.25E-02
1Actual product weight fractions for solvent-basedadhesive & sealant were: 5-15%; 40-60; and >90%. 4 7 .5% is a
mathematically-derived mid-point (i.e., mean) for use in modeling, based on the minimum and maximum inputs.

837

838
839

840

There are no dermal exposuresquantifiedfor this scenario,as this use pattern is not expected to involve
dermal contact with impeded evaporation.

841

2.3.2.6.3 Summary of Consumer Exposure Assessment
Table 2-70 displays the consumerconditionsof use evaluatedfor acute inhalationand/or dermal

842

exposures.

843
844

T able 2 -70. Eva Iua ted P athwavs fior C onsumer CODd.ti
I ODS 0 fU se
Life
Cycle
Stage

Use

Categories

Solvents for
Cleaning and
Degreasing

Product Subcategories

Form

Acute
Inhalation

Acute
Dermal

Exposure

Exposure
✓

Brake & Parts Cleaner

Aerosol

✓

Electronic Degreaser/Cleaner

✓

Electronic Degreaser/Cleaner
Aerosol Spray Degreaser/Cleaner

Aerosol
Liquid
Aerosol

Liquid Degreaser/Cleaner

✓

✓

✓

✓

Liquid

✓

✓

Gun Scrubber

Aerosol

✓

✓

Gun Scrubber

Liquid

✓

✓

Tire Cleaner

Aerosol
Aerosol

✓
✓

✓

Tire Cleaner

Liquid

✓

✓

Lubricants and
Greases

Tap & Die Fluid
Penetrating Lubricant

Aerosol

✓

Aerosol

✓

Adhesives and
Sealants

Solvent-basedAdhesive & Sealant

Liquid

✓

Mirror-edge Sealant

✓

Tire Repair Cement/Sealer

Aerosol
Liquid

Cleaning and
Furniture Care
Products

Carpet Cleaner

Liquid

✓

✓

Spot Remover

✓

✓

Spot Remover

Aerosol
Liquid

✓

✓

Arts , Crafts, and

Fixatives & Finishing Spray Coatings

Aerosol

✓

Shoe Polish

Aerosol

✓

Mold Release

Hobbv Materials
Apparel and
Footwear Care
Products

✓

✓

Aerosol

Fabric Spray

Page 173 of 691

✓

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

Life
Cycle
Stage

Categories

Product Subcategories

Other Consumer
Uses

845
846
847

848
849

850
851

852
853

Film Cleaner
Hoof Polish
Pepper Spray

Acute
Inhalation
Exposure

Form

ti'

TonerAid

Aerosol
Aerosol
Aerosol
Aerosol

Lace Wig and Hair ExtensionGlues

Unknown

✓

Acute
Dermal
Exposure

✓
✓
✓

A range in acute inhalation and acute dermal exposures is provided in Table 2-71, summarized by the
consmnercategory. Ranges provided are based on the presented user scenario descriptions(high-,

moderate-,and low-intensity)and may not reflect overall minimum and maximum exposure levels from
all iterationsof the modeling scenario, which can be seen in the SupplementalFiles [ExposureModeling
Results and Risk Estimatesfor ConsumerInhalationExposuresand Risk ExposureModeling Results
and Risk Estimatesfor ConsumerDermalExposures.Docket: EPA-HQ-OPPT-2019-0500].

.

T able 2-71 Summarvo
Consumer
Category

re onsumer E xposure L evelsb ,y Cateiory

Acute Inhalation 24-hr TWA 1
(ppm)

Solvents for Cleaning User
and Degreasing
Bystander

4.55E-02 - l .62E+02

Lubricants and
Greases

2.16E-02- 1.47E+0l

User
Bystander

8.47E--03- 4.71E+ol
421E-03 - 2.95

Adhesives and
Sealants

User

Cleaning and
Furniture Care
Products

User

5.47E-Ol - 5.26E+ol

Bystander

6.90E-02 -1.lSE+ol

Arts, Crafts. and
Hobby Mater.ials

User

2.90E-Ol- 931

Bystander

5.66E-02- 2.28

Apparel and Footwear User
Care Products
Bystander

5.96E-02- 3.38
l. l 6E--02- 6.79E--O
I

Other Consumer Uses User

1.77E-02-6.42E+Ol

Bystander

Bystander

8.83E-03- 3.22E+ol
l.30E-03 - 4.06

7.79E--05- l.57E+ol

Acute Dermal

ADR2
(mg/kg/d)

l.19E-01- l.75E+02
NA

NA

8.61E-02-4.77E+ol

NA

4.70E-02- 3.08
NA

1
The level of variationdisplayedin the ranges of consumercategoriesreflect multiple,
specific consumerconditionsof use / subcategoriesand do not reflect 1bedegree of
variationpresent within scenario-specificresults. The displayed categoryranges therefore
reflect a much .broader spread of exposureestimates.
2The range in acute dennal ADRs reflect all age groups modeled (youth and adult).

854

Page 174 of691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

855
856
857
858

859
860
861

862

2.3.2.7
Assumptions and Key Sources of Uncertainty for Consumer
Exposures
BPA's approach recognizes the need to include uncertainty analysis. One important distinction for such
an analysis is variability versus uncertainty - both aspects need to be addressed. Variability refers to the
inherent heterogeneity or diversity of data in an assessment. It is a quantitative description of the range
or spread of a set of values and is often expressed through statistical metrics, such as variance or
standard deviation, that reflect the underlying variability of the data. Uncertainty refers to a lack of data
or an incomplete understanding of the context of the risk evaluation decision.

863
864

865
866

867
868

869
870
871

872
873
874

875
876
877
878
879
880

881
882

883
884

885
886

887
888
889
890

891
892
893
894
895

896
897
898
899
900
901

Variability cannot be reduced, but it can be better characterized. Uncertainty can be reduced by
collecting more or better data. Quantitative methods to address uncertainty include non-probabilistic
approaches such as sensitivity analysis and probabilistic methods such as Monte Carlo analysis.
Uncertainty can also be addressed qualitatively, by including a discussion of factors such as data gaps
and subjective decisions or instances where professional judgment was used.
Uncertainties associated with approaches and data used in the evaluation of consumer exposures are
described below.
2.3.2.7.1 Modeling Approach Uncertainties
Deterministic vs. Stochastic
With deterministic approaches like the one applied in this evaluation of consumer exposure, the output
of the model is fully determined by the choices of parameter values and initial conditions. Stochastic
approaches feature inherent randomness, such that a given set of parameter values and initial conditions
can lead to an ensemble of different model outputs. The overall approach to the CEM modeling is
intended to capture a range oflow- to high-intensity User exposure estimates by varying only a limited
number of key parameters that represent the range of consumer product and use patterns for each
scenario. As previously mentioned the parameters selected were chemical weight fractio~ product mass,
and duration of use. All other parameters remained constant between model runs. Since not all
parameters were varied, there is uncertainty regarding the full range of possible exposure estimates.
Although these estimates are thought to reflect the range in exposure estimates for the suite of possible
exposures based on the three varied parameters, the scenarios presented are not considered bounding or
"worst-case," as there are unvaried parameters that are also identified as sensitive inputs held constant at
a central tendency value. These include the room of use volume, residential building volume, and air
exchange rate. Because EPA's largely deterministic approach involves choices regarding highly
influential factors such as mass of product used and weight fraction, it likely captures the range of
potential exposure levels although it does not necessarily enable characterization of the full probabilistic
distribution of all possible outcomes.

Aggregate Exposure
Background levels ofTCE in indoor and outdoor air are not considered or aggregated in this assessment;
therefore, there is a potential for underestimating consumer inhalation exposures, particularly for
populations living near a facility emitting TCE or living in a home with other sources of TCE, such as
TCE-containing products stored in the home. For example, the indoor air and personal breathing zone
monitoring values presented in Appendix D.2 were not considered for aggregation with modeled, usespecific acute air concentrations. Similarly, inhalation exposures were evaluated on a product-specific
basis and are based on use of a single product type within a day. not multiple products.

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Acute Exposure
EPA assumes that a consumer product would be used only once per day. This is a reasonable assumption
for most scenarios, but a Do-It-Yourself- (DIY-) type user could potentially use the same product
multiple times in one day. Additionally, based on human health hazard considerations and typical use
patterns, chronic exposures were not evaluated for TCE-containing consumer products. However, it is
possible that there would be concern for chronic exposure effects for use frequencies greater than
intermittent. For example, daily or DIY-type uses of consumer products could constitute a short-term
chronic exposure scenario or repeated-acute exposure scenario that is not captured in this evaluation.
Identified chronic non-cancer and cancer hazard endpoints (Section 3.2) are unlikely to present for these
populations based on reasonably available information, however the possibility cannot be ruled out. For
the vast majority of the consumer population which are only exposed through short-term, occasional use
ofTCE products, only acute exposure is applicable.
Dermal Exposure Approach
Dermal exposures are quantified and presented for scenarios that may involve dermal contact with
impeded evaporation based on professional considerations of the formulation type and likely use pattern.
However, there is uncertainty surrounding the assumption that such dermal contact with impeded
evaporation would occur for those scenarios. For example, for aerosol formulations, it is possible that
aerosol degreasing or cleaning products may be sprayed and left to drip or dry from the target surface. It
is also possible users would follow spraying with wiping, which could lead to some duration of dermal
contact with impeded evaporation.
There is related uncertainty surrounding the application of exposure durations for such scenarios. The
exposure durations modeled are based on reported durations of product use and may not reflect
reasonable durations of such dermal contact with impeded evaporation. In many cases, the exposure
duration modeled could exceed a reasonable duration of such dermal contact with a wet rag, for
example. Therefore, dermal exposure results based on the higher-end durations (i.e., those associated
with the moderate- and high-intensity user scenarios) may overestimate dermal exposure. Another
source of potential overestimation is the application of a single formulation density to scenarios covering
a range of specific TCE-containing products with a range of formulation densities. For such scenarios, a
single (highest) density was chosen to convert the mass used input obtained from the Westat (1987)
survey from ounces of product to grams of product. For some scenarios, this may have driven up the
mass us~ though the degree of this impact is dependent on he broadness of the density range for that
condition of use.

In the evaluation of consumer dennal exposure, P_ DER2b utilizes a measured dermal permeability
coefficient (Kp) . EPA selected a Kp of 0.019 cm/hr from Poet (2000 ) obtained from a water patch test
on human skin using TCE in aqueous solution. While it is within range of other, predicted Kp values CEM predicts a .Kpof 0.028 cm/hr and the NIOSH Skin Notation Profile for TCE calculates a Kpof
0.01197 cm/hr (Hudson and Dotson . 2017)-it is a key parameter and there is some uncertainty
surrounding the impact of applying an aqueous Kpfor the prediction of dennal flux for fonnulations of
TCE-containing consumer products, some of which contain nearly 100% TCE . While neat TCE would
be estimated to have a lower Kp based on relatively low water solubility (Table l • l) compared to its
density, TCE is an irritant that would be expected to disrupt the stratum corneum and lead to greatly
increased absorption over time.

Inhalation Modellngfor Outdoor Scenarios
The CEM model does not currently accommodate outdoor scenarios. For products that are intended to
be used outdoors, modifications to the CEM inputs were made to simulate an outdoor scenario by
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adjusting Zone 1 parameters (which represents the room of use or use environment). In modeling pepper
spray, the garage was selected as the room of use, but the room volume was changed to 16 m3 to
represent a half-dome chemical cloud around the person using the product. Additionally, the air
exchange rate for Zone 1 was set to 100 to reflect the high rate between the cloud and the rest of outside.
The interzonal ventilation rate was set to 0, which effectively blocks the exchange of air between Zone 1
and the rest of the house. Thus, the concentrations users are exposed to inside the home after product use
is zero. In the outside scenario, bystanders in the home are assumed to have zero exposures. However,
bystanders in the outdoor environment were not modeled, but could potentially be exposed to similar
levels as the user.

2.3.2.7.2 Data Uncertainties

Product Data
The products and articles assessed in this risk evaluation are largely based on EPA's 2017 Use and
Market Profile for TCE, as well as EPA's Use Report and Preliminary Information on Manufacturing,
Processing, Distribution, Use, and Disposal: TCE, which provide information on commercial and
consumer products available in the US marketplace at that time (U.S. EPA . 2017c , :U
). While it is
possible that some products may have changed since 2017, EPA believes that the timeframe is recent
enough to represent the ongoing and reasonably foreseen consumer uses. Additional sources of product
information were evaluated, including the NIH Household Product Survey and EPA's Chemical and
Products Database (CPDat), as well as available product labels and safety data sheets (SDSs). However,
it is possible that the entire universe of products may not have been identified, or that certain changes in
the universe of products may not have been captured, due to market changes or research limitations.

Use.Patterns
A comprehensive survey of consumer use patterns in the US, the Household Solvent Product: A
National Usage Survey (U.S. EPA . 1987), was used to parameterize critical consumer modeling inputs,
based on applicable product and use categories. This large survey of over 4,920 completed
questionnaires, obtained through a randomized sampling technique, is highly relevant because the
primary purpose was to provide statistics on the use of solvent-containing consumer products for the
calculation of exposure estimates. The survey focused on 32 different common household product
categories, generally associated with cleaning, painting, lubricating, and automotive care. Although
there is uncertainty due to the age of the use pattern data, as specific products in the household product
categories have likely changed over time, EPA believes that the use pattern data presented in the Westat
survey reflect reasonable estimates for current use patterns of similar product types.

994

A crosswalk was completed to select the most appropriate Westsat survey category for each consumer
conditions of use in the current risk evaluation. Although detailed product descriptions were not
provided in the Westat survey, a list of product brands and formulation type in each category was useful
in pairing the Westat product categories to the scenarios being assessed. In most cases, the product
categories in the Westat survey aligned reasonably well with the products being assessed. Where Westat
survey product categories did not align well with consumer conditions of use, professional judgment
was used to select the most appropriate Westat category. This involved considering the reasonableness
of the duration and mass used, as well as comparing the primary formulation type. For a limited number
of scenarios, technical fact sheets or labels with information on product use amounts were available, and
this information was used in the assessment as needed.

995
996
997
998

Westat' s overall respondent pool of the survey was large, but the number of users in each product
category was varied, with some product categories having a much smaller pool of respondents than
others. Product categories such as spot removers, cleaning fluids, glues and adhesives, lubricants, paints,

992
993

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000
001
002

003
004
005

006
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010
011
012
013
014
015
016
017
018
019
020
021
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024

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031
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040

041
042

paint strippers, fabric water repellents, wood stains, tire cleaners, engine degreasers, carburetor cleaners,
and specialized electronic cleaners had sample sizes ranging from roughly 500 to 2,000 users; whereas,
categories such as shoe polish, adhesive removers, rust removers, primers, outdoor water repellents,
gasket removers and brake cleaners had sample sizes of fewer than 500 users.

EmissionRate
The higher-tier Multi-Chamber Concentration and Exposure Model (MCCEM) was considered by EPA
for use in estimating inhalation exposures from consumer conditions of use; however, key data (i.e.,
chamber emission data) were not available. Therefore, the model used (CEM 2.1) estimates emission
rate based on chemical properties and emission profiles matching a spray or liquid application.
2.3.2.8

Confidence in Consumer Exposure Scenarios
Overall, there is moderate to high or high confidence in the consumer inhalation exposure modeling

approach and results. This is based on strength of the model employed, as well as the quality and
relevance of the default and user-selected/variedmodeling inputs. CEM 2.1 is peer reviewed, publicly
available, and was designed to estimate inhalation and dermal exposures from household uses of
products and articles. CEM 2.1 uses central-tendency default values for sensitive inputs such as building
and room volumes, interzonal ventilation rate, and air exchange rates. These parameters were not varied
by EPA due to EPA having greater confidence in the central tendency inputs for such factors that are
outside of a user's control (unlike, e.g.,, mass used~use duration). These defaults are sourced from
EPA's exposure factors handbook (U.S. EPA. 201 lc ). The one default value with a high-end input is the
overspray fraction, which is used in the aerosol or spray scenarios. It assumes a certain percentage is
immediately available for inhalation. However, due to TCE's physical chemical properties, this is a not a
sensitive parameter. In the 2014 TCE Risk Assessment, this parameter was varied from 1% to 25% and
resulted in almost no difference in the modeled peak air concentration(U.S. EPA. 2014b). The default
emission rate from a thin film is estimated within the model based on TCE's molecular weight and
vapor pressure, as described in the Chinn equation12 and is deemed appropriate given the lack of
consumer product chamber emission data. The confidence in the user-selected varied inputs (i.e., mass
used, use duration, and weight fraction) are moderate to high. depending on the condition of use. The
sources of these data include the Westat Survey (U.S. EPA. 1987) and company-generated safety data
sheets (SDSs). What reduces confidence in these inputs for particular conditions of use is the relevance
or similarity of the Westat survey product category for the modeled condition of use. In some cases,
professional judgment was used in selection of room of use, which sets the volume for modeling zone 1.
Table 2-72 displays the considerations and confidence ratings for the acute inhalation consumer
exposure scenarios.
Overall, there is a low to moderate confidence in the consumer dermal exposure modeling approach and
results. The same model is employed to estimate dermal exposures; however, there is greater uncertainty
related to the potential for dermal contact with impeded evaporation (i.e., dermal exposure scenarios
wherein volatilization from the skin surface is inhibited); this contributes to the lower overall confidence
in the dermal results. The dermal permeability approach was selected for modeling instead of the
fraction absorbed method. Based on rationale provided in the problem formulation, EPA determined that
only dermal exposures with impeded evaporation would be evaluated for consumer conditions of use.
This is based on the expectation that, if not inhibited from volatilizing, inhalation exposure would
account for the preponderance of exposure from consumer uses. An example of dermal contact with
12 The

value of k is determinedfrom an empiricalrelationship,developedby (Chinn, 1981), between the time required for
90% of a pure chemical fihn to evaporate(EvapTime) and the chemical's molecular weight (MW) and vapor
pressure (VP): EvapTime= 145 / (MW x VP) 0.9546, k = ln(l 0) / (EvapTimex 60), where k = first-order rate
constant for emission decline (min-1),MW = molecularweight, VP= vapor pressure.

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impeded evaporation for consumer applications would be having a TCE-soaked rag pressed firmly
against a user's fingers or hands for a period of time. Therefore,the permeability approach was deemed
more reflective of this type of dermal exposure scenario, as it does not account for losses due to
volatiliz.ationand assumes a constant flux ofTCE for the durationof the use event. In modeling these
scenarios, the same use durations sourced from the Westat survey (U.S. EPA. 1987) are applied;
however, in doing so, the model assumes that there are no losses throughout the entire use duration. It is
unlikely that dermal contact would involve impeded evaporation for the entire use duration, particularly
for central-tendencyand high-end use durations. It is more likely that such contact would be intennittent
throughout longer use durations and not constant. This leads to an overall low confidence in that input;
however, there would be greater confidence in the results obtained from the low-end use duration inputs
for any weight fraction modeled. Table 2-73 displays the considerationsand confidence ratings for the
acute dermal consumer exposure scenarios.
An additional point of confidence in the conswner modeling approachrelated to capturing variation and
estimating results for a range of exposure levels. Although a probabilisticassessment was not employed,
EPA did use up to three inputs for three key modeling parameters:mass used, use duration, and weight
fraction. The first two parameters are based on the Westat survey data, which presented a distributionof
responses. For these parameters, a low-end (l{}'hpercentile), central tendency (50th percentile), and highend (95th percentile) was used in modeling. Weight fraction inputs were based on product SDSs, so the
full range of reported weight fractions was reflected in the modeling inputs using either minimum and
maximum weight fractions or using minimumand maximwn weight fractions along with a mid-point
weight fraction. For subcategories with only one product, only one weight fraction was used in the
modeling. Otherwise, these varied parameters were varied in all possible combinations, resulting in up
to 27 iterations for a given modeling scenario.

067

068
069
070
071
072
073
074
075
076
077

Consumer exposure monitoring studies associated with conditionsof use are not available for direct
comparison with modeled results. Indoor air monitoring data are available but are not associated with
specific conditions of use or TCE-containingconsumer products and are therefore only relevant for
considerations of background levels ofTCE in homes.
While there were certain scenarios that have moderate confidenceratings rather than high confidence for
user-selected varied inputs, there are not available alternativeinputs ~t would serve to increase
confidence in themodeling estimates. For example, in modelingfilm cleaner, the alternative to applying
mass used and use duration from the rust remover Westat survey scenario is professionaljudgment,
which is unlikely to decrease uncertainty.

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078
079

.

n a a on Consumer E:xposure Md
o em2
I' Scenanos
T able 2-72 Con fidence Rati m •sfior Acuetlhlti

Brake &
Parts
Cleaner

Aerosol High

Confidence
in Model
Default
Values1
High

Electronic
Degreaser/
Cleaner

Aerosol High

High

High

High

High

High

High

Electronic
Degreaser/
Cleaner

Liquid

High

High

High

High

High

High

High

Aerosol High

High

High

High

High

High

High

High

High

High

High

High

High

High

Aerosol High

High

High

Moderate

High

Moderate Moderate
to High

High

High

High

Moderate

High

Moderate Moderate
to High

Aerosol High

High

Moderate High

High

High

Moderate
to High

Aerosol High

High

High

High

High

High

High

Liquid

High

High

High

High

High

High

High

Aerosol High

High

High

High

High

High

High

Aerosol High

High

High

High

High

High

High

High

High

High

High

High

High

High

Aerosol High

High

Moderate Moderate

High

High

High

Consumer
Condition of User

Category Subcategory
Solvents for
Cleaning
and
Dee:reasing
Solvents for
Cleaning
and
De2reaSing
Solvents for
Cleaning
and

Dem-easing
Solvents for Spray
Degreaser/
Cleaning
and
Cleaner
Del!reasine:
Solvents for Liquid
Degreaser/
Cleaning
Cleaner
and
Degreasing
Solvents for Gun
Cleaning
Scrubber
and
Dell.reasin2:
Solvents for Gun
Scrubber
Cleaning
and
Dee:reasine.
Solvents for Mold
Cleaning
Release
and
De2reasin2
Solvents for Tire Cleaner
Cleaning

Form

Liquid

Liquid

Confidence
in Model
Used1

Confidence lo User-Selected Varied Inputs 3

Overall

Mass
Used4
High

Use
Duration5
High

Weight
Fraction6
High

Room of

Confidence

Use7
High

High

and

Degreasing
Solvents for
Cleaning
and
Degreasing
Lubricants
and Greases
Lubricants
and Greases
Adhesives
and Sealants

Tire Cleaner

Tap&Die
Fluid
Penetrating
Lubricant
Solventbased
Adhesive&
Sealant
Adhesives Mirror-edge
and Sealants Sealant

Liquid

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Consumer
Condition of User
Category

Subcategory

Adhesives Tire Repair
and Sealants Cement/
Sealer
Cleaning
Carpet
and
Cleaner
Furniture
Care
Products
Cleaning
Spot
and
Remover
Furniture
Care
Products
Spot
Cleaning
and
Remover
Furniture
Care
Products
Arts, Crafts, Fixatives&
and Hobby Finishing
Materials
Spray

Form

Liquid

Confidence
in Model
Used1

High

Confidence
Confidencein User-Selected Varied lnputs3
in Model
Default
Mass
Use
Weight
Room of
Valuer
Duration5 Fraction6
Used4
·Use7
High
High
High
High
High

Overall
Confidence

High

Liquid High

High

Moderate Moderate

High

Moderate Moderate
to High

Aerosol High

High

High

High

High

High

High

Liquid

High

High

High

High

High

High

High

Aerosol High

High

Moderate Moderate

High

Moderate Moderate
to High

Coatin 2s

High
High
High
Apparel and Shoe Polish Aerosol High
High
High
High
Footwear
Care
Products
High
High
High
High
High
High
Other
Fabric Spray Aerosol High
Consumer
Uses
High
Moderate Moderate High
Moderate Moderate
Other
Film Cleaner Aerosol High
Consumer
to High
Uses
Moderate Moderate High
High
Moderate
Other
Hoof Polish Aerosol High
NA
to High
Consumer
Uses
High
High
High
Moderate Moderate
Pepper
NA
Other
Aerosol High
to High
Conswner Spray
Uses
Aerosol High
High
Moderate Moderate High
Moderate Moderate
Other
Toner Aid
to High
Consumer
Uses
1Confidence in Model Used considers whether model has been peer reviewed,as well as whether it is being applied in a manner
appropriateto its design and objective.The model used (CEM 2.1) has been peerreviewed, is publicly available, and has been
applied in a manner intended- to exposuresassociatedwith uses of householdproducts and/or articles.
2Confidence in Model Default Values considersdefault value data source(s)such as building and room volumes, interzonal
ventilationrates, and air exchangerates. The.9edefault values are all central tendencyvalues (i.e., mean or median values) sourced
from EPA's Exposure Factors Handbook(U.S. EPA, 201 lc). The one default value with a high-end input is the ovcrsprayfraction,
which is used in the aerosol or spray scenarios.It assumesa certain percentageis immediatelyavailable for inhalation.However,
due to TCE's physical chemical properties,this is a not a sensitiveparameter. In the 2014 TSCA Work Plan ChemicalRisk
Assessment for TCE (U.S. EPA, 2014b ). this parameterwas varied from I% to 25% and resulted in almost no difference in the
modeled peak air concentration.
3Confidence in User-SelectedVaried Inputs considersthe quality of their data sources,as well as relevance of the inputs for the
selected consumer conditionof use.

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Consumer

Confidence
in Model

Conditionof Us.er

Confidence
in Model

Confidence in User-Selected Varied lnputs3

Overall
Confidence
Default
Mass
Use
I
Weight
Room
of
Used
Category Subcategory j Form
Values2
Used4
Duration5 Fractioo6
Use7
4
Mass Used is primarily sourcedfrom the Westat( 1987) survey,which receiveda high-quality rating during data evaluationand has
been applied in previous agencyassessments.Two conditionsof use had productinfonnationthat was used insteadofWestat (gun
scrubber and pepper spray).
'Use Duration is primarily sourced from the Westat( 1987) survey,which receiveda high-qualityrating during data evaluation and
has been applied in previousagency assessments.One conditionof use had product informationthat was used instead of Westat
(pepper spray). Relevanceof these inputs from the Westatsurveyto the specificconsumerconditionof use they were appliedto is
considered in the reported confidenceratings.
6
Weight fraction ofTCE in productsis sourcedfrom productSafetyData Sheets(SDSs), which were not reviewedas part of
systematicreview but were taken as authoritativesourceson a product's ingredients.
7Room of use (zone 1 in modeling) is informedby responses in the Westat (1987) survey, which received a high-qualityrating
during data evaluation,althoughprofessionaljudgment is also applied for some scenarios.The reasonablenessof these judgements
is consideredin the rePOrted confidenceratinsrs.

I

080
081
082

1

I

I

.

T able 2-73 C onfidence a ml!s tior A cuet Derma IC onsumer E;m osure Md0 em2
r Scenanos
Consumer
C<inditionof User
Category

Subcategory

Confidence in
Confideace Assumption of
Confidence
in Model
Dermal
In Model
Default
Contact with
1
Used
Form
Values2
Im~ded
Evaporation'

Confidence in User-Selected
Varied Inputs4

K,5

Overall
Use
Weight Confidence
Duration6 Fraction'

Solventsfor
Cleaning and
Degreasing

Brake& Parts Aerosol Low to
Cleaner
Moderate

High

Low

Moderate Low

High

Low to
Moderate

Solventsfor
Cleaning and
Degreasing

Electronic
Degreaser/
Cleaner

Liquid

Low to
Moderate

High

Low

Moderate Low

High

Low to
Moderate

Solvents for
Cleaning and
Degreasing

Spray
Degreaser/
Cleaner

Aerosol Low to
Moderate

High

Low

ModerateLow

High

Lowto
Moderate

Solventsfor
Cleaning and
Degreasing

Liquid
Degreaser/
Cleaner

Liquid

Low to
Moderate

High

Moderate

ModerateLow

High

Low to
Moderate

Solvents for
Cleaningand
Degreasing

Gun Scrubber Aerosol Low to
Moderate

High

Moderate

ModerateLow

High

Lowto
Moderate

Solventsfor
Cleaning and
Degre-c1Sing

GIU\Scrubber Liquid Low to
Moderate

High

Moderate

Moderate Low

High

Low to
Moderate

Solvents for
Cleaning and
Degreasing

Tire Cleaner

Aerosol Low to
Moderate

High

Low

Moderate Low

High

Low to
Moderate

Solventsfor
Cleaning and
Degreasing

Tire Cleaner

Liquid Low to
Moderate

High

Moderate

Moderate Low

High

Lowto
Moderate

Cleaningand
Furniture Care
Products

Carpet
Cleaner

Liquid

Low to
Moderate

High

Moderate

Moderate Low

High

Low to
Moderate

Cleaningand
Furniture Care
Products

Spot Remover Aerosol Low to
Moderate

High

Low

Moderate Low

High

Low to
Moderate

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Consumer
Condition of User

Category

Subcategory

Confidence in
Confidence Assumption of
Confidence
in Model
Dermal
in Model
Default
Coatact with
1
Used
Form
Impeded
Values2
Evaporatfon3

Cleaning and
Furniture Care
Products

Spot Remover Liquid

Apparel and

Shoe Polish

Footwear Care
Products

Confidence In User~eleded
Varied Inputs'

Kp5

Weight
Use
Duration' Fraction7

Overall
Confidence

Lowto
Moderate

High

Moderate

Moderate Low

High

Low to
Moderate

Aerosol Lowto
Moderat.e

High

Low

Moderate Low

High

Lowto
Moderate

'Confidence in Model Used considers whether model has been peer reviewed, as well as whether it is being applied in a
manner appropriate to its design and objective. The model used {CEM2.1) has been peer reviewed, is publicly available, and
has been applied in a manner intended- to estimate exposures associated with uses of household products and/or articles. For
the purposes of dermal exposure, this confidence rating also considers the appropriatenessof the dermal permeability model
within CEM 2.1 for estimating dermal exposures with impeded evaporation and known sources of uncertainty.
2Confidencein Model De&ult Values considers default value data source{s)such as surface area to body weight ratios for the
dermal contact area. These defauh values are all central tendency values (i.e., mean or median values) sourced from EPA' s
Exposure Factors Handbook QJ.S. EPA. 201 lc).
3
Confidence in Assumption of Dermal Contact with Impeded Evaporation characterizesthe uncertainty surrounding whether
or not occluded contact is even possible or likely. Certain conditions of use have greater uncertainty over whether or not any
occluded contact is expected, i.e., the spray scenarios. The liquid formulationsare likely to result in some dermal contact with
a rag; however, there remains uncertainty related to the degree to which such contact would be occluded.
4
Confidence in User-Selected Varied Inputs considers the quality of their data sources, as well as relevance of the inputs for
the selected consumer condition of use.
5The dermal permeability coefficient (Kp)used (0.019 cilllhr) from Poet (2000) cmne from a water patch test on human skin
using TCE in an aqueous solution. While it is within range of other, predicted Kpvalues(CEM 2.1 predicts 0.028 cm/hr and
NIOSH calculates 0.01197 cm/hr), it is a key parameter and there is uncertainty surroundingthe impact of applying an
aqueous Kp for prediction of dermal flux for formulations of TCE-containingconsumer products with nearly I 00% TCE.
6Use Duration is primarily sourced from the Westat (l 987) survey, which received a high-quality rating during data evaluation
and has been applied in previous agency assessments. The dermal modeling receives a "low'' confidence for this criterion due
to the uncertainty associated with the period of time during which a dermal exposure duration is likely to be occluded, not due
to relevance or datasource.
7Weight fraction ofTCE in products is sourced from product Safety Data Sheets (SDSs), which were not reviewed as part of
systematic review but were taken as authoritative sources on a product's ingredients.

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2.3.3 Potentially Exposed or Susceptible Subpopulations
TSCA requires that a risk evaluation "determine whether at chemical substance presents an
unreasonable risk of injury to health or the environment, without consideration of cost or other non-risk
factors, including an unreasonable risk to a potentially exposed or susceptible subpopulation identified
as relevant to the risk evaluation by the Administrator, under the conditions of use." TSCA § 3(12)
states that "the term 'potentially exposedor susceptiblesubpopulation'meansa group of individuals
within the general population identified by the Administrator who, due to either greater susceptibility or
greater exposure, may be at greater risk than the general population of adverse health effects from
exposure to a chemical substance or mixture, such as infants, children, pregnant women, workers, or the
elderly."
During problem formulation (U.S. EPA. 2018d ), EPA identified potentially exposed or susceptible
subpopulations for further analysis during the development and refinement of the life cycle, conceptual
models, exposure scenarios, and analysis plan . In this section, EPA addresses the potentially exposed or
susceptible subpopulations identified as relevant based on greater exposure.EPA addresses the
subpopulations identified as relevant based on greater susceptibilityin Section 3.2.5.2.
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In developing the draft risk evaluation, the EPA analyzedthe reasonably available information to
ascertain whether some human receptor groups may have greater exposure than the general population
to the hazard posed by TCE. Exposures of TCE would be expected to be higher amongst groups living
near industrial facilities, groups with TCE containing products in their homes, workers who use TCE as
part of typical processes, and groups who have higher age and route specific intake rates compared to
the general population.
Of the human receptors identified in the previous sections, EPA identifies the following as potentially
exposed or susceptible subpopulations due to their greater exposure to TCE and considered them in the
risk evaluation :

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Workers and occupational non-users (ONUs). EPA reviewed monitoring data found in published
literature including both personal exposure monitoring data (direct exposure) and area monitoring data
(indirect exposures) and identified data sources that contain measured monitoring data and or/estimated
data for the various conditions of use (including import and processing ofTCE). Exposure estimates
were developed for users (males and female workers of reproductive age) exposed to TCE as well as
non -users or workers exposed to TCE indirectly by being in the same work area of the building. Also,
adolescents and female workers of reproductive age (>16 to less than 50 years old) were also considered
as a potentially exposed or susceptible subpopulations
Consumers/product users and bystanders associated with consumer use. TCE has been identified as
being used in products available to consumers. Sections 2.3 .2.1 and 2.3 .2.2 provide an overview of
exposure pathways considered for the consumer assessment. Furthermore, EPA identified consumers
and bystanders associated with use of TCE-containing consumer products as a potentially exposed and
susceptible subpopulation due to greater exposure as described in Section 2.3 .2.3. For example, higherintensity users (i.e., those using consumer products for longer durations and in greater amounts) were
considered and evaluated. In addition, consumers are considered to include youth and adults over age
11, but bystanders in the home exposed via inhalation are considered to include any age group, from
infant to adult, including pregnant women and/or women ofreproductive age. However, only some
individuals within the general population may use these products. Therefore, those who do use these
products are a potentially exposed or susceptible subpopulation due to greater exposure. Exposures for
these subpopulations are considered and/or evaluated in Section 2.3 .2.6 (Table 2-32 through Table
2-69).

In developing dermal exposure scenarios, EPA quantified age and gender-specific differences. For TCE,
exposure scenarios that involve potentially exposed or susceptible subpopulations considered agespecific behaviors, activity patterns , and exposure factors unique to those subpopulations. EPA used the
Exposure Factors Handbook (U.S. EPA. 201 lc ) to inform body weights , intake rates, and body surface
areas for children and adults. Distinct dermal exposure estimates are provided for female workers of
reproductive age (Section 2.3.2 .6.1), while dermal consumer exposure estimates presented for adult and
youth age groups are expected to be protective of pregnant women based on discussion in Section
2.4.2.5.1.
Other groups of individuals may experience greater exposures due to their proximity to conditions of use
that result in releases to the environment and subsequent exposures (e.g., individuals who live or work
near manufacturing, processing, use or disposal sites), however this Risk Evaluation did not assess
exposure pathways covered by programs under other environmental statutes (!l .S. EPA , 2018d ). EPA

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also considered availabledata to ascertainwhether some human receptor groups may be exposed via
exposurepathways that may be distinct to a particularsubpopulationor whether some human receptor
groups may have higher exposurevia identifiedpathways of exposuredue to unique characteristics
(e.g.,, activities, duration or locationof exposure) when comparedwith the general population.Other
152 groups of individualsmay experiencegreaterexposuresdue to their proximity to conditions of use that
153 result in releases to the environmentand subsequentexposures(e.g.• individuals who live or work near
154 manufacturing,processing, use or disposal sites), howeverthis Risk Evaluation did not assess exposure
155 pathways covered by programs under other environmentalstatutes(U.S. EPA. 2018d). EPA also
156 considered available data to ascertainwhethersome human receptorgroups·may be exposed via
157 exposurepathways that may be distinct to a particularsubpopulationor whether some human receptor
158 groups may have higher exposurevia identifiedpathways of exposuredue to unique characteristics(e.g.,
159 activities, duration or location of exposure)when comparedwith the generalpopulation.Othergroups of
individualsmay experience greater exposuresdue to their proximity to conditionsof use that result in
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161 releases to the environmentand subsequentexposures(e.g. individualswho live or work near
162 manufacturing,processing,use or disposalsites), howeverthis Risk Evaluation did not assess exposure
163 pathways covered by programs under other environmentalstatutes (U.S. EPA . 2018d ). EPA also
164 considered available data to ascertainwhether some human receptor groups may be exposed via
165 exposurepathways that may be distinct to a particularsubpopulationor whether some human receptor
groups may have higher exposurevia identifiedpathwaysof exposuredue to unique characteristics(e.g.,
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167 activities, duration or location of exposure)when comparedwith the general population.

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use. Table 2-74 presents the percentageof employedworkers and ONUs whom may experience either
greater exposure or biologicalsusceptibilitywithin select industry sectors relevant to TCE conditions of
use. The percentages were calculatedusing CurrentPopulationSurvey (CPS) data for 2017 (U.S. BLS .
2017). CPS is a monthly survey of householdsconductedby the Bureau of Census for the Bureau of
Labor Statistics and provides a comprehensivebody of data on the labor force characteristics. Statistics
for the following subpopulationsof workers and ONUs are pro_vided: adolescents,men and women of
reproductiveage, and the elderly. For the purpose of this assessment,EPA considers"reproductive age"
as age > 16 to less than 50 years old.
As shown in Table 2-74, men make up the majority of the workforcein manufacturingsectors. In other
sectors, women (includingthose of reproductiveage and elderly women)make up nearly half of the
workforce.Adolescents are generally a small part of the total workforce.Table 2-75 presents further
breakdown on the percentageof employedadolescentsby industry subsectors.As shown in the tables,
they comprise only 1.2% percent of the manufacturingworkforce,and only as high as 3.7% for other
services such as dry cleaning that fall under a COU for TCE.

.

t Sector
I edP ersons b~y A12e, Sex,an didn ustrv
ofEm 1p1oy
Table 2-74 Percem.tae:e
Age group

Sex

Adolescent

Male

(16-19 years)

Female
Male
Female
Male
Female

Reproductiveage
(16-54 years)
Elderly (55+)

Manufacturing

Wholesaleand
Retail Trade

Professionaland
Business Services

Other Services

0.8%

3.0%

0.7%

1.4%

0.4%
52.9%
22.2%
17.5%
7.3%

3.2%
42 .8%
35.4%
12.3%

0.5%
44.4%
32.8%
13.4%
9.4%

1.7%
35.2%
38.4%
13.1%
13.3%

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Source: {U.S. BLS. 2017). While statistics on pregnant women are not available, CPS provides data on the number of
employed female workers by age group, which allows for detennination of the number of employed women of reproductive
age. Percentage calculated using CPS Table 14, "Employed persons in nonagriculturalindustries by age, sex, race, and
Hispanic or Latino ethnicity."

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iy ea e
Table 2-7S. JPercentage ofE mp nove d AdolescentbDtildld

Sector

Subsector

n us:ry
t Sector
Adolescent
(16-19 years)

Manufacturing

All

1.2%

Wholesale and retail trade

Wholesaletrade

1.4%

Professionaland business
services ·

Waste managementand
remediation services

0.9%,

Repair and maintenance

3.1%

Dry cleaning and laundry services

3.7%

Other services

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Source: {U.S. BLS, 2017). Percentage ofadolescent calculated using CPS table 18b, "Employed personsby detailed industry

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and age."

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The CPS uses 2012 Census industry classification,which was derived from the 2012 NAICS. The
Census classificationuses the same basic structure as NAICS but is generallyless detailed. TCE
conditionsof use fall under the following Census industry sectors:

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Manufacturing- The Manufacturingsector comprises establishmentsengaged in the mechanical,
physical, or chemical transformationof materials, substances,or componentsinto new products.
Establishmentsin the sector are often describedas plants, factories, or mills. For TCE, this sector covers
most conditions of use that occur in an industrial setting, including: Manufacturing,Processing as a
Reactant,Formulation of Aerosol and Non-AerosolProducts,the vast majority of facilities likely
engaged in Vapor Degreasing (all degreasertypes), Cold Cleaning,MetalworkingFluids, Adhesives,
Sealants,Paints and Coatings, Other IndustrialUses, IndustrialProcessing Aids and Printing and
Copying. This sector also covers cement manufacturingfacilitiesthat may bum waste containing TCE
for energy recovery. Also - Printing and Copyingworker informationmay also be captured under the
Informationsector (see below).
•
Wholesaleand retail trade - The wholesaletrade sector comprises establishmentsengaged in
wholesalingmerchandise, generallywithout transformation,and rendering services incidental to the sale
of merchandise.Wholesalersnormally operate from a warehouse or office. This sector likely covers
facilitiesthat are engaged in the repackagingTCE or products and formulationscontainingTCE. The
retail trade sector comprises establishmentsengaged in retailing merchandiseand rendering services
incidentalto the sale of merchandise.
•
Professionaland business services- This sector comprises establishmentsthat specialize in a
wide range of services. This sector covers waste managementand remediationservices, which includes
establishmentsthat may handle, dispose, treat, and recycle wastes containingTCE.
•
Other services - This sector comprisesestablishmentsengaged in providing services not
specificallyprovided for elsewherein the classificationsystem. For TCE, this sector covers the vast
majority of commercialrepair and maintenancefacilities that are likely to use TCE for Aerosol
Applications(spray degreasing).The sector also covers the use ofTCE in spot cleaning.

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3 HAZARDS
3.1 Environmental Hazards

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3.1.1 Approach and Methodology
During scoping and problem formulation QJ.S. EPA. 2018d), EPA reviewed potential environmental
health hazards associated with TCE. EPA identified the following sources of environmental hazard data:
European Chemicals Agency (ECHA) Database (ECHA, 2017), European Union (EU) environmental
risk assessment on TCE (ECHA. 2004) EPA Chemical Test Rule Data OJ.S. EPA, 2017a) Environment
and Climate Change Canada (ECCC) Risk Assessment for Trichloroethylene (Environment Canada and
Health Canada, 1993) and Ecological Hazard Literature Search Results in Trichloroethylene (CASRN
79-01-6) Bibliography: Supplemental File for the TSCA Scope Document (U.S. EPA, 2017i).

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EPA completed the review of environmental hazard data/information sources during risk evaluation
using the data quality review evaluation metrics and the rating criteria described in the Application of
Systematic Review in TSCA Risk Evaluations (U.S. EPA, 2018b). Studies were rated high, medium, or
low for quality. The data quality evaluation results are outlined in the [Data Quality Evaluation of
Environmental Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500]and indicate that most of the
acceptable studies for TCE were rated high and moderate for quality. With the data available, EPA only
used studies with an overall quality level of high or medium for quantitative analysis during data
integration. Studies assigned an overall quality level of low were used qualitatively to characterize the
environmental hazards oftrichloroethylene. Any study assigned an overall quality level of unacceptable
was not used for data integration.

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3.1.2 Hazard Identification
Toxicity to Aquatic Organis,m
EPA identified 25 acceptable studies that contained aquatic toxicity data, including data for fish,
amphibians, aquatic invertebrates, and algae. Aquatic toxicity studies considered in this assessment are
summarized in Table 3-1 below. As stated in Section 2.1, TCE is not expected to accumulate in aquatic
organisms due to low measured BCFs and an estimated BAF.

Fish Toxicity
Acute fish data for TCE were identified in six acceptable studies representing four different species,
including fresh and saltwater species (fathead minnows [Pimephalespromelas], American flagfish
[Jordanellafloridae], bluegill [Lepomis macrochirus], and sheepshead minnow [Cyprinodon
variegatusJ).In these studies, the lethal concentrations at which 50% oftest organisms die (LCsos)
ranged from 28.28 mg/L to 66.8 mg/L (Geiger et al., 1985); (Broderius et al.. 2005; Smith et al., 1991;
Ward et al.• 1986; Buccafusco et al., 1981; Alexander et al., 1978). Ward et al. (1986) tested a saltwater
species, sheepshead minnow, and derived an LCsoof 52 mg/L. Because this value is within the of the
range of values for freshwater species, and because baseline narcosis is the expected mode of action for
TCE in both freshwater and saltwater fish (Alexander et al.. 1978): (Ward et al .• 1986); (Broderius et al.,
2005), freshwater and saltwater LCsovalues were assessed together during data integration. EPA
calculated a geometric mean of 42 mg/L using LCsosfrom high and medium quality studies. Acute fish
data for TCE also included a 96-hour ECso(the concentration at which 50% of test organisms exhibit an
effect) of21.9 mg/L for loss of equilibrium in a freshwater species, fathead minnows (Alexander et al.•
1978). This study was rated high for quality.

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Subchronic fish data were also identified in two acceptable studies representing two species. Smith et al.
(199 1) established a 10-day NOEC of 5.758 mg/Land a LOEC of2l.233 mg/L resulting in a chronic
value (ChV) of 11 mg/L for fry survival in American flagfish (Jordanel/ajloridae). Schell ( 1987)
established a 10-day LCso of82 mg/Lin Japanese medaka (Oryzias latipes) embryos. The author found
that lethality occurred at every stage of development for embryos. Schell also observed lesion
development in the embryos after exposure in a dose-dependent pattern, with higher test concentrations
resulting in earlier formation of lesions. Both abovementioned sub-chronic studies received a high rating
for quality during data evaluation.
Chronic fish data for TCE were identified in two acceptable studies representing two freshwater species,
American flagfish (Jordanel/ajloridae) and fathead minnows (Pimephalespromelas). In addition to the
subchronic value mentioned above, Smith et al. ( 1991) established a 28-day NOEC of 10.568 mg/Land
a LOEC of20.91S mg/L for fry survival in American flag.fish. This allowed the authors to establish a
28-day ChV of 14.85 for fry survival. Broderius et al. (2005 ) established an ECso for growth of 11.8
mg/Land an EC20 for growth of7.88 mg/Lin a 32-day fathead minnow study. Both studies were rated
high for quality during data evaluation.
Broderius et al. (2005 ) reported baseline narcosis as TCE's expected mode of action in fish. This is
corroborated by other studies, including W~ et al. (1986 ), which observed signs of narcosis in
sheepshead minnows, a saltwater species, with observations offish spinning at 357 mg/L. Alexander et
al. (1978) reported signs of narcosis in fathead minnows, a freshwater species, with a 96-hour EC10 of
13.7 mg/L, ECso of21.9 mg/L, and EC90 of34.9 mg/L. The effect reported was loss of equilibrium.
Two mechanistic studies were also available for fish. Hayashi et al. (1998) examined genotoxicity in
rose bitterling (Rhodeus ocellatus) embryos using a new assay developed by the authors. The authors
found an increase in structural chromosomal aberrations and micronuclei in cells from embryos,
establishing a NOEC of 300 mg/L and a LOEC of 3,000 mg/L. The authors noted the low sensitivity of
the assay and suggested using more embryos in the future. This study was rated medium for quality.
Another in vitro study, rated low for quality, derived an ECso of 11.6 mg/L for the inhibition of total
protein content in a fathead minnow cell line (Dierickx . 1993 ). Because this cellular effect is not directly
tied to a population effect, and because of the low-quality rating, this study was not used with the other
acute data to calculate a geometric mean of ECsos during data integration; however, the results
contribute to the qualitative description of mechanistic effects of TCE exposure in fish.

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Amphibian Toxicity

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For amphibians, acute data were available from four acceptable srudies, representing five different
species (green frog [Rana clamitans],wood frog [Ranasylvatica], African clawed frogs [Xenopus
laevis], American toad [Bufo americanus],and spotted salamander [Ambystomamaculatum]).All four
studies were rated either high or medium for quality during data evaluation. The studies included 96hour LCso values ranging from 412.0 mg/L to 490.0 mg/L (McDaniel et al.. 2004 ; Fort et al.. 2001 ; Fort
et al .. 1993; Fort et al .. 1991 ). During data integration, a geometric mean of all LCsos was calculated at
438mg/L.
Sub-chronic data were also available for amphibians, inc1uding 96-hr ECso values for developmental
effects ranging from 22 mg/L to> 85 mg/L (McDaniel et al.. 2004 ; Fort et al.. 2001 ; Fort et al .. 1993;
Fort et al .. 1991 ). During data integration, a geometric mean of all definitive ECs~ for developmental
effects was calculated at 34 mg/L. These developmental effects are irreversible and would result in
effects that last throughout the animals' lifetime. Developmental effects described included gut
miscoiling and microphthalmia, muscular kinking, incomplete development of the mouth, and severe
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hypognathia in African clawed frogs, and edema and dorsal flexure oftbe taiJ and notochord in tadpoles
of green frogs, wood frogs, American toads, and spotted salamanders (McDaniel et al.. 2004 ; Fort et al.,
1993; Fort et al .. 1991). As stated previously, McDaniel et al. (2004 ) reported signs of narcosis in green
and wood frog tadpoles.
Limited chronic data were also available for amphibians. McDaniel et al., (2004 ) included a chronic
toxicity test for amphibians on Americantoad tadpoles. However, chronic toxicity values for deformities
were not established, because more than 25% of control animals exhibited deformities. Mortality,
however, was below 25% in controls, and authors saw no significant difference in mortality between test
concentrations (4 mg/L and 1 mg/L) and controls. This suggests that survival rates for American toad
tadpoles would not be affected by 4 mg/L ofTCE. It should be noted that acute exposure data show
American toads are less sensitive to TCE than other amphibian species, so they may also be less
sensitive to chronic exposures.
McDaniel et al. (2004 ) reported signs of narcosis in green and wood frog tadpoles.

Aquatic InvertebrateToxicity
For aquatic invertebrates, acute data were found in seven acceptable studies representing five different
species, including fresh and saltwater species. Five of these studies included LCso or ECso values rated
high or medium for quality; these values ranged from 7.75 mg/L to 43.14 mg/L for Daphnia magna,
Ceriodaphniadubia, and Mysidopsisbahia (Dobaradaran et al.. 2012 ; Niederlehner et al .. 1998;
Abemeth v et al.. 1986; Ward et al .. 1986; LeB1anc. 1980). The only saltwater species tested. Mysidopsis
bahia, had an LC so of 14 mg/L, which is within the of the range of values for freshwater species.
Additionally, Ward et al. (1 986) and Niederlehner et al. ( 1998) reported baseline narcosis as the mode of
action for TCE in freshwater and saltwater invertebrates. Therefore, freshwater and saltwater values
were integrated together. The geometric mean of the BCsoand LCsos from high and medium quality
studies is 16 mg/L. Another study, Sanchez-Fortun et al. ( 1997), rated low for quality, established LCsos
in Artemia sa/ina larvae at three different ages; however, this study was not used during data integration,
given that medium and high-quality studies were available for invertebrates.
One subchronic study found an LCso of 1.7 mg/Lin planarian (Dugesiajaponica) over 7 days (Yoshioka
et al. , 1986). This study was rated low for quality.
Chronic data for aquatic invertebrates were identified in two acceptable studies, both rated high for
quality. One study established toxicity values for reproduction, an effect that is relevant at the
population level. Niederlehner et al. ( 1998) established aNOEC of7.1 mg/Land aLOEC of 12mg/L
for reproduction in Ceriodaphniadubia, resulting in a Ch V of 9 .2 mg/L. Niederlehner et al. ( 1998)
established a 7-day reproductive inhibitory concentration (ICso) of 11 mg/L, the concentration at which
the mean number of young decreased by 50%.
Two studies reported baseline narcosis as the mode of action for TCB in invertebrates. Ward et al.
(1986 ) observed mild intoxication in Mysidopsisbahia, a saltwater species, and Niederlehner et al.
(1998) observed behavioral changes, including narcosis and abnormal movement in Ceriodaphnia
dubia, a freshwater species.
Two studies provided mechanistic data for invertebrates. Vidal et al. (2001 ), rated high for quality,
examined mechanistic effects of an acute exposure to a freshwater clam species, Corbiculafluminea. A
one-time exposure over five days resulted a significant change in protein activity related to phase I
metabolism. Results indicated a NOEC of 1.2 mg/Land a LOEC of 3.6 mg/L for significantly increasing
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cytochrome P-450 levels, and a NOEC of3.6 mg/Land LOEC of 14mg/L for significantly decreasing
NADPH cytochrome C reductase activity (Vidal et al.. 2001). Houde et al. (2015), also rated high for
quality, examined the effects ofTCE on Daphnta magna at the cellular and life-stage levels. The authors
found a significant increase in chitinase production over 10 days, with a NOEC of0.001 mg/Land a
LOEC of 0.0 I mg/L. Chitinase is an enzyme involved in molting and therefore development in Daphnia
magna. While the study did not find a significant change in the total number of molts for the
concentrations tested, the results were very close to significant with a p = 0.051 (assuming significance
at p ~ 0.05), suggesting more tests are necessary to determine the impact of increased chitinase at the
life-stage level.

Aquatic Plant Toxicity
For aquatic plants hazard studies, algae are the common test species. Algae are cellular organisms which
will cycle through several generations in hours to days, therefore the data for algae was assessed
together regardless of duration (i.e. 48-hours to 96-hours).
There were six acceptable studies reported data on 11 species of algae, including fresh and saltwater
species, and cyanobacteria and eukaryotes. There was a wide range of toxicity values reported in the

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saltwater species found in the acceptable studies, Skeletonemacostatum, with an ECsoof 95 mg/L. This
value is within the of the range of values for freshwater species, so saltwater and freshwater species
were integrated together. EPA derived a geometric mean of 242 mg/L from the hlgh and medium quality
ECsos.A 72-hour EC10of 12.3 mg/L was also established by Brack and Rottler (l 994) measuring
biomass (a measure of growth) in Chlamydomonasreinbardtii, a freshwater eukaryotic green algae.
Additionally, several NOECs and LOECs were established. Labra et al. (2010) found a 72-hour NOEC
of0.02 mg/Land a LOEC of 0.05 mg/L for cell count (a measure of growth) in Raphidocelis
subcapitata. This study also assessed the integrity of algal cell membranes and found a dose-dependent
increase in membrane damage starting at 0.05 mg/L. Ando et al. (Ando et al.. 2003) measured relative
absorbance of chlorophyll a (an indirect measure of algal growth) in three species of algae, Selenastrum
capricornutum, Chlorella vulgaris, and Volvulinasteinii. They found no significant change in the
relative absorbance of chlorophyll a for S. capricornutumor C vulgaris during the 10-day test;
however, they established a 10-day LOEC of 0.003 mg/L for V. steinii. The authors did not report results
for biomass, although they mention the solvents were eventually fatal to V. steinii.

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26.24mg/L to 820 mg/L (Lukavsh et al.. 2011; Labra et aL 2010; Tsai and Chen. 2007; Ando et al..
2003; Brack and Rottler. 1994; Wardet al .. 1986). Wardet al. (1986) reported results on the only

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-

. tion ofTCE tior A,quati c 0 IJ:'28DISIDS
Ta bl e 3 1 E coI021.ca
. IH azardCh aract enza
Duration

Test
organism

Endpoint

Hazard value
(mg/L)I

Geometric

Effect Endpoint

Citation
(Study Quality)

Mean2
(mg/L)

Acute3
LC,o
(freshwater)

28.28-66 .8
42

Mortality

Fish
LCso
(saltwater)

EC,o
(freshwater)

52
21.9

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(Geiger et al.. 1985)
(high); (Alexander et al. ,
1978) (high); (Smith et
al.. 1991) (high);
(Broderius et al .. 2005 )
(high); (Buccafusco et al ..
1981) (medium)
cWard et al.. 1986)
(medium)

Immobilization

(Alexander et al .. 1978)
fh igh )

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Amphibian

Aquatic
Invertebrates

LC,o

412.0-490 .0

ECso/LCso
(freshwater)

7.8-33.85
16

LCso
(saltwater)
Subchron
ic/Chroni
c3

14

NOEC
LOEC
ChV

7.88
11.8
10.568
20.915
14.87

NOEC

4

ECio
EC,o

Fish

436

(Fort et al.. 2001 )
al..
Mortality
1991) (medium);(Fort ct
al .. 1993 ) (hi!!h)
(LeBlanc . 1980) (high);
(Niederlehner et al..
1998) (high); (Aberneth \
et al.. 1986) (mediwn);
Mortality and
Immobiliz.ation (Dobaradaran et al..
2012 ) (medium)
(Ward et al.. 1986)
(medium); (Fort et

(mediwn)

Growth
Growth
Fry Survival

Tadpole
Survival

(Broderius et al.. 2005 )
(high)

(Smith et al.. l 991) (high)
(McDaniel et al.. 2004 )
(medium )

(Fort et al.. 2001 )
Amphibians
ECso

22->85

34

Defonnities

(medium); (Fort et al ..
1991) (medium); {Fort et

al.. 1993) (high);
(McDaniel et al.. 2004 )
<hi1>hand medium l

Aquatic
invertebrates

NOEC
LOEC
ChV

7.1
12
9.2

ICSO

11

ECso
(freshwater)

Reproduction

2624 - 820
242

Gro\\1h

(Niederlehner et al..
1998) (high)
(Brack and Rottier .
1994) (high); (Tsai and
Chen, 2007 ) (high);
(Labra et al.. 2010 )
(medium); (Ando ct al ..
2003 ) (medium);
(Lukavsk\ et al.. 2011 )
(medium )

AJgae4

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183

(Ward et al.. I 986)

EC50
(saltwater)

95

EC10

12.3

Growth

NOEC
LOEC

0.02
0.05

Growth

CbV

0.03

LOEC

0.003

/medium \

Growth

(Brack and Rottier .
1994) (hi!!h)
(Labra et al., 2010 )
(medium)

(Ando et al.. 2003 )
( Wt:1.liUlll

)

the table are presentedin the number of significantfigures reported by the study authors.
2 Geometric mean of definitive values only (i.e., > 85 mg/L was not used in the calculation).
3 Acute and chronic hazard data include fish.invertebrates,or amphibiandata
• Because algae can cycle throughseveralgenerationsin hours to days, the data foralgaewasassessed together regardlessof duration (i.e.,
48-hrsto 96-hrs).
1Values in

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Note: Values in bold were used to derive Concentrationsof Concern (COC) as described in Section 3.1.5 of this document. All values are
listed individually with study quality in [DataQuality Eva/UOJton
of EnvironmentalHazardStudies and Data Extractionfor Environmental
HazardStudies. Docket: EPA-HQ-OPPT-2019.()500].

3.1.3 Species Seusitivity Distributions (SSDs)
A Species Sensitivity Distribution (SSD) is a type of probability distribution that uses toxicity values
from multiple species. It can be used to visualize which species are most sensitive to a toxic chemical
exposure, and to predict a concentration of a toxic chemical that is hazardous to a percentage of species.
This hazardous concentration is represented as an HCp,where p is the percent of species.

As stated previously, there were a wide range of toxicity values reported in the literature for algae
exposed to TCE. ECsoswere as low as 26.24 mg/L and as high as 820 mg/L, representing nine different
species. With such a wide range of sensitivities, it is helpful to show how TCE could be affecting algae
species as a whole. Therefore, EPA generated an SSD to help interpret the data. Figure 3-1 shows the
SSD for algae created using EPA's SSD Toolbox (Etterson. 2019). The data used in the SSD includes
ECsosmeasutjng growth from :freshwaterspecies, a saltwater species, ·cyanobacteria, eukaryotes, a
diatom, and a colonizing species. As stated in Section 3 .1.2, saltwater and freshwater species were
assessed together, because the only saltwater species, Skeletonema costatum, had an ECsowithin the of
the range of values for freshwater species.
An HCos (Hazardous Concentrationthreshold for 5% of species) for algae of 52 mg/L was derived from

this SSD.
!Figure3-1. Species Sensitivity Distribution SSD for Algae Species Usin ECsos (Etterson, 2019)

1
~AlgaeSSD

0.9

♦

HC05

Synecnococcus e/ongatus

0.8

Desmodesmus subspicatus

•

•

~ 0.7
is
~ 0.6

e

a..
a,

0.5

i:3

0.4

>

E
::,

•

Ch/orella kesslefi

U 0.3

0.2
0.1
0 L....:::;:__

1.5
206

•
___

_

RaphidoceJ/s subcapitste

__._ ____

2

_

__;L...-

_____

2.5

_._

___

3

Toxicity Value (Log 10[EC50]) mg/L

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Note: The data in this figure includesECsosmeasuringgrowth from mediwn-or high-quality studies. A black dot indicates
the toxicity value used for that species. The red diamond indicatesan HCo,.The SSD was created using a triangular
distnoution and a graphical methods fitting method (AppendixE.1}.

211

Given this data, certain algae species may be more sensitivethan others; however, there is not enough
data to make definitive conclusions.The three cyanobacteria,Mycrocystisaeruginosa, Synechococcus
leopoliensis,and Synechococcuselongatus,are distributedthroughoutthe curve and as a group do not
appear to be more or less sensitivethan the eukaryotespecies. The saltwater species, Skeletonema
costatum,also the only diatom, is one of the more sensitive species on the distribution. The species that
organizes into colonies,Mycrocystisaeruginosa,is also one of the more sensitive species represented on
the curve. However, with only one saltwater species, diatom, and colonizing species represented,
generalizationsabout the sensitivity of these taxa of algae could not be made.

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It is important note that, for consistency,this distributiononly includes ECsosto compare between
studies and species. Therefore, it does not capture some of the lowesttoxicity values reported, including
LOECs and NOECs. For example, the ChV of 0.03 mg/L for algae derived from Labra et al. (2010 ) is
not included in the algae SSD.
An SSD was also created using the acute hazarddata, including LCso and ECsodata for fish, amphibians,
and invertebrates(Figure 3-2) (Etterson. 2019 ). The input data for Figure 3-2 included ECsosand LCsos
available in the literature representing four species of fish (LCsos), one species of amphibian (LC sos),
and three species of invertebrates(LCsos/ECsos).
As stated previously,freshwater and saltwater species
were assessed together, because the saltwater values were within the of the range freshwater species in
the same taxonomic group. Additionally,for fish and invertebrates,the mode of action for freshwater
and saltwater species expectedto be the same (Broderiuset al.. 2005; Ward et al .. 1986; Alexander et
al .. 1978).

For the HCosfor acute hazard data, EPA used a model average of the gumbel, triangular,normal, and
logistic distributions (Figure 3-2). The model-averagedHCosfrom all three distributions was 9.9 mg/L,
which estimates a concentrationthat is hazardous for 5% of aquatic species. The SSDs showed aquatic
invertebrates were the most sensitive species.

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Figure 3-2. Species Sensitivity Distributions(SSDs) for Acute Hazard Data Using LCsosor ECsos
(Etterson, 2019)
1

0.9

0.8

-

norma l distribution

-

logisticdistribution
triangulardistribution
gumbeldistribution
HC05
Cyprinodon

♦

variegatus (sheepshead) •

~0.7
Lepom,s macroch,rus (btuegfll/

:c

!

0.6

£ 0.5

Pimephales promelas {fathead minno

a,

>
.=
(0

anella fbridae /Ragfish)

:i 0.4

E
::,

(.) 0.3

0.2
0.1

o L0

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241
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244
245
246
247
248
249
250

-

-~ ~~
0.5

_.!!f.
~ __
1

i..__

__

1.5

....L.._

__

2

--..L..

__

2.5

___,

3

Toxicit Value Lo 10 EC50
Note: The data in this figure includes LCsoSand EC!oSmeasuringmortality and immobilizationfrom medium- or high-quality
studies. A black dot indicatesthe toxicity value used for that species.The red diamonds indicate HC-Oss
for the nonnal,
logistic, triangular, and gumble distributions using the maximumlikelihood fitting method (Appendix E. l ).

This SSD shows that generally, invertebrates are the most sensitive taxonomic group to short-term (4896 hour) exposure to TCE. Amphibians and fish were distributed throughout the center of the
distribution, with the two frog species being the most sensitive amphibians, and American flagfish
(Jordanel/ajloridae) the most sensitive fish.

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A chronic SSD for aquatic species was not created due to insufficient data.

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3.1.4 Weight of Evidence
During the data integration stage of systematic review EPA analyzed, synthesized, and integrated the
data/information. This involved weighing scientific evidence for quality and relevance, using a Weight
of Evidence (WoE) approach (L.S. EPA. 2018b).

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During data evaluation, EPA assigned studies an overall quality level of high, medium, or low for
quality based on the TSCA criteria described in the Application of Systematic Review in TSCA Risk
Evaluations (U.S. EPA. 2018b). While integrating environmentalhazard data for TCE, EPA gave more
weight to relevanl data/information rated high or medium for quality than to data/information rated low.
Only data/infonnation rated as high, medium, or low for quality was considered for the environmental
risk assessment. Any information rated as unacceptable was not considered. EPA also considered
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relevance in selecting data/informationfor this risk evaluation, specifically biological,
physical/chemical, and environmentalrelevance (U.S. EPA. 1998):
- Biological relevance: correspondenceamong the taxa, life stages, and processes measured or
observed and the assessment endpoint.
- Physical/chemicalrelevance: correspondencebetween the chemical or physical agent tested and
the chemical or physical agent constituting the stressor of concern.
- Environmentalrelevance: correspondencebetween test conditions and conditions in the region of
concern. (U.S. EPA. 1998)

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EPA used this weight-of-evidence approach to assess haz.arddata and develop concentrations of concern
(COCs) and HCoss.Given the available data, EPA only used studies assigned an overall quality level of
high or medium to derive COCs or HCossfor each taxonomic group. EPA derived geometric means for
each trophic level that had comparabletoxicity values (e.g.,.multiple ECsosmeasuring the same or
comparable effects from various species within a trophic level). To calculate HCoss,EPA created SSDs
for algae species using comparable data (e.g., ECsosmeasuringgrowth) and for all species (e.g., ECsos
and LCsosmeasuring population effect measures, like growth,mortality, immobilization, and
deformities). Non-definitive toxicity values (e.g., ECso>85 mg/L) were not used to derive geometric
means or HCoss.

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To assess aquatic toxicity from acute exposures, data for three taxonomic groups were available: fish,
amphibians, and aquatic invertebrates.For each taxonomic group, data were available for multiple
species, and geometric means were calculated as shown in Table 3-1. The geometric mean for aquatic
invertebrates, 16 mg/L, represented the lowest toxicity value derived from each of the four taxonomic
groups. The SSD in Figure 3-2 shows that the three most sensitive species in the.distribution are aquatic
invertebrates, further substantiating that this is the most sensitive taxonomic group to acute exposures.
To assess aquatic toxicity from chronic exposures, data for three taxonomic groups were described in the
acceptable literature: fish, amphibians, and aquatic invertebrates.However, for amphibians, only a

NOEC was established. Therefore, the endpoints for fish and aquatic invertebrates (ChVs, an EC20,and
an ECso)were more biologically relevant, because they measured a toxic effect. Of these values, the
most sensitive was the EC20measuring growth in fish at 7.88 mg/L.
To assess the toxicity ofTCE to algae, data for 11 species were available from studies rated high and
medium for quality. The most sensitive endpoint reported for algae was a 10-day LOEC of 0.003 mg/L
.fromAndo et al. (2003 ). The duration of this study (10 days) was unconventional for algae tests and
difficult to compare to the more standard 48- or 96~hourstudies. Additionally,the authors were only
able to establish a LOEC and not a NOEC. Therefore, these data were considered less biologically
relevant than values from other studies. The ChV of 0.03 from Labra et al. (2010 ) was the most sensitive
endpoint from the more relevant studies. Both Ando et al. (20 03) and Labra et al. (20 10) were rated
medium for quality. An EC10of 12.3 mg/L from a high-quality study, Brack et al. ( 1994), was also
available; however, taking biological relevance into considerationas well, EPA used the ChV derived
from Labra et al. (20 l 0), because there was a wide range in toxicity values reported in the literature
between algae species. Therefore, EPA used the value from Raphidocelissubcapitata(formerly kn.own
as Pseudokirchneriellasubcapitata) from Labra et al. (2010) to represent the more sensitive algae
species in the COCs. (According to the algae SSD, Raphidocelissubcapitatais generally more sensitive
to TCE exposme than Chlamydomonasreinhartdtii,the species used in Brack et al. ( 1994).) In addition
to this ChV, EPA considered the results from the SSD for algae in assessing toxicity to algae. The SSD
represented toxicity values for nine species of algae and provided an additional line of evidence for how
TCE exposure could affect this taxonomic group.
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3.1.5 Concentrations of Concern
The concentrations of concern (COCs) for aquatic species were calculated based on the environmental
hazard data for TCE, using the weight of evidence approach described above and EPA methods (U.S.
EPA. 2016i, 2012c). For TCE, EPA derived an acute COC, a chronic COC, and an algal COC. Algae
was assessed separately and not incorporated into acute or chronic COCs, because durations normally
considered acute for other species (e.g., 48, 72 hours) can encompass several generations of algae.
After weighing the evidence and selecting the appropriate toxicity values from the integrated data to
calculate an acute, chronic , and algal COC, an assessment factor (AF) is applied according to EPA
methods (U.S. EPA. 2016i, 2012c). The application of AFs provides a lower bound effect level that
would likely encompass more sensitive speciesnot specifically represented by the available
experimental data. AFs also account for differences in inter- and intra-species variability, as well as
laboratory-to-field variability. These AFs are dependent on the availability of datasets that can be used
to characterize relative sensitivities across multiple species within a given taxa or species group.
However, they are often standardized in risk assessments conducted under TSCA, since the data
available for most industrial chemicals are limited. For fish and aquatic invertebrates (e.g .,, daphnia) the
acute COC values are divided by an AF of 5. For chronic COCs, an AF of 10 is used (U.S. EPA, 2012c).
To derive an acute COC for TCE , EPA used the geometric mean of the ECso and LCsos for aquatic
invertebrates from five different studies, all rated high or medium for quality (Qobaradaran et al.. 2012;
Niederlehner et al.. 1998; Abernethv et al., 1986; Ward et al.. 1986; LeBlanc. 1980). The geometric
mean for aquatic invertebrates represented the lowest acute value from all four taxonomic groups of
aquatic species from the integrated data for TCE. The data used to calculate the geometric mean

represent toxicity data for three species, Daphnia magna, Ceriodaphnia dubia, and Mysidopsis bahia.
To calculate an acute COC, the geometric mean, 16 mg/L, was divided by the AF of 5 for aquatic
invertebrates and multiplied by 1,000 to convert mg/L to µg/L (or ppb).
Therefore, the acute COC = (16 mg/L) / AF of 5 = 3.2 x 1,000 = 3,200 µg/L or ppb.
The acute COC for TCE is 3,200 ppb.

To derive a chronic COC, EPA used the lowest chronic toxicity value from the integrated data, an EC20
for growth in fish (fathead minnows) from a study rated high for quality (Broderius et al.. 2005). This
value, 7.88 mg/L was divided by an assessment factor of 10, and then multiplied by 1,000 to convert
from mg/L to µg/L (or ppb).
Therefore, the chronic COC = (7.88 mg/L) / AF of 10 = 0.788 x 1,000 = 788 µg/L or ppb.

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The chronic COC for TCE is 788 ppb.

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To derive an algal COC, EPA used a geometric mean of a LOEC and a NOEC for growth in
Raphidocelis subcapitata (Labra et al .. 2010). This value, 0.03 mg/L was divided by an assessment
factor of 10, and then multiplied by 1,000 to convert mg/L to µg/L (or ppb).
Therefore, the algal COC = (0.03 mg/L) / AF of 10 = 0.003 x 1,000 = 3 µg/L or ppb.
The algal COC for TCE is 3 ppb.

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Additionally, EPA used algae data representingnine species to produce an SSD, which.was used to
calculate an HCosof 52 mg/L (or 52,000 ppb). As stated previously,this HCosestimates a concentration
that is hazardous for 5% of species. The HCoscan be used in addition to the COC for algae, estimating
the concentration of TCE that is expected to protect 95% of algae species.

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The algal HCosfor TCE is 52,000 ppb.

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365

Summary of Environmental H~rd
The available environmentalhazard data indicate that TCE presents hazard to aquatic organisms. For
acute exposures to invertebrates,toxicity values ranged from 7.8 to 33.85 mg/L (integrated into a

366

geometric mean of 16 mg/L). For chronic exposures, toxicity values for fish and aquatic invertebrates
were as low as 7.88 mg/Land 9.2 mg/L, respectively. The data also indicated that TCE presents hazard
for aquatic plants, with toxicity values in algae as low as 0.03 mg/L (geometric mean between a NOEC
and a LOEC), and a wide range in toxicity between algae species (ECsosranging from 26.24 - 820
mg/L).

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3.1.6

The COCs derived for aquatic organisms are sun:unarizedin Table 3-2. EPA calculated the acute COC
for TCE at 3,200 ppb, based on the geometricmean ofLCsos and ECsosfor aquatic invertebrates, from
five studies rated either high or medium for quality (Dobaradaran et al .. 2012 ; Niederlehner et al .. 1998;
Abcrnethv et al.. 1986; Ward et al .. 1986; LcBlanc. 1980). EPA calculatedthe chronic COC for TCE at
788 ppb, based on an EC20for fathead minnows from Broderius et al. (2005), rated high for quality .
As stated previously, algae were assessed separately from other aquatic organisms, because durations
normally considered acute for other species (e.g., 48, 72 hours) can encompass several generations of
algae. EPA calculated an algal COC for TCE at 3 ppb, based on a geometric mean of a LOEC and
NOEC for growth in Raphidocelissubcapitatafrom Labra et al. (2010), a study rated medium for
quality. EPA also calculated an HCosof 52,000 ppb for algae based on the ECsos for nine species, from
studies rated medium and high for quality.
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity

Environmental Aquatic Toxicity

Concentrationof Concern

Toxicity from Acute Exposure

3,200 ppb

Toxicity from Chronic Exposure

788 ppb

Toxicity for Algae: COC based on the lowest toxicity value

3 ppb

52,000 ppb

HCosbased on ECsos
385

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393

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3.1. 7 Assumptions and Key Uncertainities for Environmental Hazard Data

While EPA determined that there was sufficient environmentalhazard data to characterize
environmental hazards ofTCE, there are uncertainties. First, assessment factors (AFs) were used to
calculate the acute and chronic concentrationsof concern for TCE. As described in Section 3.1.5, AFs
account for differences in inter- and intra-speciesvariability, as well as laboratory-to-fieldvariability
and ,are routinely usedwithin TSCA for assessing the hazard of new industrialchemicals (with very
limited environmentaltest data). Some uncertaintymay be associated with the use of the specific AFs
used in the hazard assessment.
Second, there was more acute duration data available in the literaturethan chronic duration data.
Therefore, .EPAis less certain of chronic hazard values, which was based on one fish species, than the
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acute hazard values, which was based on data from multiple species of aquatic invertebrates. However, a
few lines of evidence mitigate the uncertainty in the chronic data. For example, the fish toxicity value
that the chronic COC was based on, was from a high-quality study, relevant study. Additionally, the
acute duration data show aquatic invertebrates was the most sensitive taxonomic group, and aquatic
invertebrates was represented in chronic duration data. Also, the other chronic fish toxicity values as
well as the chronic aquatic invertebrate values were very close to the fish value used to derive the
chronic COC. Therefore, some of the uncertainties EPA had around the chronic COC were curtailed.
Third, while the toxicity values for fish, amphibians, and invertebrates are relatively consistent, there
was wide variation in the toxicity values for different species of algae. One study, Lukavsky et al. (2011 )
examined several species of algae using standardized methods within the same lab to determine whether
the variation seen in the literature was due to differences in laboratory practices, methodology used, or
species studied. They found that conducting the tests with standard methods in the same lab reduced the
variation seen in toxicity levels between species; however, ECsos were still as low as 130 mg/L and as
high as 820 mg/L for the eight species of algae tested (compared to a range of 26.24 - 820 mg/L from
the entire body of literature), indicating there is in caft a wide range in species sensitivities. Taking this
range of sensitivies into consideration, BPA used two approaches to characteriz.e haz.ard in algae. EPA
developed an algae COC, using a toxicity value of 0.03 mg/L, which represents one species. The data
show that there are other species that are less sensitive to TCE exposure. To provide more context for
this taxonomic group, EPA also used algae data from nine species to create an SSD and derive an HCos.
EPA considered the HCosanalogous to a COC. However there are pros and cons to each approach. For
example, the COC incorporates the most sensitive endpoint in a geometric mean of a NOEC and LOEC
for growth, while the HCos does not consider the most sensitive endpoints reported in the data. However,
the HCos is derived using data from nine species rather than just one, and is therefore representative of a
larger portion species in the environment.

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3.2 Human Health Hazards
3.2.1 Approach and Methodology
-= ---EPA used the approach described in Section 1.5 to evaluate, extract and integrate TCE' s human health
hazard and dose-response information.
---

-

-

-

--

- -

-

Hlllmm Health Haz~ird Assessment
D&eal'.Taludoa

Data

MwAIII--~
IFPI)·~ 411a

~

qualit)·..."a!oaliollcrittm
lbe COllftN11Ce
of

to.....,..

kie)'•~--ideali&d&Mlpmioos
eotsu.-.lu
1m1· .uidieeGd
cousdtttd iD11:11
fC'e\10\J.I
UH

Otttpa«

--

Exln.ctdelalnm
t.ty.~

and-·

Rhlc0.anctnbadoa

Data lalftl'ltdoa
~!Mad~by.-ideria&quality(a.e
~liaulaliOlll).~~.f.ollcnse-

____

.,

b_"'~
- puuoibility
_....;.----

RauNtm

duriQs
scopinf;problem
fonnula:ioll w

idttti!}-aewbuards
m,m IIIOW liuaJuft(,f
ae,,lie&ble)

or

»--Rwpeeu
.ualyiis
BellCbmule-

~=,for

e,:,~wilh
........

A.ulJds

Detamiaellle~t
ad!« quaatiWi,,. bmaa
ll<alUINb - iac:l\)ck,u

tll>ditt
Ccmli.rmpotaltiJ:
baDldJ iiktlti5cd

--

Risk C'lrnract.-nz:il1c)ll

data;

ot:

llppRlfriate.
& --• U.-Uialy

..

•'8rilWlitr

·l>alaqllllil)-

. ms

• Altfflllti,,.

~

SelectioaQf PODs

Rhll:E,ric111e•
ud

S)-.ttmatk
tt..riffl'

s,..-

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)

Figure 3-3. EPA Approach to Hazard Identification, Data Integration, and Dose-Response
Analysis for TCE

Specifically, EPA reviewed key and supporting information from previous ha7.ardassessments as well as
the existing body of knowledge on TCE' s human health hazards. These data sources included an EPA
IRIS Assessment (U.S. EPA 201 le ) and an ATSDR Toxicological Profile (ATSDR. 2014a); hence,
many of the hazards of TCE have been previously compiled and systematically reviewed. Furthermore,
EPA previously reviewed data/informationon health effects endpoints, identified hazards and conducted
dose-response analysis in the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (U.S. EPA.
2014b).
All health hazardsofTCE previously identified in these reviews were described and reviewed in this
risk evaluation, including: acute overt toxicity, liver toxicity, kidney toxicity, neurotoxicity,
immunotoxicity (including sensitization),reproductive toxicity, developmental toxicity, and cancer.
EPA relied heavily on the aforementioned existing reviews along with scientific support from the Office
of Research and Development in preparing this risk evaluation. Development of the TCE hazard and
dose-response assessments considered EPA and National Research Cowicil (NRC) risk assessment
guidance.
The new literature was screened against inclusion criteria in the PECO statement and the relevant
studies (e.g." useful for dose-response) 13 were further evaluated using the data quality criteria for
13 Some of the studies that

were excludedbased on the PECOstatementwere consideredlater during the systematicreview
process as needed. For example,EPA reviewedmode of action infonnationto qualitativelysupportthe health hazard
assessment.

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human, animal, and in vitro studies described in the Applicationof SystematicReview in TSCA Risk
Evaluations (U.S. EPA . 2018b ) (see Section 1.5). EPA skipped the screening step of the key and
supporting studies and entered them directly into the data evaluation step based on their relevance to the
risk evaluation.
EPA considered studies oflow, medium, or high confidence for hazard identification and dose-response
analysis. Information from studies that were rated unacceptable were only discussed on a case-by-case
basis for hazard ID and weight-of-scientific-evidence assessment but were not considered for doseresponse analysis.
EPA has not developed data quality criteria for all types of hazard information. This is the case for
toxicokinetics and many types of mechanistic data which EPA typically uses for qualitative support
when synthesizing evidence. As appropriate, EPA evaluated and summarized these data to determine
their utility with supporting the risk evaluation.
Following the data quality evaluation, EPA extracted the toxicological information from each relevant
study. In the last step, the strengths and limitations of the data were evaluated for each endpoint and a
weight-of-the-scientific evidence narrative was developed. Data for each selected hazard endpoint
underwent dose-response analysis. Finally, the results were summarized, and the uncertainties were
presented. The process is described in Figure 3-3 .. The weight of evidence analysis included integrating
information from toxicokinetics, toxicodynamics in relation to the key hazard endpoints: acute overt
toxicity, liver toxicity, kidney toxicity, neurotoxicity, immunotoxicity (including sensitization),
reproductive toxicity, developmental toxicity, and cancer. EPA selected human health studies that were
of high quality and relevance to move forward for dose-response analysis in order to quantitatively
assess each key hazard endpoint.
A summarytable which includes all endpoints considered for this assessment, the no-observed- or
lowest-observed-adverse-effect levels (NOAEL and LOAEL) for non-cancer health endpoints by target
organ/system, the incidence for cancer endpoints, and the results of the data quality evaluation is
provided in [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-20190500].
EPA considered points of departure (POD) from studies that were PECO relevant, scored acceptable in
the data quality evaluation, and contained adequate dose-response information. The POD is a dose or
concentration near the lower end of the observed range without significant extrapolation to lower doses.
It is used as the starting point for subsequent dose-response (or concentration-response) extrapolations
and analyses. PODs can be a no-observed-adverse-effect level (NOAEL), a lowest-observed-adverseeffect level (LOAEL) for an observed incidence, or change in level of response, or the lower confidence
limit on the dose at the benchmark dose (BMDL) 14• PODs were adjusted as appropriate to conform to
the specific exposure scenarios evaluated.
Human equivalent concentrations (HECs) and human equivalent doses (HEDs) were obtained via EPA's
previously published Physiologically-Based Pharmacokinetic (PBPK)model GJ:.S. EPA . 201 le ), which
accounts for both extrapolation from rodents to humans and human variability. The PBPK model also
allows data-based route-to-route extrapolation between oral and inhalation studies. For HEC
calculations, these values were adjusted based on 24-hr exposure durations unless otherwise noted.
Limited toxicological data are available by the dermal route for TCE and a PBPK model that would
14

The benchmark dose (BMD) is a dose or concentrationthat produces a predeterminedchange in response range or rate of
an adverse effect (called the benchmark responseor BMR) comparedto baseline.

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facilitate route-to-route extrapolation has not been developed for the dermal exposure route. Therefore,
oral HEDs were also utilized for risk estimation following dermal exposure.
Section 3.2.5 describes the dose-response assessment guiding the selection of PODs for non-cancer
endpoints. The BMD modeling results for pulmonary immunotoxicity (Selgrade and Gilmour . 2010 ),
which was not included in the 2014 TCE Risk Assessment (U.S . EPA. 2014b), are presented in Appendix
F. The full description of the PBPK and BMD model outputs for all other endpoints can be found in (U.S.
EPA . 2011e ).

3.2.2 Toxicokinetics
The toxicokinetics and PBPK modeling ofTCE were thoroughly discussed in the 2014 Risk Assessment
(U.S. EPA . 2014b ). This discussion is summarized below.
TCE is fat soluble (lipophilic) and easily crosses biological membranes. Though there are
quantitative differences across species and routes, TCE is readily absorbed into the body
following oral, dermal, or inhalation exposure. Because of its lipophilicity, TCE can cross the
placenta and also passes into breastmilk( U.S. EPA 201 le ).
Absorption following inhalation of TCE is rapid and the inhaled absorbed dose is proportional to the
exposure concentration, duration of exposure, and lung ventilation rate. Therefore, for this risk
evaluation absorption of TCE is assumed to be 100% via inhalation. Likewise, TCE is
rapidly absorbed from the gastrointestinal tract into the systemic circulation (i.e., blood)
following oral ingestion. Oral absorption of TCE has been shown to be influenced by dose of the
chemical, the dosing vehicle and stomach contents. Absorbed TCE is first transported to the
liver where it is metabolized for eventual elimination (i.e., "first-pass effect") (U .S. EPA 201 le ).
Rapid absorption through the skin has been shown by both vapor and liquid TCE contact with
the skin. In several human volunteer studies, both TCE liquid and vapors were shown to be well
absorbed in humans via the dermal route. Dermal absorption was rapid following exposures of between
20 and 30 minutes, with peak TCE levels in expired air occurring within 15 minutes (liquid) and 30
minutes (vapor) (U.S. EPA . 201 le ). Dermal exposure to TCE disrupts the stratum comeum, impacting
the barrier function of skin and promoting its own absorption. Therefore, absorption may increase at a
greater than linear rate due to increasing epidennal disruption over time (ATSDR . 2019 ). Based on this
information, this risk evaluation assumes that TCE dermal absorption under occluded scenarios is 100%.
Dermal absorption under non-occluded occupational exposure scenarios was evaluated by the Dermal
Exposure to Volatile Liquids Model in order to account for evaporation of TCE deposited on skin
(Section 2.3 .1). Consumer exposure was only evaluated for scenarios that may involve dermal contact
with impeded evaporation using a skin permeability model with a dermal permeability coefficient of
0.019 cm/hr (Section 2.3.2.4.1).
Regardless of the route of exposure, TCE is widely distributed throughout the body. TCE levels
can be found in many different human and rodent tissues including: brain, muscle, heart,
kidney, lung, liver, and adipose tissues. It can also be found in human maternal and fetal blood
and in the breast milk oflactating women (U.S. EPA 201 le ).
The metabolism ofTCE has been extensively studied in humans and rodents (U.S. EPA 201 le ).
Animals and humans metabolize TCE to metabolites to varying degrees. These metabolites are known to
play a key role in causing TCE-associated toxic effects. TCE metabolites are known to target the liver
and kidney. The two major metabolic pathways are (1) oxidative metabolism via the cytochrome P450
(CYP) mixed function oxidase system and (2) glutathione (GSH) conjugation followed by further
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biotransformations and processing with other enzymes. The liver is the major tissue for the oxidative
and GSH conjugation metabolic pathways. Both pathways are saturable, and above the saturable
concentration/dose, TCE is excreted unchanged in expired air. Table 3-3 presents the important
metabolites formed following both the CYP (oxidation) and GSH (conjugation) pathways in humans and
animals. The amount and types of metabolites formed are important for understanding the toxicity of
TCE in both animals and humans.
These major TCE metabolites as well as a number of minor metabolites are also observed in the
metabolic pathway ofTCE-related compounds (Table 3-4). This may be important in
determining exposures because people may be co-exposed to many of these solvents at the
same time. Concomitant exposures to TCE and its related compounds can affect TCE's metabolism and
increase toxicity by generating higher internal metabolite concentrations than those resulting from TCE
exposure only (U.S. EPA. 2011e).

Oxidative Metabolites

GSH Conjugation Metabolites

Chloral
(metabolized to TCOHa)
Trichloroethylene oxide
(re-a"anged to DCACb)

DCVGe

(metabolizedto DCVCr isomers)

Trichloroethaool or TCOH
(metabolized to TCOGc)
Trichloroacetic acid or TCA
(may lead to DCAd)

Abbreviations:• TCOH = trichloroethanol; bDCAC = dichloroacetyl chloride; c TCOG= trichloroethanol,

glucuronide conjugate; dDCA=dichloroacetic acid; eDCVG= S-dichlorovinyl-glutathione (collectively, the 1,2and 2,2- isomers); rDCVC = S-dichlorovinyl-L-cysteine (collectively , the 1,2- and 2,2 - isomers)

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A review of in vitro metabolism data in the liver suggested that rodents (i.e ., especially mice)
have greater capacity to metabolize TCE via the oxidation pathway (!JS. EPA, 201 le ). Jn vitro data
have also reported modest sex- and age-dependent differences in the oxidative TCE metabolism in
humans and animals. Significant variability may exist in human susceptibility to TCE toxicity given the
existence of CYP isoforms and the variability in CYP-mediated TCE oxidation (U.S. EPA, 2011 e) .

Table 3-4 Common Metabolites of TCE and Related Com ounds

Metabo

1,1,11,2,1,2,Trichloro- Dichloro- Dichloroethylene
ethane
ethane

ethane

X

Oxalic acid

X

Chloral

X

X

Chloral hydrate
(CH)

X

X

Monocbloroacetic
acid

X

X

X

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Dichloroacetic
acid (DCA)

X

X

X

Dichloroacetic
acid {TCA)

X

X

X

X

11ricbloroethaool

X

X

X

X

X

X

X

X

X

(TCOH)

Trichloroethanolglucuronide

Note: Table is the same as Table2-21 in(U.S. EPA. 2014b).

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Conjugation is a process that generally leads to detoxification. However, this is not the case for
TCE and many other halogenated alkanes and alkenes because they are biotransformed into
reactive metabolites. The eventual metabolite(s) of concern for TCE are formed several steps
from the initial GSH conjugate formed in the liver, which ultimately results in toxicity or
carcinogenicity in the kidney (U.S. EPA. 201 le).
Compared to the CYP oxidation pathway, there appear to be more significant sex and species
differences in TCE metabolism via the GSH pathway (U.S. EPA. 20 I Je). Animal data show that rates of
TCE GSH conjugation in male rats/mice are higher than females. According to some in vitro data, the
rates of DCVG production in liver/kidney cytosol are highest in humans, followed by mice, and then
rats. In vitro data also suggest that y-glutamyl transpeptidase (i.e., GGT, an enzyme involved in DCVC
production) activity in kidneys seems to be highest in rats, then humans, and then mice (U.S. EPA.
2011e). Furthennore, species-dependent enzymatic activities have been reported for the P-lyase and
FM03 enzymes (U.S. EPA. 2011e).
The majority ofTCE absorbed into the body is eliminated by the metabolic pathways discussed
above. With the exception of unchanged TCE and COi, which are excreted by exhalation, most
TCE metabolites (i.e., TCA, TCOH, GSH metabolites) are primarily excreted in urine and feces.
Elimination ofTCE metabolites can also occur through the sweat and saliva, but these excretion routes
are likely to be relatively minor( U.S. EPA. 201 le).
Varying rates ofTC E pulmonary excretion in humans have been observed in different studies (Chiu et
al., 2007; Opdam, 1989; Sato et al .. 1977). The relatively long tenninal half-lives observed (up to 44
hours) suggest that the lungs require considerable time to completely eliminate TCE, primarily due to
high partitioning to adipose tissues (!J,S. EPA. 201 le). Various laboratories have studied the urinary
elimination kinetics ofTCE and its major metabolites in humans and rodents. Animal studies have
shown that rodents exhibit faster urinary eHminationkinetics than humans, with demonstrated
elimination half-lives of just over 50 hours in humans and only approximately 16 hours in rats (Ikeda
and Imamura. 1973).

3.2.2.1
Physiologically-BasedPharmacokinetic(PBPK) Modeling Approach
Given the complicated metabolic profile ofTCE, understandingthe relationship between the external
dose/concentration(i.e., exposure) and internal dose at the target organ of interest is critical to
quantifying potential risk(s) because internal dose is more closely associated with toxicity at the target
tissue (L .S. EPA. 2006). Predictions of internal dose in chemical risk assessments are achieved by
employing PBPK modeling.

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PBPK models use a series of mathematicalrepresentationsto describe the absorption, distribution,
metabolism and excretionof a chemical and its metabolites.BecausePBPK modeling assumes that the
toxic effects in the target tissue are closely related to the internal dose of the biologicallyactive form of
the chemical,knowledgeabout the chemical's mode of action guidesthe selectionof the appropriate
dose metric. Traditionalrisk estimatesbased on applied dose carry higher uncertaintiesthan those based
on PBPK-derivedinternal dose metrics.This reduction in uncertaintyand the versatility of PBPK
approacheshave resultedin a growing interest to use these modelsin risk assessmentproducts ~
EPA. 2006).
U.S. EPA developed a comprehensiveBayesianPBPK model-basedanalysis of TCE and its metabolites
in mice, rats and humans (U.S. EPA. 201 le). This model is briefly discussedbelow to provide clarity on
how the PBPK modeling was used to estimatethe PBPK-derivedHECs.

Physiological,chemical, in vitro and in vivo data were consideredwhen building the PBPK model,
including many studies in animals and humansthat quantifiedTCE levels in various tissues following
oral and inhalation exposures.Some of these studies providedkey data/parameters for the calibrationof
the PBPK model used in the IRIS assessment(U.S. EPA 201 le). All of this informationwas used to
build a model that was able to predict different dos~metrics as measuresof potential TCE toxicity. Each
dose-metricwas developedto evaluate a differentmetabolicpathway/targetorgan effect based on the
dose-response analysis and understandingof metabolism(Table 3-5 and Figure 3-4).

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In general, an attempt was made to use tissue-specificdose-metricsrepresentingparticular pathways or
metabolites identified from available data on the role of metabolismin toxicity for each endpoint
(discussed.in more detail below). The selectionwas limited to dose metrics for which uncertainty and
variabilitycould be adequatelycharacterizedby the PBPK model. For most endpoints, sufficient
information on the role of metabolitesor mode of action was not availableto identify likely relevant
dose metrics, and more upstreammetrics representingeither parent compoundor total metabolism had
to be used.

Table 3-5 List of All of the PBPK-ModeledDose Metrics Used in the TCE IRIS Assessment
ose-Metric
dentifier
BioactDCVCBW34

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Amount of DCVC bioactivatedin the kidne
Amotmtof DCVC bioactivatedin
AmountofTCE con· ated with
Amount ofTCE oxidized in !iv
Amount ofTCE oxidizedto metabolitesother
Amount ofTCE oxidized to metaboli
Amount ofTCE oxidized in
Amount ofTCE oxidized in res
Area under the curve of venous blood concentrationofTCE
Area under the curve of blood concentrationofTCOH
Area under the curve of the liver concentrationof TCA

For developmentaltoxicity endpoints,the TCE PBPK model did not incorporatea pregnancy model to
estimate the internal dose ofTCE in the developingfetus. In this case, the maternal dose-metricwas
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used as the surrogatemeasure of target tissue dose in the developingfetus. A complete description of the
TCE PBPK model, includingthe rationale for parameter choices in animals and hwnans, choice of dose
metric, and experimental informationused to calibrate and optimizethe model is found in the TCE IRIS
assessment (U.S. EPA. 2011e).
As shown in Figw-e3-4 and Figure 3-5, several steps were needed to derive the PBPK-derived HECs
used in this assessment. First, the rodent PBPKmodel was nm to estimate rodent internal dose Points of
Departure (idPODs) for the applied dose PODs (i.e., LOAEL,NOAEL, or BMDL) that were identified
in the TCE IRJS assessment. Separately, the hwnan PBPK model was run for a range of continuous
exposures from 0.1 to 2,000 ppm or 0.1 to 2,000 mg/kg-bw/dayto establish the relationship between
human exposure air levels and internal dose for the same dose-metricevaluated in the rodent PBPK
model. This relationship was used to derive Human EquivalentConcentrations (HECs) and Human
Equivalent Doses (HEDs) corresponding to the idPOD by interpolation(U.S. EPA. 201 le ).
0.1to 2000 ppmTC£In

Rodllntnon•

air or0,1 to2000

Clf!Cjlf,tUdy

ffll/kc-bw/day

e,q1ariment1I

a,ntlnuou1 expoeure

p1radlpn

FIHd

I Dlttnbutlon l••PN~t•
i' ... cemolntyand varlabllty)

1 Distribution (oomblntd
i' uncertainty and vadabllity)
Roclentnonstudy
canc:er

re,p......,.

Human IRternel dote
at 50"' pa,centileu
func.tlcncf applied
dote

lodotnt ldPOO

for 1nv1111■n
BMDL,LOAEl

orNOAEL

lnvertfuncliona

l==='====='==
OHotconc:en
======j:
tratlon

Human Internal dat 95"' p-tlr.11
functloll cf llltfl(led

OM

Human lntemel doN
lit 99"' jNll'AlltlN H

I

functfoflol 1pplkld
doM

--+-- ----1l-______,
,l
=
~.J

EJ EJ_I

_H_ECg_g
___

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Figure 3-4 Dose-Response Analyses of Rodent Non-Cancer Effects Using the Rodent and Human
PBPKModels
Notes: Figure adaptedfromFigure 5-2 (Chapter5, TCE IRISassessment)(U.S. EPA. 20lle ). Squarenodes indicatepoint
values, circle nodes indicatedistnoutionsand the invertedtriangleindicatesa (deterministic) functional relationship.

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Human internal
dose•

Ro~nt Internal
dose

/

~ }~
..~-- .,~"

uncerwn~&
vanab ility

~
~---

•

..

-------

IdPOD
Figure adapted from Figure 5-3 (Chapter 5,

Study dose groups

LOAEL/
NOAEL

TCE IRISassessme nt)
Notes: When using benchmark dose est imates,
the idPOD is th e mo deled BMD L in
interna l dose units.

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Figure 3-5 Example of HEC99 Estimation through Interpecies, Intraspecies and Route-to- Route
Extrapolation from a Rodent Study LOAEL/NOAEL

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The rodent population model was designed to characterize study -to-study variation and used median
values of dose-metrics to generate idPODs . The rodent PBPK model did not characterize variation
within studies and assumed that the rodent idPODs were for pharmacokinetically identical animals. The
basis of that assumption was that animals with the same sex/species/strain combination were considered
pharmacokinetically iden tical and represented by the group average. In practice, the use of median or
mean internal doses for rodents did not make much difference except when the uncertainty in the rodent
dose-metric was high( U.S. EPA . 201 le ).
On the other hand, the human populat ion model characterize s toxicokinetic uncertainty and individualto-individual variation and used median, 95dtand 99t1t
percentile values of dose- metrics to general
human idPODs. The 50th, 9S111
, or 99 th percentile of the combined uncertainty and variability distribution
of human internal doses was used to derive the HEC/HED so,HEC/HED9 5 or HEC/HED99 estima tes,
respectively. The HEC 95 and HEC99 were interpreted as being the concentrations ofTCE in air for which
there is 95% and 99% likelihood, respectively, that a randomly selected individual will have an internal
dose less than or equal to the idPOD derived from the rodent study. HED values represent the same
likelihood for given administered doses of TCE. This risk evaluation presents both HEC/HEDso and
HEC/HED99 POD values .

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3.2.3

Hazard Identification

3.2.3.1
Non-Cancer Hazards
EPA previously identified human health hazard for the below endpoints in ( U.S. EPA 2011 e) and ~
EPA . 2014b ). Key and supporting studies from those publications that were used for derivation of tissuespecific PODs were reviewed along with any newer studies identified through EPA's updated literature
search beginning with studies published after the TCE IRIS assessment( U.S. EPA . 201 le ). A short
summary of the overall database and short details on any older key studies or relevant new studies are
provided here; details on all reviewed studies can be found in [DataExtractionfor Human Health

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Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500].

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Animals and humans exposed to TCE consistently experience liver toxicity. Specific effects include
the following structural changes: increased liver weight, increase in deoxyribonucleic acid (DNA)
synthesis (transient), enlarged hepatocytes, enlarged nuclei, and peroxisome proliferation.
The role of metabolites is important but not well understood. Many investigators have dosed animals
with TCE, as well as with many of its metabolites to detennine the role and potency of each in terms
of target organ toxicity. It appears that the oxidation pathway is important for the development of liver
toxicity, but the specific role of each metabolite (i.e., that ofTCA, DCA, and chloral hydrate), as well
as the parent TCE, is unclear .

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EPA did not identify any new repeat-dose experimental studies in animals or human epidemiological
studies that would contribute significant additional hazard information for this endpoint Therefore,
EPA relied primarily on conclusions from the 2014 TSCA Work Plan Chemical Risk Assessment (.!JS.
EPA . 2014b ).
Human Data
Several human studies (including those in TCE degreaser operations) reportedan association between
TCE exposure and significant changes in serum liver function tests used in diagnosing liver disease,
or changes in plasma or serum bile acids. There was also human evidence for hepatitis accompanying
immune-related generalizedskin diseases. jaundice, hepatomegaly, hepatosplenomegaly , and liver
failure in TCE-exposed workers (1!.S. EPA . 2011 e) . Cohort studies examining cirrhosis mortality and
either TCE exposure or solvent exposure did not generally identify a statistically significant
association, but due to limitations in this database these studies do not rule out an association between
TCE and liver disorders/toxicity CU
.S. EPA 2011 e). A case study published after the 2011 IRIS
Assessment reported TCE hypersensitivity-induced liver damage (Jun!!et al.. 2012 ).
Animal Data
The 2014 TSCA Work Plan Chemical Risk Assessment (U.S. EPA 2014b)reviewed many oral and
inhalation studies in rats and mice. Studies in animals exposed to TCE reported increased liver weight, a
small, transient increase in DNA synthesis, enlarged hepatocytes, increased size of nuclei of liver cells,
and proliferation of peroxisomes (U.S. EPA . 2011 e). Dose-responsive increases in relative liver weight
(compared to body weight) were observed both following administration ofTCE for 6 weeks via
gavage (Buben and O'Flahe rt" , 1985) and for up to 120 days via inhalation (Woolhiser et al.. 2006 ;
Kjellstrand et al .. 1983). Hypertrophy, histopathology, cytotoxicity, and altered serum biochemistry
were also observed in mice in (Buben and O'Flahe m . 1985) and (Kjcllstrand et al.. 1983 ). Increased
liver weight was additionally observed in (Bovcrhof et al.. 2013), identified in the EPA literature

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search, following 6hr/day inhalation exposure to a single concentration level (1 OOOppm)
ofTCE for 4
weeks.

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3.2.3.1.2 Kidney toxicity
Studies in both hwnans and animals have shown changes in the proximate tubules of the kidney
following exposure to TCE. DCVC (and to a lesser extent other metabolites) appears to be responsible
.S. FPA 2011e). Toxicokinetic
for kidney damage and kidney cancer following TCE exposure (L1
data suggest that the TCE metabolites derived from GSH conjugation (in panicular DCVC) can be
systemically delivered or formed in the kidney. Importantly , DCVC-treated animals showed the same
type of kidney damage as those treated with TCE (U.S. EPA 201 le).

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EPA did not identify new any repeat-dose experimental studies in animals or hwnan epidemiological
studies that would contribute significant additional hazard information for this endpoint. Therefore,
EPA relied primarily on conclusions from the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
EPA 2014b).
Hwnan Data
Occupational studies showed increased levels of kidney damage (proximal tubules) and end-stage
renal disease in TCE-exposed workers. Humanstudies reported increased excretion of urinary proteins
among TCE-exposed workers when compared to unexposed controls. While some of these studies
included subjects previously diagnosed with kidney cancer, other studies report similar results in
subjects who are disease free (U.S. EPA 201 le).
Animal Data
In animal studies, renal toxicity was evident in both rats and mice following inhalation or gavage

exposures. Maltoni and Cotti (1986) identifiedpathologicalchangesin the renal tubule of rats following 12 years of either oral or inhalationexposure.Similarchangeswere also observed in a chronic gavage study
in female mice conductedby NCI. (JSCL 1976), howeverthat study scored Unacceptablein EPA data
quality evaluationdue to confoundingmortality.The toxicity included damage to the renal tubules (e.g.,,
both cytomegaly and karyomegaly). In a chronic gavage study, kidney toxicity was observed in almost
100percent ofrodents at high doses (NTP, 1988). Under inhalation exposure scenarios, male rats were
more susceptible than female rats or mice to kidney toxicity. As noted earlier , this toxicity is likely
caused by DCVC formation, with possible roles for TCOH and TCA (U.S. EPA. 201 le). Increased
relative kidney weight compared to body weight was also observed in both mice and rats following
inhalation exposure over several weeks to months (Boverhof et al.. 2013; Woolhiser et al.. 2006;
Kjellstrand et al .. 1983).
3.2.3.1.3 Neurotoxicity
Neurotoxicity has been demonstrated in animal and human studies under both acute and chronic
exposure conditions (U.S. EPA. 201 le). Due to the effects on the nervous system, TCE was initially
synthesized for use as an anesthetic in hwnans in the early part of the 20th century.These anesthetic-like
effects occurred at high concentrations. CNS depressionhas been consistently observed following
acute exposure ofhwnans to TCE (see Section 3.2.3.1.7).

Among newer studies not previously discussed in (!1:.S. EPA. 2011e), a single repeat-dose
experimental study in rats (Liu et al .. 2010) along with a few epidemiological studies that identified
specific neurological outcomes were identified in EPA' s literature search. These studies only add to
and do not contradict the hazard conclusions from the 2014 TSCA Work Plan Chemical Risk
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Assessment (U.S. EPA. 2014b). Therefore, EPA primarily relied on the previous hazard conclusions.

Human Data
Evaluation of the human studies has reported the following TCE-inducedneurotoxic effects:
alterations in trigeminal nerve and vestibular :function,auditory effects, changes in vision, alterations
in cognitive function, changes in psychomotor effects, and neurodevelopmental outcomes (U.S. EPA.
201 le ).
Multiple epidemiological studies in different populations have reported TCE-induced abnormalities in
trigeminal nerve function in humans,with a few studies not reporting any association (1!.S. EPA.
2011e). The strongest evidence of human neurological hazard is for observed changes in trigeminal
nerve function or morphology and impairment of vestibular function in a High quality study on workers
exposed to TCE for a mean of 16 years (Rui jten et al.. 1991). Fewer and more limited epidemiological
studies are suggestive ofTCE exposure being associated with delayed motor function, and changes in
auditory, visual, and cognitive function or performance, and neurodevelopmentalabnormalities (U.S.
EPA. 2011e).
Human studies have consistently reported vestibular system-rel~ed symptoms such as headaches,
dizziness, and nausea following TCE exposure. Although these symptoms are subjective and selfreported, these effects have been reported extensively in human chamber, occupational, and
geographic-based/drinkingwater studies (U.S. EPA. 2011e). Additionally.several newer
epidemiological studies have found an associationbetween TCE exposure and neurodegenerative
disorders such as Amyotrophic Lateral Sclerosis (Bove et al..2014a) and Parkinson's disease (Bove et
al.. 2014b; Goldman et al .. 2012).

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Animal Data
The2014 TSCA Work Plan Chemical Risk Assessment(U.S. EPA. 2014b)reviewed many animal
studies reporting a variety of neurotoxic effects under different exposure conditions. Animal studies
have reported the following TCE-induced neurotoxic effects: morphologicalchanges in the trigeminal
nerve, disruption of the auditory system, visual changes, structural or functional changes in the
hippocampus, sleep disturbances and changes in psychomotor effects (1!.S. EPA. 2011e). Key and
supporting studies considered in this risk evaluation identified significant decreases in wakefulness
following 6 weeks ofTCE inhalation exposure (Arito et al.. 1994) and demyelination of the
hippocampus following 8 weeks of drinking water exposure (Isaacson et al.. 1990) in rats. Neuronal
degeneration (Gash et al.. 2008) and diminished sciatic nerve regeneration (Kjellstrand et al.. 1987)
were also observed following TCE exposure in rodents, however those studies scored Low and
Unacceptable, respectively in data quality evaluation. More recent studies have observed both sedative
(Wilmer et al.. 2014) and stimulatory effects (Shelton and Nicholson. 2014) ofTCE via inhalation at
doses at or above 5000 ppm. Rats administeredTCE via gavage for 6 weeks demonstrated loss of
dopaminergic neurons at 500 and 1000 mg/kg-day, with changes in behavior and reduced
mitochondrial activity with increased oxidative stress observed at 1000 mg/kg-day (Liu et al.. 2010).

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3.2.3.1.4 Immunotoxicity (including sensitization)
Im.munrrrelatedeffects following TCE exposures have been observed in both animal and human
studies. In general, these effects were associated with inducing enhanced immune responses as
opposed to immunosuppressiveeffects. Of concern are the immunrrrelated and inflammatory effects
reported in TCE-exposed animals and humans. These effects may influence a variety of other

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conditionsof considerablepublic health importance,such as cancer and atherosclerosis(U.S. EPA,
201 le ).

EPA's literature search identified a single acute inhalation study in rats that identified a novel endpoint
for impaired response to infection (Selm-adeand Gilmour. 2010). This study was discussed in the TCE
IRIS assessment (U.S. EPA. 2011e ) but was not included in the 2014 TSCA Work Plan Chemical
Risk Assessment (U.S. EPA . 2014b ). All other studies supported the hazard conclusionsof the 2014
TCE Risk Assessment (U.S. EPA . 2014b). Therefore, EPA primarily relied on the previous hazard
conclusions for all other endpoints.
Human Studies
Studies have reported a relationship between systemic autoimmunediseases, such as scleroderma, and
occupationalexposure to TCE. The TCE IRIS assessment (1;.S. EPA. 201 le)performed a metaanalysis of a number of human studies evaluatinga possible connection between scleroderma and TCE
exposure. Results indicated a significant odds ratio (OR) in men, whereas women showed a lower but

not significant OR. These results may not reflect a true gender difference because the incidence of this
disease is very low in men (approximatelyone per 100,000 per yr) and somewhat higher in women
(approximately one per 10,000 per yr). In addition,these results may be affected by gender-related
differences in exposure prevalence, the reliabilityof the exposure assessmen~gender-related
differences in susceptibilityto TCE toxicity or chance (U.S. EPA. 201 le).

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Increased levels of human inflammatorycytokineshave been observed in both workers exposed
occupationallyto TCE and infants exposed to TCE via indoor air. (U.S. EPA, 201 le). These findings
were supported by studies in mice (describedbelow) in which short exposures to TCE resulted in
increased levels of inflammatorycytokines.
The epidemiologicaldatabase also provides evidence of immunosuppressionbased on reduced IgG
antibody levels in TCB-exposedworkers (Zhanl! et al.. 2013).
Animal Data
Numerous studies have shown increased autoimmuneresponses in autoimmune-pronemice, including
changes in cytokine levels similar to those reported in human studies, with more severe effects,
jncJudingautoimmune hepatitis, inflammatoryskin lesions, and alopecia, manifesting at longer
exposure periods (U.S. EPA. 20 l le ). Key studies identified evidence of autoimmunity from chronic
TCE exposure in both non-autoimmuneprone (Keil et al.. 2009 ) and autoimmuneprone (Kaneko et al.,
2000 ) mice. Evidence oflocalized immunosuppressionhas also been reported in mice and rats
(Boverhof et al .. 2013 ; Woolhiser et al.. 2006 ; Sanders et al.. 1982). Support for immunotoxicity
hazard is further supported by decreasedthymus weight and celluJarity in the non-autoimmuneprone
mice following up to 30 weeks of drinking water exposure (Keil et al.. 2009).
Inhalationexposure to TCE has been shown to suppresspulmonaryhost defenses and enhance
susceptibilityto respiratory infectionin mice co-exposedto aerosolizedpathogenicbacteria. Increased
mortality was observed post-infectionfollowingexposureto TCE concentrationsof 50ppm or greater.
with correspondingdose-dependenteffects on bacterialclearance,percentageof infected mice, and
alveolar phagocytosis(Selgrade and Gilmour. 2010).

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Sensitization / Hypersensitivity
Limited epidemiological data do not support an association between TCE exposure and allergic
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respiratory sensitization or asthma. However, there have been a large number of case reports in TCEexposed workers developinga severe hypersensitivityskin disorder, distinct from contact dermatitis,
and often accompanied by systemic effects (e.g., hepatitis, lymph node changes, and other organ
effects). These effects appeared after inhalation exposures ranging from less than 9 to greater than 700
ppm TCE. Similar sensitiz.ation/hypersensitivity
effects have been observed in guinea pigs and mice
following TCE exposure via drinking water(U.S. EPA 201 le).

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3.2.3.1.5 Reproductive toxicity
Both the epidemiological and animal studies provide suggestive, but limited, evidence of adverse
outcomes to female reproductive outcomes. However, much more extensive evidence exists in support
of an association between TCE exposures and male reproductive toxicity (U.S. EPA 2011e).

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The available human data that associate TCE with adverse effects on male reproductive function are
limiied in sample size and provide little quantitative dose data. However,the animal data provide
strong and compelling evidence for TCE-relatedmale reproductive toxicity. Strengths of the animal
database include the presence of both functional and structural outcomes, similarities in adverse
treatment-related effects ob.servedin multiple species, and evidence that metabolism of TCE in male
reproductive tract tissues is associated with adverse effects on sperm measures in both hwnans and
animals. Additionally, some aspects of a putative mode of action (e.g.,, perturbations in testosterone
biosynthesis) appear to have some commonalities between humans and animals (U.S. EPA. 2011e).

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studies that would contribute significant additional h87.8l'dinformation for this endpoint Therefore,
EPA relied primarily on conclusions from the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
EPA. 2014b).

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Human Data
Most human studies support an association between TCE exposure and alterations in sperm density
and quality, as well as changes in sexual drive or function and serum endocrine levels. Chia et
al.( 1996) observed decreased normal sperm morphology along with hyperzoospermiain male workers
averaging over five years occupational exposure. Fewer epidemiological studies exist linking decreased
incidence of fecundability (time-to-pregnancy)and menstrual cycle disturbances in women with TCE
exposures (U.S. EPA. 201 le ).
Animal Data
Laboratory animal studies provide evidence for similar effects, particularly for male reproductive
toxicity. These animal studies have reported effects on spenn, libido/copulatory behavior, and serum
hormone levels, although some studies that assessed sperm measures did not report treatment-related
alterations ( U.S. EPA 2011e). Identifiedkey and supportingstudies have observed TCE-related
histopathological lesions in the testes or epididymides,altered in vitro sperm-oocytebinding, and
increased incidence of irregular sperm in rodents (Kan et al .. 2007; Xu et al.. 2004; Kumar et al .. 2001;
Kumar et al.. 2000). Forkert et al. (2002) also observed effects on the epididymis, however that study
was Unacceptable in data quality evaluation. Similarly, decreased in vitro fertilization resulted from
exposure of male rats to TCE in drinking water in one study (Duteaux et al.. 2004), however that
study scored a Low in data quality evaluation.
Fewer animal studies are available for the female reproductivetoxicity endpoint. While in vitro oocyte
fertilizability has been reported to be reduced as a result ofTCE exposure in rats, a number of other
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laboratory animal studies did not report adverse effects on female reproductive function effects (U.S.
EPA. 20 1le). The key study Narotsky et al. (1995) observed delayed parturition in female rats.
Exposw-eof either males or females to TCE in feed resulted in reduced successful copulation and an
associated decrease in the nwnber of live pups and litters (George et al.. 1986).

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3.2.3.1.6 Developmental Toxicity
An evaluation of the human and animal developmentaltoxicity data suggests an association between
pre- and/or postnatal TCE or TCE metabolite exposures and potential developmental adverse
outcomes. Heart malformations observed after developmental TCE exposure in animal studies were
identified in the 2014 TSCA Work Plan Chemical Risk Assessment (J,1.S. EPA 2014b) as the most
sensitive developmental toxicity endpoint for dose-response analysis. The developmental toxicity
information is briefly described below.

For developmental toxicity other than congenital heart defects EPA did not identify any repeat-dose
experimental studies in animals or human epidemiological studies that would contribute significant
additional information for this hazard. Therefore, EPA relied primarily on conclusions from the 2014
TSCA Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) for these other endpoints. For
congenital heart defects, EPA evaluated more recent epidemiological studies, mechanistic studies, and
a single experimental animal study that provide conflicting evidence for this endpoint.
Human Data
The 2014 TSCA Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) evaluated numerous human
studies that examined the possible association of TCE with various developmental outcomes, including
prenatal (e.g.,, spontaneous abortion and perinatal death, decreased birth weight, and congenital
malformations) and postnatal (e.g.,, growth, survival, developmental neurotoxicity, developmental
immunotoxicity, and childhood cancers) health outcomes. Most of these were occupational
epidemiology studies. In addition, geographically-basedepidemiological studies have been conducted
in various parts of the United States, including Arizona(Tucson Valley), Colorado (Rocky M0tmtain
Arsenal), Massachusetts, New York (Endicott), Camp Lejeune, North Carolina and Milwaukee,
Wisconsin (U.S. EPA. 201 le).
The Endicott, New York, and the Camp Lejeune studies focused on reproductive and developmental
outcomes. Some of these studies have reported associations between parental exposure to TCE and
spontaneous abortion or perinatal death, and decreased birth weight. However, other occupational and
geographically-based studies have failed to detect a positive association between TCE exposure and
developmental toxicity in humans (U.S. EPA. 201 le).
There have been some epidemiological studies that have consistently reported an increased incidence of
birth defects in TCE-exposed populations. For instance, ATSDR has conducted studies at Camp
Lejeune. North Carolina. where individuals were exposed to VOC-contaminated drinking water
(Ruckart et al.. 2014, 2013 ). TCE was one of the main contaminants found in the drinking water.
Ruckart et al. found an association between neural tube defects and TCE exposure above 5 ppb during
the first trimester of pregnancy, however either negative or null associations were identified between
TCE exposure and other developmental effects (e.g., reduced birth weight, oral cleft defects). Yauck et
al. (2004) observed a strong relative risk estimate for cardiac malformations in infants from Milwaukee,
Wisconsin born to TCE-exposed mothers aged 38 years or older. In addition to older age, increased risk
was also independently associated with other confounders including alcohol use, hypertension, and
diabetes. Forand et al., (2012) (an update for the Endicott, NY community) reported significant relative
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risk estimates for low birth weight, small for gestational age, and cardiac defects. See the below section
for further discussion of congenital heart defects.
Other studies have also identified an association between exposure to TCE exposure and
developmental effects. One study reported increased risk of spina bifida to offspring of TCE-exposed
mothers (Swartz et al.. 2015), and both statistically significant and non-significant associations have
been observed between exposure to the TCE metabolites trichloracetic acid and trichloroethanol vvith
various outcomes mcluding oral clefts, urinary tract malformations, and limb defects (Cordier et al..
2012). In contrast, (Brender et al., 2014) found no statistically significant association with neural tube
defects, spina bifida, anenocephaly, any oral cleft, cleft palate, cleft lip with or without cleft palate, any
limb defi~iency, or longitudinal or transverse limb deficiencies. The study did identify an increased risk
of septa.Iheart defects (see below section) in older mothers, however. As for human developmental
neurotoxicity, the available studies collectivelysuggest that the developingbrain is susceptible to TCE
toxicity. These studies have reported an associationwith TCE exposure and CNS birth defects and
postnatal effects such as delayed newborn reflexes, impaired learning or memory, aggressive behavior,
hearing impainnent, speech impairment, encephalopathy,impaired executive and motor function and
attentiondeficit (U.S. EPA. 2011e).
Animal Data
Many of the TCE-related developmental effects reported in humans have been observed in key and
supporting animal studies: increased fetal resorptions (Narotsky et al.. 1995), developmental
neurotoxicity(Fredriksson et al .. 1993; Ta\ lor et al .. 1985). developmentalimm1.D1otoxicity
(PedenAdams et al.. 2006), and congenital heartdefects anomalies (Johnson et al.. 2003; Dawson et al..
1993). Healy et al. (1982) observed increased resorptions, skeletal abnormalities, and decreased fetal
weight, but the study scored Unacceptable in data quality evaluation. Some of the observed effects
appear to be strain-specific (U.S. EPA. 2011e). Amongnewer studiesidentifiedin the EPA literature
search, increased locomotor and exploratory activities were observed following dr:inkiri.g
water
exposures to mice during nervous system development(Blossom et al.. 2013), however these effects
were not consistently dose-responsive.
Congenital Heart Defects
In vivo animal studies in rats and chicks have identified an association between TCE exposures and
cardiac defects15 in the developing embryo and/or fetus (U.S. EPA, 20lle ). The 2014 TSCA Work
Plan Chemical Risk Assessment (!1.S. EPA. 2014b) identified congenital heart defects following TCE
exposure via drinking water as the most sensitive human health endpoint for dose-responseanalysis
and risk evaluation based on data from (Johnson et al.. 2003) and (Dawson et al.. 1993), despite public
criticisms of insufficient data reporting and other issues in these studies. Mechanistic studies have also
examined various aspects of the induction of cardiac malformations. The critical window for cardiac
development is 1-2 weeks for rodents, 1-2 weeks for chickens, and from the 3rd to the 8th week for the
human fetus. Human studies have also identified statistically significant increased risk of
developmentalcardiac defects following TCE exposure (Brender et al.. 2014; Forand et al .. 2012;
Goldberg et al.. 1990).

15 "Cardiac" (or "heart") "defects," "malformations,"

and "abnormalities"are used throughoutthis risk evaluationto refer to
adverse findings in the developingheart. These terms, in additionto "congenitalheart defects" (CHD), are used in
experimentalanimal, epidemiological, and/or clinicalstudiesto characterizeor categorizevariousmorphological
cardiovascularoutcomes in the fetus or neonate. For the purposeof this risk evaluation,they are used interchangeably.

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The scientific literature also has examples of relatively well-conducted studies in rats and mice that did
not observe TCE-induced cardiac malformations.Most prominent among these include an inhalation
study in rats (Carney et al.. 2006) and an oral gavage study in rats (Fisher et al.. 2001). Of note however,
while (Fisher et al.. 2001) did not report statistically-significantincreases in combined cardiac and
cardiovascular effects, there was a very high background incidence in control rats and the authors did
observe a 19% increase in cardiac-specific defects (significance not calculated) following TCE
treatment compared to controls. During the development of this risk evaluation, a study was completed
that also did not report a statistically significant increase in cardiac defects following TCE exposure via
drinking water (Charles River Laboratories. 2019). Several epidemiological studies also report either
negative (Lagakos et al.. 1986) or equivocal (Yauck et al.. 2004; Bove et al .. 1995) statistical
associations between TCE exposure and heart defects. Gilboa et al. (2012) identified a statistically
significant association of perimembranous ventricular septal defects with exposure to chlorinated
solvents as a class, but not to TCE alone.
In previous assessments EPA concluded that the weight of evidence supports TCE exposure posing a
potential hazard for congenital malformations, including cardiac defects in offspring (Makris et al.; U.S.
EPA. 2014b, 2011e). Given both the conflicting results and the publication of newer animal,
epidemiological, and in vitro studies since the completion of the 2014 TCE Risk Evaluation, EPA reevaluated the weight of evidence for congenital heart defects (see Section 3.2.4.1.6 and Appendix 0).

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Overt Toxicity Following Acute/Short Tenn Exposure

Acute studies in animals consist of single exposures at high doses specifically designed for assessing
the dose at which lethality occurs or for examining overt toxicity. The interim acute exposure
guideline levels (AEGLs) document for TCE was consulted and used in this assessment to briefly
summarize the acute toxicity data (NAC/ AEGL. 2009).
In humans, TCE odors can be detected at concentrations of ~50 ppm. It was once commonly used as
an anesthetic agent with concentrations ranging from 5,000 to 15,000ppm for light anesthetic use and
from 3,500 to 5,000 ppm for use as an analgesic. Information on the toxicity of TCE in humans comes
from either case reports in the medical/occupationalliterature or experimentalhuman inhalation
studies. Lethality data in humans have been reported following accidental exposure to TCE. However,
there is insufficient information about the exposure characterizationof these incidents (NAC/AEGL,
2009),
Human inhalation studies have shown that acute exposure to TCE results in irritation and central
nervous system (CNS) effects in humans. Mild subjective symptoms and nose and throat irritation
were reported by human volunteers exposed to 200 ppm TCE for 7 hrs/day on the first day of exposure
during a 5-day exposure regimen. The study also reported minimal CNS depression following TCE
exposure (NAC/AEGL. 2009). Laboratory studies have additionally demonstrated acute effects of
TCE on the respiratory lract in the form of both localized irritation and broad fibrosis, likely
dependent on oxidative metabolism. (U.S. EPA 201 le).
CNS depression and effects on neurobehavioral functions were seen in human volunteers exposed to
1,000 ppm TCE for a 2-br period. In the same studies, volunteers were also exposed to 100 or 300
ppm TCE for 2 hrs. Some subjects had similar CNS effects at the middle concentration (300 ppm),
with no such effects observed at the 100 ppm. A different study reported slight to marginal
neurobehavioral effects after exposure to 300 ppm TCE for 2.5 hrs. Cardiac arrhythmias have also
been reported in humans exposed to high concentration of TCE. Several animal studies have reported
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neurobehavioral effects and the potential for inducing cardiac sensitization following acute inhalation
exposure to TCE (NAC/AEGL. 2009).
The NIOSH Skin Notation Profile for TCE (I Judson and Dotson. 2017) summarizes data providing
evidence for skin irritation and/or corrosion from dermal TCE exposure, with effects including rashes,
blistering, and burning sensations. Eye effects and CNS effects also resulted following simultaneous
vapor inhalation along with percutaneous penetration. Skin irritation potential varied greatly among
individuals in volunteer studies, with some exhibiting extreme pain and others hardly reporting any
effects. Studies on both humans and animals demonstratethat TCE is a moderate skin sensitizer, with
hypersensitivity reactions observed following exposure to both TCE and various metabolites.
3.2.3.2

Genotoxicity and Cancer Huards

3.2.3.2.1 Kidney cancer
The TCE IRIS assessment concluded that TCE is "carcinogenicto humans" based on convincing
evidence of a causal relationship between TCE exposure in humans and kidney cancer. A review of
TCE by the International Agency for Research on Cancer (IARC) also supported this conclusion
(IARC. 2014). The carcinogenic classificationwas based on a review of more than 30 human studies,
including studies in TCE degreasing operations, and meta-analysesof the cohort and case- control
studies. Relative risk estimates for increased kidney cancer were consistent across a large number of
epidemiological studies of different designs and populations from different cowitries and industries
(Appendix C,(U.S. EPA. 201 lb). This strong consistency of the epidemiologic data on TCE and
kidney cancer argues against chance, bias, and confowidingas explanations for the elevated kidney
cancer risks (U.S. EPA. 2011e).

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Cancer bioassays with TCE in animals (i.e., both gavage and inhalation exposure routes) did not show
increased kidney tumors in mice, hamsters, or female rats, but did show a slight increase in male rats.
1078 Kidney tumors in rats are relatively rare ill.S. EPA 2011e).

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The toxicokinetic data and the genotoxicity of DCVC further suggest that a mutagenic mode of action
is involved in TCE-induced kidney tumors, although cytotoxicity followed by compensatory cellular
proliferation cannot be ruled out. As for the mutagenic mode of action, both genetic polymorphisms
(GST pathway) and mutations to tumor suppressor genes have been hypothesized as possible
mechanistic key events in the formation of kidney cancers inhumans (!l .S. EPA. 201 le).
3.2.3.2.2 Liver cancer

U.S. EPA concluded that TCE exposure causes liver tumors in mice but nof rats and the meta-analysis
of human data on liver and gallbladder/biliarypassages indicated" .. .a small, statisticallysignificant
increase in risk!'. Multiple TCE metabolites (i.e., and thus pathways) likely contribute to TCE-induced
livertumors (U.S. EPA 201 le).

1091 Previous meta-analyses of the cohort, case-control,and community (geographic) studies reporting liver
l092 and biliary tract cancer, primary liver cancer, and gallbladder and extra-hepatic bile duct cancer (see
[093 Appendix C in (U.S. EPA. 201 lb)) reported a small, statistically significant summaryrelative risk
I094 (RRm, overall RR from meta-analysis) for liver and gallbladder/biliarycancer with overall TCE
l 095 exposure. However, the meta-analyses reported a lower, nonstatistically significant RRm for primary
l 096 liver cancer when using the highest exposure groups (U.S. EPA. 2011b).
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l098 With respect to liver carcinogenicity, TCE and its oxidativemetabolites TCA, DCA, and CH are
Page 215 of 691

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clearly carcinogenic in mice, with strain and sex differences in potency. Data in other laboratory animal
species are limited; thus, except for DCA which is carcinogenic in rats, inadequate evidence exists to
evaluate the hepatocarcinogenicityof TCE and its metabolites in rats or hamsters (U.S. EPA. 2011e).

3.2.3.2.3 Cancer of the immune system
Human studies have reportedcancers of the immunesystem resultingfrom TCE exposure. Lymphoid
tissue neoplasms arise in the immune system and result from events that occur within immature
1105 lymphoid cells in the bone marrow or peripheral blood (leukemias), or more mature cells in the
l106 peripheral organs (non-Hodgkin's lymphoma). The broad category of lymphomas can be divided into
l107 specific types of cancers, including non-Hodgkin's lymphoma, Hodgkin lymphoma, multiple
1108 myeloma, and various types of leukemia (e.g.,, acute and chronic forms oflymphoblastic and myeloid
ll09 leukemia). Leukemia during childhood has been observed in a number of studies in children exposed
to TCE, however this association has not been confirmed (U.S. EPA. 201 le ).
lll0

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One of the three cancers for which the TCE IRIS assessment based its cancer findings wa~ nonHodgkin's lymphoma (NHL) (the other two being kidney and liver cancer) (U.S. EPA. 201 le ). The
human epidemiological database identifies a statistically significant association between TCE exposure
and NHL (Appendix C, (U.S. EPA. 2011b). Further support comes from animal studies reporting rates
oflymphomas and/or leukemias following TCE exposure (U.S. EPA 201 le).
3.2.3.2.4 Other cancers
Reproductive System
The effects of TCE on cancers of the reproductive system have been examined for males
and females in both epidemiological and experimental animal studies. The epidemiological
literature includes data on prostate in males and cancers of the breast and cervix in females. The
experimental animal literature includes data on prostate and testes in male rodents; and uterus,
ovary, mammary gland , vulva, and genital tract in female rodents. The evidence for these cancers is
generally not robust (U.S. EPA 201 le).

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Other cancers
There is limited evidence of increased risk for esophageal cancer following TCE exposure in males only.
The available evidence is not statistically sensitive enough for infonning quantitative evaluations of
esophageal cancer risk from TCE. There is some evidence of association for bladder or urothelial cancer
and high cumulative TCE exposure, however the available studies examine multiple sites and do not
completely account for potential confounding factors. In several studies examining the relationship
between TCE exposure and cancer of the brain or central nervous system (CNS), the data does not
provide strong evidence in either direction, although there is some association ofTCE exposure with
CNS cancers in children (l.. S. EPA 201 le).
3.2.4

Weight of Scientific Evidence

3.2.4.1
Non-Cancer Hazards
The EPA literature search (U.S. EPA. 2017i) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of scientific evidence (WOE) conclusions
for all non-cancer endpoints other than congenital heart defects. For the previous WOE evaluations of all
other endpoints, see the 2011 EPA IRIS Assessment (U.S. EPA, 201 le ) and the 2014 TSCA Work Plan
Chemical Risk Assessment (U.S. EPA 2014b).
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3.2.4.1.1 Liver toxicity

The EPA literature search (U.S. EPA. 2017i) did not identify any new evide~ce that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this haz.ard.
Animal data demonstrating increased liver weight, cytotoxicity, hypertrophy, and peroxisome
proliferation is supportedby human data demonstrating changes in plasma or bile acid liver enzyme
levels and hypersensitivity-inducedliver damage. Overall, liver toxicity following TCE exposure is
supported by the weight of evidence. Therefore, this haz.ardwas carried forward for dose-response
analysis.

l 152 3.2.4.1.2 Kidney toxicity
1153 The EPA literature search (U.S. EPA. 20 l 7i) did not identify any new evidence that significantly
1154 contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
1155
l 156 The kidney is one of the J}lOresensitive targets ofTCE , with toxicity resulting from conjugative
1157 metabolites such as DCVC. Both animal and human studies consistently observe induction of kidney
1158 toxicity (e.g., damage to renal tubules and nephropathy) and progression of existing kidney disease.
1159 Overall, kidney toxicity following TCE exposure is supported by the weight of evidence. Therefore, this
1160 hazard was carried forwardfor dose-response analysis.

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3.2.4.1.3 Neurotoxicity
The EPA literature search (U.S. EPA 2017i) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard

In addition to anesthetic effects at high concentrations, human evidence concludes that TCE exposure
induces abnormalities in trigeminal nerve function, and TCE exposure has also been associated with
neurodegenerative disorders. These effects have been confirmed in animal studies which additionally
demonstrate a variety of neurological effects from TCE exposure. Overall, neurotoxicity following TCE
exposure is supported by the weight of evidence. Therefore, this hazard was carried forward for doseresponse analysis.
3.2.4.1.4 Immunotoxicity

The EPA literature search( U.S. EPA 2017i) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
Both animal and human studies demonstrate that TCE exposure can result in either autoimmune
responses or imrnunosuppression.There is also evidence of both systemic and localized hypersensitivity
resulting in skin sensitization and autoimmune hepatitis. Selgrade et al (2010) demonstratedreduced
response to respiratory infection. There are no other available studies that examined respiratory
immunotoxicity,however this endpoint is consistentwith other data on immunosuppression.Overall,
immunotoxicity following TCE exposure is supported by the weight of evidence. Therefore, this hazard
was carried forward for dose-response analysis, including both systemic and respiratory endpoints.
There is only qualitative information available for sensitization and hypersensitivity, so this hazard was
not carried forward for dose-response analysis.
3.2.4.1.5 Reproductive toxicity

The EPA literature search (U.S. EPA. 2017i) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
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Both human and animal data provide strong evidence for male reproductive effects from TCE. Effects
observed include effects on sperm, male reproductive organs, hormone levels, and sexual behavior.
There is insufficient evidence for determining whether TCE contributes to female reproductive toxicity.
Overall, male reproductive toxicity following TCE exposure is supported by the weight of evidence.
Therefore, this hazard was carried forward for dose-response analysis.
3.2.4. 1.6 Developmental Toxicity
The EPA literature search (LT.S. EPA. 2017 i) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) conclusions for this
hazard other than for congenital heart defects.

There is substantial evidence from both animal and human studies that TCE exposure is associated with
various developmental outcomes, ranging from decreased birth weight to pre- and postnatal mortality.
Other hazardsalso present following developmental exposure, including developmental immunotoxicity
and developmental neurotoxicity. While the epidemiological literature does not consistently observe
developmental effects, effects that have been observed in multiple human studies have been
corroborated by animal data.
Overall. based on suggestive epidemiologicdata and fairly consistent laboratory animal •
developmental toxicity following TCE exposure is supported by the weight of evidence. Therefore, this
hazard was carried forward for dose-response analysis.
Developmental toxicity endpoints will be considered for both acute and chronic scenarios. Although
developmental studies typically involve multiple exposures, they are considered relevant for evaluating
single exposures because EPA has determined that certain developmental effe.cts may result from a
single exposure during a critical window of development (Davis et al.. 2009 ; Van Raaij et al.. 2003 ;
U.S. EPA. 1991). This is consistent with EPA's Guidelines for Reproductive Toxicity Risk Assessment
(U.S. EPA. 1996) and Guidelines for Developmental Toxicity RiskAssessment( U.S. EPA, 1991),
which state that repeated exposure is not a necessary prerequisite for the manifestation of
developmental toxicity.

Congenital Heart Defects
The developmental cardiac toxicity endpoint for TCE has been widely discussed since the release of the
2011 IRIS A~essment (U.S. EPA. 201 le). EPA previously published weight of evidence (WOE)
analyses both as part of the 2014 TCE Risk Assessment and as a distinct manuscript (Makris et al..
2016 ), which concluded that the totality of data does support congenital heart defects as a human health
hazard for TCE. These WOE analyses utilized modified Bradford-Hill criteria {Hill. 1965) to evaluate
the overall evidence for causality following study quality review. Recently, (Wikoff et al .. 2018 )
published a WOE analysis focusing only on animal and epidemiological data that came to the opposite
conclusion using a Risk of Bias assessment for internal study validity. During the development of this
risk evaluation, EPA received a study sponsored by the Halogenated SoJvents Industry Alliance (HSIA)
(Charles River Laboratories. 2019) that attempted to replicate the (Johnson et al., 2003 ) study,
1231 examining the incidence of developmental cardiac defects following administration of TCE to rats via
l232 drinking water.
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Charles River Study

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EPAJJ00/Rl6 / 00. (U.S. EPA 2016i)], which advocates presenting evidence on a semiqualitative scale

on the basis of three evidence areas: reliability, outcome/strength, and relevance (see Appendix G.2.1 for
more details on selection of approach and methodological details).
In short, the overall grade for each study was defined by the lowest-amplitude score of each evidence
area, and those overall study grades were integrated to select a representative overall summary score for
each line of evidence (epidemiological, in vivo, or mechanistic). Independently, the area scores of each
study were averaged to obtain integrated areas scores for each line of evidence, however these were not
used to determine the overall summary score. Functionally, this scoring methodology is similar to that
used by (Wikoff et al.. 2018), although that analysis focused on data quality reliability through a risk of
bias assessment. Importantly, (Wikoff et al., 2018) did not evaluate any mechanistic data, which may
explain the different overall conclusions between that study and this analysis. Importantly, this WOE
assessment also incorporated data on TCE metabolites, which are believed to be the toxicologically
active agent for many of the observed cardiac effects as well as other developmental outcomes.

The overall weight-of-evidence for TCE-induced congenital cardiac defects is presented in Table 3-6.
Epidemiological, toxicological and mechanistic studies were available. The epidemiology studies as a
group provide suggestive evidence for an effect ofTCE on cardiac defects in humans (summary score of
+). Oral in vivo studies provided ambiguous to weakly positive (0/+) results for TCE itself, but positive
results for its TCA and DCA metabolites( +), while inhalation studies contributed negative evidence(-).
Overall, the in vivo animal toxicity studies provided mixed, ambiguous evidence for an effect of TCE
L304 (summary score of 0). Mechanistic studies provided strong and consistent supporting infonnation for
1305 effects ofTCE and metabolites on cardiac development and precursor effects (summary score of ++).
1306
1307 The database overall was determined to be both reliable and relevant. Integration of the three evidence
1308 areas resulted in an overall summary score of (+), demonstrating positive overall evidence that TCE can
1309 produce cardiac defects in humans (based on positive evidence from epidemiology studies, mixed
1310 evidence from animal toxicity studies, and stronger positive evidence from mechanistic studies). See
1311 Appendix G.2 for the complete WOE narrative and methodology. The complete scoring table and
1312 detailed evaluation of all studies is presented in [Data Table for Developmental Cardiac Toxicity Weight
1313 of Evidence Analysis. Docket: EPA-HQ-OPPT-2019-0500) .

1314
1315

Table 3-6. Overall Summary scores b1yL"meo fE v1'dence tior Cardiac Defects from TCE
Evidence Area
Summary
Score
Epidemiol ogy studies

+

In vivo animal toxicity studies

0

Mechanistic studie s
Overall

++

+

1316

1317 The differences in observed responses across studies may be partially attributed to experimental design
1318

differences. These differential responses may also represent varying susceptibility among mammalian

1319 species, strains, and populations. It is possible that animals showing a greater incidence of defects
1320

following TCE exposure represent an especially susceptible population, and genetic drift may preclude a

1321 true replication of previous srudy conditions (Makris et al., 2016).
1322
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Char les River Laboratories (20 19) administered TCE to pregnant rats via drinking water at
concentrations of 0ppm, 0.25ppm, l.Sppm, 500ppm, and lO00ppm in reverse osmosis-filtered water
from gestation day 1 through 21, with 3 mg/ml retinoic acid (RA) serving as a positive control. The
study did not observe a statistically significant increase of interventricular septal defects in TC.&treated
fetuses (2.4% in negative control, 3.7% at highest dose).
While the results of the Charles River study (2019 ) results contradict the results observed by (Johnson et
al..2003 ) and (Dawson et al., 1993), EPA concludes that the Char les River study methodology was
insufficiently sensitive and therefore does not sufficiently replicate the study conditions of those earlier
studies. In short , the methodology and positive contro l data indicate that the Charles River study (2019 )
was primarily focused on ventricular septal defects (VSDs) and therefore did not sufficiently examine
the complete range of potential cardiac defects. The Johnson study (2003 ) specifically described
assessment of valves and observed both valve and atrial septal defects using their laboratory dissection
and examination methodology. In contrast , while the Stuckhardt and Poppe dissection method ~
)
used by the Charles River study should allow visualization of valves, the Charles River study did not
report valve defects in any TCE group or the RA positive control group even though many other
published reports have identified valve defects following administration of TCE or RA. Additionally,
the Stuckhardt and Poppe method ( 1984) does not include examination of the heart for atrial septal
defects, and the Charles River study did not report any atrial septal defects in either the RA positive
control group or the TCE groups. In fact, the Charles River study (2019 ) observed a similar percentage
ofVSDs as (Johnson et al., 2003 ). Considering total VSDs, 3.5% of fetuses showed a VSD in Charles
River vs 3.8% in Johnson at the highest dose, with 1.5% in Charles River vs 2.2% in Johnson at l.5ppm.
When considering only membranous VSDs (the only type observed in the Charles River study), observed
incidences were actually higher in Charles River at the highest dose (3 .5% vs 2. 86% ). Meanwhile, a
substantial percentage of the total cardiac defects observed in (Johnson et al.. 2003 ) were valvular or
atrial.
As further evidence for the narrower focus of (Charles River Laboratories, 2019 ), the defects observed
from exposure to the retinoic acid (RA) positive control were also somewhat limited compared to the
broader RA literature (which did identify atrial septal defects). Additionally, the other oral TCE study
(Fisher et al., 200 I), which did not identify a statistically significant increase in cardiac defects
following TCE administration at a high dose via gavage, identified a significant number of additional
defects that match those identified in (Johnson et al .• 2003) and (Dawson et al .. 1993) (including atrial
septal and valve defects). Therefore, (Charles River Laboratories, 2019) insufficiently replicates the
methodology of (Johnson et al., 2003 ), and its results do not undermine the conclusions of that study. For
a more detailed analysis of the (Charles River Laboratories. 2019) study, see Appendix G.l.

WOE Analysis
In order to address the conflicting results of the previous WOE assessments (!J.S. EPA. 2014b: Makris
et al .• 2016; (Wikoff et al ., 2018), in support of this risk evaluation EPA performed another WOE
analysis. This analysis included all relevant primary literature cited in (Makris et al.. 2016 ), the 2014
TCE Risk Assessment (U.S. EPA, 2014b ). and any additional on-topic studies identified in the
systematic review literature search (U.S. EPA. 2017i ). Additionally, EPA also incorporated any newer
studies published after the end date of the literature search, including an in vitro mechanistic study
(Harris et al., 2018 ) and the recent ly completed in vivo drinking water study (Charles River
Laboratories, 2019 ), comprising 45 studies in total (42 scoring Acceptable). After reviewing a sampling
of recent literature on systematic approaches to performing weight-of~evidence evaluation, EPA adopted
the methodo logy described in [Weight of Evidence in Ecological Assessment. Risk Assessment Forum.
Page 219 of 691

 3.2.4.2
CancerHazards
Meta-analyses were performed in the 2011 EPA TCE IRIS Assessment (Appendix C, (Q.S. EPA.
201 lb)) in order to statistically evaluate the epidemiological data for NHL, kidney cancer, and liver
cancer. The IRIS Assessment also investigated the association ofTCE with lung cancer, primarily as a
means to examine smoking as a potential confounder for the kidney cancer studies (Appendix C, (U.S.
EPA. 201 lb )). In that assessment EPA identified a statistically significant association between TCE
exposure and NHL, kidney cancer, and liver cancer. An association was not identified for lung cancer,
suggesting that there was no confounding from smoking. The assessment concluded that TCE is
carcinogenic to humans by all routes of exposures, most strongly supported by the data on kidney
cancer. The consistency of increased kidney cancer relative risk (RR) estimates across a large number of
independent studies of different designs and populations from different countries and industries provided
compelling evidence given the difficulty, a priori, in detecting effects in epidemiologic studies when the
RRs were modest and the cancers were relatively rare, indicating that individual studies had limited
statistical power. This strong consistency of the epidemiologic data on TCE and kidney cancer argued
against chance, bias, and confounding as explanations for the elevated kidney cancer risks.

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For this risk evaluation, EPA performed new meta-analyses incorporating both the initial group of
studies assessed in the 2011 EPA TCE IRIS Assessment and any newer, on-topic studies of Acceptable
data quality identified in the literature search performed according to the Applicationof Systematic
Review in TSCA Risk Evaluations(U.S. EPA. 2018b). EPA utilized similar methodology as was
employed in the 2011 EPA TCE IRIS Assessment (U.S. EPA. 201 le) and included sensitivity analyses
as needed to partition the results based on both heterogeneity and study quality. When more than one
report was available for a single study population, only the most recent publication or the publication
reporting.the most informative data for TCE was selected for inclusion in the meta-analysis. See
Appendix H for full details and results.

1412
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3.2.4.l.1 Meta-Analysis Results
The initial results of meta-analyses for NHL, kidney cancer and liver cancei;showed moderate
heterogeneity among studies, due largely to the influence of the study by Vlaanderen et al. (2013).
Random-effects models are consequently preferred to fixed-effects models due to the degree of

The IRIS Toxicological ReviewofTCE (U.S. EPA 201 le) also cited other lines of supporting evidence
for TCE carcinogenicity in humans by all routes of exposure:

"First, multiple chronic bioassaysin rats and mice have reported increasedincidencesof tumors with
TCE treatment via inhalationand gavage, includingtumors in the kidney, liver, and lymphoidtissues target tissues of TCE carcinogenicityalso seen in epidemiologicalstudies."
"A second line of supportingevidencefor TCE carcinogenicityin humansconsists of toxicokineticdata
indicating that TCE is well absorbedby all routes.of exposure,and that TCE absorption,distribution,
metabolism, and excretionare qualitativelysimilar in humansand rodents. "

"Finally, availablemechanisticdata do not suggest a lack of humancarcinogenichazardfrom TCE
exposure. "
A statistically significant association was not identified for lung cancer and it was not considered as
contributing to the overall oral slope factor or inhalation unit risk. However, the results of the lung
cancer meta-analysis were interpreted to minimize any concern for confounding effects of smoking on
the other cancers.

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Mode of Action

A number of studies have been conducted to elucidate the mode of action for TCE-related cardiac
teratogenicity. During early cardiac morphogenesis, outflow1ract and atrioventricular endothelial cells
differentiate into mesenchymal cells. These mesenchymal cells have characteristics of smooth musclelike myofibroblasts and form endocardial cushion tissue, which is the primordia of septa and valves in
the adult heart. Many of the cardiac defects observed in humans and laboratory species involved septal
and valvular structures. Thus, a major research area has focused on the disruptions in cardiac valve
formation in avian in ovo and in vitro studies following TCE treatment. These mechanistic studies
have revealed TCE's ability to alter the endothelialcushion development, which could be a possible
mode of action underlying the cardiac defects involving septal and valvular morphogenesis in rodents
and chickens. Other modes of actions may also be involved in the induction of cardiac malformation
following TCE exposure. For example, studies have reported TCE-related alterations in cellular Ca2+
fluxes during cardiac development (Caldwell et al.. 2008; Selmin et al.. 2008; Collier et al.. 2003).
Of note, early stages of cardiac development are quite similar across various species (Makris et al.. 2016).
Therefore, these mechanistic data provide support to the plausibility ofTCE-relatcd cardiac effects in
humans (U.S. EPA 201 le).

1334
1335
1336
1337
1338
l339
l 340 Several in vitro studies have observed non-monotonic dose responses in gene activation and other
l34 l molecular changes following TCE exposure at varying concentrations (£alb vkin et al.. 2011 ; Makwana
l 342 et al .. 2010 ). Specifically, TCE exposure induced expression of oxidative stress genes (Makwana et al..
l 343 2010 ) and increased DNA hypennethylation of a calcium-ATP pump promoter in developing cardiac
l344 tissue (Palbykin et al .. 2011 ) only at lower and not higher doses, resulting in multimodal calcium
l345 responses (Caldwell et al.. 2008). TCE also increased significantly increased gene expression of the
1346 oxidative metabolism enzyme CYP2Hl specifically in cardiac tissue only at the lower dose ((Makwana
1347 et al., 2013)). In (Harris et al.. 2018), expression of genes involved in cardiac development and
l 348 metabolism were either reduced (low dose) or increased (high dose), depending on the administered
l 349 concentration. These results may explain the non-monotonic polynomial dose-response observed in
l350 (Johnson et al.. 2003), whereby toxicological outcomes present at different doses equating to either
l35 l inhibition or activation of particular gene expression (Harris et al.. 2018). This differential gene
1352 expression would in turn lead to dose-specific downstream metabolic and phenotypic effects.
l353
1354 Overall, an association between increased congenital cardiac defects and TCE exposure is supported by
l355 the weight of evidence, in agreement with previous EPA analyses (!.I.S. EPA. 2014b: Makris et al..
l356 2016). Therefore, this endpoint was carried forward for dose-response analysis.
l357

3.2.4.1.7 Overt Toxicity Following Acute/Short Term Exposure

l358

There is strong evidence for overt toxicity in humansfollowing acute exposure to high concentrations of

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TCE. AEGL guidelines indicate the concentrations at which increasing levels of toxicity are established
following acute inhalation exposure to TCE. High concentrations ofTCE have been shown to result in
respiratory and dermal irritation, CNS depression, cardiac arrhythmia, and even death.
While overt toxicity following acute or short term exposure to TCE is supported by the weight of
evidence, studies examining the acute outcomes described above were not selected for assessing acute
risks due to a lack of sufficient dose-response information. EPA considered more sensitive endpoints for
estimation of risks following acute TCE exposure, namely all developmental toxicity endpoints and
reduced response to respiratory infection (Selgrade and Gilmour. 2010). Other acute studies described
above were not selected for assessing acute risks due to a lack of sufficient dose-response infonnation.

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heterogeneity. These reduced the influence of the (Vlaanderen et al.. 2013) study and demonstrated
stronger positive associations (greater meta-RR value) of all cancers with exposure to TCE, although the
liver cancer meta-RR was not significant. The evidence for an association between TCE exposure and
NHL was further strengthened by a subsequent meta-analysis on studies reporting cohorts categorized as
experiencing "high" exposure to TCE, which demonstrated a greater meta-RR compared to "any"
exposure.

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3.2.4.2.2 Mode of Action
Kidney Cancer
Genotoxicity
The predominant mode of action (MOA) for kidney carcinogenicity involves a genotoxic mechanism
through formation of reactive GSH metabolites (e.g., DCVC, DCVG). This MOA is well-supporte4 as
toxicok.ineticdata indicates that these metabolites are present in both hwnan blood and urine, and these
metabolites have been shown to be genotoxic both in vitro and in animal studies demonstrating kidneyspecific genotoxicity (U.S. EPA. 201 le).

The study ofVlaanderen et al. (2013) carries very large statistical weight due to its large sample size,
but its sensitivity to detect any true effect ofTCE is likely to be low. The study is based on a large
general population cohort with exposures estimated by linkingjob titles recorded in national census data
to a job-exposure matrix. The prevalence and average intensity ofTCE exposure are low in the study
population and the indirect method of estimating exposures has significant potential to misclassify
exposure. Further, the study was not scored High for data quality in EPA's review (it scored Medium).
There was therefore reason to believe that omitting the Vlaanderen et al.(2013) study would improve the
sensitivity of meta-analytic results for all three cancers. In sensitivity analyses omitting the study of
(Vlaanderen et al.. 2013), between-study heterogeneity wa:ssignificantly reduced or eliminated
1432 Resulting meta-RRs for exposure to TCE were strengthened and were statistically significant for all
1433 three cancers.
1434
1435 Analyses stratified by a data quality score also indicated strQngerassociations of all cancers with TCE
l 436 exposure in studies that scored High for data quality compared to studies that scored Medium or Low;
1437 notably, the latter group included the influential study of (Ylaanderen et al.. 2013), Studies that scored
1438 high showed no heterogeneity of effects for NHL and kidney cancer, but moderate heterogeneity
1439 remained for liver cancer.
1440
1441 In summary, meta-analyses accounting for between-study heterogeneity, influential observations, and
l442 data quality consistently indicate positive associations of NHL, kidney cancer and liver cancer with
1443 exposure to TCE. This conclusion generally agrees with that of other governmental and international
l 444 organizations. The International Agency for Research on Cancer (IARC) (IARC, 2014) found sufficient
1445 evidence for the carcinogenicity ofTCE in humans. IARC definitively stated that TCE causes kidney
1446 cancer and determined that a positive associated has been identified for NHL and liver cancer. Based on
1447 the weight of evidence when accounting for both these authoritiative assessments and the results of
l 448 EPA' s meta-analyses, cancer was carried forward for dose-response analysis, incorporating extra cancer
1449 risk from all three cancer types.

Cytotoxicity and other mechanisms
Observed nephrotoxicity in both hwnan and animal studies, especially at elevated concentrations,
provides some evidence of a cytotoxic MOA. Data comparing relative dose-response analysis of
nephrotoxicity and kidney cancer incidence suggests that cytotoxicity can occur at doses below those
causing carcinogenicity in animal bioassays, however this data also indicates that nephrotoxicity is not
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sufficient or rate-limiting for renal carcinogenesis. Therefore, a causal or predictive link between
cytotoxicity and carcinogenicity cannot be established. There is inadequate experimental support for
other potential MOAs such as peroxisome proliferator activated receptor alpha (PPARa) induction, a2µglobulin nephropathy, and formic acid-related nephrotoxicity (U.S. EPA. 201 le ).
Conclusion
There is clear evidence of a genotoxic MOA for kidney cancer , either on its own or in combination with
other mechanisms. While the kidney is highly sensitive to TCE-induced cytotoxicity, the contribution of
cytotoxicity toward kidney carcinogenesis cannot be determined. Renal cytotoxicity may instead serve
as a promoter step in tumorigenesis following genotoxic initiation, or it may merely represent an
independent pathway of toxicity (U.S. EPA. 2011e).

Liver Cancer
Genotoxicity
The strongest data supporting mutagenic potential ofTCE or potential liver metabolites comes from data
on the intermediate metabolite chloral hydrate (CH), which induces a variety of genotoxic effects both in
vitro and in vivo. The peak in vivo con~entrations of CH in tissue are substantially less than is required
for induction of genotoxicity in many in vitro assays, however there is some evidence of in vivo
genotoxicity at doses comparable to those inducing cancer in chronic bioassays. Overall, the data are
insufficient to conclude that a mutagenic MOA is operating, however it cannot be ruled out. (U .S. EPA ,
201le ).

PPARa receptor activation
While strong evidence exists for TCA-mediated PP ARa receptor activation (resulting in downstream.
perturbation of cell apoptosis and proliferation signaling) based on observed peroxisome proliferation
and increased marker activity in rodents treated with TCE, TCA, or DCA, this appears to occur at a
higher dose than what induces liver tumors in mice. TCE, TCA, and DCA have been found to be weak
peroxisome proliferators, and some data suggests that PPARa activation may not be sufficient for
carcinogenesis . The available data clearly supports a role of PPARa activation in liver tumorigenesis,
however any key causal effects are likely mediated by multiple mechanisms and neither causality ,
sufficiency, or necessity of PPARa. signaling in liver carcinogenicity can be established (U.S. EPA .
201 le ).
Other mechanisms
There is limited evidence for a tumorigenic role of increased liver weight, growth selection, cytotoxicity,
oxidative stress, and/or glycogen accumulation. Heritable epigenetic changes such as altered DNA
methylation patterns, which disrupt the balance of gene expression and may lead to over- or underexpression of various tumor suppressors and promoters, have been associated with liver cancer and
other tumors in general. Additionally, TCE has been shown to promote hypomethylation (resulting in
increased gene expression) in vivo and ex vivo in liver tissue. DNA hypomethylation can be sufficient
for liver carcinogenesis based on choline/methionine deficiency studies, however the applicability of
this mechanism to TCE-induced carcinogenesis is unknown as these changes could either be causally or
consequentially related to carcinogenicity (U.S. EPA , 201 le ).
Conclusions
The available data is inadequate to support any singular MOA. TCE-induced liver carcinogenesis
appears to be very complex and likely involves multiple contributing mechanisms. The strongest
evidence exists for involvement of both genotoxicity and PPARa activation, however a causal
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relationship cannot be establishedbecausethe dose levels required to elicit outcomes through both
MOAs are higher than.thosedemonstratingtumorigenicactivity (U.S. EPA 201 le).
Non-Hodgkin Lymphoma

There is insufficientdata availablefor suggestingany particularMOA for NHL.
Overall Conclusions

TCE is carcinogenicby a genotoxicmode of action at least for kidney cancer, while a predominant
mode of action cannot be determinedfor the other tumor types . Per EPA Guidelines for CarcinogenRisk
Assessment(U.S. EPA . 2005 ), overall,the totality of the availabledata/informationand the WOE
analysis for the cancer endpoint was sufficientto support a linear non-threshold model. The application
of a linear non-thresholdmodel is justified based on the genotoxicMOA for kidney cancer, the
combined relative contributionsof multiple tumor types, and the positive associations observed via
meta-analysisfor all three cancers in epidemiologicalstudies based on low-level,environmental
exposure levels (as opposed to relying on extrapolationfrom high doses in a rodent bioassay).

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3.2.4.3
Summary of Human Health Hazards Used to Evaluate Acute and
Chronic Exposures
3.2.5

Dose-Response Assessment

1530
3.2.5.1
Selection of Studies for Dose-Response Assessment
l 531 The EPA evaluated data from studies describedabove (Section 3.2.3.1) to characterizethe dose1532 response relationships ofTCE and selected studiesand endpointsto quantify risks for specific exposure
1533 scenarios. One of the additional considerationswas that the selectedkey studies had adequate
l 534 informationto perform dose-responseanalysis for the selectedPODs. The EPA defines a POD as the
1535 dose-responsepoint that marks the beginningof a low-doseextrapolation. This point can be the lower
1536 bound in the dose for an estimatedincidence,or a change in response level from a dose-responsemodel
1537 (i.e., BMD), a NOAEL or a LOAEL for an observedincidenceor change in the level of response.
1538
l539 Based on the weight of the evidenceevaluation,six health effect domains were selected for non-cancer
1540 dose-responseanalysis: (1) liver; (2) kidney; (3) neurological; (4) immunological;(5) reproductive;and
1541 (6) developmental.Additionally,dose-responseanalysis was performed for cancer based on observed
1542 incidences of kidney cancer, liver cancer, and non-Hodgkinlymphoma.These hazards have been carried
1543 forward for dose-responseanalysis. While there is also evidenceto support overt toxicity following
1544 acute exposure, endpoints for these effects were not carried forward for dose-responseanalysis. For a
1545 complete discussion, see Section 3.2.4.1.
1546
1547 Studies that evaluated each of the health effect domains were identifiedin Section 3.2.3 , and are
l 548 considered in this section for dose-responseanalysis. In order to identify studies for dose-response
1549 analysis, several attributes of the studies were reviewed.Preferencewas given to studies using designs
l55O reasonably expected to detect a dose-relatedresponse. Chronic or subchronicstudiesare genefally
1551 preferred over studies of less-than-subchronicduration for deriving chronic and subchronicreference
l 552 values. Studies with a broad exposurerange and multiple exposure levels are preferredto the extent that
L553 they can provide information about the shape of the exposure-responserelationship.Additionally,with
1554 respect to measurementof the endpoint, studies that can reliably measure the magnitudeand/or degree
1555 of severity of the effect are preferred.
1556

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Experimental animal studies considered for each haz.ardand effect were evaluated using systematic
review quality considerations discussed in the Systematic Review Methods section. Only studies that
scored an acceptable rating in data evaluation were considered for use in dose-response assessment. In
addition to the data quality score, considerations for choosing from among these studies included study
duration, relevance of study design, and the strength of the toxicological response. Details on these
considerations for each endpoint are provided below.
Given the different TCE exposures scenarios considered (both acute and chronic), different endpoints
were used based on the expected exposure durations. For non-cancer effects and based on a weight-ofevidence analysis of toxicity studies from rats, risks for developmental effects that may result from a
single exposure were considered for both acute (short-term) and chronic (long-term, continuous)
exposures, whereas risks for other adverse effects (e.g.,, liver toxicity, kidney toxicity, neurotoxicity,
immunotoxicity, and reproductive toxicity) were only considered for repeated (chronic) exposures to
TCE. Although developmental studies typically involve multiple exposures, they are considered relevant
for evaluating single exposures because EPA has determined that certain developmental effects may
result from a single exposure during a critical window of development (Davis et al., 2009; Van Raai1et
al.. 2003; U.S. EPA. 1991). This is consistent with EPA's Guidelines for Reproductive Toxicity Risk
Assessment (U.S. EPA. 1996) which state that repeated exposure is not a necessary prerequisite for the
manifestation of developmental toxicity. Consequently,in this risk evaluation EPA accepted the
Agency's default assumption and concluded that developmental endpoints are applicable when assessing
acute exposures, where it is assumed that the risk of their occurrence depends on the timing and
magnitude of exposure. 1bis is a health protective approach and assumes that a single acute exposure
could lead to the same effects if that exposure occurs during a critical window within the pregnancy
term. A single acute study examiningpulmonary immunotoxicity following 3h TCE inhalation exposure
(Selgrade and Gilmour. 2010) was also consideredfor acute exposure scenarios.Overt toxicity studies
(Section 3.2.3.1. 7) were not used for the acute POD because they were often only single-dose studies
and the doses at which acute toxic effects or lethality were observed were significantly higher than those
that caused toxic effects in developmental studies.

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3.2.5.1.1 Liver toxicity
The 2014 TSCA Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) detennined that the studies
of (Woolhiser et al.. 2006; Buben and O'Flahert,. 1985; Kjellstrand et al.. 1983) were suitable for the
dose-response assessment of the liver health effects domain. These three studies reported dose~nsive increases in liver/body weight ratios. (Buben and O'Flahert\ . 1985) and Q(jellstrand et al..
1983) also reported cytotoxicity and bistopathologyin mice. All three of these studies scored Medium
or High in EPA's data quality evaluation [DataQualityEvaluation of Human Health Hazard Studies.
Docket: EPA-HQ-OPPT-2019-0500)and were therefore utilized for dose-responseanalysis.

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3.2.S.1.2 Kidney toxicity
The 2014 TSCA Work Plan Chemical Risk Assessment(U.S. EPA 2014b) considered five animal
studies reporting kidney toxicity for further non-cancer dose-response analysis. (Maltoni et al .. 1986),
(NCI, 1976) and (NTP. 1988) reported histological changes in the kidney, whereas (!(j ellstrand et al. ,
1983) and (Woolhiser et al.. 2006) reported increased kidney/body weight ratios (!I.S. EPA, 201 le ).
NCI (1976) scored Unacceptable in EPA's data quality evaluation [DataQualityEvaluation of Human
Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500]and therefore was excluded from doseresponse analysis. All of the other studies scored Medium in data quality and were therefore utilized for
dose-response analysis.

Page 226 of 691

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3.2.5.1.3 Neurotoxicity

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Among the human studies, (Ruijten et al., 1991) was the only epidemiological study that the IRIS
program deemed suitable for further evaluation in the TCE' s dose-response assessment for
neurotoxicity. Only the following four animal studies were considered suitable for dose-response
l608 analysis for the neurotoxicity endpoint in the 2014 TSCA Work Plan Chemical Risk Assessment ~
l609 EPA, 20 14b): (Arito et al., 1994). Qsaacson et al., 1990). (Gash et al .• 2008). and (Kjellstrand et al .•
l610 1987). Kjellstrand (1987) scored Unacceptable in in EPA's data quality evaluation [Data Quality
1611 Evaluation of Human Health Hazard Studies. Docket: EPA -HQ-OPPT-2019-0500] and therefore was
l612 excluded from dose-response analysis. Gash et al. (2008) scored a Low in data evaluation and was also
l 613 not carried forward to dose--responseanalysis given the other,•higher quality studies available. Ruijten
l614 et al. (I 991). Arito et al. (1994), and Isaacson et al. (1990) all scored Medium or High for data quality
l 615 and were therefore utilized for dose-response analysis.

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3.2.5.1.4 lmmunotoxicity
Only the following four animal studies were suitable for the 2014 TSCA Work Plan Chemical Risk
Assessment (U.S. EPA. 2014b ) non-cancer dose-response analysis for the immunotoxicity endpoint:
(Keil et al .• 2009), (Kaneko et al.• 2000), (Sanders et al., 1982). and (Woolhiser et al., 2006). For this
Risk Evaluation, EPA also assessed the endpoint of acute pulmonary immunotoxicity observed in
(Selgrade and Gilmour. 2010). In Selgrade et al (2010). mice were infected via respiration with
aerosolized S zooepidemicus bacteria following 3h TCE exposure. Mortality, bacterial, clearance from
the lung, percent of mice infected, and phagocytic index were assessed following co-exposure. Mortality
was selected as the most statistically sensitive endpoint due to a larger numbers of mice per exposure
group and more dose groups.All of these studies scored Medium or High in EPA' s data quality
evaluation [Data Quality Evaluation of Human Health Hazard Studies . Docket : EPA- HQ-OP PT 2019-05 00J and were therefore utilized for dose-response analysis.

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3.2.5. 1.5 Reproductive toxicity

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Among the human studies, (Chia et al .• 1996) was the only epidemiological study that the 2014 TSCA
Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) deemed suitable for further evaluation in the
TCE' s dose-response assessment for reproductive toxicity. Only the following eight reproductive
animal toxicity studies were considered suitable for non-cancer dose-response analysis in the 2014
TSCA Work Plan Chemical Risk Assessment (!1,S. EPA. 2014b): (Kwnar et al.. 2000), (Kumar et al.,
200 1), (Kan et al., 2007). (Xu et al., 2004). (Narotsky et al., 1995). (George et al., 1986), (Duteaux et
al.• 2004). and (Forkert et al., 2002). Forkert et al. (2002) scored Unacceptable in EPA's data quality
evaluation and therefore was excluded from dose-responseanalysis, however it had the same POD as
(Kan et al .• 2007). which scored Medium. Duteaux et al. (2004) scored a Low for data quality and was
not carried forward to dose-response analysis given the other, higher quality studies available. The
remaining studies all scored Medium or High for data quality [Data Quality Evaluation of Human
Health Hazard Studies. Docket : EPA-HQ-OPPT -2019-0500] and were therefore utilized for doseresponse analysis.

l 642 3.2.5.1.6 Developmental toxicity
1643 The 2014 TSCA Work Plan Chemical Risk.Assessment(U.S. EPA. 2014b) found 5 animal studies that
l 644 were suitable for non-cancer dose- response analysis for the following developmental outcomes: pre1645 and postnatal mortality; pre- and postnatal_growth; developmental neurotoxicity; and congenital heart
l646 malformations (Appendix L of that document).
1647
l 648 Although the focus of the discussion below is on these 5 studies and corresponding endpoints, it is
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important to mention that developmental immunotoxicity has alsobeen demonstrated in TCE-treated
animals. The most sensitive immune system response was reported by (eeden-Adams et al., 2006). In
this study, B6C3F 1 mice were exposed to TCE via drinking water. Treatment occurred during mating
and through gestation to TCE levels of 0, 1.4, or 14 ppm. After delivery, pups were further exposed for
either 3 or 8 more weeks at the same concentrationlevels that the dams received in drinking water.
Suppressed PFC response was seen in male pups after 3 and 8 weeks of exposure, whereas female pups
showed the suppression of PFC response and delayed hypersensitivityat 1.4ppm following 8 weeks.
At the higher concentration {14 ppm), both of these effects were observed again in both males and
females following 3 or 8 weeks of postnatal exposure. A LOAEL of0.37 mg/kg-bw/day served as a
POD for the decreased PFC and increased delayed hypersensitivity responses {U.S. EPA, 2011 e).
Whlle this endpoint exhibits one of the lower PODs among developmentaltoxicity studies, the study
scored a ''Low" in EPA' s data quality evaluation [Data Quality Evaluation of Human Health Hazard
Studies. Docket: EPA-HQ-OPPT-2019-0500} due to concerns over statistical reliability and dose
precision {difficult to calculate precise dosage}.Additionally, it could not be accurately PBPK modeled
because exposure occurred in utero, through nursing, and after weaning. Therefore, this study was not
considered further for dose-response assessment, although developmental immunotoxicity will still be
considered qualitatively.
Pre- and Postnatal Mortality and Growth
The following two studies were considered suitable for non-cancer dose-response analysis for pre- and
postnatal mortality and growth effects in the 2014 TSCA Work Plan Chemical Risk Assessment {U.S.
BPA, 2014b): (Heah et al.. 1982)_and (Narotsk, et al.. 1995). Healy et al. (1982) scored Unacceptable·
in in EPA's data quality evaluation [Quality Evaluation of Human Health Hazard Studies. Docket:
EPA-HQ-OPPT-2019-0500} and therefore was excluded from dose-response analysis. (Narotsky et al.,
1995) scored a High and was therefore utilized for dose-response analysis.
Developmental Neurotoxicity
There is evidence of alterations in animal brain development and in behavioral parameters (e.g.,.
spontaneous motor activity and social behaviors}following TCE exposure during the development of
the nervous system. Among all of the available studies, there were two oral studies that reported
behavioral changes which were used in the dose-response evaluation for developmental toxicity:
(Fredriksson et al., 1993) and (Ta\ lor et al.. 1985). {Taylor et al.. 1985) scored a Low in EPA's data
quality evaluation due to the same issues as (Peden-Adams et al., 2006) and was not considered further
for dose-response assessment (Fredriksson et al., 1993) scored a Medium despite some uncertainty
concerning the statistical validity of its sampling methodology [Data Quality Evaluation of Human
Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500}and was therefore utilized for doseresponse analysis.
Congenital Heart Defects
The fetal cardiac defects reported in (Dawson et al., 1993) and (Johnson et al.. 2003) were identified as
the most sensitive endpoint within the developmental toxicity domain and across all of the health
effects domains evaluated in the TCE IRIS assessment. Johnson et al. {Johnson et al.. 2003) reported
data from different experiments over a several-year period in which pregnant Sprague-Dawley rats {913/group; 55 in control group) were exposed to TCE via drinking water. Treatment of pregnant rats
occurred during the entire gestational period (i.e., GD Oto GD22). The study was a follow-up to
Dawson et al. ( 1993), which demonstrated increasing incidence of congenital heart defects at the
highest two dose groups that were later pooled and re-analyz.edin (Johnson et al.. 2003).
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Much of the controversy surrounding the reliabilityof the (Johnson et al.. 2003) study relates to the
pooling of control animals and data across several years, including the use of different vehicles (tap
water vs distilled water). EPA therefore compared the data from (Johnson et al.. 2003) and from
(Dawson et al.. 1993), the earlier study comprisingthe highest two doses of the (Johnson et al.. 2003)
study in which <latawas not pooled and only a single vehicle was used. Unfortunately, EPA was unable
to use a nested benchmarkdose (BMD) model because individualpup data could not be easily tracked
to a particular dam, so this data is less statistically reliable. Both studies scored a "Medium" in in
EPA' s data quality evaluation [Data Quality Evaluationof Human Health Hazard Studies. Docket:
EPA-HQ-OPPT-2019-0500],which incorporated all available information on the two studies,
including subsequent errata and communicationsto EPA (Johnson et al.. 2014; Johnson. 2014, 2008;
Johnson et al.. 2005). While the original publications (especially (Johnson et al.. 2003)) had severe
l708 data and methodology reporting issues, many of the data quality conc~ms from the original study were
1709 mitigated by the information provided in these updates. Because both studies passed data evaluation
l 710 with the same score and statistics could only be performed using a pup as the statistical unit for
l 711 (Dawson et al.. 1993), EPA decided to utilize the (Johnson et al.. 2003) data for dose-response
l 712 analy~-i~which .has increased statistical sensitivity from the additional two dose levels and allowed a
1713 nested design for BMD modeling analysis in order to account for litter effects. Additionally, some
l 714 defects originally identified in (Dawson et al.. 1993) were later reclassified or recharacterized in
l 715 (Johnson et al.. 2003), so (Johnson et al.. 2003) contains the more updated analysis.

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3.2.S.1.7 Cancer
The 2019 meta-analysis of a11relevant studies examining kidney cancer, liver cancer, or NHL
(Appendix H) crune to the same conclusion as the previous EPA meta-analysis in the 2011 IRIS
Ac;sessment(U.S. EPA. 201 le ). Therefore, EPA utilized the same inhalation unit risk and oral slope
factor estimates as were derived in (U .S. EPA. 201 lc) and cited in the 2014 TSCA Work Plan Chemical
Risk Assessment (U.S. EPA. 2014b ). A linear non-thresholdassumption was applied to the TCE cancer
dose-response analysis because there is sufficient evidence that TCE-induced kidney cancer operates
primarily through a mutagenic mode of action while it cannot be ruled out for the other two cancer types.
The 2011 IRIS Assessment( U.S. EPA 201 le) selected the epidemiological kidney cancer data
Charbotel et al (2006) as the best representative dose-response data for derivation of an oral slope factor
and inhalation unit risk value as a case-control study with quantitative cumulative exposure estimates
based on a task-exposure matrix based on decades of measurement. Charbotel et al (2006) received a
High score for dataquality both overall and for the exposure domain in EPA' s dataevaluation [Data
Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500].Therefore,
EPA relied on its previous dose-response analysis from this study.

3.2.5.2
PotentiallyExposed and SusceptibleSubpopulations(PESS)
TSCA reqwres that a risk evaluation "determine whether at chemical substance presents an
unreasonable risk of injury to health or the environment, without consideration of cost or other non-risk
L735 factors, including an unreasonable risk to a potentially exposed or susceptible subpopulation identified
l 736 as relevant to the risk evaluation by the Administrator, under the conditions of use." TSCA § 3(12)
l 737 states that ''the term 'potentially exposed or susceptiblesubpopulation' means a group of individuals
1738 within the general population identified by the Administrator who, due to either greater susceptibility or
l 739 greater exposure, may be at greater risk than the general population of adverse health effects from
1740 exposure to a chemical substance or mixture, such as infants, children, pregnant women, workers, or the
l741 elderly."
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 During problem formulation (l '.S. EPA. 20 18d), EPA identified potentially exposed or susceptible
subpopulations for further analysis during the development and refinement of the life cycle, conceptual
models, exposure scenarios, and analysis plan. In this section, EPA addresses the potentially exposed or
susceptible subpopulations identified as relevant based on greater susceptibility.EPA addresses the
subpopulations identified as relevant based on greater exposurein Section 2.3.3.

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There is some evidence that certain populations may be more susceptible to exposure to TCE. Factors
affecting susceptibility examined in the available studies on TCE include lifestage, gender, genetic
polymorphisms, race/ethnicity, preexisting health status, and lifestyle factors and nutrition status.
Factors that affect early lifestage susceptibility include exposures during gestation, such as
transplacental transfer, and during infancy, such as breast milk ingestion, early lifestage-specific
toxicokinetics, and early lifestage-specific health outcomes including developmental cardiac defects.
Gender-specific differences also exist in toxicokinetics (e.g.,, cardiac outputs, percent body fat,
expression of metabolizing enzymes) and susceptibility to toxic endpoints (e.g.,, gender-specific effects
on the reproductive system, gender differences in baseline risks to endpoints such as scleroderma or
liver cancer). Genetic variation likely has an effect on the toxicokinetics ofTCE. Pre-existing
diminished health status may alter the response to TCE exposure. Individuals with increased body mass
may have an altered toxicokinetic response due to the increased uptake of TCE into fat. Other conditions
that may alter the response to TCE exposure include diabetes and hypertension, and lifestyle and
nutrition factors such as alcohol conswnption, tobacco smoking, nutritional status, physical activity, and
socioeconomic status (U.S . EPA. 201 le ). Among life stages, the most susceptible is likely to be
pregnant women and their developing fetus based on the hazard findings from reviewing the available
literature for this assessment, which conclude that developmental toxicity is among the most sensitive
acute health effects associated with TCE exposure. Among pregnant women, older women may be
especially susceptible to TCE-induced cardiac defects in their offspring. Maternal age is known to have
a large influence on the incidence of congenital heart defects, and multiple studies cited in this Risk
Evaluation identified a significantly stronger association of TCE with developmental cardiac defects
(Brender et al.. 2014 ; Yauck et al..2004 ). Additional maternal risk factors for susceptibility to
congenital car<liova&:u1ar defects include diabetes, infection status, drug exposure, and stress, among
others (Jenkins et al.. 2007).

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Significant variability in humansusceptibility to TCE toxicity may result from differences in
metabolic potential, given the existence of CYP isofonns and the variability in CYP-mediated TCE
oxidation (U.S. EPA. 201 le ). Increased enzymatic activity of cytochrome P450 2El (CYP2El) and
glutathione-S-transferase (GST) polymorphisms may influence TCE susceptibility due to effects on
the production of toxic metabolites (U.S. EPA . 201 le ). More specifically, there appears to be
greater susceptibility to TCE-induced kidney cancer in those individuals that carry an active
polymorphism in a gene associated with the GST metabolic pathway. Particularly, the gene is
associated with the P-lyase gene region which is responsible for converting DCVC to the unstable
intermediate DCVT. Also, there are some human studies suggesting a role for mutations to the tumor
suppressor gene, von Hippel Lindau (VHL gene). This tumor suppressor gene appears to be
inactivated in certain TCE-induced kidney cancers( U.S. EPA. 2011e ). In the 2014 TCE risk
evaluation (U.S. EPA . 2014b ), EPA performed a population analysis to systematically estimate
uncertaintyand variability across several metabolic factors, including human variability related to
oxidative metabolism and glutathione conjugation as a result of GST activity. Integration of these
factors into a probabilistic model resulted in a distribution of human equivalent concentrations/doses
(HECs/HEDs) for each endpoint. HEC99/HED99values representing the most metabolically
sensitive 1% of the population, a susceptible subpopulation, were used for risk evaluation, and EPA
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utilized the same analysis for this assessment.

3.2.S.3

Derivation of Points of Departure (P9Ds)

Point of departures (PODs) were identified for those studies that had suitable data for dose-response
analysis, described above. PODs can be a NOAEL or LOAEL for an observed incidence, or change in
level of response, or the lower confidence limit on the dose at the benchmark dose (BMDL ). PBPK
modeling was used to estimate internal dose PODs (idPOD) and subsequently the human equivalent
concentrations/doses (HECs/HEDs) based on the oral and inhalation PODs identified in earlier steps .
The PBPK modeling integrated internal dose-metrics based on TCE's mode of action and the role of
different TCEmetabolites in toxicity (U.S. EPA 201 le). Note thatthe effects within the same health
effect domain were generally assumed to have the same relevant internal dose-metrics, with some
exceptions. Compared to the 2014 TSCA Work Plan Chemical Risk Assessment, an additional POD
from Selgrade (2010 ) has also been added for acute exposure scenarios.

3.2.5.3.1 BMD Modeling and Uncertainty Factors (UFs)
A set of dose-response models were applied to empirically model the dose-response relationship in the
range of the observed data. The models in EPA's Benchmark Dose Software were applied. Consistent
with EPA's BenchmarkDose TechnicalGuidanceDocument(U.S. EPA 2012a ), the benchmark dose
(BMD) and 95% lower confidence limit on the BMD (BMDL) were estimated using a benchmark
response (BMR) to represent a minimal, biologically significant level of change, when possible. The
BMR is represented by a specified percentage change, or relative deviation (RD), for continuous data.
The BMR for dichotomous data is repre sented by a specified incidence , or extra risk (ER). In the
absence of information regarding the level of change that was considered biologically significant, a
BMR of 1 standard deviation (SD) from the control mean for continuous data or a BMR of 10% ER for
dichotomous data was used to estimate the BMD and BMDL, and to facilitate a consistent basis of
comparison across endpoints, studies, and assessments. Endpoint-specific BMR.s are described further
below. Where modeling was feasible, the estimated BMDLs were used as points of departure (PODs).
Further details, including the modeling output and graphical results for the model selected for each
endpoint, can be found in the 2011 EPA IRIS Assessment (U.S. EPA 201 le ) and Appendix G (for
(Sel grade and Gilmour . 2010 )). A comparison of results from updated BMDL modeling runs with
results from (U.S. EPA . 2011e ) for (Johnson et al. , 2003) are provided in Appendix N. Where doseresponse modeling was not feasible, NOAELs or LOAELs were also identified and are summarized.

1821 Selecting the model to use for POD computation
The following approach is recommended for selecting the model(s) to use for computing the BMDL to
serve as the POD for a specific dataset according to EPA Benchmark Dose Guidance (U.S. EPA .
2012a ).
L826 I) Assess goodness-of-fit, using a value of a= 0.1 to determine a critical value (ora = 0.05 or a =0.01)
ff
if there is reason to use a specific model( s) rather than fitting a suite of models.
1822
1823

lil!

l ~i
l829
lffjq
1832
l833
1834
1835
1836
1837

2) Further reject models that apparently do not adequately describe the relevant low- dose portion of the
dose-response relationship, examining residuals and graphs of models and data.
3) As the remaining models have met the recommended default statistical criteria for adequacy and
visually fit the data, any of them theoretically could be used for detennining the BMDL. The remaining
criteria for selecting the BMDL are necessarily somew~at arbitrary and are suggested as defaults.
4) If the BMDL estimates from the remaining models are sufficiently close (given the needs of the
assessment), reflecting no particular influence of the individual models, then the model with the lowest
Page 231 of691

 Akaike's Information Criteria (AIC)16 may be used to calculate the B.MDLfor the POD. This criterion is
intended to help arrive at a single BMDL value in an objective, reproducible manner. If two or more
models share the lowest AIC, the simple average or geometric mean of the BMDLs with the lowest AIC
may be used. Note that this is not the sameas "model averaging", which involves weighing a fuller set
of adequately fitting models. In addition, such an average has drawbacks, including the fact that it is not
a 95% lower bound (on the average BMD); it is just the average of the particular BMDLs under
consideration (i.e., the average loses the statistical properties of the individual estimates).

l838
1839
L840
1841
l842
l843
1844
l845
1846
l 847
l 848
1849
l 850
l 851
1852
1853

5) If the BMDL estimates from the remaining models are not sufficiently close, some model dependence
of the estimate can be assumed. Expert statisticaljudgment may help at this point to judge whether
model uncertainty is too great to rely on some or all of the results. If the range of results is judged to be
reasonable, there is no clear remaining biological or statistical basis on which to choose among them,
and the lowest BMDL may be selected as a reasonable conservative estimate. Additional analysis and
discussion might include consideration of additional models, the examination of the parameter values for
the models used, or an evaluation of the BMDs to determine if the same pattern exists as for the
BMDLs. Discussion of the decision procedure should always be provided.

l 854

6) In some cases, modeling attempts may not yield useful results. When this occurs and the most

1855
1856
1857
1858
l859
1860
1861
1862

biologically relevant effect is from a study considered adequate but not amenable to modeling, the
NOAEL (or LOAEL) could be used as the POD. The modeling issues that arose should be discussed in
the assessment, along with the impacts of any related data limitations on the results from the alternate
NOAEL/LOAEL approach.

l§~

1865
1866
1867
1868
1869
1870
1871
l 872
1873
1874

l§16

1877
1878
1879
1880

UncertaintyFactors
After the PODs were determined for each study/endpoint,UFs were used to derive acceptable
benchmark margins ofmxposure (MOEs). UFs are used to address differences between study conditions
and conditions of human environmental exposure. These include:
(a) Extrapolatingfrom laboratory animals to humans (UFA):
If a POD is derived from experimental animal data, it is divided by an UF to reflect pharmacokinetic and
phannacodynamic differences that may make humans more sensitive than laboratory animals.For oral
exposures, the standard value for the interspecies UF is 10, which breaks down (approximately) to a
factor of3 for phannacokinetic differences (which is removed if the PBPK model is used) and a factor
of 3 for pharmacodynamic differences. For inhalation exposures, ppm equivalence across species is
generally assumed or other cross-species scaling is perfonned, in accordance with U.S. EPA inhalation
dosimetry guidance (U.S. EPA. 1994b), in which case, residual phannacokinetic differences are
considered to be negligible, and the standard value used for the interspecies UF is 3, which is ascribed to
pharmacodynamic differences. These standard values were used for all of the PODs based on laboratory
animal data in this assessment.
(b)Human (intraspecies) variability (UFH):
Sensitive humans could be adversely affected at lower exposures than a general study
population; consequently, PODs from general-populationstudies are divided by an UF to address
sensitive humans. Similarly, the animals used in most laboratory animal studies are considered to be
16

Akaike's InformationCriteria-a measure of infonnation loss from a dose-responsemodel that can be used to
compare a set of models. Among a specifiedset of models,the model with the lowest AIC is consideredthe best. If two or
more models share the lowestAIC, an averageof the BMDLscould be used, but averagingwas not used in this assessment
because for the one occasion in which models shared the lowest AIC, a selectionwas made based on visual fit.

Page 232 of 691

 typical or average responders, and the human (intraspecies)variability UF is also applied to PODs from
l882 such studies to address sensitive subgroups. The standard value for the human variability UF is 10,
1883 which breaks down (approximately)to a factor of 3 for pharmacokineticvariability (which is removed if
1884 the PBPK model is used) and a factor of 3 for pharmacodynamicvariability. This standard value was
used for all of the PODs in this assessment.
1887 (c) Uncertaintyin extrapolatingfromsubchronicto chronicexposures(UFs):17
l 888 Chronic risk estimates apply to long-term exposure over decades, but sometimes the best {or only)
l889
available data come from less-than-lifetimestudies. Lifetime exposure can induce effects that may not
l890 be apparent or as large in magnitude in a shorter study; consequently,a dose that elicits a specific level
l 891 of response from a lifetime exposure may be less than the dose eliciting the same level of response from
1892 a shorter exposure period. Thus, PODs based on subchronic exposure data are generally divided by a
l893 subchroni~to-chronic UF, which has a standard value of 10. If there is evidence suggesting that
l 894 exposure for longer time periods does not increase the magnitude of an effect, a lower value of 3 or one
1895 might be used. For some reproductiveand developmentaleffects, chronic exposure is that which covers
1896 a specific window of exposure that·is relevant for eliciting the effect, and subchronic exposure would
correspond to an exposure that is notably less than the full window of exposure.
l899 (d) Uncertaintyin extrapolatingfrom LOAELs to NOAELs (UFL):
1900 PODs are intended to be estimates of exposure levels without appreciable risk under the study
1901 conditions so that, after the application of appropriate UFs for interspecies extrapolation, human
1902 variability, and/or duration extrapolation,the absence of appreciable risk is conveyed. Under the
l903 NOAEL/LOAEL approach to determining a POD, however, adverse effects are sometimes observed at
1904 all study doses. If the POD is a LOAEL, then it is divided by an UF to better estimate a NOAEL. The
1905 standard value for the LOAEL-to-NOAELUF is 10, although a value of 3 is sometimes used if the
l906
effect is considered minimally adverse at the response level observed at the LOAEL or is an early
1907 marker for an adverse effect. For NOAEL or BMDL values, the UFL is 1.
1908
1909 For this assessment, when an endpoint can be BMD and PBPK modeled, default cumulative acute UF ::e:
1910 10 (UFA and UFHboth = 3 for toxicodynamics (UFro); UFs and UFL= 1) and default cumulative
1911 chronic UF = 100 (UFs = 10 if the study covers less than 10% of lifetime).
1912
1913 POD Selection Metrics
1914 The below sections present all studies considered for dose-responseanalysis. From this list, the studies
1915 were selected from each health domain /organ system that best represent each available endpoint. For
1916 some health domains with multiple endpoints this resulted in multiple studies being selected for
1917 consideration in risk estimation. In selecting the most representative studies and PODs, EPA
1918 considered factors such as data quality evaluation score, species, exposure duration, dose range,
1919 cumulative uncertainty factor, and relevance. Dose metric selection is based on a determination of
l 920 which to:xicokineticmeasure is most predictive of localized effects from TCE exposure. These factors
1921 were evaluated for each independent endpoint, and EPA considereduse of the most health-protective
1922 POD only after first considering each of the above factors. See the 2011 EPA TCE IRIS Assessment
1923 (U.S. EPA. 2011 e) for more detailson do8€>-metric
and benchmarkresponse(BMR)determinationsfor all
1924 endpoints except that from Selgrade and Gilmour (2010 ). BMD modeling results for (Selurade and
l925 Gilmour. 2010 ) are presented in Appendix F.
l881

l1§~

l§~i

17 Chronic exposure covers > l 0%

of expectedlifetime. Rodent studies exceeding90 days of exposure are considered
chronic, and rodent studies covering from 4 weeks to 90 days of exposureare consideredsubchronic.For human studies.
chronic exposureexceeds 7-8years, on average (U.S . EPA. 1994b).

Page233 of 691

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1927
1928
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1930
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1936
1937
1938
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1940
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1942
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1944
1945
1946
1947
1948
1949
1950
1951
l952
1953
l954
1955
1956
1957
1958
l 959
1960
l 961
1962
l 963
1964
l 965
l966

l967
1968
1969
1970
1971
l 972
l973

3.2.5.3.2 Non-Cancer PODs for Acute Exposure

Acute exposure in humans is defined for occupationalsettings as exposure over the course of a single
work shift (8 hours) and for consumers as a single 24-hour day. Although developmental studies
typically involve multiple exposures, they are considered relevant for evaluating single exposures
because EPA has determined that certain developmentaleffects may result from a single exposure
during a critical window of development (Davis et al.. 2009; Van Raaij et al.. 2003; U.S. EPA. 1991).
This is consistent with EPA's Guidelinesfor ReproductiveToxicityRisk Assessment (U.S. EPA, 1996),
which state that repeated exposure is not a necessary prerequisite for the manifestation of
developmental toxicity. Therefore, developmental endpoints were considered relevant for calculating
risks associated with acute occupational or consumer exposure. Single-exposure studies identifying a
dose-responsive specific health outcome were also considered for deriving PODs representative of
risks following acute exposures.
HECs for developmental toxicity were adjusted to reflect a 24-hr value, consistent with both
occupational and consumer exposure values. The POD from Selgrade (2010), a 3hr acute inhalation
study, was adjusted to a 24hr HEC value for occupational risk estimates due to limited available
occupational exposure information below 8hr time periods. The 3hr POD was used without adjustment
for estimation of consumer risks due to available exposure estimates for 3hr time periods.
DevelopmentalToxicity Endpoints
--Prenatal Mortality
(Narotskv et al.. 1995) was also discussed above in the reproductive toxicity section, but also
identified mortality to the developing fetus following in utero TCE exposure. F344 timed-pregnant
rats (8-12 dams/group) were treated with TCE by gavage during GD 6 to 15. The BMDLo1for
increased resorptions was 32.2 mg/kg-bw/day (U.S. EPA. 201 le).
-- DevelopmentalNeurotoxicity
(Fredriksson et al., 1993) treated male NMRI mouse pups (12/group, selected from 3-4 litters) with
TCE via gavage (0, 50, or 290 mg/kg-bw/day)during postnatal days (PND) 10 to 16. Locomotor
behavior was evaluated at PND 17 and 60. TCE-treatedmice showed decreased rearing activity at both
dose levels on PND 60, but not PND 17, resulting in a LOAEL of 50 mg/kg-bw/dayas a POD GJ:.S.
EPA. 201 le).
- Congenital Heart Malformations
(Johnson et al.. 2003) reported a statistically and biologically significant increase in the formation of
heart defects at the 0.048 mg/kg-bw/day and higher dose levels (concentrations of 0, 0.00045, 0.048,
0.218 or 129mg/kg-bw/day)measured on both an individual fetus basis and a litter basis. A BMDL01
HEC99of 0.0037 ppm and HED99of 0.0052 mg/kg-bw/daywere identified as the inhalationand oral
PODs, respectively, for heart malformations in the 2014 TSCA Work Plan Chemical Risk Assessment
(U.S. EPA 2014b). EPA quantified the totality of cardiac defects insteadof any particular defect, as
cardiac teratogens can result in a diverse constellationof effects (e.g., retinoic acid, see Appendix
G.1.2.2).

The BMR selection from the 2014 TSCA Work Plan Chemical Risk Assessment (!1.S. EPA. 2014b)
for (Johnson et al.. 2003) was also reassessed based on the non-monotonic dose-response, decreased
incidence from control at the 2.5ppb dose level, and reduced statistical power due to a less than
recommended number of litters assessed for each dose group. These concerns were discussed as part
of a re-analysis of the 2011 dose-response assessmentin (Makris et al., 2016), which acknowledged
Page 234 of 691

 l974
1975
1976
1977
l 918
1919
1980
1981
1982
l983
1984
1985
1986
l 987
1988
1989
1990
1991
1992
1993
l994
L995
L996
1997
l998

the uncertainty inherent in a selection of a 1% BMR:
«BMDinferenceat the 1% extra-risklevel is highly uncertain,becauseBMD and BMDL values vary
by several orders of magnitudedepend-ingon the modelingassumptions.This is attributed in part to
the lack of monotonicityat the lowestdose and the apparentsupralinearityof the overall exposureresponse relationship.Additionaldoses would be requiredto betterspecify the curve shape in the lowdose region.More reliable inferencecan be madefor higher BMRs...

There is substantialmodel andparameter uncertaintyat the 1% level of extra risk, although 1% is the
appropriateBMR based on severity of the effect (i.e., cardiacmalformations).These uncertaintiescan
be attributedprimarily to having toofew datapoints in the low-doserange, where more data would be
required to adequatelycharacterizethe dose-response shape. Uncertaintydecreasesfor higher BMR
levels (5% and 100/4extra risk), although 10% exceeds the range of the datafor some models".
In reevaluating the BMR, EPA considered both biological and statistical factors:
1. The biological severity of the effect
2. The range of observable data relative to the BMR and resulting BMDL
3. The influence of study design and sample size on statistical sensitivity
4. Confidence in the model fit and variance

After considering all these factors, EPA determined that the biological severity of the effect,
potentially lethal heart defects, strongly supported a BMR of 1%. For statistical considerations, EPA
referred to the nested BMD modeling results from Appendix F.4.2.l in (U.S. EPA. 201 lb ). In these
results, the .BMDL for both a 1% and 5% BMR easily fall within the experimental dose range,
increasing confidence in the target BMRs. The observed incidence for the lowest dose in (Johnson et
al.. 2003 ) was reduced from controls, adding uncertainty to the modeling estimate, however
l 999 overlapping confidence intervals indicate that the results are not statistically different. A larger sample
2000 size for the treated groups may have increased the statistical sensitivity at lower doses. The BMD
WOl model actually displays better visualfit at the lower end of the dose range, near the control, suggesting
l002 that a lower BMR may actually represent a more accurate model estimate.
l003

2004 In evaluating model fit, EPA determined that the BMD:BMDL ratio was adequate (3.1), indicating
2005 reasonably small variance, however the reported p-value for the original model fit was quite poor
2006 (p=0.0128) based on limitations with the 2011 version ofBMDS resulting in an inappropriate choice
2007 of subgroups. In response, an Rprogram was written to test other subgroups (U.S. EPA. 201 lb ) which
2008 demonstrated an adequate model fit. EPA also performed a Rao-Scott transformation to adjust for
2009 intra-litter correlation in a later publication (Makris et al.. 2016), resulting in very similar BMD
Wl0 modeling results to the original 2011 assessment (U.S. EPA. 201 lb ). Due to limitations with the 2011
Wl 1 modeling software, EPA decided to re-nm the BMD modeling on the (Johnson et al.. 2003) dataset in
W12 order to increase confidence in the model fit and resulting BMDL. The resulting BMDLs and model fit
2013 based on AICs were identical to that of the 2011 IRIS Assessment (11.S. EPA 201 la) (within
2014 rounding error). Of note, the p-value = 0.661 for the selected model at a 1% BMR (based on applied
2015 dose) in the updated model run (Appendix N) is significantly improved, demonstrating strong model
2016 fit in the updated BMDS software and confinning the 2011 conclusions of a good model fit.
·
2017
W18 Based on the above considerations and the improved model fit from the updated BMD modeling run,
W19 EPA determined that use of a 1% BMR is most appropriate for risk estimation. The difference
2020 between the 1% and 5% BMR.POD values is 5.2-fold. Results for both 1% and 5% extra risk BMR
!021 options (along with 10%) are presented in Appendix N.
Page 235 of 691

 W22

W23
2024
2025
W26
2027
2028
W29
?030
W31
?032
?033
2034
?035
?036
2037
lQ38
?039
?040
W41
?042
?043
?044
2045
2046
2047
2048
,049

-

Immunotoxicity
-- Response to infection
In addition to the previously described developmentaltoxicity studies, (Sehrrade and Gilmour. 2010)
was deemed suitable for dose-response analysis of immunotoxicitybased on observed decreased
response to infection. In Selgrade et al (2010), female CD-1 mice were infected via respiration with
aerosolizedS. zooepidemicus bacteria following3h exposure to 0, 5, 10, 25, 50, 100, or 200 ppm ofTCE.
Mortalitywas assessed for all dose groups, with statisticallysignificant and dose-responsiveincreases
observed at 50 ppm and above. Bacterial clearancefrom the lung, percent of mice infected, and phagocytic
index were also assessed for 0, 50, 100, and 200ppm dose groups. This study examined pulmonary
immunologicalresponses to respiratory infection following inhalation ofTCE and is therefore only
applicable to inhalation exposure. The inclusionof the Selgradeand Gihnour (2010 ) study is an addition
to this risk evaluation and was not previously evaluated for dose-response analysis in the 2014 TSCA
Work Plan Chemical Risk Assessment (U.S. EPA. 2014b ). This study was discussed in the 2011 IRIS
Assessment (U.S. EPA. 2011 e) but was excludedfrom the 2014 Risk Assessment in an oversight.
For (Selgrade and Gilmour. 2010 ), B:MDmodelingwas perfonned on the endpointsof mortality and
percentageof mice infected (see [PersonalCommunicationto OPPT. Raw Data Values.fromSelgrade
and Gilmour, 2010. Docket: EPA-HQ-OPPT-2019-0500]).A reliableBMDL could not be obtained from
the percentage infected data because BMDs and BMDLs from all models were well below the lowest
data point and cannot be considered reliable. For mortality, a BMR of 1% increase was selected due to
the severity of the effect. Based on evidence of systemic chronic immunosuppression(Keil et al., 2009 ),
this acute endpoint was applied to systemic exposure.Based on assumed ppm equivalence across species
(U.S. EPA. 201 le), the BMDL1also serves as the HEC for 3hrexposure, while 1.74ppm is the HEC for
24hr exposure. Route-to-route extrapolationand allometric scaling based on values from (U.S. EPA,
1988) and subsequent allometric scaling results in a dermal HED of2.74 mg/kg.
. of se Iecte d stu d.1es cons1.d ere d fior acut e expo sure scenanos
T abl e 3-7: Dose-res ponse smaI 1s1S

Target
Organ/ Species
System

Duration

Rat
Gestational
(female) days 6to I~

DevelopRat
mental
(female)
Effects
Rat
(male
pups)

22 days
throughout
gestation
(gestation.al
dav!. 0 tn2n

PODType 1
(applieddose)

BMDL 01=32.2 Increased
mg/ kg--bw/day resorptions

. 3hr/day, single

Infection

BMDLo1=
13.9ppm

Decreased

rearing
activity
Mortality
following
infeetion

HECso HEC,, HEDso HEim

Metric

(ppm)

TotMetab
BW34

57

BMDLo1=
Congenital
TotOx
0.0207 mg/kgheart
Metab
malformatio ns BW34
bw/day

Postnatal days LOAEL=SO
10 to 16
mg/kg -bw/day

Rat dose; followed
Immune
System (female) by respiratory

Dose

Effect

0.0ll

(ppm) (mg/kg (mg/kg
23

29

2

UFS=l; UFA=3;
UFH=3; UFL=l;
Tota l UF=lO

UFs=l; UFA= 3;
0.0037 0.0058 0.0052 UFH=3; UFL= I;
TotalUF=l0

TotMeta b
BW34

8

3

4.2

N/A4

N/A 4

1.744

N/A4

Page236 of691

28

Uncertainty
Factors (UFs)

4.1

Reference

Data
Quality

(NilJ:Otl!k
, i.t
al., 129~)

High
(1.3)

(Johnsonet Medlun
al., 2003)
(1.9)

l.JFS=l; UFA= 3;
(Eredriks~Qll Mediun
UFH=3; UFL=I0;
1.rnl..122~
) ().7)
Tota l UF=l 00

UFs=l; UFA= 3; (Setgract1i!mli
High
2.744.S UFH=l0; lJFL-"l;
Gilmoyr,
(1.6)
2010 )
TotalUF =30

 1

POD type can be NOAEL, LOAEL,or BMDL. EPAadjusted all valuesto continuousexposure.
chronic UF; UFA=interspeciesUF; UFH=intraspeciesUF; UFL=LOAELto NOAEL UF.
3
Sec {DataQuality Evaluationo[Hum<mHealth HazardStudies.Docket:EPA-HQ-OPPT-2019-0500)
for full evaluation by metric.
4 Data from Selgrade and Gilmour. 2010) was not subjectto PBPK modelingdue to uncertaintyconcerning the most appropriate dose metric. The BMDLvalue
adjusted for a 24hr exposure will be used as the POD for occupationalrisk estimates, while the 3hr value will be used for consumer risk estimates.This value ii
presented in the HEC99column but does not represent any particularpercentilesince it was not PBPK-modeled.
5
A dermal HED was obtained through route-to-routeextrapolationusing breathing ra,e and body weightdata on inale CD-I mice (insufficientfemale data was
available) from (U.S. EP \. 1988) and allometric scaling based on (U.S. EPA. 201Jd) using adosimetric adjustment factorof0.14 for mice.
2 UFs=subchronicto

2050

W51
W52
W53
2054
W55
W56
W51
2058

Table 3-7 presents the derived PODs from all studies consideredfor dose-response analysis of acute
exposure scenarios. EPA selected studies representativeof the distinct endpoints of prenatal mortality,
congenital defects, developmentalneurotoxicity,and responseto infection. Most of the developmental
toxicity studies utilized the PBPK dose metric ofTotMetabBW34, or the total amount TCE metaboliz.ed
per unit adjusted body weight This dose metric was selected because for these endpoints there is
insufficient information for site-specificor mechanism-specificdetenninations of an appropriatedoseme1ric,however in general TCE toxicity is associatedwith metabolitesrather than the parent compound.
TotOxMetab34,or the total amount TCE oxidiz.edper unit adjusted body weight, was used for deriving
W59 HEC/HED values for congenital heart defects because evidence demonstrating effects from TCA and
2060 DCA (see Section 3.2.4.1.6) suggests that oxidative metabolism is important for TCE-induced heart
W61 malformations.
2062
2063 The LogProbit model was selected for BMD modeling results of (Sel!!rade and Gilmour. 2010) data
W64 because it was the model with the lowest AIC, using a BMR of 1% based on the endpoint of mortality.
2065 Data from (Narotskv et al.. 1995) and (Johnson et al.. 2003) were also BMD modeled. A BMR of 1%
2066 ER was selected for (Johnson et al.. 2003) based on the severityof the effect and absence of a strong
?067 statisticaljustification for raising the value (see discussion above). A BMR of 1% was also selected for
2068 (Narotsk\ et al .. 1995) because of the severity of the effect (full-litter resorptions) and low background
2069 response. A LOAEL was used as a POD for (Fredriksson et al.. 1993), which was not BMD modeled.
UF does not apply, so UFs = I for all studies. See Section
2070 For acute exposures, subchronic-to--chronic
?071 3.2.2.1 and (U.S. EPA 2011e) for more details on TCE PBPK modeling, dose metric selection, and
W72 BMR selection.
2073

2074 Differences from standard UF values are explained below:
W15 A UFAvalue of3 was applied to (Sel!!rade and Gilmour. 2010) because cross-species scaling based on
2076 blood:air partition coefficientor allometric scaling for body weight was used to adjust the HEC/HEDas
?077 necessary.
W78
2079 The selected studies are bold in the table above. The endpoints were each represented by a single study.
W80 While there are some methodological and statistical concerns about (Johnson et al.. 2003) and
2081 (Fredriksson et al.. 1993), based on the WOE for the endpoints and data quality scores of at least
2082 Medium, all four of the studies will be utiliz.edfor quantitativerisk estimation following acute
?083 exposures. There is also some inherent uncertaintyextrapolatingfrom the response to pulmonary
W84 infection observed in (Selgrade and Gilmour. 2010) to a systemicresponse across multiple exposure
2085 routes, but an acute systemic response to infection is likely based on the systemic immunosuppression
2086 observed in a chronic study (Keil et al.. 2009 ).
2087 3.2.5.3.3 Non-Cancer PODs for Chronic Exposures
W88 Chronic exposure was defined for occupational settings as exposure reflecting a 40-hour work week.
2089 Chronic exposure was not considered relevant to to consumersbased on expected use patterns (Section
W90 2.3.2.7.1). Non-cancer endpoints selected as most relevant for calculating risks associated with chronic
Page 237 of 691

 2091 (repeated) occupational exposures to TCE included effects on the to the liver, kidney, nervous system,
?092 immune system, reproductive system, and development, with all HECs adjusted to reflect a 24-hr value,
W93 consistent with calculated occupational exposure values.
W94
W95 Liver toxicity
W96 -- Increased liver weight and cytotoxicity/h.ypertrophy
W97 (Kjellstrand et al., 1983) exposed NMRI male mice (10-20/group) with up to nine different TCE
W98 concentrations. These concentrations ranged from 37 to 3,600 ppm and included an air control group.
W99 Exposures were conducted for various durations (1, 2, 4, 8, 16, or 24 hrs/day) and for different time
?100 frames (from 30 to 120 days). EPA calculated a benchmark concentration lower-bound confidence
2101 limit of21.6 ppm based on the 10% benchmark response (BMDL10)for increased liver/body weight
no2 ratios, with cytotoxicity and histopathology also observed.
H03
2104 (Buben and O'Flahertv. 1985) exposed Swiss-Cox male mice (12-15 group) to TCE by gavage. Mice
nos were exposed to a range ofTCE doses (100 to 3,200 mg/kg-bw/day plus control) for 5 days/week for 6
2106 weeks. A BMDL1oof82 mg/kg-bw/day was identified as the POD for increased liver/body weight
2107 ratios, with cytotoxicity and histopathology also observed.
2108
2109 In (Woolhiser et al.. 2006), Sprague-Dawley female rats (16/group)were exposed to TCE via
2110 inhalation at concentrations of 0, 100, 300, or 1,000 ppm for 6 hrs/day, 5 days/week for 4 weeks. A
2111 BMDL10of 25 ppm was estimated for increased liver/body weight ratio.
2112 Table 3..S: Dose-response analysis of selected studies considered for evaluation of liver toxicity
Target
Oraan Species
Svstem

Duration
I

PODType 1
(appieddollle)

Effect

Dose

Metric

Continuous and
intermittent
Increased AMetLivl
exposures,
Mouse variabletime BMDL1o=21.6 liver/body BW34
ppm
(male)
weight ratio
periods for 30and
l20days
cytoto:dcity/
Liver
BMDL10= 82 hypertropbJ AmetLivl
Mouse
6week s
BW34
mg/kg-bw
/day
(male)
Increased
6 hr/day, 5
BMDLui=25 liver/b<,dy ArnetLivl
Rat
days/week for 4
BW34
female
ppm
weight ratio
weeks

HECsoHEC,, HEDso 1IED9' Uncertainty
(ppm)

25

(ppm) (mg/kg) (mg/ka

9.1

9.0

Factors (UFs) 2

Data
Reference QualityJ

7.9

UFS=l; UFA= 3; (K ieJlotT<>ntiPf
Medium
UFH=3; UFL=l;
id., 1983)
(1.8)
TotaJUF=I0

JO

UFs= l; UFA=3 ; (B11!2m
!!ml
High
UFH=3; UFL=l; QTl!!l!em,
(1.3)
1985)
Total UF=JO

16

UFS=l; UFA= 3;
(:WQQlhiser
et Medium
lJFH=3; lJFL=l;
al., 2006)
(2)*
Total UF=lO

I

32

11

53

19

12

19

1 POD type

can be NOAEL, LOAb7...,or BMDL. EPA adjustedall valuesto continuous exposure.
UFs=subchronic to chronic UF; UFA=interspeciesUF; UFH=intraspeciesUF; UFL=LOAELto NOAEL UF.
3 See [DataQuality Evaluationof HumanHealth HazardStudies. Dock£t: EPA-HQ-OPPT-2019-0500] for full evaluationby metric. * Woolhiser 2006
was downgraded from a High, with calculatedscore = 1.3.
2

?113
2114
2115
2116
2117
2118
2119

Table 3-8 presents the derived PODs from all studies considered for dose-response analysis. Increased
liver/body weight ratio was the only endpoint modeled from all studies based on the dose metric
AMetLivlBW34 , or the amount ofTCE oxidized in liver per unit adjusted body weight This dose metric
was selected because evidence suggests that hepatic oxidative metabolism is involved in TCE liver
toxicity and dose-response relationships using this metric showed greater consistency than other
considered metrics. All studies were BMDL modeled. A BMR of 10% RD was used to represent a
?120 minimal, biologically significant amount of change in relative liver weight. See Section 3.2.2.1 and (1!.S.
2121 EPA. 2011e) for more details on TCE PBPK modeling, dose metric selection, and BMR selection.
2122
Page 238 of 691

 ?123
2124
?125
2126
2127
2128
H29
2130
H31
2132
H33
2134
2135
2136
2137
?138
H39
H 40
2141
?142
2143
2144
2145
H46
2147
2148
H49
HSO
2151
2152
2153
2154
2155

The data from (Kjellstrand et al.. 1983) was selected to represent the liver toxicity hazard. (Woolhiser et
al.. 2006) was excluded from further consideration becauseadditional signs of toxicjty were not
observ~ indicating that the increased liver weight was likely merely adaptive. (Kjellstrand et al.. 1983)
was selected over (Bubenand O'Flahert\. 1985) because it covered up to 120 days exposure as opposed
to only 42 days. Additionally, (Kjellstrand et al.. 1983) utilized the widest dose range of any study,
imparting more precision in the POD estimate.
Kidney toxicity
--Kidney Pathology

(Maltoni et al., 1986) exposed Sprague-Dawleymale rats (116-124/group) to TCE via inhalation (0,
100, 300, or 600 ppm) for 7 hrs/day, 5 days/week for 104 weeks (and allowed all rats to continue
unexposed until they died). The investigatorsalso conducted an oral (gavage) study that dosed rats
with a range of TCE doses (50 to 250 mg/kg-bw/day) for 4-5 days/week for 52 weeks. BMDL10
values of 40.2 ppm and 34 mg/kg-bw/day were calculated for the inhalation and gavage studies ,
respectively, based on renal tubular pathological changes (meganucleocytosis) (U.S. EPA. 201 le).
In another oral (gavage) study (NTP, 1988), the National ToxicologyProgram exposed Marshall female
rats (44-50/group) to TCE (i.e., 0, 500, or 1,000mg/kg-bw/day) for 5 days/week for l 04 weeks. Rats
developed toxic nephropathy following TCE exposure. A BMDLosof9 .45 mg/kg- bw/day was
calculated for the observed kidney effects (U.S. EPA 201 le).
--Increased Relative Kidney Weight
(Woolhiser et al.. 2006) conducted an inhalation study that exposed Sprague-Dawley female rats
(16/group) to 0, 100, 300 or 1,000ppm TCE for 6 hrs/day for 5 days/weeks for 4 weeks. At the end of
the study, rats exhibited increased kidney/body weight ratios and a BMDL10of 15.7 ppm was estimated
fortheseeffects (U.S. EPA 201 le).

Increased kidney/body ·weight ratios were also seen in (Kjellstrand et al., 1983). NMRI male mice (1020/group) were exposed to a range ofTC E concentrations (37 to 3,600 ppm) for 30 to 120 days on
continuous and intermittent exposure regimens. A BMDL10of 34.7 ppm was identified as the POD for
increased kidney/body weight ratios (U.S. EPA. 201 le).
Table 3-9: Dose-response analysis of selected studies considered for evaluation of kidney toxicity

Target
Organ
Species
System

Duration

PODType 1
(IRJlmdme)

Effect

Dose HECso REC" HEDso RED,,
Uncertainty
Metric (ppm) (ppm) (mg/kg ] (mg/kg' Factors (Ut's) 2

Data
Reference Qaality3

UFS=I ; UFA= 3;
ABioact
Medium
5days/week BMDLos= 9.45
Toxic nephropathy DCVC 0.042 0.0056 0.033 0.0034 UFH=3; lJFL=J; (NTP, 1288)
(2)*
(female) for 104weeks mg/kg-bw/day
Total UF=JO
BW34
Rat

Pathology
ABioad
14-Sdays/wed BMDL1oa34
cbanaes in renal DCVC 0.19
for52week.s mg/kg-bw/day
BW34
tubule
-Oral

0.025

7hrs/day , 5
ABioact
Rat
BMDL10=40.2 Pathology changes DCVC 0.28
(male) days/week for
in renal tubule
ppm
Kidney -Inhal.
2 years
BW34

0.038

Rat

(male)

6hr /day, 5
BMDL10= 15.7
Rat
days/week for
(female)
ppm
4week s

Increasedkidney ABioact
DCVC 0.099
weight/body
BW34
wei2htratio

Page 239 of 691

0.013

0.15

UFS=l; UFA=3; (Maltoni et Medium
0.015 UFH=3; UFL=l;
al., 1986)
(2)*
TotalUF=lO

UFS=l ; UFA==3;
0.023 UFH=3; UFL= I;
Total UF• JO
UFs=l;UFA = 3;
0.o78 0.0079 UFH=3; UFL= I;
Total UF=JO
0.22

(M~ltoni ct Medium
(2) *
al., 1986)
(Woolhiser Medium
et al.. 2006) (2)*

 Continuous
and
Increased kidney AMet
UFS"'l;UFA=3; (Kjellstrand Medium
Mouse intermittent BMDL10= 34.7
weight/body
0.88
0.12
0.07
GSH
9.69
UFH=3; UFL=I; et al .. 1983) (1.8)
(male) exposures for
ppm
weight ratio
BW34
Total UF=JO
30-120days
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 UFS=subchronic to chronic UF; UFA=interspeciesUF; UFH=intra.5pCCies
UF; UFL=LOAELto NOAEL UF.
3 See [DataQuality Evaluation efHuman Health Hazard Studies. Docket: EPA-HQ-OPPT-2019
-0500] for full evaluation by metric. *Nf P 1998 was
~owngraded from a High, with calculated score= 1.2;Maltoni 1986 was
downlmlliedfrom a Hill.h,with calculated scores = l.4 (oral) and 1.3 (inhalation): Woolhiser2006 was downaraded from a Hi2h with calculated score= 1.3.

H56
H57
2158
H59
2160
2161
2162
'163
2164
H 65
2166
H67
?168
2169
?170
H 71
2172
?173
H74
2175
2176
H 77
H 78
2179
H 80
?181
H 82
2183
2184
?185
H86
H87
H88
H 89
90
?191
H 92
2193
2194
?195

n

Table 3-9 presents the derived PODs from all studies considered for dose-response analysis. The studies
considered for dose-response analysis identified either indications of kidney pathology or increase
kidney/body weight ratio . All rat studies utilized ABioactDCVCBW34, or the amount ofDCVC
bioactivated in the kidney per unit adjusted body weight, because GSH-conjugative bioactivation of
TCE into metabolites such as DCVC in the kidney is expected to be responsible for kidney toxicity.
AMetGSHBW34, or the amount ofTCE conjugated with GSH per unit adjusted body weight, was
utilized for mice studies because PBPK information on DCVC activation in mice is not available. All
studies were BMDL modeled. ABMRof5%
ER was used for (NTP . 1988) because toxic nephropathy is
a severe toxic effect. (Maltoni et al.. 1986) used a BMR of 10% ER because meganuclocytosis is
considered minimally adverse, while both studies examining increased relative kidney weight used a
standard BMR of 10% RD. See Section 3.2.2.1 and (U.S. EPA. 201 le ) for more details on TCE PBPK
modeling, dose metric selection, and BMR selection.
Differences from standard UF values are explained below:
(Woolhiser et al.. 2006) and (K iellstrand et al.. 1983) were assigned UFs = 1 despite shorter exposure
duration because no differences were observed in severity of relative kidney weight increases between 30
and 120 days in (Kiellstrand et al .. 1983).
EPA determined that kidney pathology was a better indicator of adverse kidney effects than increased
relative organ weight and therefore only that endpoint was selected to represent kidney toxicity. While
there are concerns about the procedureof continuing observation until spontaneous death in (Maltoni et
al.. 1986) due to the potential for confounding effects from autophagy or infection, there are unlikely to
be significant artifacts from this methodology affecting the interpretation of kidney lesions. There was
random allocation to study groups and kidney lesions were not observed in the control or lowest dose
group. Therefore, background false positives were not an issue and the observed dose-response is
expected to be independent of this confounder. Additionally, a 2011 review of pathology results from
other cancer studies performed in this laboratory (Ramazzini Institute) by the N1P Pathology Working
Group (Malarke , and Bucher. 2011 ) found good agreement on the interpretation of most solid tumors
and only identified significant differences among inflammatory cancers of the blood and respiratory
tract.
Both (Maltoni et al., 1986) and (NTP, 1988) scored a Medium in data quality, however (Maltoni et al ..
1986) tested exposure over a sufficiently similar duration with a more appropriate dose range. The
elevated doses in (tl TP . 1988) resulted in massive nephrotoxicity and introduce large uncertainty in
BMD modeling the effects at low doses well below the tested doses with a BMR well below the
observed effect incidence in the study. Therefore, the BMDL and resulting HEC/HED from (Maltoni et
al.. 1986) was considered more reliable. Among the inhalation and oral results from (Maltoni et al.,
1986), with few other differences among the data the lower resulting oral POD was selected to represent
the endpoint in order to be health-protective. Of note, this represents a change from the 2014 TSCA Work
Page 240 of691

 H96
H97
?198
?199
2200
220I
?202
2203
2204
2205
2206
!207
2208
!209
2210
2211
2212
2213
2214
!215
2216
!217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
!230
?231
232

Plan Chemical Risk Assessment (U.S. EPA, 2014b), which selected the POD from QiTP. 1988) to
represent kidney toxicity.
Neurotoxicity

-· CNS Depression
(Ari to et al.. 1994) exposed Wist&' male rats (5/group) to TCE via inhalation to concentrations of 0,
SO,100, or 300 ppm for 8 hrs/day, 5 days/week for 6 weeks. Exposure to all of the TCE concentrations
significantly decreased the amount of time spent in wakefulness during the exposure period. Some
carry over was observed in the 22 hr-post exposure period, with significant decreases in wakefulness
seen at 100 ppm TCE. Significant changes in wakefulness- sleep elicited by the long-term exposure
appeared at lower exposure levels. The LOAEL for sleep changes was 12 ppm (i.e., LOAEL, adjusted
for continuous exposure) (U.S. EPA 201 le).
-- Trigeminal nerve effects
(Ruijten et al.. 1991) evaluated the TCE exposures and possible health effects of 31 male printing
workers (mean age: 44 yrs) and 28 unexposed control subjects (mean age: 45 yrs). The exposure
duration was expressed as "cumulative exposure" (concentration x time). Using historical monitoring
data, mean exposures were calculated as 704 ppm x number of years worked, where the mean number
of years was 16 (range: 160-2,lS0ppmx yr)( U.S. EPA 20lle ). The study measured the trigeminal
nerve function by using the blink reflex, but no abnormal findings were observed. However, the study
found a statistically significant average increase in the latency response time in TCE-exposed workers
on the masseter reflex test, another test commonly used to measure the integrity of the trigeminal
nerve. The POD derived from the dataset was a LOAEL of 14 ppm( U.S. EPA, 2011e).

-- Neuronal demyelination
(Isaacson et al .. 1990) dosed weanling Sprague-Dawleymale rats (12/dose group) via the oral route
(drinking water) in an experimental protocol for an 8-week period. The control group had unexposed
rats for 8 weeks. The experimental group#l exposed rats to 47 mg/kg-bw/day TCE for 4 weeks and
then no TCE exposure for 4 weeks. The experimental group#2 exposed rats to 47 mg/kg-bw/day TCE
for 4 weeks, no TCE exposure for the following 2 weeks, and then 24 mg/kg-bw/day TCE for the final
2 weeks. Rats in group#2 reported a decreased latency to find the platform in the Morris water maze
test. While these results actually suggest increased cognitiveperformance,all of the TCE-treated groups
exhibited hippocampal demyelination,with effects more severe in the twice-exposed group. The
LOAEL for neurodegenerative effects (i.e., demyelination in the hippocampus)was 47 mg/kg-bw/day
(U.S. EPA. 201 le).
Table 3-10: Dose-response analysis
of se Iecte d stu d"
1es cons1.dered fior eva uation of neuro I•021ca1flit
e ec s
I

Target
Organ
System Species Duration

PODType 1
(applieddose)

Rat 8 hrs/day,5 LOAEL =
(male) days/weeks
12ppm
for6 weeks

Human
(both Meanof16
sexes)
years

Nervous
system

8week s
(male) ' intermittent)
Rat

LOAEL=
14ppm

Dose

HECs, DEC,, BEDst BED,,

Uncertainty
Factors (UFs) 2

Data
Quality 3

Effect

Metric

(ppm)

SignlOcant
decreases in
wakefulness

TotMetab

13

4.8

6.6

6.S

14

5.3

7.4

7.3

UFs=l; UFA= 1;
(Ruijtcn ct Medium
1.JFH=3;UFL•3;
al., 1991)
(1.7)
TotalUF-10

18

7.1

9.4

9.2

UFS=l0; UFA=3;
(Isaacsonet Medium
lJFH=3; UPL=l O;
al., 1990)
(2)*

(ppm) ~mg/kg mg/kg)

UFs=3; UFA=3;

BW34

Trigeminalnerve
effects(IncreasedTotMetab
latencyin
BW34
masseterreflex)

LOAEL=4?
Demyelinationof TotMetab
mg/kgBW34
hippocamp\!5
bw/day

Page 241 of691

UFH=3; UFL•I0;
Total UF-300

Total UF=JOOO

Reference

(Arito et al. Medium
1994)
(2)*

 1 POD type

canbe NOAEL, LOAEL, or BMDL. EPA adjustedall valuesto continuousexposure.

2 UFS==subchronic
to chronicUF; UFA=interspeciesUF; UFH=intraspeciesUF; lJFL=LOAELto NOAELUF.
3
See [DataQualityEvaluationof Human HealthHazard Studies. Docket:EPA-HQ-OPPT-2019-0500] for full evaluationby metric. • Arito 1994 was
downgradedfrom a High, with calculatedscore = 1.6; Isaacson 1990
was downgradedfrom a High, with calculatedscore = 1.6

2233
2234
?235
?236
?237
?238
2239
2240
2241
?242
?243
2244
2245
?246
2247
?248
?249
?250
2251
?252
?253
2254
?255
?256
?257
?258
?259
2260
?261
2262
?263
?264
?265
2266
?267
?268
?269
?270
?271
2272
?273
?274
?275
2276

Table 3-10 presents the derived PODs from all studies considered for dose-response analysis. The
available datasets for considering neurotoxicity included single studies for each of the three endpoints of
central nervous system (CNS) depression, trigeminal nerve effects, and neuronal demyelination. The
TotMetabBW34 dose metric, or the total amount TCE metabolized per unit adjusted body weight, was
used for all three studies. Thise dose metric was selected because for these endpoints there is insufficient
information for site-specific or mechanism-specific determinations of an appropriate dose-metric,
however in general TCE toxicity is associated with metabolites rather than the parent compound.
LOAELs were used as PODs for all studies, and none were BMD modeled. See Section 3.2.2.1 and
(U.S. EPA. 201 le) for more details on TCE PBPK modeling and dose metric selection.
Differences from standard VFvalues are explained below:
(Arito et al.. 1994) was assigned UFs = 3 (instead of 10) despite being only a 6 week study because
eITect:sob:served al 6 weeks exposure were only minimally different than effects at 2 weeks (differences
observed post-exposure).
(Ruijten et al.. 1991) was assigned UFs = 1 because the data was based on a mean of 16 years ofhwnan
exposure. UF 1 = 3 (instead of 10) due to the observed effect being an early marker and representing a
minimal degree of change.
EPA did not select (Isaacson et al.. 1990), demonstrating demyelination of the bippocampus, to
represent the neurotoxicity hazard because dosing during the study was not continuous and the resulting
POD was subject to a large cumulative uncertainty factor (1000). (Arito et al.. 1994) and (Ruijten et al..
1991) were both considered for use in quantitative risk estimation as thc;y were relatively well-conducted
studies examining independent endpoints within the hazard of neurological effects.
Immunotoxicity
-- Thymus Effects I Autoimmunity

(Keil et al.. 2009) exposed B6C3Fl mice (IO/group), a standard test strain not genetically prone to
develop autoimmwie disease, to TCE via drinking water for 27 or 30 weeks at concentrations in water
of 0, 1.4, or 14 ppm (0.35 or 3.5 mg/kg-bw/day). The study reported a significant decrease in thymus
weight concentrations and thymic cellularity as well as an increase in autoantibodies to ssDNA and
dsDNA. A LOAEL of 0.35 mg/kg-bw/day was identified as the POD for the thymic and autoimmune
effects (U.S. EPA. 201 le).
-- Autoimmunity
(Kaneko et al.. 2000) exposed auto-immune prone mice (5/group) to TCE via inhalation at
concentrations of 0, 500, 1,000, or 2,000 ppm for 4 hrs/day, 6 days/week, for 8 weeks. At
concentrations 2'.:500 ppm, mice exhibited dose-related liver inflammation, splenomegalyand
hyperplasia oflymphatic follicles. Immunoblasticcell formation in lymphatic follicles was observed in
mice treated with 1,000 ppm TCE. The LOAEL of 70 ppm (adjusted for continuous 24hr exposure)
was identified for these effects (U.S. EPA 2011e).
-- Immunosuppression
In (Sanders et al., 1982), male and female CD-1 mice (7-25/group)were given TCE in drinking water

Page 242 of 691

 2277
2278
?279
2280
?281
?282
2283
2284
?285
2286
2287

concentrations of 0, 0.1, 1.0, 2.5, or 5 .0 mg/mL (0, !'8, 217, 393 or 660 mg/kg-bw/day) for 4 or 6
months. Female mice showed decreased humoral immunity at 2.5 and 5 mg/mL (393 or 660 mg/kgbw/day), whereas cell-mediatedimmunity and bone marrow stem cell colonization decreased at all four
concentrations. Male mice were relatively unaffected after both 4 and 6 months of exposure. A LOAEL
of 18 mg/kg-bw/day was identified as the POD for immunosuppressiveeffects (L1.S. EPA . 2011e).

Another study that was previously discussed for liver and kidney effects (Woolhiser et al.. 2006) also
reported immunosuppressiveeffects. Sprague-Dawleyfemale rats (16/group) were treated with 0, 100,
300 or 1,000ppm TCE for 6 hrs/day, 5 days/week for 4 weeks. Four days prior to study termination,
the rats were immunized with sheep red blood cells (SRBC), and within 24 hrs following the last
exposure to TCE, a plaque-forming cell (PFC) assay was conducted to determine effects on splenic
2288 anti-SRBC IgM response. At 1,000 ppm, rats demonstrated a 64% decrease in the PFC assay response.
?289 A BMDL1s0of24.9 ppm was identified for this immunosuppressiveeffect(U.S. EPA. 201 le).
2290
. of seIected stud'1esCODSI
"dered Iior eva ua 10no nnmune eftiects
2291 Table 3-11 Dose-response anaI1ys1s

..

Target
Or,:an
System Species

Duration

PODType 1
(llA)lied
dose)

Effect

Dose HEC50 HE½9HEDso HED,,
Metric (ppm) (ppm) (mg/kg (mg/kg

Decreasein
Mouse
LOAEL= 0.35 thymusweight TotMetab
(female) 27-30 weeks mg/kg-bw/day and thymus BW34 0.092 0.033 0.049
cellularity
Autoimmunity
(increased
Mouse 27-30weeks LOAEL=0.35 anti- dsDNA TotMetab 0.092
mg/kg-bw/day
BW34
(female)
andssDNA
antibodies)
Mouse
(males ;
Autoimmunity
lmm.wie auto- 4hrs/day,6 LOAEL=70
(changes in TotMetab 97
system immune days/week
immunoreactiveBW34
ppm
prone for8weeks
organs)

0.033

0.049

Uncertainty
Factors (UFs) 1

Data
Reference Quality'

UFS=l; UFA= 3;
0.048 UFH=3; UFL=IO; (Keil et al.,
2009)
Total UF=1004
UFs=,1;UFA=-3;
(Keil ct al. ,
0.048 UFJl=3; UF'L==3;
2009)
4
Total UF=30

High
(1.6)

High
(1.6)

37

44

42

UFS=IO;UFA= 3; (Kaneko et
lJFH=l ; UFL=lO; al., 2000)
Total UF=300

1.7

2.5

2.5

UFs=l; UFA= 3;
(S!l!ldla]1.t High
UFH=3; lJFl,=10; al., 1982)
(1.4)
Total UF01 100

High
(1.5)

strain)

16 or24
TotMetab
ImmunoMouse
LOAEL = 18
4.8
wuks (4 or
mg/kg-bw/day suppression BW34
(female)
6montbs)

UFS=IO;UFA= 3;
6hrs/day, S
Immuno- TotMetab
BMDL1so=
UFH=3; UFL=I; (Woolhi:i!ilT High
14
14
29
11
days/week
suppression BW34
(female)
24.9ppm
Total UF Q100 et al.. 2006) (1.1)
for4 weeks
1 POD type can be NOAEL,LOAEL,or BMDL. The IRJSprogramadjusted all values to continuousexposure.
2 UFs=subchronicto chronic UF; UFA=interspeciesUF; UFH=intraspecies
UF; UFL=LOAELto NOAELUF.
3 See [DataQuality Evaluationof Human HealthHazardStudies. Docket: EPA-HQ-OPPT-2019-0500]for full evaluation by metric.
4 Two different effects were reportedby Keil et al, (2009): decreasedthymic weight and cellularityand autoimmunity
. A total UF of l 00 was used for the
thymus toxicity, whereas a total UF of30 was used for the autoimmuneeffects . The TCE IRIS assessmentallocated different LOAEL-to-NOAEL
uncertainty factors(UFL) based on the severity of the effects,whichresulted in differenttotal UF (U.S. EPA.2011e).
Rat

i292
l293

Table 3-11 presents the derived PODs from all studies considered for dose-response analysis. These
2294 studies covered the endpoints of thyroid effects, ·autoimmunity, and immunosuppression.The
l295 TotMetabBW34 dose metric, or the total amount TCE metabolized per unit adjusted body weight, was
2296 used for all three studies. This dose metric was selected because for these endpoints there is insufficient
2297 information for site-specific or mechanism-specificdeterminations of an appropriate dose-metric,
2298 however in general TCE toxicity is associated with metabolites rather than the parent compound.
2299 LOAELs were used as PODs for all studies except (Woolhiser et al.. 2006), which was BMD modeled
Page 243 of691

 BOO
?301
?302
2303
2304
?305
2306
2307
2308
2309
?310
?311
B 12
B 13
?314
?315
?316
?317
?318
2319
2320
2321
2322
2323
2324
2325
2326
?327
2328
2329
2330
2331
?332
2333
?334
2335
2336
2337
2338
2339
?340
?341
?342
?343
2344
?345
?346
?347

with a BMR of 1 SD because it was unclear what should oonstitute the cutoff point for a minimal,
biologically significant change. See Section 3.2.2.1 and (U.S. EPA. 201 lc ) for more details on TCE
PBPK modeling, dose metric selection, and BMR selection.
Differences from standard UF values are explained below:
(Keil et aL 2009) was assigned lJFL = 3 (instead of 10) due to the observed effect being considered an
early, subclinical or pre-clinical early marker of disease.
Decreased thymus weight and cellularity as observed in (Keil et al., 2009) was not considered for use in
risk estimation because EPA determined that this effect is insufficiently adverse compared to the other
endpoints. Of note, elimination of this endpoint and corresponding change in total UF represents a change
from the 2014 TSCA Work Plan Chemical RiskAssessment( U.S. EPA. 2014b). The data from( Keil et
al.. 2009) was selected to represent autoimmunity however, because the study was oflonge .r duration than
(Kaneko et al.. 2000) with a smaller cumulative uncertainty factor. (Sanders et aL 1982) was selected to
represent immunosuppression because the study was of a much longer duration than (Woolhiser et al..
2006).
Reproductive toxicity
-- Male Reproductive Effects

(Chia et al.. 1996) examined a cohort of 85 workers in an electronics factory. The workers provided
urine, blood, and sperm samples. The mean urine TCA level was 22.4 mg/g creatinine (range: 0.8-

136.4mg/g creatinine). In addition, 12 workers provided personal 8-hr air samples, which resulted in a
mean TCE exposure of29.6 ppm (range: 9-131 ppm). There were no controls in the study. Males
experienced decreased percentage of normal sperm morphology and hyperzoospermia. A BMDL10of
1.4 ppm was identified as the POD for these effects (U.S. EPA 2011e).
(Xu et al.. 2004) exposed male CD-1 mice (27/group) to TCE at concentration of0 or 1,000 ppm for 6
hrs/day, 5 days/week for 6 weeks. Inhalation exposure to TCE did not result in altered body weight,
testis and epididymis weights, sperm count, or sperm morphology or motility.
Percentages of acrosome-intact sperm populations were similar between treated and control animals.
However, decreased in vitro sperm-oocytebinding and reduced in vivo fertilization were observed in
TCE-treated male mice. A LOAEL of 180 ppm (adjusted for continuous 24hr exposure) was identified
as the POD for these effects (U.S. EPA 2011e).
(Kumar et al.. 2000) and (Kumar et al.. 2001),exposed male Wistar rats by inhalation at concentrations
of O or 376 ppm TCE. Both study protocols exposed rats for 4 hrs/day, 5 days/week, but had variable
duration scenarios. For instance, (Kumar et al.. 2000) treated rats for the following exposure durations:
2 weeks (to observe the effect on the epididymal sperm maturation phase), 10 weeks (to observe the
effect on the entire spennatogenic cycle), 5 weeks with 2 weeks of rest (to observe the effect on
primary spermatocytes differentiation to sperm), 8 weeks with 5 weeks of rest (to observe effects on an
intermediate stage of spermatogenesis), or 10 weeks with 8 weeks of rest (to observe the effect on
spermatogonial differentiation to spenn). (Kumar et al.. 2001) exposed rats for either 12 or 24 weeks.
(Kumar et al.. 2000) reported altered testicular histopathology, increased sperm abnormalities, and
significantly increased pre- and/or postimplantation loss in litters in the groups with 2 or 10 weeks of
exposure, or 5 weeks of exposure with 2 of weeks rest. Multiple sperm effects were observed in another
study by Kumar (2001). After 12 weeks ofTCE exposure, rats exhibited decreased number of
spermatogenic cells in the seminiferous tubules, fewer spennatids as compared to controls, and the
Page 244 of 691

 2348 presence of necrotic spennatogenic cells. Following 24 weeks of exposure, male rates showed reduced
B49 testes weights and epididymal sperm count and motility, testicular atrophy, smaller tubules,
?350 hyperplastic Leydig cells, and a lack of spermatocytes and spennati.ds in the tubules. Testicular marker
ns1 enzymes were altered at both 12 and 24 weeks of exposure. A LOAEL of 45 ppm was identified as the
?352 POD for the sperm and male reproductiveeffects reported in both studies (U.S. EPA. 201 le).
?353
?354 (Kan et al .. 2007) _alsoprovided evidence for the damage to the epididymis epitheliwn and sperm.
?355 CD•l male mice (4/group) were exposure by inhalation to 0 or 1,000-ppmTCE for 6 hrs/day, 5
?356 days/week for 1 to 4 weeks. As early as 1 week after TCE exposure, exposed mice showed
2357 degeneration and sloughing of epithelial cells. These effects increased in severity at 4 weeks of
2358 exposure. A LOAEL of 180 ppm (adjusted for continuous 24hr exposw-e)was identified as a POD for
?359 the effects in the epididymis epithelium.
2360
2361 -- Female ReproductiveEffects
2362 (Narotsk} et al .. 1995 ) administered TCE to F344 timed-pregnantrats (8-12 dams/group) by gavage.
2363 Dams were exposed to TCE doses of 0, 10.1, 32, 101, 320, 475, 633,844 or 1125 mg/kg-bw/day during
2364 gestational days (GD) 6 to 15. The study was a prequel to a complicatedprotocol with other chemicals
2365 in a mixture study. Delayed parturition was observed at ~475 mg/kg- bw/day. The LOAEL for female
2366 reproductive effects was 475 mg/kg-bw/day (U.S. EPA , 201 le).
2367
2368 -- Diminished Reproductive Behavior
?369 George et al. ( 1986) ad.ministeredTCE to both male and female F344 rats (20 each treated, 40 each
2370 controls) in feed with estimated doses of 0, 72, 186, or 389 mg/kg-bw/day. Breeders were exposed for
2371 one week premating and then for 13 weeks while cohabitating. Pregnant females were subsequently
?372 exposed throughout gestation (an additional 4 weeks). Copulation was reduced equally following
?373 either exposed males or exposed females cohabitating with control mates (highest dose only
?374 examined). 1bis corresponded with a dose-responsive decrease in the number of litters produced per
B75 breeding pair and the number of live pups per litter.
2376
'377 Ta ble 3 12 Dose-res >onse ana•·l ·su. of seIected stud'1escons1'dere d tioreva Iua ti ODO f repro d UCtiv ee ftiects

- .

~

Target
Organ
System

Species

Duration

PODType 1
(applied dose

Dose
Metric

Effect

HECso HEC" HED!i(} HEim
(ppm) (ppm) (mg/kg (mg/ke

Uncertainty
Facton (UFs)

2

Data
Reference Quality

Measured

I

values after an
Human 8-hr workshift; BMDL10=
(male) mean 5.1 years _Uppm
on the job

Hyper-

zoospermla

TotMetab
BW34

4hrs/day, 5
days/week,2- 10
weeks exposed,
Spermeffects and
2-8weeks
LOAEL==45
TotMetab
imalereproductive BW34
Rat
unexposed.
ppm
tract effects
Reproductive (male)
4hrs/day, S
system
days/week for
12 or24 weeks
6 hrs/day, 5 ILOAEL= 180
days/weekfor 1ppm
(IJ?1ll
e)
4weeks

Mouse

Effects on
epididymis
epithelium

TotMetab
BW34

UFs=l0; UFA"' 1;
1.4

0.5

0.74

0.73

UFH=3;UFIFI;
TotaJUF=30

(Chia etal., Mediuo
1996)

(1.8)

(Kumar et Mediurr
32

190

Page 245 of 691

13

67

16

80

16

73

UFS=IO;UFA= 3;
lJFH=3; UFL=l O;
Total UF=/000

al.. 2000 )

( 1.7)

(Kumar et
al., 2001)

High
( 1.4)

UFS=10; UFA=3;
(Kan et al., Med.iurr
UFH=3; UFL=lO; 2007)
(2)•
Total UF=/000

 Spermeffects
(decreasedin
vi1rosperm-

6 hrs/day, S
Mouse
days/weekfor 6 LOAEL= 180 oocyte binding
(male)
weeks
ppm
and in vivo
fertilization)
WAEL=
Rat 9 days (during
(female gestationaldays 475mg/kgdams)
6 to 15)
bw/day

Delayed
parturition

TotMctab
BW34

fotMetab
BW34

190

67

80

73

UFS==I0;UFA= 3;
(Xu et al.,
UFH=3;UFL=lO; 2004)
Total UF-1000

98

37

47

44

UFs=l; UFA=3; ili arotsky
et al.,
UFH•3; UFI.rlO;
1995)
Total UF=J00

Breeders
Decreased
UFS=l; UFA= 3;
exposed 1 week
copulation;
Rat
l.JFH=3;UFL=IO; (George et
fotMetab 204
prematingand ILOAEL= 389
reducednumbers
(male/
71
85
77
UFI)=l
al., 1986)
then for 13 mg/kg-bw/day of live litters/pair BW34
female)
weeks
Total
UF=JOO
and pups/litter
cohal>itating
1 POD type can be NOAEL, LOAEL, or BMDL. The IRISprogramadjustedall valuesto continuousexposure.
2
UFS=subchronicto chronicUF; UFA911terspeciesUF; UFH=intraspeciesUF; UFL=LOAEL to NOAEL UF.
3 Sec [DataQuality Evaluationof Human Health HazardStudies. Docket:EPA.
-HQ-OPPT-2019..()500)
for full evaluatiooby metric. •Kan 2007 was
owngradedfrom a High, with calculated score = 1.6.

2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
n90
n91
2392
2393
n94
2395
2396
2397
2398
?399
2400
?401
2402
2403
?404
!405

Table 3-12 presents the derived PODs from all studies considered for dose-response analysis. The
majority of studies identified effects indicative of male reproductive toxicity, with one study
demonstrating female reproductive toxicity. The TotMetabBW34dose metric, or the total amount TCE
metabolized per unit adjusted body weight, was used for all three studies. This dose metric was selected
because for these endpoints there is insufficient information for site-specific or mechanism-specific
determinations of an appropriate dose-metric, however in general TCE toxicity is associated with
metabolites rather than the parent compound. For (Chia et al.. 1996), the 2011 IRIS Assessment (Q .S.
EPA 201 le) notes some additional wcertainty in the dose estimate because exposure groups were
defined by ranges and exposure was estimated by conversion of urinary TCA. LOAELs were used as
PODs for all studies except (Chia et al .. 1996), which was BMD modeled with a standard BMR of 10%
extra risk. The 2011 IRIS Assessment (U.S. EPA. 201 le) indicates some wcertainty in the biological
signficance of this Bl\1Rbecause the study used a lower cutoff to define hyperzoospermiathan other
studies. See Section 3.22.1 and (U.S. EPA. 201 le) for more details on TCE PBPK modeling, dose
metric selection, and BMR selection.
For male reproductive toxicity, (Chia et al.. 1996) was selected over the other studies because it was a
hwnan study over a mean 5.1 year period compared to the other studies which were in mice and all for
only a few weeks except for (Kumar et al., 2001). Additionally, (Chia et al.. 1996) only has a
cumulative uncertainty factor of 30, compared to 1000 for the other three studies. (Narotskv et al.,
1995) received a High in data quality evaluation and was deemed suitable for quantitative assessment
of female reproductive toxicity based on delayed parturition (giving birth). While (George et al., 1986)
received a High in data quality evaluation, it is unclear whether the observed effects are a result of true
reproductive toxicity or merely behavioral changes (i.e. unsuccessful copulation vs. reduced libido).
Effects on copulation are also likely downstream of any specific male or female reproductive
endpoints, which have more sensitive PODs than (George et al.. 1986). Therefore, the POD for
reduced copulation was not selected to represent the reproductive toxicity hazard.

Z406 Developmental toxicity
?407 As described above in Section 3.2.5.3.2,developmental effects may result from single as well as

!408 repeated exposures at a developmentally critical period; therefore the same endpoints are relevant for
2409 both acute and chronic exposure scenarios. The only difference between acute and chronic exposure
Page 246 of 691

High
{1.4)

High

(1.3)

High
(1.1)

 UlO scenarios in evaluating developmental toxicity is the benchmark MOE for( Fredriksson et al.. 1993). The
?411 subchronic-to-chronicUFs = 3 for chronic exposure, because the study only exposed pups during
?412 postnatal days 10-16,suggesting that exposure during a longer period of development may have
?413 exacerbated the observed effects (UF s would not = 10 because neurological development only occurs
?414 over a portion of a lifetime). This results in a cumulativeUF and benchmark MOE of 300. See Section
2415 3.2.5.3.2 for a detailed description of the developmentaltoxicity endpoints.

?416
2417
?418
?419
2420
?421
?422
~~~~

?425
?426

3.2.S.3.4 Cancer POD for LifetimeExposures
EPA utilized linear low-dose extrapolation for derivation of PODs accounting for all three cancer types.
Regarding low-dose extrapolation, a key consideration in determining what extrapolation approach to
use is the mode(s) of action. However, mode-of-action data are lacking or limited for each of the cancer
responses associated with TCE exposure, with the exception of the kidney tumors (see Section
3.2.4.2.2). For the other TCE-induced cancers, the mode(s) of action is unknown. When the mode(s) of
action is identified as genotoxic or cannot be clearly defined, EPA generally uses a linear approach to
estimate low-dose risk (U.S. EPA. 2005), based on the following general principles:
1) A chemical's carcinogenic effects may act additively to ongoing biological processes,

given that diverse human populations are already exposed to other agents and have

!j~l substantial background incidences of various cancers.

2429
?430
2431
2432
2433
?434

2) A broadening of the dose-response curve (i.e., less rapid fall-off of response with decreasing dose) in
diverse human populations and, accordingly, a greater potential for risks from low-dose exposures (Lutz
et al.. 2005; Zeise et al .. 1987) is expected for two reasons: First, even if there is a threshold
concentration for effects at the cellular level, that threshold is expected to differ across individuals.
Second, greater variability in response to exposures would be anticipated in heterogeneous populations
than in inbred laboratory species under controlled conditions (due to, e.g.,, genetic variability, disease
status, age, nutrition, and smoking status).

2437
2438
?439
2440
?441
?442
2443

3) The general use oflinear extrapoJationprovides reasonable upper-bound estimates that
are believed to be health-protective (U.S. EPA. 2005) and also provides consistency
across assessments.

~~1i

Dose-response analysis of kidney cancer utilized ABioactDCVCBW34,or the amount ofDCVC
bioactivated in the kidney per unit adjusted body weight, for the same rationale as described above for
kidney non-cancer effects. Dose-response modeling for kidney cancer from Charbotel et al. (2006) was
2444 performed by linear regression weighted by the inverse of variances for RR estimates. Consistent with
?445 EPA 's Guidelines for Carcinogen Risk Assessment (U.S. EPA. 2005), the same data and methodology
?446 were also used to estimate the exposure level (ECx: -effective concentration corresponding to an extra
?447 risk ofx%) and the associated 95% lower confidence limit of the effective concentration corresponding
!448 to an extra risk of 1% (LECx [lowest effective concentration], x = 0.01). A 1% extra risk level is
!449 commonly used for the determination of the POD for epidemiological data. Use of a 1% extra risk level
?450 for these data is supported by the fact that, based on the actuarial program, the risk ratio (i.e., Rx/Ro) for
!451 an extra risk of l % for kidney cancer incidence is 1.9, which is in the range of the ORs reported by
!452 Charbotel et al (ORs range from 1.16 - 2.16 across exposure tertiles). Thus, 1% extra risk was selected
?453 for determination of the POD, and, consistent with EPA 's Guidelines for Carcinogen Risk Assessment
2454 (U.S. EPA. 2005), the LEC value corresponding to that risk Jevel was used as the actual POD. For more
?455 details, see Section 5.2.2 in the 2011 IRIS Assessment (U.S. EPA. 201 le).
2456
!457 The inhalation unit risk (IUR) for TCE is defined as a plausible upper bound lifetime extra risk

Page 247 of691

 2458
2459
2460
2461
2462
2463
2464
2465
U66
2467
2468
2469
2470
2471
2472
?473
?474
2475
2476
2477
?478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
!502
2503
2504

of cancer from chronic inhalation of TCE per unit of air concentration. The estimate of the inhalation
unit risk for TCE is 2.20 x 10·2 per ppm (2 x 10·2 per ppm [4 x 10-6per µg/m3]) rounded to one
significant figure), based on human kidney cancer risks reported by Charbotel et al. (2006) and adjusted
4-fold upward for potential additional risk for NHL and liver cancer. This estimate is based on Highquality human data, thus avoiding the uncertainties inherent in interspecies extrapolation. This value is
supported by inhalation unit risk estimates demonstrating multisite carcinogenicity in several rodent
bioassays, the most sensitive of which range from 1 x 10·2 to 2 x 10·1 per ppm [2 x 10-6to 3 x 10·5 per
µg/m3] .
The IUR from Charbotel et al. (2006) (calculated as 5.49 x 10·3 per ppm) was adjusted by a factor of
four to account for estimating risk to all three cancer types combined (i.e., lifetime extra risk for
developing any of the three types of cancer) versus the extra risk for kidney cancer alone. Although only
the Charbotel et al. (2006) study was found adequate for direct estimation of inhalation unit risks, the
available epidemiologic data provide sufficient information for estimating the relative potency ofTCE
across tumor sites. Section 5.2.2 of the 2011 IRIS Assessment (U.S. EPA. 20lle ) describes the process
for this adjustment In short, extra lifetime cancer risks were summed across the three cancer types and
the ratio of the sum of the extra risks to the extra risk for kidney alone was derived. EPA calculated this
ratio using two sets of data: the summary RR estimates from the 2011 meta-analyses for NHL, kidney
cancer, and liver cancer, and the SIR estimates for all three cancer types from the Raaschou-Nielsen et
al. (2003) study. The value for the ratio of the sum of the extra risks to the extra risk for RCC alone was
3.28 from the first calculation (using meta-analysis results) and 4.36 from the second calculation (using
Raaschou-Nielsen et al. data). The geometric and arithmetic mean of these two values is 3.8, and EPA
decided to round up to 4 based on the imprecision of the adjustment factor.
The oral slope factor (OSF) for TCE is defined as a plausible upper bound lifetime extra risk of
cancer from chronic ingestion of TCE per mg/kg/day oral dose. The estimate of the oral slope factor is
4.64 x 10-2 per mg/kg/day ( 5 x 10·2 per mg/kg/day rounded to one significant figure), resulting from
PBPK model-based route-to-route extrapolation of the inhalation unit risk estimate based on the human
kidney cancer risks reported in Charbotel et al. (2006) and adjusted 5-fold upward for potential risk for
NHL and liver cancer. For this adjustment, individual IUR estimates were first obtained for each site
based on the ratios of extra risk relative to kidney. Those site-specific IUR estimates were then
extrapolated to the equivalent OSFs using site-specific dose metrics18, and those individual OSFs were
summed to obtain a ratio of 5.0 relative to kidney cancer alone. Uncertainty in the PBPK model-based
route-to-route extrapolation is relatively low, however variability stemming from the requirement of
using distinct dose-metrics for the different target tissues resulted in a larger 5-fold adjustment, as
opposed to the 4-fold adjustment calculated for the IUR. Extrapolation using different dose-metrics
yielded expected population mean risks within about a two-fold range, and, for any particular dosemetric, the 95% CI for the extrapolated population mean risks for each site spanned a range of no more
than about threefold. The resulting combined OSF value is supported by oral slope factor estimates from
multiple rodent bioassays, the most sensitive of which range from 3 x 10·2 to 3 x 10·1 per mg/kg/day.
EPA decided not to use the TIJRor OSF to calculate the theoretical cancer risk associated with a single
(acute) exposure to TCE. NRC (2001) published methodology for extrapolating cancer risks from
chronic to short-term exposures to mutagenic carcinogens, however these methods were published with
the caveat that extrapolation oflifetime theoretical excess cancer risks to single exposures has great
uncertainties. Thus, this risk evaluation plan risk assessment for TCE does not estimate excess cancer
risks for acute exposures because the relationship between a single short-term exposure to TCE and the
18

Kidney: ABioactDCVCBW34;NHL: TotMetabBW34; Liver: AMetLivlBW34

Page 248 of 691

 ?505 induction of cancer in humans has not been established in the current scientific literature. Risk estimates
?506 for cancer will be based on lifetime exposure durations, represented as Lifetime Average Daily
?507 Concentration/Dose (LADC/LADD).
?508
3.2.S.4
Selected PODs for Human Health Hazard Domains
?509 Table 3-13 and Table 3-14 list the studies and correspondingHECs, HEDs, and UFs that EPA is using
?510 in the TCE Risk Evaluation following acute and chronic exposure. Table 3-15 provides the cancer
?511 PODs for evaluating lifetime exposure. Key studies in Table 3-13 and Table 3-14 are briefly described
?512 in Section 3.2.5.1. Presenting PODs for the HEC/HEDsoand HEC/HED99values is intended to provide
?513 a sense of the difference between the median and 99% confidencebound for the combined uncertainty
?514 and variability. Calculations of HECsot99and HEDso199ratios generally showed a 2-3 fold difference
2515 for the various studies described in Section 3.2.5.3. The exception was for studies reporting kidney
?516 effects, which showed high HEC5ot9'Jand HED 50199 ratios (7 to 10-fold)due to larger uncertainty in
?517 the rodent internal dose estimates for the GSH metabolismdose metrics (e.g., ABioActDCVCBW34)
?518 (U.S. EPA, 201 le ) and greater influence of human variability. Confidencein these metrics was lower
?519 for mouse data due to an absence of GSD-specific in vivo data, however uncertainty was similar as to
?520 other metrics for rat and human data (U.S. EPA. 201 le). The HEC/HEDwvalues represent the PODs
?521 that are expectedto be protectiveof sensitive subpopulations,accountingfor the majority of identified
?522 toxicokinetichuman variability.
?523
.d er ed fior acu t e exposure scenanos
. o f seIec ted stud'1es CODSI
Ta ble 3-13 D ose-response ana I1vs1S
.
'524

.

1

Target
Organ/ Species
System

Duration

POD Type
(an,lieddole)

Effect

Dose HEC• HEC,, HED!IOBED,,
Metric (ppm) (ppm) (mg/kg (mg/kg)

Rat Gestationaldays BMDLo1= 32.2 Increased TotMetab
BW34
(female)
6to 15
mg/kg-bw/day resorptions
Developmental
Rat
Effects (female)
Rat
(male

pups)

22 days
BMDLo1=
Congenital
TotOx
throughout
Metab
0.0207mg/kgheart
gestation
malformations BW34
(gestationaldays
bw/day
0to22 1
Decreased TotMetab
Postnatal days LOAEL=50
10 to 16
mg/kg-bw/day rearing activity BW34

57

23

29

28

Uncertainty
Facton (UFs)

UFs=l; UFA= 3; (Narotsk\ et
UFH=3; lJFL=l;
al .. 1995)
Total UF=l0

UFS=l; UFA= 3;
0.012 0.0037 0.0058 0.0052 UFH=3; lJFL=l;
TotalUF=lO

8

3

4.2

4.1

Data
Reference Quality

(J2hnsonet
al., 2003)

High

MediutI

lJFS=l;UFA = 3;
(fredriksson
lJFH=3; UFr,=10; eta!. , 1923) Medium
Total lJFa l 00

3hr/day, single
UFS=l; UFA= 3; (S!:l!m!Qe
and
Mortality
dose; followed BMDLo1=
1 N/A 1 2.74 1.2 UFH=lO;llFL =l;
Gilmour,
1.74
High
NIN
NIA'
following
13.9ppm
System (female) by respiratory
2010)
infection
Tota/UF=30
infection
1 Data from (Selgrade and Gilmour, 20 l 0) was not subject to PBPK modelingdue to uncertaintyconcerning the most appropriatedose metric.The BMDL valu
adjusted for a 24hr exposurewill be us«l as the POD for occupationalrisk estimates,whilethe 3hr value will be used for conswnerrisk estimates. This value i:
presented in the HEC99column but does not represent eny particularpercentilesince it was not PBPK-modeled.
2 A dermal HBD was obtained through route-to-routeextrapolationusing breathingrate and body weight data on male CD-1 mice (insufficientfemale data was
available) from (U.S. EPA, 1988) and allometricscaling based on (U.S. EPA.2Ql Id) usinga dosimetricadjustmentfactor of 0.14 for mice.
Immune

Rat

Page 249 of 691

 !526

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

.

I . of se1ected stu d.1escon.s1
.dered tior chromc exposure sceoanos
T able 3-14 Dose-respoimseama1ys1s
Target
Organ Speeiea
System

Liver

Kidney

Duration

Continuousand
intermittent
Mouse
exposures,variable
(male)
time periods for 3012Odays
Rat
(male)
-Oral
Rat

(male)

4-5 days/week for
S2 weeks
8hrs/day, S
days/weeksfor 6
weeks

Nervous
System Human
(both Mean of 16 years
sexes)

Immune
System

Mouse
(female)

27-3Oweeks

Mouse
(female)

16 or 24 weeks
(4 or 6 months)

PODType
(an>lkdcbe)

BMDL10=
21.6ppm

Effect

REC,. HEJ>se BED,,
DoseMetrie HEC50
(ppm) (ppm) (mg/kg) (mg/kg)

Increased liver/body
weight ratio and
AMetLivl
cytotoxicity/
BW34
hypertrophy

25

Trlgeminalnerve
effects(increased
latency in masseter
reflex)
Autoimmunity
LOAEL=O.35 ( increasedantimg/kg-bw/day dsDNAand ssDNA
antibodies)

7.9

0.025

0.15

O.Ots

13

4.8

6.6

6.5

TotMetab
BW34

14

5.3

7.4

7.3

TotMetab
BW34

0.092

0.033

0.049

0.048

4.8

1.7

2.5

2.5

UFS-1; UFA-=3; (Sanders et al.,
UFH=3;UFL=lO;
1982)
Total UF=JOO

1.4

0.5

0.74

0.73

UFS=lO; UFA= l;
UFH=3; UFL=I;
TotalUF=3O

LOAEL= 18
TotMetab
Immtmosuppression
mg/kg-bw/day
BW34

Measured values
BMDLIO=
Human after an 8-ltr work
(male)
shift;
mean
5.1
years
1.4 ppm
Reproon the job
ductive
System
Rat
LOAEL=475
(female 9 days (during
darns) gestational days 6-15) mg/kg-bw/day

UFS=l; UFA= 3; (Kjellstrandet
lJFH=3; UFL=l;
al., 1983)
TotolUF=JO

9.0

Significant decrCMCS TotMetab
in wakefulness
BW34

LOAEL=
14ppm

Reference

9.1

BMDL10=34 Pathologychanges in ABioact
0.19
renal tubule
DCVCBW34
mg/kg-bw/day
LOAEL=
12ppm

Uneertaiaty
Factors(UFs)

Decreasednonnal
sperm morphology TotMetab
BW34
and hyperzoospennia

UFS=l;UFA=3;
(Maltoni et al..
UFH=3;UFL=l;
1986)
Total UF=JO
UFs=3;UFA=3;
(Arito et al.
UFH=3;UFL=lO;
1994)
Total UF=300

Data
Quality

Medium

Medium

Medium

UFs=t; UFA-=l; (Ruijten et al_,_.
Medium
UFH=3;l.JFL=3;
1991)
TotalUF=JO
UFS""l;UFA= 3;
UFH=3;UFL=3;
Total UF=30

(Keil et al.,
::?009
)

(Chia et al.
1996)

High

High

Medium

UFS=l; UFA= 3;
(Narotsb et al.,
High
UFH=3; UFL=lO;
1995)
Total UF=IOO
UFs=l;UFA=3;
(Narotsk, et al.,
TotMetab
Rat Gestationaldays 6 to BMDLo1=32.2
Increasedresorptions
51
High
UFH=3;UFL=l;
23
29
28
(female)
1995)
BW34
15
mg/kg-bw/day
Total UF=lO
22days
BMDLo1=
UFS=l; UFA= 3;
Develop- Rat
{Johnsonet al.~
Congenitalheart TotOx Metab
(gestational
days
0.0207
mg/kg0.012 0.0037 0.0058 0.0052 UFH=3;l.JFL=l;
Medium
mental (female)
malfonnations
BW34
2003)
0-22)
TotalUF•IO
bw/dav
Effects
Rat
UFs=3; UFA= 3;
LOAEL=5O Decreasedrearing
Postnataldays
TotMetab
(Fredrikssonet
(male
4.1
Medium
8
3
4.2
UFH=3;
l.JFL=lO; al., 1993)
mg/kg-bw/day
activity
BW34
10-16
pups)
Total UF=300
Delayed parturition

TotMetab
BW34

98

!527

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. tso fD eoarture tior L 1fiet·1meExposure scenanos
Table 3-15..Cancer P om
0

POD Type

Oral Slope Factor

Inhalation Unit Risk

Extra Risk Benchmark

POD (extra risk per
dose/concentration)

0.0464 per mg/kg

0.022 per ppm

1 X 104

As stated in Section 3.2.5.3.4, these PODs represent the plausible upper bound lifetime extra risk
of cancer per unit dose or air concentration. The linear non-threshold assumption underlying the
derivation of these values is appropriate based on the mutagenic mode of action for kidney cancer (with
an unclear mode of action for the other two cancer types). The PODs are derived from a single High
quality kidney cancer study (Charbotel et al.. 2006) and the combined estimates account for the
additional relative contribution from the other two cancers.

For TCE, EPA, consistent with OSHA (878 F.2d 389 (D.C. Cir. 1989) and 2016 NIOSH guidance
(Whittaker et al.• 2016), used 1 x l 04 as the benchmark for the purposes of this risk determination for
individuals in industrial and commercial work environments subject to Occupational Safety and Health
Act (OSHA) requirements. It is important to note that lxl0 4 is not a bright line and EPA has discretion
to find unreasonable risks based on other benchmarks as appropriate based on analysis. It is important to
note that exposure related considerations (duration, magnitude, population exposed) can affect EPA's
estimates of the excess lifetime cancer risk (ELCR) . Cancer assessment is only applicable to evaluation
of occupational exposure scenarios, because consumer exposures were only evaluated as acute scenarios
(Section 2.3.2.2). .
3.2.6

Assumptions and Key Sources of Uncertainty for Human Health Hazard

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EPA has high confidence in the overall weight of scientific evidence. EPA did not identify any
information that would question the previous WOE regarding the evaluation of liver, kidney,
neurological, immunological, reproductive toxicity, and developmental toxicity (other than cardiac
malformations). For cancer, EPA performed an updated meta-analysis that found positive statistical
associations between human TCE exposure and cancer of kidney, liver, and NHL types, in agreement
with the previous meta-analyses performed in 2011 (Appendix C, (U.S. EPA 201 lb ). For cardiac
malformations, EPA performed a thorough WOE assessment (Appendix 0.2), examining all pertinent
studies in the available literature. While some uncertainty remains in the dose-response analysis of the
(Johnson et al.. 2003) study and the resulting POD, there is medium confidence in the qualitative
relevance of the endpoint to human toxicity based on the results of the WOE.

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3.2.6.2
Derivation of PODs, UFs, and PBPK Results
Conceptually, the POD should represent the maximum exposure level at which there is no appreciable
risk for an adverse effect in the study population under study conditions (i.e., the threshold in the doseresponse refationship). In fact, it is not possible to know that exact exposure level even for a laboratory
study becauseof experimental limitations (e.g.,, the ability to detect an effect, the doses used and dose

3.2.6.1
Hazard Identification and Weight of Evidence
There is high confidence in the database for human health hazard. All studies considered for doseresponse analysis scored either Medium or High in data quality evaluation and were determined to be
highly relevant to the pertinent health outcome. EPA selected the best representative study for each
identified endpoint from among a broad selection of studies, taking into account factors such as data
quality evaluation score, species, exposure duration, dose range, cumulative uncertainty factor, and
relevance.

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spacing, measurement errors, etc.), and POD approximationslike the doses used (i.e., a NOAEL) an
exposure level which is modeled from the available doses used (i.e., BMDL) are used. The application
ofUFs is intended to account for this uncertainty/variability to allow for estimating risk for sensitive
human subgroups exposed continuously for a lifetime. While the selection ofUFs is informed by
available data, the true necessary extent of adjustment most appropriate for capturing all relevant
uncertainty and variability is unknown.

If a BMDL is used as the POD, there are uncertainties regarding the appropriate dose-response model to
apply to the data, but these should be minimal if the modeling is in the observable range of the data.
There are also uncertainties about what BMR to use to best approximate the desired exposure level
(threshold, see above). For continuous endpoints, in particular, it is often difficult to identify the level of
change that constitutes the threshold for an adverse effect.

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For each of these types of PODs, there are additional uncertaintiespertaining to adjustments to the
administered exposures (doses). Typically, administered exposures (doses) are converted to equivalent
continuous exposures (daily doses) over the study exposure period under the assumption that the effects
are related to concentration x time, independent of the daily (or weekly) exposure regimen (i.e., a daily
exposure of 6 hours to 4 ppm is considered equivalent to 24 hours of exposure to 1 ppm). However, the
validity of this assumption is generally unknown, and, if there are dose-rate effects, the assumption of C
x t equivalence would tend to bias the POD downwards.

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For the PBPK analyses in this assessment, the actual administered exposures are taken into account in
the PBPK modeling, and equivalent daily values (averaged over the study exposure period) for the dosemetrics are obtained. EPA determined that the peer-reviewed PBPK model sufficiently accounted for
any variability and uncertainties in route-to-route extrapolation,and therefore inhalation and oral data
were considered equivalently relevant. Nonetheless, this PBPK model, like any model, does not
incorporate all possible sources of biological uncertainty or variability.
The PBPK-based POD estimates include uncertainties about the appropriate dose-metric for each effect,
although there was better information about relevant dose-metrics for some effects than for others (see
Section 3.2.5.3). The 2011 TCE IRIS Assessment determinedthat the PBPK model was most reliable
for dose metrics of oxidative metabolism flux .There remains substantial uncertainty in the extrapolation
of GSH conjugation from mice to humans due to limitations in the available data. This dose metric is
specifically applicable to kidney endpoints, which are believed to result from renal bioactivation through
GSH conjugation. In this manner, the HEC/HED99values (which account for both modeling unce.rtainty
and interspecies/intraspeciestoxicokinetic variability) may potentially overestimate kidney toxicity for a
proportion of the population, however use of these values are expected to sufficiently account for the
majority of human toxicokinetic variability, including increased biological susceptibility (see Section
3.2.5 .2). Of note, there was significantly less wicertainty for extrapolation of rat GSH conjugation data,
which was used for the selected kidney PODs, compared to data from mice. Despite any limitations of
the model, overall uncertainty for the selected PODs is reduced by the use of a PBPK model. Use of the
PBPK model resulted in data-derived HEC/HED99values replacing default assumptions and uncertainty
factors that would have otherwise been used such as allometric scaling and a UFTKof3 in accounting for
for both interspecies and intraspecies toxicokinetic variability. Data-derived values are always preferred
to default uncertainty adjustments and improve confidence in the adjusted PODs.

3.2.6.3
Cancer Dose Response
Potential sources of uncertainty associated with Charbotel et al. (2006 ) include the modest sample size
of the study and localized population (86 kidney cancer cases, 37 associated with TCE exposure from a
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specific region in France), the retrospective estimation ofTCE in study subjects, and potential
confounding effects from exposure to other degreasing agents. These uncertainties do not significantly
affect confidence in the study results because Charbotel et al. (2006) was a well conducted, High quality
study that used a comprehensive exposure assessment with a detailed occupational questionnaire and
sensitivity and regression analyses found no statistical effect on the cancer POD from a sensitivity
analysis adjusting for exposure to other chemicals. (U.S. EPA 2011e).
The two major sources of uncertainty in quantitative cancer risk estimates are generally interspecies
extrapolation and high-dose to low-dose extrapolation. The unit risk estimate for kidney cancer
incidence derived from the Charbotel et al. (2006) results is not subject to interspecies uncertainty
because it is based on human data. A major uncertainty remains in the extrapolation from occupational
exposures to lower environmental exposures. There was some evidence of a contribution to increased
kidney cancer risk from peak exposures; however, there remained an apparent dose-response
relationship for RCC risk with increasing cwnulative exposure without peaks, and the odds ratio (OR)'
for exposure with peaks compared to exposure without peaks was not significantly elevated (Charbotel
et al.. 2006) Although the actual exposure-response relationship at low exposure levels is unknown, the
conclusion that a mutagenic mode of action is operative for TCE-induced kidney tumors supports the
linear low-dose extrapolation that was used (U.S. EPA. 2005). The weight of evidence also supports
involvement of processes of cytotoxicity and regenerative proliferation in the carcinogenicity of TCE.
although not with the extent of support as for a mutagenic mode of action. In particular, data linking
TCE-induced proliferation to increased mutation or clonal expansion are lacking, as are data informing
the quantitative contribution of cytotoxicity. Because any possible involvement of a cytotoxicity mode
of action would be additional to mutagenicity, the dose-response relationship would nonetheless be
expected to be linear at low doses. Therefore, the additional involvement of a cytotoxicity mode of
action does not provide evidence against the use of linear extrapolation from the POD.
The upward adjustment of the cancer PODs based on additional contributions from liver and NHL
cancer was based on peer-reviewed methodology as explained in the 2011 IRIS Assessment (U.S. EPA.
201 1e). This approach is reasonable, however it is unknown whether these statistical methods resemble
the true combined extra risk from these three cancers. Additionally, the IUR adjustment was rounded up
to 4-fold from a mean of 3.8 and route-to-route extrapolation results in a 5-fold adjustment for the OSF.
When combined with the above factors and the fact that the cancer PODs represent upper-bound values,
these uncertainties may potentially lead to overestimation of risk. but any differences from the true
IUR/OSF values are unlikely to vary by more than ~2-fold.
3.2.6.4

Confidence in Human Health Hazard Data Integration

Acute Non-Cancer
There is medium to high overall confidence in the database, weight of evidence, and dose-response for
acute non-cancer endpoints. There are four endpoints relevent to acute exposure scenarios, covering
three distinct endpoints from developmental toxicity studies and an immunological endpoint from an
acute co-infection study. Three of the four studies scored Medium in data quality, while one
developmental endpoint scored High. The PODs cover several orders of magnitude, with benchmark
MOEs of either 10 or I 00. Confidence is reduced from a high due to the data quality scores, the wide
range of PODs, and controversy over the most sensitive POD, from (Johnson et al.. 2003). Confidence is
raised from the robust WOE analysis performed on the cardiac toxicity endpoint (see Appendix G), the
presence of a variety of endpoints including a study using acute TCE administration, and reduced
uncertainty factors due to the use of a PBPK model or allometric scaling.
Chronic Non-Cancer
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There is high overall confidence in the database, weight of evidence, and dose-response for chronic noncancer endpoints. There are eleven endpoints relevant to chronic exposure scenarios across six health
domains. Seven of the studies scored Medium in data quality, while the other four scored High. The
PODs cover several orders of magnitude with benchmarkMOEs ranging from 10 to 300. Confidence is
high because there is strong WOE in support of all health effects, the PODs for three most sensitive
endpoints differ by within an order of magnitudefrom each other, and the majority of PODs and have
reduced uncertainty factors due to the use of a PBPK model.
Cancer
There is medium to high overall confidence in the database,weight of evidence, and dose-responsefor
cancer. Meta-analyses on the full database of relevant epidemiologicalstudies confirm a statistically
significant association between human exposure to TCE and the incidence of kidney cancer , liver
cancer, or NHL. The IUR/OSF is derived from a High quality study (Charbotel et al.. 2006) on kidney
cancer, with the PODs adjusted upward to account for the additional two cancer sites. Confidence is
slightly reduced due to some uncertainty over the precision of the dose-responseestimate in accounting
for all three cancer sites and in the GSH metabolism dose metrics but remains medium-highdue to
strong evidence for a mutagenic mode of action.

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4 RISK CHARACTERIZATION
4.1 Environmental Risk
EPA took fate, exposure, and environmentalhazard into consideration to characterize environmental risk
ofTCE. EPA determined that no further analysis beyond what was presented in the problem formulation
document would be done for environmental exposure pathways for sediment for aquatic and terrestrial
organisms, or land application ofbiosolids, water, or soil pathways for terrestrial organisms, in this risk
evaluation. As stated in Section 2.1 Fate and Transport, TCE is not expected to accumulate in
wastewater biosolids, soi~ sediment, or biota. TCE is expected to volatilize from the water surface or
from moist soil as indicated by its physical chemical properties (e.g., Henry's law constant) and by
microbial biodegradation under some conditions. The EP1 Suite™ volatilization module estimates that
the half-life of TCE in a model river will be 1.2 hours and the half-life in a model lake will be 110 hours.
Biodegradation ofTCE in the environment is dependent on a variety of factors and thus, a wide range of
degradation rates have been reported (ranging from days to years). TCE is not expected to accumulate in
aquatic organisms due to low measured BCFs and estimated BAF.

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Environmental exposure pathways for surface water for aquatic organisms are assessed and presented in
this draft risk evaluation. As stated in Section 2.2 Environmental Exposures, modeled surface water
concentrations ofTCE ranged from 1.27E-5ppb to 9,937.5 ppb from facilities releasing the chemical to
surface water. Measured surface water concentrations near facilities range from 0.4 ppb to 447 ppb from
published literature (1976-1977) . Measured surface water concentrations in ambient water range from
below the detection limit to 2.0 ppb in the Water Quality Portal (2013-2017) and from below the
detection limit to 37 ppb in the published literature (1996-2001). Because the WQP data was more
recent than the data from published literature, it was more relevant to this risk evaluation and was
therefore used to assess risk in ambient water.

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As stated in Section 3.1 Environmental Hazards, the available environmental hazard data indicate that
TCE presents hazard to aquatic organisms. For acute exposures to invertebrates, toxicity values ranged
from 7.8 to 33.85 mg/L (integrated into a geometric mean of 16 mg/L) . For chronic exposures, toxicity
values for fish and aquatic invertebrates were as low as 7.88 mg/Land 9.2 mg/L, respectively. These
data also indicated that TCE presents hazard for aquatic plants, with toxicity values in algae as low as
0.03 mg/L (geometric mean between a NOEC and a LOEC), and a wide range in toxicity between algae
species (ECsos ranging from 26.24 - 820 mg/L) .
A total of 25 aquatic environmental hazard studies were identified for TCE as acceptable. They were
given mostly high and medium quality ratings during data evaluation (See [DataQuality Evaluation of
EnvironmentalHazard Studies and EnvironmentalHazard Data Extraction Table. Doc'/ret:EPA-HQOPPT-2019-0500]).The [Data QualityEvaluationof EnvironmentalHazard Studies. Docket: EPA-HQOPPT-2019-0500]document presents details of the data evaluations for each study, including scores for
each metric and the overall study score.

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Given TCE' s conditions of use under TSCA outlined in the problem formulation (U.S. EPA. 2018d),
EPA determined that environmental exposures are expected for aquatic species, and risk estimation is
discussed in Section 4.1.2 Risk Estimation for Aquatic.

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4.1.1 Risk Estimation Approach _
EPA used modeled exposure data from E-FAST, as well as monitored data from the Water Quality
Portal (www.waterqualitydata.us) and available literature,to characterizethe risk ofTCE to aquatic
species. Risk quotients (RQs) were calculatedusing modeled surface water concentrationsfrom EFAST, monitored data, available literature, and the COCs calculatedin the hazard section of this
docwnent (Section 3.1.5). An RQ is defined as:

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RQ = Predicted EnvironmentalConcentration/ Effect Level or COC
An RQ equal to 1 indicates that environmentalexposures are the same as the COC. If the RQ is above 1,
the exposure is greater than the COC. If the RQ is below 1, the exposure is less than the COC. The
COCs for aquatic organisms shown in Table 3-2 and the environmentalconcentrationsshown in Section
2.2.6.2 were used to calculate RQs. (U.S. EPA. 1998)

EPA consideredthe biological relevance of the species that the COCs were based on when integrating
the COCs with surface water concentrationdata to produce RQs. For example, certain biological factors
affect the potential for adverse effects in aquatic organisms. Life-historyand the habitat of aquatic
organisms influences the likelihood of exposure above the hazard benchmark in an aquatic environment.

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Frequency and duration of exposure also affect potential for adverse effects in aquatic organisms,
especially for chronic exposures. Therefore,the number of days that a COC was exceeded was also
calculated using E-FAST. The days of exceedancemodeled in E-FAST are not necessarily consecutive
and could occur sporadically throughout the year. For TCE, EPA assumed continuousaquatic exposure
for the longer exposure scenarios (i.e. 117•365 days per year of exceedance of a COC), and more of an
interval or pulse exposure for shorter exposure scenarios (i.e. 1-40 days per year of exceedances of a
COC). Due to the volatile properties ofTCE, it is more likely that a chronic exposure duration will occur
when there are long-term consecutive days of release versus an interval or pulse exposure which would
more likely result in an acute exposure duration.
4.J .2 Risk Estimation for Aquatic
To characterizepotential risk due to TCE exposure, RQs were calculated based on modeled data from EFAST for sites that had surface water dischargesofTCE accordingto TRI and DMR data (see Table
4-1). Surface water concentrationsof TCE were modeled for 214 releases: 6 manufacturingreleases, 7
processing as a reactant releases, 51 open top vapor degreasingreleases, 1 spot cleaning and carpet
cleaning releases, 4 repackaging releases, 104 adhesives sealants paints and coatings, 6 industrial
processing aid releases, 1 commercialprinting and copying releases, 11 other industrial use releases, 5
other commercial uses releases, and 4 process solvent recycling and worker handling of wastes. Direct
releases from facilities (releases from an active facility directly to surface water) were modeled with two
scenarios based on high-end and low-end days of release. Indirect facilities (transfer of wastewater from
an active facility to a receiving POTW or non-POTWWWTP) were only modeled with a high-end days
of releases scenario. As stated in Section 2.2.3, the maximum releases :frequency(200 to ~65 days) is
based on release estimates specific to the facility's condition of use and the low-end releases frequency
(20 days) is an estimateof releases that couldleadto chronicrisk for aquaticorganisms.

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These facilities were modeled in E-FAST and all RQs are listed in Appendix E.2. As stated previously,
the frequency and duration of exposure affects potential for adverse effects in aquatic organisms.
Therefore, the nwnber of days a COC was exceeded was also calculated using E-FAST. Facilities with
RQs and days of exceedance that indicate risk for aquatic organisms (facilities with an acute RQ ~ 1, or
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a chronic RQ 2::1 and 20 days or more of exceedance for the chronic COC) are presented in Table 4-1.
All facilities were below these thresholds for manufacturing, spot cleaning and carpet cleaning, and
commercial. printing and copying, indicating no risks to aquatic organisms for these conditions of use.

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Processing as a Reactant:
Of the 443 facilities processing TCE as a reactant (including 440 unknown sites modeled in E-FAST),
one facility had acute RQs 2::1, or chronic or algae RQs 2::1 with 20 days or more of exceedances.
As_swning 20 days of releases, Praxair Technology Center in Tonawanda, NY had a chronic RQs of 3.81
with 20 days of exceedance, and an algae COCs representing the most sensitive species of algae of
1,000 with 20 days of exceedance. In other words, the surface water concentration modeled for this
facility was 3.81 times higher than the COC for chronic exposures, and 1,000 times higher than the COC
for the most sensitive species of algae. Assuming 260 days of releases from the facility, the algae RQ
representing the most sensitive species was 56.33 with 350 days of exceedance. However, for algae
species as a whole, RQs for this site were 0.06 assuming 20 days of release and 0.00 assuming 350 days
of release, meaning the concentration did not exceed the COC of 52,000 ppb which represents nine
different species of algae. Therefore,there may be riskfor some of the most sensitive species of algae at

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this site, but not for algae species as a whole. Risks were identifiedat this site for other aquatic
organismsfor chronic exposures, with a surface water concentration3.81 times higher than the chronic
COC and 20 days of exceedance.
Repackaging:
Of the six facilities repackaging TCE, one had algae RQs 2::1 with 20 days or more of exceedances.
Assuming 20 days of release per year, Hubbard-Hall Inc in Waterbury, CT had an RQ for the most
sensitive species of alge as high as 113.04 with 20 days of exceedance. Assuming this facility released
TCE for 250 days per year, the RQ is 9.06 with 194 days of exceedance. However, for algae species as a
whole, RQs for this site were 0.01 for 20 days of releases, and 0.00 for 250 days, meaning the
concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.

Therefore, there may be risk for some of the most sensitive species of algae at these sites, but not for
algae species as a whole. No risks were identifiedfor other aquatic organisms in this condition of use.
Open-top Vapor Degreasing:
Of the 64 open-top vapor degreasing facilities, three sites had acute RQs 2::1, or chronic or algae RQs 2::
1 with 20 days or more of exceedances. Assuming 20 days of releases, US Nasa Michoud Assembly
Facility in New Orleans, LA had acute RQs of3.l l, a chronic RQs of 12.61 with 20 days of exceedance,
and an algae COCs representing the most sensitive species of algae of 3,312.50 with 20 days of
exceedance. Assuming 260 days ofrelese from the facility, the algae RQ representing the most sensitive
species was 255.21 with 260 days of exceedance. However, for algae species as a whole, RQs for this
site were 0.01 assuming 260 days ofrelease, -arid 0.19 assuming 20 days of release, meaning the
concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.

Therefore, there may be riskfor some of the most sensitive species of algae at this site, but notfor algae
species as a whole. Risks were identified at this sitefor other aquatic organismsfor acute and chronic
exposures, with a surface water concentration3.11 times himer than the acute COC and 12.61 times
higher than the chronic COC and 20 days of exceedance.
GM Components Holdings LLC in Lockport, NY had an RQ for the most sensitive species of algae of
3.66 with 117 days of exceeqance, assuming 260 days of release per year. Assuming 20 days of release,
this site has an RQ for the most sensitive species of algae of 48.16 with 20 days of exceedance.
However, for algae species as a whole, RQs for this facility were 0.00 for this site, meaning the
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concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.
Therefore,there may be riskfor some of the most sensitive species of algae at this site, but notfor algae
species as a whole.
Akebono Elizabethtown Plant in Elizabethtown, KY had an RQ for the most sensitive species of algae
of 1.62 with 27 days of exceedance, assuming 260 days of release per year. However, for algae species
as a whole, RQs for this facility were 0.00 for this site, meaning the concentration did not exceed the
COC of 52,000 ppb which represents nine different species of algae. Therefore,there may be riskfor
some of the most sensitive species of algae at this site, but notfor algae species as a whole.
Adhesives, Sealants, Paints, and Coatings:

Of the 54 facilities using TCE as adhesives, sealants, paints, and coatings, one site had algae RQs ~ 1
with 20 days or more of exceedances. Raytheon Company in Portsmouth, RI had an RQ for the most
sensitive species of alge as high as 44.44, assuming 20 days of release per year. In other words, the
surface water concentration modeled for this facility was 44.44 times higher than the COC for the most
sensitive species of algae (3 ppb). Additionally,this COC was exceeded for 20 days. Assuming this
facility released TCE for 250 days per year, the RQ is 3.61 with 250 days of exceedance. However, for
algae species as a whole, RQs for this facility were 0.00, meaning the concentration did not exceed the
COC of 52,000 ppb which represents nine different species of algae. Therefore,there may be riskfor
some of the most sensitive species of algae at this site, but notfor algae species as a whole. No risks
were identifiedfor other aquatic organismsfor this conditionof use.
Other Industrial Uses:
Of the 21 facilities with other industrial uses ofTCE, three sites had algae RQs ~ 1 with 20 days or more

of exceedances. Eli Lilly And Company-Lilly Tech Ctr in Indianapolis, IN had an RQ for the most
sensitive species of alge of 3.01, assuming 250 days of release per year. In other words, the surface
water concentration modeled for this facility was 3.01 times higher than the COC for the most sensitive
species of algae (3 ppb). Additionally, this COC was exceeded for 35 days. Washington Penn Plastics in
Frankfort, KY had an RQ for the most sensitive species of alge of 2.51, assuming 250 days of release
per year. Additionally, this COC was exceeded for 22 days. Keeshan and Bost Chemical Co., Inc. in
Manvel, TX had an RQ for the most sensitive species of algae of 66.67 with 20 days of exceedance,
assuming 20 days of release per year. Assuming 350 days of release, this site has an RQ for the most
sensitive species of algae of 3.17 with 350 days of exceedance. However, for algae species as a whole,
RQs for these facilities were 0.00, meaning the concentration did not exceed the COC of 52,000 ppb
which represents nine different species of algae. Therefore,there may be riskfor some of the most
sensitivespecies of algae at these sites, but notfor algae species as a whole.No risks were identifiedfor
other aquatic organismsfor this conditionof use.
Industrial Processing Aid:

Of the six industrial processing aid facilities, one site had algae RQs ~ 1 with 20 days or more of
exceedances. Entek International LLC in Lebanon, OR had an RQ for the most sensitive species of alge
as high as 46.11, assuming 20 days of release per year. In other words, the surface water concentration
modeled for this facility was 46.11 times higher than the COC for the most sensitive species of algae (3
ppb). Additionally, this COC was exceeded for 20 days. Assuming this facility released TCE for 300
days per year, the RQ is 3.10 with 140 days of exceedance. However, for algae species as a whole, RQs
for this facility were 0.00, meaning the concentration did not exceed the COC of 52,000 ppb which
represents nine different species of algae. Therefore,there may be risk/or some of the most sensitive

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 186
187

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
species of algae at this site, but notfor algae species as a whole. No risks were identifiedfor other
aquatic organismsfor this condition of use.

188
I 89

Other Commercial Uses:

190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225

Of the nine facilities with other commercial uses ofTCE, one site had algae RQs ~ 1 with 20 days or
more of exceedances. Park Place Mixed Use Developmentin Annapolis, MD had an RQ for the most
sensitive species of alge as high as 36.67, assuming 20 days of release per year. In other words, the
surface water concentration modeled for this facility was 36.67 times higher than the COC for the most
sensitive species of algae (3 ppb). Additionally,this COC was exceeded for 20 days. Assuming this
facility released TCE for 250 days per year, the RQ is 3.00 with 250 days of exceedance. However, for
algae species as a whole, RQs for this facility were 0.00, meaning the concentration did not exceed the
COC of 52,000 ppb which represents nine different species of algae. Therefore, there may be riskfor
some .of the most sensitive species of algae at this site, but notfor algae species as a whole. No risks
were identifiedfor other aquatic organisms in this condition of use.
Process Solvent Recycling and Worker Handling of Wastes:

Of the five facilities with other commercial uses ofTCE, three sites had algae RQs ~ 1 with 20 days or
more of exceedances. Asswning 20 days of release per year, Clean Water Of New York Inc in Staten
Island, NY had an RQ for the most sensitive species of alge as high as 46.08 with 20 days of
exceedance. Assuming this facility released TCE for 250 days per year, the RQ is 3.92 with 250 days of
exceedance. Assuming 20 days of release, Veolia Es Technical Solutions LLC in Middlesex, NJ had an
RQ for the most sensitive species of alge of 11.91 with 20 days of exceedance. And assuming 250 days
of releases, Clean Harbors Deer Park LLC in La Porte, TX had an RQ for the most sensitive species of
alge of 2.86 with 110 days of exceedance. However, for algae species as a whole, RQs for at all three
facilities were 0.00, meaning the concentration did not exceed the COC of 52,000 ppb which represents
nine different species of algae. Therefore, there may be riskfor some of the most sensitive species of
algae at these sites, but not/or algae species as a whole. ·No risks were identified/or other aquatic
organisms in this condition of use.
Wastewater Treatment Plants (WWTPs):

Of the nine WWTPs, one site had algae RQs ~ 1 with 20 days or more of exceedances. New Rochelle
STP in New Rochelle, NY had an RQ for the most sensitive species of alge of 4.26, assuming 20 days of
release per year. This means that the surface water concentration modeled for this facility was 4.26 times
higher than the COC for the most sensitive species of algae (3 ppb). Additionally, this COC was
exceeded for 20 days. Assuming this facility released TCE for 365 days per year, the RQ is only 0.23
with Odays of exceedance. A WWTP is likely to be operating at greater than 20 days of release,
therefore the RQ associated with the high-end days of release scenario (365 days) is likely more
representative of actual conditions. Therefore, no risks to aquatic species werefor thisfacility or
condition of use.

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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-1. EnvironmentalRisk Quotientsfor FacilitiesReleasini TCE to Surface Water as Modeled in E-FAST(RQs > l in bold)

Name, Location, and ID of
Active Releaser Facility •

Release
Mediab

Modeled Facility or
Industry Sector in

EFASTC

EFAST
Waterbody
Typed

Days of
Release
e

Release
(kg/day)£

7Ql0

swc

COCType

(ppb) S

coc
(ppb)

Days of
Exceedance
{days/year)

Risk
Quotient

h

OES: Processin2as a Reactant

Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281

350
Surface
Water

NPDES NY000028l

0.00169

169

Stillbody
20

0.03

3000

Acute
Chronic
Algae
Algae (HCos)
Acute
Chronic
Ahzae
AhaaeffiCos)

3,200
788
3
52,000
3.,200

NA

788

20
20

3
52.000

0
350
0

NA
0

0.05
0.21
56.33

0.00
0.94
3.81

1.000.00
0.06

OES: Reoacka2fa2
Acute
Chronic
250
27.18
1.108
Off-site
A)J!:ae
Hubbard-HallInc,
WasteReceiving Facility:
Algae lHCos)
water
Recycle Inc.; POTW Surface water
Waterbury, CT
Acute
Treatme
{Ind.)
NPDES: Unknown
Chronic
nt
20
13.85
339.11
Algae
Alsme (HCos}
OES: OTVD (lncludes releases for Closed-LoopDeereasina, Conveyoriud De2reasina.Web Degreasin1., and Metalworkin11:
Fluids'
US Nasa Michoud Assembly
Facility,
New Orleans, LA
NPDES: LA0052256

26P
Surface
Water

SurrogateNPDES
LA0003280

Akebono ElizabethtownPlant

260
Surface
Water

NPDES NY0000558

765.63

Still body
20

GM ComponentsHoldings
LLC,
Lockport, NY
NPDES: NYOOOOSS8

1.96

25.44

0.13

9937.5

10.97

Surface water

Surface water

20

1.71

144.47

260

0.07

4.87

Page 260 of 691

3,200
788
3
52000
3,200

NA
0
194
0

788
52000

1
20
0

Acute

3.200

NA

Chronic
Ale.ae(COC)
Als.?ae<HCos)
Acute
Chronic
Algae
AlgaelHCM)
Acute
Chronic
Ahtae (COC)
Ahiae <HCos)
Acute
Chronic
Ale.ae
Abtae ffiCos)
Acute

788

0
260

3

3
52,000
3,200
788
3
52000
3,200
788
3
52 000
3,200
788
3
52000
3.200

NA

0

NA
20
20
0
NA
0
117
0
NA
0
20
0
NA

0.01
0.03
9.06

0.00
0.11
0.43
113.04

0.01
0.24
0.97
255.21
0.01
3.ll

12.61
3.312.50

0.19
0.00
0.01
3.66

0.00
0.05
0.18
48.16

0.00
0.00

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
;

Name, Location, and ID of
Active ReleaserFacility•

Release
Mediab

Modeled Facility or
Industly Sector in
EFAST 0

EFAST
Waterbody

Days of
Release

Typed

C

Release
(kg/day)f

7Q10

swc

(ppb)'

Eliz.abethtown,KY
NPDES: KY0089672
Surface
Water

SurrogateNPDES
KY0022039
20

0.897

COCType

62.38

coc

Days of
Exceedance

(ppb)

(days/year)

Risk
Quotient

h

Chronic
Abtae( COC)
AlJZ.
ae (HCos)
Acute
Chronic
Algae
Alr.rae
_lli_Cos)

788
3
52_1000
3~00
788
3
52 000

0
27
0
NA
0

O.Ql

16

20.79

0

0.00

Acute
Chronic
AlgaeJCOC)
AlgaeJHCos)
Acute
Chronic
Algae( COC)

3,200

NA
0
250
0
NA
0
20

0.00
0.01
3.61
0.00
0.04
0.17
44.44

0
NA
0
0
0

0.00
0.00
0.00
0.11
0.00

3 200
788
3
52.000
3_1200

NA
0
35
0

0.00
0.01
3.01
0.00

NA

0.04

788

0
17

37.70

1.62
0.00
0.02
0.08

OES: Adhesiv~, Sealants , Paints. and Coadnl!S

RaytheonCompany,
Portsmouth,RI
NPDES: RI0000281

Surface
Water

0.013

10.83

20

0.160

133.33

NPDES Rl0000281
Still body

POTW

250

No info on receiving
facility; Adhesives
and SealantsManuf.

250

0.013

0.32

Algae (HCos)
Acute
Chronic
Algae(COC)
Algae{HCos)

788
3
52 000
3_,200
788
3

siooo
3,200
788
3
52,000

OES: Other Industrial Uses

Eli Lilly And CompanyLilly Tech Ctr,
Indianapolis,IN

250
Surface
Water

NPDES IN0003310

20

250

Surface
Water

SurrogateNPDES
KY0028410

9.03

Surface water

NPDES: IN0003310

WashingtonPenn Plastics,
Frankfort,KY
NPDES: KY0097497

1.553

19.410

0.032

113.09

7.53

Surfacewater
20

0.399

Page 261 of 691

94.12

Acute
Chronic
AlgaeJCOC)
Algae ( HCos)
Acute
Chronic
Algae
Al~ae (HC os}
Acute
Chronic
Algaei COC)
AlgaeiHCos)
Acute
Chronic
Algae

3
52,000
3,200
788
3

NA
0
22

52,000

0

3 200
788

NA
0
13

3

0

0.14
0.00
0.00
0.01
2.51

0.00
0.03
0.12
31.37

 INTERAGENCYDRAPT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility •

Release
. Media b

Keeshan and Bost Chemical
Co., Inc.,
Manvel, TX
NPDES: TX0072168

Modeled Facility or
Industry Sector in
EFASTC

EFAST
Waterbody
Typed

Days of
Release

•

350
Surface
Water

NPDES TX0072168

Release
{kg/day) f

0.000095

7Ql0

swc

(ppb) a

9.50

Still body
20

0.002

COCType

200.00

coc
(ppb)

Days of
Exceedance
(days/year)

Risk
Quotient

b

Ah!ae (HCos)
Acute
Chronic
Al~ae
Algae<HCos)
Acute
Chronic
Ale.ae
Ale.ae<HCos)

52,000
3,200
788
3
52000
3.200

Acute
Chronic
Al11.ae{COC}
Ah!ae {HCos)
Acute
Chronic
Al!!ae(COC)
Algae (HCos)

3,200
788
3
52,000
3,200
3
52000

Acute
Chronic
Ale.ae(COC)
Ah!ae <HCos)
Acute
Chronic
Aleae (COC)
Algae <HCos)
Acute
Chronic
Ah!ae(COC)
Al~effiCo5)
Acute
Chronic
Aleae
Alg;ae(HCos)
Acute

788

3
52000

0
NA
0
350
0
NA
0
20
0

0.00
0.00
0.01
3.17

0.00
0.06
0.25
66.67

0.00

OES: Industrial Processine Aid

Entek International LLC,
Lebanon, OR
NPDES:N /A

Off-site
Waste..
water
Treatine
nt

300
No info on receiving
facility; POTW(Ind.)

0.38

9.3

Surface water
20

5.65

138.34

NA
0
140
0
0
0
20

0.00
0.01
3.10

0

0.00
0.04
0.18
46.11
0.00

3.200
788
3
52 000
3,200
788
3
52,000

NA
0
250
0
NA
0
20
0

0.00
0.01
3.00
0.00
0.03
0.14
36.67
0.00

3,200
788
3
52000
3.200
788
3
52000
3,200

NA
0
250
0
NA
0
20
0
NA

0.00
0.01
3.92
0.00
0.04
0.18

788

OES: Other Commercial Uses

Park Place Mixed Use
Development,
Annapolis, MD
NPDES: MD0068861

Surface
Water

Surrogate NPDES
MD0052868

250

0.00027

9

20

0.00334

110

Still body

OES: Process Solvent Recvclin2 and Worker Handlin2 of Wastes

Clean Water Of New York
Inc,
Staten Island, NY
NPDES: NY0200484

Surface
Water

Surrogate NPDES
NJ0000019

250

0.004

11.76

20

0.047

138.24

250

24.l

2.85

Still body

Still body

Page 262 of 691

46.08

0.00
0.00

 INTERAGENCYDRAFT- DO NOT CUE OR QUOTE
Name, Location, and ID of
Active Releaser Facility •

Release
Media b

Off~site
Waste-

Veolia Es Technical Solutions
LLC,
water
Middlesex,NJ
Treatme
NPDES: NJ0020141
nt

Clean Harbors Deer Parle
LLC,
La Porte, TX
NPDES: TX0005941

Modeled Facility or
lndus1IySector in
EFASTC

EFAST

Days of
Release

Waterbody
Typed

e

Receiving Facility:
MiddlesexCnty UA;
NPDES NJ0020141

20

250

Off-site
Wastewater
Treatme
nt

POTW(Ind.)

Release
(kg/day) f

301.78

0.35

7Ql0

swc

(ppb)S

35.72

8.57

Surface water
20

4.36

COCType

106.75

coc
(ppb)

Days of
Exceedance
(days/year)
h

Chronic
AlRae(COC)
A.12.lle
(HCos)
Acute
Chronic
Ale.ae
AJQaefHCo5)
Acute
Chronic
AIR.ae(COC)
Alaae <HCos)
Acute
Chronic
AIR.ae
Alaae lHCos)

788
3

52.000
3 200
788
3

52000
3 200
788
3

52000
3 200
788
3

52000

0
0
0
NA
0
20
0
NA
0
110
0
NA
0
19
0

Risk
Quotient
0.00
0.95
0.00
0.01
0.05
11.91
0.00
0.00
0.01
2.86

0.00
0.03
0.14
35.58

0.00

OES : Wastewater Treatment Plants lWWTP}

New RochelleSTP,
New Rochelle, NY
NPDBS: NY0026697

Surface
Water

NPDES NY0026697

365

0.043

0.7

20

0.786

12.79

Still body

Acute
Chronic
Alllae (COG)
Al2ae mCos)
Acute
Chronic
Al2ae (COCl
Ale.ae<HCos
)

3,200
788
3
52000
3 200
788
3
52000

0
0
0
NA
0

0.00
0.00
0.23
0.00
0.00
0.02

20

4.26

0

0.00

NA

227
228
229

230
231
232

233
234
235
236
237
238

a Facilities actively releasing trichloroethylenewere identifiedvia DMR, TRI, and CDR databasesfor the 2016 reporting year.
b.Release media are either direct (release from active facility directly to surface water) or indirect (transfer of wastewater from active facility to a receiving POTW or
non-POTW WWTP facility). A wastewatertreatment removal rate of 81% is applied to all indirect releases, as well as direct releases from WWTPs.
c.Ifa valid NPDES of the direct or indirect releaser was not available in EFAST, the release was modeled using either a surrogate representativefacility in EFAST (based
on location) or a representativegeneric industry sector. The name of the indirect releaser is provided. as reported in TRI.
d.EF AST uses ether the "surface water" model, for rivers and streams, or the "still water" model, for lakes, bays, and oceans.
e.Modeling was conducted with the maximum days of release per year expected. For direct releasing facilities,a minimum of20 days was also modeled.
f. The daily release amount was calculated from the reported annual release amount divided by the number of release days per year.
g.For releases discharging to lakes, bays, estuaries, and oceans, the acute scenario mixing zone water concentrationwas reported in place of the 7Ql O SWC.
h .To determinethe PDM days of exceedancefor still bodies of water, the release days provided by the EPA Engineers should become the days of exceedanceonly if the
predicted surface water concentrationexceedsthe COC. Otherwise,the days of exceedancecan be assumedto be zero.

Page 263 of 691

 INTFRAGENCY DRAFT - DO NOT CITE OR QUOTE
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261

EPA also used surface water monitoring data from the Water Quality Portal (WQP) and from the
published literature to characterize the risk ofTCE to aquatic organisms. For the most part these
monitored surface water concentrations reflect concentrations ofTCE in ambient water. There was one
US study (U.S. EPA . 1977) that had measurements reflecting near-facility monitoring data. The other
monitored data collected in the US reflect ambient concentrations.
Monitored data from one US study (U.S. EPA . 1977) in the published literature reporting near-facility
concentrations ofTCE collected between 1976 and 1977ranging from 0.4 to 447 µg/L. While these data
reflect historical levels ofTCE, they are helpful to compare measured near-facility concentrations to the
modeled near-facility concentrations from E-FAST. The measured concentrations in this study
encompases the range of the modeled estimates across all OES with the exception of two sites, that
release to still water bodies.
EPA also had monitored data reflecting ambient water concentrations. EPA' s Storage and Retrieval
(STORET) data and USGS 's National Water Information System (NWIS) data were extracted on Oct
3 rd, 2018 from the WQP. These data show an average concentration for TCE of0 .33 ± 0.29 µg/L or ppb
in surface water from 2,273 measurements taken throughout the US between 2013 and 2017. The
highest value recorded during these years was 2 µg/L or ppb, which was measured in 2017. Table 4-2
shows that none of the RQs for aquatic species are greater than or equal to 1. The RQs for algae range
:from O to 0.67. Acute and chronic RQs for other aquatic species are all very close to 0.

Table 4-2. RQs Calculated using Monitored EnvironmentalConcentrations from Water Quality
Portal
Monitored Surface Water
AlgaeRQ
RQ using Acute RQusing
Concentrations (ppb) from
2013-2017

262
263
264
265
266
267
268
269
270
271
272
273
274

using COC using HCosof

C0Cof3,200
ppb

Chronic COC of
788ppb

of3 ppb

52,000 ppb

Mean (Standard Deviation):
0.33 (0.29) ppb

0.11

0.0

0.0

0.0

Maximum: 2 ppb

0.67

0.0

0.0

0.0

The published literature show monitored data in six U.S. studies encompassing 1,177 surface water
samples collected from river and oceans throughout the nation between 1979 and 2001. Reported
concentrations ofTCE ranged from below the detection limit (0.0001 to 0.08) to 17.3 µg/L or ppb , with
reported central tendency values ranging from 0.0002 to 1. 17 µg/L (USGS , 2006 ; Sauer . 1981; Sin£h et
al.. 1983; USGS . 2003 ; Robinson et al .. 2004 ). The maximum concentration was collected from the
Charles River in Boston, Massachusetts (an urban area) between 1998 and 2000 (Robinson et aL 2004).
The next highest TCE concentration was 2.0 µg/L, collected during a large nationwide survey of surface
water for drinking water sources (rivers and reservoirs) between 1999 and 2000 (11SOS, 2003 ). Table
4-3 shows an RQs for algae range from Oto 5.77 using monitored surface water concentrations from the
grey literature. Acute RQs for other aquatic organisms range from Oto 0.01, and chronic RQs range
from Oto 0.02 .

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 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
275
276

Table 4-3. RQs Calculated using Monitored Environmental Concentrations from Published
Literature
Monitored Surface
AlgaeRQ
RQ using Acute RQusing
COCof3,200
Water Concentrations
Chronic COC of
788 ppb
(ppb) from 2013-2017
using COC of 3 using HCos of ppb

ppb

277
278
279
280
281
282
283
284
285
286
287
288
289

290
291
292
293
294
295
296
297

52,000ppb

Central tendency values: 0.00 -0 .39
0.0002 - 1.17 ppb

0.00

0.00

0.00

Maximum: 17.3 ppb

0.00

0.01

0.02

5.77

To compare the modeled data with the monitored data, EPA conducted a watershed analysis by
combining monitored data from WQP with predicted concentrations from modeled facility releases,
using the geospatial analysis outlined in Section 2.2. A geographic distribution of the concentrations is
shown in Figure 4-1.and Figure 4-2. (east and west US) for the maximum days of release scenario, and
in Figure 4-3. and Figure 4-4. (east and west US) for the 20-days of release scenario. The co-location of
TCE releasing facilities and monitoring stations in a HUC is shown in Figure 4-5. for HUCs in North
Carolina and in Figure 4-6. for the HUC in New Mexico. The modeled estimates are only shown in
Figme 4-5. and Figure 4-6. for the higher release frequency scenarios, which are associated with lower
predicted surface water concentratj.ons. The surface water concentrations were compared to the COCs in
these maps.
Figure 4-1 . to Figure 4-6. show that, while some facilities releasing TCE to surface water were co-located with monitoring locations , none were downstream from facilities. The monitored data, which
represents concentrations of TCE in ambient water, generally show lower concentrations than the
modeled surface water concentrations from E-F AST, which represents concentrations near facilities
releasing TCE. The modeled and monitored data together indicate that risk to aquatic organisms from
TCE exposure is more likely in areas near the facilities, rather than in ambient water.

Page 265 of 691

 INTERAGENCY DRAFf - DO NOT CITE OR QUOTE

298
299
300

Figure 4"1. Concentrations ofTrichloroetbylene from Releasing Facilities (Higher Release
Frequency Scenarios) and WQX Monitoring Stations: Year 2016, East US.
[Note: All indirect releases are mapped at the receiving facility unless the receiving facility is unknown.]

•

•

••

-

•
• • •• •

•

•

~

•

•

••••..•
•

■

•

■

•

"• ••

•

• • ••

••
•
•

• • ••

•
c••
• • •
•
••"
•

• ..
•

•

Concentration
Levels

•

• •

....

a

-~
■

•

■

•• •

■

• •
•
•

--

ni

Concentration Type

3-7871,19/L

Modeled - Direct Release (250 - 365 days/yr)

< 3 µgtl (below all COCs)

Modeled - Indirect Release (250 - 365 days/yr)

Not detected

Measured - NWIS/STORET Monitoring Sites

; Daysof exceedance ~ 20 days
States with no modeled or measured
concentrations

301
302

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304
305

Figure 4-2. Concentrations of Trichloroethylene from Releasing Facilities (Higher Release
Frequency Scenarios) and WQX Monitoring Stations: Year 2016, West US.
ote: All indirect releases are ma d at the receivin facilit unless the receiving facility is unknown .]

•
•
•

•

•
•

COncentrationLevels
•

Concenlration Type

-

3- 787 µgll

Modeled • Dired Release (250 - 365 days/yr)

< 3 µg/L (below all COCs)

Modeled • Indirect Release (250 - 365 days/yr)

Not detected

Measured - NWIS/STORETMonitoringSites
A Days of exceedance 2: 20 days
· :·· ·"}States with no modeled or measured

306

~-- • ooncentrations

307

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Figure 4-3. Concentrations of Trichloroethylene from Releasing Facilities (20 Days of Release
Scenario) and WQX Monitoring Stations: Year 2016, East US.
[Note: All indirect releases are mapped at the receiving facility unless the receiving facility is unknown.]

•
II

..

•

I ••
• ••

•

•

•

•

•

•

788 - 51 999 JJS,,L

a
a

3 - 787 1,19
.1
1.
< 3 1,1g
1L <below all COCs,

Not detected

311

••

•
•

If!

,oottoJ@Jfonbtvtts

I

•
••
•
• • •

•
•

• •
••

.
,
.,

- . '

•

ft

t, ■•.:

• •
•
• •
•
•
• .• • ••
• ••

•• •

..

.•

•

••
ConcenlAtlon
IYa

-

Modeled • Direct Release 120 days iyn
Measured • IIIWISISTORET MonitonngSites
Daysof e,cceedance
~ 20 days
Slates Mth no modeled or measured
concenlfat,ons

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Figure 4-4. Concentrations ofTrichloroethylene Releasing Facilities (20 Days of Release Scenario)
and WQX Monitoring Stations: Year 2016, West US.
ote: All indirect releases are ma ed at the receivin facili unless the receiving facility is unknown.]

•

•
•

•

•

•

J

•

..
~

Concantratlon
Lpvels

a

3- 787 µgll
< 3 µgll (below all COCs}
Not deteeted

315
316

300
-Mle~

ConcemrationType
Modeled• Direet Release(250 - 365 days/yr)
Measured• NWISISTORETMonitoring Sites
· Days of exceedance~ 20 days
-~States withno modeledor measured
-concentrations

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Figure 4-5. Co-location of Tricbloroethylene -Releasing Facilities and WQX Monitoring Stations at
the HUC 8 Level in NC

""

I Pl'

1

I1100009004461

lt.rn

j~ IUOOIS01492!

J

f'I

'

l

I'-~ .... I

tt\ ♦ 1«:01

.,/

I

O ~0•11101
II

Pl,., l.u
W !ti l II t

1001504747

-y

~-7
Q

,.r

'

l----·

Ill ~.
>

'l u,,
:?Ill

~)..___
lN<:00894941

,--1 l'lh' I 1ui..h
(

111,
,1,,11111111,

IJ1(lllOOJ45939j

Concentrat;ons
Measured • NWIS/STORET Monitoring Sites

On

I ,I

ls..

Not detected
Modeled - Indirect Release (250 - 365 days/yr)

•

Below all COC

Modeled • Direct Rei.ase (250 - 365 days/yr)

319

10(

0

_:::JMi

Below all COC
HUC•8 boundary

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320
321

Figure 4-6. Co-location ofTrichloroethylene-Releasing Facilities and WQX Monitoring Stations at
the HUC 8 Level in NM

J}
ll.S. Location

Concentrations
Measured • NWIS/STORET Monitoring Sites
.. Not detected

Modeled• Indirect Release (250 - 365 days/yr)
• Belowall COO
Modeled• Direct Release (250 - 365 days/yr)
BelowallCOC
HUC-8 boundary

_

322
323

$G$Tt.t

r;r.~fl..ait

r~~l1'f'd1:,gr!lph'

OOl.-t

,._____._

50

,.t , ,le~

04\.tl•!'lt~0-'"'%it)fi(.

201i

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325
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328
329
330
331
332
333

4.1.3 Risk Estimation for Sediment
EPA did not quantitatively assess exposure to sediment organisms, because TCE is not expected to
partition to sediment, based on physical-chemical properties. TCE is expected to remain in aqueous
phases and not adsorb to sediment due to its water solubility (> 1280 m g/L) and low partitioning to
organic matter (log Koc= 1.8-2.17). Limited sediment monitoring data for TCE that are available
suggest that TCE is present in sediments, but because TCE bas relatively low partition to organic matter
(log Koc= 1.802.17) and biodegrades slowly (19% biodegradation in 28 days (ECB2004)],
TCE concentrations in sediment pore water are expected to be similar to the concentrations in the
overlying water or lower in the deeper part of sediment which anaerobic condition prevails. Thus, the
TCE detected in sediments is likely from the pore water.

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340

4.1.4 Risk Estimation for Terrestrial --EPA did not quantitatively assess exposure to terrestrial organisms through soil, water, or biosolids.
TCE is not expected to partition to soil but is expected to volatilize to air, based on its physical-chemical
properties. Review of hazard data for terrestrial organisms shows potential hazard; however, physicalchemical properties do not support an exposure pathway through water and soil pathways to terrestrial
organisms.

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TCE is not anticipated to partition to biosolids during wastewater treatment. TCE has a predicted 81%
wastewater treatment removal efficiency, predominately due to volatilization dwing aeration. Any TCE
present in the water portion ofbiosolids following wastewater treatment and land application would be
expected to rapidly volatilize into air. Furthermore, TCE is not anticipated to remain in soil, as it is
expected to either volatilize into air or migrate through soil into groundwater.
TCE is expected to volatilize to air, based on physicochemical properties. However, the emission
pathways to ambient air from commercial and industrial stationary sources or associated inhalation
exposure of terrestrial species were out of the scope of the risk evaluation because stationary source
releases ofTCE to ambient air are adequately assessed and any risks effectively managed when under
the jurisdiction of the Clean Air Act (CAA) .

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4.2 Human Health Risk

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359

4.2.1

Risk Estimation Approach
The use scenarios-:populations of interest and toxicologicalendpoints used for acute and chrmtlc
exposures are are presented in Table 4-4.

Table 4-4. Use Scenarios, Populations of Interest and Toxicological Endpoints Used for Acute and
Chronic Exposures
Workers: 1
Acute- Adolescent and adult workers ~ 16 years old) exposed to TCE for

a single 8-hr exposure
.
Chronic- Adolescentand adult workers ~16 years old) exposed to TCE
for the entire 8-hr workday for 260 days per year for 40 working years
Occupational Non-User:
Population of Interest and
Exposure Scenario

Acute or Chronic-Adolescentand adult worker ~16 years old) exposed
to TCE indirectly by being in the same work area of the building
Consumers

2

Acute- Youth and adult consumers ~11 years old) exposed to TCE for a
short period of time during use 3
Bystanders:

Acute- Individuals of all ages exposed to TCE through consumer use of
another individual.
Non-Cancer Point of Departures (POD):

Health Effects,
Concentration and Time
Duration

HEC-ppm;
POD HECs represent 24hr values and exposure concentrationshave been
adjusted to match the time duration for inhalation exposure.
Note: Selgrade 20 IOPOD is a 3h acute value that has been adjusted to
match the 24hr value for workers (3h exposure values were used for
consumers).
HED- mg/kg; for dermal risk estimates
Non-Cancer Health Effects:

4

Acute- Developmentaleffects and pulmonary immunotoxicity
Chronic- Liver effects, kidney effects, neurological effects, immune
effects, reproductiveeffects, and developmental effects
Uncertainty Factors {UF)
used in Non-Cancer Margin
of Exposure (MOE)
calculations

Benchmark MOEs: Vary by endpoint
Benchmark MOE= (UFs) x (UF ,J x (UFH)x (UFi,) 5

1Adult workers (> 16 years old) include both

healthyfemale and male workers.
EPA beJievesthat the users of these productsare generallyadults,but young teenagersand even younger childrenmay be
users or be in the same room with the user while engagingin variousconditionsof use. Sincethere are not surveydata for · I
consumerbehaviorpatterns or a way to create varyingbehaviorpatterns for differentage groups,the indoor air concentrations'
shown in Table 4-4. Use could be extended to all users.
3 EPA believesthat the users of these productsare generallyadults,but young teenagersand even youngerchildrenmay be
users or be in the same room with the user while engagingin variousconditionsof use. Sincethere are not surveydata for
2

1

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consumerbehavior patterns or a way to create varyingbehavior patterns for differentage groups, the indoor air concentrations
shown in Table 4-5 could be extendedto all users.
4 Female workers of childbearingage are the populationof interest for reproductiveand developmentaleffects. For other
health effects (e.g.,, liver, kidney, etc.), healthy femaleor male workers were assumedto be the population of interest.
5
UFs""Subchronic
to chronic UF; UFA=interspeciesUF; UF!FintraspeciesUF; UFL=LOAEL to NOAEL UF

360
361
362
363
364
365
366
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368
369
370
371

The EPA uses a Margin of Exposure (MOE) approach to assessing non-cancer risk. The MOE is the
ratio of the point of departure (POD) dose divided by the human exposure dose. The MOE is compared
to the benchmark MOE. If the MOE exceeds the benchmark MOE, this indicates the potential for risk to
human health.
Acute or chronic MOEs (MOEacute
or MOEohronic)
were used in this assessment to estimate non- cancer
risks using Equation 4-1.
Equation 4-1. Equation to Calculate Non-Cancer Risks Following Acute or Chronic Exposures
Using Margin of Exposures

Non - cancer Hazard value (POD)

372
373
374

MOEacuteorchronic

Where:
MOE
Hazard Value (POD)
Human Exposure

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388
389
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391
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= ------------Human Exposure

= Margin of exposure (unitless)
= HEC (ppm) or RED (mg/kg)
= Exposure estimate (in ppm or mg/kg) from occupational exposure
assessment
= Exposure estimate (in ppm or mg/kg) from consumer exposure
assessment

Acute Concentrations (ACs) in ppm and acute Average Daily Doses (ADDs) were used to calculate
occupational non-cancer risks following acute inhalation or dennal exposure, respectively. Average
Daily Concentrations (ADC) and non-cancer chronic ADDs were used for calculating occupational noncancer risks following inhalation or dennal chronic exposure, respectively. ADD values accounted for
modeled evaporation, representing an estimated absorbed dose. Lifetime Average Daily Concentrations
(LADC) and cancer Chronic Retained Doses (CRDs) were used for calculating occupational cancer
risks. See Appendix J for more details on the derivation of chronic exposure values from acute
concentrations/doses.
Consumer risks via inhalation were calculated based on maximum Time-Weighted Average (TWAs) for
either 3h or 24h periods and consumer risks via dermal exposure were calculated based on Acute Dose
Rate (ADR). See Section 2.3.1.3.1 for more details on consumer exposure).
EPA used margin of exposures (MOEs) to estimate acute or chronic risks for non-cancer based on the
following:
• themost sensitiveand robust HEDs within each health effectsdomain reported in the literature;
• the endpoint/study-specificUFs applied to the HEDs per EPA RfD Guidance(U.S. EPA. 2002); and
• the exposure estimates calculatedfor TCE uses examinedin this risk assessment(see Section 2.3 Human Exposures).

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398
399

400
401
402
403

404
405
406
407
408

409
410
411
412
413
414
415

416
417
418

419
420

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422
423
424
425

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435

436

MOEs allow for the presentation of a range of risk estimates. The occupational exposure scenarios
considered both acute and chronic exposures, while consumer exposure scenarios considered only acute
exposures. Although Westat (1987) survey data indicate that use frequencies for high-end product users
(i.e., those reflecting 95th percentile annual use :frequencies) may use products up to 50 times per year, or
approximately one time per week, available toxicologicaldata are based on either single or continuous
TCE exposure. There is uncertainty regarding the extrapolationfrom continuous studies in animals to the
case of repeatedintennittent human exposures. Identified chronic non-cancer and cancer haz.ard
endpoints (Section 3.2) are unlikely to present for these populations, however they cannot be ruled out.
Therefore, while certain consumers at the high-end :frequencyof use may potentially be at risk for chronic
hazard effects, the effects of this intermittent exposure is unknown and based on reasonably available
information EPA is unable to develop risk estimates for this population. For the vast majority of the
consumer population which are only exposed through short-term,occasional use of TCE products, only
acute effects are relevant
Different adverse endpoints were used based on the expected exposure durations. For non-cancer
effects, risks for developmental effects were evaluated for acute (short-term) exposures, whereas risks
for other adverse effects (liver toxicity, kidney toxicity, neurotoxicity,immunotoxicity, reproductive
effects, and developmental effects) were evaluated for repeated (chronic) exposures to TCE.
The total UF for each non-cancer POD was the benchmark MOE used to interpret the MOE risk
estimates for each use scenario. The MOE estimate was interpreted as human health risk if the MOE
estimate was less than the benchmark MOE (i.e. the total cumulative UF) . On the other hand,the MOE
estimate indicated negligible concerns for adverse human health effects if the MOE estimate exceeded
the benchmark MOE. Typically, the larger the MOE relative to the benchmark MOE for that endpoint,
the more unlikely it is that a non-cancer adverse effect would occur.
Extra cancer risks for chronic exposuresto TCE were estimatedusing Equation 4-2. Estimates of extra
cancer risks should be interpretedas the incrementalprobabilityof an individualdevelopingcancer over a
lifetime as a result of exposureto the potential carcinogen(i.e., incrementalor extra individual lifetime
cancer risk). For purposes of this risk evaluation,EPA considersextra risk of 1 x lo-4(or lE-4 in shorthand)
to be the benchmarkfor occupationalrisk estimation.

Equation 4-2. Equation to Calculate Extra Cancer Risks
Risk== Human Exposure (LA.DC}x POD {/UR or OSF}

Where:
Risk = Extra cancer risk (unitless)
Human exposure = Exposure estimate (ppm or mg/kg/day) from occupational exposure
assessment
POD= Inhalation unit risk (0.022 per ppm) or oral slope factor (0.0464 per mg/kg-day)

437

438

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440
441
442
443
444

Risk estimates were calculated for all of the studies per health effects domain that EPA considered
suitable for the risk evaluation of acute and chronic exposure scenarios in this risk evaluation for TCE.
EPA used a previously developed peer-reviewed PBPK model in order to obtain both HECs and HEDs
from animal toxicological studies involving either oral or inhalation administration of TCE. The PBPK
model does not account for dermal exposure, so EPA relied on traditional route-to-route extrapolation
from oral HED values. EPA conservatively assumes 100% absorption through all routes based on
available toxicokinetic data. EPA did not evaluate TCE exposure through the oral route because the
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44 7
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route is out of scope for this evaluation (U.S. EPA . 20 17d). The volatile properties of TCE suggest that
the majority of dermally deposited. TCE would quickly evaporate except in occluded scenarios.
Therefore, inhalation is expected. to be the predominant route of human exposw-e for most conditions of
use. Dermal exposure was considered for occupational scenarios while accounting for evaporation
according to modeling from (Kastin g and Miller , 2006 ) (see Section 2.3.1.2.5). For consumers, dermal
exposure was only considered for scenarios resulting in dennal contact with impeded evaporation (See
Section 2.3.2.2.2).

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455
456
457
458
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460

4.2.1.1
Representative Points of Departure for Use in Risk Estimation
All PODs listed in Table 3-13 will be used for risk estimation of acute exposure scenarios. For chronic
exposure scenarios, due to the large number of relevant endpoints, risks will be assessed using a single
endpoint representative of each health domain. EPA considers all of the endpoints identified in Table
3-14 to be similarly relevant to human health haz.ard from TCE exposure. Therefore risk estimates for
chronic exposure scenarios will be presented for only those endpoints representing the most sensitive and
robust data within each health domain, with the presumption that evaluation of risks for these endpoints
would also account for all other less sensitive yet relevant endpoints. These PODs are presented in Table
4-5. For complete MOE tables displaying risk estimates for all chronic endpoints, see [Risk Calculator
for Occupational Exposures. Docket: EPA-HQ-OPPT-2019-0500) .

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464

Table 4-5: Most Sensitive Endpoints from Each Health Domain for Risk Estimation
of ChronicExposure Scenarios
Target Organ /
System

Effect

HEC,, BED,,
(ppm) (mwk2)

Congenitalheart
malformations

0.0037 0.0052

UFS=l; UFA= 3;
(Johnson et al.,
lJFH==3;UFL=l;
Medium
2003)
Total UF=l0

0.025

0.ol5

UFs=t; UFA= 3;
(Maltoni et al.,
UFH=3;UFL= l;
fil§ )
Total UF=JO

High

Autoimmunity(increased
LOAEL.. 0.35
anti-dsDNAand -ssDNA 0.033
mg/kg-bw/day
antibodies)

0.048

UFS=l;UFA=3;
UFH=3;UFL•3;
Total UF=30

(Keil et al.,
2®2 )

High

BMDL,o= 1.4 Decreasednormal sperm
morphology and hyperppm
zoospermia

0.5

0.73

UFS=I0; UFA= I;
UFH=3;UFL=l;
TotalUF=30

(Chia etal..
1222)

Medium

Significant decreases in
wakefulness

4.8

6.5

UFs=3; UFA= 3;
lJFH=3; UFL-10;
Total UF=300

(AritQ ~t s1l,.
1994)

Medium

Increased liver/body
weight ratio and
cytotoxicity/hypertrophy

9.1

7.9

UFS=l; UFA=3 ;
(Ki1:llstr!!ml
1.1
Medium
UFH=3;UFL- 1;
al., 1983)
Total UF=JO

POD Type

BMDLo,=
Developmental
0.0207mg/kgEffects
bw/day

Kidney

Immune System

Reproductive
System

Nervous System

Liver

465
466
467
468
469
470
471
472

473

BMDL,0 -=34
mg/kg-bw/day

Pathologychanges in
renal tubule

LOAEL• 12
ppm

BMDL10=21.6
ppm

I

Uncertainty
Factors (UFs)

Reference

Data
Quality

HEC/HED99values will be used for risk estimation. These upper-end outputs from the PBPK model are
expected to be protective of susceptible subpopulations, acc01mtingfor the majority of identified
toxicokinetic human variability. The toxicokinetic metric of the interspecies and intraspecies uncertainty
factors has been eliminated based on the use of these data-derived values, resulting in a reduced UFAand
UFttof 3.

4.2.2 Risk Estimation for Occupational Exposures ~y Exposure Scenario
Risk estimates via inhalation and dermal exposw-e are provided below for workers and ONUs following
acute (single day), chronic (40-year), or lifetime (78 year) TCE exposw-e. Inhalation risk estimates are

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based on either monitoring or modeling exposure data. Non-cancer endpoints were applied to acute and
chronic exposures while cancer risk estimates are provided for adjusted lifetime exposure. Both are
presented for exposure scenarios where both data types are available. All dermal risk estimates are based
on modeling data as discussed in Section 2.3.1.2.5. When sufficient data was not available for
quantifying ONU exposures, risk estimates for workers were applied to ONUs assuming that ONU
exposure may be as high as workers in various circumstances. For details on the exposure estimates for
each exposure scenario, see Section 2.3.1.
For occupational scenarios,EPA evaluated the impact of potential respirator use based on respirator APF of
10 and 50 in the below tables.The calculatednon-cancerMOE or extra cancer risk with respiratoruse is then
compared to the benchmarkMOE to detennine the level of APF requiredto mitigate risk for all health
domaiils. EPA does not evaluate respiratoruse for occupationalnon-usersbecause they do not directly
handle TCE and are unlikely to wear respirators. In addition, EPA believes small commercial facilities
performing spot cleaning, wipe cleaning, and other related commercial uses as well as commercial
printing and copying are unlikely to have a respiratory protection program. For dermal protection, EPA
evaluated the impact of glove use up to the maximum possible PF of20 for industrial scenarios and PF
of 10 for commercial scenarios (see Table 2-20). For complete MOE tables displaying risk estimates for
all endpoints and all PPE options, see [Risk Calculatorfor OccupationalExposures. Docket: EPA-HQOPPT-2019-0500].

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.
T a ble 4-6 0 ccu 1attonaI RiskE stimationManu facturme:

.

Inhalation (Monitorin&)

Endpoint

Dermal (Modeling)

Benchmark
NoPPE
APF • l0
NoPPE
NoPPE
GlovePF=5 GlovePF=10 Glove PF-20
APF = 50
Exposure Level Worker MOE Worker MOE Worker MOE ONUMOE 1 Worker MOE WorkerMOE Worker MOE WorkerMOE
MOE
ACUTE NON-CANCER

Developmental Cardiac Toxicity
(JohniiQn et !)I.. 2003)

10

Developmental Neurotoxicity
(Fredrikssog ~t al,. 1993)

100

Developmental Mortality
Q:larotsk~ et 11I.,1225)

10

lmmWtotoxicity Responseto infection
(Selll,!adeangGilmour, 2010)

30

High End

4.3E-03

4.3E-02

0.21

4.3E-03

2.JE-03

1.]E-02

2.lE-02

4.SE-02

Central'f endency

3.0E-02

0.30

l.5

3.0E-02

6,8E-03

3.4E~02

6.8.E-02

0.14

High End

3.5

34.8

173.9

3.5

1.8

8.9

17.8

35.6

Central Tendency

24.0

239.9

1,199.4

24.0

5.3

26.7

S3.4

106.7

High End

26.7

266.6

1,333.0

26.7

12.2

60.8

121.5

243.0

Central Tendency

183.9

1,839.1

9,195.6

183.9

36.5

182.3

364.5

729.0

High End

l.0

20.2

100.8

2.0

1.2

5.9

11.9

23.8

Central Tendency

13.9

139.1

695.7

13.9

3.6

17.8

35.7

71.3

CHRONICNON-CANCER
Liver
(Kiellstrand et al., 1283)

10

Kidney
(Maltoni et al., t986)

10

Neurotoxicity
(Arito et al ., 1994)

300

Immunotoxicity
(Keil et al ., 2009)

30

Reproductive Toxicity
(Chia et al., l996 )

Developmental Toxicity
ilJ,,2003)
(12hnson !.11

High End

15.4

154.0

770.0

15.4

5.0

25.0

50.1

100.l

Central Tendency

106.2

1,062.4

5,311.8

106.2

15.0

75.1

150.2

300.3

4.lE-02

0.42

2.1

4..lE-02

9.SE-03

4.SE-02

9.SE-02

0.19

Central Tendency

0.29

2.9

14.6

0.29

2.9E-02

0.14

0.29

0.57

HighEnd

8.1

81.l

406.2

8.1

4.1

20.6

41.2

82.4

Central Tendency

56.0

560.4

2,801.8

56.0

12.4

61.8

123.5

247.1

5.6E-02

0.56

2.8

5.6E-02

3.0E-02

0.15

0.30

0.61

Central Tendency

0.39

3.9

19.3

0.39

9.lE-02

0.46

0.91

t.8

High End

0.85

8.5

42.3

0.85

0.46

2.3

4.6

9.2

Central Tendency

5.8

58.4

291.9

5.8

1.4

6.9

13.9

27.7

High End

6.JE-03

6.3E-02

0.31

6.3E-03

3.3E-03

1.6E-02

3.JE-02

6.6E-02

Central Tendency

4.JE-02

0.43

2.2

4.3E-02

9.9E-03

4.9E-02

.9.9E-02

0.20

High End

High End

30

10

LIFETIMECANCER RISK
Combined can cer Risk Kidney, NHL, Liver

I X 10'4

High End

6.7E-03

6.7E-04

J.JE-04

6.7E-03

3.SE-02

7.SE-03

3.SE-03

J.9E-03

Central Tendency

7.SE-04

7.SE-05

l. SE--05

7.SE-04

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold texVpink shading indicates MOE< benchmark MOE. The highest PPE scenarios displayed are considered plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.

Page 278 of 691

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494
495
496
497
498
499

500
501

MOE results for Manufacturingutilized monitoring inhalation exposure data (with dennal modeling) and are presented in Table 4-6.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEsremained below the benchmark MOE for cardiac
toxicity at both exposure levels via dermal and inhalation routes even when assuming the highest plausible APF and glove PF protection.

502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517

Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for
multiple endpoints at high-end inhalation exposure and for multiple endpoints at both high-end and central·tendency inhalation exposure even
when assuming the highest plausible APF. MOEs remained below the benchmark MOE for multiple endpoints at both dermal exposure levels
even when assuming the highest plausible glove PF.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dennal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances.. Risk estimates remained above the benchmark for
cancer at high-end inhalation exposure even when assuming the highest plausible APF. Risk estimates remained above the benchmark for
multiple endpoints at both dermal exposure levels even when assuming the highest plausible glove PF.

Page 279 of 691

 518

INTERAGENCY DRAFT - DO NOT CifE OR QUOTE
'
T a ble 4-7 0 ccu >atmnalRi skE stlDlationJProcessmg as a Reactant

.

Inhalation {Monitoring)

Dermal (Modeling)

Benchmark
NoPPE
APF=lO
APF=S0
NoPPE
NoPPE
GloveJ>F=S GlovePF=t0 GlovePF=Z0
Exposure Level WorkerMOE WorkerMOE WorkerMOE ONUMOE 1 WorkerMOE Worker MOE WorkerMOE WorkerMOE
MOE

Endpoint

ACUTENON-CANCER
DevelopmentalCardiac Toxicity
(lohn~n et al., 200J)

10

DevelopmentalNeurotoxicity
(Fredrikssonet al., 199J)

100

DevelopmentalMortality
iliarotsk): et al., 1995)

10

Immunotoxicity Responseto infection
(S~!grndeand GilmQW:,
2010)

30

High End

4.3E-03

4.3E-02

0.21

4.31A>3

2.JE-03

J.lE-02

2.JE-02

4.SE-02

Central Tendency

3.0E-02

0.30

1.S

3.0IA>2

6.SE-03

3.4&-02

6.SE-02

0.14

High End

3.S

34.8

173.9

3.S

1.8

8.9

17.8

35.6

Central Tendency

24.0

239.9

1,199.4

24.0

5.3

26.7

53.4

106.7

High End

26.7

266.6

1,333.0

26.7

12.2

60.8

121.5

243.0

Central Tendency

183.9

1,839.1

9,195.6

183.9

36.S

182.3

364.5

729.0

High End

2.0

20.2

100.8

2.0

1.2

S.9

11.9

23.8

Central Tendency

13.9

139.1

695.7

13.9

3.6

17.8

35.7

71.3

CHRONICNON-CANCE.R
Liver
(Ki~Ustrand~ al.• 1983)

10

Kidney
(M!lltoniet al., 1986)

10

Neurotoxicity
(AritQet al,. l 994}
Immunotoxicity
(Keil et al., 2002)
, ReproductiveToxicity
(Chia et !!l-.1996)

1

Deve)opmentalToxicity
(John~n !;t al., iooJ)

High End

15.4

154.0

770.0

15.4

5.0

25.0

50.1

100.1

Central Tendency

106.2

1,062.4

5,311.8

106.2

15.0

75.1

150.2

300.3

4.lE-02

0.42

2.1

4.2E-02

9.SE-03

4.SE-02

9.SE-02

0.19

Central Tendency

0.29

2.9

14.6

0.29

2.9E-02

0.14

0.29

0.57

High End

8.1

81.2

406.2

8.1

4.1

20.6

41.2

82.4

Central Tendency

56.0

560.4

2,801.8

56.0

12.4

61.8

123.5

247.1

5.6E-Ol

0.56

2.8

5.6E-02

3.0E-02

0.15

0.30

0.61

Central Tendency

0.39

3.9

19.3

0.39

9.lE-02

0.46

0.91

1.8

High End

0.85

8.5

42.3

0.85

0.46

2.3

4.6

9.2

Central Tendency

5.8

58.4

291.9

5.8

1.4

6.9

13.9

27.7

High End

6.JE-03

6.3&-02

0.31

6.3E-03

3.3E-4>3

].6E-Ol

3.3E-02

6.6E-02

Central Tendency

4.JE-02

0.43

2.2

4.3E-02

9.9E-03

4.9E-02

9.9.E-02

0.20

High End

I

300

30

30
10

High End

LIFETIME CANCER RISK

CombinedCancerRisk -

Kidney,NHL,Liver

] X

10"4

High End

6.7E-03

6.7.E-04

l.3E-04

6.7E-03

3.SE-02

7.SE-03

3.SE-03

1.9.E-03

Central Tendency

7.SE-04

7.SE-05

I.SE-OS

7.SE-04

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicatesMOE< benchmarkMOE. The highest PPE scenariosdisplayed are consideredplausiblefor this exposurescenario.
1
EPA is unable to estimate ONU exposures separately from workers.

Page 280 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
519
520

521
522
523
524
525
526

527
528
529
530
531
532

533
534
535

MOE results for Processingas a Reactant utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table
4-7.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for cardiac
toxicity at both exposure levels via dermal and inhalation routes even when assumingthe highest plausible APP and glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for
multiple endpoints at high-end inhalation exposure and for multiple endpoints at both high-end and central tendency inhalation exposure even
when assuming the highest plausible APP. MOEs remained below the benchmark MOE for multiple endpoints at both dermal exposure levels
even when assuming the highest plausible glove PF.

536
537

538
539

540
541
542
543

Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer a1 both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. Risk estimates remained above the benchmark for
cancer at high-end inhalation exposure even when assuming the highest plausible APP. Risk estimates remained above the benchmark for
multiple endpoints at both dermal exposure levels even when assumingthe highest plausible glove PF.

Page 281 of 691

 544

INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
. ataon- B ateh O1oen TOD Vanor De2r eas101z-l nhalation
. Monit9rm2 Data
T able 4 8.0 ccu ,ationa I RiskE stim

-

Inhalat ion (Monitorin g)

Benchmark
Endpoint

MOE

NoPPE
APF=l0
APF = S0
Expos ure Level Worker MOE Worker MOE Worker MOE

Derma l (Modeling)
NoPPE
ONUMOE

Glove PF=.20
NoPPE
GlovePF=S GlovePF=l0
Worker MOE Worker MOE Worker MOE Worker MOE

ACUTENON-CANCER

Developmental Cardiac Toxicity
(Johnsonet al.•2003)

10

Developmental •
Neurotoxicity
(Fredrikssonet al., 122J)

100

DevelopmentalMortality
Q:!!.1!'.Qtsk
:t et al., I225
)

10

lmmunotoxicity•
Responseto infection
(S!il1rade
.1 and Qilmour, 2010)

30

High End

1.4E-04

1.4E-03

7.lE-03

1.2E-03

2.JE-03

l.tE-Ol

l.3E-Ol

4.5E-Ol

CentralTendency

8.0E-04

8.0E-03

4.0E-02

1.0E-Ol

6.SE-03

3.4E-02

6.SE-02

0.14

High End

0.12

1.2

5.8

0.99

1.8

8.9

17.8

35.6

Central Tendency

0.65

6.5

32.6

8.1

5.3

26.7

53.4

106.7

High End

0.89

8,9

44.4

7.6

12.2

60.8

121.5

243.0

Central Tendency

5.0

50.0

250.0

62.3

36.5

182.3

364.5

729.0

6.7E-02

0.67

3.4

0.57

1.2

5.9

11.9

23.8

0.38

3.8

18.9

4.7

3.6

17.8

35.7

71.3

High End
CentralTendency

CHRONIC NON-CANCER

Liver
)
(Kjellstrandet al., 1283

10

Kidney
(Maltoni"1lll-, 1286)

IO

Neurotoxicity
(Arito et fil., 1994)

300

Immunotoxicity
(Keil et al., 2009)

30

ReproductiveToxicity
(Chia et al., 192§)

30

DevelopmentalToxicity
(Johnson et ill,, 2003)

10

High End

0.51

5.1

25.6

4.4

5.0

25.0

50.1

100.1

CentralTendency

2.9

28.9

144.4

36.0

15.0

75.l

150.2

300.3

High End

1.4E-03

UE-02

7.0E-02

UE-02

9.5E-03

4.SE-02

9.5E-02

0.19

CentralTendency

7.9E-03

7.9E-02

0.40

9.9E-02

2.9E-02

0.14

0.29

0.57

0.27

l.7

13.S

2.3

4..1

20.6

41.2

82.4

l.S

15.2

76.2

19.0

12.4

61.8

IZ3.5

247.l

High End

1.9E-03

t.9E-02

9.3E-02

1.6E-02

3.0E-02

0.15

0.30

0.61

CentralTendency

1.0E-02

0.10

0.52

0.13

9.lE-02

0.46

0.91

1.8

High End

2.8E--02

0.28

1.4

0.24

0.46

2.3

4.6

9.2

0.16

1.6

7.9

2.0

1.4

6.9

13.9

27.7

High End

2.lE-04

2.tE-03

1.0E-02

I.8E-03

3.3E-03

l.6E-02

3.3E-02

6.6E-02

CentraJTendency

l.lE-03

l.lE-02

5.9E-02

1.SE-02

9.9E-03

4.9E-02

9.9E-02

0.20

High End
CentralTendency

Central'fendency

LIFETIME CANCER RISK

CombinedCancerRisk Kidney, NHL, Liver

IX

High End

0.20

l.0E-02

4.0E-03

2.JE-02

3.SE-02

7.SE-03

3.SE-03

t.9E-03

2.SE-02

2.SE-03

5.5E--04

2.ZE-03

9.7E-03

t.9E-03

9.7E-04

4.9E-04

J()-4

Central Tendency

Bold text/pinkshading indicatesMOE< benchmarkMOE. The highestPPE scenariosdisplayedare plausiblefor this exposurescenario.

545
Page 282 of 691

 546

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
t'nonaI R'ISk E st'1mat'ion- Batch O1penTop V apor De2reas102- lnh a Ia ti on Mod emg
r D ata
T abl e 4-9. 0 CCU 1>a
Inhalation (Modeling)

Dermal (Modeling)

Benchmark
.Endpoint

I

NoPPE
APF=lO
APF=50
NoPPE
NoPPE
GlovePF=5 GlovePF-=10 GlovePF=l0
Exposure Level WorkerMOE WorkerMOE WorkerMOE ONUMOE WorkerMOE WorkerMOE Worker-MOEWorkerMOE

MOE

ACUTE NON-CANCER

DevelopmentalCardiac Toxicity
I (JQhn~n ~t al., 2003 )

2.9E-05

2.9E-04

1.4~

4.7E-05

2.3~

UE-02

2.JE-02

4.SE-02

3.lE-04

3.2E-03

l.6E-02

6.lE-04

6.8E--03

3.4E-Ol

6.8E-02

0.14

2,3E-02

0.23

1.2

3.8E-02

1.8

8.9

17.8

35.6

CentralTendency

0.26

2.6

12.9

0.50

5.3

26.7

53.4

106.7

High End

0.18

1.8

8.9

0.29

12.2

60.8

121.5

243.0

Central Tendency

2.0

19.8

99.l

3.8

36.5

182.3

364.5

729.0

l.3E-02

0.13

0.67

2.2E-02

1.2

5.9

11.9

23.8

0.15

1.5

7.5

0.29

3.6

17.8

35.7

71.3

High End

10

Developmental •
Neurotoxicity
(J:redrikssonet al., 1993)

100

Developmental Mortality
fNar2tskv et al., 1995)

10

ImmunotoxicityResponse to infection
(SelK,!adeand Gilmour, 20I0)

30

CentralTendency

I

High End

High End
1

Central Tendency

CHRONIC NON-CANCER

Liver
(Kjellstrang~ al., 12il)

10

Kidney
(Maf!Oniet al., 1986)

10

Neurotoxicity
(Arito et al., 1994)

Immunotoxicity
(Keil et al ., 2009)

ReproductiveToxicity
(£ hill £UI.. 1996)

Development.alToxicity
(fabn~n !lt m.,
2003)

300

30

30

IO

High End

0.10

1.0

5.1

0.17

5.0

25.0

50.l

100.1

CentralTendency

l.l

11.4

51.2

2.l

15.0

75.1

150.2

300.3

High End

2.8E-04

2.8E-03

1.4E-02

4.6E-04

9.5E-03

4.8E-Ol

9.SE--02

0.19

CentralTendency

3.lE-03

3.lE-02

0.16

6.0E-03

2.9E-02

0.14

0.29

0.57

HighEnd

S.4E-02

0.54

2.7

8.9E-02

4.1

20.6

41.2

82.4

0.60

6.0

30.2

l.l

12.4

61.8

123.S

247.1

l.9E-02

6.lE-04

3.0E-02

0.15

0.30

0.61

CentralTendency
High End

3.7E-04

3.7~

CentralTendency

4.lE-03

4.lE-02

0.21

8.0E-03

9.IE-02

0.46

0.91

1.8

High End

!.6E--03

S.6E-02

0.28

9.3E-03

0.46

2.3

4.6

9.2

Central Tendency

6.3E-02

0.63

J.l

0.12

1.4

6.9

13.9

27.7

High End

4.lE-05

4.lE-04

2.lE-03

6.9E-05

3.3E--03

l .6E-02

3.3E-02

6.6E-0.2

Central Tendency

4.6E-04

4.6E-03

2.3E-02

8.9E-04

9.9E--03

4.9E-02

9.9E-02

0.20

0.46

3.8E-02

7.5E-03

3.8E--03

1.9E--03

3.4E-02

9.7E-03

1.9E-03

9.7E-04

4.9E-04

LIFETIME CANCER RISK

Combined Cancer Risk Kidney, NHL, Liver

l X 10"4

High End

CentralTendency

I

0.78

7.SE-02

l.6E-02

6.5E-02

6.5E-03

1.3~

Bold text/pink shading indicates MOE< benchmark MOE. The highest PPE scenariosdisplayed are considered plausible for this exposure scenario.

547

Page 283 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585

MOE results for Batch Open Top VaporDegreasingutilized both monitoring and modeling inhalation exposure data (with dennal modeling).
Results are presented in Table 4-8 and Table 4-9.
Acute Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via both inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple
endpoints based on monitoring and for all endpoints based on modeling at both high-end and central tendency inhalation exposure levels.
Based on both monitoring and modeling data, MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure
levels even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for cardiactoxicity at both dermal
exposure levels even when assuming the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via both inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple
endpoints based on monitoring and for all endpoints based on modeling at both high-end and central tendency inhalation exposure levels.
Based on both monitoring and modeling data, MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via
dermal and inhalation routes even when assuming the highest plausible APF and glove PF protection.
Cancer Risk Estimates:
Based on both monitoring and modeling data, extra risk estimates for workers were above the benchmark level for cancer at both high-end
and central tendency exposure levels via both inhalation and dennal routes. Based on both monitoring and modeling data, risk estimates for
ONUs were also above the benchmark for cancer at both high-end and central tendency inhalation exposure levels. Based on both monitoring
and modeling data, risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes even
when assuming the highest plausible APF and glove PF protection.
OSHA PEL considerations
The OSHA PEL for TCE is 100 ppm (8hr TWA). The monitoring dataset for this OES included some data points above the PEL value. In an
alternative approach, EPA calculated central tendency and high end values for the measurements lower than the PEL. This resulted in a
reduction of the high-end acute exposure estimate from 25.92ppm to 19.23 ppm and the central tendency acute exposure estimate from 4.60
ppm to 4.26 ppm. Chronic high-end and central tendency exposures are reduced from 17.75 ppm and 3.15 ppm to 13.17 ppm and 2.92 ppm,
respectively. Lifetime exposures are reduced from 9.10 ppm and 1.25 ppm to 6.75 ppm and 1.15 ppm, respectively. The reduced exposures do
not significantly affect the risk estimates, since exposures were only reduced by up to ~30%. Based on PEL-capped exposure estimates, the
acute and chronic central tendency MOEs for the cardiac toxicity endpoint (with benchmark MOE= 10) are 8.7E-04 and 1.3E-03,
respectively. The central tendency cancer extra risk (benchmark= IE-04) is 2.6E-02. Therefore, the MOEs remains orders of magnitude
below the benchmark MOE (or above the benchmark for cancer risk) when using only PEL-capped exposure estimates. Full details are
provided in [OccupationalRisk Estimate Calculator.Docket #EPA-HQ-OPPT-2019-0500}.

Page 284 of 691

 586

INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
t h Closed-L 0 0 1) Vapor De~ueasmg
T a bl e 4-10 0 ccupati onal Risk E sfunafion- Bac

.

Inhalation (Monitoring)

Dermal (Modeling)

Benchmark
Endpoint

MOE

NoPPE
APF=10
APF=SO
NoPPE
NoPPE
GlovePF=-S GlovePF=10 GlovePF=l0
Exposure Level WorkerMOE WorkerMOE WorkerMOE ONUMOE 1 WorkerMOE WorkerMOE WorkerMOE WorkerMOE
ACUTE NON-CANCER

DevelopmentalCardiacToxicity
(Johlll!Qn
et al., 200l )

10

DcvelopinentalNeurotoxicity
(Fredrikssonet al., 1993)

100

DevelopmentalMortality
(~!!rol~kvct al., J9()5)

10

lmmunotoxicityResponseto infection
(Selgradeang Qilmour. 2010)

30

High End

7.6E-03

7.6E-Ol

0.38

7.6E-03

2.3.E-03

1.lE-02

l.JE-02

4.SE-02

CentralTendency

2.4E-02

0.24

1.2

2.4E-02

6.8E-03

3.4E--02

6.SE-02

0.14

High End

6.2

61.9

309.5

6.l

1.8

8.9

17.8

3S.6

CentralTendency

19.7

196.6

983.0

19.7

5.3

26.7

53.4

106.7

High End

47.5

474.5

2,372.S

47.5

12.2

60.8

121.S

243.0

CentralTendency

150.7

1,507.3

7,536.5

150.7

36.5

182.3

364.5

729.0

High End

3.6

35.9

179.5

3.6

1.2

5.9

11.9

23.8

CentralTendency

11.4

114.0

570.1

11.4

3.6

17.8

35.7

71.3

CHRONICNON-CANCER
Liv«
(Kjell§trand~ al,. 198~)

10

Kidney

10

(Maltoniet i!l,. 1286)
Neurotoxicity
(Arit2 s.t111.
, I994>

300

Immunotoxicity
(Keil ~tal .• 2QQ9
)

30

Reproductive Toxicity
<Chms.{ !!I.. t 99@

30

Developmental Toxicity
(lohns2n et ru,,2003)

10

High End

27.4

274.1

1,370.5

27.4

5.0

25.0

50.1

100.1

CentralTendency

87.1

870.7

4,353.S

87.1

15.0

75.1

150.2

300.3

7.SE-02

0.75

3.8

7.5E-02

9.SE-03

4.8£-02

9.SE-02

0.19

CentralTendency

0.24

2.4

12.0

0.24

2.9E-02

0.14

0.29

0.57

High End

14.5

144.6

722.9

14.S

4.1

20.6

41.2

82.4

Central Tendency

45.9

459.3

2,296.3

45.9

12.4

61.8

123.5

247.1

9.9E-02

0.99

5.0

9.9E-02

3.0E--02

0.15

0.30

0.61

CentralTendency

0.32

3.2

15.8

0.32

9.lE-02

0.46

0.91

1.8

High End

1.5

15.1

75.3

1.5

0.46

2.3

4.6

9.2

Central Tendency

4.8

47.8

239.2

4.8

L4

6.9

13.9

1.7.7

High End

1.lE--02

0.11

O.S6

1.lE..02

3.3.E-03

1.6.E-02

3.3E-02

6.6E-02

CentralTendency

3.!E-02

0.35

1.8

3.SE-02

9.9E-03

4.9£-02

9.9E-02

0.lt)

High End

High End

LIFETIME CANCER RISK

CombinedCancerRisk •
Kidney, NHL, Liver

Ix 104

High End

3.7E-03

3.7E-04

7.SE-05

3.7E-03

3.8E-02

7.SE--03

3.8E-03

1.9E-03

CentralTendency

9.lE--04

9.lE-05

l.8E-05

9.IE-04

9.7E-03

l.9E-03

9.7E--04

4.9E-04

Bold text/pink shadingindicatesMOE < benchmark MOE.The highestPPE scenariosdisplayedare plausible for thisexposurescenario.
1 EPA is unable to estimate ONU exposuresseparately from workers.

Page 285 of 691

 INTERAQENCYDRAFT - DO NOT CITE OR QUOTE
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613

MOE results for Batch Closed-LoopVaporDegreasingutilized monitoring inhalation exposure data (with dermal modeling) and are
presented in Table 4-10.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. 1berefore EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for cardiac
toxicity at both exposure levels via dermal and inhalation routes even when assuming the highest plausible APF and glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for
multiple endpoints at high-end inhalation exposure and for immunotoxicity at both high-end and central tendency inhalation exposure even
when assuming the highest plausible APP. MOEs remained below the benchmark MOE for multiple endpoints at both dermal exposure levels
even when assuming the highest plausible glove PF.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances. Risk estimates were not above the benchmark for highend inhalation exposure when assuming APP = 50 or for central tendency inhalation exposure when assuming APF = 10. Risk estimates
remained above the benchmark for multiple endpoints at both dermal exposure levels even when assuming the highest plausible glove PF.

Page 286 of 691

 614

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
'edV apor D1e2reasm2 - lhlti
s
s
a on- C onvevonz
n a a on M om·torm2 Da ta
Tabl e 4-11 0 ccupat1onalRikEtimti

.

Inhalation (M.onitoring)

Dermal (Mbdeling)

Benchmark
Endpoint

MOE

APF==10
APF==50
NoPPE
NoPPE
NoPPE
GlovePF=5 GlovePF=lO GlovePF=20
Exposure Level WorkerMOE WorkerMOE WorkerMOE ONUMOE 1 WorkerMOE WorkerMOE WorkerMOE WorkerMOE
ACUTE NON.CANCER

Developmental•
CardiacToxicity
(lohn~n i.t i!,!., 2003)

10

Developmental Neurotoxicity
(Fredrikssogei.al., l22J)

100

Developmental-

Mortality
ili arotsk~ ~t!!I., 1995)
ImmunotoxicityResponseto infection
(Selgrade ood Gilmour, 2010)

IO

High End

1.JE-04

2.3E-03

t.lE-01

1.3E-04

2.3E-03

l.lE-01

1.3E-02

4-.SE-02

CentralTendency

3.4E-04

3.4E-03

1.7E-01

3.4E--04

6.8E-03

3.4E-02

6.SE-02

0.14

High End

0.19

1.9

9.3

0.19

1.8

8.9

17.8

35.6

CentralTendency

0.18

2.8

13.9

0.28

S.3

26.7

53.4

106.7

High End

1.4

14.3

7 1.4

1.4

12.2

60.8

121.5

243.0

CentralTendency

2.1

21.3

106.5

1.1

36.5

182.3

364.5

729.0

High End

0.11

1.1

5.4

0.11

1.l

S.9

11.9

23.8

CentralTendency

0.16

1.6

8.1

0.16

3.6

17.8

35.7

71.3

30

CHRONICNON-CANCER

Liver
{Kjellstrand1,t al., 1983)
Kidney

(Maltoni et al11 19~§)
Neurotoxicity
. (Arito et al., I 994)

10

10
300

ltmnunotoxicity
(Keil et al., 2QQ2
)

30

ReproductiveToxicity
(Chia et al,. !9%)

30

DevelopmentalToxicity
(John~n el al., 2003)

10

0.83

8.3

41.3

0.83

5.0

25.0

50.l

100.1

1.2

12.3

61.5

1.2

15.0

75.1

150.2

300.3

High End

2.3E-03

2.3E-02

0.11

1.3E-03

9.SE-03

4.SE-02

9.5E-02

0.19

CentralTendency

3.4E-03

3.4E-02

0.17

3.4E-03

2.9E-02

0.14

0.29

0.57

High End

0.44

4.4

21.8

0.44

4.1

20.6

41.2

82.4

CentralTendency

0.65

6.5

32.S

0.65

12.4

61.8

123.5

247.1

High End

3.0E-03

3.0E-01

0.15

3.0E-03

3.0E-01

0.15

0.30

0.61

Central Tendency

4.SE-03

4.5E-02

0.22

4.SE-03

9.lE-02

0.46

0.91

1.8

High End

4.5.E-02

0.45

2.3

4.SE-02

0.46

2.3

4.6

9.2

CentralTendency

6.81:-02

0.68

3.4

6.8E-Ol

1.4

6.9

13.9

27.7

High End

3.4E-04

3.4E-03

1.7E-02

3.4.E-04

3.3E-03

1.6E-02

3.3E-02

6.6E-02

CentralTendency

S.OE-04

S.OE-03

2.SE-02

5.0E-04

9.9E-03

4.9E-02

9.9E-02

0.10

High End
CentralTendency

LIFETIMECANCERRISK
Combine.cl
Cancer Risk Kidney, Nlil,, Liver

JX

10"4

High End
CentralTendency

0.12

1.lE-02

2.SE-03

0.12

3.SE-02

7.5E-03

3.SE-03

l.9E-03

6.5.E-02

6.SE-03

l.3E-03

6.SE-02

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicatesMOE < benchmarkMOE. The highestPPE scenariosdisplayedare plausiblefor this exposurescenario.
1 EPA is unable to estimateONU exposuresseparatelyfrom workers.

Page 287 of 691

 615

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
. dV apor De2reasm2- lhlf
Table 4-12 0 ccupa f1onalRikEt'
s s una f100- Conveyonze
n a a 10n MdlinDt
o e lit aa

.

Dermal (Modeling)

Inhalation (Modeling)

Benchmark
NoPPE
APF--10
GlovePF=S GlovePF=I0 GlovePF-20
APF=50
NoPPE
NoPPE
ExposureLeve) WorkerMOE Worker MOE Worker MOE ONUMOE WorkerMOE WorkerMOE WorkerMOE WorkerMOE
MOE

Endpoint

ACUTE NON-CANCER

1Developmental Cardiac Toxicity
(JQhnsonet !l!..200) )

High End

3.6E-06

3.6E-05

1.8E--04

S.9E-06

2.3E-03

1.IE-02

2.3E-02

4.SE-02

Central Tendency

2.7E-04

2.7E-03

l.4E-02

4.8~

6.SE-03

3.4E-02

6.8E-02

0,14

High End

3.0E-03

3,0E-02

0.15

4.8E-03

1.8

8.9

17.8

35.6

0.22

2.l

11.0

0.39

5.3

26.7

53.4

106.7

2.3E-02

0.23

1.1

3.7E-02

12.2

60.8

121.5

243.0

1.7

16.9

84.6

3.0

36.5

182.3

364.S

729.0

1.7E-03

1.7E-02

8.6E-02

2.SE-03

J.2

5.9

11.9

23.8

0.13

1.3

6.4

0.22

3.6

17.8

35.7

71.3

10

DevelopmentalNeurotoxicity
(Fredcikssonet !!.I
.. 1993)

100

DevelopmentalMortality
(~ ~Q~k:t et al., l 22~)

10

lmmunotoxicityResponseto infection
(Sel&[adeand GilmoYr.,
2010)

30

Central Tendency
High End
Central Tendency
High End
CentralTendency

CHRONIC NON-CANCER

High End

Liver
(Kjellstrang et al., 1283)

10

Kidney
(MaltQ!li ~ al., 1986)

10

I

: Neurotoxicity
(Arito et!!]., 1994)

300

Imm.onotoxicity
(Keil et al,. 2Q02)

30
I

ReproductiveToxicity
(Chia ct al., 1226)
DevelopmentalToxicity
(JohnSQ!let al., 2003)

30
I

10

l.3E-02

0.13

.0.65

2.lE-02

5.0

25.0

50.l

100.l

0.98

9.8

48.8

1.7

15.0

75.1

150.2

30-0.3

High End

3.6E-OS

3.6E-04

1.8E-03

5.8E-05

9.SE-03

4.8E-02

9.SE-02

0.19

CentralTendency

2.7E-03

2.7E-02

0.13

4.7E-03

2.9E-02

0.14

0.29

0.57

High End

6.9E-03

6.9E-02

0.35

l.JE-02

4.1

20.6

41.2

82.4

0.52

s..2

25.8

0.90

12.4

61.8

123.5

247.1

High End

4.7E-OS

4.7E--04

2.4E-03

7,7E-05

3.0E-02

0.15

0.30

0.61

CentralTendency

3.SE-03

3.SE-02

0.18

6.2E-03

9.lE-02

0.46

0.91

1.8

High End

7.2E-04

7.2E-03

3.6E-02

1.lE-03

0.46

2.3

4.6

9.2

CentralTendency

5.4E-02

0.54

2.7

9.4E-02

1.4

6.9

13.9

27.7

High End

5.3E-06

S.3E-05

2.7E-04

8.6E-06

3.3E-03

l.6E-02

3.JE-02

6.6E-02

CentralTendency

4.0E-04

4.0E-03

2.0E-02

6.9E-04

9.9E-03

4.9E-02

9.9E-02

0.20

CentralTendency

CentralTendency

LIFETIME CANCER RISK

CombinedCancer Risk Kidney, NHL, Liver

l X 10-4

High End

6.1

0.61

0.12

3.7

3.8E-02

7.SE-03

3.SE--03

1.9E-03

Central Tendency

0.12

1.2E-02

2.JE-03

7.9E-02

9.7E-03

J.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicatesMOE < benchmarkMOE. The highest PPE scenariosdisplayedare plausiblefor this exposurescenario.

616
Page 288 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
617
618
619

MOE results for ConveyorlzedVaporDegreasingutilized both monitoring and modeling inhalation exposure data (with dermal modeling).
Results are presented in Table 4-11 and Table 4-12.

620

621
622

623
624

625
626

627
628

629
630
631
632
633
634
635
636

637
638
639
640
641

642
643
644
645
646

Acute Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via inhalation and for most endpoints via the dermal route. EPA is unable to estimate ONU exposures
separately from workers based on monitoring data. Therefore, BPA provided risk estimates for ONUs assuming ONU exposure may be as
high as workers in various circumstances.ONU risk estimates were below the benchmark MOE for all endpoints at both high-end and central
tendency inhalation exposure levels based on modeling data. Based on both monitoring and modeling data, MOEs remained below the
benchmark MOE for multiple endpoints at both inhalationexposure levels even when assuming the highest plausible APF. MOEs remained
below the benchmarkMOE for cardiac toxicity at both dermal exposure levels even when assuming the highest plausible glove PF protection.
Chronic Non-CancerRisk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via both inhalation and dennal routes. EPA is unable to estimate ONU exposures separately from workers
based on monitoring data.Therefore,EPA provided risk estimates for ONUs assuming ONU exposure may be as high as.workers in various
circumstances.ONU risk estimates were below the benchmark MOE for all endpoints at both highwendand central tendency inhalation
exposure levels based on modeling data. Based on both monitoring and modeling data, MOEs remained below the benchmark MOE for
multiple endpoints at both exposure levels via dermal and inhalationroutes even when assuming the highest plausible APF and glove PF
protection.
Cancer Risk Estimates:
Based on both monitoring and modeling data, extra risk estimates for workers were above the benchmark level for cancer at both high-end
and central tendency exposure levels via both inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from
workers based on monitoringdata. Therefore,EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in
various circumstances.ONU risk estimates were above the benchmark at both high-end and central tendency inhalation exposure levels based
on modeling data. Based on both monitoring and modeling data, risk estimates remained above the benchmark for cancer at both exposure
levels via dermal and inhalationroutes even when assuming the highest plausible APF and glove PF protection.

647

648
649

Page 289 of 691

 650

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
apor Def!reasmg
s s ma tion - WbV
e
T abl e 4 13 0 ccupa f100a IRikEti

- .

Dermal (Modeling)

Inhalation (Modeling)

Endpoint

•

Benchmark

MO:£

NoPPE
APF=lO
APF=~
No PP£
NoPPE
GlovePF=S GlovePF=l0 GlovePF=20
ExposureLevel WorkerMOE WorkerMOE WorkerMOE ONUMOE WorkerMOE WorkerMOE WorkerMOE WorkerMOE
ACUTENON-CANCER

Developmental •
Cardiac Toxicity
(Johnson ctal., 2003)

10

Developmental•
Neurotoxicity
(Frednbson et al .. 1993)

100

DevelopmentalMortality
(Narotsk>:et al., 19'15)

10

Immunotoxicity •
Response to infection
(Selg[adeand Gilmour, 2Ql0)

30

High End

7.9E--04

7.9E-03

3.9E-02

1.2E-03

2.3E-03

I. IE-02

2.3E-02

4.SE-02

Central Tendency

1.9E-03

I.9E-02

9.3E-02

3.5E-03

6.SE-03

3.4E-02

6.8:E-02

0.14

0.64

6.4

31.8

0.94

J.8

8.9

17.8

35.6

Central Tendency

1.5

15.1

75.7

2.9

5.3

26.7

53.4

106.7

High End

.f.9

48.8

244.0

7.1,

12.2

60.8

121.5

243.0

Central Tendency

11.6

116.l

580.4

22.1

36.S

182.3

364.5

729.0

High End

0.37

3.7

18.5

1.7E-03

1.1

5.9

11.9

23.8

Central Tendency

0.88

8.8

43.9

5.2E-03

3.6

17.8

35.7

71.3

High End

CHRONICNON-CANCER
Liver
(KJellstrang~t al., l 98J)

10

Kidney
(Malto!]jet al., 1986)

10

Neurotoxicity
(ArjlQ et al., 1994)

300

Immunotoxicity
(Keil et !lJ.,2002)

30

Reproductive Toxicity
(Cilia eta! .. !9'J6)

30

Developmental Toxicity
(John~ ~t al., 200J)

10

High End

2.8

28.2

140.9

4.2

s.o

25.0

50.l

100.1

Central Tendency

6.7

67.l

335.3

12.7

15.0

75.1

150.2

300.3

High End

7.7E-03

7.7E-Ol

0.39

l.lE-Ol

9.SE-03

4.SE-02

9.SE-02

0.19

Central Tendency

l,SE-02

0.18

0.92

3.SE-02

2,9E-02

0.14

0.29

0.57

High End

l.S

14.9

74.3

2.2

4.1

20.6

41.2

82.4

Central Tendency

3.5

35.4

176.8

6.7

12.4

61.8

123.S

247.1

High End

J.0E-02

0.10

0.5)

t .SE-02

3.0E-02

0.15

0.30

0.61

Central Tendency

2.4E-02

0.24

1.2

4.6E-02

9.lE-02

0.46

0.91

1.8

High End

0.15

1.5

7.7

0.1,3

0.46

2.3

4.6

9.1,

Central Tendency

0.37

3.7

18.4

0.10

t.4

6.9

13.9

27.7

High End

l .JE-03

t .lE-02

5.7E-02

1.7E-03

3.3E-03

1.6E-02

3.3E-02

6.6E-02

Central Tendency

2.7E-03

2.7E-02

0.14

5.2E--02

9.9E-03

4.9E-02

9.9E-02

0.1,0

LIFETIME CANCER RISK
Combined Cancer Risk Kidney, NHL, Liver

High End

2.9E-02

2.9E-03

5.8E-04

1.9E-()2

3.8E-02

7.SE-03

3.SE-03

L9E-03

Central Tendency

1.lE-02

1.IE-03

2.3E-04

5.9E-03

9.7E-03

1.9E-03

9.7E-04

4.9E--04

1 X J()-4

Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.

65 1

Page 290 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
652
653
654

655
656
657
658
659
660
661
662
663
664

665
666

MOE results for Web Vapor Degreasing utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-13.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central tendency
inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure levels even when
assuming the highest plausible APF. MOEs remained below the benchmark MOE for cardiac toxicity at both dermal exposure levels even
when assuming the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers wete below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assuming the highest plausible APF and glove PF protection.

667

668
669
670
671
672
673
674
675
676
677

Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer at both high-end and central tendency
inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APF and glove PF protection.

Page 291 of 691

 678

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
.
T abl e 4-14 0 ccuoaf1onaI Risk Esflillation - C0 Id Cleanm2

.

Inhalation (Modeling)

Endpoint

Benchmark
ExposureLevel
MOE

NoPPE
APF = lO
APF = 50
Worker MOE WorkerMOE WorkerMOE

Dermal (Monitoring)
NoPPE
ONUMOE

NoPPE
Glove PY.,,5 GlovePF-10
GlovePF=20
Worker MOE WorkerMOE WorkerMOE Worker MOE

ACUTENON-CANCER

Developmental •
Cardiac Toxicity
(Johnsonet al., 2003 )

10

Developmental •
Neurotoxicity
(Fredriksson et al., 199J)

100

Developmental Mo.rtality
<Nll:Qt§k
i ~ al., 1225)

10

Immunotoxicity Response to infection
(Selarade i.md{WIDQY!
, 2Ql Q)

30

High End

1.9E-04

1.9E-03

9.7&-03

3.2E-04

2.3E--OJ

I.IE--02

2.JE--02

4.5E-02

CentralTendency

3.3E--03

3.JE--02

0.17

6.0E-03

6.8E-03

J.4E--02

6.SE-02

0.14

High End

0.16

1.6

7.9

0.26

1.8

8.9

17.8

35.6

CentralTendency

2.7

27.0

135.l

4.9

5.3

26.7

53.4

106.7

High End

l.l

12.1

60.3

2.0

12.2

60 .8

121.5

243 .0

20.7

207.2

1,036 .0

37.5

36.S

182.3

364.5

729 .0

9.lE-02

0.91

4.6

0.15

1.2

5.9

11.9

23.8

J.6

15.7

78.4

2.8

3.6

17.8

35.7

7 1.3

Central Tendency
High End
Central Tendency

CHRONIC NON-CANCER

Liver
(Kjell:tttandet aJ., 1983)

10

Kidney
(MaJtoniet al_ l98~)

10

Ncurotoxicity
(AritQ et al., 1994)

Jmmwiotoxicity
(Keil et al., 2009)
ReproductiveToxicify

300

30

(Chi!! et al., 1996)

30

Developmental Toxicity
(John~n et al,. 200J )

10

High End

0.69

6.9

34.7

1.2

5.0

25.0

50.l

100.1

CentralTendency

12.0

Jl 9.7

598.7

21.7

15.0

75.1

150.2

300 .3

High End

1.9E--03

1.9E-o2

9.5E-02

3.2E-03

9.SE-03

4.8E--02

9.SE-02

0.19

CentralTendency

J.JE-02

0.33

1.6

6.0E--02

2.9E-02

0.14

0.29

0.57

High End

0.37

3.7

18.3

0.61

4.1

20.6

41.2

82.4

CentralTendency

6.3

63.2

3 15.8

11.4

12.4

61.8

123.5

247.1

High End

2.SE-03

2.sE-02

0.13

4.2£-03

3.0E--02

0.15

0.30

0.61

CentralTendency

4.3E-02

0.43

2.2

7.9E--02

9.lE-02

0.46

0.91

1.8

High End

3.SE-02

0.38

1.9

6.JE-02

0.46

2.3

4.6

9.2

0.66

6.6

32.9

1.2

1.4

6.9

13.9

27.7

High End

2.8E--04

2.SE-03

UE-02

4.7E-04

3.3E--03

l.6E--02

3.3E--02

6.6E--02

Central Tendency

4.9E-03

4.9E--02

0.24

8.SE-03

9.9E-03

4.9E--02

9.9E-02

0.20

Central Tendency

LIFETIME CANCER RISK

Combined Cancer Risk Kidney, NHL, Liver

1 x lo-4

High End

CentralTendency

0.11

1.lE-02

2.3E--03

6.9E-02

3.SE-02

7.5E-03

3.8E-03

l .9E--03

6.lE-03

6.lE-04

1.2E-04

3.3E--03

9.7E--03

1.9E-03

9.7E-04

4.9E--04

Bold text/pink shading indicatesMOE < benchmarkMOE. The highestPPE scenariosdisplayedare plausible for this exposurescenario.

679
680

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
681

682

MOE results for Cold Cleaningutilized modeling inhalationexposuredata (with dermal modeling) and are presented in Table 4-14.

683
684
685
686
687

688
689
.690
691
692
693
694

Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmarkMOE for most endpointsat both high-endand central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmarkMOE for multiple endpoints at both high-end and central tendency
inhalationexposure levels. MOEs remainedbelow the benchmarkMOE for cardiac toxicity at both exposure levels via dermal and inhalation
1
routes even when assumingthe highest plausible APF and glove PF protection.
ChronicNon-CancerRisk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendencyexposure levels via both
inhalation and dermal routes. MOEs for ONUs were also below the benchmarkMOE for multiple endpoints at both high-end and central
tendency inhalationexposure levels. MOEs remained below the benchmarkMOE for multiple endpoints at both exposure levels via dermal
and inhalationroutes even when assuming the highest plausible APF and glove PF protection.

695
696

697
698

699
700
701
702

Cancer Risk Estimates:
Extra risk estimatesfor workers were above the benchmarklevel for cancer at both high-end and central tendencyexposure levels via both
inhalationand dermal routes. R1skestimates for ONUs were also above the benchmarkfor cancer at both high-endand central tendency
inhalationexposure levels. Risk estimates remained above the benchmarkfor cancer at both exposure levels via dermal and inhalationroutes
even when assumingthe highest plausible APF and glove PF protection.
I

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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Tabl e 4-15 0 ccuoa tiona l Risk E stim at'ion- AerosoI A,ppI1ca
I' ti ODS

.

1

Inhalation (Modeling)

Endpoint

Benchmark
NoPPE
APF = lO
APF=SO
ExposureLevel Worker MOE Worker MOE Worker MOE
MOE

Dermal (Modeling)
NoPPE
ONUMOE

NoPPE
GlovePF=S GlovePF=to GlovePF=20
Worker MOE Worker MOE Worker MOE Worker MOE

ACUTE NON-CANCER

DevelopmentalCardiac Toxicity
(John~ et al., 200J)

lO

Developmental•
Neurotoxicity
(FredrikssQnet al., 199l)

100

Developmental•
Mortality
~aro~ )' et al., 1995)

10

ImmunotoxicityResponse to infection
(Selgradeand Gilmour, 2010)

30

High End

4.6E-M

4.6E-03

2.3E--02

l.lE-02

UE-03

7.lE-03

1.4E-02

2.9E-02

Central Tendency

l.!E-03

l.SE-02

7.JE-02

7.9E-02

4.JE-03

2.lE-02

4.JE-02

8.6E-02

High End

0.38

3.8

18.8

8.7

1.1

5.7

11.3

22.7

Central Tendency

u

11.8

59.0

64.3

3.4

17.0

34.0

68.0

High End

2.9

28.8

143.9

66.3

7.7

38.7

77.4

154.8

Central Tendency

9.0

90.4

452.2

492.9

23.2

116.1

232.2

464.3

High End

0.22

2.2

10.9

5.0

0.76

3.8

7.6

15.1

Central Tendency

0.68

6.8

34.2

37.3

2.3

11.4

22.7

45.4

CHRONIC NON-CANCER

Liver
(Kjellstrandet al., 1983)

10

Kidney
(MaltQniet al., 1986)

10

Neurotoxicity
(Aril2 et al. , 1991)

300

Immunotoxicity
(Keil et al,. 2009)

30

ReproductiveToxicity
(Chia ct 111.
. 19%)

30

DevelopmentalToxicity
(J2bnsonet aL, 2003)

10

High End

1.7

16.6

83.1

38.2

3.2

15.9

31.9

63.8

Central Tendency

5.2

52.3

261.3

284.4

9.6

47.8

95.6

191.3

HighEod

4.6E-4)3

4.6E-02

0.23

0.11

6.lE-03

3.0E-02

6.1E-02

0.12

Central Tendency

1.4E-02

0.14

0.72

0.78

1.SE-02

9.IE-02

0.)8

0.36

High End

0.88

8.8

43.8

20.2

2.6

13.J

26.2

52.S

Central Tendency

2.8

27.6

137.9

150.0

7.9

39.3

78.7

157.4

High End

6.0E-03

6.0E-02

0.30

0.14

J.9&-02

9.7&-02

0.19

0.39

Central Tendency

l.9E-02

0.19

0.95

1.0

5.8E-02

0.29

0.58

l.2

High End

9.lE-02

0.91

4.6

2.1

0.29

1.5

2.9

5.9

0.29

2.9

14.4

15.6

0.88

4.4

8.8

17.7

High End

6.8E-04

6.SE-03

3.4E-02

t.6E-02

2.tE-03

l.OE-02

2.JE-02

4.2E-02

Central Tendency

2.lE-03

2.lE-02

0.11

0.12

6.3E-03

3.lE-02

6.JE-02

0.13

Central Tendency

LIFETIMECANCERRISK
CombinedCancer Risk •
Kidney, NHL, Liver

1 X 1()-4

High End

4.9E-02

4.9E-03

9.7E-04

2.0E-03

5.9E--02

1.2E-02

5.9E-03

l.9E-03

Central Tendency

l.4E-02

1.4E-03

2.9E-04

2.6E-04

1.5&-02

3.0E-03

l.SE-03

7.6E-04

Bold text/pink shading indicatesMOE < benchmarkMOE. The highest PPE scenariosdisplayedare plausible for this exposure scenario.

704
705

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 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
706
707
708
709
710
711

712
713
714
715
716

717
718
719

MOE results for Aerosol Applications utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4--15.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via inhalation
and dennal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central tendency
inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal and
inhalation routes even when assuming the highest plausible APF and glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendency exposure levels via both
inhalation and dermalroutes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dennal
and inhalation routes even when assuming the highest plausible APF and glove PF protection.

720
721
722

723
724
725

Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer at both high-end and central tendency

inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APP and glove PF protection.

726

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 727

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-16. Occupational Risk Estimation - Spot Cleanine and Wipe Cleanin e (and Other Commercia l Uses) - Inha lation Monitoring Data
Inhalation (Monitoring)

Endpoint

Benchmark
MOE
ExposureLevel

Dermal (Modeling)

NoPPE
APF ==10
APF=50
No PPE
NoPPE
GlovePF=S
GlovePF =lO Glove PF-=20
Worker MOE WorkerMOE WorkerMOE ONUMOE 1 WorkerMOE WorkerMOE WorkerMOE WorkerMO:E
ACUTE NON-CANCER

Developmental•
CardiacToxicity
(Johnson et al., 2003)

10

Developmental•
Neurotoxicity
(Fr~ksson et al,. 1993)

100

DevelopmentalMortality
~arotsk ~ et al., 1995)

10

Immunotoxicity Responseto infection
(S~lgradeand GllmQyr,2010)

30

High End

3.9E-03

3.9E-02

0.19

3.9E-03

t.4E-03

7.ZE-03

t.4E-02

Central Tendency

2.9E-02

0.29

1.4

2.9E-02

4.3E-03

2.2E-02

4.3E-02

High End

3.2

31.6

157.8

3.l

t.l

5.7

11.3

Central Tendency

235

235.l

1,175.3

23.5

3.4

17.0

34.0

High End

24.2

242.0

1,210.1

24.2

7.7

38.7

77.4

Central Tendency

180.2

1,802.2

9,010.9

180.2

23.2

116.1

232.2

1.8

18.3

91.5

1.8

0.76

3.8

7.6

13.6

136.3

681.7

13.6

l.3

11.4

22.7

High End
Central Tendency

N /A 2

CHRONICNON-CANCER

Liver
(Kiellfilr4!!d
~t al.• 1983)

IO

Kidney
(Maltoniet al., 1986)

10

Neurotoxicity
. (Arit2 et al., 1994)
Immunotoxicity
(Keil et al., 2009)
ReproductiveToxicity
. (~his! !i:lal......122§)

DevelopmentalToxicity
{121!.n~net al., 2003)

300

30

30

High End

13.5

135.5

677.3

13.S

2.7

13.6

27.2

Central Tendency

100.9.

1,008.7

5,043.7

100.9

9.3

46.3

92.7

3.7E--02

0.37

1.9

3.'TE-02

5.2E-03

l.6E-02

5.lE-02

High End

Central Tendency

0.28

2.8

13.9

0.28

l.SE-02

8.8E-02

0.18

High End

7.1

71.5

357.3

7.1

2.2

11.2

22.4

CentralTendency

53.2

532.l

2,660.4

53.2

7.6

38.l

76.3

4 ..9E-02

0.49

2.5

4.9E-02

l .7E-02

8.3E-Ol

0.17

Central Tendency

0.37

3.7

18.3

0.37

5.6E-02

0.28

0.56

High End

0.74

7.4

37.2

0.74

0.25

1.3

2.S

Central Tendency

5.5

SS.4

277.1

5.5

0.86

4.3

8.6

2.SE-02

0.28

).4

2.SE-02

1.SE-03

9.0E-03

l.8E-02

0.21

2.1

10.5

0.21

6.lE-03

3.lE-02

6.tE-02

High End

High End
10

Central Tendency

N/A2

LIFETIMECANCER RISK

Combined Cancer Risk Kidney,NHL, Liver

1 X 10-4

High End
' Central Tendency

7.6E-03

7.6E-04

t.5E-04

7.6E-03

6.9E-02

t.4E-02

6.!>E-03

7.9E-04

7.9E--05

l.6E--05

7.9E~4

l .6E--02

3.JE-03

l .6E-03

Bold tex1/pinkshading indicatesMOE < benchmark MOE. The highest PPE scenarios displayed are plausiblefor this exposure scenario.
1
EPA is unable to estimateONU exposuresseparately from workers.
2 Glove PF =20 is only applicableto industrial settings (See Section 2.3.1).

Page 296 of 691

N/A2

 728

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-17. Occupational Risk
Estimation
- Spot Cleanine, and Witpe Cleanin2 (and Other Commercial Uses) - Inhalation Modelin g Data
..

.

Inhalation (Modeling)

Endpoint

APF..,10
Benchmark
NoPPE
APF • SO
ExposureLevel Worker MOE Worker MOE WorkerMOE
MOE

Dermal {Modeling)
NoPPE
ONUMOE

NoPPE
GJovePF=5 Glove p,-,,10 GlovePF=l0
Worker MOE WorkerMOE Worker MOE Worker MOE

ACUTENON-CANCER
DevelopmentalCardiacToxicity
(Johnson et gl., 2003)
. Developmental•
Neurotoxicity
(fredriksson et al., 1993)

10

High End

4.0E-03

4.0E-02

0.20

6.JE-03

l.4E-03

7.2&-03

1.4E-02

CentralTendency

1.2&-02

0.12

0.58

2.JE-02

4.3E-03

2.2E-02

4.3&-02

High End

3.2

32.5

162.5

5.1

Ll

5.7

11.3

Central Tendency

9.4

93.7

468.3

18.8

3.4

17.0

34.0

High End

24.9

249.1

1,245.5

39.4

7.7

38.7

77.4

Central Tendency

71.8

718.0

3,590.0

144.l

23.2

116.1

232.2

High End

1.9

18.8

94.2

3.0

0.76

3.8

7.6

Central Tendency

5.4

54.3

271.6

10.9

2.3

11.4

22.7

100

Developmental•
Mortality
(tlarotskv ~l al11 1995)

10

lmmunotoxicityResponseto infection
(Sel&rad~an!.}Gilmour, 2010)

30

N/A1

CHRONICNON-CANCER
Liver
(Kjellstrand~tal., I 9Sl)

10

Kidney
(Mal!Qniet fil., 1986)

10

Neurotoxicity
(Arito et al., 1994)
Immunotoxicity
(Keil ~t al,. 2002)
ReproductiveToxicity

(Chiaet al., I 99~
DevelopmentalToxicity
{!2hns2n !.1AL2003)

High End

14.0

139.6

697.9

22.1

2.7

13.6

27.2

Central Tendency

40.3

402.7

2,013.3

80.S

9.3

46.3

92.7

J.SE-02

0.38

1.9

6.lE-02

5.2E-03

2.6E-02

5.2E-02

Central Tendency

0.11

1.1

5.5

0.21

1.8E-02

8.SE-02

0.18

High End

7.4

73.6

368.1

11.7

2.2

11.2

22.4

Central Tendency

21.2

l12.4

1,061.9

41.5

7.6

38.l

76.3

5.lE-02

0.51

2.5

8.0E-02

l.7E-02

8.JE-02

0.17

Central Tendency

0.15

1.5

7.3

0.29

5.6E-02

0.28

0.56

High End

0.77

7.7

38.3

1.2

0.25

1.3

2.5

Central Tendency

2.2

22.l

110.6

4.4

0.86

4.3

8.6

High End

5.7E-03

5.7E-02

0.28

9.0E-03

1.8E-03

9.0E-03

1.SE-02

Central Tendency

l.6E-02

0.16

0.82

3.3E-02

6.tE-03

3.J.E-02

6.lE-02

High End

300

High End

30

30

10

N/A 1

LIFETIME CANCER RISK

I

Combined Cancer Risk Kidney, NHL, Liver

1 X 10-4

High End

S.SE-03

5.8E-04

l.2E-04

3.6E-03

6.9E-02

l.4E-02

6.9.E-03

Central Tendency

1.8E-03

1.BE-04

3.7E-05

9.2&-04

l.6E-02

3.lE-03

l.6E-03

Bold text/pink shading indicatesMOE < benchmarkMOE. The highestPPE scenariosdisplayed are plausible for this exposurescenario.
1 Glove PF =20 is only applicableto industrialsettings (Sec Section 2.3.1).

729
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N/A1

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
730
731
732
733
734
735
736
737
738
739
740
741

MOE calculations for Spot Cleaningand WipeCleaningutilized both monitoring and modeling inhalation exposure data (with dermal
modeling). This data also applies to the exposure scenario of OtherCommercialUses.Results are presented in Table 4-16 and Table 4-17.
Acute Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end
and central tendency exposure levels via both inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circwnstances
based on monitoring data. ONU risk estimates were below the benchmark MOE for multiple endpoints at both high-end and central tendency
inhalation exposure levels based on modeling data. Based on both monitoring and modeling data, MOEs remained below the benchmark
MOE for cardiac toxicity at both exposure levels via inhalation and for multiple endpoints via the dennal route even when assuming the
highest plausible APF and glove PF protection.

742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766

Chronic Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via both inhalation and dermal routes EPA is unable to estimate ONU exposures separately from workers.
Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstancesbased on
monitoring data. ONU risk estimates were below the benchmark MOE for multiple endpoints at both high-end and central tendency inhalation
exposure levels based on modeling data. Based on both monitoring and modeling data, MOEs remained below the benchmark MOE for
immunotoxicityat both exposure levels via inhalation and for multiple endpoints via the dermal route even when assuming the highest
plausible APF and glove PF protection.
Cancer Risk Estimates:
Based on both monitoring and modeling data, extra risk ·estimates for workers were above the benchmark level for cancer at both high-end
and central tendency exposure levels via both inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances
based on monitoring data. ONU risk estimates were above the benchmark at both high-end and central tendency inhalation exposure levels
based on modeling data Based on both monitoring and modeling data, risk estimates remained above the benchmark for cancer at high-end
inhalation exposure levels and both dermal exposure levels even when assuming the highest plausible APF and glove PF protection. Risk
estimates were not above the benchmark for central tendency inhalation exposure when assuming APF = 10 based on monitoring data or
when assuming APF = 50 based on modeling data.
PPE Considerations
EPA is presentingrisk estimatesfor respiratoryprotectionup to APF = 50 as a what-ifscenario,howeverEPA believesthat smallcommercial
facilitiesperformingspot cleaning,wipe cleaning,and other relatedcommercialuses are unlikelyto have a respiratoryprotectionprogram.
Therefore,the use of respiratorsis unlikelyfor workers in these facilities.

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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
pati-on al Risk E sfuna t·10n- F ormual ti ODO f AerosoI an d N on-AerosoIP roducts
T abl e 4-18 0 cc111

.

-

Inhalation (Monitoring)

Benchmark
Endpoint

MOE

NoPPE
APF=lO
APF=50
Exposure Level Worker MOE Worker MOE Worker MOE

I

-

Dermal (Modeling)
NoPPE
ONUMOE

1

NoPPE
GlovePF=S GlovePF=lO GlovePF=26
Worker MOE Worker MOE Worker MOE Worke r MOE

ACUTE NON-CANCER

DevelopmentalCardiacToxicity
(l2hnson et al., 2003)

10

DevelopmentalNeurotoxicity
(Er~rik§§Qn et al., 1993)

100

9.7E-03

9.7E--02

0.49

9.7E-03

2.JE-03

1.lE-02

2.JE-02

4.SE--02

Central Tendency

22.4

224.3

1,121.3

22.4

6.SE-03

3.4E-02

6.8E-02

0.14

High End

79

78.9

394.7

7.9

1.8

8.9

17.8

3S.6

18,182.7

181,827.5

909,137.3

18,182.7

5.3

26.7

53.4

106.7

60.S

605.3

3,026.3

60.5

12.2

60.8

121.S

243.0

139,401.1

1,394,010.5

6,970,052.6

139,401.1

36.5

182.3

364.5

729.0

4.6

45.8

228.9

4,6

1.2

5.9

ll.9

23.8

10,546.0

105,459.9

527,299.6

10,546.0

3.6

17.8

35.7

71.3

High End

Central Tendency
High End

Developmental Mortality
(Narotsky et !!,1
., 1995)

10

Immw1otoxic
ity •
Responseto infection
(Selgrndeand Gilmoµr. 2010)

30

Central Tendency
High End
Central Tendency

CHRONICNON-CANCER
Liver
(Ki~ll~l!lmdet al., !2~J)

10

. Kidney
(Maltoni et 11!
.. 1282)

10

Neurotoxicity
(AritQet al., 1994)

300

Immunotoxicity
(Keil et al., 2009)

30

ReproductiveToxicity
(Chill ~t al.. 1222
)

30

Developmental Toxicity
(Johnson et At..200l)

IO

35.0

349.6

1,748.2

35.0

5.0

25.0

50.1

100.1

Central Tendency

80,525.3

805,253.2

4,026,266.0

80,525.3

1S.0

7S.I

150.2

300.3

High End

9.6&-02

0.96

4.8

9.6E-Ol

9.5E-03

4.SE-02

9.SE-02

0.19

221.2

2,212.2

11,061.2

221.2

2.9E-02

0.14

0.29

0.57

18.4

184.4

922.1

18.4

4.1

20.6

41.2

82.4

42,474.9

424,748.9

2,123,744.7

42,474.9

12.4

61.8

123.5

247.1

High End

0.13

1.3

6.3

0.13

3.0E-02

0.15

0.30

0.61

CentralTendency

292.0

2,920.1

14,600.7

292.0

9.lE-02

0.46

0.91

1.8

1.9

19.l

96.1

19

0.46

2.3

4.6

9.2

CentralTendency

4,424.5

44,244.7

221,223.4

4,424.5

1.4

6.9

13.9

27.7

High End

1.4E-O:l

0.14

0.71

l.4&-02

3.3E-03

1.6E-02

3.3E-Ol

6.6E-02

32.7

327.4

1,637.1

32.7

9.9E-03

4.9E-02

9.9E-02

0.20

High End

Central Tendency
High End
Central Tendency

High End

Central Tendency

LIFETIMECANCER RISK
Combined Cancer Risk Kidney, NHL, Liver

1 X }()-4

High End

2.9E-03

2.9E-04

5.9E-0S

2.9E-03

3.SE-02

7.SE-03

3.SE-03

t .9E-03

Central Tendency

9.9E-07

9.9E-08

2.0E-08

9.9E-07

9.7E-03

l.9E-03

9.7E-04

4.9E-04

Bold tex:t/pinkshading indicat~ MOE < benchmark MOE. The highestPPE sc.enariosdisplayed are plausible for this exposurescenario.
1
EPA is unable to estimateONU exposures separatelyfrom workers.

768

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 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

769
770

771
772
773
774
775
776
777
778
779
780
781

782
783
784

785
786
787

MOE results for Formulationof Aerosol and Non-AerosolProductsutilized monitoring inhalation exposure data (with dennal modeling) and
are presented in Table 4-18.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmarkMOE for multiple endpoints at high-end inhalation exposures,but MOEs were above the
benchmarkMOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers.Therefore,EPA provided risk estimates for ONUs assumingONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remainedbelow the benchmarkMOE
for cardiac toxicity at high-end inhalationexposure even when assuming the highest plausible APF. MOEs remained below the benchmark
MOE for cardiac toxicity at both dermal exposure levels even when assumingthe highest plausible glove PF protection.
Chronic Non-CancerRisk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalationexposures, but MOEs were above the
benchmarkMOE for all endpoints at central tendency inhalationexposures. EPA is unable to estimate ONU exposures separately from
workers.Therefore,EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmarkMOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmarkMOE
for multiple endpoints at high-end inhalationexposure and at both dermal exposure levels even when assuming the highest plausible APF and
glove PF protection.

788

789
790
791
792
793
794
795
796
797
798

Cancer Risk Estimates:

Extra risk estimates for workers were above the benchmarklevel for cancer at at high-end inhalationexposures,but risk estimates were below
the benchmark for cancer at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from workers.
Therefore,EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.Risk
estimates were above the benchmarkat both dermal exposure levels. Risk estimates were not above the benchmarkfor high-end inhalation
exposure when assuming APF = 50. Risk estimates remained above the benchmarkfor cancer at both dermal exposure levels even when
assuming the highest plausible glove PF protection.

Page 300 of 691

 199

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

.

T abl e 4-19 0c cupationa I Risk Es.timation- Repacka.i2m1?
Inhalation (Monitoring)
Endpoint

Benchmark
Exposure Level
MOE

NoPPE
APF=lO
APF=50
Worker MOE Worker MOE Worker MOE

Dermal (Modeling)
NoPPE
ONUMOE

NoPPE
1

GlovePF-5

GlovePF=l0

GlovePF=lO

Worker MOE Worker MOE Worker MOE WorkerMOE

ACUTE NON-CANCER
Developmental Cardiac Toxicity
, 2003)
(lohnson et ll:!.

10

Developmental•
Neurotoxicity
(Fredriksson1.tal., l92J)

100

DevelopmentalMortality
(N_arotsk1et al., l 995)

10

Immunotoxicity•
Responseto infection
(Selgrage and Gilmour, 2010)

30

9.7E-03

9.7E-02

0.49

9.7E-03

1.3E-03

1.lE-01

l.3:E-01

4.5E-Ol

Central Tendency

22.4

224.3

1,121.3

224

6.SE--03

3.4E-02

6.8:E-01

0.14

High End

7.9

78.9

394.7

7.9

1.8

8.9

17.8

35.6

18,182.7

181,827.5

909,137.3

18,182.7

5,3

26.7

53.4

106.7

60.S

605.3

3,026.3

60.5

12.2

60.8

121.5

243.0

139,401.1

1,394,010.5

6,970,052.6

139,401.1

36.S

182.3

364.S

729.0

4.6

45.8

228.9

4.6

1.2

5.9

11.9

23.8

10,546.0

105,459.9

527,299.6

10,546.0

3.6

17.8

35.7

71.3

High End

Central Tendency
High End

CentralTendency
High End
Central Tendency

CHRONICNON-CANCER
Liver
(Kjell~trand et al., 1983)

10

Kidney
(M!!ltQlliet al., 1986)

10

35.0

349.6

1,748.2

35.0

5.0

25.0

50.1

100.l

80,525.3

805,253.2

4,026,266.0

80,525.3

15.0

75.l

150.2

300.3

9,6E-02

0.96

4.8

9.6E-02

9.SE-03

4.SE-02

9.SE-02

0.19

221.2

2,212.2

11,061.2

221.2

2.9E-01

0.1'

0.29

0.57

18.4

184.4

922.l

18.4

4.1

10.6

41.2

82.4

4Z474.9

424,748.9

2,123,744.7

42,474.9

12.4

61.8

123.5

247.l

High End

0.13

1.3

6.3

0.13

3.0E-02

0.lS

0.30

0.61

Central Tendency

292.0

2,920.1

14,600.7

292.0

9.lE-02

0.46

0.91

1.8

1.9

19.2

96.l

1,9

0.46

2.3

4.6

9.2

Central Tendency

4,424.5

44,244.7

221,223.4

4,424.S

1.4

6.9

13.9

27.7

High End

UE--02

0.14

0.71

1.4E-Ol

3.3E-03

1.6E-Ol

3.3E--01

6.6E-Ol

32.7

327.4

1,637.1

32.7

9.9E-03

4.9E-02

9.9E-02

0.20

High End

Central Tendency
High End

Neurotoxicity
(Arito et al., 1994)

300

lnpnunotoxicity
(Keil et al., 2Q09)

30

ReproductiveToxicity
({;hia et al.. 1996)

30

DevelopmentalToxicity
(J2hnson et !!I,,. 2Q03}

10

Central Tendency
High End
Central Tendency

High End

Central Tendency

I

LIFETIME CANCERRISK
Combined Cancer Risk Kidney, NHL.Liver

1 X 10"'

High End

2.9E-03

2.9.E--04

5.9E-05

2.9E-03

3.8E-02

7.5E-03

3.8E-03

l.9&-03

Central Tendency

9.9E-07

9.9E-08

2.0E-08

9.9E-07

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicatesMOE < benchmarkMOE. The highest PPE scenariosdisplayed are plausible for this exposurescenario.
1
EPA is unable to estimateONU exposuresseparatelyfrom workers.

800

Page 301 of691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
801
802

MOE results for Repackagingutilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table 4-19.

803
804
805
806

807
808

809

810
811
812
813
814
815
816
817
818
819

820
821
822
823
824
825
826

Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE
for multiple endpoints at high-end inhalation exposure even when assuming the highest plausible APF. MOEs remained below the benchmark
MOE for cardiac toxicity at both dermal exposure levels even when assuming the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE
for multiple endpoints at high-end inhalation exposure and at both dermal exposure levels even when assuming the highest plausible APF and
glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at at high-end inhalation exposures, but risk estimates were below
the benchmark for cancer at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from workers.
Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circwnstances. Risk
estimates were above the benchmark at both dermal exposure levels. Risk estimates were not above the benchmark for high tendency
inhalation exposure when assuming APF = 50. Risk estimates remained above the benchmark for cancer at both dermal exposure levels even
when assuming the highest plausible glove PF protection.

827
828
829
830
831
832
833
834

835
836
837

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 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

838

I rki ng Flw.d s- I nha Iatton
• Momtorm2
. . Data
Table 4-20. 0 ccupat1onaI Risk Est'1mati on- M e¢awo

-

-

Dermal(Modeling)

lnhalatlon (Monitoring)

Benchmark
Endpoint

MOE

NoPPE
APF = lO
APF= SO
No PPE
NoPPE
Glo\'ePF=S GlovePF=l0 GlovePF=l0
Exposore Level WorkerMOE WorkerMOE WorkerMOE ONU MOE1 WorkerMOE WorkerMOE WorkerMOE WorkerMOE
ACUTE NON-CANCER

Developmental CardiacToxicity

High End

1.SE-04

l.SE-03

7.4E-03

l.!E--04

2.SE-03

1.4E-02

2.SE-02

5.6:E-02

Central Tendency

l.6E-04

l.6:E-03

8.0E-03

1.6E--04

8.5E-03

4.2E-02

8.SE-02

0.17

High End

0.12

1.2

6.0

0.12

2.2

11.l

22.2

44.5

Central Tendency

0.13

1.3

6.5

0.13

6.7

33.4

66.7

133.4

High End

0.92

9.2

45.8

0.92

15.2

75.9

151.9

303.8

Central Tendency

0.99

9.9

49.5

0.99

45.6

227.8

45S.6

911.3

High End

6.9:E-02

0.69

3.5

6.9E-02

1.5

7.4

14.9

29.7

Central Tendency

7.!E--02

0.75

3.7

7.SE-02

4.5

22.3

44.6

89.2

10

(Johnson et al., 2003 )

Developmental Neurotoxicity
I CFredrikssonet al., 1993)

100

Developmental Mortality
(~arotsk~ !;t al., 1225)

10

Jmmunotoxicity Response to infection
(Selg,rndeand Gilmour, 2010)

30

CHRONIC NON~CANCER

Liver
(Kjellstrandet al. 12~~
)

Kldney
(MaltQniet al,. I 986)

10

10

Neurotoxicity
(AritQ ~ al., 1994)

300

Immunoto.xicity
(Keil et al., 2002)

30

Reproductive Toxicity
(Q!Jii ~t a!., 1996)

Developmental Toxicity
(John~n et 11l
11 2003)

30

High End

0.53

5.3

26.4

0.53

6.3

31.3

62.6

125.1

Central Tendency

0.57

5.7

28.6

0.!7

18.8

93.8

187.7

375.4

High End

1.SE-03

LSE-02

7.3E-02

1.SE-03

1.2E-02

5.9E--02

0.12

0.24

Central Tendency

1.6E-03

1.6E-02

7.9E-02

1.6E-03

3.6E-02

0.18

0.36

0.71

High End

0.28

2.8

13.9

0.28

5.1

25.7

51.5

l03.0

Central Tendency

0.30

3.0

15.1

0.30

15.4

77.2

154.4

308.9

High End

1.9E-03

1.9E-02

9..6E-02

l.9E--03

3.SE-02

0.19

0.38

0.76

Central Tendency

2.lE-03

2.lE-02

0.10

2.lE--03

0.11

0.57

1.1

2.3

High End

:2.9E-02

0.29

1.S

2.9E-02

0.58

2.9

5.8

11.6

Central Tendency

3.lE-02

0.31

1.6

3.lE-02

1.7

8.7

17.3

34.7

High End

2.2E-04

2.lE-03

l .lE-02

2.lE-04

4.lE-03

2.tE-02

4.lE-02

8.lE-02

Central Tendency

2.JE-04

2.3&-03

UE-02

2.JE-04

1.2E-02

6.lli-02

0.12

0.25

10

LIFETIME CANCER RISK

Combined Cancer Risk Kidney, Nfil , Liver

1 X 10"4

High End

0.19

l .9E-02

3.9E-03

0.19

3.0E-02

6.0E-03

3.0E-03

1.SE-03

Central Tendency

0.14

1.4E-02

2.SE-03

0.14

7.SE-03

1.6E-03

7.SE--04

3.9E-04

Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposurescenario.
1EPA is unable to estimateONU exposuresseparately from workers.

Page 303 of 691

I

 839

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
t Iwoirkinif! FlUl.d S • In h aIaf10n M0dClin12 D aat
T abl e 4 21 0 ccupa ti onaI Risk E sf1ma ti on - M ea

-

I

.

In halation (Modeling)

Endpoint

Benchmark
Exposure Level
MOE

NoPPE
APF=l0
APF=S0
WorkerMOE Worker MOE WorkerMOE

Derma l (Modeling)
NoPPE
ONUMOE

1

NoPPE
GlovePF=5 GlovePF=IO GlovePF=20
WorkerMOE Worker MOE Worker MOE WorkerMOE

ACUTE NON-CANCER

DevelopmentalCardiac Toxicity
(J2hnson et al., 2003)

10

Developmental Neurotoxicity
(Fredriksson et al,. 1993)

100

Developmental Mortality

10

iliarotslq et al., 199~}
ImmunotoxicityResponse to infection
~lgradi: llllQGilmQY[.2QlQ)

4.3E-02

0.43

2.1

2.SE-03

t.4E-02

2.SE-02

5.CiE-02

Central Tendency

0.16

1.6

7.9

0.16

8.SE-03

4.2E-02

8.SE-02

0.17

High End

34.6

346.2

1,730.8

34.6

2.2

11.l

22.2

44.5

Central Tendency

128.6

1,.285.7

6,428.6

128.6

6.7

33.4

66.7

133.4

High End

265.4

2,653.8

13,269.2

265.4

15.2

75.9

151.9

303.8

Central Tendency

985.7

9,857.1

49,285.7

985.7

45.6

227.8

455.6

911.3

High End

20.1

200.8

1,003.8

20.1

1.S

7.4

14.9

29.7

Central Tendency

74.6

745.7

3,728.6

74.6

4.5

22.3

44.6

89.2

High End

. 30

4.3~

CHRONIC NON-CANCER

I

Liver
(Kje]lstransjet al., 1283)

10

Kidney
(Malto11
i et al., 1986)

10

Neurotoxicity
(Aritoetal ., 1994)

300

Immunotoxicity
(Keil et al .. 2009)

30

'

· ReproductiveToxicity
. (Chia et aL 1996)
DevelopmentalToxicity
(Johnson et Ill-,20Q3)

30

High End

151.7

1,516.7

7,583.3

151.7

6.3

31.3

62.6

125.1

Central Tendency

568.8

5,687.5

28,437.S

568.8

18.8

93.8

187.7

375.4

High End

0.42

4.2

20.8

0.42

l.2E-02

5.9E-02

0.12

0.24

Central Tendency

1.6

15.6

78.1

1.6

3.6E-02

0.18

0.36

0.71

High End

80.0

800.0

4,000.0

80.0

5.1

25.7

51.S

103.0

Central Tenden.cy

300.0

3,000.0

15,000.0

300.0

15.4

77.2

1S4.4

308.9

High End

0.55

5.5

27.5

0.55

3.SE-02

0.19

0.38

0.76

Central Tendency

2.1

20.6

103.1

2.1

0.11

0.57

1.l

2.3

High End

8.3

83.3

416.7

8.3

0.58

2.9

5.8

11.6

Central Tendency

31.3

312.5

1,562.5

31.3

1.7

8.7

17.3

34.7

6.lE-02

0.62

3.1

6.2E-02

4.IE-03

2.1~

0.23

2.3

11.6

0.23

l.2E-02

High End

10

Central Tendency

4.IE-02

8.lE-02

6.2&-02

0.12

0.25

LIFETIMECANCERRISK
Combined Cancer Risk Kidney, NHL, Liver

IX IO-"

High End

6,6E-04

6.6E-05

l.3E-05

6.6E-04

3.0E-02

6.0E-03

3.0E-03

1.SE-03

Central Tendency

l.JE-04

l. 3E-05

2.6E-06

1.JE-04

7.SE-03

l.6E-03

7.8.E--04

3.9E-04

Bold text/pink shading indicates MOE < benchmarkMOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimateONU exposures separately from workers.

840
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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
841
842
843
844
845
846
847
848
849
850
851
852
853
'854
855
856
857
858
859
860
861
862
863
864

865
866

867
868
869
870
871

MOE calculationsfor MetalworkingFluids utilized both monitoring and modeling inhalation exposure data (with dermal modeling). Results
are presented in Table 4-20 and Table 4-21.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints based on monitoring and for cardiac toxicity based on modeling at both
high-end and central tendency exposure levels via inhalation. Based on both monitoring and modeling data, EPA is unable to estimate ONU
exposures separately from workers.Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in
various circumstances. Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for multiple
endpoints via dermal exposure. MOEs remained below the benchmark MOE for multiple endpoints based on monitoring and for cardiac
toxicity based on modeling at both exposure levels via inhalation and for cardiac toxicity at both dermal exposure levels even when assuming
the highest plausible APF and glove PF protection based on monitoring data.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints based on monitoring and for multiple endpoints based on modeling at
both high-end and central tendency exposure levels via inhalation. Based on both monitoring and modeling data, EPA is unable to estimate
ONU exposures separatelyfrom workers.Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as
workers in various circumstances.Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all
endpoints via dermal exposure.MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal and
inhalationroutes even when assumingthe highest plausible APF and glove PF protection based on monitoring data. For modeling data, MOEs
were not below the benchmark MOE at central tendency exposure level when assuming APF = 50, although MOEs were below the
benchmark MOE for multiple endpoints via the dermal route even when assuming the highest plausible glove PF protection.
Cancer Risk Estimates:
Based on both monitoring and modeling data, extra risk estimates for workers were above the benchmark level for cancer at both high-end
and central·tendency exposure levels via both inhalation and dermal routes. Based on both monitoring and modeling data, EPA is unable to
estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as
high as workers in various circumstances. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and
inhalationroutes even when assuming the highest plausible APF and glove PF protection based on monitoring data. For modeling data, risk
estimates were not above the benchmark at either inhalation exposure level when assuming APP = 10, although risk estimates were above the
benchmark via the dermal route even when assuming the highest plausible glove PF protection.

872
873

Page 30S of 691

 874

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Tab le 4-22. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatin2s (Industr ial Settine:l
Inhalation (Monitoring)
Benchmark

Endpoint

MOE

NoPPE

APF=l0

APF=S0

ExposureLevel Worker MOE Worker MOE Worker MOE

Der mal (Modeling)
NoPPE
ONUMOE

NoPPE
GlovePF=5
GlovePF=l0
GlovePF=20
Worker MOE Worker MOE WorkerMOE Worker MOE

ACUTE NON-CANCER

Developmental•
Cardiac Toxicity
· (Johnson et al. , 2003 )
Developmental Neurotoxicity
(Fredrikssonet al .• 1993)

High End

2.SE-04

2.SE-03

JAE--02

1.lE-02

2.SE-03

1.3.E-02

2.SE-02

5.0E-02

Central Tendency

2.4E-03

2.4E-02

0.12

1.lE-02

7.SE-03

3.BE-02

7.SE-02

0.JS

High End

0.23

1.3

11.4

9.0

2.0

9.9

19.8

39.5

Central Tendency

1.9

19.4

97.1

9.6

S.9

29.7

59.3

118.6

High End

1.7

17.5

87.4

69.0

13.5

67.5

135.0

270. 0

Central Tendency

14.9

148.8

744.l

73.3

40 .5

202.5

405 .0

810.0

High End

0.13

1.3

6.6

5.2

t.3

6.6

13.2

26.4

1.1

11.3

56 .3

5.5

4.0

19.8

39.6

79.3

10

100

DevelopmentalMortality
iliarotsk ~ ~ al., 199~)

10

Immunotoxicity Response to infection
(Selg[l!g~and Q:ilmQyt, ~QIQ)

30

Central Tendency

CHRONICNON-CANCER

Liver
(KjeJlstrandet al., 19~ )
Kidney

(Maltoni et al., 1986)
Neurotoxicity

10

High End

1.0

10.1

50.5

39.9

5.6

27.8

55.6

111.2

Central Tendency

8.6

86.0

429 .9

42.4

16.7

83.4

166.8

333.7

High End

2.SE-03

2.8E-02

0.14

0.11

l.lE-02

5.3E-02

0.11

0.21

Central Tendency

2.4E-02

0.24

l.l

0.12

3.2E--02

0.16

0.32

0.63

High End

0.53

5.3

26.6

21.0

4.6

22 .9

45.8

91.S

Central Tendency

4.5

45.3

226 .7

22.3

13.7

68.6

137.3

274.5

High End

3.7E-03

3.7E-Ol

0.18

0.14

3.4E-02

0.17

0.34

0.68

Central Tendency

3.lE-02

0.31

1.6

0.15

0.10

0.51

1.0

2.0

High End

5.SE-02

0.55

l.8

2.2

0.51

2.6

5,1

10.3

0.47

4.7

23.6

2.3

J.5

7.7

15.4

30.8

High End

2.lE-03

2.l E-02

0.11

t.6E-02

3.7E-03

l.8E-02

3.7E-02

7.3E-02

Central Tendency

1,SE-02

0.18

0.90

l.7E-02

l.lE-02

5.SE-02

0.11

0.22

10

(Arj!Q et al., 1994)

300

Immunotoxicity
(&,i i et al., 2009)

30

Reproductive Toxicity
(Chi1 ct it!.. 1996)

30

DevelopmentalToxicity
(JQhn:!Qn
~ al., 20Q3)

10

Central Tendency

LIFETIMECANCERRISK
Combined Cancer Risk ~
Kidney, NID...,Liver

High End

0.10

1.0E-02

2.0E-03

2.6E-03

3.4E-02

6.SE-03

3.4E-03

1.7E-03

9.JE-03

9.3E-04

J.9E-04

l.9E-03

8.7E-03

1.7E-03

8.7E--04

4.4E-04

l X 10"4

Central Tendency

Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenariosdisplayed are plausible for this exposure scenario.

Page 306 of 691

 INTERAGENCY DRAFf - DO NOT CITE OR QUOTE
875
876
877

MOE results for Adhesives, Sealants, Paints, and Coatings (IndustrialSetting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-22. Inhalation exposures are estimated to be identical for industrial and commercial workers.

878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900

Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high--end and central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints .at both high-tmd and central tendency
inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure levels even when
assuming the highest plausible APF. MOEs remained below the benchmark MOE for cardiac toxicity at both dermal exposure levels even
when asswning the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high--end and central tendency exposure levels via both
inhalation and dermal routes. MOEs for ONUs ~ere also below the benchmark MOE for multiple endpoints at both high--end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assuming the highest plausible APF and glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high--end and central tendency exposure levels via both
inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer at both high-end and central tendency
inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APF and glove PF protection.

Page307 of 691

 1N1ERAGENCY DRAFT - DO NOT CITE OR QUOTE
901

Table 4-23. OccupationalRisk Estimation - Adhesives, Sealants, Paints, and Coatin2s (CommercialSettine:)
Inhalation (Monitoring)

Benchmark
Endpoint

MOE

NoPPE
APF•10
APF==~
Exposure Level WorkerMOE WorkerMOE Worker MOE

Dermal (Modeling)
NoPPE
ONUMOE

NoPPE
GlovePF=5 GlovePF=l0 GlovePF=20
WorkerMOE WorkerMOE WorkerMOE Worker MOE

ACUTENON-CANCER
DevelopmentalCardiac Toxicity
(Jonnson et al.. 2003)

10

Developmental Neurotoxicity
(£redriksson et al., 1993)

100

Developmental . Mortality
• (!':1:arotsk
x et al.. 19'12)
bnmunotoxicityResponse to infection
rnelgi:adeand Gilmour, 2010)

High End

2.SE-04

2.SE-03

l.4E-02

1.IE-02

J.6E-03

8.0E--03

1.6.E-02

Central Tendency

2.4E-03

2.4E-02

0.12

l.lE--02

4.SE--03

2.4&-02

4.8.E-02

0.23

2.3

11.4

9.0

1.3

6.3

12.6

Central Tendency

1.9

19.4

97.1

9.6

3.8

18.9

37.8

High End

1.7

17.5

87.4

69.0

8.6

43.0

86.0

Central Tendency

14.9

148.8

744.1

73.3

25.8

129.0

258.0

High End

0.13

1.3

6.6

5.2

0.84

...2

8.4

1.1

11.3

56.3

5.5

2.5

12.6

25.2

High End

10

30

Central Tendency

NIN

CHRONICNON-CANCER
Liver
(KjellstTandet al., 12~3)

10

Kidney
.• 1986)
(Maltoniet ru

10

Neurotoxicity
(Ari lQet al., 1994)

300

lmmunotoxicity
(IS.eil~t a!.• 2009)

30

ReproductiveToxicity
(Qhia et al ., 1996)

30

DevelopmentalToxicity
(J2bn~ et ru
..200J)

10

High End

l .O

10.1

50.5

39.9

3.5

17.7

35.4

Central Tendency

8.6

86.0

429.9

42.4

10.6

53.1

106.3

High End

2.8E--03

2.SE--02

0.14

0.11

6.7E--03

3.4E-02

6.7&-02

Central Tendency

2.4E--02

0.24

1.2

0.12

2.0E--02

0.10

0.20

High End

0.53

5.3

26.6

21.0

2.9

14.6

29.1

Central Tendency

4.S

45.3

226.7

22.3

8.7

43.7

87.4

High End

3.7E--03

3.7&-02

0.18

0.14

2.lE-02

0.11

0.22

Central Tendency

3.JE--02

0.31

t.6

O.JS

6.SE..02

0.32

0.65

High End

5.!E-02

0.55

2.8

2.2

0.33

1.6

3.3

0.47

4.7

23.6

2.3

0.98

4.9

9.8

High End

4.lE-04

4.lE--03

2.lE-02

I.6E-02

2.3&-03

l.2E-02

2.JE--02

Central Tendency

3.5.E-03

3.SE-02

0.17

1.7E-02

7.0E-03

3.5E-02

7.0E-02

Central Tendency

NIA1

LIFETIMECANCERRISK
Combined Cancer Risk Kidney, NHL, Liver

1 x lo-4

High End
Central Tendency

0.10

1.0E--02

2.0E-03

2.6E--03

5.3&-02

1.lE--02

5.3E--03

9,3E--03

9.3E-04

1.9E--04

l.9E-03

l.4&-02

2.7E-03

l.4E-03

Bold text/pink shading indicatesMOE< benchmark MOE.The highest PPE scenarios displayedare plausible for this exposurescenario under a rigorous PPE program.
'Glove PF =20 is only applicableto industrialsettings (See Section 2.3.1).

902

Page 308 of 691

N/A1

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925

MOE results for Adhesives,Sealants,Paints, and Coatings(CommercialSetting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-23. Inhalation exposures are estimated to be identical for industrial and commercial settings.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assumingthe highest plausible APF and glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assuming the highest p1ausibleAPF and glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer at both high-end and central tendency
inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APF and glove PF protection.

926
927

Page 309 of 691

 928

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-24. Occupational Risk Estimation - Industrial Processinl! Aid (12 hr)
Inhalation (Monitoring)

Benchmark
Endpoint

MOE

NoPPE
APF=lO
APF==SO
Exposure Level WorkerMOE WorkerMOE WorkerMOE

Dermal (Modeling)

NoPPE
ONUMOE

NoPPE
GlovePF=S GlovePF=lO GlovePF"'20
WorkerMOE WorkerMOE WorkerMOE WorkerMOE

ACUTE NON-CANCER
DevelopmentalCardiacToxicity
(JQhnsonet al., 2003)

10

DevelopmentalNeurotoxicity
(Fredrikssonet al., 1993)

100

DevelopmentalMortality
(~aro~k~• ~L!!I.. 199~)

10

lmmunotoxicity•
Response to infection
(Selgrade and Gilmour, 2010)

30

High End

5.8E-04

5.8E-03

2.9E-02

2.SE--03

l,JE--03

1.IE-02

2.3E--02

4.SE-02

Central Tendency

l.7E-03

l.7E-02

8.7E-02

5.6E-03

6.8E-03

3.4E-02

6.SE-02

0.14

0.47

4.7

23.4

2.1

1.8

8.9

17.8

35.6

Central Tendency

1.4

14.l

70.6

4.6

S.3

26.7

53.4

106.7

High End

3.6

35.9

179.6

15.8

12.2

60.8

121.5

243.0

Central Tendency

10.8

108.2

540.9

35.1

36.5

182.3

364.5

729.0

High End

0.27

2.7

13.6

1.2

l.l

5.9

11.9

23.8

Centtal Tendency

0.82

8.2

40.9

2.7

3.6

17.8

35.7

71.3

High End

CHRONICNON-CANCER
Liver
(Kjellstrand et al., 1983)

10

Kidney
(Maltgni et ~I-.1986)

10

Neurotoxicity
(Arito et al., 1994)

300

Immunotoxicity
(Keil et al., 2009)

30

ReproductiveToxicity
(Chhu;ul .. 199!!
)

30

DevelopmentalToxicity
(l!1hnsonet al., 2003)

10

High End

2.1

20.7

103.7

9.2

5.0

25.0

50.1

100.1

Central Tendency

6.2

62.5

312.5

20.3

15.0

75.1

150.2

300.3

High End

5.7E-03

5.7E-02

0.28

2.SE-02

9.SE-03

4.SE--02

9.SE-Ol

0.19

Central Tendency

l.7E-02

0.17

0.86

5.6E--02

2.9E-02

0.14

0.29

0.57

High End

1.1

10.9

54.7

4.8

4.1

20.6

41.2

82.4

Central Tendency

3.3

33.0

164.8

10.7

12.4

61.8

123.S

247.1

High End

7.SE--03

7.SE-02

0.38

3.JE--02

3.0E--02

0.15

0.30

0.61

Central Tendency

2.3E-02

0.23

1.1

7.3E-02

9.l.E-02

0.46

0.91

1.8

High End

0.11

t.l

5.7

o.so

0.46

2.3

4.6

9.2

Central Tendency

0.34

3.4

17.2

1.1

1.4

6.9

13.9

27.7

High End

8.4E-04

8.4E-03

4.2E-02

3.7E-03

3.3E--03

1.6E-02

3.3E-02

6.6E..()2

Central Tendency

2.SE-03

2.SE-02

0.13

8.lE-03

9.9E-03

4.9E-02

9.9E-02

0.20

LIFETIME CANCER RISK

CombinedCancer Risk •
Kidney, NHL, Liver

I X IQ-I

High End

4.9E..()2

4.9E-03

9.9E--04

1.lE--Ol

3.8E-02

7,SE-03

3.8E-03

l.9E-03

Central Tendency

l.JE..()2

l.3E-03

2.SE-04

3.9E-03

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicates MOE < benchmark MOE. The highestPPE scenariosdisplayed are plausiblefor this exposurescenario.

929
930
Page 310 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958

MOE results for IndustrialProcessingAid utilized 12hr monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-24.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central tendency
inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure levels even when
assuming the highest plausible APF. MOEs remained below the benchmark MOE for cardiac toxicity at both dermal exposure levels even
when assuming the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assuming the highest plausible APF and glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both

inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer ·at both high-end and central tendency
inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APF and glove PF protection.

Page 311 of 691

 959

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
. IP.nntin. g an dC ODVlD
. ~
lRikE.
T abl e 4-25 0 ccupat1ona s stimat1on - Commerc1a

.

Inhalation (Monitoring)
Benchmark
Endpoint

MOE

NoPPE

APF=l0

APF = S0

Dermal (Modeling)
NoPPE

ExposureLevel Worker MOE Worker MOE Worker MOE ONUMOE

1

NoPPE
GlovePF-=5 GlovePF=lO Glove PF=20
Worker MOE WorkerMOE WorkerMOE Worker MOE

ACUTE NON-CANCER

DevelopmentalCardiacToxicity
(John§Qnet al., 2003)

10

DevelopmentalNeurotoxicity

100

S.3E-03

S.3E-02

0.26

5.3E-03

CentralTendency

0.13

1.3

6.5

High End

4.3

42.9

High End

UE-03

2.lE-02

4.IE-02

0.13

l.ZE-02

6.2E-02

0.12

2 14.7

4.3

3.2

16.2

32.4

(Fr~riksson et al.. 1993)

CentralTendency

105.9

1,058.8

5,294.1

105.9

9.7

48.6

97.1

Developmental Mortality
{Narotsk:t:et al., 1995)

High End

32.9

329.3

1,646.4

32.9

22.1

110.6

221.1

10

CentralTendency

811.8

8,117.6

40,588.2

811.8

66.3

331.7

663.4

Im.munotoxicity Response to infection
(Sehuad~and Gilmour, 201Q)

High End

2.5

24.9

124.6

2.5

2.2

10.8

21.6

30
Ce~tralTendency

61.4

614.1

3,070.6

61.4

6.5

32.S

64.9

NA2

CHRONICNON-CANCER

Liver
(Kjellstrandet al., 1983)

10

Kidney
(Maltoniet al., l 98~)

10

Neurotoxicity
..1224)
(Arito ~ ru

300

Immunotoxicity
(Keil et al., 2!!Q2)

30

ReproductiveToxicity
(Chia ~t al., 1996)

30

DevelopmentalToxicity
(Johnson!,l i!I., 2003)

10

19.0

190.2

951.0

19.0

9.1

45.5

91.1

468.9

4,689.2

23,445.9

468 .9

27.3

136.6

273.3

5.2E-02

0.52

2.6

5.2E-02

1.'TE-02

8.6E-02

0.17

Central Tendency

1.3

12.9

64.4

1.3

5.2E-02

0.26

0.52

High End

10.0

100.3

501.6

10.0

7.5

37.5

74.9

247.3

2,473.4

12,367.1

247.3

215

112.4

224 .8

6.9E-02

0.69

3.4

6.9E-02

5.SE-02

0.28

0.55

Central Tendency

1.7

17.0

85.0

1.7

0.17

0.83

1.7

High End

1.0

10.5

52.3

1.0

0.84

4.2

8.4

2S.8

251.6

1,288.2

25.8

2.5

12.6

25.2

7.7E-03

7.7E--02

0.39

7.7£..03

6.0E-03

3.0E-02

6.0E-02

0.19

t.9

9.S

0.19

l.8E-02

9.0E-02

0.18

High End
Central Tendency
High End

Central Tendency
High End

CentralTendency
High End

CentralTendency

NA 2

LIFETIMECANCERRISK

Combined CancerRisk Kidney,NHL, Liver

1 x lo-4

High End

5.4E-03

S.4E-04

1.IE-04

S.4E-03

2.lE-02

4.tE-03

2.lE-03

Central Tendency

l.7E-04

l.7E-OS

3.4E-06

1.7E-04

5.3E-03

1.lE-03

5.3E-04

Bold text/pink shadingindicatesMOE< benchmarkMOE. The highest PPE scenariosdisplayedare plausiblefor this exposurescenariounder a rigorous PPE program.
1 BPA is unable to estimateONU exposuresseparately from workers.
2 Glove PF =20 is only applicable to industrial settings (See Section2.3.1).

960

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961
962
963
964
%5
966
967
968

969
970
971
972
973
974
975

MOE results for CommercialPrinting and Copyingutilized monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-25.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE cardiac toxicity at both high-end and central tendency exposure levels via both inhalation
and dermal routes. EPA is unable-toestimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for ONUs
assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the benchmark MOE for cardiac
toxicity via inhalation and for multiple endpoints via dermal exposure at both exposure levels even when assuming the highest plausible APF
and glove PF protection.

Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via
inhalation and for all endpoints via the dermal route. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA
provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances. MOEs remained below the
97r6 benchmark MOE for cardiac toxiciiy via inhalation and for multiple endpoints via dennal exposure at both exposure levels even when
977 assuming the highest plausible APF and glove PF protection.
978
979 Cancer Risk Estimates:
980 Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
981 inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. Therefore, EPA provided risk estimates for
982 ONUs assuming ONU exposure may be as high as workers in various circumstances. Risk estimates remained above the benchmark at high•
983 end inhalation exposure but were not above the benchmark at central tendency inhalation exposure when assuming APF = 10. Risk estimates
984 remained above the benchmark at both dermal exposure levels even when assuming the highest plausible glove PF protection.
985
986 PPE Considerations
987 EPA is presentingrisk estimatesfor respiratoryprotectionup to APF = 50 as a what-if scenario,howeverEPA believesthat small commercial
988 facilitiesperformingcommercialprinting and copyingare unlikelyto have a respiratoryprotectionprogram.Therefore,the use of respiratorsis
989 unlikelyfor workers in these facilities.

990
991

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. l Uses
T able 4-26 0 ccupa fwna l R"
IS k Esti mati on- 0th er I ndustria

.

Inhalation (Monitorin&)
Endpoint

Dermal (Modeling)

Benchmark
NoPPE
APF=lO
APF=50
NoPPE
Exposure Level WorkerMOE WorkerMOE WorkerMOE ONUMOE
MOE

NoPPE
GlovePM
GlovePF 10 GlovePF=20
WorkerMOE WorkerMOE WorkerMOE WorkerMOE
0

1

ACUTE NON-CANCER

DevelopmentalCardiacToxicity
(JOh!!SOD
et al., 200J)

10

DevelopmentalNeurotox.icity
(freQ[jkssQD~ti!l...J22l )

100

DevelopmentalMortality
(N!!rotskyet al., 1W5)
ImmunotoxicityResponseto infection
(Setgrndeand Gilm2ur, 2QlQ)

IO

100

High End

4.JE-03

4.3E-02

0.21

4.JE--03

2.3E--03

1.lE--01

2.3E-Ol

4.5E-02

Central Tendency

3,0E--02

0.30

1.5

3.0E--02

6.SE-03

3.4E-02

6.SE-02

0.14

High End

3.S

34.8

173.9

3.5

1.8

8.9

17.8

35.6

CentralTendency

24.0

239.9

1,199.4

:u.o

5.3

26.7

S3.4

106.7

High End

26.7

266.6

1,333.0

26.7

12.2

60.8

121.5

243.0

Central Tendency

183.9

1,839.1

9,195.6

183.9

36.5

182.3

364.S

729.0

High End

2.0

20.2

100.8

2.0

1.2

5.9

11.9

23.8

CentralTendency

13.9

139.1

695.7

13.9

3.6

17.8

35.7

71.3

CHRONICNON-CANCER

Liver
(Kiell§trandet al., 1983)

10

Kidney
(Maltoni ct al.. 19~6)

10

Neurotoxicity
(Arito et !!I., 1994)

300

Immunotoxicity
(K!,:ilet al., 2009)

30

ReproductiveToxicity
(Chia~t al., 1996)

30

, DevelopmentalToxicity
(lohnsonet al., 200J)

10

High End

15.4

154.0

770.0

15.4

5.0

25.0

50.l

100.1

CentralTendency

106.2

1,062.4

5,311.8

106.2

15.0

75.1

150.2

300.3

4,2E--02

0.42

2.1

4.2E--02

9.5E--03

4.SE--02

9.SE-01

0.19

CentralTendency

0.29

2.9

14.6

0.29

2.9E-02

0.14

0.29

0.57

High End

8.1

81.2

406.2

8.1

4.1

20.6

41.2

82.4

CentralTendency

56.0

560.4

2,801.8

56.0

12.4

61.8

123.5

247.1

5.6E--02

0.56

2.8

5.6E--02

3.0E--02

0.15

0.30

0.61

High End

High End
CentralTendency

0.39

3.9

19.3

0.39

9.lE-02

0.46

0.91

1.8

High End

0.85

8.5

42.3

0.85

0.46

2.3

4.6

9.2

Central Tendency

5.8

58.4

291.9

5.8

J.4

6.9

13.9

27.7

High End

6.3E--03

6.3E--02

0.31

6.JE--03

3.3E--03

1.6E--02

3.3E--02

Central Tendency

4.3E-02

0.43

2.2

4.3E-02

9.9E--03

4.9E-02

9.9E-02

0.20

6.6E--0.2

LIFETIME CANCER RISK

CombinedCancer Risk Kidney, NHL, Liver

1 X (0"4

High End

6.7E--03

6.7E-04

l.3E--04

6.7E-03

3.SE-02

7.SE--03

3.8E--03

1.9E-03

CentralTendency

7.SE--04

7.SE-05

l.SE-05

7.SE--04

9.7E--03

1.9E--03

9.7E--04

4,9E--04

Bold text/pink shadingindicatesMOE < benchmarkMOE. The highestPPE scenariosdisplayedare plausible for this exposure scenario.
1 EPA is unable to estimateONU exposuresseparatelyfrom workers.

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993
994
995
996
'997
998
999
l 000

MOE results for Other Industrial Uses utilized monitoring inhalationexposure data (with dennal modeling) and are presented in Table 4-26.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalationand dermal routes. EPA is unable to estimate ONU exposures separately from workers.Therefore,EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances.MOEs remained below the benchmark MOE for cardiac
toxicity at both exposure levels via dermal and inhalationroutes even when assuming the highest plausible APF and glove PF protection.

lOOl
l 002
l 003

l 004
1005
l 006
l 007

1008
l 009
l O10
l O11
l012
l013
l 0 14

ChronicNon-Cancer Risk Estimates:
MOEs for workers were below the benchmarkMOE for multiple endpoints at both high-end and central tendency exposure levels via
inhalationand for all endpoints via the dermal route. EPA is unable to estimate ONU exposures separately from workers.Therefore,EPA
provided risk estimates for ONUs assuming ONU exposuremay be as high as workers in various circumstances.MOEs remained below the
benchmarkMOE for multiple endpointsat both exposure levels via dermal and inhalation routes even when assuming the highest plausible
APF and glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposuresseparately from workers.Therefore, EPA provided risk estimates for
ONUs assuming ONU exposure may be as high as workers in various circumstances.Risk estimates remained above the benchmark at highend inhalation exposurebut were not above the benchmark at central tendency inhalation exposure when assuming APF = 10. Risk estimates
remainedabove the benchmarkat both dermal exposure levels even when assuming the highest plausible glove PF .

1015

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.
- P. rocess SoIvent R ecycrmi an d Wor ker Han dlin j!O f W astes
Table 4-27. Occupational RiskE stimat1on
Inhalation (Monitoring)

Benchmark
Endpoint

MOE

Exposure Level

NoPPE
APF=l0
APF=50
Worker MOE WorkerMOE Worker MOE

Dermal (Modeling)
NoPPE
NoPPE
GlovePF==5 GlovePF=l0
GlovePF=20
ONU MOE 1 Worker MOE Worker MOE Worker MOE Worker MOE

ACUTE NON-CANCER

DevelopmentalCardiac Toxicity
(JQhn~n et al., 2003)
Developmental•Neurotoxicity
(Fredriksson ~ JW-3)

,t ..

9.7E-02

0.49

9.7E-03

l.3E-03

l.lE-02

2.3E-02

4.5E-Ol

Central Tendency

22.4

224.3

l, 121.3

22.4

6.SE-03

3.4E-02

6.8E-02

0.14

High End

7.9

78.9

394.7

7.9

1.8

8.9

17.8

35.6

18,182.7

181,827.5

909,137.3

18,182.7

5.3

26.7

53.4

106.7

60.5

605.3

3,026.3

60.5

12.2

60.8

121.S

243.0

139,401.1

1,394,010.5

6,970,052.6

139,401.1

36.5

182.3

364.S

729.0

4.6

45.8

228.9

4.6

1.2

5.9

11.9

23.8

10,546.0

105,459.9

527,299.6

10,546.0

3.6

17.8

35.7

71.3

100
Central Tendency
High End

Developmental10

Mortality

(l':!arotsk): et al.,

9.7E-03

High End
10

122~
)

Imrnunotoxicity· Response to infection
(S~lgradeand Gilmoyi:,2010)

Central Tendency
High End

100

Central Tendency

CHRONICNON-CANCER

Liver
(Kiflllstrandet al.. 1983)

10

Kidney
(Malto!!iet al., 1986)

10

Neurotoxicity
(Arit2 !,~H!I., 1994)

300

Immunotoxicity
(Keil ct al., 2009)

30

ReproductiveToxicity
(~bill ~ al,. 199~

DevelopmentalToxicity
(John~n et al., 2003)

30

10

35.0

349.6

1,748.2

35.0

5.0

25.0

50.1

100.1

Central Tendency

80,525.3

805,253.2

4,026,266.0

80,525.3

15.0

75.1

150.2

300.3

High End

9.6E-02

0.96

4.8

9.6E-02

9.SE--03

4.8E-Ol

9.SE-02

0.19

221.2

2,212.2

11,061.2

221.2

2.9E-02

0.14

0.l9

0.57

18.4

184.4

922.1

18.4

4.1

20.6

41.2

82.4

42,474.9

424,748.9

2,123,744.7

42,474.9

12.4

61.8

123.5

247.1

High End

0.13

1.3

6.3

0.13

3.0E-02

O.lS

0.30

0.61

Central Tendency

292.0

2,920.1

14,600.7

292.0

9.lE-02

0.46

0.91

1.8

1.9

19.2

96.1

1.9

0.46

2.3

4.6

9.l

CentralTendency

4,424.5

44,244.7

221,223.4

4,424.5

1.4

6.9

13.9

27.7

High End

UE-02

0.14

0.71

l.4E-02

3.3E-03

J.6E-02

3.3E-02

6.6E-Ol

32.7

327.4

1,637.1

32.7

9.9E-03

4.9E-02

9.9E-02

0.20

High End

Central Tendency
High End
Central Tendency

High End

, Central Tendency

LIFETIMECANCER RISK
'

Combined CancerRisk Kidney, NHL, Liver

1 x lo-4

High End

2.9E-03

2.9E-04

5.9E-0S

2.9.E-03

3.8E-02

7.SE-03

3.SE-03

l.9E-03

Central Tendency

9.9E-07

9.9E-08

2.0E-08

9.9E-07

9.7E-03

1.9E-03

9.7E-04

4.9E-04

Bold text/pink shading indicates MOE < benchmarkMOE. The highest PPE scenariosdisplayedare plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
l017
l018
l 0 19
l020
l 021
1022
l 023
l 024
l 025
l 026
l 027
l028
l 029
l 030
l 031
l 032
l 033
t 034
l 035

MOE results for Process Solvent Recyclingand WorkerHandlingof Wastesutilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-27.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE
for cardiac toxicity at high-end inhalation exposw-eand at both dermal exposure levels even when assuming the highest plausible APF and
glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances.
MOEs were below the benchmark MOE for multiple endpoints at both dermal exposw-elevels. MOEs remained below the benchmark MOE
for multiple endpoints at high-end inhalation exposure and at both dermal exposure levels even when assuming the highest plausible APF and
glove PF protection.

1036

1037
1038
l039
L040
l 041
l042
1043
1044
1045
1046
1047
l048

Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at at high-end inhalation exposures, but risk estimates were above
the benchmark MOE for cancer at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from workers.
Therefore, EPA provided risk estimates for ONUs assuming ONU exposure may be as high as workers in various circumstances. Risk
estimates were not above the benchmark at central tendency inhalation exposure when assuming APF = 50. Risk estimates remained above
the benchmark at both dermal exposure levels even when assuming the highest plausible glove PF protection.

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049
050
051
052
053
054
055
056
057
058
059

060
061
062
063
064

065
066

067
068

069

4.2.3 Risk Estimation for Consume! Exposures by Exposure Scenario
Risk estimates via inhalation and dermal routes are provided below for consumers and bystanders
following acute exposure. Risk estimates were presented for differing exposure assumptions,
categorized as high, moderate, or low intensity users based on variation in weight fraction, mass of
product used, and duration of use/exposure duration. Risk estimates primarily utilized central tendency
values for other modeling parameters (e.g., room volume, air exchange rate, building volume) and
therefore do not necessarily represent an upper bound of possible exposures. See Section 2.3.2.6.1 for
more details on the characterization of consumer exposure and [CEM Modeling Results and Risk
Estimates. Docket# EPA-HQ-OPPT-2019-0500] for MOE estimates of all modeled scenarios.
As discussed in Section 2.3 .2.2, EPA is unable to develop risk estimates for chronic exposure scenarios
or cancer based on repeated, intermittent human exposures because available toxicological data is based

on either single, acute exposure or continuous, chronic exposures. It is therefore unknown whether there
is any risk for chronic non-cancer or cancer associated with regular, intermittent exposures at the very
high end of use frequencyldentified chronic non-cancer and cancer hazard endpoints (Section 3.2) are
unlikely to present for these populations, however they cannot be ruled out. Therefore, while certain
conswners at the high-end frequency of use may potentially be at risk for chronic hazard effects, the
effects of this intermittent exposure is unknown and based on reasonably available infonnation EPA is
unable to develop risk estimates for this population.For the vast majority of the consumer population
which are only exposed through short-term, occasionaluse ofTCE products, only acute effects are
relevant

070
071
072
073
074
075
076

077
078
079
080
081
082
083
084

085
086
087
088

089
090
091

092
093
094

095
096
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 097

098

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-28. ConsumerRisk Estimation- Solventsfor Cleaningand Degreasing - Brake and Parts
Cleaner
Benchmark
10

Scenario

Consumer
Receptor

100

10

30
Acute
DevelopmentalEffects DevelopmentalEffects
Developmental Effects
Immunotoxicity
Congenital
Developmental
Increased Resorptions
Response to Infection
HeartDefects
Neurotoxicity
(Narotsky et aL, 1995) (Selgrade andGilmour,
(lobnson et al., 2003) (f redriks~n et al., 1993)
201Q)

InhalationExposure
HighIntensity
User
ModerateIntensity
User
LowIntensity
User

User

6.4E-OS

S.2E-02

0.40

3.SE-02

Bystander

2.2E-04

l.8E-Ol

1.4

0.14

User

4.IE-04

0.33

2.5

0.21

Bystander

l.6E-03

1.3

10

0.94

User

S.lE-03

4.2

32

2.7

Bystander

2.0E-02

17

127

12

Dermal Exposure
6.SE-05

S.4E-02

0.37

3.6E-02

Youth (16-20 years)

7.3E-0S

S.7E-02

0.39

3.SE-02

Youth (11-15 years)

6.7E-0S

S.3E-02

0.36

3.SE-02

Adult ~21 years)

9.lE-04

0.72

4.9

0.48

Youth {16-20years)

9.7E-04

0.77

5.2

O.Sl

Youth {11-15years)

8.9E-04

0.70

4.8

0.47

Adult (~21 years)

4.lE-02

32

220

22

Youth (16-20 years)

4.4E-02

34

235

23

Youth (11-15 years)

4.0E-02

32

215

ll

Adult ~l
HighIntensity
User

years)

Moderate -

Intensity
User

Low-

Intensity
User

099
100

101
102
103
104

105
106

MOE results for Brake and Parts Cleanerare presented in Table 4-28.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dermal MO.Eswere below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensityuser inhalation exposure
levels.

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 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
107
108

Table 4-29. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol
Electromc
. D•e2:re
aser/Cleaner
Benchmark
10
Scenario

Consumer
Receptor

100

10

30

Acute
DevelopmentalEffed s DevelopmentalEffects
DevelopmentalEffects
Immunotoxicity
Congenital
Developmental
Increased Resorptions Response to Infection
HeartDefects
Neurotoxicity
(Narotsky
et al..1995) <SeJ......r1.. and Gilmour
et al. 1993
(Johnson et al.1 200J ) (Fr..t1n1rc:con
2010 )

Inhalation Exposure
HighIntensity
User

User

9.8E-0S

8.0E-02

0.61

5.0E-02

Bystander

4.9E-04

0.40

3.0

0.28

ModerateIntensity
User

User

2.3E-03

1.9

15

1.2

Bystande
r

1.JE-02

10

78

7.1

Low-

User

6.7.E-02

54

414

33

Intensity
User

Bystander

0.34

277

2 123

193

I

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

109
110

MOE results for Aerosol Electronic Degreaser/Cleanerare presented in Table 4-29.

111
112
113
114
115
116

MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for congenital heart defects at high, medium, and low-intensity user
inhalation exposure levels and for multiple endpoints at high and medium-intensity exposure levels.

117
118
119
120
121
122
123
124

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Table 4-30. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
Del!l'ease~/Cl eaner
Benchmark
10
100
10
30
Scenario

Consumer
Receptor

DevelopmentalEffects
Congenital
Heart Defects
(Johnsonet al.• 2003)

DevelopmentalEffects
Acute
Developmental
DevelopmentalEffects
Immunoto:xicity
Neurotoxicity
Increased Reso:rptions Response to Infection
(fredriksson et al,
(Narotsky et al., 1995) (Selgrade and Gilmour,
2010)

1993\

Inhalation Exposure
HighIntensity
User

User

1.0E-04

8.3E-02

0.64

S.lE-02

Bystander

S.lE-04

0.41

3.2

0.29

ModerateIntensity
User

User

l.6E-03

1.3

9.9

0.79

Bystander

8.SE-03

6.9

53

4.8

Low-

User

2.lE-02

17

132

11

Intensity
User

Bystander

0.11

88

674

61

Dermal Exposure
Adult ~ 21 years)

1.2E-04

9.SE-03

0.65

6.4E-02

Youth (16-20years)

1.JE-04

0.10

0.70

6.SE-02

Youth (11-15 years)

J.2E-04

9.JE-02

0.64

6.lE-02

Adult ~1 years)
ModerateIntensity Youth {16-20years)
User
Youth {11-15years)

l.SE-03

1.4

9.7

9.SE-01

l.9E-03

1.5

10

1.0

l.SE-03

1.4

9.6

9.4E-01

7.JE-03

5.7

39

3.8

7.SE-03

6.1

42

4. 1

7.lE-03

5.6

38

3.7

HighIntensity
User

Adult ~21 years)
LowIntensity Youth (16-20 years)
User
Youth (11-15 years)

127
128
129
130
131
132
133

134
135

I

I

MOE results for Liquid Electronic Degreaser/Cleaner are presented in Table 4-30 .
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels .

Page 321 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
136
137

Table 4-31. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
Dee.r easer /Cl eaner
Benchmark
10
Scenario

Consumer
Receptor

100

Developmental Effects
Congenital

Heart Defects
(Joh9son ~ al, 2003 )

I

10

3G

DevelopmentalEffects

Acute
Immunotoxicity

Developmental Effects
Developmental
Neurotox icity
Q:redriksson ~t al,.
1993)

Increased Resorptions
(t:!arotsfil'.et al., 1995)

Respon se to In fection
(Selgrad~ and QilmQm:,

2010)

Inhalation Exposu re
HighIntensity
User

User

2. 3E-0S

1.8E-02

0.14

t .lE-02

Bystan der

7.9E-OS

6.4E-02

0.49

4.9E-02

Moderate -

Use r

9.0E-05

7.3E-02

0.56

4.6E-02

User

Bystande r

3.6E-04

0.29

2.2

0.21

Low-

User

6.0E-04

0.48

3.7

0.31

Intensity
User

Bystander

2.SE-03

2.0

15

1.4

Intensity

I
!

Dermal Exposure
High Intensity
User

ModerateIntens ity
User

Adu lt ~21 years)

7.3E-05

S.7E-0'.2

0.39

3.SE-02

Youth (l6-20year s)

7.8E-OS

6.l E-02

0.42

4.lE-02

Youth (11 -15 years)

7.lE-05

S.6E-O2

0.38

3.7E-02

Adult ~21 years)

S.SE-04

0.46

3.1

0.31

Youth (16--20years)

6.2E-04

8.49

3.3

0.33

Youth (11-15 years)

5.7E-04

0.45

3.1

0.30

Adult (~ 1 years)

2.9E-03

2.3

16

1.S

Youth (16-20 years)

3.tE-03

2.4

17

1.6

Youth (1 1-15 years)

2.8E-03

2.2

15

1.5

Low-

Intensity
User

138
139
140
141
142
143
144
145
146

MOE results for Aerosol Spray Degreaser/Cleaner are presented in Table 4-31 .
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Denna! MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels.

Page 322 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
147
148

Table 4-32. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid
lD1e2reaser:
. /Cleaner
Benchmark
10

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunoto:xlclty
Congenital
Neurotoxicity
IncreasedResorptions Response to Infection
HeartDefects
(I:redriksson
et
Al-.
(Nm:otsky
~ ft.1.,
I 22~ (Selgrnd~~ Gilmour,
(Johnson et al., 2003)
1993)
WQ )

Inhalation Exposure
I

High-

User

2.SE-05

2.0E-02

0.16

1.3E-02

Intensity
User

Bystander

1.0E-04

8.3E-02

0.64

6.lE-02

User

2.4E-04

0.19

1.5

0.12

Bystander

l .2E-03

1.0

7.8

0.70

User

1.4E-03

1.2

8.8

0.71

Bystander

7.6E-03

6.2

47

4.3

ModerateIntensity
User
LowIntensity
User

Dermal Exposure
Adult~!

years)

3.0E-05

2.4E-02

0.16

l.6E-02

Youth (16-20 years)

3.2E-OS

2.6E-02

0.17

1.7E-02

Youth (I 1-15 years)

3.0E-05

2.3E-02

0.16

1.6E-02

Adult ~ 1 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

2.4E-04

0.19

1.3

0.13

2.6E-04

0.20

1.4

0.14

2.4E-04

0.19

1.3

0.13

1.SE-03

1.4

9.8

0.96

Youth (16-20 years)

1.9E-03

1.5

10

1.0

Youth (11-15 years)

1.8E-03

1.4

9.6

0.94

High-

Intensity
User

Adult ~l
LowIntensity
User

years)

149

150
151
152
153
154

155
156
157

MOE results for Liquid Degreaser/Clea ner are presen ted in Table 4-32.
MOEs for consumer user s were below the benchmark MOE for multiple endpoints at high, medium , and
low-intens ity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MO Es for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation expos ure
levels .

Page 323 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table 4-33. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Gun
Scrubber
-

158
159

Benchmark
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
lmmunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Response to Infection
Heart Defects
(Narotsfl'. et al, 199~) ~elpde and Qilmgur.
(t~drik~SO!l et fil:.
(Johnson ~l ill., 200J)
1993)
2010 )

100

10

Scenario

Consumer
Receptor

Inhalation Exposure
HighIntensity
User
I

Moderatelntensity
User

User

S.0E-02

40

309

26

Bystander

0.20

) 64

1255

120

User

4.7E-02

38

294

24

Bystander

0.25

202

1551

141

--

LowIntensity
User

User

8.lE-02

66

506

41

Bystander

0.44

354

2715

247

Der mal Exposure
Adult ~1 years)

7.5E-0S

S.9E-02

0.41

4.0E-02

Youth (I 6-20 years)

8.I E-05

6.4E-02

0.43

4.lE-02

Youth (11-15 years)

7.4E-05

S.SE-02

0.40

3.9E-02

Adult ~1 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

6.0E-04

0.48

3.2

0.32

6.4E-0 4

0.51

3.5

0.34

S.9E-04

0.46

3.2

0.31

7.SE-03

S.9

41

4.0

Youth (16-20 years)

8.0E-03

6.3

43

4.2

Youth (11-15 years)

7.3E-03

5.8

40

3.9

HighIntensity

User

Adult ~1

LowIntensity
User

years)

160
161
162

MOE results for Aerosol Gun Scrubber are presented in Table 4-33.

163

MOEs for consumer users were below the benchmark MOE for multip le endpoints at high, medium, and

164

low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the

165

benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for congenital heart defects at high, medium, and low-intens ity user inhalation

166
167

exposure levels.

Page 324 of 691

 168

169

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-34. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Gun
Scrubber
·
Benchmark
100
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions
to Infection
Response
Heart Defects
Cf'redrikssonet al.,
(N aro!§Jo'.
~t !ll., 1995) (Selgradeand Gilmour,
Us!bnsonet al., 2003 )
1993)
2010 )
10

Scenario

Consu1Rer
Receptor

Inhalation Exposure
HighIntensity
User

User

5.SE-02

47

361

30

Bystander

0.24

191

1465

140

ModerateIntensity
User

User

5.SE-02

45

343

28

Bystander

0.29

236

1809

164

Low-

User

S.9E-02

48

370

30

Bystander

0.30

247

1893

172

Intensity

User

Dermal Exposure
Adult (221 years)

3.3E-0S

2.6E-02

0.18

l.7E-02

3.SE-0S

2.7E-02

0.19

1.SE-02

3.lE-05

2.SE-02

0.17

1.7E-02

2.6E-04

0.21

1.4

0.14

2.8E-04

0.22

1.5

0.15

2.SE-04

0.20

1.4

0.13

l.9E-03

1.5

10

1.0

2.tE-03

1.6

11

1.1

l.9E-03

1.5

10

1.0

High-

Intensity Youth (16-20 years)
User
Youth (11-15 years)
Adult (221 years)
ModerateIntensity Yo·uth (16-20 years)
User
Youth (11-15 years)
Adult (221 years)
Low-

Intensity Youth (16-20 years)
User
Youth (11-15 years)

170
171
172
173
174
175
176
177
178

MOE results for Liquid Gun Scrubber are presented in Table 4-34.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high , medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dennal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for congenital heart defects at high, medium, and low-intensity user inhalation
exposure levels.

179
180
Page 325 of 691

I

 181

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
·
. - Solvents tior Cleanm2
. an d D eueasm. g - M 0 ldR eIease
T abl e 4-35 C onsumer Risk Estunatmn

.

Benchmark

10
I

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
lmmunotoxicity
Congenital
Neurotoxicity
IncreasedResorptions Responseto Infection
Heart Defects
{Eredrikssonet !IL
(Narotsk;yet al., 1995) (Selgmd!:DndQilmour,
(J2hn~n ~t 1.1I.,
io03 )

1993)

2010 )

Inhalation Exposure
HighIntensity
User

User

2.3E-04

0.18

1.4

0.11

Bystander

l.l.E-03

0.91

7.0

0.64

ModerateIntensity
User

User

2.lE-03

1.7

13

1.1

Bystander

J.JE-02

9.2

71

6.4

Low-

User

2.JE-02

17

130

11

Intensity
User

Bystander

0.11

87

667

61

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

182
183
184
185
186
187
188
189
190

MOE results for Mold Release are presented in Table 4-35.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystander s
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.

Page 326 of 691

 191
192

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table 4-36. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Tire
Cleaner
Benchmark
10

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
lmmunotoxicity
Congenital
Neurotoxic:ity
Increased Resorptions
Response to Infection
Heart Defects
(Narotsky et al., I 99~
(Fredrikssonet al.,
(Selgrade
and Gilmour*
(Johnson et al., 2003)
1993)
2010 )

Inhalation Exposure
I

HighIntensity

User

2.4E-04

0.19

1.S

0.13

User

Bystander

S.4E-04

0.44

3.4

0.32

Moderate-

User

8.9E-04

0.72

5.5

0.46

User

Bystander

3.6E-03

2.9

22

2.0

LowIntensity
User

User

6.4E-03

5.2

40

3.3

Bystander

2.6E-02

21

164

15

Intensity

-

-

Dermal Exposure
HighIntensity

Adult P-21 years)

3.3E-04

0.26

1.8

0.17

Youth (16-20 years)

3.SE-04

0.28

1.9

0.19

Youth (11-15 years)

3.lE-04

0.26

1.7

0.17

1.3E-03

1.0

7.1

0.70

1.4E-03

1.1

7.6

0.74

l.3E-03

1.0

6.9

0.68

Adult (~21 years)

5.7E-03

4.5

31

3.0

Youth (16-20 years)

6.0E-03

4.8

33

3.2

Youth (I 1-15 years)

5.SE-03

4.4

30

2.9

User

Adult P-21 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

LowIntensity
User

193
194

MOE results for Aerosol Tire Cleaner are presen ted in Table 4-36.

195
196
197
198
199
200
201

MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-inte nsity expos ure levels via both inhalation and dermal routes. Dermal MOE s were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels.

Page 327 of 691

I

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
202
203

Table 4..37, Consumer Risk Estimation • Solvents for Cleaning and Degreasing- Liquid Tire
Cleaner
Benchmark
10

Scenario

Consumer
Receptor

100

10
30
Developmental Effects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions
Response to Infection
Heart Defects
(Ere~~~Qn ~ ru,
.• (Naro~~ ~ BL 199~) {Selgradeand GilmQur.
(Johnson et al., 2QQ3)
2010 )
1993)

lnha)ation Exposure
High-

User

7.8E-0S

6.3E-02

0.48

4.2E-02

Intensity
User

Bystander

2.4E-04

0.20

1.5

0.14

User

4.0E -04

0.32

2.S

0.21

Bystander

1.6E-03

t.J

9.9

0.92

User

2.0E-03

1.6

12

1.0

Bystander

8.3E-03

6.7

51

4.7

ModerateIntensity
User
LowIntensity
User

Dermal Exposure
Adult~l

S.9E-05

4.7&-02

0.32

3.lE-02

Youth (16-20 years)

6.3E-05

S.0E-02

0.34

3.3:&-02

Youth (11-15 years)

S.8E-05

4.6E-02

0.31

3.0E-02

Adult ~21 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

2.4E-0 4

0.19

1.3

0.12

2.SE-04

0.20

1.4

0.13

2.3E-04

0.18

1.2

0.12

7.1.E-04

0.56

3.8

0.37

Youth (16-20 years)

7.6E-04

0.60

4.1

0.40

Youth (ll-15 years)

6.9E -04

0.55

3.7

0.37

years)

HighIntensity

User

Adult ~1
LowIntensity
User

204
205
206
207
208
209
210
211
212

years)

MOE results for Liquid Tire Cleanerare presented in Table 4-37.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels.

Page 328 of 691

 213

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
T abl e 4-38 Consumer Risk ESflDla ti on- Lu bncan
. ts and Greases- Tapan d n·1eFl Ul'd

.

Benchmark
100
10
30
DevelopmentalEffects
Acute
DevelopmeatalEffects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Responseto Infection
Heart Defects
(Fredriksson~ §l,.
W:arotskx
~ 111.,
l 99~ (Selgradeand Gilmour,
(Jghnsonet al., 2003)
20)0)
1993)
10

Scenario

Consumer
Receptor

Inhalation Exposure
High-

User

2.SE-04

0.20

1.6

0.13

Intensity
User

Bystander

l.3E -03

1.0

7.8

0.71

User

2.4E-03

1.9

15

1.2

Bystander

l.3E-02

10

79

7.1

User

1.3E-02

II

83

6.8

Bystander

4.3E-0l

35

270

28

ModerateIntensity
User
LowIntensity
User

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

214
215
216
217
218
219
220
221

MOE results for Tap and Die Fluid are presented in Table 4-38.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium , and
low-intensity inhalation exposure levels. Dermal exposure was not quantified . MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels .

Page 329 of 691

 -

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

.

T abl e 4 39 C onsumer RikEf
s s nna fion - Lb'
u racan ts an dG reases - Penet ra tin2 Lb'
u r1cant
Benchmark

222

10

Scenario

Consumer
Receptor

100

10

30

Developmental Effects
Acute
Developmental Effects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Response to Infection
Heart Defects
<Fredrik§§Qnet al.,
(Narotskl'.et al., 1995) (Selg;mdeand Qilm2ur.
2003)
(Johnson et ru.,
1993)
2010)

Inhalation Exposure
Hi_ghIntensity
User

User

3.2E -04

0.26

2.0

0.16

Bystander

I.6E-03

1.3

9.8

0.89

ModerateIntensity
User

User

5.4E-03

4.4

33

2.7

Bystander

2.9E-02

23

179

16

Low-

User

0.17

139

1065

86

Intensity
User

Bystander

0.88

712

5460

496

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.
223
224
225
226
227
228
229
230
231

MOE results for Penetrating Lubricant are presented in Table 4-39 .
MO Es for consumer users were below the benchmark MOE for congenital heart defects at high,
medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and medium intensity exposure levels. Dermal exposure was not quantified. MOEs for bystanders were below the
benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
defects at all user intensity inhalation exposure levels.

Page 330 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
232
233

Table 4-40. Consumer Risk Estimation - Adhesives and Sealants - Solvent-Based Adhesive and
Sealant
Benebmark
100
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions
Response to Infection
Heart Defects
10

Consumer
Receptor

Scenario

(Johnson et al., 2003)

(!:redrikssonet al.,

(Narotsk~ ~t al., 1995)

(Selgmde and GilmQur,
2010 )

1993)

Inhalation Exoosure
HighIntensity
User

1.l&-04

9.3E-02

0.71

S.6E-02

Bystander

9.lE-04

0.74

5.7

0.52

3.7E-03

3.0

23

1.8

Bystander

3.6E-02

29

223

20

User

0.42

340

2604

207

Bystander

2.8

2300

17636

)602

· User

ModerateIntensity
User
LowIntensity
User

User

I

Dermal ,e~posures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

234
235
236
237
238
239
240
241
242

MOE results for Solvent-Based Adhesive and Sealant are presented in Table 4-40.
MOEs for consumer users were below the benchmark MOE for congenital heart defects at high,
medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and mediumintensity exposure levels. Dermal exposure was not quantified. MOEs for bystanders were below the
benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
defects at all user intensity inhalation exposw-e levels.

Page 331 of691

 INTERAGENCY DRAFT - 00 NOT CITE OR QUOTE
'
l t - Mir ror Edli?e S ea1ant
and Seaans
T abl e 4-41 C onsumer Risk E sfIDla f10n • Adh es1ves

.

243

Benchmark
10

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
Developmental Effects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions
Response to Infection
Heart Defects
(Eredrik.s~onet !!l,
(NM:Qtifil'.et al,. 1992) (Seluade and Qilmour .
(John:1Qnet1I-, 20Ql)
1993)
2010\

Inhalation Exposure
High-

User

J.JE-03

0.90

6.9

0.57

Intensity
User

Bystander

4.7:E-03

3.8

29

2.7

ModerateIntensity
User

User

3.JE-03

2.7

21

1.7

Bystander

l.8E -02

15

114

10

Low-

User

0.17

134

1028

83

Intensity
User

Bystander

0.91

737

5651

513

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

244
245
246
247
248

249
250
251
252

MOE results for Mirror Edge Sealant are presented in Table 4-41.
MOEs for consumer users were below the benchmark MOE for congenital heart defects at high,
medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and medium.intensity exposure levels. Denna! exposure was not quantified. MOEs for bystanders were below the
benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
defects at all user intensity inhalation exposure levels.

Page 332 of 691

 253

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
. an d SeaIanst T"
T abl e 4-42 C onsumer Risk Estima tion- Adhes1ves
ire Repa1r Cement /SIea er

.

Benchmark
100
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunotoxkity
Congenital
Neurotoxicity
Increased Resorptions
Response to Infection
Heart Defects
(Fredrikssonet al.,
(liarotsky et al., 1995) (SS!lmde and Gilmour,
(Johnson et al., 2003)
1993)
201Q)
10

Scenario

Consomer
Receptor

Inhalation Exposure
HighIntensity
User
ModerateIntensity
User

User

3.lE-04

0.25

1.9

0.17

Bystander

9.7E-04

0.79

6.1

0.57

User

S.6E-03

4.S

35

2.9

Bystander

2.JE-02

18

141

13

Low-

User

6.2E-02

so

385

Intensity
User

Bystander

0.23

188

1444

32
133
-

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

254
255
256
257
258
259

260
261
262

MOE results for Tire Repair Cement/Sealer are presented in Table 4-42.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints for high and medium-intensity users and for
congenital heart defects at all user intensity inhalation exposure levels.

Page 333 of 691

--

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
.
. - Cleanm2
. and Furn1
·t ure C are Pro ducts Carnet Cleaner
Tabl e 4-43 Consumer Risk Estimatlon

.

263

-

Benchmark
10

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
Developmental Effects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Response to Infection
Heart Defects
(t:!!![Qtskvs:till,, l 92~) (Selgmde ~ Gibnow;.
(l:redriksson et ~1.,
(J2hnson et al,,200~)
2010 )
1993)

Inhalation Exposure

I

High-

User

7.0E-05

5.7E-02

0.44

3.6E-02

Intensity
User

Bystander

3.2E-04

0.26

2.0

0.18

ModerateIntensity
User

User

5.8E-04

0.47

3.6

0.29

Bystander

2.9E-03

2.4

18

1.7

Low-

User

3.4E-03

2.7

21

1.7

Intensity
User

Bystander

l.6E- 02

13

99

9.0

Dermal Exposure
HighIntensity
User
I

Adult ~ l years)

1.l E-04

8.8E-02

0.60

S.9E-02

Youth (16-20 years)

1.2E-04

9.4E-02

0.64

6.3E-02

Youth (I 1-15 years)

l.tE-04

8.6E-02

0.59

S.7E-02

6.7E-04

0.53

3.6

0.35

7.tE-04

0.56

3.8

0.38

6.6E-04

0.52

3.5

0.35

Adult (~1 years)

1.3E-02

II

72

7.1

Youth (16-20 years)

t. 4E-02

11

77

7.5

Youth (11-15 years)

1.3E-02

10

70

6.9

Adult ~1 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

Low-

Intensity
User

264
265
266
267
268
269
270
271
272

MOE results for Carpet Cleaner are presented in Table 4-43.
MOEs for consumer users were below the benchmark MOE for multiple endpoint-; at high, medium, and
low-intensity exposure levels via both inhalation and dennal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels.

Page 334 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
273

274

Table 4-44. Consumer Risk Estimation - Cleaning and Furniture Care Products - Aerosol Spot
Remover
Benchmark
10

Scenario

Consumer
Receptor

100

10

30

Acute
DevelopmentalEffects DevelopmentalEffects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
IncreasedResorptions Response to Infection
HeartDefects
(Fredriksson~t al..
(Narotskv ~ gl,. 1995) ~lgrade and Gilmour.
(Johnson et al., 2003)
1993)
201Q
}

InhalationExposure
HighIntensity

User

1.lE-04

9.3E-02

0.71

5.6E-Ol

User

Bystander

1.1E-03

0.87

6.7

0.61

ModerateIntensity

User

9.8:£..04

0.79

6.1

0.47

User

Bystander

9.9E-03

8.0

61

S.6

LowIntensity
User

User

6.SE-03

S.3

41

3.2

Bystander

S.4E-02

43

333

30

DermalExposure
9.4E-04

0.74

5.1

0.50

Youth (16-20 years)

1.0E-03

0.79

5.4

0.53

Youth ( 11-15 years)

9.2E-04

0.73

5.0

0.49

S.7E-03

4.S

31

3.0

Youth (16-20 years)

6.0E-03

4.8

33

3.2

Youth (11-15 years)

5.SE-03

4.4

30

2.9

5.7E-02

4S

305

30

Youth ( 16-20 years)

6.0E-02

48

325

32

Youth (11-15 years)

S.SE-02

44

297

29

Adult ~I
HighIntensity
User

Adult ~1
ModerateIntensity
User

Adult ~l

years)

years)

years)

Low-

Intensity
User

275
276
277
278

279
280
281
282
283

MOE results for Aerosol Spot Remover are presented in Table 4-44.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium , and
low-intensity exposure levels via both inhalation and dennal routes. Dennal MOEs were below the
benchmark MOE for multiple endpoin~sand all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
levels .

Page 335 of691

I

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
284
285

Table 4-45. Consumer Risk Estimation - Cleaning and Furniture Care Products - Liquid Spot
Remover
Benchmark
JOO
10
30
DevelopmentalEffects
Acute
Developmental Effects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Response to Infection
Heart Defects
<Narotskyet al.. 1995) (Selgrade and Gilmour.
<Fredrikssonet al..
(Jg!l!Mon~ al., 200:!)
1993)
2010 )
10

Scenario

Consumer
Receptor

I

Inhalation Exposure
High-

User

9.3E-05

7.SE-02

0.58

4.7E-02

Intensity
User

Bystander

4.6E-04

0.37

2.9

0.26

ModerateIntensity
User

User

7.SE-04

0.63

4.9

0.39

Bystander

4.lE-03

3.4

26

2.4

Low-

User

6.8&-03

5.5

42

3.4

Intensity
User

Bystander

3.4E-Ol

28

214

19

Dermal Exposure
Adult (2'..21years)

J.6E-04

0.13

0.87

8.SE-02

Youth (16-20 years)

1.7E-04

0.14

0.93

9.lE-02

Youth (11-15 years)

t.6E-04

0.13

0.85

8.4E-02

Adult (2:21years)

9.8E-04

0.77

5.3

0.51

1.0E-03

0.82

S.6

0.55

9.SE-04

0.75

5.1

0.50

Adult ~21 years)

1.SE-02

12

79

7.7

Youth (16-20 years)

1.6E-02

12

84

8.2

Youth (11-15 years)

1.4E-02

11

77

7.S

High-

I

Intensity
User

ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

Low-

Intensity
User

286
287
288
289
290
291
292
293
294

MOE results for Liquid Spot Remover are presented in Table 4-45.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dennal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints at high, mediwn, and low-intensityuser inhalation exposure
levels.

Page 336 of 691

 295
296

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table 4-46. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and
Fin'1sb'm i Sipra y Coatm ~s
Benchmark
10

Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
DevelopmentalEffects
DevelopmentalEffects
Developmental
Immunotoi:icity
Congenital
Neurotoxicity
increased Resorptions
Response to Infection
Heart Defects
(l:redrikssonet al.,
{N arots)&yet al., 1995) (Selgradeand Qilmour,
Qobnson et al., 20Q3)
1993)
2010)

Inhalation Exposure
HighIntensity
User

User

4.0E-04

0.32

2.5

0.20

Bystander

1.6E-03

1.3

10

0.92

ModerateIntensity
User

User

2.SE-03

2.0

15

1.2

Bystander

l.3:E-02

11

83

7.6

Low-

User

l.3E-02

10

79

6.4

Intensity
User

Bystander

6.s:E-02

S3

407

37

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

297
298
299
300
301
302
303
304

MOE results for Fixatives and Finishing Spray Coatingsare presented in Table 4-46.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.

Page 337 of 691

 305

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
.
.
A,ppare I and F 00 twear C are P ro ducts - Shoe Pr0 ISh
T abl e 4-47 C onsumer RiskE stamation-

.

Benchmark

10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions
Response to Infection
Heart Defects
(N;y:otslfyet al., 199~) (S~lgrade imd GiLtrumr..
(F1:edrik~iQn
~t al.,
(J2hnson et ;u.,200l )
1993)
2010\
10

Scenario

Consumer
Receptor

100

Inhalation Expo sure
HighIntensity
User
ModerateIntensity
User
LowIntensity
User

User

1.IE-03

0.89

6.8

0.55

Bystander

5.SE-03

4.4

34

3.1

User

1.lE-02

8.8

67

5.4

Bystander

S.9E-02

48

366

33

User

6.2E-02

50

386

31

Bystander

3.2E-0I

258

1977

180

-

Dermal Exposure
Adult (?:21 years)

1.7E-03

1.4

9.3

0.91

Intensity Youth (16-20 years)
User
Youth (l 1-15 years)

1.SE-03

1.45

9.9

0.97

1.7E-03

1.3

9.1

0.89

Adult ~1 years)
ModerateIntensity Youth (16-20 years)
User
Youth (11-15 years)

1.0E-02

8.l

56

s.s

1.lE -02

8.7

60

5.8

1.0.E-02

8.0

54

5.3

Adult ~ 1 years)

0.10

82

560

55

Youth (16-20 years)

0.11

87

596

58

Youth (I 1-15 years)

0.10

80

545

53

High-

LowIntensity
User

306
307
308
309
310
311
312
313
314

MOE results for Shoe Polish are presented in Table 4-47.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
defects at all user intensity inhalation exposure levels.

Page 338 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

.

T a'bl e 4-48 Consumer Ris kE s f1ma f100- 0th er Consumer Uses - Fa brte
. Sipray

315

Benchmark
100
IO
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Response to Infection
Heart Defects
(.Eredriksson et al,
(Narotskv et al., 122~ (Sel2tade and Qilmour,
(JohnsQnet al., 2003)
1993)
201Q)
10

Scenario

Consumer
Receptor

Inhalation Exposure
I

High-

User

5.8:E--OS

0.12

0.91

7.l .E-02

I Intensity

User

Bystander

2.4E-04

0.94

7.2

0.66

ModerateIntensity
User

User

3.6E-04

0.72

5.5

0.43

Bystander

1.9E-03

7.3

56

5.1

User

l.9E-03

4.1

31

2.5

Bystander

9.SE-03

33

251

23

LowIntensity
User

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

316
317

MOE results for Fabric Spray are presented in Table 4-48 .

318
319
320
321
322

MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels .

323

Page 339 of 691

 324

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table 4-49 . Consumer Risk Estimation - Other Con sumer Uses - Film Cleaner
Benchmark
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immu otoxicity
Congenital
Neurotoxicity
IncreasedResorptions Response to Infection
Heart Defects
(Eredriks~onet a},.
(Narotskyet al., 1225) (Se!gmdeand Gilmom.
(JQ!lll~QD
ll!i!,l.,20Ql )
1993)
2010)
10

Scenario

Consumer
Receptor

100

Inha_lation Exposure
High-

User

5.8E-05

4.7E-02

0.36

3.0E-02

Intensity
User

Bystander

2.4E-04

0.19

1.5

0.13

I

ModerateIntensity
User

User

3.6E-04

0.29

2.2

0.18

Bystander

1.9E-03

1.6

12

1.1

Low-

User

1.9E-03

1.5

12

0.93

Intensity
User

Bystander

9.SE-03

7.7

59

5.4

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.
325
326
327
328
329
330
331
332

MOE results for Fabric Spray are presented in Table 4-49.
MO Es for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-inten sity inhalation exposure levels. Dermal expos ure was not quantifie d. MOEs for bystan ders
were be low the benchmark MOE for multiple endpoints at high, medium , and low-intensity user
inhal ation exposure levels.

Page 340 of 691

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INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table 4-50. Consumer Risk Estimation - Other Consumer Uses - Hoof Polish
Benchmark
10
Scenario

Consumer
Receptor

100

10

30

DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
lmmunotoxicity
Congenital
Neurotoxicity
IncreasedResorptions Responseto Infection
HeartDefects
Q:redrikssonet al.,
(liarotsky et al., 1995) {Selizradeand Gilmow:,
(Johnson et al., 2003)
1993\
2010)

Inhalation Exnosure
HighIntensity

User

1.7E-03

1.4

10

0.79

User

Bystander

0.34

272

2084

157

Moderate-

User

14

106

8.0

1.7:E-02

Intensity

User

Bystander

7.8

6307

48351

3653

LowIntens ity

User

0.12

97

747

56

User

Bystander

48

38519

295309

22309

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.
334
335
336
337
338
339
340
341
342

MOE results for Hoof Polish are presented in Table 4-50.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation expo sure levels. Dermal expo sure was not quantified. MOEs for bystanders
were below the benchmark MOE for congenital heart defects only for high and medium-intensity users.
MOE s for bystanders were not below the benchmark MOE for any endpoint at low-intensity inhalation
exposure levels.

Page 341 of 691

 343

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
T a bl e 4 -51 C onsumer RiskE s fImaflOD - 0th er Consumer Uses- P epper S1p ra y

.

Benchmark
10

Scenario

Consumer
Reeeptor

100

10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
Developmental Effects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Responseto Infection
HeartDefects
(f rcdriks~n et al.,
CNarotskyet al., 1225
) (S~luademillGilmour,
(JohnsQnet at., 2003)
1993)
2010 )

Inhalation Exposure
Single
Scenario

User
Bystander

169

0.21

1297

98

Not modeled due to simulated outdoor scenario - can be considered equal to user.

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

344
345
346
347
348
349
350
351

MOE results for Pepper Spray are presented in Table 4-51.
MOEs for consumer users were below the bench.markMOE for congenital heart defects. Dermal
exposure was not quantified. MOEs for bystanders are expected to be equivalent to users.
Table 4-52. Consumer Risk Estimation - Other Consumer Uses - Toner Aid
Benchmark
100
10
30
DevelopmentalEffects
Acute
DevelopmentalEffects
Developmental
DevelopmentalEffects
Immunotoxicity
Congenital
Neurotoxicity
Increased Resorptions Responseto Infection
Heart Defects
(f redrik~SQI!
1ttal.,
<Narots!syet al., 199~) (Selgmdr:and Qilmour.
(John:iOllet a],, 2QQ;D
1993)
2010)
10

Scenario

Consumer
R«eptor

Inhalation Exposure
HighIntensity
User

User

4.2E-04

Bystander

l.7E-03

0.34

2.6

0.21

1.4

11

0.97

--

ModerateIntensity
User

User

2.6E-03

2 .1

16

1.3

Bystander

J.4E-02

11

88

8.0

Low-

User

t.4E-02

11

84

6.8

Bystander

6.9E-02

56

43 1

39

Intensity

User

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

352
353
354
355
356
357
358

MOE results for Toner Aid are presented in Table 4-52.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the bench.markMOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 342 of 691

 359

360

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table 4-53. Consumer Risk Estimation - Other Consumer Uses - Lace Wig and Hair Extension
Glues
Benchmark
100

10

Scenario

Consumer
Receptor

DevelopmentalEffect,
Congenital
HeartDefects
(Johnson ~1 2003 )

w.,

DevelopmentalEffects

Developmental
Neurotoxicity
(Eredriksson~t !}.,
1993)

30
Pulmonary
DevelopmentalEffects
Immunotoxicity
Increased Resorptions Response to Infection
(Haro~ et al,. 1995) (Selgrade and QilmQur,
2010)
10

Inhalation Exposure
Moderate
to HighIntensity
User
Lowto
ModerateIntensity
User

User

3.8E-03

3.1

23

1.9

Bystander

3.0E-02

24

186

17

User

3.0E-02

25

189

15

Bystander

0.30

239

1836

167

Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.

361
362
363
364
365
366
367
368
369

MOE results for Lace Wig and Hair Extension Glues are presented in Table 4-53.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at moderate to high
and low to moderate-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs
for bystanders were below the benchmark MOE for multiple endpoints for mode rate to high highintensit y users and for congenital heart defects at both user intensity inhalation exposure levels.

370
371
372
373

Page 343 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
374
375
376
377
378
3 79
380
381
382
383
384
385
386
387
388
389

390
391
392
393
394
395
396
397
398
399
400
401

402
403
404
405
406
407
408
409
410
411

412
413
414
415
416
417
418

4.3 Assumptions and Key Sources of Uncertainty for Risk Characterization
4.3.1 Environmental Risk Characterization

----

There were some uncertainties related to environmental risk for TCE, with some leading to potentially
underestimating risk and some leading to potentially overestimating risk. As mentioned in Section 3.1. 7,
there were uncertainties regarding the hazard data for aquatic species; however, some of the uncertainty
was mitigated by the use of multiple lines of evidence supporting the assessment of hazard.
There were also uncertainties around surface water concentrations used to determine the environmental
risk. EPA used E-F AST, monitored data, and data from available literature to characterize acute and
chronic exposures of TCE to aquatic organisms. In some ways the E-F AST estimates are
underestimating exposure, because data used in E-F AST include TRI and DMR data. TRI does not
include smaller facilities with fewer than 10 full time employees, nor does it cover certain sectors, which
may lead to underestimates in total TCE releases to the environment. In other ways the E-F AST
estimates are overestimating exposure, because TCE is a volatile chemical, and E-FAST doesn't take
volatiliz.ation into consideration; and, for static water bodies, E-FAST doesn't take dilution into
consideration. This may have led to an over estimation of surface water concentrations for the two
facilities with environmental risks, as both release to still water bodies. Additionally, both facilities with
risk showed 20 days of exceeding the chronic COC. (The 20-day chronic risk criterion is derived from
partial life cycle tests [e.g.,, daphnid chronic and fish early life stage tests] that typically range from 21
to 28 days in duration.) However, there is uncertainty about whether those 20 days would be
consecutive, because the days of exceedance modeled in E-F AST occur sporadically throughout the
year. Because TCE is a volatile chemical, it is more likely that a chronic exposure duration will occur
when there are more days of exceedances.
The reasonably available monitored data was limited temporally and geographically. Aquatic
environmental conditions such as temperature and composition (i.e., total organic carbon, water
hardness, dissolve oxygen, and pH) can fluctuate with the seasons, which could affect TCE
concentrations in water and sediment pore water. In addition, TCE monitoring data was collected only in
certain areas, and within a limited number of states in the U.S. There were no measurements available
immediately downstream from facilities releasing TCE to surface water; these data are only a limited
representation of ambient water.

4.3.2

Human Health Risk Characterization

4.3.2.1
Occupational Exposure Considerations
Air concentrations. In most scenarios where data were available, EPA did not find enough reasonably
available data to determine complete statistical distributions of actual air concentrations for the workers
exposed to TCE. Ideally, EPA would like to know 50th and 95th percentiles for each exposed
population. In the absence of percentile data for monitoring, the air concentration means and medians
(means are preferred over medians) of the data sets served as substitutes for 50th percentiles (central
tendencies) of the actual distributions, whereas high ends of ranges served as substitutes for 95th
percentiles of the actual distributions. However, these substitutes are uncertain and are not as reliable as
the true percentiles. For instance, in the few cases where enough data were found to determine statistical
means and 95th percentiles, the associated substitutes (i.e., medians and high ends of ranges) were
shown to overestimate exposures, sometimes significantly. While it most air concentration data
represent real exposure levels, EPA cannot determine whether these concentrations are representative of
the statistical distributions of actual air concentrations to which workers are exposed. It is unknown
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whether these uncertaintiesoverestimateor underestimateexposures.The range of air concentration
estimates from central tendencyto high-endwas generallynot large (e.g.,, less than 20-fold for most
exposure scenarios).Because of this the results of risk characterizationwere generally not sensitive to
the individual es_timatesof the central tendencyand high-endseparatelybut rather were based on
considering both central tendencyand high-endexposureestimateswhich increase the overall
confidence in the risk characterization.
Exposures for ONUs can vary substantially.Most data sources do not sufficientlydescribe the proximity
of these employees to the exposuresource. As such, exposurelevels for the "occupational non-user"
category will have high variabilitydependingon the specific work activity performed. It is possible that
some employees categorizedas "occupationalnon-user" have exposuressimilar to those in the ''worker"
category depending on their specificwork activity pattern. Therefore,in the absence of specific
monitoring or modeling data, worker risk estimateswere appliedto ONUs. It is unknown whetherthese
uncertainties overestimateor underestimateexposures.
Additionally, some data sources may be inherentlybiased For example,bias may be present if exposure
monitoring was conductedto address concernsregardingadversehuman health effects reported
following exposures during use. These sourcesmay cause exposuresto be overestimated.

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Where data were not available,the modelingapproachesused to estimate air concentrationsalso involve
uncertainties.Model parameter values did not all contain distributionsknown to represent the modeled
scenario. It is also uncertainwhether the model equationsgenerateresults that represent actual
workplace air concentrations.It is unknownwhetherthese uncertaintiesoverestimateor underestimate
exposures.

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Averaging Times. EPA cannot determinehow accuratelythe assumptionsof exposure frequencies
(days/yr exposed) and exposed workingyears may represent actual exposurefrequencies and exposed
working years. For example,tenure is used to represent exposedworking years, but many workers may
not be exposed during their entire tenure. It is unknownwhether these uncertaintiesoverestimateor
underestimateexposures, althoughthe high-endvalues may result in overestimateswhen used in
combinationwith high-endvalues of other parameters.

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See Section 2.3.1.3 for more details on uncertaintiesand assumptionsunderlyingthe occupational
exposure assessment.

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4.3.2.2
Consumer/BystanderExposure Considerations
Inhalation and dermal exposuresare evaluatedfor acute exposurescenarios,i.e., those resulting from
short-term or daily exposures.Chronic exposurescenariosresultingfrom long-termuse of household
consumerproducts are not evaluated.Althoughhigh-end frequenciesof consumeruse may approach up
to 50 times per year, or approximatelyone time per week, availabletoxicologicaldata is based on either
single or continuous TCE exposure.There is uncertaintyregardingthe extrapolationfrom continuous
studies in animals to the case of repeated, intermittenthuman exposures.Therefore,while consumersat
the high-end :frequencyof use (such as DIY-types)may be at risk for chronichazard effects, EPA is
unable to developrisk estimatesfor this population.While certain consumersat the high-endfrequency
of use may potentiallybe at risk for chronichazard effects, the effects of this intermittentexposureis
unknown and based on reasonablyavailableinformationEPA is unable to developrisk estimatesfor this
population.For the vast majority of the conswnerpopulationwhich are only exposedthrough short-term,
occasionaluse of TCE products, only acute effectsare relevant

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EPA's approach recognizes the need to include uncertainty analysis. An important distinction for such
an analysis concerns variability versus sensitivity - both aspects need to be addressed. The TSCA Risk
Evaluation Rule (40 CFR 702.33) defines variability as "the inherent natmal variation, diversity, and
heterogeneity across time and/or space or among individuals within a population". Variability is often
expressed through statistical metrics, such as variance or standard deviation, that reflect the underlying
variance of the data. Sensitivity refers to an analysis of the predictability of a response variable, whereby
a change in a given parameter or asswnption affects a response variable.
Uncertainty is defined in the TSCA Risk Evaluation Rule (40 CFR 702.33) as ''the imperfect knowledge
or lack of precise knowledge of the real world either for specific values of interest or in the description
of the system." Variability cannot be reduced, but it can be better characterized. Uncertainty can be
reduced by collecting more or better data. Quantitative methods to address uncertainty include nonprobabilistic approaches such as sensitivity analysis and probabilistic methods such as Monte Carlo
analysis. Uncertainty can also be addressed qualitatively, by including a discussion of factors such as
data gaps and subjective decisions or instances where professional judgment was used .

With these approaches, the output of the model is fully determined by the choices of parameter values
and initial conditions. Stochastic approaches feature inherent randomness, such that a given set of
parameter values and initial conditions can lead to an ensemble of different model outputs. Because
EPA's largely deterministic approach involves choices regarding low, mediwn, and high values for
highly influential factors such as chemical mass and frequency /duration of product use, it likely captures
the range of potential exposure levels although it does not necessarily enable characterization of the full
probabilistic distribution of all possible outcomes.

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Certain inputs to which model outputs are sensitive, such as zone volumes and airflow rates, were not
varied across product-use scenarios. As a result , model outcomes for extreme circumstances such as a
relatively large chemical mass in a relatively low-volume environment likely are not represented among
the model outcomes. Such extreme outcomes are believed to lie near the upper end (e.g.,, at or above the
90th percentile) of the exposure distribution.

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See Section 2.3 .2.7 for more details on uncertainties and assumptions underlying the consumer exposure
assessment.

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4.3.2.3

Dermal AbsorptionConsiderations

The occupational and consumer assessment approaches utilize different models for estmating dermal
absorption. As discussed in Section 2.3 .2.5 .1, the occupational exposure assessment used a fractional
absorption model that accounts for evaporation of volatile chemicals such as TCE . In contrast, the
consumer assessment used a permeability model that incorporates duration of use and was only applied
to exposure scenarios where evaporation was believed to be impeded. There are several parameters that
must be estimated for each of the respective models, including quantity deposited on skin, surface area
of contact, evaporative flux, film thickness, and exposure duration. Many of these are Jikely to vary not
only by condition of use but also the particulars of the individual activity patterns on a daily basis.
Therefore, these parameters can only be approximated and the absorption estimates may either
underestimate or overestimate the actual exposure of any particular worker or consumer on a given day,
however they serve as a reasonable generalized approximation if not a higher-end bound.
The choice of one model over the other is primarily driven by the exposure scenario that needs to be
assessed and the information available. For example, EPA does not know the exact duration of exposure
for occupational loading and unloading hence EPA used the engineering model for occupational
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exposure assessment since it is event based and does not requirea duration input. In contrast, for
consumer applications there is infonnation available for duration of use, hence the permeability model
can be used for these exposure scenarios with greater confidence. Overall, the two mod~ls are
considered appropriate for their respective uses based on the reasonably available information.

4.3.2.4
Confidencein Risk Estimates
There is varying confidence in inhalation exposure estimates from different occupational risk scenarios,
ranging from low-to-mediumto medium-to-high(see Table 2-12). Despite some OES with low to
medium overall confidence, many of these are further supported by the availability of both monitoring
and modeling data, despite the uncertainties within each (see Table 2-27). Additionally, the data quality
scores for monitoring data ranged from medium to high, and the inhalation modeling approach was peer
reviewed during the 2014 TCE risk assessment process (U.S. EPA. 2014b) (for a subset of COUs).
There is medium confidence in the occupational dennal modeling approach, which was developed from
a peer-reviewed publication (Kasting and Miller. 2006). There is medium to high confidence in
consumer inhalation exposure modeling (see Section 2.3.2.8), however there is low to moderate
confidence in consumer dermal exposure modeling due to uncertaintiesrelated to absorption (as
discussed above) and assumptions regarding impeded evaporation for particular conditions of use.
For all exposure scenarios, the confidence in the risk estimates is raised due to the presence of both
central tendency and high end estimates for occupational scenarios and low-, moderate-, and highintensity user estimates for consumer scenarios. Any reduced confidence in individual exposure
estimates is mitigated by the use of a range of exposure estimates, which cover a variety of different
assumptions to account for any uncertainty and variability. Therefore, while there is lower confidence in
various occupational inhalation estimates and for consumer dermal exposure estimates, there is high
confidence in the overall approach and it is unlikelythat any refinement of risk estimates would result in
variation of more than a few fold in either direction.
The human health database covers a wide range of endpoints, with most health effects supported by
animal,. epidemiological, and mechanistic evidence. There is medium to high confidence in the
integration of human health data for both acute non-cancer and cancer, with high confidence for chronic
non-cancer endpoints (see Section 3.2.6for more details).

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In considering risk estimates relative to the benchmark MOE, identified risks are typically present for
multiple endpoints, at both high-end and central tendency (or high and medium-intensity user scenarios
for consumers) exposure levels, for both inhalation and dermal exposure, and based on both monitoring
and modeling data, when available (Sections 4.5.1.1 and 4.5.1.2). In accounting for the totality of
uncertainties, confidence levels for each scenario, and range of risk estimates provided for the different
aspects of the risk evaluatio~ EPA determinedthat there is medium to high confidence in the risk
estimates and conclusions based on inhalation exposure and medium confidence in the risk estimates
based on dermal exposure.

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4.4 Other Risk Related Considerations

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EPA identified lifestage, gender, genetic polymorphisms, race/ethnicity, preexisting health status, and
lifestyle factors and nutrition status as factors affecting biological susceptibility.The use ofHEC/HED99
POD values derived from relevant PBPK dose metrics accounts for the vast majority oftoxicokinetic
variation across the population. By relying on the 99th percentile output of the PBPK model, these values
are expected to ·be protective of particularly susceptible subpopulations, including those with genetic
polymorphisms resulting in increased activity of bioactivating enzymes. The (Selcrade and Gilmour.
2010) study accounts for pre-existing infection concurrent with TCE exposure, representing a
susceptible status that applies intermittently to the entire population. Cardiac malformations are most
strongly associated with offspring of older mothers (Brender et al.. 2014; Yauck et al.. 2004). While
inconsistencies in the data on cardiac malformations (Appendix 0.2) suggt:stthat l.ht:remay nor be a risk
for all individuals, inclusion of risk estimates for cardiac malformations is protective of susceptible
mothers (Jenkins et al., 2007) and their offspring.

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4.4.2 Aggregate and Sentinel Exposures
·section 2605(b)(4)(F)(ii) ofTSCA requires the EPA, as a part of the risk evaluation, to describe whether
aggregate or sentinel exposures under the conditions of use were considered and the basis for their
consideration. The EPA has defined aggregate exposure as "the combinedexposuresto an individual
from a single chemical substance across multiple routes and across multiplepathways" (40 CFR §
702.33). In this risk evaluation, EPA determined that aggregating dermal and inhalation exposure for
risk characterization was not appropriate due to uncertainties in quantifying the relative contribution of
dermal vs inhalation exposure, since dermally applied dose could evaporate and then be inhaled.
Aggregating exposures from multiple routes could therefore inappropriatelyoverestimate total exposure,

4.4.J Potentially Exposed ~r Susceptible Populations _ _
EPA identified workers, ONUs, consumers, and bystanders as potentially exposed populations. EPA
provided risk estimates for workers and ONUs at both central tendency and high-end exposure levels for
all COUs. Consumer and bystander risk estimates were provided for low, medium, and high intensities
of use, accounting for differences in duration, weight fraction, and mass used. Dermal risk estimates
were calculated for both average workers and women of childbearing age [OccupationalRisk Estimate
Calculator.Docket# EPA-HQ-OPPT-2019-0500], based on differences in delivered dose accounting for
differing body weight and hand size. Exposures differ by only ~ 10% between these groups, so this
difference is relatively insignificant considering the magnitude of risk estimates relative to the
benchmark MOE. Accordingly, the risk characterization section only presents dermal risk estimates for
average adult workers (Section 4.2.2). Similarly, risk estimates were provided for each of the three
lifestages that are expected to potentially be directly exposed through consumer use, namely 11-15 year
olds, 16-20 year olds, and adults 21 and over (Section 4.2.3). These risk estimates also only varied by a
small percentage relative to the magnitude of risk estimates relative to the benchmark MOE. EPA
determined that bystanders may include lifestages of any age.

For inhalation exposures, risk estimates did not differ between genders or across lifestages because both
exposures and inhalation hazard values are expressed as an air concentration. EPA expects that
variability in human physiological factors (e.g., breathing rate, body weight, tidal voume) which may
affect internal delivered concentration or dose is sufficiently accounted for in the PBPK model, although
some differences among lifestages or between working and at-rest individuals may not have been
accounted for. The use of HECIHEim values is expected to account for the vast majority of
physiological differences among individuals.

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as simply adding exposures from different routes without an available PBPK model for those routes
would compound uncertainties.EPA also did not consider aggregate exposure among individuals who
may be exposed both in an occupationaland consumer context because there is insufficient information
available as to the likelihoodof this scenario or the relative distributionof exposures from each
pathway. Additionally, background levels of TCE in indoor and outdoor air from either environmental
releases or additional exposure sources are not consideredor aggregated in this assessment because they
are out of scope for this assessment, even if originatingfrom in-scope COUs.
EPA defines sentinel exposure as "the exposureto a single chemicalsubstance that representsthe plausible
upper bound of exposurerelative to all other exposureswithina broad category of similar or related
exposures" (40 CFR § 702.33).In terms of this risk evaluation,EPA considered sentinel exposures by
considering risks to populations who may have upper bound exposures - for example, workers and
ONUs who perform activities with higher exposurepotential, or consumers who have higher exposure
potential (e.g.,, those involved with do-it-yourselfprojects) or certain physical factors like body weight
or skin surface area exposed. EPA characterizedhigh-end exposures in evaluating exposure using both
monitoring data and modeling approaches.Where statistical data are available, EPA typically uses the
95th percentile value of the available dataset to characterizehigh-end exposure for a given condition of
use. For consumer and bystander exposures, EPA characterizedsentinel exposure through a "highintensity use" category based on both product and user-specificfactors.

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4.5 Risk Conclusions
4.5.1

Environmental Risk Conclusions
Risks to aquatic organisms like fish and invertebrates were identified near one open-top vapor
degreasing facility and one facility that processes TCE as a reactant (See Table 4-54). These facilties

had an acute RQ ~ 1, or a chronic RQ ~ I and 20 days or more of exceedance for the chronic COC.
Risk to the most sensitive species of algae were identified near 521 facilities (with 20 days or more of
exceedances for 461 of these facilities, and more than 100 days exceedances for 10 facilities); however,
as a taxonomic group, 95% of algae species not experience risk. (They had RQs ~ I using the algae
COC of 3 ppb but RQs < 1 using the algae HCosof 52,000 ppb.) These facilities are not included in
Table 4-54 in this section, but are in Table 4-1 for reference.
EPA did not identify risks to aquatic organisms in the ambient water where monitored data were
available. Monitored data from the Water Quality Portal and the available literature show no
exceedances of the acute COC, chronic COC, or the algae COCs in ambient water. Monitored data from
literature showed some exceedances of the algae COC of3 ppb; however, these monitors were near
facilities and therefore do not represent ambient concentrations.

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Near-facility monitoring data report levels ofTCE ranging from 0.4 to 447 µg/L (1!.S. EPA. 1977).
These data show that measured near-facility concentrations compare to the modeled near-facility
concentrations from E-FAST. With the exception of two sites, the measured concentrations in this study
encompases the range of the modeled estimates across all OES from E-FAST.
Open-top Vapor Degreasing:

One out of 64 open-top vapor degreasing facilities had releases of TCE to surface water that indicate
risk to aquatic orgnaisms. U.S. NASA Michoud Assembly Facility in New Orleans, LA had an acute RQ
~ 1 (RQ = 3.11). In other words, the surface water concentration modeled for this facility was 3.11 times
higher than the acute COC of3,200 ppb, indicating risk to aquatic organisms from acute exposures. The
facility also had a chronic RQ of 12.61with 20 days of exceedance. In other words, the surface water
concentration was 12.61 higher than the COC of788 for 20 days. Therefore,EPA identified risk to
aquatic organisms at this sitefor acute and chronic exposuresto TCE.

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Processing as a Reactant:

650

One out of 443 facilties (including 440 unknown sites modeled in E-FAST) that process TCE as a
reactant had releases of TCE to surface water that indicate risk to aquatic organisms like fish and
invertebrates. Praxair Technology Center in Tonawanda, NY had a chronic RQs of 3.81 with 20 days of
exceedance. In other words, the surface water concentration modeled for this facility was 3.81 times
higher than the COC for chronic exposures. Therefore,EPA identifiedrisk to aquatic organisms at this
site for chronic exposures to TCE.

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Of the 443 facilities processing TCE as a reactant (including 440 unknown sites modeled in E-FAST),
one facility had acute RQs ~ 1, or chronic or algae RQs ~ 1 with 20 days or more of exceedances.
Assuming 20 days of releases, Praxair Technology Center in Tonawanda, NY had a chronic RQs of3.81
with 20 days of exceedance, and an algae COCs representing the most sensitive species of algae of
1,000 with 20 days of exceedance. In other words, the surface water concentration modeled for this
facility was 3.81 times higher than the COC for chronic exposures, and 1,000 times higher than the COC
for the most sensitive species of algae. Assuming 260 days of releases from the facility, the algae RQ
representing the most sensitive species was 56.33 with 350 days of exceedance. However, for algae
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species as a whole, RQs for this site were 0.06 assuming20 days of release and 0.00 assuming 350 days
of release, meaningthe concentrationdid not exceed the COC of 52,000 ppb which represents nine
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Table 4-54. Facilities with Acute or Chronic Risk Identified for Aquatic Ore:anisms(ROs > 1 in bold)
Name, Location, and
ID of Active Releaser
Facility•

Release
Media b

ModeledFacility or
Industry Sector in
EFASTC

EFAST
Waterbody
Typed

Days of
Release
e

Release
(kg/day}r

7Ql0

swc

(ppb) g

COCType

coc
(ppb)

Days of
Exceedance
(days/year}

Risk
Quotient

h

OES: Processinit as a Reactant

l670
1671
l672
l673
1674
1675
1676
1677
l678
1679
1680

Acute
3,200
NA
0.05
Chronic
788
0
0.21
350
0.00169
169
Praxair Technology
Alllae
3
350
56.33
Center,
0.00
Ahtae <HCo,)
0
Surface
52.000
NPDES NY0000281
Still body
Tonawanda,NY
Water
Acute
NA
0.94
3.200
NPDES: NY0000281
Chronic
788
20
3.81
20
0.03
3000
3
20
1.000.00
AlJ?ae
Algae <HCo
s)
52,000
0
0.06
OES: OTVD (Includes releases for Closed-Loop Desueasing, Conveyorized De2reasin2.1 Web De ~easio2. and Metalworkine Fluids)
3,200
Acute
NA
0.24
788
Chronic
0
0.97
US Nasa Michoud
260
1.96
765.63
Algae(COC)
3
260
255 .21
Assembly Facility,
52,000
0
Algae (HCos)
0.01
New Orleans, LA
Surface
SurrogateNPDES
Still body
NPDES: LA0052256
Water
LA0003280
Acute
3,200
NA
3.11
788
20
12.61
Chronic
20
25.44
9937.5
Ale.ae
3
20
3.312.50
Algae (HCos)
52.000
0.19
0
a. Facilities activelyreleasing trichloroethylenewere identified via DMR. TRI, and CDR databases for the 2016 reportingyear.
b.Release media are either direct (release from active facility directly to surfacewater) or indirect(transfer of wastewaterfrom active facilityto a receivingPOTW or
non-POTWWWTP facility).A wastewatertreatmentremoval rate of 81% is appliedto all indirectreleases,as well as direct releases from WWTPs.
c. Ifa valid NPDES of the direct or indirect releaser was not availablein EFAST, the release was modeled using either a surrogaterepresentativefacility inEFAST (based
on location)or a representativegeneric industrysector. The name of the indirectreleaser is provided, as reported in TRI.
d. EFAST uses ether the "surface water" model, for rivers and streams,or the "still water'' model, for lakes, bays, and oceans.
e. Modelingwas conductedwith the maximumdays ofrelease per year expected.For direct releasingfacilities,a minimwn of20 days was also modeled.
f. The daily release amountwas calculatedfrom the reported annualrelease amowit dividedby the number of release days per year.
g. For releases dischargingto lakes, bays, estuaries,and oceans,the acute scenariomixing zone water concentrationwas reported in place of the 7Ql0 SWC.
h. To determinethe PDM days of exceedancefor still bodies of water, the release days provided by the EPA Engineersshould become !he days of exceedanceonly if the
predicted surface water concentrationexceedsthe COC. Otherwise,the days of exceedancecan be assumedto be zero.

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4.5.1

Human Health Risk Conclusions

4.5.1.1
Summary of Risk Estimates for Workers and ONUs
Table 4-55 summarizesthe risk estimates for inhalationand dennal exposures for all occupational
exposure scenarios.Risk estimates that exceed the benchmark(i.e. MOEs less than the benchmark MOE
or cancer risks greater than the cancer risk benchmark)are highlightedby holding the number and
shading the cell in gray. When both monitoring and modelinginhalationexposures were available, EPA
presented the most reliable data source in the table. The occupationalexposure assessment and risk
characterizationare describedin more detail in Sections2.3.l and 4.2.2, respectively. Specific links to
the relevant risk characterizationsections are listed in Table 4-55 in the Occupational Exposure Scenario
column.
Of note, the risk summarybelow is based on the most sensitivenon-cancerendpoint (cardiac toxicity) as
well as cancer. For the majorityof exposure scenarios,risks were identified for multiple endpoints in
both acute and chronic exposure scenarios. Risk estimates are also presented considering PPE up to
respirator APF 50 and glove PF 10 or 20. When risks did not exceed the benchmark, the lowest
protection factor that results in no risk is shown (e.g., if risks do not exceed the benchmark for APF 10
and above, the risk estimate for APF 10 is shown).

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Inhalation Exposure
For acute and chronic exposures via inhalation without PPE (i.e. no respirators) there are risks for
workers relative to the benchmarksfor all the exposure scenariosat the high~endexposure level for noncancer effects (i.e. cardiac toxicity) from both acute and chronic exposure durations as well as for
cancer. Occupationalnon-users (ONUs) are expectedto have lower exposure levels than workers in
most instances but exposurescould not always be quantifiedand risk estimates for ONUs may be similar
to workers in some circumstances.Therefore, when separate ONU exposure estimates were not
available, EPA provided risk estimates for ONUs based on worker values assuming that ONU exposure
may be as high as workers in various circumstances.Acute and chronic non-cancer risks to ONUs were
indicated for all exposure scenarios.
When only considering central tendency inhalation exposure level, risks for any endpoint were not
identified to workers or ONUs for the followingexposure scenarios:
• Formulationof Aerosol and Non-AerosolProducts
• Repackaging
• Process SolventRecyclingand Worker Handlingof Wastes
When respiratorsare worn (either APF 10 or 50) there are risks relative to the benchmarks for noncancer effects (i.e. cardiac toxicity) and for cancer for workers (ONUs are assumed to not consistently
wear respirators) from both acute and chronic exposure durationsfor the majority of exposure scenarios.
Risks were identifiedfor at least cardiac toxicity assuming respiratoruse up to APF = 50 at high-end
exposure level for all exposure scenarios. Risks for any endpoint were not identifiedwhen assuming the
maximum plausible APF (up to APF =50) and central tendency exposure levels for the same exposure
scenarios that did not demonstraterisk without PPE:
• Fonnulation of Aerosol and Non-AerosolProducts
• Repackaging
• Process SolventRecyclingandWorkerHandlingof Wastes
Dermal Exposure
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731
732

For acute and chronic exposures via dermal contact without PPE (i.e. no gloves) there are risks relative
to the benchmarks for non-cancer effects (i.e. cardiac toxicity) and for cancer for workers (ONUs are
assumed to not have direct dermal contact with TCE) for all exposure scenarios. Risks are still identified
for all exposure scenarios when gloves are worn even when assuming the maximum applicable glove
protection (either PF 10 or 20).

Page 354 of 691

 l 733

lNTERAGENCYDRAFT- DO NOT CITE OR QUOTE
f1onaI Ri.skSummarvTable
Table 4-55 0c c111pa

.

'

Risk Estimates for No PPE

Ure Cycle
Stage/
Category

Subcategory

Occupational
Exposure
Scenario

Acute Non- Chronic
Acute Non- Chronic
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Route and Exposure (benchmark (benchmark (benchmark (benchmark. (benchmark (benchmark
Duration
Level MOE= 10) MOE= 10)
= lo-4) MOE= 10) MOE= 10)
= 104 )

Exposure

Population

High-

Inhalation
Worker
ManufactureDomestic
manufacture

Domesticmanufacture

ManufacturingTable4-6

(APFSO)

2.2
(APFSO)

7.SE-05
(APF 10)

3.8:E.ol

4.SE-02
(PF20)

6.6:E-02
(PF20)

1.9E-03
(PF20)

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PF20)

4.9E~
(PF20)

4.3E-03

6.3E-03

6.7E-03

N/A

Central
Tendency

3.0E-02

4.JE-02

7.SE-04

N/A

High End

9.7E-03

1.4E-02

2.9E-03

0.49
(APFSO)

3.7
(APFS0)

5.9E-0S
(APF 50)

Central
Tendency

22.4

32.7

9.9E-07

224
(APF 10)

327
(APP 10)

9.9E-08
(APF 10}

HighEnd

2.3E-03

3.3E-03

3.SE-02

4.5E-02
(PF20)

6.6E-02
(PF20)

l.9E-03
(PF20)

Central
Tendency

6.SE-03

9.9E-03

9.7.E--03

0.14
(PF20)

0.20
(PF20)

(PFZO)

HighEnd

9.7E-03

1.4E-02

2.9E-03

N/A

Central
Tendency

22.4

32.7

9.9E-07

NIA

4.JE..03

6.JE-03

6.7E-03

0.21
(APFSO)

0.31
(APFSO)

1.3E-04
(APFSO)

Central
3.0E-02
Tendency

4.3E-02

7.SE-04

I.S
(APFSO)

2.2
(APFSO)

7.SE-05
(APF 10}

Central
Tendency
End
Central
High-

Inhalation

Inhalation

I

Worker
Import

RepackagingTable 4-19

Dennal

ONU
(upper

Inhalation

limit)

Processing Processing as a
reactant/
intennediate

l.3E-04
(APF50)

Tendency

limit)

Intermediate in industrial
Processing as a
gas manufacturing(e.g.,,
Reactantmanufacture of
Table 4-7
fluorinated gases used as

End

High-

Worker

Inhalation

0.21
(APF50)

0.31
(APFSO)

End

High -

Dermal

ONU
(upper

ManufactureImport

Risk Estimates with PPE

End

Page 355 of 691

4.3E-03

6.3E-03

6.7E-03

3.0E-02

4.3E-02

7.SE-04

2.3.E-03

3.3E-03

6.SE-03

1.5

4.9E-04

 INTERAGENCYDRAFT- DONOT CITE OR QUOTE
Risk Esti mates for No PPE

Life Cycle
Stage/
Category

Subcategory

Occupational
Exposure
Scenario

Exposure
Route and
Population Duration

refrigerants,foam
blowing agents and
solvents)

Dennal

ONU
(upper

Inhalation

limit)

Solvents (for cleaning or
degreasing)

Inhalation

Adhesives and sealant
chemicals

Processing Incorporation
into formulation, Solvents (which become Formulation of
mixture or
part of product
Aerosol and Nonreaction product formulation or mixture) Aerosol Products
(e.g.,, lubricants and
Table 4-18
greases, paints and
coatings, other uses)
Processing incorporated
into articles

Worker
Dermal

Solvents {booomesan
integral component of
articles)

Solvents (for cleaning or
degreasing)

Repackaging Table 4-19

HighEnd

2.3E-03

3.3E-03

3.8E-0:2

4.!E-02
(PF20)

6.6E-Ol
(PF20)

l.9E-03
(PF20)

Central
Tendency

6.8E-03

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PFlO)

4.9FAM
(PF20)

HighEnd

4.3E-03

6.3E-03

6.7E-03

N/A

Central
Tendency

3.0E-02

4.3E-02

7.SE-04

N/A

3.7

HighEnd

9.7E-03

7.3E-02

2.9E-03

0.49
(APF 50)

(APF50)

5.9E-05
(APF 50)

Central
Tendency

22.4

32.7

9.98-07

224
(APF 10)

327
(APF 10)

9.9E-08
(APF 10)

HighEnd

2.3E-03

3.JE-03

3.SE-02

4.5E-02
(PF20)

6.6E-02
(PFlO)

1.9E-03
(PFlO)

Central
Tendency

6.SE-03

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PF 20)

4.9E-04
(PF20)

9.7E-03

7.3E-02

2.9E-03

N/A

Central
Tendency

22.4

32.7

9.9E-07

NIA

HighEnd

9.7E-03

1.4E-02

2.9E-03

0.49
(APF50)

(APF SO)

5.9E-05
(APF 50)

Central
Tendency

22.4

32.7

9.9E-07

224
(APF 10)

327
(APF 10)

9.9E-08
(APF 10)

2.3E-03

3.3E-03

3.8E-02

4.SE-02
(PF20)

6.6E-02
(PF20)

l.9E--03
(PFlO)

6.SE-03

9.9E-03

9.7E--03

0.14
(PF20)

0.20
(PFlO)

4.9FAM
(PF20)

End
Inhalation

Inhalation
Processing Repackaging

Acute Non- Chronic
Acute Non- Chronic
Cancer Non-Cancer Cancer
Cancer Non-Cancer
Cancer
Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
Level MOE = 10) MOE = 10)
= Io-4) MOE = IO) MOE = 10) - I0-4)

High-

ONU
(upper
limit)

Risk Estima tes with PPE

Worker

High-

Denna!

End
Central
Tendency

Page 356 of 691

3.7

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Risk Estimatesfor No PPE
Life Cycle
Stage/
Category

Subcategory

Acute Non- Chronic
Acute Non- Chronic
Occupational
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Exposure
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
Population Duration
= I0-4) MOE= IO) MOE= IO)
Scenario
Level MOE= 10) MOE= 10)
= I0-4)

ONU
(upper
limit)

High-

Inhalation

9.7E-03

1.4:E-Ol

l.9:E--03

N/A

22.4

32.7

9.9E-07

NIA

9.7E-03

1.4.E-02

2.9E-03

22.4

32.7

9.9E-07

2.JE-03

3.JE-03

Central
Tendency

6.8E-03

HighEnd
Central
Tendency

End
Central
Tendency
High-

Inhalation

Processing Recycling

Recycling

Process Solvent
Recycling and
Worker Handling
of WastesTable4-27

Workers
Dermal

(upper

Inhalation

limit)

Distribution

Inhalation
(Monitoring
Batch Open-Top
Batch vapor d~
Vapor Degreasing
(e.g.., open-top, c1osedWorlcers
- Table 4-8 and
loop)
Table4-9

Central
Tendency
End

0.49
(APFSO)

3.7
(APF50)

(APF SO)

224
(APF 10)

327
(APF 10)

9.9E-08
(APF 10)

3.8E--Ol

4.5E-02
(PF 20)

6.6E-02
(PF20)

1.9E-03
(PF20)

9.9E-03

9.7E--03

0.14
(PF .20)

0.20
(PFZO)

4.9E-04
(PF20)

9.7E-03

1.4E-0l

2.9E-03

NIA

22.4

32.7

9.9E-07

N/A

5.9E-05

Distribution is accounted for as part of otl1.erCOUs

Distribution

Industria1/

commercial use
Solvents (for
cleaning or
degreasing)

End

High-

ONU

Distnbution in
commerceDistn"bution

Risk Estimateswith PPE

Data)·

Dermal

High-

7.1&-03

1.0E--02

(APFSO)

(APFSO)

4.0E-03
(APFSO)

4.0E-02

(APFSO)

S.9E--02
(APFS0)

5.SE-04
(APFSO)

3.8E-Ol

4.SE-02
(PF20)

6.6E-02
(PF20)

l.9E--03
(PF20)

9.7E-03

0.14
(PF20)

0.20
(PF20)

4.9E-04
(PF20)

1.4E-04

2.lE-04

0.20

Central
Tendency

8.0E-04

1.lE-03

2.SE-02

HighEnd

2.3E-03

3.3E-03

Central
Tendency

6.SE-03

9.9E-03

End

Page 357 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Risk Estimates for No PPE
Life Cycle

Stage/
Category

Subcategory

Acute Non- Chronic
Acute Non- Chronic
Occupational
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Exposure
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
= I0-4) MOE = 10) MOE= 10) = lo--4)
Level MOE= 10) MOE = 10)
Scenario
Population Duration
High-

ONUs

1.2E-03

1.SE-03

2.3E-02

NIA

1.0E-02

l.SE-02

2.2E-03

N/A

1.t:E-02

3.7E-03

0.38
(APFSO)

0.56
(APFS0)

7.SE-05
(APF 50)

3.SE-02

9.lE-04

1.2
(APF50)

1.8
(APF 50)

9.IE-05
(APF IO)

2.JE-03

3.3E-03

3.SE-02

4.SE-02
(PF20)

6.6E-02
(PF20)

l.9E.-03
(PF20)

Central
6.SE-03
Tendency

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PF20)

4.9E-04
(PF20}

7.6E-03

1.lE-02

3.7E-03

NIA

Central
2.4E-02
Tendency

3.SE-02

9.lE-04

NIA

3.4E-04

0.12

1.lE-02
(APF50)

1.7E-02
(APFSO)

2.SE-03
(APFSO)

S.OE-04

6.SE-02

1.7E-02
(APFSO)

2.SE-02
(APFSO)

1.3E-03
(APFSO)

2.3E-03

3.3E-03

3.SE-02

4.SE-02
(PF20)

6.6E -02
(PF20)

1.9E-03
(PF20)

Central
6.SE-03
Tendency

9.9E-03

9.7E-03

0.14
(PF 20)

0.20
(PF20)

4.9E-CM
(PF20)

2.3E-04

3.4E-04

0.12

N/A

Central
3.4E-04
Tendency

S.0E-04

6.SE-02

NIA

Inhalation
End
(Monitoring
8
Data)
Central
Tendency
High-

Inhalation
Workers

Industrial/
commercial use
Solvents (for
cleaning or
degreasing)

Risk Estimates with PPE

Batch ClosedLoop Vapor
DegreasingTable 4-10

I

Inhalation
(Monitoring
Data) 3
Workers
C-0nveyorized
In-line vapor degreaser
VaporDegreasing
(e.g.,, conveyorized,web
- Table 4-11 and
cleaner)
Table 4-12

End

High-

End

Inhalation
(Monitoring

Data) 8

2.3E-04

Central
3.4E-04
Tendency
High-

Dermal

ONU
(upper
limit)

End

High-

Inhalation

7.6E-03

Central 2.4:E-02
Tendency
High-

Dermal

ONU
(upper
limit)

End·

End

High-

End

Page 358 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUO'IE
Risk Estimatesfor No PPE
Life Cycle
Stage/
Category

Subcategory

AcuteNon- Chronic
Acute Non- Chronic
Exposure
Occupational
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Exposure
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
= 10-4) MOE= 10) MOE= 10) = lo-4)
Population Duration
Level MOE = 10) MOE= 10)
Scenario
Inhalation
Workers
Web Vapor
DegreasingTable4-l3

HighEnd

1.lE-03

2.9E-02

3.9E-02
(APFSO)

(APFSO)

5.8E-04
(APFSO)

Central
1.9E-03
Tendency

2.7E-03

1,lE-02

9.3E-02
(APF50)

0.14
(APFSO)

2.3E-04
(APFSO)

2.3E-03

3.3E-03

3.8E-Ol

4.SE-02
(PF20)

6.6E-02
(PF20)

(PF20)

Central
6.8E-03
Tendency

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PFlO)

(PF20)

1.lE-03

1.7E-03

1.9E-02

NIA

Central
3.5E-03
Tendency

S.2E-02

5.9E-03

NIA

1.9E-04

2.8E-04

0.11

9.7E-03
(APFSO)

1.4E-02
(APFSO)

2.3£-03
(APFSO)

Central
3.3E-03
Tendency

4.9E-03

6.2E-03

0.17
(APFSO)

0.24
(APFSO)

1.2E-04
(APFS0)

2.3E-03

3.3E-03

3.8E-02

4.SE-02
(PFl0)

6.6E-0l
(PF20)

(PF20)

Central
6.8E-03
Tendency

9.9E-03

9.7E-03

0.14
(PF20)

0.20
(PFlO)

(PF20)

3.lE-04

4.7E-04

6.9E-02

N/A

Central
6.0E-03
Tendency

8.SE-03

3.3E-03

NIA

6.SE-04

4.9E-02

2.IE-03

1.4E-02

High-

Dermal

ONU

IndustriaV
commercialuse
Solvents(for
cleaningor
degreasing)

Inhalation

End

End

High-

Inhalation
Worker
Cold Cleaning•
Table4-l4

Dermal

ONU

Aerosolspray
degreaser/cleaner

Aerosol
ApplicationsTable4-15

Inhalation

End

HighEnd

HighEnd

HighWorker

Inhalation

S.7E-02

7.9E-04

High-

Cold cleaner

Risk Estimateswith PPE

End

4.6E-04

Central
Tendency l.sE-03

Page 359 of 691

1.9&-eJ
4.9~4

1.9E-03
4.9&04

2.3E-02
(APF50)

3.4E-Ol
(APF50)

9.7E-04
(APF50)

7.JE-02

0.11
(APFSO)

(APF50)

(APFSO)

2.9E-04

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Risk Estimatesfor No PPE
Life Cycle
Stage/

Category

Subcategory

Occupational
Exposure
Scenario

Acute Non- Chronic
Acute Non- Chronic
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Route and Exposure (benchmark
(benchmark (benchmark (benchmarlc (benchmark (benchmark
= 10-4) MOE= 10) MOE= 10) = J0-4)
Population Duration
Level MOE= 10) MOE= 10)

Dermal

HighEnd

1.4:E-03

2.IE-03

5.9E-02

2.9E-02
(PF20)

4.lE-02
(PF20)

2.9E-03
(PF20)

Central
Tendency

4.JE-03

6.3E-03

1.SE-02

8.6E-02
(PF20)

0.13
(PF20)

7.6E-04
(PF20)

1.lE-02

J.6'E-02

2.0E-03

NIA

7.9E-02

0.12

2.6E-04

NIA

4.3E-02

6.2E-02

6.6E-04

2.1
(APFS0)

3.1
(APFSO)

1.3E-05
(APF50)

Central
Tendency

0.16

0.23

1.3E-04

7.9
(APFSO)

11.6
(APF 50)

2.6E-06
(APF 50)

HighEnd

2.8.E-03

4.lE-03

3.0E-02

5.6:E-02
(PF20)

8.2E-02
(PF20)

1.SE-03
(PF20)

Central
Tendency

8.5E-03

l.lE-02

7.IE-03

0.17
(PF20)

0.25
(PF20)

3.9E-04
(PF20)

4.3E-02

6.2E-02

6.6E-04

NIA

0.16

0.23

1.3E-04

NIA

4.6E-04

6.8E-04

4.9:E-02

2.3E-02
(APFSO)

3.4E-02
(APFSO)

9.7E-04
(APFSO)

1.SE-03

l.1.E-03

1.4E-02

7.3E-02
(APFS0)

0.11

2.9E-04

(APF SO) (APF50)

HighEnd

1.4E-03

2.lE-03

5.9E-02

2.9E-0l
(PF20)

4.lE-02
(PF20)

2.9E-03
(PF20)

Central
Tendency

4.3E-03

6.3E-03

1.5E-Ol

8.6E-02
(PF20)

0.13
(PFl0)

7.6E-04

High-

Mold release
ONU

Inhalation

End
Central

Tendency
Inhalation
(Modeling
Data)l>
Worker
Tap and die fluid
Industrial/
commercial use
- Lubricants and
greases/
lubricants and
lubricant
additives

Metalworking
Fluids - Table
4-20 and Table
4-21

Denna!

(upper

High-

End

High-

ONU
Inhalation

limit)

End
Central
Tendency
High-

Inhalation
Penetrating lubricant

Aerosol
Applications Table 4-15

Risk Estimates with PPE

End
Central

Tendency

Worker

Dermal

Page 360 of 691

(PF20)

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Risk Estimates for No PPE
Life Cycle
Stage/
Category

Subcategory

Occupational
Exposure
Scenario

AcuteNon- Chronic
AcuteNon- Chronic
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Route and Exposure (benchmark(benchmark(benchmark (benchmark (benchmark (benchmark
= 10-4) MOE= 10) MOE= 10) = 10-4)
Level MOE= 10) MOE= 10)
Population Duration
HighONU

Industrial/
commercialuse
- Lubricantsand

Inhalation

Inhalation

Solvent-basedadhesives
and sealants

End

Tire repair cement/
sealer

Adhesives,
Sealants,Paints,
andCoatingsTable 4-22 and
Table4-23

Worker

Dermal
(Industrial)

(closed systems)

Heat exchangefluid

Other Industrial
Uses Table4-26

3.SE-03

9.3E-03

2.SE-03

3.7E-03

Central
7.SE-03
Tendency

1.4E-02
(APFSO)

2.lE-02
(APFSO)

2.0E-03
(APFSO)

0.12

0.17

(APFSO)

(APFSO)

1.9E-04
(APFSO)

3.4E-02

5.0E-02
(PFl0)

7.3E-02
(PF20)

1.7E-03
(PF20)

1.lE-02

8.7E-03

0.15
(PF20)

0.22
(PFl0)

4.4E-04
(PF20)

2.3E-03

5.3E-02

1.6E-02
(PF 10)

2.3E-02
(PF 10)

5.3E-03
(PF 10)

7.0E-03

1.4E-02

4.8E-02
(PF 10)

7.0E-02
(PF 10)

1.4E-03
(PF 10)

1.1E-G2

1.6E-02

2.6E-03

N/A

Central
1.2E-G2
Tendency

1.7E-02

1.!,JE-03

NIA

4.3E-03

6.3E-03

6.7E-03

0.21
o.31
(APFSO) (APF50)

3.0E-02

4.3E-02

7.5E-04

1.!
(APF50)

2.2
(APF50)

7.SE-05
(APF 10)

2.3E-03

3.3E-03

3.8E-02

4.SE-02

6.8E-03

9.9E-03

9.7E-03

6.6E-02
(PF20)
0.20
(PF20)

1.9E-03
(PF 20)
4.9E-04
(PF20)

End

End

End
Central
High-

Dermal

NIA

2.4E-03

Tendency

Worker

2.6:E-04

Central
Tendency

1.6E-03

Dermal
(Commercial) Central
4.8E-03
Tendency

Inhalation

0.12

0.10

High-

IndustriaV
commercialuse Functionalfluids

NIA

4.lE-04

End

Inhalation

2.0E-03

l.8:E-04

High-

ONU

l.6:E-02

HighEnd

HighMirror edge sealant

l.lE-02

Central
7.9E-02
Tendency

High-

greases/

lubricants and
lubricant
additives

Risk Estimates with PPE

End

Central
Tendency

Page 361 of 691

(PF20)
0.14
(PF20)

1.3:E-04
(APFSO)

 INTERAGENCYDRAr I - DO NOT CITE OR QUOTE
-

--

Life Cycle
Stage/
Category

Subcategory

~

Risk Estimatesfor No PPE

Acute Non- Chronic
Acute Non- Chronic
Occupational
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Exposure
Route and Exposure (benchmark(benchmark (benchmark (benchmark(benchmark(benchmark
Scenario
Population Duration
Level MOE= 10) MOE= 10)
= 104 ) MOE= IO) MOE= 10) = 104 )
ONU
(upper
limit)

Inhalation

HighEnd

4.3E-03

6.JE-03

, .7E-03

N/A

Central
3.0E-02
Tendency

4.3E-02

7.5E-04

N/A

2.SE-04

4.lE-04

0.10

l.4E-02
(APFSO)

2.lE-02
(APFSO)

2.0E-03
(APFSO)

Central
2.4E-03
Tendency

3.5E-03

9.3E-03

0.12
(APFSO)

0.17
(APF50)

t.9E-04
(APF50)

3.7E-03

3.4E-02

S.0E-02
(PF20)

7.3E-02
(PF20)

l.7E-03
(PF20)

l.lE-02

8.7E-03

0.1S
(PFlO)

0.22
(PF20)

4.4E-04
(PF20)

2.JE-03

5.3E-02

l.6E-02
(PF 10)

2.3E-02
(PF 10)

S.3E-03
(PFl0)

7.0E-03

l.4E-02

4.SE-02
(PFl0)

7.0E-02
(PF 10)

1.4E-03
(PF 10)

1.6E-02

2.6E-03

NIA

1.7E-02

1.9E-03

N/A

2.SE-02

7.6E-03

0.19
1.4
(APFSO) e (APFS0)C

0.21

7.9E--04

1.4
(APFSO)"

1.SE-03

6.9E-02

1.4E-02
(PF 10)

1.SE-02
(PF 10)

6.9E-03
(PFI0)

2.SE-03

1.6E-Ol

2.0E-02
(PF 10)

2.SE-02
(PF 10)

1.6E-03
(PF 10)

High-

Inhalation

End

High-

Industrial/
commercialuse
Paints and
coatin~

Adhesives,
Sealants,Paints,
Diluent in solvent-based
and Coatingspaints and coatings
Table 4-22 and
Table 4-23

Worker

End

HighEnd

Cleaningwipes

Spot Cleaning
and Wipe
Cleaning- Table
4-16 and Table
4-17

1.6E-03

Dermal
(Commercial) Central
Tendency 4.SE-03

ONU

Carpet cleaner

l.SE-03

Dennal
(Industrial) Central
7.SE-03
Tendency

High End

Industrial/
oommercialuse Cleaningand
furniture care
products

Risk Estimateswith PPE

Inhalation

Inhalation
(Modeling
Data)b
Worker
Dermal

l.lE-02

Central
1.2E-02
Tendency
HighEnd

3.9E-03

Central
2.9E-02
Tendency
HighEnd

1.4E-03

Central
2.0E-03
Tendency

Page 362 of 691

1.SE-04
(APFSO)"

10.5
7.9E-05
(APF SO)C (APP 10) 0

 INTERAGENCYDRAFT - DO l\.JOTCITE OR Ql Olf
Risk Estimates for No PPE

AcuteNonLife Cycle
Stage/
Category

Industrial/
commercial use Launruyand
dishwashing
products

Occupational

Exposure
Subcategory

Scenario

Population

ONU

Inhalation
(Modeling
Data)b

Inhalation

9.0E-03

3.6E-03

NIA

2.3E-02

3.JE-02

9.2E-04

N/A

l.4E-03

2.lE-03

0.10

7.2E-02
(APFS0)

0.11
(APFS0)

2.0E-03
(APFS0)

l.SE--02

9.3E-03

0.61
(APFS0)

0.90
(APFS0)

l.9E-04
(APFSO)

2.SE~03

3.7E-03

3.4E-02

S.0E-02
(PF20)

7.JE-02
(PF20)

t.7E-03
(PFlG)

Central
7.SE-03
Tendency

1.lE-02

8.7E-03

0.15
(PF20)

0.22
(PF20)

4.4E-04
(PF20)

2.3E-03

5.3E-02

t.6E-02
(PF 10)

2.3E-02
(PF 10)

5.3E-03
(PFlO)

7.0E-03

1.4E-02

4.SE-02
(PF 10)

7.0E-02
(PF 10)

1.4E-03
(PF 10)

1.lE-02

1.6E-02

2.6E-03

N/A

Central
1.2E-02
Tendency

l.7E-02

l.9E-03

N/A

Central
Tendency

End

Central
1.2E--02
Tendency
High.-

Fixatives and finishing
spray coatings

Adhesives,
Sealants,Paints,
and Coatings Table 4-22 and
Table4-23

Worker

Industrial/
commercialuse Industrial
Corrosion
Corrosioninhibitors and
ProcessingAid inhibitorsand
anti-scaling agents
Table 4-24
anti-scaling
agents

Dermal
(Industrial)

End

High-

End

l.6E-03

Dermal
(Commercial) Central
4.SE-03
Tendency

ONU

Worker

Chronic

6.3E-03

End

High-

Industrial/
commercialuse Arts, crafts and
hobby materials

Acute Non-

Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer Cancer
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
= 10-4) MOE= 10) MOE = 10) = 10-4)
Duration
Level MOE = 10) MOE = 10)

High-

Spot remover

Chronic

Risk Estimateswith PPE

Inhalation

HighEnd

HighEnd

5.8E-04

X.4E-04

4.9E-02

2.9E-02
(APF50)

4.lE-02
(APF50)

9.9E-04
(APFSO)

Central
Tendency

1.7E-03

2.SE-03

1.3E-02

X.7E-02
(APFS0)

0.13

(APFSO)

2.SE-04
(APF50)

Inhalation

l734

Page 363 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

Life Cycle

Stage/
Category

Subcategory

Occupational
Exposure
Scenario

Risk Estimatesfor No PPE
Risk Estimateswith PPE
AcuteNon- Chronic
AcuteNon- Chronic
Cancer
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer (benchmark
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark
Population Duration Level MOE = 10) MOE= 10) = 104)
MOE= 10) MOE = 10) = 104)

Process solvent used in
battery manufacture

Dermal

HighEnd

Process solvent used in
polymer fiber spinning,
tluoroelastomer
manufacture and
Alcantara manufacture

Industrial
Processing Aid Table4-24

Inhalation
Workers
Dermal

ONU
(upper
limit)
Industrial/
commercial use
Automotivecare
products

Brake and parts cleaner

Aerosol
Applications Table 4-15

Workers

0.20
(PF20)

4.9E-04
(PFl0)

9.7E-03

l.SE-03

3.7E-03

1.lE-02

NIA

Central
S.6E-03
Tendency

8.2E-03

3.9E-03

NIA

S.3E-03

7.7E-03

S.4E-03

0.39
0.26
1.lE-04
(APFSO)C (APFSO)C (APFSO)C

Central
Tendency

0.13

0.19

1.7E-04

6.S
9.S
l.7E-OS
(APFSO)C (APFSO) C (APF IO)C

HighEnd

4.lE-03

6.0E-03

l.tE-0.2

4.tE-02
(PF 10)

6.0E-02
(PF10)

2.lE-03
(PF 10)

Central
S.7E-03
Tendency

8.4E-03

5.3E-03

S.7E-02
(PF 10)

8.4E-Ol
(PF 10)

5.3E-04

High-

Toner aid

0.14
(PFl0)

9.9E-03

Inhalation

Precipitant used in betacyclodextrin manufacture

Commercial
Printing and
Copying Table 4-25

(PF20)

6.8E-03

Central

End

Extraction solvent used in
caprolactammanufacture

Industrial/
commercial use
Ink, toner and
colorant
products

1.9~3

(PF20)

3.8E-02

High-

ONU

6.6:E-Ol

(PF20)

3.3E-03

Tendency
Industrial/
commercial use
Processing aids

4.SE-02

2.3:E-03

Inhalation

Inhalation

End

HighEnd

S.3E-03

7.7E-03

5.4E-03

NIA

Central
Tendency

0.13

0.19

l.7E-04

NIA

HighEnd

4.6E-04

6.SE-04

4.9E-Ol

Central
Tendency

l.SE-03

2.lE-03

t.4E-02

Page 364 of 691

2.JE-02
(APF50)

3.4E-02
(APF50)

7.JE-02

0.11

(APFSO)

(APF50)

(PF 10)

9.7E-04
(APFSO)
2.9E-04
(APFSO)

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
---Life Cycle
Stage/

Category

Subcategory

Occupational
Exposure
Scenario

Risk Estimatesfor No PPE
Risk Estimateswith PPE
Acute Non- Chronic
Acute Non• Chronic
Cancer
Exposure
Cancer Non-Cancer Cancer
Cancer Non-Cancer (benchmarlc
(benchmark
Route and Exposure (bench.mark (bench.mark
(bench.mark(benchmark
4
Population Duration Level MOE= 10) MOE= 10) ==104) MOE= 10) MOE= 10) = 10 )

Dermal

ONUs

IndustriaV
commercial use
Apparel and
footwear care
products

Inhalation
(Modeling
D~)b

I

Worker

Shoe polish

Hoof polishes
Gun Scrubber
Industrial/
commercialuse
Other uses

Lace wig and hair
extensionglues
1

Inhalation

Pepper spray

Other miscellaneous
· industrial and commercial
uses

Denna)

Other Commercial
Uses
(Spot Cleaning
and Wipe
Cleaning)Table 4-16 and
Table 4-17

HighEnd

8.6£.02
(PF20)

0.13
(PF20)

7.6E-04

2.tE--03

5.9~2

Central
4.3E-03
Tendency

6.JE-03

1.S~

t.6E-02

2.0E-03

N/A

0.12

2.6E-04

N/A

5.7E-03

5.8E-03

0.20
0.28
(APFSO)C (APF50)

1.6E-02

t.8E-03

0.58
(APFSO)C

3.7E-05
0.82
(APFSO)C (APF 10) c

1.SE-03

6.9E-02

1.4.E-02
(PF 10)

1.SE-02
(PF 10)

6.9E-03
(PFl0)

2.8E-03

1.6E-02

2.0E-02
(PF 10)

2.8E-02
(PF 10)

(PF10)

HighEnd

1.lE-02

Central
7.9E-02
Tendency
HighEnd

4.0E-03

Central
1.lE-02
Tendency
·High1.4E-03
End
Central
2.0E-03
Tendency
HighEnd

ONUs

4.2E-02
(PF20)

2.9E-03

(PF20)

1.9E-02

1.4E-03

6.3E-03

9.0E-03

3.6E-03

N/A

3.3E-0l

9.2E-04

N/A

Inhalation
(Modeling
Data)b

Central
2.3E-02
Tendency

l735

Page 365 of 691

(PF20)
(PF20)

0

1.2E-04
(APF50) e

1.6E--03

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Risk Estimates for No PPE

Acute NonLife Cycle
Stage/
Category

Subcategory

Occupational
Exposure
Scenario

Population

Industrialpre-treatment

Disposal

Industrial wastewater
treatment

Publicly owned treannent
works (POTW)

Chronic

Acute Non-

Risk Estimates witb PPE

Chronic

Acute Non•

Chronic

Cancer Non-Cancer' Cancer Non-Cancer Cancer Non-Cancer
Exposure
Route and Exposure (benchmark (benchmark (benchmark (benchmark (benchmark (benchmark
Duration
Level
MOE = 10) MOE= 10) MOE = 10) MOE = 10) MOE= 10) MOE = 10)
HighEnd

9.7E-03

l.4E-02

l.9:E-03

0.49
(APFS0)

0.71
(APF50)

5.9E-05
(APF 50)

Central
Tendency

22.4

32.7

9.9E-07

224
(APF 10)

327
(APF IO)

9.9E-08
(APF 10)

HighEnd

2.3E-03

3.3E-03

3.SE-02

4.5E-02
(PF20)

6.6E-02
(PF20)

1.9E-03
(PF20)

Central
Tendency

6.SE-03

9.9E-03

9,7E-03

0.14
(PF20)

0.20
(PF20)

4.9E-o4
(PF20)

l.4E-02

2.9E-03

NIA

9.9E-07

NIA

Inhalation
Process Solvent
Recycling and
Worker Handling
of WastesTable 4-27

Workers

Dermal

ONU
(upper
limit)

High9.7E-03
End
Inhalation
Central
22.4
Tendency

32.7

• Monitoring data was selected as most representativebased on the EPA data hierarchy where high-quality monitoringdata is preferred over modeling results or exposure limits.
b Modeling data was selected as most representativebecause the monitoring dataset contained a very low number of datapoints.
0
EPAbelieves that small commercial facilities perfonning spot cleaning, wipe cleaning, and other related commercialuses as well as co~mercial printing and copying are unlikely to
have a respiratory protection program. Therefore, the me of respirators is unlikely for workers in these facilities.
N/A = Not Applicable.ONUs are not expected to wear respiratory protection.

l736

Page 366 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR Ql 01£
737
738
739
740
741
742
743
744
745
746

747

4.5.1.2
Summary of Risk Estimates for Consumers and Bystanders
Table 4-56 summarizesthe risk estimates for CNS effects from acute inhalation and dennal exposures
for all consumer exposure scenarios. Risk estimates that exceed the benchmark (i.e. MOEs less than the
benchmark MOE) are highlightedby holding the number and shading the cell in gray. The consumer
exposure assessment and risk characterizationare describedin more detaiJin Sections 2.3.2 and 4.2.3 ,
respectively. Specific links to the relevant risk characterizationsections are listed in Table 4-56 in the
Consumer Condition of Use Scenariocolumn.

Of note, the risk summarybelow is based on the most sensitive non-cancerendpoint (cardiac toxicity)
For the majority of exposure scenarios,risks were identifiedfor multiple endpoints in acute exposure
scenarios.

748

749
750
751
752
753
754
755
756
757
758
759
760

Inhalation
For acute inhalation exposuresthere are risks for non-cancereffects (i.e. cardiac toxicity) for consumer
users and bystanders relative to the benchmarksfor all the exposure scenarios at both medium and highintensity user exposure levels.
Dermal
For acute dermal exposuresthere are risks for non-cancereffects (i.e. cardiac toxicity) for consumer
users (bystanders are assumed to not have direct dermal contact with TCE}relative to the benchmarks
for all the exposure scenarioswhere dermal exposure is expected at both medium and high-intensityuser
exposure levels.

.

Table 4-56 Consumer Ri skS ummary T abl e
Life Cycle
Stage/
Category

Subcategory/
Consumer
Condition of Use
Scenario

Exposure
Route and
Population Duration
Inhalation

Brake and Parts
Cleaner•
Table4-28

Aerosol electronic
Consumer Use - degreaser/cleanerSolvents (for
Table4-29
cleaning or
degreasing)
Liquid electronic
degreaser/cleanerTable4-30

User

Age
High-Intensity ModerateLow-Intensity
Group
User
Intensity User
User
NIA

6.4E-OS

4.lE-04

5.lE-03

21+

6.SE-05

9.lE-04

4.lE-02

16-20

7.3.E-05

9.7E-04

4.4E-02

11-15

6.7E-OS

8.9E-04

4.0E-02

Bystander

Inhalation

NIA

9.lE-04

l.6E-OJ

l.OE-Ol

User

Inhalation

NIA

9.SE-05

2.3E-03

6.7E-0l

Bystander

Inhalation

NIA

4.9E-04

1.3E-02

0.34

Inhalation

NIA

l.OE-04

1.6E-03

2.lE-02

21+

l.lE-04

1.8E-03

7.3E-03

16-20

l.3E-04

1.9E-03

7.SE-03

11-15

l.lE-04

I.SE-OJ

7.lE-03

Inhalation

NIA

5.lE-04

8.SE-03

0.11

Inhalation

NIA

2.3E-OS

9.0E-OS

6.0E-04

Dermal

21+

7.3E-OS

S.SE-04

l.9E-OJ

User

Bystander
Aerosol spray
degreamlrlcleaner-

Dermal

Acute Non-Cancer
(benchmark MOE= 10)

User

Dermal

Page 367 of 691

 INTERAGENCYDR.<\.FT
- DO NOT CITE OR QUOTE
Life Cycle
Stage/
Category

Subcategory/
Consumer
Conditionof Use
Scenario

Exposure
Routeaod
Population Duration

Acute Non-Cancer
(benchmarkMOE= 10)
Age High-Intensity Moderate- Low-Intensity
Group
User
Intensity User
User
16-20

1.sE~os

6.2E-04

3.lE-03

11-15

7.lE-OS

S.7E-04

2.8E-03

Bystander Inhalation

NIA

7.9E-05

3.6E-04

2.5E-03

Inhalation

NIA

2.SE-05

l.4E-04

1.4E-03

21+

3.0E-05

l.4E-04

I.SE-03

16-20

3.2E·05

2.6E-04

t.9E-03

11-15

3.0E-05

2.4E-04

l.8E-03

Bystander Inhalation

NIA

1.0E-04

l.2E-03

7.6E-03

Inhalation

NIA

S.0&02

4.7E-02

8.lE-02

21+

7.SE-05

6.0E-04

7.5E-03

16-20

8,lE-05

6.4E-04

8.0E-03

11-15

7.4E-05

S.9E-04

7.JE-03

Bystander Inhalation

NIA

0.20

0.25

0.44

Inhalation

NIA

5.SE-02

S.sE-02

5.9E-02

21+

3.3E-05

2.6E-04

1.9E-03

16-20

3.SE-05

2.SE-04

2.lE-03

11-15

3.lE-05

2.SE-04

1.9E-03

NIA

0.24

0.29

0.30

Inhalation

NIA

2.3E-04

2.lE-03

2.lE-02

Bystander Inhalation

NIA

1.lE-03

1.lE-02

0.11

Inhalation

NIA

2.4E-04

8.9E-04

6.4E-03

21+

3.JE-04

1.3E-03

5.7FAJ

16-20

3.SE-04

1.4E-03

6.0E-03

11-15

3.2E-04

l.3E-03

5.SE-03

NIA

5.4E-04

3.6E-03

2.6E-02

NIA

7.SE-05

4.0E-04

l.0E-03

21+

S.9E-05

2.4E-04

7.lE-04

16-20

6.JE-OS

2.SE-04

7.6E-04

11-15

5.SE-05

2.3E-04

6.9E-04

Bystander Inhalation

NIA

l.4E-04

1.6E-03

8.3E-03

User
Inhalation
Tap and Die Fluid Table 4-38
Consumer Use Bystander Inhalation
Lubricantsand
User
Inhalation
greases
Penetratinglubricant
-Table 4-39
Bystander Inhalation

NIA

l.SE-04

2.4E-03

1.3E-02

NIA

t.3E-03

1.3E-02

4.3E-02

NIA
NIA

3.lE-04

5.4E-03

0.17

1.6E-03

2.9E-02

0.88

Table 4-31

Liquid
degreaser/cleanerTable 4-32

Aerosol gun
scrubberTable4-33

Liquid gun

scrubberTable4-34

User

User

User

Dermal

Dermal

Dermal

Bystander Inhalation
Mold ReleaseTable 4-35

Aerosol Tire Cleaner
-Table4-36

User

User

Dermal

Bystander Inhalation
Inhalation
Liquid Tire Cleaner Table4-37

User

Dermal

Page 368 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
I

Subcategory/
Consumer
Condition of Use
Scenario

Life Cycle
Stage/
Category
I

Acute Non-Cancer
(benchmark MOE = 10)

Exposure
Route and
Population Duration

Solvent-based
adhesives and
sealants Table 4-40

User

ConswnerUseAdhesives and
Mirror edge sealant sealants
Table 4-41
I
I

Age
High-Intensity
ModerateGroup
User
Intensity User

Inhalation

NIA

1.lE-04

3.7E-03

0.42

Bystander Inhalation

NIA

9.tE-04

3.6E-Ol

1.8

NIA

t.tE-03

3.3E-03

0.17

NIA

4.7E-03

t.SE-02

0.91

Inhalation

NIA

3.lE-04

S.6E-03

6.2E-0l

Bystander Inhalation

NIA

9.7E-04

l.3E-02

0.23

Inhalation

NIA

7.0E-05

5.SE-04

3.4E-03

User

Inhalation

Bystander Inhalati<>n

Tire repair cement/
sealerTable4-42

User

User

Carpet cleaner Table4-43

Dermal

Aerosol Spot
RemoverTable 4-44

Arts, crafts,and

hobby materials

1

Consumer use Apparel and
footwear care
products

Fixatives and
finishing spray
coatings Table4-46

Shoe polish •
Table4-47

i

Consumer use Other conswneli

uses

Fabric spray Table 4-48
Film cleaner Table4-49

t.3E-Ol

16-20

1.2E-04

7.lE-04

1.4E-Ol

11-15

1.tE-04

6.6E-04

1.3E-Ol

Inhalation

NIA

1.tE-04

9.SE-04

6.SE-03

21+

9.4E-04

5.7E-03

5.7E-02

16-20

1.0E-03

6.0E-03

6.0E-02

11-15

9.lE-04

S.SE-03

5.5E-02

NIA
NIA

1.lE-03

9.9E-03

5.4E-02

9.3E-05

7.8E-04

6.SE-03

21+

1.6E-04

9.SE-04

1.5E-02

16-20

1.7E-04

t.OE-03

1.6E-Ol

11-15

l.6E-04

9.5E-04

1.4E-02

NIA

4.6E-04

4.lE-03

3.4E-02

NIA

4.0E-04

2.sE-03

1.3E-02

Bystander Inhalation

NIA

l.6E-03

l.3E-02

6.SE-02

Inhalation

NIA

l.lE-03

1.lE-02

6.lE-02

21+

t.7E-03

1.0E-02

0.10

16-20

1.8E-03

1.IE-02

0.11

11-15

l,7E-03

1.0E-02

0.10

Dermal

Inhalation

Consumer use "'

6.7E-04

NIA

Bystander Inhalation

User

J

1.lE-04

Bystander Inhalation

User

Liquid Spot
RemoverTable 4-45

21+

3.lE-04

I

Consumer use Cleaning and
furniture care
products

Low-Intensity
User

Bystander
User

User

Dermal
Inhalation·
Inhalation

Dermal

I

2.9E-03

J.6:E-02

Bystander

Inhalation

NIA

S.5E-03

5.9E-Ol

3.lE-01

User

Inhalation

NIA

5.8E-OS

3.6E-04

J.9E-03

Bystander

Bystander

NIA

2.4E-04

1.!)E-03

9.5E-03

User

Inhalation

5.8E--OS

3.6E-04

1.9E-03

Bystander

Bystander

NIA
NIA

2.4E-04

1.9E-03

9.SE-03

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Life Cycle
Stage/
Category

Subcategory/
Consumer
Condition of Use
Scenario

Acute Non-Cancer
(benchmark MOE = 10)

Population

Exposure
Route and
Duration

Age
Group

High-Intensity
User

Hoof polish-

User

Inhalation

N/A

1.7E-03

1.7E-0l

0.12

Table 4-50

Bystander

Bystander

N/A

0.34

7.8

48

Pepper spray Table 4-51

User
Inhalation

NIA

User

Inhalation

NIA

4.2E-04

2.6E-03

1.4E-02

Bystander

Bystander

NIA

l.7E-03

l.4E-Ol

6.9E-02

User

Inhalation

NIA

3.SE-03

3.0E-02

Bystander

Bystander

NIA

3.0E-02

0.30

Toner aidTable4-52
Lag e wig and hair
extension glues Table 4-53

ModerateIntensity User

Low-Intensity
User

0.21

Bystander

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5 RISK DETERMINATION
5.1 Unreasonable Risk
5.1.1 Overview
In each risk evaluation under TSCA section 6(b), EPA determineswhether a chemical substance
presents an unreasonablerisk of injury to health or the environment,under the conditions of use. These
determinationsdo not considercosts or other non-risk factors. In making these determinations,EPA
considers relevant risk-related factors, including,but not limited to: the effects of the chemical substance
on health and hwnan exposure to such substanceunder the conditions of use (including cancer and noncancer risks); the effects of the chemical substance on the environmentand environmental exposure
under the conditions of use; the population exposed (includingany potentially exposed or susceptible
subpopulations(PESS)); the severity of hazard (includingthe nature of the hazard, the irreversibility of
the hazard); and uncertainties. EPA also takes into considerationthe Agency's confidence in the data
used in the risk estimate. This includes an evaluationof the strengths, limitations and uncertainties
associated with the informationused to informthe risk estimate and the risk characterization. This
approach is in keeping with the Agency's final rule, Proceduresfor ChemicalRisk Evaluation Underthe
Amended Toxic SubstancesControlAct (82 FR 33726).19
Under TSCA, conditions of use are defined as the circumstances,as determinedby the Administrator,
under which the substance is intended, known, or reasonably foreseento be manufactured,processed,
distributed in commerce,used, or disposed of. TSCA §3(4).

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An unreasonable risk may be indicated when health risks under the conditions of use are identified by

comparing the estimated risks with the risk benchmarksand where the risks affect the general
population or PESS, identified as relevant. For workers (which are one example of PESS), an
unreasonable risk may be indicatedwhen risks are not adequatelyaddressedthrough expected use of
workplace practices and exposure controls, including engineeringcontrols or use of personal protective
equipment (PPE). An unreasonablerisk may be indicated when environmentalrisks under the conditions
of use are greater than environmentalrisk benchmarks.The risk estimates contribute to the evidence
EPA uses to detennine unreasonablerisk.
EPA uses the term "indicates unreasonablerisk" to indicate EPA concern for potential unreasonable
risk. For non-cancer endpoints, "less than MOE benchmark" is used to indicatepotential unreasonable
risk; this occurs if an MOE value is less than the benchmarkMOE (e.g.,, MOE 0.3 < benchmark MOE
30). For cancer endpoints, EPA uses the term "greater than risk benchmark"to indicate potential
unreasonable risk; this occurs, for example, if the lifetimecancer risk value is greater than 1 in 10,000
(e.g.,, cancer risk value is 5x10·2 which is greater than the standard range of acceptablecancer risk
benchmarks of lx10 4 to lxl0" 6) . For environmentalendpoints,to indicatepotential unreasonablerisk
EPA uses a risk quotient (RQ) value "greater than l" (i.e., RQ > 1). Conversely,EPA uses the term
"does not indicate unreasonable risk" to indicatethat it is unlikely that EPA has a concern for potential
unreasonablerisk. More details are describedbelow.

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19 This risk determinationis being issued underTSCAsection6(b) and the terms used, such as unreasonablerisk, and the
considerationsdiscussedare specific to TSCA. Other statuteshave differentauthoritiesand mandatesand may involve risk
considerationsother than those discussedhere.

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The degree of llllcertainty surrounding the MO Es, cancer risk or RQs is a factor in determining whether
or not unreasonable risk is present. Where uncertainty is low, and EPA has high confidence in the
hazard and exposure characterizations (for example, the basis for the characterizations is measured or
monitoring data or a robust model and the haz.ards identified for risk estimation are relevant for
conditions of use), the Agency has a higher degree of confidence in its risk determination. EPA may also
consider other risk factors, such as severity of endpoint, reversibility of effect, or exposure -related
considerations, such as magnitude or number of exposures, in determining that the risks are
unreasonable under the conditions of use. Where EPA has made assumptions in the scientific evaluatio~
whether or not those assumptions are protective will also be a consideration. Additionally, EPA
considers the central tendency and high-end scenarios when determining the unreasonable risk. Highend risk estimates (i.e., 95th percentile) are generally intended to cover individuals or sub-populations
with greater exposure (PESS) and central tendency risk estimates are generally estimates of average or
typical exposure .

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EPA may make a no unreasonable risk determination for conditions of use where the substance's hazard
and exposure potential, or where the risk-related factors described previously, lead EPA to determine
that the risks are not unreasonable.

5.1.1

Risks to Human Health

S.l.1.1 Determining Non-Cancer Risks
Margins of exposure (MOEs) are used in EPA's risk evaluations as a starting point to estimate noncancer risks for acute and chronic exposures. Toe non-cancer evaluation refers to potential adverse
health effects associated with health endpoints other than cancer, including to the body's organ systems,
such as reproductive/developmental effects, cardiac and lung effects, and kidney and liver effects. The
MOE is the point of departure (POD) (an approximation of the no-observed adverse effect level
(NOAEL) or benchmark dose level (BMDL)) for a specific health endpoint divided by the exposure
concentration for the specific scenario of concern. The benchmark for the MOE that is used accounts for
the total uncertainty in a POD, including, as appropriate: (1) the variation in sensitivity among the
members of the human population (i.e., intrahuman/intraspecies variability); (2) the uncertainty in
extrapolating animal data to humans (i.e., interspecies variability); (3) the uncertainty in extrapolating
from data obtained in a study with less-than-lifetime exposure to lifetime exposure (i.e ., extrapolating
from subchronic to chronic exposure); and (4) the uncertainty in extrapolating from a lowest observed
adverse effect level (LOAEL) rather than from a NOAEL. MOEs can provide a non-cancer risk profile
by presenting a range of estimates for different non-cancer health effects for different exposure scenarios
and are a widely recognized point estimate method for evaluating a range of potential non--cancer health
risks from exposure to a chemical.
A calculated MOE that is less than the benchmark MOE indicates the possibility of risk to human health.
Whether those risks are unreasonable will depend upon other risk-related factors, such as severity of
endpoint, reversibility of effect, exposure-related considerations (e.g." duration, magnitude, frequency
of exposure, population exposed), and the confidence in the information used to inform the hazard and
exposure values. If the calculated MOE is greater than the benchmark MOE, generally it is less likely
that there is risk.

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Uncertainty factors (UFs) also play an important role in the risk estimation approach and in determining
unreasonable risk. A lower benchmark MOE (e.g.,, 30) indicates greater certainty in the data (because
fewer of the default UFs relevant to a given POD as described above were applied). A higher benchmark
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MOE (e.g.,, 1000) would indicate more uncertainty in risk estimation and extrapolation for the MOE for
specific endpoints and scenarios. However, these are often not the only uncertainties in a risk evaluation.
S.1.1.2 Determining Cancer Risks
EPA estimates cancer risks by determining the incremental increase in probability of an individual in an
exposed population developing cancer over a lifetime (excess lifetime cancer risk (ELCR)) following
exposure to the chemical llllder specified use scenarios. Standard cancer benchmarks used by EPA and
other regulatory agencies are an increased cancer risk above benchmarks ranging from 1 in 1,000,000 to
1 in 10,000 (i.e., lxl0-6 to lxl0-4) depending on the subpopulationexposed. Generally, EPA considers 1
x 10-6to lx 104 as the appropriate benchmark for the general population, consumer users, and nonoccupational PESS.20

For the subject chemical substance, the EPA, consistent with case law and 2017 NIOSH guidance,21
used 1 x 104 as the benchmark for the purposes of this risk determination for individuals in industrial
and commercial work environments subject to Occupational Safety and Health Act (OSHA)
requirements. It is important to note that 1x10-4is not a bright line and EPA has discretion to make risk
determinations based on other benchmarks as appropriate. It is important to note that exposure-related
considerations (duration, magnitude, population exposed) can affect EPA' s estimates of the excess
lifetime cancer risk.
5.~
Determining Environmental Risk
___
_
To assessenvironmental risk, EPA identifies and evaluates environmental hazard data for aquatic,
sediment-dwelling, and terrestrial organisms exposed under acute and chronic exposure conditions. The
environmental risk includes any risks that exceed benchmarks to the aquatic environment from levels of
the evaluated chemical released to the environment (e.g.,,smface water, sediment, soil, biota) under the
conditions of use, based on the fate properties, release potential, and reasonably available environmental
monitoring and hazard data.

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Environmental risks are estimated by calculating a RQ. The RQ is defined as:

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RQ = EnvironmentalConcentration/ Effect Level
An RQ equalto I indicates that the exposures are the same as the concentration that causes effects. If the
RQ is greater than l, the exposure is greater than the effect concentration and there is potential for risk

presumed. If the RQ is less than 1, the exposure is less than the effect concentration and unreasonable
risk is not likely. The Concentrations of Concern (COC) or hazard value for certain aquatic organisms
are used to calculate RQs for acute and chronic exposures.For environmentalrisk, EPA is more likely to
As an example, when EPA 's Office of Water in 2017 updatedthe Hwnan Health Benchmarks for Pesticides, the
benchmadcfor a ''theoretical upper-bound excess lifetime cancer risk" from pesticides in drinking water was identified as I in
1,000,000 to 1 in 10,000 over a lifetime of exposure (EPA. Human Health Benchmarks for Pesticides: Updated 2017
Technical Document January2017. https://www.epa.gov/sites/production/files/2015-10/documents/hh-benchmarkstechdoc.pdt). Similarly, EPA's approach under the Cl~ Air Act to evaluate residual risk and to develop standards is a twostep approach that includes a ''presumptive limit on maximum individual lifetime [cancer] risk (MIR) of approximately 1 in
IO thousand" and consideration of whether emissions standardsprovide an ample margin of safety to protect public health "in
consideration of all health information, including the number of persons at risk levels higher than approximately 1 in 1
million, as well as other relevant factors" (54 FR 38044, 38045, September 14, 1989).
21 International Union.UAW v. Pendergrass, 878 F.2d 389 (D.C. Cir. 1989), citing Industrial Union Department,AFL-CIO v.
American Petroleum Institute, 448 U.S. 607 (1980) (''Benzene decision"), in which it was foundthat a lifetime cancer risk of
1 in 1,000 was found to be clearly significant;and NIOSH (Whittaker et al., 2016). Current intelligencebulletin 68: NlOSH
chemical carcinogen policy, available at https://www.cdc.gov/niosh/docs/2017-100/pdf/2017-IOO.pdf.
20

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determine that there is unreasonable risk if the RQ exceeds 1 for the conditions of use being evaluated.
Consistent with EPA's human health evaluations, the RQ is not treated as a bright line and other riskbased factors may be considered (e.g.,, exposure scenario, uncertainty, severity of effect) for purposes of
making a risk determination.

5.2 Risk Determinations for TCE
EPA's determinations of unreasonable risk for specific conditions of use ofTCE listed below are based
on health risks to workers and occupational non-users (ONUs) during occupational exposures, and to
consumers and bystanders during exposures to consumer uses.
As described in section 4, significant risks associated with more than one adverse effect (e.g.,
developmental toxicity, reproductive toxicity, liver toxicity, kidney toxicity, immunotoxicity,
neurotoxicity, and cancer) were identified for particular conditions of use. In table 5-1 and section 5.3
below, EPA identifies developmental cardiac malfonnations as the driver endpoint for the conditions of
use that EPA has preliminarily detennined present unreasonable risks. This is the effect that is most
sensitive, and it is expected that addressing risks for this effect would address other identified risks.
•

Workers: EPA evaluated workers' acute and chronic inhalation and dermal exposures for cancer
and non-cancer risks and determined whether any risks are unreasonable. The drivers for EPA's
determination of unreasonable risk for workers are developmental cardiac toxicity resulting from
acute and chronic inhalation and dermal exposures, and, for most conditions of use, cancer
resulting from chronic inhalation and dennal exposures. The determinations reflect the severity
of the effects associated with the occupational exposures to TCE and incorporate consideration
of expected PPB. EPA expects there is compliance with federal and state laws, such as worker
protection standards, unless case-specific facts indicate otherwise, and therefore existing OSHA
regulations for worker protection and hazard communication will result in use of appropriate
PPE consistent with the applicable SDSs. Estimated numbers of workers are in section 2.3 .1.2. 7.

•

Occupational Non-Users (ONU§): EPA evaluated ONU acute and chronic inhalation exposures
for cancer and non-cancer risks and determined whether any risks are unreasonable. The drivers
for EPA's determination of unreasonable risks to ONUs are developmenta1 cardiac toxicity
resulting from acute and chronic inhalation and cancer resulting from chronic inhalation. The
determinations reflect the severity of the effects associated with the occupational exposures to
TCE and the expected absence of PPE for ONUs. For dennal exposures, because ONUs are not
expected to be dermally exposed to TCE, dennal risks to ONUs generally were not evaluated.
For inhalation exposures, EPA, where possible, used monitoring or modeling information to
estimate ONU exposures and to describe the risks separately from workers directly exposed. For
some conditions of use, EPA did not separately calculate risk estimates for ONU s and workers.
For these conditions of use, there is uncertainty in the ONU risk estimates since the data or
modeling did not distinguish between worker and ONU inhalation exposure estimates. ONU
inhalation exposures are expected to be lower than inhalation exposures for workers directly
handling the chemical substance; however, the relative exposure of ONUs to workers in these
cases cannot be quantified. To account for this uncertainty, EPA considered the central tendency
risk estimate when determining ONU risk for those conditions of use for which ONU exposures
were not separately estimated. Estimated numbers of occupational non-users are in section
2.3.1.2.7.

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•

Consumers: EPA evaluated consumer acute inhalation and dermal exposures for non-cancer risks
and determinedwhether any risks are unreasonable.The driver for EPA' s detennination of
unreasonablerisk is developmentalcardiac toxicity from acute inhalation, and, usually, from
dermal exposure as well. Generally, risks for consumerswere indicated by acute inhalation and
dermal exposure at low, medium, and high intensity use. Estimated numbers of consumers are in
section2.3.1.2.7.

•

Bystanders (from consumer uses): EPA evaluatedbystander acute inhalation exposures for noncancer risks and determined whether any risks are unreasonable.The driver for EPA's
determinationof unreasonablerisk is developmentalcardiac toxicity from acute inhalation
exposure. Generally, risks for bystanders were indicatedby acute inhalation exposure scenarios
at low, medium, and high intensity use. Because bystanders are not expected to be dermally
exposed to TCE, dermal non-cancer risks to bystanderswere not evaluated. Estimated numbers
of bystanders are in section 2.3.1.2.7.

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As described below, risks to the environmentand general population either were not relevant for these
conditions of use or were evaluated and not found to be unreasonable. For the conditions of use where
EPA found no unreasonable risk, EPA describes the estimatedrisks in Section 4.5.1 (Table 4-55 and
Table 4-56).

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•

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EPA did not identify any additional scenarios indicatingunreasonablerisk for aquatic organisms
from exposures to TCE in surface waters. For aquatic organisms like aquatic invertebratesand fish,
one facility had an acute RQ greater than 1 (RQ = 3.11), exceeding the acute COC of 3,200 ppb and
indicating risk to aquatic organisms from acute exposures. 1bis facility is one of 59 facilities
modeled by EPA that use TCE for open-top vapor degreasing(see Section 4.4.1 ). Another facility
had an acute RQ of 0.94 indicating some uncertaintyabout whether it would also pose risks to ·
aquatic organisms from acute exposures. This facility is one of 11 facilities modeled by EPA that
process TCE as a reactant (see Section 4.5.1). Both facilities had chronic RQs greater than 1,
exceeding the chronic COC of 788 ppb for 20 days. The over 400 facilities with potential risks to the
most sensitive algae species (exceeding the algae COC of 3 ppb) did not show risks for algae species
as a whole, as they showed no risks for 95% of algae species (no exceedancesof the algae COC of
52,000 ppb). Monitored data from the Water Quality Portal and grey literature show no exceedances
of the acute COC and the chronic COC in ambient water. Monitored data from literature showed
some exceedances of the algae COC of3; however, these monitors were near facilities and therefore
do not represent ambient concentrations. Therefore,EPA did not identify risks for acute or chronic
exposure durations in ambient water for areas where monitored data were available. Given the
uncertainties in the modeling data and exceedanceofRQ for only two data points out of 70, and no
exceedances of RQ from monitoring data, EPA does not consider these risks unreasonable (see
Section 4.5.1).

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Environmental risks: EPA concludedthat environmentalexposures are expected for aquatic
species for the conditions of use within the scope of the evaluation. EPA identified risks from acute
and chronic exposures for aquatic organisms like aquatic invertebratesand fish near two facilities
releasing TCE to surface water and risks to the most sensitive algae species near over 400 facilities.

•

General population: Exposure pathways to the general population are covered by other statutes and
consist of: the ambient air pathway (i.e., TCE is listed as a HAP in the Clean Air Act (CAA)), the
drinking water pathway (i.e., National Primary Drinking Water Regulations(NPDWRs) are
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promulgated for TCE under the Safe DrinkingWater Act), ambient water pathways (i.e., TCE is a
priority pollutant with recommended water quality criteria for protection of human health under the
CWA), the biosolids pathway (i.e., the biosolids pathway for TCE is currently being addressed in the
CWA regulatory analytical process), disposal pathways (TCE disposal is managed and prevented
from further environmental release by RCRA and SDWA regulations). As described above, other
environmental statutes administered by EPA adequately assess and effectively manage these
exposures. EPA believes that the TSCA risk evaluation should focus on those exposure pathways
associated with TSCA conditions of use that are not subject to the regulatory regimes discussed
above because those pathways are likely to represent the greatest areas of concern to EPA.
Therefore, EPA did not evaluate hazards or exposures to the general population in this risk
evaluation, and there is no risk determination for the general population (U.S. EPA. 2018d ).
Table 5-1 below presents an overview of risk determinations by condition of use. An in-depth
explanation of each determination follows the table, in section 5.3.
. ti ons b1vCon d.ti
I on ofU se
Table 5-1. Summary o fU nreasona bl e RiskD e termma
Condition of Use

Unreasonable Risk Determination

Manufacture - Domestic Manufacture

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Manufacture - Import (includes repackaging and
loading/wtloading)

Presents an unreasonable risk of injury to health
(workers)

Does not present an unreasonable risk of injury to
health (occupational non-users)
Processing - Processing as a reactant/intermediate in
industrial gas manufacturing (e.g.,, manufacture of
fluorinated gases used as refrigerants, foam blowing
agents and solvents)

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Processing - Incorporation into formulation, mixture or
reaction product - Solvents (for cleaning or degreasing);
adhesives and sealant chemicals; solvents (which
become part of product formulation or mixture) (e.g.,,
lubricants and greases, paints and coatings, other uses)

Presents an unreasonable risk of injury to health
(workers)

Processing - Incorporation into articles - Solvents
(becomes an integral components of articles)

Does not present an unreasonable risk of injury to
health (occupational non-users)
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to

health (occupational non-users)
Processing - Repackaging - Solvents (for cleaning or
degreasing)

Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to

health (occupational non-users)
Processing - Recycling

Presents an unreasonable risk of injury to health
(workers)

Does not present an unreasonable risk of injury to
health (occupational non-users)
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Condition of Use

Unreasonable Risk Determination

Di&tributionin Commerce

Presents an unreasonable risk of injury to health
(workers and ~cupational non-usen)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Batch vapor degreaser (open-top)

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Batch vapor degreaser (closed-loop)

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - In-line vapor degreaser (conveyorized)

Presents an unreasonable risk of injury to health
(workers and ~cupational non-users)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing)- In-line vapor degreaser (web cleaner)

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Cold cleaner

Presents an unreasonable risk of injury to health
(workers and ~cnpational non-users)

Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Aerosol spray degreaser/cleaner;mold
release

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Lubricants and
Presents anunreasonable risk ofinjury to health
greases/lubricants and lubricant additives - Tap and die (workers and occupational non-users)
fluid
Industrial/Commercial Use - Lubricants and
Presents an unreasonable risk of injury to health
greases/lubricants and lubricant additives - Penetrating (workers and occupational non-users)
lubricant
Industrial/Commercial Use - Adhesives and sealants Solvent-based adhesives and sealants; tire repair
cement/sealer; mirror edge sealant

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - FW1ctionalfluids (closed
systems) - Heat exchange fluid

Presents an unreasonable risk of injury to health
(workers and ~cupational non-users)

Industrial/Coinmercial Use - Paints and coatings Diluent in solvent-based paints and coatings

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Cleaning and furniture
care products - Carpet cleaner; wipe cleaner

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Laundry and dishwashing Presents an unreasonable risk of injury to health
(workers and occupational non-users)
products- Spot remover
Industrial/Commercial Use - Arts, crafts and hobby
materials - Fixatives and :finishingspray coatings

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/Commercial Use - Corrosion inhibitors and
anti-scaling agents - Corrosion inhibitors and antiscaling agents

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

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Condition of Use

Unreasonable Risk Determination

Industrial/ Commercial Use - Processing aids - Process Presents an UJireasonable risk of injury to health
solvent used in battery manufacture;process solvent
(workers and occupational non-users)
used in polymer fiber spinning, fluoroelastomer
manufacture, and Alcantaramanufacture; extraction
solvent used in caprolactam manufacture;precipitant
used in beta-cyclodextrinmanufacture
Industrial/CommercialUse - Ink, toner and colorant
products - Toner aid

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Industrial/CommercialUse - Automotivecare products Presents an unreasonable risk of injury to health
- Brake and parts cleaners
(workers and occupational non-users)
Industrial/CommercialUse - Apparel and footwear care Presents an unreasonable risk of injury to health
products - Shoe polish
(workers and occupational non-users)
Industrial/CommercialUse - Other commercialuses Hoof polishes; gun scrubber; lace wig and hair
extension glues; pepper spray; other miscellaneous
industrial and commercial uses

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Disposal

Presents an unreasonable risk of injury to health
(workers and occupational non-users)

Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Brake and parts cleaner
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Aerosol electronic degreaser/cleaner
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Liquid electronic degreaser/cleaner
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Aerosol spray degreaser/cleaner
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Liquid degreaser/cleaner
(consumers and bystanders)
Consumer Use- Solvents (for cleaning or degreasing)- Presents an UJireasonable risk of injury to health
(consumers and bystanders)
Aerosol gun scrubber
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Liquid gun scrubber
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Mold release
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Aerosol tire cleaner
(consumers and bystanders)
Consumer Use- Solvents (for cleaning or degreasing)- Presents an unreasonable risk of injury to health
Liquid tire cleaner
(consumers and bystanders)

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Cohdition of Use

Unreasonable Risk Determination

Consumer Use-Lubricants and greases- Tap and die
fluid

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Lubricants and greases- Penetrating
lubricant

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Adhesives and sealants - Solventbased adhesive and sealant

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Adhesives and sealants - Mirror edge
sealant

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Adhesives and sealants - Tire repair
cement/sealer

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Cleaning and furniture care products Carpet cleaner

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Cleaning and furniture care products Aerosol spot remover

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Cleaning and furniture care productsLiquid spot remover

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Arts, crafts, and hobby materials Fixatives and finishing spray coatings

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Apparel and footwear care products Shoe polish

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Other consumer uses - Fabric spray

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Other consumer uses - Film cleaner

Presents an unreasonable risk of injury to .health
(consumers and bystanders)

Consumer Use - Other consumer uses - Hoof polish

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Other consumer uses - Pepper spray

Presents an unreason~ble risk of injury to health
(consumers)

Consumer Use - Other consumer uses - Toner aid

Presents an unreasonable risk of injury to health
(consumers and bystanders)

Consumer Use - Other consumer uses - Lace wig and
hair extension glues

Presents an unreasonable risk of injury to health
(consumers and bystanders)

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234
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5._3 Detailed Risk Determinations by Condition of Use

------------

5.3.1

Manufacture - Domestic manufacture

Section 6(b)(4)(A) unreasonable risk determinationfor domestic manufactureofTCE:
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Presents an unreasonable risk of injury to health (workers and occupational non-users
(ONUs)).

Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dennal
exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, non-Hodgkin Lymphoma (NHL)): Benchmark= lxl0-4.
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 1.5 and 0.21 (central tendency and high-end) with PPB
(respirator APF 50).
o Chronic inhalation MO Es 2.2 and 0.31 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high~end) with PPE (gloves
PF =20).
o Chronic dermal MO Es 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-6)
• Cancer:
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-6)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOE 3.0E-02 (central tendency).
o Chronic inhalation MOE 4.3E-02 (central tendency). (Table 4-6)
• Cancer: Inhalation: 7.5E-04 (central tendency). (Table 4-6)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not indicate unreasonable risks with expected respiratory protection (APF 50), all other
risk estimates indicate risk even with expected respiratory and dermal protection. EPA did not separately
calculate risk estimates for ONUs and workers. There is wicertainty in the ONU risk estimate since the
data did not distinguish between worker and ONU inhalation exposure estimates. ONU inhalation
exposures are expected to be lower than inhalation exposures for workers directly handling the chemical
substance; however, the relative exposure of ONUs to workers in these cases cannot be quantified. To
account for this uncertainty, EPA considered the central tendency estimate when determining ONU risk.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA assessed inhalation exposures during manufacturing using monitoring data
submitted by the Halogenated Solvents Industry Alliance (HSIA). BPA estimated dermal exposures
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using the Dermal Exposure to Volatile Liquids Model because dermal exposure data wasnot reasonably
available for the condition of use.

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Life Cycle Stage

Manufacture

Category

Domestic Manufacture

Subcategory

Domestic manufacture

287
288

5.3.2

Manufacture - Import (includes repackaging and loading.lunloading)

289
290
291
292
293

Section 6(b)(4){A) unreasonable risk determination for import ofT CE:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonabl e risk of injury to health (occupational non-users ).

294

Unreasonable risk driver - workers :
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.

295
296
297

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299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327

Driver benchmarks - workers:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark = lxl0- 4 •
Risk estimate - workers :
• Developmental cardiac toxicity:
o Acute inhalation MOE 0.49 (high-end) with PPE (respirator APF 50).
o Chronic inhalation MOE 0. 71 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPB (gloves
PF= 20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-19) .
• Cancer :
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-19)

RiskConsiderations: For workers , all pathways of occupational exposure for this condition of use at the
high end indicate non-cancer risks even with expected respiratory and dermal protection. Acute and
chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not indicate
risks in the absence of PPE . Acute and chronic dermal exposures at the central tendency for cancer and
non-cancer effects indicate risks even with expected dermal protection (PF = 20) . Cancer risk estimates
from inhalation exposures do not indicate risk when expected PPE (APF 50) was considered. EPA did
not separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk
estimate since the data did not distinguish between worker and ONU inhalation exposure estimates.
ONU inhalation exposures are expected to be lower than inhalation exposures for workers directly
handling the chemical substance ; however, the relative exposure of ONUs to workers in these cases
cannot be quantified. To account for this uncertainty , EPA considered the central tenden~y estimate
when determining ONU risk . The high volatility ofTCE and potentially severe effects from short term
exposure are f~tors when weighing uncertainties. EPA assessed inhalation exposure s during import
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using the repackaging exposure scenario. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dermal exposure data was not reasonably available for the condition
ofuse.

Life Cycle Stage
Manufacture

Category
Import

Subcategory
Import

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337
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340
341
342
343
344
345
346
34 7
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350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371

5.3.3

Processing - Processing as a reactant/intermediate in industrial gas manufacturing
(e.g.,, manufacture of fluorinated gases used as refrigerants, foam blowing agents
and solvents)

Section 6(b){4)(A) unreasonable risk determination for processing ofTCE as a reactant/intermediate:
• Presents an unreasonable risk of injury to heal.th (workers and occupational non•usen).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0-4.
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 1.5 and 0.21 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MO Es 2.2 and 0.31 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.5E-02 (central tendency and high-end) with PPE (gloves

PF=20).

o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-7)
•

Cancer:
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-7)

Risk estimate - ONUs:
•

•

Developmental cardiac toxicity:
o Acute inhalation MOE 3.0E-02 (central tendency).
o Chronic inhalation MOE 4.3-02 (central tendency). (Table 4-7)
Cancer:
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o

Inhalation: 7.5E-04 (central tendency). (Table 4-7)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not indicate risks with expected respiratory protection (APF 50), all other risk estimates
indicate risk even with expected respiratory and dermal protection (PF = 20). EPA did not separately
calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the
data did not distinguish between worker and ONU inhalation exposure estimates. ONU inhalation
exposures are expected to be lower than inhalation exposures for workers directly handling the chemical
substance; however, the relative exposure ofONUs to workers in these cases cannot be quantified; To
account for this uncertainty, EPA considered the central tendency estimate when determining ONU risk.
The high volatility of TCE and potentially severe effects from short tenn exposure are factors when
weighing uncertaint;i.es.EPA did not identify inhalation exposure monitoring data related to processing
TCE as a reactant. Therefore, EPAused monitoring data from the manufacture ofTCE as surrogate data
for the processing condition of use. EPA believes the handling and TCE concentrations for ooth
conditions of use to be $imilar. EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for the condition of use.
Category

Life Cycle Stage

Processing

Processing as a Reactant/
Intermediate

Subcategory

Intermediate in industrial gas
manufacturing (e.g., manufacture of
fluorinated gases used as refrigerants,
foam blowing agents .and solvents)

390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405

5.3.4

Processing- Incorporation into formulation, mixture or reaction product - Solvents
(for cleaning or degreasing); adhesives and sealant chemicals; solvents (which
become part of product formulation or mixture) (e.g., lubricants and greases, paints
and ~oatings, other uses)

Section 6(b}(4)(A) unreasonable risk determination for incomoration ofTCE intoformulation, mixture,
reaction product, or articles:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users) .
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.

406
407
408
409
410
411
412

Driver benchmarks - workers:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •
Risk estimate - workers:
• Developmental cardiac toxicity:
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o Acute inhalation MOE 0.49 (high-end) with PPE (respirator APF 50).
o Chronic inhalation MOE 3.7 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 0.14 and 4.5E-02 (central tendency and high-end) with PPE (gloves
PF =20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-18)
Cancer:
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-18)

Risk Considerations: For workers, all pathways of occupational exposure for this condition of use at the
high end indicate non-cancer risk even with expected respiratory and dermal protection. Acute and
chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not indicate

risks in the absence of PPE. Acute and chronic dermal exposures at the central tendency for cancer and
non-cancer effects indicate risks in the absence of PPE. Cancer risk estimates from inhalation exposures
do not indicate risk when expected PPE (APF 50) was considered. EPA did not separately calculate risk
estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the data did not
distinguish between worker and ONU inhalation exposure estimates. ONU inhalation exposures are
expected to be lower than inhalation exposures for workers directly handling the chemical substance;
however, the relative exposure ofONUs to workers in these cases cannot be quantified. To account for
this uncertainty, EPA considered the central tendency estimate when determining ONU risk. The high
volatility ofTCE and potentially severe effects from short term exposure are factors when weighing
uncertainties. EPA did not identify inhalation exposure monitoring data related to using TCE when
formulating aerosol and non-aerosol products. Therefore, EPA used monitoring data from repackaging
as a surrogate. EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage

Processing

Category

Processing- Incorporationinto
fonnulation,mixtureor
reactionproduct

Subcategory

•
•

•

Solvents(for cleaningor
degreasing)
Adhesivesand sealantchemicals
Solvents(which becomepart of
productformulationor mixture)
(e.g.,, lubricantsand greases,
paintsand coatings,other uses)

440
441
442
443
444
445
446
447
448
449
450
451

S.3.5

Processing - Incorporation into articles - Solvents (becomes an integral components
of articles)

Section 6{b)(4){A)unreasonable risk determination for incomoration ofTCE into articles as solvents
that become integral components of articles:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk ofinjuryto health (occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
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460
461
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463

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467
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471
4 72
473
474
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483
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•

Cancer resulting from chronic dermal exposures.

Driv er benchmarks - workers :
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •
Risk estimate - workers :
• Developmental cardiac toxicity :
o Acute inhalation MOE 0.49 (high-end)with PPB (respirator APP 50).
o Chronic inhalation MOE 3.7 (high-end) with PPE (respirator APF 50).
o Acute dennal MO Es 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
Pf =20) .
o Chronic dennal MOEs 0.20 and 6.6E-02(central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-18)
• Cancer:
o Dermal : 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-18)
Risk Considerations: For workers, all pathways of occupational exposure for this condition of use at the
high end indicate non-cancer risk even with expected respiratory and dermal protection. Acute and
chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not indicate
risks in the absence of PPE. Acute and chronic dermal exposures at the central tendency for cancer and
non-cancer effects indicate risks in the absence of PPE. Cancer risk estimates from inhalation exposures
do not indicate risk when expected PPE (APF 50) was considered . EPA did not separately calculate risk
estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the data did not
distinguish between worker and ONU inhalation exposure estimates. ONU inhalation exposures are
expected to be lower than inhalation exposures for workers directly handling the chemical substance;
however, the relative exposure ofONUs to workers in these cases cannot be quantified . To account for
this uncertainty, EPA considered the central tendency estimate when determining ONU risk. The high
volatility ofTCE and potentially severe effects from short term exposure are factors when weighing
uncertainties. EPA did not identify inhalation exposure monitoring data related using TCE when
formulating aerosol and non-aerosol products. Therefore, EPA used monitoring data from repackaging
as a surrogate. EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dennal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Processing

Category

Subcategory

Processing- incorporatedinto Solvents (becomesan integralcomponents
of articles)
articles

487

488
489
490
491
492

5.3.6 Processing- Repackaging- Solvents (for cleaning or degreasing)
Section 6(b)(4)(A) unreasonable risk detennination for processing and repackaging of TCE as a solvent
for cleaning or degreasing:
• Presents an unreasonable risk of injury to health (work.en).
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512
513
514
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518
519
520
521
522
523
524
525
526
527
528
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530
531
532

•

Does not present an umeasonable risk of injury to health (occupational non-users).

Umeasonable risk driver - workers :
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures .
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Developmental cardiac toxicity : Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark = lxl0-4.
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOE 0.49 (high-end) with PPE (respirator APF 50).
o Chronic inhalation MOEs 0.71 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF = 20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-19).
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-19)
Risk Considerations: For workers, all pathways of occupational exposure for this condition of use at the
high end indicate non-cancer risk even with expected respiratory and dermal protection . Acute and
chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not indicate
risks in the absence of PPE. Acute and chronic dennal exposures at the central tendency for cancer and
non-cancer effects indicate risks even with expected dermal protection (PF=20). Cancer risk estimates
from inhalation exposures do not indicate risk when expected PPE (APF 50) was considered . EPA did
not separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk
estimate since the data did not distinguish between worker and ONU inhalation exposure estimates.
ONU inhalation exposures are expected to be lower than inhalation exposures for workers directly
handling the chemical substance; however, the relative exposure ofONUs to workers in these cases
cannot be quantified. To account for this uncertainty , EPA considered the central tendency estimate
when determining ONU risk. The high volatility ofTCE and potentially severe effects from short tenn
exposure are factors when weighing uncertainties. EPA assessed inhalation exposures during import
using the repackaging exposure scenario. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dermal exposure data was not reasonably available for the condition
ofuse.

Ca1egory

Life Cycle Stage
Processing

Processing - repackaging

Subcategory
Solvents (for cleaning or degreasing)

533
534
535
536

5.3.7 Processing - Recycling
Section 6(b)( 4 )(A) unreasonable risk determination for recycling of TCE:
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538
539
540
541
542
543

•
•

Presents an unreasonable risk of injury to health (workers).
Does not present an unreasonable risk of injury to health (occupational non-users).

Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.

544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577

Driver benchmarks - workers:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 •
Risk estimate - workers:

•

•

Developmental cardiac toxicity:
o Acute inhalation MOE 0.49 (high-end) with PPE (respirator APP 50).
o Chronic inhalation MOE 0.71 (high-end) PPE (respirator APF 50).
o Acute dermal MOEs 0.14 and4.5E-02 (central tendency and high-end) with PPE (gloves
PF=20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-27)
Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-27)

Risk Considerations: For workers, all pathways of occupational exposure for this condition of use at the
high end indicate non-cancer risk even with expected respiratory and dermal protection. Acute and
chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not indicate
risks in the absence of PPE. Acute and chronic dermal exposures at the central tendency for cancer and
non-cancer effects indicate risks in the absence of PPE. Cancer risk estimates from inhalation exposures
do not indicate risk when expected PPE (APP 50) was considered. EPA did not separately calculate risk
estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the data did not
distinguish between worker and ONU inhalation exposure estimates. ONU inhalation exposures are
expected to be lower than inhalation exposures for workers directly handling the chemical substance;
however, the relative exposure of ONUs to workers in these cases cannot be quantified. To account for
this uncertainty, EPA considered the central tendency estimate when determining ONU risk. The high
volatility of TCE and potentially severe effects from short term exposure are factors when weighing
uncertainties. EPA did not identify inhalation exposure monitoring data related to using TCE when
formulating aerosol and non-aerosol products. Therefore, EPA used monitoring data from repackaging
as a surrogate for recycling. EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for the condition of use.
Category

Life Cycle Stage
Processing

Recycling

578
579
580

5.3.8 Distribution in Commerce

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583
584
585
586
587

Section 6(b)(4 )(A) unreasonable risk determination for distribution of TCE:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Risk Considerations: A quantitative evaluation of the distribution of TCE was not included in the risk
evaluation because exposures and releases from distribution were considered within each condition of

use.
Life Cycle Stage
Distribution in commerce

588
589
590
591
592
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595
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598
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600
601
602
603
604
605
606
607
608
609
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611
612
613
614

5.3.9

Distribution

Subcategory
Distribution in commerce

Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor
degreaser (open-top)

Section 6(b){4)(A) unreasonable risk determination for industrial/commercial use ofTCE as a solvent
for batch vapor degreasing (open-top):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = l 0.
• Cancer (liver, kidney, NHL): Benchmark = lxl0-4.
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 4.0E-02 and 7.l E-03 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 5.9E-02 and 1.0E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dennal MOEs 0.14 and 4.SE-02 ( central tendency and high-end) with PPE (gloves

615
616
617
618
619
620
621
622
623

Category

PF= 20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-8)
•

Cancer:
o Inhalation: 5.5E-04and 4.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end} with PPE (gloves PF=
20) . (Table 4-8)
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Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs1.0E-02 and l .2E-03(central tendency and high-end).
o Chronic inhalation MOEs 1.SE-02and 1.8E-03 (central tendency and high-end). (Table
4-8)

•

Cancer:
o Inhalation: 2.2E-03 and 2.3E-02 (central tendency and high-end). (Table 4-8)

Risk Considerations:For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from NIOSH investigations at twelve sites using TCE as a degreasing solvent in
OTVDs. Due to the large variety in shop types that may use TCE as a vapor degreasing solvent, it is
unclear bow representative these data are of a ''typical" shop. Therefore, EPA supplemented the
identified monitoring data using the Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation
Exposure Model. EPA's inhalation exposure modeling is based on a near-field/far-field approach, where
a vapor generation source located inside the near-field diffuses into the surrounding environment.
Workers are assumed to be exposed to TCE vapor concentrationsin the near-field, while occupational
non-users are exposed at concentrationsin the far-field. These estimateswere used for determining
worker and ONU risks. For workers, EPA estimated dermalexposures using the Dermal Exposure to
Volatile Liquids Model because dermal exposure data was not reasonablyavailable for the condition of
use.

648

Life Cycle Stage

ltidustrial/commercial use

Category

Solvents(for cleaning or

Subcategory
Batch vapordegreaser(open-top)

degreasing)

649
650
651
652
653
654
655

5.3.10 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor
_,,__,__
degreaser (closed-loop)

____

Section 6(b){4)(A)unreasonable risk determination for industrial/commercialuse ofTCE as a solvent
for batch vapor degreasing (closed-loop):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).

656
657
658
659
660
661
662
663
664

Unreasonable risk driver ...;.
workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.

665
666

Driver benchmarks - workers and ONUs:
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703

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Developmental cardiac toxicity: Benchmark MOE = 10.
Cancer (liver, kidney, NHL) : Benchmark= lxl0 4 •

Risk estimate - workers:
•

•

Developmental cardiac toxicity:
o Acute inhalation MOEs 1.2 and 0.38 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 1.8 and 0.56 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 ( central tendency and high-end) with PPE (gloves
PF =20) .
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) .with PPE
(gloves PF= 20). (Table 4-10)
Cancer:
o Denn.al: 4.9E-04 and l .9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-10)

Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOE 2.4E-02 (central tendency).
o Chronic inhalation MOE 3.5E-02 (central tendency). (Table 4-10)
• Cancer (liver, kidney, NHL):
o Inhalation: 9.lE-04 (central tendency). (Table 4-10)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures at the central tendency and high end do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
EPA did not separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU
risk estimate since the data did not distinguish between worker and ONU inhalation exposure estimates.
ONU inhalation exposures are expected to be lower than inhalation exposures for workers directly
handling the chemical substance; however, the relative exposure of ONUs to workers in these cases
cannot be quantified. To account for this uncertainty, EPA considered the central tendency estimate
when determining ONU risk. The high volatility of TCE and potentially severe effects from short term
exposure are factors when weighing uncertainties. EPA identified inhalation exposure monitoring data
from a European Chemical Safety report using TCE in closed degreasing operations. EPA estimated
dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.
Life Cycle Stage

Industrial/commercial use

Category

Solvents(for cleaningor
degreasing)

Subcategory

Batchvapordegreaser (closed-loop)

706
707

708

5.3.11 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor
degreaser(conveyorized)

709

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Section 6(b)(4)(A) unreasonable risk detennination for industrial/commercialuse ofTCE as a solvent
for in-line vapor degreasing (conveyorized):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable riskdriver- ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: BenchmarkMOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 •

Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 1.7E-02 and 1.lE-02(central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalationMOEs 2.5E-02 and l.7E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF=20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-11)
• Cancer (liver, kidney, NHL):
o Inhalation: l.3E-03 and 2.5E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-11)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOE 3.4E-04 (central tendency).
o Chronic inhalation MOE 5.0E-04 (central tendency). (Table 4-11)
• Cancer (liver, kidney, NHL):
o Inhalation: 6.5E-02 (central tendency). (Table 4-11)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dennal protection (APF 50 and PF= 20). The high volatility ofTCE and
potentially severe effects from short term exposure are factors when weighing wicertainties. EPA
identified inhalation exposure monitoring data from NIOSH investigationsat two sites using TCE in
conveyorized degreasing. Due to the large variety in shop types that may use TCE as a vapor degreasing
solvent, it is unclear how representative these data are of a "typical" shop. Therefore, EPA supplemented
the identified monitoring data using the Conveyorized Degreasing Near-Field/Far-FieldInhalation
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760

Exposure Model. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for this condition of use.
Life Cycle Stage
Industrial/commercial use

Category

Solvents(for cleaning or
degreasing)

Subcategory

In-line vapor degreaser (conveyorized)

761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
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781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800

5.3.12 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor
degreaser (web cleaner)
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE as a solvent
for in-line vapor degreaser (web cleaner):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers. and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 •
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 9.3E-02 and 3.9E-02 (central tendency and high-end) with PPB
(respirator APF 50).
o Chronic inhalation MO Es 0.14 and 5.7E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF= 20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-13)
• Cancer:
o Inhalation: 2.3E-04 and 5.8E-04 (central tendency and high-end) with PPE (respirator
APP 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). {Table 4-13)

Riskestimate - ONUs:
•

Developmental cardiac toxicity:
o Acute inhalation MOEs 3.SE-03 and l.2E-03 (central tendency and high-end).
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819

o Chronic inhalation MOEs 5.2E-02 and 1.7E-03 (central tendency and high-end). (Table
4-13)
•

Cancer:
o Inhalation: 5.9E-03 and 1.9E-02 (central tendency and high-end). (Table 4-13)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE . For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF 50 and PF= 20). The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify any inhalation exposure monitoring data related to the use ofTCE in web degreasing.
Therefore, EPA assessed inhalation exposures during web degreasing using the Web Degreasing NearField/Far-Field Inhalation Exposure Model. EPA's inhalation exposure modeling is based on a nearfield/far-field approach, where a vapor generation source located inside the near-field diffuses into the
surroun ding environment. Workers are assumed to be exposed to TCE vapor concentrations in the nearfield, while occupational non-users are exposed at concentrations in the far-field. These estimates were
used for determining worker and ONU risks. For workers, EPA estimated dermal exposures using the
Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available
for the condition of use.

Life Cycle Stage
Industrial/commercial use

Category
Solvents (for cleaningor
degreasing)

Subcategory
In-line vapor degreaser (web cleaner)

820
821
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825
826
827
828
829
830
831
832
833
834
835
836
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838

5.3.13 Industrial/Commercial Use - Solvents (for cleaning or degreasing)- Cold cleaner
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE as a solvent
for cold cleaning :
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures .
• Cancer resulting from chronic inhalation and dermal exposures.
Umeasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs :
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark = lxl0 4 .

839
840
841
842
843

Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 0.17 and 9.7E-03 (central tendency and high-end) with PPE
(respirator APF 50).
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862
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870
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o
o
o
•

Chronic inhalation MO Es 0.24 and 1.4E-02 (central tendency and high-end) with PPE
(respirator APF 50).
Acute dermal MO Es 0.14 and 4.5E-02 (central tendency and high-end) with PPE (gloves
PF=20).
Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-14)

Cancer:
o Inhalation: l.2E-04 and 2.3E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-14)

Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 6.0E-03 and 3.2E-04 (central tendency and high-end).
o Chronic inhalation MOEs 8.8E-03 and 4.7E-04 (central tendency and high-end). (Table
4-14)
• Cancer:
o Inhalation: 3.3E-03 and 6.9E-02 (central tendency and high-end). (Table 4-14)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates except cancer inhalation risks
at the central tendency indicate unreasonable risk even with expected respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA did not identify inhalation exposure monitoring data for the Cold Cleaning
condition of use. Therefore, EPA used the Cold Cleaning Near-Field/Far-Field Inhalation Exposure
Model to estimate exposures to workers and ONUs. EPA's inhalation exposure modeling is based on a
near-field/far-field approach. where a vapor generation source located inside the near-field diffuses into
the surrounding environment. Workers are assumed to be exposed to TCE vapor concentrations in the
near-field, while occupational non-users are exposed at concentrations in the far-field. These estimates
were used for determining worker and ONU risks. For workers, EPA estimated dermal exposures using
the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably
available for the condition of use.

878
Life Cycle Stage
Industrial/commercial use

Category
Solvents (for cleaning or
degreasing)

Subcategory
Cold cleaner

879
880
881
882
883
884
885
886

5.3.14 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner;~old release
Section 6{b)(4}{A}unreasonable risk determination for industrial/commercial use ofTCE as a solvent
for aerosol spray degreaser/cleaner and for mold release:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
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904
905
906

907
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917
918
919
920
921
922
923
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926
927
928
929
930
931
932
933

Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dennal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 .
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 7.3E-02 and 2.3E-02 (central tendency and high-end) with PPB
(respirator APF 50).
o Chronic inhalation MOEs 0.11 and 3.4E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 8.6E-02 and 2.9E-02 (central tendency and high-end) with PPE
(gloves PF = 20).
o Chronic dermal MO Es 0.13 and 4.2E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-15)
• Cancer:
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-15)
Risk estimate-ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 7.9E-02 and l.lE-02 (central tendency and high end).
o Chronic inhalation MOEs 0.12 and l.6E-02 (central tendency and high end). (fable 4-15)
• Cancer:
o Inhalation: 2.6E-04 and 2.0E-03 (central tendency and high end). (Table 4-15)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF=50 and PF=20). EPA estimated ONU exposures could
be as high as worker exposures as a high-end estimate. The high volatility of TCE and potentially severe
effects from short term exposure are factors when weighing uncertainties. EPA did not identify
inhalation exposure monitoring data related to the use of TCE in aerosol degreasers. Therefore, EPA
estimated inhalation exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA's
inhalation exposure modeling is based on a near-field/far-field approach, where a vapor generation
source located inside the near-field diffuses into the surrounding environment Workers are assumed to
be exposed to TCE vapor concentrations in the near-field, while occupational non-users are exposed at
concentrations in the far-field. These estimates were used for determining worker and ONU risks. For
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workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dennal exposure data was not reasonably available for the condition of use.

Category

Life Cycle Stage

Industrial/commercial use

Solvents (for cleaning or
degreasing)

Subcategory

•
•

Aerosol spray degreaser/cleaner
Mold release

938
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5.3.15 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant
additives - Tap and die fluid

Section 6{b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE as a lubricant,
grease/lubricant, and lubricant additive in tap and die fluid:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark = Ix 104 .
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 8.0E-03 and 7.4E-03 (central tendency and high-end) with PPB
(respirator APF 50).
o Chronic inhalation MOEs 1.2E-02 and 1.lE-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dennal MOEs 0.17 and 5.6E-02 (central tendency and high-end) with PPB (gloves
PF =20).
o Chronic dermal MOEs 0.25 and 8.2E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-20)
• Cancer:
o Inhalation: 2.8E-03 and 3.9E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 3.9E-04 and 1.5E-03 (central tendency and high-end) with PPE (gloves PF=
20). {Table 4-20)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
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INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
o Chronic inhalation MOEs 2.3E-04 (central tendency). (Table 4 -20)
Cancer:
o Inhalation: 1.4E-01 (central tendency). (Table 4-20)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. EPA did not separatelycalculate risk estimates for ONUs
and workers. There is uncertainty in the ONU risk estimate since the data did ·not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility ofTCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
identified inhalation exposure monitoring data from OSHA facility inspections at two sites using TCE in
metalworking fluids. EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dermal exposure data was not reasonably available for the condition of use.

Life Cycle Stage
Industrial/commercial use

Category
Lubricantsand
greases/lubricantsand
lubricant additives

Subcategory
Tap and die fluid

994
995
996

5.3.16 Industrial/CommereialUse - Lubricants and greases/lubricants and lubricant
~dditives - Penetrating lubrican
_ t__
_

997
998
999

000
001
002
003
004
005

Section 6(b)(4)(A) unreasonable risk determination for industrial/commercialuse ofTCE as penetrating
lubricant:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.

006
007
008

009

Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.

010

011
012
013
014
015
016
017
018

Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark = lxl0 4 .
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 7.3E~02and 2.3E-02 (central tendency and high-end) with PPB
(respirator APF 50).

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o Chronic inhalation MOEs 0.11 and 3.4E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 8.6E-02 and 2.9E-02 (central tendency and high-end) with PPE
(gloves PF = 20).
o Chronic dermal MOEs 0.13 and 4.2E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-15)
Cancer (liver, kidney, NHL):
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-15)

Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 7.9E-02 and 1.lE-02 (central tendency and high-end).
o Chronic inhalation MOEs 0.12 and l.6E-02 (central tendency and high-end). (Table 4-15)
• Cancer (liver, kidney, NHL):
o Inhalation: 2.6E-04 and 2.0E-03 (central tendency and high-end). (Table 4-15)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. EPA estimated ONU exposures could be as high as worker
exposures as a high-end estimate. The high volatility of TCE and potentially severe effects from short
term exposure are factors when weighing uncertainties. EPA did not identify inhalation exposure
monitoring data related to the use of TCE in aerosol degreasers. Therefore, EPA estimated inhalation
exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA estimated dermal
exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not
reasonably available for the condition of use.

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048
Life Cycle Stage

Industrial/commercialuse

Category

Lubricants and
greases/lubricantsand
lubricantadditives

Subcategory

Penetrating lubricant

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054

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S.3.17 Industrial/Commercial Use -Adhesives and sealants - Solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant

Section 6(bX4)(A) unreasonable risk determination for industrial/commercialuse ofTCE as an adhesive
and sealant in solvent-based adhesives and sealants, tire repair cement/sealer. and mirror edge sealant:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
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Cancer resulting from chronic inhalation and dennal exposures.

Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.

Driver benchmarks - workers and ONUs:
•
•

Developmental cardiac toxicity: Benchmark MOE= 10.
Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •

Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 0.61 and 7.2E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 0.90 and 0.11 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.15 and 5.0E-02 (central tendency and high-end) with PPB (gloves
PF =20).
o Chronic dennal MOEs 0.22 and 7.3E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-22)
• Cancer:
o Inhalation: 1.9E-04 and 2.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.4E-04 and l.7E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-22)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs l.2E-02 and 1.lE-02 (central tendency and high-end).
o Chronic inhalation MO Es 1.7E-02 and l .6E-02 (central tendency and high-end) . (Table
4-22)
• Cancer:
o Inhalation: l.9E-03 and 2.6E-03 (central tendency and high-end). (fable 4-22)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence ofPPE. For workers, all risk estimates indicate unreasonable risk even
with expected respiratory and dennal protection with the exception of cancer inhalation risks at the
central tendency. The high volatility ofTCE and potentially severe effects from short term exposure are
factors when weighing uncertainties. EPA identified inhalation exposure monitoring data from a NIOSH
Health Hazard Evaluation report (Chrostek, 1981) using TCE in coating applications and from OSHA
facility inspections (OSHA, 2017) at three sites using TCE in adhesives and coatings. The OSHA data
also provided two data points where the worker job description was "foreman." EPA assumed this data
is applicable to ONU exposure. EPA estimated dennal exposures using the Denna! Exposure to Volatile
Liquids Model because dennal exposure data was not reasonably available for the condition of use.

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Life Cycle Stage
Industrial/commercial use

Category
Adhesives and sealants

Subcategory

•

Solvent-basedadhesives and

•
•

Tire repair cement/sealer
Mirror edge sealant

sealants

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5.3.18 Industrial/CommercialUse - Functionalfluids (closed systems) - Heat exchange

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fluid

Section 6(b)(4)(A) unreasonable risk determination for industrial/commercialuse ofTCE as a functional
fluid {closedsystems) for heat exchange fluid:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).

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Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.
Umeasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers andONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •

Riskestimate - workers:
•

•

Risk

•

Developmental cardiac toxicity:
o Acute inhalation MOEs 1.5 and 0.21 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 2.2 and 0.31 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPB (gloves
PF =20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-26)
Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-26)

estimate- ONUs:
Developmental cardiac toxicity:
o Acute inhalation MOE 3.0E-02 (central tendency).
o Chronic inhalation MOE 4.3E-02 (central tendency). (Table 4-26)
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Cancer:
o Inhalation:7.SE-04(central tendency).(Table 4-26)

Risk Considerations:For workers and ONUs, all pathwaysof occupationalexposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not j.ndicateunreasonablerisks with expectedrespiratoryprotection (APF 50), all other
risk estimates indicate unreasonablerisk even with expectedrespiratory and dermal protection. EPA did
not separately calculate risk estimatesfor ONUs and workers. There is uncertainty in the ONU risk
estimate since the data did not distinguishbetween worker and ONU inhalation exposure estimates.
ONYinhalation exposuresare expectedto be lower than inhalationexposures for workers directly
handling the chemical substance; however, the relative exposure_of ONUsto workers in these cases
cannot be quantified. To account for this uncertainty,EPA consideredthe central tendency estimate
when determining ONU risk. The high volatilityofTCE and potentially severe effects from short term
exposure are factors when weighinguncertainties.EPA did not identify inhalation exposure monitoring
data related to using TCE for other industrial uses. Therefore,EPA used monitoring data from
loading/unloadingTCE during manufacturingas a surrogatefor this condition of use. EPA estimated
dermal exposures using the Dermal Exposureto Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.

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Category

Functionalfluids (closed
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Subcategory

Heat exchange fluid

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5.3.19 Industrial/Commercial Use -Paints and coatings- Diluent in solvent-based paints
and coatings

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Section 6fb}(4)(A)unreasonablerisk determination for industrial/commercialuse ofTCE in paints and
coatings as a diluent in solvent-basedpaint and coatings:

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•
•

Presents an unreasonable risk of injury to health (workers and occupational non-users).
Does not present an unreasonablerisk of injury to the environment(aquatic, sediment dwelling

and terrestrial organisms).
Unreasonablerisk driver - workers:
• Developmentalcardiac toxicity resulting from acute and chronic inhalationand dermal
exposures.
,. Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmentalcardiac toxicity resulting from acute and chronic inhalationexposures.
• Cancer resulting from chronic inhalation exposures.

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Driver benchmarks-workers and ONUs:
• Developmental cardiac toxicity: BenchmarkMOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 •
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1%
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Riskestimate
•

•

- workers:
Developmental cardiac toxicity:
o Acute inhalation MOEs 0.61 and 7.2E-02 (central tendency and high-end) with PPE
(respirator APF 50) .
o Chronic inhalation MOEs 0.90 and 0.1 l (central tendency and high-end) with PPE
(respirator APF 50) .
o Acute dermal MO Es 0.15 and 5.0E-02 (central tendency and high-end) with PPE (gloves
PF=20) .
o Chronic dermal MOEs 0.22 and 7.3E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-22)
Cancer :
o Inhalation : 2.0E-03 (high-end) with PPE (respirator APF 50).
o Dermal: 4.4E-04 and 1.7E-03 (central tendency and high-end) with PPE (gloves PF =
20) . (Table 4-22)

Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MO Es 1.2E-02 and 1. lE-02 (central tendency and high-end).
o Chronic inhalation MOEs l.7E-02 and l.6E-02 (central tendency and high-end). (Table
4-22)
• Cancer :
o Inhalation: 1.9E-03 and 2.6E-03 (central tendency and high-end). (Table 4-22)

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Risk Considerations : For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate unreasonable risk even
with expected respiratory and dermal protection with the exception of cancer inhalation risks at the
central tendency. The high volatility ofTCE and potentially severe effects from short tenn exposure are
factors when weighing uncertainties. EPA identified inhalation exposure monitoring data from a NIOSH
Health Hazard Evaluation report (Chrostek, 1981) using TCE in coating applications and from OSHA
facility inspections (OSHA, 2017) at three sites using TCE in adhesives and coatings. The OSHA data
also provided two data points where the worker job description was "foreman." EPA assumed this data
is applicable to ONU exposure. EPA estimated dennal expo sures using the Dermal Exposure to Volatile
Liquids Model because dennal exposure data was not reasonably available for the condition of use.

Life Cycle Stage
Industrial/commercial use

Category
Paints and coatings

Subcategory

Diluent in solvent-based paints and
coatings

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5.3.20 Industrial/Commercial Use - Cleaning and furniture care products - Carpet
cleaner; wipe cleaning
Section 6(b)(4)(A) unreasonable risk detennination for industrial /commercial useofTCE in cleaning
and furniture care products for carpet cleaning and wipe cleaning, and in laundry and dishwashing
products as a spot remover:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
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Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs :
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures .
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE== 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •

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Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 1.2E-02 and 4.0E-03 (central tendency and high-end) without
respiratory PPE.
o Chronic inhalation MOEs l.6E-02 and 5.7E-02 (central tendency and high-end) without
respiratory PPE.
o Acute dermal MOEs 4.3E-02 and I .4E-02 (central tendency and high-end) with PPE
(gloves PF = 10).
o Chronic dermal MOEs 6.lE-02 and 1.SE-02 (central tendency and high-end) with PPE
(gloves PF= 10). (Table 4-17)
• Cancer :
o Inhalation: l .SE-03 and 5.SE-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: l .6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-17)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 2.3E-02 and 6.3E-03 (central tendency and high-end).
o Chronic inhalation MOEs 3.3E-02 and 9.0E-03(central tendency and high-end). (Table 417)
• Cancer:
o Inhalation: 9.2E -04 and 3.6E-03 (central tendency and high-end). (Table 4-17)

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Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not indicate risks with expected respiratory protection (APF 10), all other risk estimates
indicate risk even with expected respiratory and dermal protection. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
identified minimal inhajation exposure monitoring data related to the spot cleaning using TCE.
Therefore, EPA supplemented the identified monitoring data using the Near-field/Far-field Exposure
Model. EPA's inhalation exposure modeling is based on a near-field/far-field approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Workers are
assumed to be exposed to TCE vapor concentrations in the near-field, while occupational non-users are
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exposed at concentrations in the far-field. These estimates were used for determining worker and ONU
risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dermal exposure data was not reasonably available for the condition of use.

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Subcategory

Category
Cleaning and furniture care

•
•

products

Carpet cleaner
Wipecleaning

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S.3.21 Industrial/Commercial Use - Laundry and dishwashing products - Spot remover

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Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE in laundry and
dishwashing products as a spot remover:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Umeasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Umeasonable risk driver- ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.

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310
311
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320

Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 •
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 1.2E-02 and 4.0E-03 (central tendency and high-end) without
respiratory PPE.
o Chronic inhalation MOEs l.6E-02 and 5.7E-03 (central tendency and high-end) without
respiratory PPE.
o Acute dermal MOEs 4.3E-02 and l.4E-02 (central tendency and high-end) with PPE
(gloves PF = 10).
o Chronic dermal MOEs 6.1E-02 and 1.SE-02 (central tendency and high-end) with PPE
(gloves PF = 10). (Table 4-17)
• Cancer:
o Inhalation: l.8E-03 and 5.8E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table4-17)
Riskestimate - ONUs:
• Developmental cardiac toxicity:
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o Acute inhalation MOE 2.3E-02 (central tendency).
o Chronic inhalation MOE 3.3E-02 (central tendency). (Table 4-17)
Cancer:
o Inhalation: 9.2E-04 and 3.6E-03 (central tendency and high-end). (Table 4-17)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not indicate risks with expectedrespiratory protection (APF 10), all other risk estimates
indicate risk even with expected respiratory and dermal protection. The high volatility of TCE and
potentially severe effects from short term exposure are factors when we1ghing uncertainties. EPA
identified minimal inhalation exposure monitoring data related to the spot cleaning usingTCE.
Therefore, EPA supplemented the identified monitoring data using the Near-field/Far-field Exposure
Model. EPA' s inhalation exposure modeling is based on a near-field/far-field approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Workers are
assumed to be exposed to TCE vapor concentrations in the near-field, while occupational non-users are
exposed at concentrations in the far-field. These estimates were used for determinjng worker and ONU
risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dennal exposure data was not reasonably available for the condition of use.

Category

Life Cycle Stage

Industrial/commercial use

Laundryand dishwashing

Subcategory

Spot remover

products

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5.3.22 Industrial/Commercial Use - Arts, crafts and hobby materials - Fixatives and
finishing spray coatings

Section 6(b)(4 )(A) unreasonable risk determination for industrial/commercial use of TCE in arts. crafts
and hobby materials as a fixative and finishing spray coating:
• Presents an unreasonable risk of injury to health (worken and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures .
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lx10 4 .

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Risk estimate - workers:
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•

•

Developmental cardiac toxicity:
o Acute inhalation MOEs 0.61 and 7.2E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MO Es 0.90 and 0.11 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOEs 0.15 and 5.0E-02 (central tendency and high-end) with PPB (gloves
PF= 20).
o Chronic dermal MOEs 0.22 and 7.3E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-22)
Cancer:
o Inhalation: 1.9E-03 and 2.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.4E-04 and 1.7E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-22)

Risk estimate-ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs l.2E-02 and 1.lE-02 (central tendency and high-end).
o Chronic inhalation MOEs 1.7E-02 and l.6E-02 (central tendency and high-end). (Table
4-22)
• Cancer:
o Inhalation: 1.9E-03 and 2.6E-03 (central tendency and high-end). (Table 4-22)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate unreasonable risk even
with expected respiratory and dermal protection with the exception of cancer inhalation risks at the
central tendency. The high volatility of TCE and potentially severe effects from short term exposure are
factors when weighing uncertainties. EPA identified inhalation exposure monitoring data from a NIOSH
Health Haz.ard Evaluation report (Chrostek, 1981) using TCE in coating applications and from OSHA
facility inspections (OSHA, 2017) at three sites using TCE in adhesives and coatings. The OSHA data
also provided two data points where the worker job description was "foreman." EPA assumed this data
is applicable to ONU exposure. EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for the condition of use.

Life Cycle Stage
Industrial/commercial use

Category
Arts, crafts and hobby
materials

Subcategory
Spot remover

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5.3.23 Industrial/Commercial Use - Corrosion inhibiton and anti-scaling agents Corrosion inhibitors and anti-scaling agents

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404

Section 6{b)(4)(A) unreasonable risk detennination for industrial/commercial useofTCE as corrosion
inhibitor, and anti-scaling agent:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
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Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks -workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark:= lxl 04 .
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 8.7E-02 and 2.9E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 0.13 and 4.2E-02 (central tendency and high-end) with PPB
(respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF=20).
o Chronic dennal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPB
(gloves PF = 20). (Table 4-24)
• Cancer:
o Inhalation: 2.SE-04 and 9.9E-04 (central tendency and high-end) with PPB (respirator
APF 50).
o Dermal: 4.9E-04 and l.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-24)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 5.6E-03 and 2.3E-03 (central tendency and high-end).
o Chronic inhalation MOEs 8.2E-03 and 3.7E-03 (central tendency and high-end). {Table
4-24)
• Cancer:
o Inhalation: 3.9E-03 and 1.lE-02 (central tendency and high-end). (Table 4-24)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a European Commission (EC) Technical Report (European Commission, 2014,
3970806). The data was supplied to the EC as supporting documentation in an application for continued
use ofTCE under the REACH Regulation. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dermal exposure data was not reasonably available for the condition
ofuse.
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Life Cycle Stage

Industrial/commercial use

Category

Corrosion inhibitors and
anti-scaling agents

Subcategory

Corrosion inhibitors and anti-scaling
agents

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5.3.24 Industrial/Commercial Use - Processing aids - Process solvent used in battery
manufacture; process solvent used in polymer fiber spinning, tluoroelastomer
manufacture, and Alcantara manufacture; extraction solvent used in caprolactam
manufacture; precipitant used in beta-cyclodextrin manufactur !_

Section 6(b)(4}(A) unreasonable risk determination for industrial/commercial use ofTCE in processing
aids as a process solvent used in battery manufacture, polymer fiber spinning,fluoroelastomer
manufacture. and Alcantara manufacture, as an extraction solvent used in caprolactam manufacture, and
as a precipitant used in beta-cyclodextrinmanufacture:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Umeasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 •
Risk estimate-workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 8.7E-02 and 2.9E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 0.13 and 4.2E-02 (central tendency and high-end) with PPE
(respirator APF 50).
·
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF =20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF= 20). (Table 4-24)
• Cancer:
o Inhalation: 2.SE-04 and 9.9E-04 (central tendency and high-end) with PPB (respirator
APF 50).
o Dermal: 4.9E-04 and l .9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-24)
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Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 5.6E-03 and 2.SE-03 (central tendency and high-end).
o Chronic inhalation MOEs 8.2E-03 and 3.7E-03 (central tendency and high-end). (Table

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507
508
509
510
511
512
513

4-24)
•

Cancer :
o Inhalation: 3.9E-03 and l.IE-02 (central tendency and high-end). (Table 4-24)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility .ofTCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a European Commission (EC) Technical Report (European Commission, 2014,
3970806). The data was supplied to the EC as supporting documentation in an application for continued
use of TCE under the REACH Regulation. EPA estimated dennal exposures using the Dermal Exposure
to Volatile Liquids Model because dennal exposure data was not reasonably available for the condition
ofuse .

Life Cycle Stage
Industrial/commercial use

Category
Processing aids

Subcategory

•

•
•

•

Process solvent used in battery
manufacture
Process solvent used in polymer
fiber spinning, fluoroelastomer
manufacture, and Alcantara
manufacture
Extraction solvent used in
caprolactam manufacture
Precipitant used in betacyclodextrin manufacture

514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529

5.3.25 Industrial/Commercial Use-Ink,

toner, and colorant products-Toner

aid

Section 6(b)(4)(A} unreasonable risk detennination for industrial/comm ercial use ofTCE as an ink,
toner, and colorant product as a toner aid :
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures .
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
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540
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545
546
547
548
549
550
551
552
553
554
555

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557
558

559
560
561
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565

Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 .
Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 0.13 and 5.3E-03 (central tendency and high-end) without
respiratory PPE.
o Chronic inhalation MOEs 0.19 and 7.7E-03 (central tendency and high-end) without
respiratory PPE.
o Acute dermal MOEs 0.12 and 4.lE-02 (central tendency and high-end) with PPE (gloves
PF= 10).
o Chronic dermal MOEs 0.18 and 6.0E-02 (central tendency and high-end) with PPB
(gloves PF= 10). (Table 4-25)
• Cancer:
o Inhalation: 5.4E-03 (high-end) without respiratory PPE. (Table 4-25)
o Dermal: 5.3E-04 and 2.lE-03 (central tendency and high-end) with PPE (gloves PF=lO).
(Table 4-25)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 0.13 and 5.3E-03 (central tendency and high-end).
o Chronic inhalation MOEs 0.19 and 7.?E-03 (central tendency and high-end) . (Table 4-25)
• Cancer:
o Inhalation: 5.4E-03 (central tendency and high-end) . (Table 4-25)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a European Commission (EC) Technical Report (European Commission, 2014,
3970806). The data was supplied to the EC as supporting documentation in an application for continued
use ofTCE under the REACH Regulation. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dennal exposure data was not reasonably available for the condition

ofuse.

566
567
Life Cycle Stage
Industrial/commercial use

Category

Subcategory

Ink, toner and colorant
products

Toner aid

568
569
570

5.3.26 Industrial/Commercial Use-Automotive

care pro~ucts-Brake

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605

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612
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Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE for automotive
care products as a brake and part cleane r:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - wor kers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs :
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures .
• Cancer resulting :from chronic inhalation exposures.
Driver benchmarks - workers and ONU s:
• Development.al cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0-4.
Risk estimate-workers :
• Developmental cardiac toxicity:
o Acute inhalation MOEs 7.3E-02 and 23E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic inhalation MOEs 0.11 and 3.4E-02 (central tendency and high-end) with PPE
(respirator APF SO).
o Acute dermal MOEs 8.6E-02 and 2.9E-02 (central tendency and high -end) with PPE
(gloves PF = 20).
o Chronic dermal MOEs 0.13 and 4.2E-02 (central tendency and high-end) with PPB
(gloves PF = 20). (Table 4-15)
• Cancer:
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-15)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOE 7 .9E-02 and 1.IE-02( central tendency and high-end.
o Chronic inhalation MOE 0.12 and l.6E-02(central tendency and high-end). (Table 4-15)
• Cancer:
o Inhalation : l.4E-Ol (central tendency) (Table 4-15)
Risk Considerations: For workers and ONU s, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE . For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF =50 and PF=20) . EPA estimated ONU exposures could
be as high as worker exposures as a high-end estimate. The high volatility of TCE and potentiallysevere
effects from short term exposure are factors when weighing uncertainties. EPA did not identify
inhalation exposure monitoring data related to the use of TCE in aerosol degreasers . Therefore, EPA
estimated inhalation exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA's
inhalation exposure modeling is based on a near -field/far-field approach, where a vapor generation
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source located inside the near-field diffuses into the surrounding environment. Workers are assumed to
be exposed to TCE vapor concentrations in the near-field, while occupational non-users are exposed at
concentrations in the far-field. These estimates were used for determining worker and ONU risks. For
workers, EPA estimated dennal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Industrial/commercial use

Category
Automotive care products

Subcategory
Brake and parts cleaners

625

626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644

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5.3.27 Industrial/Commercial Use - Apparel and footwear care products - Shoe polish
Section 6<bX4){A) unreasonable risk determination for industrial/commercial use ofTCE in apparel and
footwear care products as a shoe polish:
• Presents an unreasonable risk of injury to health (workers and occupational non-users}.
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks- workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
• Cancer (liver, kidney, NHL): Benchmark== lxl0-4 .

Riskestimate
•

•

- workers:
Developmental cardiac toxicity:
o Acute inhalation MOEs 1.2E-02 and 4.0E-03 (central tendency and high-end) without
respiratory PPE.
o Chronic inhalation MOEs 1.6E-02 and 5.7E-03 (central tendency and high-end) without
respiratory PPE.
o Acute dermal MOEs 4.3E-02 and l.4E-02 (central tendency and high-end) with PPB
(gloves PF = 10).
o Chronic dermal MOEs 6. lE-02 and l .8E-02 (central tendency and high-end) with PPB
(gloves PF= 10). (Table 4-17)
Cancer:
o Inhalation: l.8E-03 and 5.SE-03 (central tendency and high-end) without respiratory
PPB.
o Dermal: l.6E-03 and 6.9E-03 (central tendency and high-end) with PPB (gloves PF=
10). (Table 4-17)

Risk estimate- ONUs:
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683

•

•

Developmental cardiac toxicity:
o Acute inhalation MOEs 2.3E-02 and 6.3E-03 (central tendency and high-end).
o Chronic inhalation MOEs 3.3E-02 and 9.0E-03 (central tendency and high-end). (Table
4-17)
Cancer:
o Inhalation; 9.2E-04 and 3.6E-03 (central tendency and high-end). (Table 4-17)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposmes do not indicate risks with expected respiratory protection (APF 10), all other risk estimates
indicate risk even with expected respiratory and dermal protection. The high volatility ofTCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
identified minimalinhalation exposure monitoring data related to the spot cleaning using TCE.
Therefore, EPA supplemented the identified monitoring data using the Near-field/Far-field Exposure
Model. EPA's inhalation exposure modeling is based on a near-field/far-field approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Workers are
asswned to be exposed to TCE vapor concentrations in the near-field, while occupational non-users are
exposed at concentrations in the far-field. These estimates were used for determining worker and ONU
risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dermal exposure data was not reasonably available for the condition of use.

Life Cycle Stage
Industrial/commercial use

Category
Apparel and footwear care
products

Subcategory
Shoe polish

684
685
686
687
688
689
690
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692
693
694
695
696
697
698
699
700
701
702
703

5.3.28 Industrial/Commercial Use - Hoof polishes; gun scrubber; lace wig and hair
extension glues; pepper spray; other miscellaneous industrial and commercial uses
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE in other
commercial uses for hoof polishes, gun scrubber, lace wig and hair extension glues, pepper spray, and
other miscellaneous industrial and commercial uses:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dennal
exposures.
• Cancer resulting from chronic inhalation and dennal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and O1'.TUs:
• Developmental cardiac toxicity: Benchmark MOE = 10.
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720
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722
723
724
725

•

742

4

•

Risk estimate - workers:
• Developmental cardiac toxicity:
o Acute inhalation MOEs l.2E-02 and 4.0E-03 (central tendency and high-end) without
respiratory PPE.
o Chronic inhalation MOEs l.6E-02 and 5.7E-03 (central tendency and high-end) without
respiratory PPE.
o Acute dermal MOEs 4.3E-02 and l.4E-02 (central tendency and high-end) with PPE
(gloves PF= 10).
o Chronic dermal MOEs 6.lE-02 and l.8E-02 (central tendency and high-end) with PPE
(gloves PF = I 0). (Table 4-17)
• Cancer:
o Inhalation: l.6E-02 and 5.SE-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF=
10). (Table 4-17)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOEs 2.3E-02 and 6.3E-03 (central tendency and high-end).
o Chronic inhalation MOEs 3.3E-02 and 9.0E-03 (central tendency and high-end). (Table

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730
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735
736
737
738
739
740
741

Cancer (liver, kidney, NHL): Benchmark= lxl0

4-17)
•

Cancer:
o Inhalation: 9.2E-04 and 3.6E-03 (central tendency and high-end). (Table 4-17)

Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers , while cancer risk estimates for inhalation
exposures do not indicate risks with expected respiratory protection (APF 10), all other risk estimates
indicate risk even with expected respiratory and dermal protection. The high volatility ofTCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
identified minimal inhalation exposure monitoring data related to the spot cleaning using TCE.
Therefore, EPA supplemented the identified monitoring data using the Near-field/Far-field Exposure
Model. EPA' s inhalation exposure modeling is based on a near-field/far-field approac~ where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Workers are
assumed to be exposed to TCE vapor concentrations in the near-field, while occupational non-users are
exposed at concentrations in the far-field. These estimates were used for determining worker and ONU
risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dermal exposure data was not reasonably available for the condition of use.

743
744

Life Cycle Stage
Industrial/commercial use

Category
Other commercial uses

Subcategory

•

•
•
•
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Hoof polishes
Gun scrubber
Lace wig and hair extension glues
Pepper spray

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Life Cycle Stage

Category

Subcategory

•

Other misc.ellaneousindustrialand
commercial uses

745
746
747
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787

S.3.29 Disposal

Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use ofTCE for disposal:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Umeasonable risk driver - workers:
• Developmental cardiac toxicity resulting from acute and chronic inhalation and dermal
exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Developmental cardiac toxicity resulting from acute and chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Developmental cardiac toxicity: Benchmark MOE= 10.
• Cancer (liver, kidney, NHL): Benchmark= lxl0 4 .
Risk estimate-workers:
• Developmental cardiac toxicity:
o Acute inhalation MOE 0.49 (high-end) with PPE (respirator APF 50).
o Chronic inhalation MOE 0.71 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 0.14 and 4.SE-02 (central tendency and high-end) with PPE (gloves
PF =20).
o Chronic dermal MOEs 0.20 and 6.6E-02 (central tendency and high-end) with PPE
(gloves PF = 20). (Table 4-27)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF=
20). (Table 4-27)
Risk estimate - ONUs:
• Developmental cardiac toxicity:
o Acute inhalation MOE 22.4 (central tendency).
o Chronic inhalation MOE 32. 7 (central tendency). (Table 4-27)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while cancer risk estimates for inhalation
exposures do not indicate unreasonable risks with expected respiratory protection (APF 50), all other
risk estimates indicate unreasonable risk even with expected respiratory and dermal protection. EPA did
not separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk
estimate since the data did not distinguish between worker and ONU inhalation exposure estimates.
ONU inhalation exposures are expected to be lower than inhalation exposures for workers directly
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handling the chemical substance; however, the relative exposure of ONUs to workers in these cases
cannot be quantified. To account for this uncertainty, EPA considered the central tendency estimate
when determining ONU risk. The high volatility of TCE and potentially severe effects from short term
exposure are factors when weighing uncertainties. EPA did not identify inhalation exposure monitoring
data related to using TCE for other industrial uses. Therefore, EPA used monitoring data from
loading/unloading TCE during manufacturing asa surrogate for this condition of use. EPA estimated
dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.

7%
797
Life Cycle Stage

Disposal

Category

Disposal

Subcategory

•
•
•

Industrial pre-treatment
Industrial wastewater treatment
Publicly owned treatment works
(POTW)

798

799
800
801
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803
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810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828

S.3.30 Consumer Use - Solvents (for cleaning or degreasing) - Brake and parts cleaner

Section 6{b){4)(A) unreasonable risk determination for consumer use ofTCE as a solvent for brake and
parts cleaners :
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation and dennal exposures.
Unreasonable risk driver - bystanders :
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE = 10.
Risk estimate- consumers:
• Developmental cardiac .toxicity: Acute inhalation MOE 4.lE-04 (moderate intensity user) .
• Acute dennal MOE 9.lE-04 (moderate intensity user). (Table 4-28)
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 1.6E-03 (moderate intensity user).
(Table 4-28)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures . Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders, the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
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used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage
Consumer use

Category
Solvents (for cleaning or
degreasing)

Subcategory
Brake and Parts cleaner

832
833
834
835

S.3.31 Consumer Use - Solvents (for cleaning or degreasing)- Aerosol electronic
degreaser/cleaner

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843
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849
850
851
852
853
854
855

Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for aerosol
electronic degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE = 10.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 2.3E-03 (moderate intensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE l .3E-02 (moderate intensity user).
(Table 4-29)

856

Risk Considerations: Risk estimates for consumer users and bystanders at the mediwn intensity use

857
858
859
860
861
862
863

scenarios of acute inhalation exposures indicate risk. Consumer and bystander unreasonable risk
determinations reflect the severity of the effects associated with acute exposures. Dermal exposures
were not quantified for this scenario, as consumer dermal exposure with impeded evaporation is not
expected, and bystanders are not expected to be dermally exposed to TCE. For the consumer exposure
scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

864
Life Cycle Stage
Consumer use

Category
Solvents (for cleaning or
degreasing)

865

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872
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874

5.3.32 Consumer Use - Solvents (for cleaning or degreasing) - Liquid electronic
degreaser/cleaner

Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for liquid
electronic degreaser/cleaner:
•

Presents an unreasonable risk of injury to health (consumers and bystanders).

Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.

875
876

877

Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

878
879
880

Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE = I 0.

881

882
883
884
885
886
887
888

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 1.6E-03 (moderate intensity user).
• Acute dermal MOE l.8E-03 (moderate intensity user). (Table 4-30)

Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 8.SE-03 (moderate intensity user).
(Table 4-30)

889

890
891
892

893
894

895
896
897
898

Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dennally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the samemodel (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

899

Life Cycle Stage

Consumer use
900
90 l
902
903
904
905
906
907

Category

Solvents (for cleaning or
degreasing)

Subcategory

Liquid electronic degreaser/cleaner

5.3.33 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner

Section 6(b)(4}(A) unreasonable risk determinationfor consumer use of TCE as a solvent foraerosol
spray degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
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908
909
910
911
912
913
914
915
916
917
918

•

Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.

Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE

= 10.

Risk estimate - consumers :
• Developmental cardiac toxicity: Acute inhalation MOE 9.0E-05 (moderate intensity user).
• Acute dermal MOES;SE-04 (moderate intensity user). (Table 4-31)

919
920
921
922
923
924

925
926
927
928
929
930
931

932

Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 3.6E-04 (moderate intensity user).
(Table 4-31)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and denna l exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same mode l (CEM 2 .1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

933
Life Cycle Stage

Consumer use

Category

Subcategory

Solvents (for cleaning or
degreasing)

Aerosol spray degreaser/cleaner

934
935
936
937
938
939

5.3.34 Consumer Use - Solvents (for cleaning or degreasing) - Liquid degreaser/cleaner

Section 6(b)(4}(A) unreasonable risk determination for consumer use ofTCE as a solvent for liquid
degreaser /cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).

940
941

942
943
944
945

Unreasonable risk driver - consumers :
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures .
Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

946
947
948
949

Driver benchmarks-consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE

= 10.

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950
951
952
953
954

Risk estimate - consumers :
• Developmental cardiac toxicity: Acute inhalation MOE 2.4E-04 (moderate intensity user) .
• Acute dermal MOE 2.4E-04 (moderate intensity user). (Table 4-32)

955

- Developmental cardiac toxicity: Acute inhalation MOE 1.2E-03 (moderate intensity user) . (Table 4-32)

956
957
958
959
960
961
962
963
964
965

Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk . Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures . Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated . For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Risk estimate - bystander s:

966
Life Cycle Stage
Conswner use

Category

Subcategory

Solvents (for cleaning or
degreasing)

Liquid degreaser/cleaner

967

968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990

5.3.35 Consumer Use - Solvents (for cleaning or degreasing) -Ae~sol

gun scrubber

Section 6(b)(4XA) unreasonable risk determination for consumer use ofTCE as a solvent for aerosol
gun scrubber :
• Presents an unreasonable risk of injury to health (consumers and bystanden).
Unreasonable risk driver - conswners :
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.
Unreasonable riskdriver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation .
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE

= 10.

Risk estimate - consumers :
•
•

Developmental cardiac toxicity: Acute inhalation MOE 4.7E-02 (moderate intensity user) .
Acute dermal MOE 6.0E-04 (moderate intensity user). (Table 4-33)

Risk estimate - bystanders :
• Developmental cardiac toxicity : Acute inhalation MOE 0.25 (moderate intensity user). (Table 433)

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 991
992
993
994

995
996
997

998
999

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Risk Considerations:All pathways of consumerand bystander exposurefor this condition of use
indicate risk. Consumerand bystanderunreasonablerisk detenninationsreflect the severity of the effects
associatedwith acute exposures.Risk estimatesfor consumerusers at the medium intensity use
scenarios of acute inhalationand dermal exposuresindicaterisk. For bystanders the risk estimates for
the medium intensityuse scenarioof acute inhalationindicaterisk. Becausebystanders are not expected
to be dennally exposed to TCE, dermal risks to bystanderswere not evaluated. For the consumer
exposure scenario for bystanders,inhalationexposureswere estimatedusing the same model (CEM 2.1)
used to estimate exposureto users. CEM 2.1 is a two-zonemodel that allows for the estimation of air
concentrationsa user and bystander(s)would be exposedto followingan exposure event.

000

Life Cycle Stage
Consumer use

Category

Solvents(for cleaning or
degreasing)

Subcategory

Aerosol gun scrubber

001
002

5.3.36 Consumer Use - Solvents (for cleaning or degreasing) - Liquid gun scrubber

003
004

005
006
007
008

009
010
011
012
013
014

015
016
017
018
019
020
021
022

023
024
025
026
027

028
029
030
031

Section 6(b){4)(A)unreasonablerisk determinationfor consumeruse ofTCE as a solvent for liguid gun
scrubber:
•

Presents an unreasonable risk of injury to health (consumers and bystanders).

Unreasonablerisk driver - consumers:
• Developmentalcardiac toxicity resultingfrom acute inhalationand dermal exposures.
Unreasonablerisk driver- bystanders:
• Developmentalcardiac toxicity resultingfrom acute inhalation.
Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE = I 0.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE 5.SE-02 (moderate intensity user).
• Acute dermal MOE 2.6E-04 (moderateintensityuser). (Table 4-34)
Risk estimate - bystanders:
• Developmentalcardiac toxicity: Acute inhalationMOE 0.29 (moderate intensity user). (Table 434)
Risk Considerations:All pathways of consumerand bystander exposure for this condition ofuse
indicate risk. Consumer and bystander unreasonablerisk determinationsreflect the severityof the effects
associatedwith acute exposures. Risk estimates for consumerusers at the medium intensityuse
scenarios of acute inhalation and dermal exposuresindicaterisk. For bystandersthe risk estimates for
the medium intensityuse scenario of acute inhalationindicaterisk. Because bystandersarenot expected
to be dermally exposedto TCE, dermal risks to bystanderswere not evaluated.For the consumer
exposure scenario for bystanders, inhalationexposureswere estimated using the same model (CEM 2.1)
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032
033
034

used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage
Consumer use

Category
Solvents (for cleaning or
degreasing)

Subcategory
Liquid gun scrubber

035
036
037
038
039
040
041
042
043
044
045
046
047
048
049
050
051
052
053
054
055
056
057
058
059
060

061
062
063
064
065

5.3.37 Consumer Use - Solvents (for cleaning or degreasing) - Mold release
Section 6(b)( 4 )(A) unreasonable risk determination for consumer use of TCE as a solvent for mold
release:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.
Unreasonable risk driver- bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE = I 0.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 2.lE-03 (moderate intensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE l.lE-02 (moderate intensity user). (Table
4-35)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander unreasonable risk
determinations reflect the severity of the effects associated with acute exposures. Dermal exposures
were not quantified for this scenario, as consumer dermal exposure with impeded evaporation is not
expected, and bystanders are not expected to be dennally exposed to TCE. For the conswner exposure
scenario for bystanders, inhalation exposures were estimated using the samemodel (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s} would be exposed to following an exposure event.

066
Life Cycle Stage
Consumer use

Category
Solvents (for cleaning or
degreasing}

067

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068

5.3.38 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire cleane_r _

069

070
071
072
073
074
075
076
077
078
079
080
081
082
083
084
085
086
087
088

089
090
091
092
093
094
095
096
097
098
099
100
101

Section 6(b)(4)(A) unreasonable risk determinationfor conswner use of TCE as a solvent for aerosol tire
cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmentalcardiac toxicity resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resultingfrom acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalation MOE 8.9E-04 (moderate intensity user).
• Acute dermal MOE 1.3E-03 (moderateintensity user). (Table 4-36)
Risk estimate - bystanders:
• Developmentalcardiac toxicity: Acute inhalation MOE 3.6E-03 (moderate intensity user) .
(Table 4-36)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonablerisk determinationsreflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event

Life Cycle Stage

Consumer use

Category

Solvents (for cleaning or
degreasing)

Subcategory

Aerosol tire cleaner

102

I 03
104
105
106
107
108

5.3.39 Consumer Use - Solvents (for cleaning or degreasing)~ Liquid tire cleaner

Section 6(b)(4)(A) unreasonable risk determinationfor consumer use ofTCE as a solvent for liguid tire
cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
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110
111
112
113

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Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.
Unreasonable risk driver- bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

114

115
116
117

Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE= 10.

118

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 4.0E-04 (moderate intensity user).
• Acute dermal MOE 2.4E-04 (moderate intensity user). (Table 4-37)

119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136

Risk estimate- bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 1.6E-03(moderate intensity user).
(Table 4-37)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage

Consumer use

Category

Solvents (for cleaning or
degreasing)

Subcategory

Liquid tire cleaner

137
138
139
140
141
142
143
144
145

S.3.40 Consumer Use - Lubricants and greases - Tap and die fluid

Section 6(b)(4)(A) unreasonable risk determination for consumer use ofTCE as a lubricant and grease
in tap and die fluid:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.

146
147
148
149
150

Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks- consumers andbystanders:
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 151
152
153

•

INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Developmentalcardiac toxicity: BenchmarkMOE= 10.

154

Risk estimate- consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE 2.4E-03 (moderate intensity user).

155
156

Risk estimate - bystanders:

157
158
159
160
161

162
163

164
165
166
167

168

•

Developmentalcardiactoxicity: Acute inhalationMOE l .3E-02 (moderate intensity user).
(Table 4-38)

Risk Considerations:Consumer and bystanderunreasonablerisk detenninations reflect the severityof
the effects associatedwith acute exposures.Risk estimatesfor consumerusers at the medium intensity
use scenariosof acute inhalationindicaterisk. For bystandersthe risk estimates for the medium intensity
use scenario of acute inhalationindicaterisk. Dermal exposureswere not quantified for this scenario,as
consumer dermal exposurewith impededevaporationis not expected,and bystanders are not expected
to be dennally exposed to TCE. For the consumerexposurefor scenariobystanders, inhalation
exposures were estimatedusing the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimationof air concentrationsa user and bystander(s)would be
exposed to followingan exposure event

169
170

Life Cycle Stage

Consumer use

Category

Lubricantsand greases

Subcategory

Tap and die fluid

171

172
173

5.3.41 Consumer Use - Lubricants and greases - Penetrating lubricant

174
175

176
177
178

179
180
181

182
183
184
185
186
187
188
189
190

191
19Z

Section 6(b)(4)(A)unreasonablerisk determinationfor consumeruse of TCE as a penetratinglubricant:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonablerisk driver - consumers:
• Developmentalcardiac toxicity resulting from acute inhalationexposures.
Unreasonablerisk driver - bystanders:
• Developmentalcardiac toxicity resultingfrom acute inhalation.
Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE S.4E-03(moderateintensity user).
Risk estimate- bystanders:

•

Developmentalcardiac toxicity: Acute inhalationMOE 2.9E-02 (moderateintensity user).
(Table 4-39)
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193
194
195
196

197
198
199
200
201
202
203

Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander unreasonable risk
detenninations reflect the severity of the effects associated with acute exposures. Dermal exposures
were not quantified for this scenario, as consumer dermal exposure with impeded evaporation is not
expect~ and bystanders are not expected to be dermally exposed to TCE. For the consumer exposure
scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage

Consumer use

Category

Lubricants and greases

Subcategory

Penetrating lubricant

204
205
206

5.3.42 Consumer Use - Adhesives and sealants - Solvent-based adhesive and sealant

207
208
209
210

Section 6(b)(4)(A) unreasonable risk determination for consumer use ofTCE in adhesives and sealants
as solvent-based adhesive and sealant:
• Presents an unreasonable risk of injury to health (consumers and bystanders).

211
212

Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.

213
214
215
216
217
218
219
220
221
222
223

224
225
226

Unreasonable risk driver- bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks- consumers andbystanders:
• Developmental cardiac toxicity: Benchmark MOE= 10.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 3.7E-03 {moderateintensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 3.6E-02 {moderateintensity user).
{Table 4-40)

227

Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use

228
229
230
231
232
233
234

scenarios of acute inhalation exposures indicate risk. Consumer and bystander unreasonable risk
determinations reflect the severity of the effects associated with acute exposures. Dermal exposures
were not quantified for this scenario, as consumer dermal exposure with impeded evaporation is not
expected, and bystanders are not expected to be dermally exposed to TCE. For the consumer exposure
scenario for bystanders~inhalation exposures were estimated using the same model {CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.
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235
Life Cycle Stage

Consumer use

Category

Subcategory

Adhesives and sealants

Solvent-based adhesive and sealant

236
237

238

5.3.43 Consumer Use -Adhesives

and sealants - Mirror edge sealant

239

240
241
242
243
244
245
246
247

248
249
250
251
252
253

254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269

Section 6(b}(4 )(A) unreasonablerisk determinationfor conswner use of TCE in adhesives and sealants
as mirror edge sealant:
• Presents an unreasonable risk of injury to health (cons um en and bystanders).
Unreasonable risk driver - consumers:
• Developmentalcardiac toxicity resulting from acute inhalationexposures.
Unreasonablerisk driver - bystanders:
• Developmentalcardiac toxicity resulting from acute inhalation.
Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalation MOE 3.3E-03(moderate intensity user).

Riskestimate - bystanders:
•

Developmentalcardiac toxicity: Acute inhalationMOE 1.8E-02 (moderate intensity user).
(Table 4-41)

Risk Considerations: Risk estimates for consumer users and bystandersat the mediwn intensity use
scenarios of acute inhalation exposures indicaterisk. Consumerand bystander unreasonable risk
determinationsreflect the severity of the effects associatedwith acute exposures. Dermal exposures
were not quantified for this scenario,as consumer dermal exposure with impeded evaporation is not
expected, and bystanders are not expected to be dermally exposed to TCE. For the consumer exposure
scenario for bystanders, inhalation.exposures were estimatedusing the same model (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s)would be exposedto following an exposure event.

Life Cycle Stage

Consumer use

Category

Adhesives and sealants

270
271
272
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Subcategory

Mirror edge sealant

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5.3.44 Consumer Use - Adhesives and sealants- Tire repair cement/sealer

273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
2%
297
298
299
300
301
302

Section 6(b)(4)(A)unreasonablerisk determinationfor consumeruse of TCE in adhesives and sealants
as tire repair cement/sealer:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonablerisk driver - consumers:
• Developmentalcardiac toxicity resulting from acute inhalation exposures.
Unreasonablerisk driver - bystanders:
• Developmentalcardiac toxicity resulting from acute inhalation.
Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE 5.6E-03 (moderate intensity user).
Risk estimate- bystanders:
• Developmentalcardiac toxicity: Acute inhalationMOE 2.3E-02 (moderate intensity user).
(Table 4-42)
Risk Considerations:Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander unreasonable risk
determinationsreflect the severity of the effects associatedwith acute exposures. Dermal exposures
were not quantified for this scenario, as consumer dermal exposure with impeded evaporation is not
expected, and bystanders are not expected to be dennally exposed to TCE. For the consumer exposure
scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposedto following an exposure event.

303

304
Life Cycle Stage

Consumer use

Category

Adhesives and sealants

Subcategory

Tire repair cement/sealer

305
306

307
308
309
310
311
312
313
314

S.3.45 Consumer Use-Cl!aning and furniture care products -Carpet cleaner

Section 6(b)(4)(A) unreasonablerisk determinationfor consumer use of TCE in cleaning and furniture

careproducts as carpetcleaner:
•

Presents an unreasonablerisk of injury to health (consumers and bystanders).

Unreasonablerisk driver - conswners:
• Developmentalcardiac toxicity resulting from acute inhalation and dermal exposures.
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315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339

Umeasonable risk driver- bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: Benchmark MOE= 10.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 5.8E-04 (moderate intensity user).
• Acute demtal MOE 6.7E-04 (moderate intensity user). (Table 4-43)
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 2.9E-03 (moderate intensity user).
(Table 4-43)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indi~
risk. Consumer and bystander unreasonable risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for .
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage
Conswner use

Category
Cleaning and furniture care
products

Subcategory
Carpet cleaner

340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356

5.3.46 Consumer Use - Cleaning and furniture can, products - Aerosol spot remover
Section 6(b)( 4 )(A) unreasonable risk determination for consumer use of TCE in cleaning and furniture
care products as aerosol spot remover:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers :
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.
Unreasonable risk driver- bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity : Benchmark MOE= 10.
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357
358
359
360
361
362
363

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 9.8E-04 (moderate intensity user).
• Acute dermal MOE 5.7E-03 (moderate intensity user). (Table 4-44)
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 9.9E-03(moderate intensity user). (Table
4-44)

364
365
366
367
368
369
3 70
371
3 72
373
374

Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonable risk detenninations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.

Life Cycle Stage
Consumer use

Category
Cleaning and furniture care
products

Subcategory
Aerosol spot remover

375
376

5.3.47 Consumer Use - Cleaning a'!_dfurniture care products - Liquid spot remover_ _

377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398

Section 6{b}(4}{A}unreasonable riskdetermination for consumer use ofTCE in cleaning and
care products asliguid spot remover:
• Presents an unreasonable risk of injury to health (consumers and bystanden).

furniture

Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

Driver benchmarks - consumers andbystanders:
•

Developmental cardiac toxicity: Benchmark MOE= 10.

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 7.8E-04(moderate intensity user).
• Acute dermal MOE 9.8E-04 (moderate intensity user). (Table 4-45)
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 4.2E-03(moderate intensity user). (Table

4-45)
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399
400
401

402
403
404

405
406
407

408
409

Risk Considerations:All pathways of consumer and bystander e~posure for this condition of use
indicate risk . Consumer and bystander unreasonablerisk determinationsreflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medium intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected
to be dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s)would be exposed to following an exposure event.
Life Cycle Stage

Consumer use

Category

Subcategory

Cleaningand furniture care Liquid spot remover
products

.410
411
412

S.3.48 Consumer Use - Arts, crafts, and hobby materials- Fixatives and finishing spray
coatings

413
414

415
416
417

418
419
420

Section 6(b)(4)(A) unreasonable risk detennination for consumer use ofTCE in arts, crafts, and hobby
materials as fixative and finishing spray coating:
•

Presents an unreasonable risk of injury to health (consumers and bystanders).

Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.

421

422
423

Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

424
425
426

Driver benchmarks - conswners and bystanders:
• Developmental cardiac toxicity: Benchmark MOE = 10.

427
428
429
430

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 2.SE-03 (moderate intensity user).

431

Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 1.3E-02(moderateintensity user). (Table

432
433

4-46)

434
435
436
437

438
439
440

Risk Considerations: Consumer and bystander unreasonable risk determinationsreflect the severity of
the effects associated with acute exposures. Risk estimates for consumer users at the medium intensity
use scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Dennal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
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442
443

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exposures were estimated using the same model (CEM 2.1) used to estimate exposureto users. CEM 2 .1
is a two-zonemodel that allows for the estimation of air concentrationsa user and bystander(s)would be
exposed to following an exposure event.

444
Life Cycle Stage

Consumeruse

Category
Arts, crafts, and hobby

Subcategory

Fixatives and finishing spray coatings

materials
445
446
447
448

449
450
451
452

453
454
455
456
457
458
459
460
461
462

463
464

5.3.49 Consumer Use - Apparel and footwear ~re products - Shoe polish

Section 6(b)(4)(A) unreasonablerisk determinationfor consumeruse ofTCE in apparel and footwear
care products in shoe polish:
• Presents an unreasonable risk ofinjury to health (consumers and bystanden).
Unreasonablerisk driver - consumers:
• Developmentalcardiac toxicity resultingfrom acute inhalationand dermal exposures.
Unreasonablerisk driver - bystanders:
• Developmentalcardiac toxicity resulting from acute inhalation.
Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE = 10.
Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalation MOE l.IE-02 (moderate intensity user).
• Acute dermal MOE l.0E-02 (moderateintensityuser). (Table 4-47)

465
466
467

468
469
4 70
471
472
473
474
4 75
476
477
478
479

Risk estimate - bystanders:
• Developmentalcardiac toxicity: Acute inhalationMOE 5.9E-02 (moderate intensity user).
(Table 4-47)
Risk Considerations: All pathways of conswnerand bystander exposure for this condition of use
indicate risk. Consumer and bystander unreasonablerisk determinationsreflect the severity of the effects
associatedwith acute exposures.Risk estimates for consumer users at the medium intensity use
scenariosof acute inhalation and dermal exposures indicate risk. For bystanders the risk estimates for
the medhnn intensity use scenario of acute inhalation indicaterisk. Because bystandersare not expected
to be dermally exposed to TCE, dermal risks to bystanderswere not evaluated. For the consumer
exposure scenario for bystanders, inhalation exposureswere estimated using the same model (CEM 2.1)
used to estimate exposure to users. CEM 2.1 is a two-zonemodel that allows for the estimation of air
concentrationsa user and bystander(s)would be exposed to following an exposure event.

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Life Cycle Stage
Consumer use

Category
Appareland footwearcare
products

Subcategory

Shoe polish

480
481
482

5.3.S0 Consumer Use - Other consumer uses - Fabric spray

483
484
485
486
487

488

Section 6(b)(4)(A) unreasonablerisk determinationfor consumeruse of TCE in fabric spray:
•

Presents an unreasonable risk of injury to health (consumers and bystanders).

Unreasonablerisk driver - consumers:
• Developmentalcardiac toxicity resulting from acute inhalationexposures.

489
490
491
492

Unreasonablerisk driver - bystanders:
• Developmentalcardiac toxicity resultingfrom acute inhalation.

493
494

Driver benchmarks- consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.

495
496
497
498
499
500

501

Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE 3.6E-04 (moderate intensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalationMOE l.9E-03(moderateintensity user) . (Table

4-48)

502

503
504

505
506
507
508
509

510
511

Risk Considerations:Consumer and bystanderunreasonablerisk determinationsreflect the severity of
the effects associated with acute exposures.Risk estimates for consumerusers at the medium intensity
use scenarios of acute inhalation indicaterisk. For bystandersthe risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Denna! exposureswere not quantified for this scenario, as
consumer dermal exposure with impeded evaporationis not expected,and bystandersare not expected
to be dennally exposed to TCE. For the consumer exposure scenariofor bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimationof air concentrationsa user and bystande.r(s)would be
exposed to following an exposureevent.

512
513
Life Cycle Stage

Consumer use

Category
Other consumeruses

Subcategory

Fabric spray

514
515
516

5.3.51 Consumer Use- Other consumer uses - Film cleaner

517

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518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546

Section 6(b)(4)(A) unreasonable risk detennination for consumer use ofTCE in film cleaner.
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Developmental cardiac toxicity resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation .
Driver benchmarks - conswners and bystanders:
• Developmental cardiac toxicity: Benchmark MOE

= 10.

Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 3.6E-04(moderate intensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE l .9E-03(moderate intensity user). (Table
4-49)
Risk Considerations: Conswner and bystander unreasonable risk determinations reflect the severity of
the effects associated with acute exposures. Risk estimates for consumer users at the medium intensity
use scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrations a user and bystander(s) would be
exposed to following an exposure event

Life Cycle Stage
Consumer use

Category
Other consumer uses

Subcategory
Film cleaner

547
548
549
550
551
552
553
554

5.3.52 C!nsumer Use - Other consumer uses - Hoof polish

Section 6lb)(4)(A} unreasonable riskdetennination for consumer useofTCE in hoof polish :
• Presents an unreasonable risk of injury to health (consumers and bystanders).

555

Unreasonable risk driver - conswners:
• Developmental cardiac toxicity resulting from acute inhalation exposures.

556
557
558

Unreasonable risk driver - bystanders:
• Developmental cardiac toxicity resulting from acute inhalation.

559
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 560

561
562
563
564
565
566
567
568
569
570
571
572

573
574

575
576
577
578

INTERAGENCYDRAFT - DO NOT CrTE OR QUOTE
Driver benchmarks - consumers and bystanders:
• Developmental cardiac toxicity: BenchmarkMOE= 10.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 1.7E-02(moderate intensity user).
Risk estimate - bystanders:
• Developmental cardiac toxicity: Acute inhalation MOE 7.8 (moderate intensity user). (Table 450)
Risk Considerations: Consumer and bystander unreasonable risk determinations reflect the severity of
the effects associated with acute exposures. Risk estimates for consumer users at the medium intensity
use scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dennally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrationsa user and bystander(s) would be
exposed to following an exposure event.

579
Life Cycle Stage

Consumer use

Category

Other consumer uses

Subcategory

Hoof polish

580
581
582

5.3.53 Consumer Use - Other consumer uses - Pepper spray

583
584
585
586

Section 6(b)(4)(A) unreasonable risk determinationfor consumer use of TCE in ixmper spray:

587

Unreasonable risk driver - consumers:
• Dev~lopmentalcardiac toxicity resulting from acute inhalation exposures.

588
589

590
591
592
593
594
595
596
597
598
599
600

•

Presents an unreasonable risk of injury to health (consumers).

Driver benchmarks - consumers:
• Developmental cardiac toxicity: BenchmarkMOE = 10.
Risk estimate - consumers:
• Developmental cardiac toxicity: Acute inhalation MOE 0.2l(moderate intensity user).
Risk Considerations: Consumer unreasonable risk determinationsreflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation indicate risk. For bystanders the risk estimates of acute inhalation were not
estimated. Dermal exposures were not quantified for this scenario, as conswner dermal exposure with
impeded evaporation is not expected, and bystanders are not expected to be dermally exposed to TCE.

601

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Life Cycle Stage

Consumeruse

Category

Other consumer uses

Subcategory

Pepper spray

602
603
604

S.3.54 Consumer Use - Other consumer uses - Toner aid

605
606
607
608
609
610
611

Section 6(b)(4)(A) unreasonablerisk determinationfor consumer use of TCE in toner aid:
• Presents an unreasonable risk of injury to health (consumen and bystanders).
Unreasonablerisk driver- consumers:
• Developmentalcardiac toxicity resulting from acute inhalationexposures.

612
613

Unreasonablerisk driver- bystanders:
• Developmentalcardiac toxicity resulting from acute inhalation.

614
615
616

Driver benchmarks - consumers and bystanders:
• Developmentalcardiac toxicity: BenchmarkMOE= 10.

617
618

619
620
621
622
623

624
625
626
627
628
629
630
631

Risk estimate - consumers:
• Developmentalcardiac toxicity: Acute inhalation MOE 2.6E-03 (moderate intensity user).
Risk estimate - bystanders:
• Developmentalcardiac toxicity: Acute inhalation MOE 1.4E-02(moderate intensity user).
(Table 4-52)

Risk Considerations: Consumerand bystander unreasonablerisk determinationsreflect the severity of
the effects associated with acute exposures.Risk estimates for consumerusers at the medium intensity
use scenarios of acute inhalationindicate risk. For bystandersthe risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Dermal exposureswere not quantified for this scenario, as
consumer dermal exposure with impeded evaporationis not expected, and bystanders are not expected
to be dermally exposed to TCE.
Life Cycle Stage

Consumer use

Category

Other consumer uses

Subcategory

Toner Aid

632
633
634
635
636
637
638

S.3.55 Consumer Use - Other consumer uses - Lace wig and hair extension glues

Section 6{b)(4)(A)unreasonablerisk determinationfor consumeruse ofTCE in lace wig and hair
extension glues:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
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640

INTERAGENCYDRAFT- DO "IOT CITE OR QUOTE
Unreasonablerisk driver - consumers:
• Developmentalcardiactoxicity resultingfrom acute inhalationexposures.

641

642
643

Unreasonablerisk driver- bystanders:
• Developmentalcardiactoxicity resultingfrom acute inhalation.

644

645
646

Driver benchmarks- consumersand bystanders:
• Developmentalcardiactoxicity: BenchmarkMOE = 10.

647

648
649
650
651
652
653
654
655

656
657
658
659

660
661
662
663
664
665

Risk estimate- consumers:
• Developmentalcardiac toxicity: Acute inhalationMOE 3.0E-02 (moderate intensity user).
Risk estimate- bystanders:
• Developmentalcardiac toxicity: Acute inhalationMOE 0.30 (moderate intensity user). (Table 453)
Risk Considerations:Consumerand bystander unreasonablerisk determinationsreflect the severityof
the effects associatedwith acute exposures.Risk estimatesfor consumerusers at the medium intensity
use scenarios of acute inhalationindicate risk. For bystandersthe risk estimates for the medium intensity
use scenario of acute inhalation indicaterisk. Dermal exposureswere not quantified for this scenario, as
consumer dermal exposure with impededevaporationis not expected,and bystanders are not expected
to be dennally exposedto TCE. For the consumerexposurescenariofor bystanders,inhalation
exposures were estimatedusing the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimationof air concentrationsa user and bystander(s)would be
exposed to followingan exposure event.

Life Cycle Stage

Consumer use

Category

Other consumeruses

666
667

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Subcategory

Lace wig and hair extensionglues

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1

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1

APPENDICES

2

3
4

AppendixA
A.1

REGULATORYHISTORY

Federal Laws and Regulations

5
6

--=--------------------

Table Aox A-1. Federal Laws and Remlations
Statutes/Regulations
Description of Authority/Regulation

Description of Regulation

EPA Regulations

Toxics Substances
Control Act (TSCA) Section 6(a)

Provides EPA with the authority to
prohibit or limit the manufacture
(includingimport), processing,
distributionin commerce,use or disposal
of a chemical if EPA evaluates the risk
and concludes that the chemical presents
an unreasonablerisk to human health or
the environment.

Proposed rule under section 6 of
TSCA to address the unreasonable
risks presented by TCE use in
vapor degreasing (82 FR 7432 ;
January 19. 2017).

TSCA - Section 6(a)

Provides EPA with the authority to
prohibit or limit the manufacture
(including import), processing,
distribution in commerce, use or disposal
of a chemical if EPA evaluates the risk
and concludes that the chemical presents
an unreasonablerisk to human health or
the environment

Proposed rule under section 6 of
TSCA to address the unreasonable
risks presented by TCE use in
commercial and consumer aerosol
degreasing and for spot cleaning at
dry cleaning facilities (81 FR
91592 ; December 16, 2016).

TSCA - Section 6(b)

Directs EPA to promulgate regulations to
establish processes for prioritizing
chemicals and conducting risk
evaluations on priority chemicals. In the
meantime, EPA is directed to identify
and begin risk evaluations on
10 chemical substancesdrawn from the
2014 update of the TSCA Work Plan for
Chemical Assessments.

TCE is on the initial list of
chemicals to be evaluated for
unreasonable risks under TSCA
(81 FR 91927 , December 19,

Once EPA determines that a use of a
chemical substance is a significant new
use under TSCA section 5(a), persons are
required to submit a significant new use
notice (SNUN) to EPA at least 90 days
before they manufacture (including
import) or process the chemical
substance for that use.

SignificantNew Use Rule (SNUR)
(81 FR 20535 ; April 8, 2016).
TCE is subject to reporting under
the SNUR for manufacture
(includingimport) or processing of
TCE for use in a consumer product
except for use in cleaners and
solvent degreasers, film cleaners,
hoof polishes, lubricants, mirror

TSCA - Section 5(a)

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2016).

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Statutes/Regulations

Description of Authority/Regulation

Description of Regulation
edge sealants and pepper spray.
This SNUR ensures that EPA will
have the opportunity to review any
new consumer uses of TCE and, if
appropriate, take action to prohibit
or limit those uses.

TSCA - Section 8(a)

The TSCA section 8(a) CDR rule
requires manufacturers (including
importers) to give EPA basic exposurerelated information on the types,
quantities and uses of chemical
substances produced domestically and
imported into the United States .

TCE manufacturing (including
importing), processing and use
information is reported under the
CDR rule (76 FR 50816 , August
16, 2011).

TSCA - Section 8(b)

EPA must compile, keep current and
publish a list (the TSCA Inventory) of
each chemical substance manufactured,
processed or imported in the United
States.

TCE was on the initial TSCA
Inventory and was therefore not
subject to EPA's new chemicals
review process (60 FR 16309 ,
March 29, 1995).

TSCA- Section 8(e)

Manufacturers (including imports),
processors and distributors must
immediately notify EPA if they obtain
information that supports the conclusion
that a chemical substance or mixture
presents a substantial risk of injury to
health or the environment.

28 substantial risk notifications
received for TCE (U.S. EPA,
Chem View. Accessed April 13,
2017).

TSCA - Section 4

Provides EPA with authority to issue
rules and orders requiring manufacturers
(including importers) and processors to
test chemical substances and mixtures.

Seven studies received for TCE
(U.S. EPA, Chern View. Accessed
April 13, 2017).

Emergency Planning
and Community Rightto-Know Act (EPCRA)
- Section 313

Requires annual reporting from facilities
in specific industry sectors that employ
IO or more full time equivalent
employees and that manufacture, process,
or otherwise use a TRI-listed chemical in
quantities above threshold levels. A
facility that meets reporting requirements
must submit a reporting form for each
chemical for which it triggered reporting,
providing data across a variety of
categories, including activities and uses
of the chemical, reJeases and other waste
management (e.g.,, quantities recycled,
treated, combusted) and pollution
prevention activities (under sect ion 6607

TCE is a listed substance subject
to reporting requirements under
40 CFR 372.65 effective as of
January 1, 1987.

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Statutes/Regulations

Description of Authority/Regulation

Description of Regulation

of the Pollution PreventionAct). These
data include on- and off-site data as well
as multimediadata (i.e., air, land and
water).
Federal Insecticide,
Fungicide, and
Rodenticide Act
(FIFRA) - Section 6

FIFRA governs the sale, distributionand TCE is no longer used as an inert

ingredient in pesticide products.
use of pesticides. Section 3 ofFIFRA
generally requires that pesticide products
be registered by EPA prior to distribution
or sale. Pesticides may only be registered
if, among other things, they do not cause
"unreasonableadverse effects on the
environment." Section 6 ofFIFRA
provides EPA with the authorityto
cancel pesticide registrationsif either: (1)
the pesticide, labeling, or other material
does not comply with FIFRA or (2) when
used in accordancewith widespread and
commonly recognizedpractice,the
pesticide generally causes unreasonable
adverse effects on the environment.

Clean Air Act (CAA) - Defines the original list of 189 HAPs.
Lists TCE as a HAP (42 U.S.C.
7412(b)(l)).
Section 112(b)
Under l 12(c) of the CAA, EPA must
identify and list source categories that
emit HAPs and then set emission
standards for those listed source
categories under CAA section 112(d).
CAA section 112(b)(3)(A) specifies that
any person may petition the
Administratorto modify the list of HAPs
by adding or deleting a substance.Since
1990, EPA has removed two pollutants
from the original list, leaving 187 at
present.
CAA - Section 112(d)

Section 112(d) states that the EPA must
establish a Natfonal Emission Standards
for Hazardous Air Pollutants (NESHAP)
for each category or subcategoryof
major sources and area sources ofHAPs
(listed pursuant to Section 112(c)).The
standards must require the maximum
degree of emission reduction that EPA
determines to be achievable by each
particular source category. Different
criteria for maximum achievablecontrol
technology (MACT) apply for new and
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EPA has promulgated a number of
NESHAPregulating industrial
source categories that emit
trichloroethyleneand other HAP.
These include, for example, the
NESHAP for Halogenated Solvent
Cleaning(59 FR 61801; December
2, 1994),among others.

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Statutes/Regulations

Description of Authority/Regulation

Description of Regulation

existing sources. Less stringent
standards, known as generally available
control technology (GAC1) standards,
are allowed at the Administrator's
discretion for area sources.

'

CAA- Sections 112(d) Risk and technology review (RTR) of
and 112 (f)
section 112(d) MACT standards. Section
l l 2(f)(2) requires EPA to conduct risk
assessments for each source category
subject to section l 12(d) MACT
standards, and to determine if additional
standards are needed to reduce remaining
risks. Section 112(d)(6) requires EPA to
review and revise the MACT standards,
as necessary, talcinginto account
developments in practices, processes and
control technologies.

EPA has promulgated a number of
RTR NESHAP (e.g.,, the RTR
NESHAP for Halogenated Solvent
Cleaning (72 FR 25138 ; May 3,
2007) and will do so, as required,
for the remaining source
categories with NESHAP.

CWA - Sections
30l(b), 304(b) , 306,
and 307(b)

Requires establishment of Effluent
Limitations Guidelines and Standards for
conventional, toxic, and
non-conventional pollutants. For toxic
and non-conventional pollutants, EPA
identifies the best available technology
that is economically achievable for that
industry after considering statutorily
prescribed factors and sets regulatory
requirements based on the performance
of that technology. Regulations apply to
existing and new sources.

TCE is designated as a toxic
pollutant under section 307(a)(l)
of the CWA and as such, is subject
to effluent limitations.

CWA-Section 307(a)

Establishes a list of toxic pollutants or
combination of pollutants under the to
the CWA The statute specifies a list of
families of toxic pollutants also listed in
40 CFR 401.15. The "priority pollutants"
specified by those families are listed in
40 CFR part 423, Appendix A. These are
pollutants for which best available
technology effluent limitations must be
established on either a national basis
through rules, or on a case-by-case best
professionaljudgement basis in National
Pollutant Discharge Elimination System
(NPDES) permits.

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Statutes/Regulations

Description of Authority/Regulation

Description of Regulation

Safe Drinking Water
Requires EPA to publish a nonAct (SOWA)- Section enforceablemaximum contaminantlevel
1412
goals (MCLGs) for contaminantswhich
1. may have an adverse effect on the
health of persons; 2. are known to occur
or there is a substantiallikelihoodthat
the contaminantwill occur in public
water systems with a frequencyand at
levels of public health concern;and 3. in
the solejudgement of the Administrator,
regulation of the contaminantpresents a
meaningfulopportunityfor health risk
reductions for persons served by public
water systems. When EPA publishes an
MCLG, EPA must also promulgatea
National PrimaryDrinking Water
Regulation (NPDWR)which includes
either an enforceablemaximum
contaminantlevel (MCL), or a required
treatment technique. Public water
systems are required to comply with
NPDWRs

EPA issued drinking water
standards for TCE pursuant to
section 1412 of the SDWA. EPA
promulgated the NPDWR for TCE
in 1987 with a MCLG of zero an
enforceable MCL of 0.005 mg/L
(52 FR 25690, July 8, 1987).

RCRA - Section 3001

Directs EPA to develop and promulgate
criteria for identifyingthe characteristics
of haz.ardouswaste, and for listing
hazardous waste, taking into account
toxicity, persistence,and degradabilityin
nature, potential for accumulationin
tissue and other related factors such as
flammability, corrosiveness,and other
hazardous characteristics.

TCE is included on the list of
commercial chemical products,
manufacturing chemical
intermediatesor off-specification
commercial chemical products or
manufacturingchemical
intermediatesthat, when disposed
(or when formulations containing
any one of these as a sole active
ingredient are disposed) unused,
become hazardous wastes pursuant
to RCRA 3001. RCRA Hazardous
Waste Status: D040 at 0.5 mg/L;
FOO1, F002; U228

Comprehensive
Environmental
Response,
Compensationand
Liability Act
(CERCLA) - Section
102(a)

Authorizes EPA to promulgate
regulations designatingas hazardous
substances those substanceswhich, when
released into the environment,may
present substantialdanger to the public
health or welfare or the environment.
EPA must also promulgateregulations
establishingthe quantity of any
hazardous substance the release of which
must be reported under Section 103.
Page 463 of 691

TCE is a hazardous substancewith
a reportable quantity pursuant to
section 102(a) ofCERCLA (40

CFR 302.4) and EPA is actively
overseeingcleanup of sites
contaminatedwith TCE pursuant
to the National ContingencyPlan
(NCP) (40 CFR 751).

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Statutes/Regulations

Description of Authority/Regulation

Descript ion of Regulation

Section 103 requires persons in charge of
vessels or facilities to report to the
National Response Center if they have
knowledge of a release of a hazardous
substance above the reportable quantity
threshold.
Other Federal Regulations

OSHA

Requires employers to provide their
workers with a place of employment free
from recognized hazards to safety and
health, such as exposure to toxic
chemicals, excessive noise levels,
mechanical dangers, heat or cold stress or
unsanitary conditions.

In 1971, OSHA issued
occupational safety and health
standards for TCE that included a
Permissible Exposure Limit (PEL)
of l 00 ppm TWA, exposure
monitoring, control measures and
respiratory protection (29 CFR
1910.1000).
While OSHA has established a
PEL for TCE, OSHA has
recognized that many of its
permissible exposure limits (PELs)
are outdated and inadequate for
ensuring protection of worker
health. Most ofOSHA's PELs
were issued shortly after adoption
of the Occupational Safety and
Health (OSH) Act in 1970, and
have not been updated since that
time . Section 6(a) of the OSH Act
granted the Agency the authority
to adopt existing Federal standards
or national consensus standards as
enforceable OSHA standards. For
TCE, OSHA recommends the use
of the NIOSH REL of2 ppm (as a
60-minute ceiling) during the
usage of TCE as an anesthetic
agent and 25 ppm (as a 10-hour
TWA) during all other exposures.

Atomic Energy Act

The Atomic Energy Act authorizes the
Department of Energy to regulate the
health and safety of its contractor
employees

Page 464 of 691

10 CFR 851.23, Worker Safety
and Health Program, requires the
use of the 2005 ACGIH TLVs if
they are more protective than the
OSHA PEL. The 2005 TLV for
TCE is 50 ppm.

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Statutes/Regulations

Federal Food, Drug,
and Cosmetic Act
(FFDCA)

Description of Authority/Regulation

Provides the FDA with authorityto
overseethe safety of food, drugsand
cosmetics.

Description of Regulation

Tolerances are established for
residues of TCE resulting from its
use as a solvent in the manufacture
of decaffeinated coffee and spice
oleoresins (21 CFR 173.290).

7

8

9
10
11

A.2

State Laws and Regulations

---------------------

Table Apx A-2. State Laws and Rel!lllations
State Actions

Description of Action

California Code of ·
Regulations (CCR), Title 17,
Section 94509(a)

Lists standards for VOCs for consumerproducts sold, supplied, offered
for sale or manufacturedfor use in California. As part of that
regulation,use of consumer general purpose degreaser products that
contain TCE are banned in Californiaand safer substitutes are in use
(17 CCR, Section 94509(a).

State Pennissible Exposure
Limits (PELs)

Most states have set PELs identicalto the OSHA 100 ppm 8-hour
TWA PEL. Nine states have PELs of 50 ppm. California's PEL of
25 ppm is the most stringent(CCR, Title 8, Table AC-1).

VOC regulations for
consumer products

Many states regulate TCE as a VOC. These regulations may set VOC
limits for con~er products and/or ban the sale of certain consumer
products as an ingredient and/or impurity. Regulated products vary
from state to state, and could include contact and aerosol adhesives,
aerosols;electronic cleaners,footwear or leather care products and
general degreasers, among other products. California (Title 17,
California Code of Regulations,Division 3, Chapter 1, Subchapter8.5,
Articles 1, 2, 3 and 4), Connecticut(R.C.S.A Sections 22a-174-40,
22a-174-41, and 22a-l 74-44), Delaware (Adm. Code Title 7, 1141),
District of Columbia(Rules 20-720,20-721, 20-735, 20-736, 20-737),
Illinois (35 Adm Code 223), Indiana ( 326 IAC 8-15), Maine (Chapter
152 of the Maine DepartmentofEnviromnental Protection
Regulations),Maryland (COMAR26.11.32.00to 26.11.32.26),
Michigan (R 336.1660and R 336. 1661),New Hampshire (Env-A
4100) New Jersey (Title 7, Chapter 27, Subchapter24), New York (6
CRR-NY III A 235), Rhode Island (Air Pollution Control Regulation
No. 31) and Virginia (9VACS Chapter45) all haveVOC regulations or
limits for consumerproducts. Some of these states also require
emissionsreporting.

Other

TCE is on California Proposition65 List of chemicals known to cause
cancer in 1988 or birth defects or other reproductiveharm in 2014
(CCR Title 27, section 27001). TCE is on California's Safer Consumer
Products RegulationsCandidateList of chemicalsthat exhibit a hazard
trait and are on an authoritativelist (CCR Title 22, Chapter 55).
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12

13
14
15

A.3

'International Laws and Regulations

------=------------------

Tablle Aox A-3. Rem.datorv Actions b, 1 Other Governments and Tribes
Country/ Organization

Requirements and Restrictions

Canada

TCE is on the Canadian List of Toxic Substances (CEPA
1999 Schedule 1). TCE is also regulated for use and sale
for solvent degreasing wider Solvent Degreasing
Regulations(SOR/2003-283)(Canada Gazette, Part II on
August 13, 2003). The purpose of the regulation is to
reduce releases ofTCE into the environment from solvent
degreasing facilitiesusing more than 1000 kilograms of
TCE per year. The regulation includes a market
intervention by establishingtradable allowances for the
use of TCE in solvent degreasing operations that exceed
the 1000 kilograms threshold per year.

European Union

In 2011, TCE was added to Annex XIV (Authorisation
list) of regulation(EC) No l 907/2006 - REACH
(Registration,Evaluation, Authorization and Restriction
of Chemicals).Entities that would like to use TCE needed
to apply for authorizationby October 2014, and those
entities without an authorization must stop using TCE by
April 2016. The European Chemicals Agency (ECHA)
received 19 applications for authorization from entities
interested in using TCE beyond April 2016.
TCE is classifiedas a carcinogen category 1B, and was
added to the EU REACH restriction of substances
classified as carcinogen category 1A or 1B wider the EU
Classificationand Labeling regulation (among other
characteristics)in 2009. The restriction bans the placing
on the market or use of TCE as substance, as constituent
of other substances,or, in mixtures for supply to the
general public when the individual concentration in the
substance or mixtureis equal to or greater than 0.1 % w/w
(Regulation (EC) No 1907/2006 -REACH (Registration,
Evaluation, Authorizationand Restriction of Chemicals)).
Previous regulations, such as the Solvent Emissions
Directive (Directive 1999/13/EC) introduced stringent
emission controls of TCE.

Australia

In 2000, TCE was assessed (National Industrial Chemicals
Notification and Assessment Scheme, NICNAS (2000),
Trichloroethy/ene.Accessed April, 132017).

Japan Chemical Substances
Control Law

TCE is regulated in Japan under the following legislation:
Page 466 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Act on the Evaluationof ChemicalSubstances and Regulationof
Their Manufacture, etc. (Chemical Substances Control Law;
CSCL)
Act on Confmnation. etc. of Release Amounts of Specific
Chemical Substances in the Environment and Promotion of
Improvementsto the ManagementThereof
IndustrialSafetyand HealthAct (ISHA)
Air PollutionControlLaw
Water PollutionControlLaw
Soil ContaminationCountermeasuresAct
Law for the Controlof HouseholdProducts ContainingHarmful
Substances
(National Institute of Technology and Evaluation (NITE)
Chemical Risk Information Platform (CHIRP), Accessed
April 18, 2017).
Australia, Austria, Belgium,
Canada, Denmark, Finland,
France, Germany, Hungary,
Ireland, Israel, Japan, Latvia,
New Zealand, People's Republic
of China, Poland, Singapore,
South Korea, Spain, Sweden,
Switzerland, United Kingdom

Occupational exposure limits for TCE (GESTIS
International limit values for chemical agents
(Occupational exposure limits. OELs) database . Accessed
April 18, 2017).

16
17
18

Page 467 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
19
20
21
22
23

~!
26
27
28
29
30
31
32
33

34
35
36
31
38
39
40

41
42
43
44

45
46
47
48
49

50
51

Appendix B

LIST OF SUPPLEMENTALDOCUMENTS

List of supplemental documenls:

Associated Systematic Review Data Quality Evaluation and Data Extraction DocumentsProvides additional detail and information on individual study evaluations and data extractions
including criteria and scoring results:

Physical/ChemicalProperties,Fate and Transport
a. Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Quality

Evaluationof Physical•ChemicalPropertiesStudies
b. Risk Evaluationfor Trich/oroethylene,SystematicReviewSupplementalFile: Data Quality
Evaluationof EnvironmentalFate and TransportStudies
c. Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Extraction
for EnvironmentalFate and TransportStudies
OccupationalExposures and Releases
d Risk Evaluationfor Trichloroethylene,SystematicReviewSupplementalFile: Data Quality
Evaluationof EnvironmentalReleasesand OccupationalExposure Data

e. Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Quality
Evaluationof EnvironmentalReleasesand OccupationalExposure CommonSources
f

Risk Evaluationfor Trichloroethylene,SystematicReviewSupplementalFile: List of Key and
SupportingStudiesfor EnvironmentalReleasesand OccupationalExposure

Consumer and EnvironmentalExposures
g. Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Quality
Evaluation/or Data Sources on Consumerand EnvironmentalF,xposure
h. Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Extraction

Tablesfor EnvironmentalMonitoringData

52

53
54
55
56

i. Risk Evaluationfor Trichloroethylene,SystematicReviewSupplementalFile: Data Extraction
for BiomonitoringData

58

EnvironmentalHazard
Risk Evaluationfor Trichloroethylene,SystematicReviewSupplementalFile: Data Quality
Evaluationof EnvironmentalHazard Studies

59
60
61

k Risk Evaluationfor Trichloroethylene,SystematicReview SupplementalFile: Data Extraction
for EnvironmentalHazard Studies

57

62
63
64
65
66

j.

Human Health Hazard
l. Risk Evaluationfor Trichloroethylene,SystematicReviewSupplementalFile: Data Quality
Evaluationof Human Health Hazard Studies -Animal and MechanisticData
Page 468 of691

 67
68
69
70

11
72
73

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
m. Risk Evaluationfor Trich/oroethylene,Systematic Review Supplemental File: Data Quality
Evaluation of Human Health Hazard Studies - EpidemiologicalData
n. Risk Evaluationfor Trichloroethylene, Systematic Review Supplemental File: Updatesto the
Data Quality Criteriafor EpidemiologicalStudies

74

o. Risk Evaluationfor Trich_loroethylene
, Systematic Review Supplemental File: Data Extraction
for Human Health Hazard Studies

75
76
77

p. Risk Evaluationfor Trichloroethylene,Systematic Review Supplemental File: List of Key and
Supporting Studiesfor Human Health Hazard Assessment

78

I?

Associated Supplemental Information Documents - Provides additional details and infonnation
on exposure, hazard and risk assessments:

82
83
84

q. Risk Evaluationfor Trichloroethylene, Supplemental InformationFile: Environmental
Releases and OccupationalExposureAssessment

79

Occupational Exposures and Releases

85
86
87

r. Risk Evaluationfor Trichloroethylene, SupplementalInformation File: Risk Calculatorfor
Occupational Exposures

88
89
90

91

Consumer and Environmental Exposures

s. Risk Evaluationfor Trichloroethylene,SupplementalInformatwn File: Aquatic Exposure
Modeling Outputsfrom E-FAST

92
93
94

95
96
97
98
99
100

t.

RiskEvaluationfor Trichloroethylene, SupplementalInformationFile: Consumer Exposure
Assessment Model Input Parameters

u. Risk Evaluationfor Trichloroethylene,SupplementalInformation File: Exposure Modeling

Results and Risk Estimatesfor ConsumerInhalation Exposures

v. Risk Evaluationfor Trichloroethylene,Supplemental InformationFile: Exposure Modeling
Results and Risk Estimatesfor ConsumerDermal Exposures

101
102

103
104

105
106
107
108
109

Human Health
w. Risk Evaluationfor Trichloroethylene, SupplementalInformation File: Data Tablefor

Developmental Cardiac Toxicity Weightof EvidenceAnalysis
x. Risk Evaluationfor Trichloroethylene, Supplemental InformationFile: Personal
Communication to OPPT. Raw Data Values.fromSelgrade and Gilmour, 2010

110

111
112
113
114
Page 469 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
115
116
117
118
119
120

Appendix C

ENVIRONMENT AL EXPOSURES

A break-out of facility-specific modeling results organized per OES, with predicted surface water concenttatiuns and associated days of COC
exceedance,are included in Table_ Apx C-1. These facility-specificmodeling results are utilized and discussed in environmentalrisk
characterizationpresented in Section 4.1.2.

1rabffeApx C-1. Facility-S 1>ecific
Aquatic Exposure Modelin2 Results
Name, Location, and ID of
Active Releaser Facility

Release
Media 1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody

Typel

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

swc

350

1.266

0.00156

0.0051

20

22.150

0.0273

0.0897

Days or
Release4

7Ql0

6

(ppb)

coc
(ppb)

Days of
Exceedance7
(days/yr)

OES: Manufacturin2

Axiall Corporation,
Westlake, LA
NPDES: LA0007129

Olin Blue Cube,
Freeport.TX
NPDES: Not available

Surface
Water

NPDES

Surface

LA0007129

water

350

Off-site
Wastewater
Treatment

Organic
Chemicals
Manuf.

Surface
water

Off-site
Wastewater
Treatment

Organic
Chemicals
Manuf.

Surface
water

Solvents& Chemicals,
Pearland,TX
NPDES:Not available
Surface
Water

Organic
Chemicals
Manuf.

0.069

026

2.42

20

1.200

4.51

42.14

350

0.015

0.0564

0.53

20

0.265

1.01

9.48

350

0.Dl5

0.30

2.77

20

0.265

5.34

49.91

Surface
water

Page 470 of 691

3
788
52,000
3
788
52,000
3

788
52,000
3
788
52,000
3
788
52,000
3

788
52,000
3
788
52,000
3
788
52,000

0
0
0
0
0
0

37
0
0
11
0
0
17
0
0
5
0
0

40
0
0
12
0

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of

Release

Active Releaser Facility

Media1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

Release•

Release 5
(kg/day)

350

0.015

Days of

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

coc
(ppb)
3

Surface
Water
Occidental Chemical Corp

Surrogate

NPDES
KS0043036

0.07

788
52,000
3

20

Wichita,
Wichita,KS
NPDES: KS0096903 and
Organic Chem MFG SIC

0.02

Surface
water
0.265

0.27

1.33

788
52,000
3

Off-s ite

Wastewater
Treatment

Organic
Chemicals
Manuf.

350

0.015

0.0564

0.53

Surface

788
52,000
3

water
20

0.265

1.01

9.48

788
52,000

Days of
Exceedance'
(days/yr)
0
0
0
0
0
0
17
0
0

5
0
0

OES: Processing as a Reactant
Off-site
Wastewater
Treatment

Organic
Chemicals
Manufacture

350

0.005

0.0188

0.18

52,000
3

Surface
water
20

0.089

0.33

3.13

788
52,000
3

440 unknown sites•
NPDES: Not applicable
Surface
Water

Organic
Chemicals
Manufacture

350

NPDES
KY0003603

0.0989

0.92

788
52,000
3

350

Surface
Water

0.005

Surface
water
20

Arkema Inc.
Calvert City, KY
NPDES: KY0003603

3
788

0.089

0.017

1.76

0.000197

Surface
water
20

0.295

0.00342

350

0.0128

0.0000158

Page 471 of 691

16.45

0.00073
7

0.128

788
52,000
3

788
52,000
3

788
52,000
3

5
0
0
2
0
0

23
0
0
7
0
0
0
0
0
0
0
0
0

 INTERAGENCYDRAFT - DO NOT CI IF OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media 1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody

Type'

Days or
Release4

Release5
(kg/day)

Harmonic
MeanSWC

swc 6

7Q10

(ppb)

(ppb)

coc
(ppb)

Days of
Exceedance 7
(days/yr)

788
0
52,000
0
Surface
NPDES
Surface
3
0
Water
LA0006181
water
0.00090
20
0.224
0.000276
788
0
7
52,000
0
3
350
350
0.00169
n/a
169.00
788
0
Praxair Technology Center,
52,000
0
Surface
NPDES
Tonawanda,NY
Still body
Water
NY0000281
3
20
NPDES: NY0000281
20
0.030
n/a
3000.00
788
20
52,000
0
OES: OTVD (Includes releases for Closed-LoopDeereasing, Conveyori7.edDegreasing,Web De2reasin1,and MetalworkingFluids)
3
0
0.00502
260
0.005
O.ot88
788
0
Texas Instrwnents, Inc.,
52,000
0
Surface
NPDES
Surface
Attleboro, MA
Water
MA0001791
water
3
0
NPDES: MA0001791
20
0.067
0.0673
0.25
788
0
0
52,000
3
0
260
0.002
0.00711
0.0425
788
0
Accellent Inc/Collegeville
52,000
0
Surface
NPDES
Surface
Microcoax, Collegeville,PA
Water
PA0042617
water
3
0
NPDES: PA0042617
20
0.029
0.10
0.62
788
0
52,000
0
3
0
0.0113
260
0.001
0.0619
788
0
Ametek Inc. U.S. Gauge Div.,
SUtTogate
52,000
0
Surface
Surface
Sellersville,PA
NPDES
water
Water
3
0
NPDES: PA0056014
PA0020460
20
0.011
0.12
0.68
788
0
52,000
0
3
0
Atk-AlleganyBallistics Lab
Surface
NPDES
Surface
260
0.000669
0.0031J
0.0005
(Nirop),
Water
WV0020371
water
788
0
0.00005
18

Honeywell International Geismar Complex,
Geismar, LA
NPDES: LA000618l

Page 472 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID of

Active Releaser Facility

Release
Media1

Industry

EFAST
Waterbody

Sector in

Type3

Facility or

Days or

Release5

Release4

(kg/day)

Harmonic
MeanSWC
(ppb)

7Qt0
SWC6
(ppb)

coc
(ppb)

Days of
Exceedaoce7
(days/yr)

EFAST2
Keyser, WV
NPDES: WV0020371

52,000
3

20
Handy & Hannan Tube
Co/East Norriton, Norristown,
PA
1436
NPDES: PAOOl
US Nasa Micboud Assembly
Facility,
New Orleans, LA
NPDES: LA00S2256

Surface
Water

Surface
Water

LLC,

Akebono Elizabethtown Plant,
Elizabethtown,KY
NPDES: KY0089672

Delphi Harrison Thennal
Systems,
Dayton,OH
NPDES: OH000943l

Surface
Water

Surface
Water

Surface
Water

0.00803

0.0373

Surrogate
NPDBS
LA0003280

NPDES
NY0000558

Surrogate
NPDES
KY0022039

NPDES
OH0009431

765.63

1.96

n/a

20

25.44

n/a

9937.50

260

0.13

3.14

10.97

20

l.71

41.38

144.47

260

0.07

1.15

4.87

20

0.897

14.77

62.38

260

Still body

Surface

water

Surface
water

260

0.04

0.ol75

0.0752

20

0.465

0.20

0.87

260

0.03

0.000631

0.00301

Surface
water

~

Chemours Company Fe LLC,

788
52,000

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikeJyto
exceed the most sensitive COC using the most conservative input assumptions.

GM Components Holdings
Lockport, NY
NPDES: NY0000558

0.0061

0
0
0
0

Page 473 of 691

3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

788
52,000
3
788
52,000
3
788
52,000
3

260
0
0
20
20
0
117
0
0
20
0
0
27
0
0
16
0
0
0
0
0
0
0
0
0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location, and ID or
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

Days of

Release'

4

(kg/day)

Release

Harmonic
MeanSWC
(ppb)

7Q10
SWC'
(ppb)

coc
(ppb)

788

Washington, WV
NPDES:WV0001279
Surface
Water

NPDES
WV0001279

Surface
water

52,000
3
20

0.334

0.00703

0.0335

788
52,000
3

Equistar Chemicals Lp,
LaPorte, TX
NPDES: TX0119792

Surface
Water

PrimaryMetal
Forming
Manuf.

260

0.02

0.46

2.22

788
52,000
3

Surface
water
20

0.218

5.06

24.44

788
52,000
3

260

GE Aviation,
Lynn, MA
NPDES: MA0003905

Surface
Water

Certa Vandalia LLC,
Vandalia, OH
NPDES: OH0122751

Surface
Water

GM Components Holdings
LLC Kokomo Ops,
Kokomo, IN
NPDES:IN0001830
Amphenol Corp-Aerospace

Operations,
Sidney,NY

Surface
Water

NPDES
MA0003905

PrimaryMetal
Fonning

Manuf.

NPDES
IN0001830

NPDES
NY0003824

n/a

0.0425

788
52,000
3
788
52,000
3

Still water
20

0.128

n/a

0.54

260

0.01

0.23

1.11

788

20

0.107

2.46

11.89

52,000
3
788
52,000
3

260

0.01

0.0387

020

788

Surface
water

52,000
3

Surface
water
20

Surface
Water

0.01

Surface
water

0.086

0.33

1.73

788
52,000
3

260

Page 474 of 691

0.01

0.00882

0.0486

788
52,000

Days of
Exceedauce7
(days/yr)
0
0
0
0
0
38
1
0
12
1
0
0
0
0
0
0
0
28
0
0
9
1
0
0
0
0

0
0
0
0
0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active ReleaserFacility

Release
Media1

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Type3

Harmonic
MeanSWC

7Ql0
SWC6

(ppb)

(ppb)

Days of
Release4

ReJeaseS
(kg/day)

20

0.082

0.0723

0.40

260

0.01

0.000076

0.00040
0

coc
(ppb)

Days of
Exceedance7
(days/yr)

EFAST2
NPDES:NY0003824

EmersonPower Trans Corp,
Maysville,KY
NPDES: KY0100196

Olean Advanced Products,
Olean, NY
NPDES: NY0073547

HollingsworthSaco Lowell,
Easley, SC
NPDES: SC0046396

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
KY0020257

Surrogate
NPDES
NY0027162

PrimaryMetal
Forming

Manuf.

Surface
water
0.081

0.000995

0.00522

260

O.oI

0.00462

0.0188

20

0.068

0.0314

0.13

260

0.00469

0.11

0.52

20

0.061

1.40

6.78

water

Surface
water

260

Sandusky Plant,

Surface

NPDES

Surface

Sandusky,MI
NPDES: M10028142

Water

Ml0028142

water

Timken Us C-OrpHonea Path,
Honea Path, SC
NPDES: SC0047520

20

Surface
Water

Surrogate
NPDES
SC0000698

Surface

0.00360

0.21

1.76

20

0.047

2.69

23.04

260

0.00355

0.20

1.06

20

0.0462

2.63

13.77

water

Page 475 of 691

0
0
0

3

3

788
52,000

3

3

Surface

TrelleborgYSH Incorporated

3
788
52,000

788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000

3
0
0
0
0
0
0
0
0
0

24
0
0
6
I
0

1
0
0

4
0
0
2
0
0

3

5

788

0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled

Name, Location, and ID of
Active Releaser Facility

Johnson Controls
Incorporated,
Wichita, KS
NPDES: KS0000850
National Railroad Passenger
Corporation(Amtrak)
WilmingtonMaintenance
Facility,
Wilmington,DE
NPDES: DE0050962
ElectroluxHome Products
(Formerly Frigidaire),
Greenville,MI
NPDES: MI0002135

Rex Heat Treat Lansdale Inc,
Lansdale, PA
NPDES: PA0052965

Carrier Corporation,
Syracuse,NY
NPDES: NY000l 163
Cascade Corp (0812100207),

Release

Medla 1

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Facility or
Industry
Sedor in
EFAST2

NPDES
KS0000850

Primary Metal

Fonning
Manuf.

NPDES
MI0002135

Surrogate
NPDES
PA0026182

NPDES
NY0001163

EFAST

Waterbody
Type3

Days of
Release◄

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7QIO

swc6
(ppb)

260

0.00228

0.0068

0.0548

20

0.0296

0.0898

0.72

Surface
water

260

0.00203

0.0467

0.230

20

0.026

0.60

2.89

Surface
water

260

0.00201

0.00644

0.0171

20

0.026

0.0834

0.22

260

0.00194

0.00896

0.0523

Surface
water

Surface
water
20

0.025

0.12

0.67

260

0.00177

n/a

0.220

20

0.023

n/a

2.84

260

0.00117

0.0269

0.130

Still water

Page 476 of 691

coc
(ppb)

52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788

52,000
3
788
52,000
3
788
52,000
3
788

52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

Days of
Exceedance7
(days/yr)
0
0

0
0
0
0
0
21

0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Springfield, OH
NPDES: OH00857I 5
Surface
Water

USAF-WurtsmitbAfb,
Oscoda, MI
NPDES: MI0042285

Surface
Water

AAR Mobility Systems,
Cadillac,MI
NPDES: MI0002640

Surface
Water

Modeled
Facility or

EFAST

Industry

Waterbody

Sector ln
EFAST2
PrimacyMetal
Forming
Manuf.

Type3

Surface
water

Surrogate
NPDES
M10028282

Surface
water

Surrogate
NPDES
MI0020257

Surface
water

Days of
Relcase4

20

5
Relea8"1
(kg/day)

0.015

Harmonic
MeanSWC
(ppb)

0.35

7Q10
SWC'6
(ppb)

1.67
0.00075

260

0.00115

0.000320

20

0.015

0.00417

0.00983

260

0.001]2

0.00413

0.00916

3

coc
(ppb)

788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

20

0.014

0.0517

0.11

260

0.00107

n/a

0.130

20

0.014

n/a

1.69

260

0.000500

0.00178

788
52,000
3

Eaton Mdh CompanyInc,
Kearney,NE
NPDES: NE0114405

Lake Region Medical,
Trappe,PA
NPDES:PA0042617

Motor ComponentsL L C,
Elmira, NY
NPDES: NY000408l

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
NE0052647

NPDES
PA0042617

NPDES
NY0004081

Still water

0.0106

Surface
water

Surface
water

20

0.007

0.0249

0.15

260

0.00096

0.0143

0.0618

Page 477 of691

788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788

52,000

Days of
Exceedance7
(days/yr)
0
0
3
0
0

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0

 INTERAGENCYDR.AFT- DO NOT CITE OR QUOTE
-~-Modeled

Name, Location, and ID of
Active Releaser Facility

Release
Media 1

Facility or
Industry
Sector In

EFAST
Wate rbody
Type3

Days of
Release 4

ReJease5
(kg/day)

Harmonic
Mea n SWC
(ppb)

7QI0
SWC 6
(ppb)

20

0.0125

0.19

0.83

260

0.000897

0.0206

0.0997

20

0.012

0.28

1.33

260

0.000806

0.0378

0.0821

20

0.010

0.47

1.02

260

0.000747

0.0172

0.0830

20

0.0 10

0.23

1.11

260

0.000742

0.00808

0.0336

20

0.010

coc
(ppb)

Days of
Exceedance 7
(days/yr)

1

EFAST

Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244

Surface
Water

GE (Greenville) Gas Turbines
LLC,
Greenville, SC
NPDES: SC0003484

Surface
Water

Parker Hannifin Corporation,

Waverly,OH
NPDES: OH0I04132

Surface
Water

Primary Metal
Forming
Manuf.

Surface
water

NPDES
SC0003484

Surface
water

Primary Metal
Forming
Manuf.

Surface
water

3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

Mahle Engine Components
Usa Inc,
Muskegon, MI
NPDES: MI0004057

General Electric Company Waynesboro,
Waynesboro, VA
NPDES: VA0002402

Surface
Water

Surface
Water

NPDES
MI0004057

NPDES
VA0002402

Surface
water

Surface
water

0.11

0.45

260

0.000733

0.00241

0.00705

20

0.010

0.0329

0.0962

Page 478 of 691

788
52,000
3
788

52,000
3
788
52,000
3
788

0
0
0
17
0
0
2
0
0
0
0
0
0
0
0
16
0
0
2
0
0
0
0
0
0
0
0
0

0
0
0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active ReleaserFacility

Release
Media1

Modeled
Facility or
Industry
Sector In
EFAST2

EFAST
Waterbody

Type3

Days of
Release•

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10
SW~

(ppb)

coc
(ppb)
52,000
3

Globe Engineering Co Inc,
Wichita, KS
NPDES: KS0086703

Gaystoo Corp,

Dayton, OH
NPDES: OH0127043

StyrolutionAmerica LLC,
Channahon, IL
NPDES: 1L0001619

RemingtonAnns Co Inc,
Ilion, NY
NPDES: NY0005282

United Technologies
Corporation,Pratt And
Whitney Division,
East Hartford, CT
NPDES: CT0001376

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
KS0043036

Surrogate
NPDES
OH002488l

NPDES
IL0001619

NPDES
NY0005282

NPDES
CT0001376

260

0.00173

0.00175

0.00853

788
52,000

20

0.023

0.0232

0.110

260

0.000643

0.000281

0.00121

788
52,000
3
788
52,000
3

20

0.008

0.0035

0.0150

Surface
water

3

Surface
water

0.00022

260

0.000637

0.0000845

20

0.008

0.00106

0.00278

260

0.000612

0.000291

0.00079
9

20

0.008

0.00380

0.0104

260

0.000480

0.0000218

20

0.006

0.000273

0.00103

260

0.000470

0.000629

0.00292

Surface
water

Surface
water

Surface
water

1

0.00008

22

788

52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

Page 479 of 691

788
52,000
3

Days of
Exceedance7
(days/yr)

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled

Name, Location, and ID of
Active Releaser Facility

Atk-AlleganyBallistics Lab
(Nirop),
Keyser, WV
NPDES: WV0020371

Release
Media1

Swface
Water

Sperry & Rice Manufacturing
CoLLC,
Brookville, IN
NPDES: INOOOI473

Surface
Water

Owt Industries,
Pickens, SC
NPDES: SC0026492

Surface
Water

Boler Company,
Hillsdale, Ml
NPDES: MI005365l

Mccanna Inc.,
Carpentersville,IL
NPDES: IL0071340

Cutler Hammer,
Horseheads,NY
NPDES: NY0246174

Surface
Water

Surface
Water

Surface
Water

Facility or
Industry
Sector in
EFAST2

NPDES
WV0020371

NPDES
IN0001473

NPDES
SC0026492

Surrogate
NPDES
Ml0022136

Surrogate
NPDES
!10027944

Surrogate
NPDES
NY0004081

EFAST
Waterbody

Type3

Surface
water

Release5

Days of
Release4

(kg/day)

Harmonic
MeanSWC
(ppb)

7Ql0
SWC6
(ppb)

20

0.006

0.00803

0.0373

260

0.000328

0.00117

0.00569

Surface
water
20

0.004

0.0143

0.0694

260

0.000314

0.000820

0.00213

20

0.004

0.0104

0.0272

Surface
water

260

0.000269

0.00461

0.0514

0.23

260

0.000268

0.000260

0.00091
1

Surface
water

260

0.000238

Page 480 of 691

0.00291

0.00352

788

52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

0.0102

0.0153

Days of
Exceedance7
(days/yr)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

52,000

0
0
0
0
0
0
0
0
0
0

3

0

788

0
0

52,000
3
788
52,000
3

0.003

0.003

788
52,000
3
788
52,000
3

788

20

20

(ppb)

0.0204

Surface
water

Surface
water

coc

788

52,000
3
788
52,000

0

0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled
Name, Location,and ID of
Active Reteaser Facility

Release
Media1

Facilityor

EFAST

Industry
Sector in
EFAST1

Waterbody

Type)

Days of
Release•

Release5
(kg/day)

20

0.003

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

coc
(ppb)
3

0.0443

0.19

788
52,000
3

US Air Force Offirtt Afb Ne,
Offutt AF B, NE
NPDES: NE0121789

Troxel Company,
Moscow, TN
NPDES: TN000045 l

Surface
Water

Surface
Water

Austin Tube Prod,
Baldwin,MI
NPDES: MI0054224

Surface
Water

LS Starrett Precision Tools,
Athol, MA
NPDES: MA0001350

Surface
Water

Avx Corp,
Raleigh, NC
NPDES: NC0089494

Surface
Water

Primary Metal
Forming
Manuf.

NPDES
TN0000451

Primary Metal
Forming
Manuf.

NPDES

MA0001350

Primary Metal
Forming
Manuf.

260

0 .000159

0.00366

0.0177

20

0.002

0.0460

0.22

0.00074

260

0.000134

0.000254

20

0.002

0.00379

0.0111

260

0.000114

0 .00262

0.0127

Surface
water

1

788
52,000
3
788
52,000
3
788
52,000
3

788
52,000

Surface
water
20

0.001

0.023

0.11

260

0.000102

0.000339

0.00153

20

0.001

0.00333

0.Ql5

Surface
water

Surface
water

788
52,000
3

Surface
water

0.0000883

0.00203

0.00981

20

0.001

0.023

0.11

Page 481 of 691

0
0
0

5
0
0
2
0
0
0
0
0
0
0
0
3
0
0

3

I

788
52,000
3
788
52,000

0
0
0
0
0
0
0
0
2
0
0
I
0

3

260

Days of
Exceedance7
(days/yr)

788
52,000
3
788
52,000
3
788

 INTERAGENCYORA.I1 - DO NOT ClfE: OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

Days of
Release4

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10

SWC6
(ppb)

coc
(ppb)

52,000
Indian Head Division, Naval
Surface Warfare Center,
Indian Head, MD
NPDES: MD0003158
General Dynamics Ordnance
Tactical Systems,
Red Lion, PA
NPDES:PA0043672
Trane Residential Solutions Fort Smith,
Fort Smith, AR
NPDES: AR0052477
Lexmark International Inc.,
Lexington, KY
NPDES: KY0097624
Alliant Techsystems
Operations LLC,
Elkton,MD
NPDES:MD0000078
Daikin Applied America, Inc.
(Fonnally Mcquay
International),
Scottsboro, AL
NPDES: AL0069701
BeechcraftCorporation,
Wichita, KS
NPDES: KS0000183
Federal-Mogul Corp,
Scottsville, KY
NPDES: KY0106S85
Cessna Aircraft Co (Pawnee
Facility),
Wichita, KS
NPDES: KS0000647
N.G.I,
Parkersbur~ WV

Days of
Exceedance7
(days/yr)
0

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservativeinput assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input asswnptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to he <0.02 kg/year were not modeled, as they were determinedto be unlikely to
exceed the most sensitive COC using the most conservativeinput assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be WJlikelyto
exceed the most sensitive COC usin2 the most conservative input assumotions.

Page482 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

NPDES: WV0003204
Hyster-YaleGroup, Inc,
Sulligent, AL
NPDES: AL0069787
Hitachi ElectronicDevices
(Usa},Inc.,
Greenville,SC
NPDES: SC0048411

Release
Media 1

Modeled
Facility or
Industry
Sector in
EFAST2

Surface
Water

Annualreleases estimated to be<0.02 kg/year were not modeled, as they were determinedto beunlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto beunlikely to
exceed the most sensitive COC using the most conservative input assumptions.

EFAST
Waterbody
Type3

Days of
Release 4

Release 5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

coc
(ppb)

Days of
Exceedance7
(days/yr)

OES: Spot Cleanin2 and Carpet Cleaning

3
Surrogate
NPDES
100023981

300

0.00008

0.00020S

0.00388

788

0
0
0
0
0
0

Boise State University,
Boise, ID
NPDES: IDG911006

Surface
Water

Venetian Hotel And Casino,
Las Vegas, NV
NPDES: NV0022888

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were detennined to be unlikelyto
exceed the most sensitiveCOC using the most conservativeinput assumptions.

63,746 unknown sites
NPDES: All POTW SIC

Surface
Water or
POTW

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to
exceed the most sensitiveCOC using the most conservative input assumptions.

Surface
water

20

0.001

0.00256

0.0485

52,000
3
788
52,000

OES: Re >ackaging

Hubbard-HallInc,
Waterbwy, CT
NPDES: Unknown

OiltankingHouston Inc,
Houston, TX
NPDES: TX0091855

Off-site
Wastewater
Treatment

Surface
Water

Receiving
Facility:
Recycle Inc.;
POTW(Ind.)

Surrogate
NPDES
Tx:0065943

250

1.108

5.33

27.18

20

13.85

66.45

339.11

250

0.003

0.32

6.52

Surface
water

Surface
water
20

Page 483 of 691

0.041

4.36

89.13

3

194

788
52,000
3
788
52,000
3

0
0

20
1
0
2

788

0

52,000
3
788
52,000

0
4

0
0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,and ID of
Active Releaser Facility

Sl Gabriel Tenninal,
Saint Gabriel, LA
NPDES: LA0005487

Vopak Tenninal Westwego
Inc,
Westwego, LA
NPDES: LA0124583
Research SolutionsGroup Inc,
Pelham,AL
NPDES: AL0074276
Carlisle Engineered Products
Inc, Middlefield, OH
NPDES: OH0052370

Release
Media1

Surface
Water

Modeled
Fadlltyor
Industry
Sector lo
.EFAST2

NPDES
LA0005487

EFAST
Waterbody
Type3

Days or
Release4

Release5

250

0.00550

Surrogate
NPDES
LA0042064

SWC'

(ppb)

(ppb)

0.00000677

0.00002
23

Surface
water
20

Surface
Water

(kg/day)

Harmonic
MeaoSWC

0.069

0.0000850

250

0.00468

0.00000576

20

0.058

0.0000714

Surface
water

7Q10

0.00027
9

0.00001

89
0.00023
5

coc
(ppb)
3
788
52,000
3

788
52,000
3

788
52,000
3
788
52,000

Days of
Exceedance7
(days/yr)
0
0
0

0
0
0
0
0
0
0
0

0

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservativeinput assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.
OES: Process Solvent Recyclio2 and Worker Handling of Wastes

Clean Water Of New York
Inc,
Staten Island, NY
NPDES: NY0200484

Surface
Water

Surrogate
NPDES
NJ0000019

250

0.004

20

0.047

n/a

11.76

Still body
n/a

138.24

3
788
52,000
3

250

788

0
0

52,000
Reserve Environmental
Services,
Ashtabula, OH
NPDES: 080098540

Surface
Water

Veolia Es Technical Solutions
LLC,
Middlesex, NJ
NPDES: NJ0020141

Off-site
Wastewater
Treatment

0
0
20

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.
Receiving
Facility:
Middlesex
CntyUA;

3
250

24.l

n/a

2.85

Still body

20

Page 484 of 691

301.78

n/a

35.72

788
52,000
3

788

0
0
0
20
0

 INTERAGENCYDRAFT - DO NOT CITF OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Clean Harbors Deer Parle
· LLC,
LaPorte, TX
NPDES: TX0005941

Clean Harbors El Dorado
LLC,
El Dorado, AR
NPDES: AR0037800

Release
Media'

Off-site
Wastewater
Treatment

Off~site
Waste•
water
Treatment

Modeled
Facility or
Industry
Sector in
EFAST2
NPDES
NJ0020141

EFAST
Waterbody
Type3

Days of
Release•

·aelease5
(kg/day)

250
POTW(Ind)

7Ql0
SWC6

(ppb)

(ppb)

1.68

coc
(ppb)

20.92

(days/yr)

0

3

52,000
3
788
52,000
3
788
52,000

110
0
0
19
0
0
6
0
0
11
0
0

3

8

7.28

788
52,000

0
0

3

0

0.00716

788
52,000

0
0

3

0

788

0

52,000

0

8.57

788
52,000
3

4.36

Days of
Exceedanee 7

52,000

Surface
water
20

POTW(Ind.)

0.35

Harmonic
MeanSWC

106.75

250

0.04

0.19

0.98

20

0.455

2.21

11.26

Surface
water

788

OES: Adhesives,Sealants,Paints, and Coatings
Able ElectropolishingCo Inc,
Chicago,IL
NPDES: Not available

POTW

Adhesives and
Sealants
Mamlf.

Surface
water

250

250
Garlock Sealing Technologies,
Palmyra, NY
NPDES: NY0000078

Surface

Water

NPDES
NY0000078

Aerojet Rocketdyne8, Inc.,
East Camden, AR
NPDES: AR0051071,
ARR00A521, ARR00A520

Surface
Water

Surface
Water

0.00033

0.86

0.00252

Surface
water
20

Ls Starrett Co,
Athol.MA
NPDES: MAR05B615

0.298

0.00407

0.0312

0.0889

Annualreleases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.
Adhesives and
Sealants
Manuf.

Surface
water

250
20

Page 485 of 691

0.013
0.160

0.20
2.42

1.67
20.57

3

0

788
52,000

0

3

0
3

 INTERAGENCYDRAfT - DO NOT CITE OR QUOTE
Name, Location , and ID of
Active Releaser Facility

Relea se
Media 1

Modeled
Facility or

Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

POTW

Day s of

Release•

250

250

8
,

Best One Tire & Service
Nashville,TN
NPDES:Not available

Surface
Water

Adhesives and
Sealants

Manuf.

Surface
water

POTW

20

250

250

BridgestoneAircraft Tire
(Usa), Inc. 8,
Mayodan
, NC
NPDES:Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

ClaytonHomeslnc8,
Oxford,NC
NPDES: Not available

Surface
Water

20

250

Adhesivesand
Sealants

Manuf.

250

Release 5
(kg/day )

0.0 13

0.013

0.160

0 .013

0.013

0. 160

0.0 13

0.0 13

Harmonic

7Q10

Meao SWC
(ppb)

SWC'
(ppb)

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

0.32

1.67

20.57

0.32

1.67

20.57

0.32

1.67

Surface

water
20

Page 486 of 691

0 .160

2.42

20.57

coc

Days of

(ppb)

Exceedance7
(days/yr)

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52 ,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788-

0

 INTERAGENCY DRAFT- D0~01
Nam~ Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST

EFAST
Waterbody

Type3

Days of

Release•

CIIf< OR QUOTE
Releaw
(kg/day)

Harmonic

7Q10

MeanSWC

SWC'
(ppb)

(ppb)

coc
(ppb)

1

52,000
3

POTW

250

2S0
Cmh Manufacturing, Inc.
Dba Schult Homes - Plant
9588,
Richfield, NC
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250
Surface
Water
Delphi Thermal Systems8,
Lockport,NY
NPDES: NY0000558

Green Bay Packaging Inc Coon Rapids8,
Coon Rapids, MN
NPDES: Not available

Surface
Water

0.0374

0.013

020

0.160

2.42

0.013

0.013

0.0374

0.31

0.32

1.67

20.57

0.32

1.10

NPDES
NY0000558
Surface
water

POTW

0.013

No info on
receiving
facility;
Adhesives and
Sealants
Mamlf.
Adhesives and
Sealants
Manuf.

20

250

Surface
water

250
20

Page 487 of 691

0.160

0.013

0.013
0.160

3.87

0.0374

0.20
2.42

13.50

788
52,000

3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000

Days of
Exceedance 7
(days/yr)
0
0
0
0
0
0
0
3
0
0
0
0
0
2
0
0

3
788
52,000

11

3

0

788

0

52,000

0

3

0
0

0.32

1.67
20.57

788

0
0

52,000

0

3

3

 INTERAGENCYDRAFT - DO NOT ClTt' OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry

Sector in

EFAST
Waterbody
Type3

Days of
Release4

Release5
(kg/day)

Harmonic
MeanSWC

swc 6

(ppb)

(ppb)

7Q10

coc
(ppb)

Days of
Exceedance7
(days/yr)

EFAST2

POTW

250

250

Mastercraft Boat Company8,
Vonore, TN
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

Michelin Aircraft Tire
Company8,
Norwood, NC
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface

water

250

POTW

M-Tek, Inc8,
Manchester, TN
NPDES: Not available

Surface
Water

20

Adhesives and
Sealants
Manuf.

250

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.013

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

0.32

1.67

20.57

0.32

1.67

20.57

0.32

i.67

Surface
water
20

Page 488 of 691

0.160

2.42

20.57

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media 1

Modeled
Facility or
lodus1ry
Sector in
EFASyi

EFAST
Waterbody
Type3

POTW

Days of
Release•

250

250
Surface
Water
Olin Corp1 ,
East Alton, IL
NPDES: IL0000230

No info on
receiving
facility;
Adhesives
Sealants
Manuf.

20

Surface
Water

250

and

Adhesives and
Sealants
Manuf

Surface
water

POTW

Parrish Tire Company8,
Yadkinville,NC
NPDES: Not available

Surface
Water

0.013

(ppb)

0.0374

0.08

7Ql0
SWC6
(ppb)

0.32

0.18

20

250

Adhesives and
Sealants
MantU:

Surface
water

0.160

1.03

2.26

coc
(ppb)

250
20

Page 489 of 691

0.013

0.013

0.160

0.013

0.013
0.160

0.0374

0.20

2.42

0.0374

0.20
2.42

Days of
Exceedance 7
(days/yr)

52,000

0

3
788
52,000

0
0
0

3

0

788
52,000

0

3

7

788

3

0
0
0

788

0

52,000

0

3

788

0
0

52,000

0

3
788

3

52,000

0

3
788

0

52,000

0

3

0

52,000

250
Parker Hannifin Corp Paraflex Division•,
Manitowoc, WI
NPDES: Not available

0.013

Harmonic
MeanSWC

NPDES
IL0000230
Surface
water

POTW

Release5
(kg/day)

0

0.32

1.67

20.57

0.32

1.67
20.57

0

0

788

0

52,000

0

3

3

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Locatlon, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST1

EFAST
Waterbody
Type3

POTW

Days of
Release4

250

250

Republic Doors And Frames8,
Mckenzie, TN
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

Ro-Lab Rubber
Company Inc.8,
Tracy,CA
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

Royale Comfort Seating, Inc. 8
- Plant No. 1,
Taylorsville,NC
NPDES: Not available

Surface
Water

20

250

Adhesives and
Sealants
Manuf.

250

Release5
(kg/day)

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.013

Harmonic
MeanSWC
(ppb)

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

7Ql0

SWC6
(ppb)

0.32

1.67

20.57

0.32

1.67

20 .57

0.32

1.67

Surface
water
20

Page 490 of 691

0.160

2.42

20.57

coc
(ppb)

Days of
Exceedance7
(days/yr)

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active ReleaserFacility

Release
Media1

Modtlcd
Facility or

Industry
Sector in
EFAS'P

EFAST
Waterbody
Typel

POTW

Days of
Rclease4

250

Release5
(kg/day)

0.013

Harmonic
MeanSWC

7Ql0
SWC6

(ppb)

(ppb)

0.0374

0.32

coc
(ppb)

52,000

0

3

0
0

788
52,000

250

Snider Tire, Inc. 1,

Surface
Water

Statesville,NC
NPDES: Not available

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Snyder Paper Corporation8,
Hickoiy, NC
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

StellanaUs1,
Lake Geneva, WI
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

0.013

0.160

0.013

0.013

0.160

0.013

0.013

0.20

2.42

0.0374

0.20

2.42

0.0374

020

1.67

20.57

0.32

1.67

20.57

0.32

1.67

Surface
water
20

Page 491 of 691

0.160

2.42

20.57

Days of
Exceedance'
(days/yr)

3

0
0

788

0

52,000

0

3

788

3
0

52,000

0

3
788
52,000
3

0

788
52,000

0

3

3

788

0

52,000

0

3
788

0
0

52,000

0

3

0

788

0

52,000

0

0
0
0
0

3

3

788

0

52,000

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

POTW

Days of
Release4

250

250

Thomas Built Buses Courtesy Road8,
High Point, NC
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

Unicel Corp8,
Escondido, CA
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

Acme Finishing Co Llc8.
Elk Grove Village, IL
NPDES: Not available

Surface
Water

POTW

Adhesivesand
Sealants
Manuf.

Release5
(kg/day)

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.013

Harmonic
MeanSWC
(ppb)

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

7QJO

SWC'
(ppb)

0.32

1.67

20.57

0.32

1.67

20.57

0.32

1.67

Surface
water
20
250

Page 492 of 691

0.160
0.013

2.42
0.0374

20.57
0.32

coc
(ppb)

Days of
Exceedance7
(days/yr)

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3
788

3

52,000

0

3

0

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID or
Active Releaser Facility

Release
Media 1

Surface
Water
Aerojet Rocketdyne, Inc. 8,
Rancho Cordova, CA
NPDES: CA0004l l 1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type'

Surface
Water

No info on
receiving
facility;
Adhesives and
Sealants
Manuf.

0.013

0.000295

20

0.160

0.00363

250

Adhesives and
Sealants
Mamlf.

Surface
water

POTW

Amphenol Corp Aerospace Operations8,
Sidney, NY
NPDES: NY0003824

7Q10

swc~
(ppb)

0.00081
8

NPDES
NY0003824

0.013

0.013

0.0374000

0.20

0.0101

0.32000
0

1.67

20

0.160

2.42

20.57

250

0.013

0.0374

0.32

250
Surface
Water

Harmonic
MeaoSWC
(ppb)

250

250
Allegheny Cnty Airport Autb/
Pgh Intl Airports,Coroapolis
Pittsburgh. PA
NPDES: Not available

Release5
(kg/day)

NPDES
CA0004l l1
Surface
water

POTW

Days or
Release4

0.013

0.0115

0.0631

Surface
water
20

0.160

0.14

0.78

coc
(ppb)

788
52,000
3
788
52,000

0
0
0
0
0

3
788
52,000
3
788

0
0
0

52,000

0

3
788
52,000
3
788
52,000
3

0
0
0
3
0
0
0

788

0

52,000
3

0
0

788
52,000

0
0

3

0
0
0

788
52,000

Page 493 of 691

Days of

Exceedance7
(days/yr)

0

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOIE
Name, Location,and ID of
Active Releaser Facility

Release
Media'

POTW

Modeled
Facility or
Industry
Sector in
EFAST2
No info on
receiving
facility;
Adhesivesand
Sealants
Manuf.

EFAST
Waterbody
Type3

Days of
Release4

250

250

AprotechPowertrain8,
Asheville,NC
NPDES:Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Coating& ConvertingTech
Corp/
AdhesiveCoatings•,
Philadelphia,PA
NPDES:Not available

Surface
Water

Adhesivesand
Sealants

Manuf.

Surface
water

POTW

CorpusChristi Army Depot8,
Corpus Christi,TX
NPDES:Not available

Surface
Water

20

250

Adhesivesand
Sealants
Manut:

250

Release5
(kg/day)

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.013

Harmonic
MeanSWC

7Q10
SWC6

(ppb)

(ppb)

0.03740

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

Page 494 of 691

0.160

2.42

(ppb)

Days of
Exceedance 7

(days/yr)

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

0.3200

1.67

20.57

0.32

1.67

20.57

0.32

1.67

Surface
water
20

coc

20.S7

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Locatlon, and ID of
Active Releaser Facility

Release
Media 1

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Type3

Days of
Release•

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7QIO

swc

6

(ppb)

coc
(ppb}

2

Days of
Exceedance 7
(days/yr)

EFAST
52,000

POTW

Electronic Data Systems
Camp Pendleton8, Camp
Pendleton, CA
NPDES: Not available

Surface
Water

250

Adhesives and
Sealants
Manuf.

Surface
water

POTW

Surface
Water

0.013

0.20

1.67

20

0.160

2.42

20.57

250

0.013

0.0374

0.32

Adhesives and
Sealants
Manuf.

Surface
water

20

250

250

Surface
Water

0.32

0.013

0.20

1.67

3
788
52,000
3

0
0

788
S2,000
3
788
52,000
3
788

0
0
3
0
0
0
0

52,000

0

3

3
788
52,000

0
0
0
3
0
0

3

0

788

0

52,000

0
0

788
52,000

POTW

Goodrich Corporation8,
Jacksonville, FL
NPDES:Not available

0.0374

250

250

Florida Production
Engineering,Inc. 8,
Onnond Beach,FL
NPDES: Not available

0.013

0
0
0

Adhesivesand
Sealants
Manuf.

0.160

0.013

0.013

2.42

0.0374

0.20

20.57

0.32

1.67

Surface
water
20

Page 495 of 691

0.)60

2.42

20.57

3
788..
52,000

0

3

3

788

0

52,000

0

0

 INTERAGENCYDRAFT - DONOT CITE OR QUOTE
Name. Location, and ID of
Active Releaser Facility

Release
Media 1

Modeled
FaciUtyor
Industry
Sector in

EFAST
Waterbody
Type3

Days of
Release4

Release 5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10

SWC6
(ppb)

coc
(ppb)

Days of
Exceedance 7
(days/yr)

EFAST2
POTW

250

250

Kasai North America Inc8,
MadisonPlant,Madison,MS
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Kirtland Air ForceBase•,
Albuquerque,NM
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Marvin Windows & Doors8,
Warroad.MN
NPDES: Not available

Surface
Water

POTW

Adhesivesand
Sealants
Manuf.

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.013

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

0.32

1.67

20.57

0.32

1.67

20.57

0.32

1.67

Surface
water
20
250

Page 496 of 691

0.160
0.013

2.42
0.0374

20.57
0.32

3

0

788
52,000

0

3

0

788

0

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Re]ease
Media 1

Modeled
Facility or
Industry
Sector in
EFAST1

EFAST
Waterbody

Type3

Days of
Release•

250

Release5
(kg/day)

0.013

Harmonic
MeanSWC
(ppb)

0.20

7Ql0
SWC6
(ppb)

1.67

Surface

Mcneilus Truck &
ManufacturingInc8,
Dodge Center, MN
NPDES: Not available

Water

Adhesivesand
Sealants
Manuf.

Swface
water

POTW

20

250

0.160

0.013

2.42

0.0374

20.57

0.32

coc
(ppb)

788 .

0

52,000

0

3

0

788

0

52,000

0

3
788

3

52,000

0
0

3
788
52,000
3

250

Metal Finishing Co. 3 Wichita (S Mclean Blvd),
Wichita,KS
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

20

250

POTW

250

MurakamiManufacturingUsa
Inc', CampbellsVIlle,KY
NPDES: Not available

Surface
Water

POTW

Adhesives and
Sealants
Manuf.

Surface
water

20

250

Page 497 of 691

0.013

0.160

0.013

0.013

0.160

0.013

0.20

2.42

0.0374

0.20

2.42

0.0374

1.67

20.57

0.32

1.67

20.57

0.32

Days of
Exceedance7
(days/yr)

788
52,000

0

0
0
0
0

3

0
3

788

0

52,000

0

3
788

0

52,000

0

3

0

788

0

52,000
3

0
3

788

0

52,000

0

0

3

0

788

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST1

EFAST
Waterbody
Type3

Days of
Release4

250

Peterbilt Motors Denton
Facility8,
Denton, TX
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

PortsmouthNaval Shipyard8,
Kittery,ME
NPDES:Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

2S0

R.D.Henry & Co. 8,
Wichita, KS
NPDES: Not available

Surface
Water

POTW

Adhesivesand
Sealants
Manuf.

Surface
water

20

250

Page 498 of691

Release5
(kg/day)

0.013

0.160

0.013

0.013

0.160

0.013

0.013

0.160

0.013

Harmonic
MeanSWC
(ppb)

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

7QIO

SWC6
(ppb)

1.67

20.57

0.32

1.67

20.57

0.32

1.67

20.57

0.32

coc
(ppb)

Days of
Exceedance 7
(days/yr)

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3
788
52,000

0

3
788

3
0

52,000

0

3

0

788

0

0
0

52,000

0

3

0

788

0

52,000

0

3
788

3

52,000

0

3

0

788

0

52,000

0

0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID of
Active Releaser Faclllty

Retease
Media 1

Facility or
Industry
Sector in

EFAST

Waterbody

Type3

Days of
Release4

250

Release5

Harmonic

7Qt0

(kg/day)

MeanSWC
(ppb)

SWC6

0.013

n/a

10.83

(ppb)

coc
(ppb)

Days of
Exceedance7
(days/yr)

EFAST2

3
Surface
Water

NPDES
RI0000281

RaytheonCompany',

20

Portsmouth,RI
NPDES: RI0000281

0.160

n/a

133.33

Still body

POTW

No info on
receiving
facility;
Adhesives and
Sealants

250

0.013

0.03740

250

788

0

52,000

0

3

20

788

0
0

52,000
3

0

788

0

52,000

0

0.32

Manu f.

250
Rehau lnc8,
Cullman.AL
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface

water

POTW

20

250

250
RotochopperInc8,
Saint Martin. MN
NPDES: Not available

Surface
Water

0.160

0.013

0.013

0.20

2.42

0.0374

0.20

1.67

20.57

0.32

1.67

0

788

0

52,000
3

0
3

788

0

52,000
3

0

52,000

0
0
0

3

0

788
3

0
0
3

788

52,000
Adhesives and
Sealants

Manuf.
POTW

0.013

3 .

Surface
water

20

250

Page 499 of 691

0.160

0.013

2.42

0.0374

20.57

0.32

788

0

52,000

0

3

0

788

0

 INTERAGENCYDRAFT - DO NOl CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sedor in

EFAST
Waterbody
Type3

Days of
Release4

Release5
(kg/day)

Harmonic

7Ql0

MeanSWC
(ppb}

SWC6
(ppb)

coc
(ppb)

Days of
7
Exceedance

(days/yr)

EFAST2

250

Rubber Applications',
Mulberry,FL
NPDES: Not available

Surface
Water

Adhesives and
Sealants
Manuf.

Surface
water

POTW

20

250

250

Sapa Precision Tubing
Rockledge, Llc8,
Rockledge, FL
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Thomas & Betts8,
Albuquerque, NM
NPDES: Not available

Surface
Water

POTW

Adhesives and
Sealants
Manuf.

Surface
water

20

250

Page 500 of 691

0.013

0.160

0.013

0.013

0.160

0.013

0.013

0.160

0.013

0.20

2.42

0.0374

0.20

2.42

0.0374

0.20

2.42

0.0374

1.67

20.57

0.32

1.67

20.57

0.32

1.67

20.57

0.32

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Re1easerFacility

Release
Medla1

Modeled
Facility or

EFAST

Industry
Sector in

Waterbody
Type)

Release5

Days of
Release•

(kg/day)

250

0.013

Harmonic
MeaaSWC
(ppb)

7Ql0
SWC6
(ppb)

0.20

1.67

coc
(ppb)

2

Days of
Exceedance7
(days/yr)

EFAST

Thomas Built Buses - Fairfield
Road8,
High Point, NC
NPDES: Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

Timoo,
Oba Haeco Americas
AirframeServices8,
Greensboro, NC
NPDES:Not available

Surface
Water

Adhesivesand
Sealants
Manuf.

Surface
water

POTW

20

250

250

TrelleborgCoated Systems
Us, lnc8 Grace AdvancedMaterials,
Rutherfordton, NC
NPDES: Not available

Surface
Water

POTW

Adhesivesand
Sealants
Manuf.

Surface
water

20

250
250

Page 501 of 691

0.160

0.013

0.013

0. 1·60

0.013

0.013

0.160

0.013
0.013

2.42

0.0374

0 .20

2.42

0.0374

0.20

2.42

0.0374
0.20

20.57

0.32

1.67

20.57

0.32

1.67

20.57

0.32
l.67

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

U.S. Coast Guard YardCurtis Bay8,
Curtis Bay, MD
NPDES: Not available

Release
Media 1

Surface
Water

Modeled
Facility or
Industry
Sector in
EFAST2

Adhesives and
Sealants
Manuf.

EFAST
Waterbody
Type 3

Surface
water

POTW

Days of
Re lease•

20

250

250

Viracon Inc8,
Owatonna, MN
NPDES: Not available

Surface
Water

Adhesives and
Sealants

Manuf.

Surface
water

POTW

Harmon ic
MeanSWC
(ppb)

Release 5
(kg/day)

0.160

2.42

0.013

0.0374

0.013

0.20

7Ql0
SWC6
(ppb)

20.57

0.32

1.67

coc
(ppb)

Days of
Exceedance 7
(days/yr)

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788

0

52,000

0

3

0

788

0

52,000

0

3

3

788

0

52,000

0

3

0

788
52,000

0

3
788
52,000
3
788
52,000
3
788
52,000
3
788

0
0
0
0
0
0
0
0
0
0
0

~

20

250

0.160

0.013

2.42

0.0374

20.57

0.32

0

OES: Industrial Processing Aid
Occidenta l Chemical Corp
Niagara Plant,
Niagara Falls, NY
NPDES: NY0003336

Stepan Co Millsdale Road,
Elwood, IL
NPDES : IL0002453

Surface
Water

Surface
Water

NPDES
NY0003336

NPDES
IL0002453

300

0.019

n/a

0.14

20

0.292

n/a

2.200

300

0.001

0.00016

0.00041
9

20

0.008

0.00128

0.00335

Still body

Surface
water

Page 502 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST2

--

EFAST
Waterbody
Type:,

Entek InternationallLC,
Lebanon,OR
NPDES:N/A

Off-site
Wastewater
Treatment

No info on
receiving
facility;
POTW(Ind.)

Surface .
water

National Electrical Carbon
Products
Dba Morgan Adv Materials,
Fostoria, OH
NPDES: OH0052744

Off-site
Wastewater
Treatment

Receiving
Facility:City
of Fostoria;
NPDES
OH0052744

Surface
water

PPG IndustriesInc Barberton,
Barberton,OH
NPDES: 080024007

Off-site
Wastewater
Treatment

Receiving
Facility:City
of Barberton;
NPDES
OH0024007

Surface
water

Daramic LLC,
Corydon,IN
NPDES: IN0020893

Surface
Water

NPDES
IN0020893

Days of
Release4

Relea~e5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

300

0.38

1.82

9.30

20

5.65

27.11

138.34

300

0.008

0.0336

0.15

20

300

0.115

0.005

0.50

0.00478

2.32

0.0141

20

0.070

0.067

0.20

300

0.008

0.00572

0.0206

Surface
water
20

0.114

0.0816

0.29

coc
(ppb)
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

788
52,000
3
788
52,000
3
788
52,000
3
788

Days of
Exceedance7
(days/yr)

0
140
0
0

20
0
0
0
0

0
1
0
0
0
0

0
0
0
0
0
0
0

52,000
3
788
52,000

0
0

3

0

788
52,000

0
0

3

0

788

0

0

OES: CommercialPrintingand Copying
Printing And Pub Sys Div,
Weatherford,OK
NPDES: OK0041785

Surface
Water

Printing

Surface
water

250

20

Page 503 of 691

0.00020

0.00250

0.000662

0.00827

0.00292

0.0365

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name. Location.and ID of
Active ReleaserFacility

Release
Medla1

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Typel

Days of
Release4

Release5
(kg/day)

Harmonic
MeanSWC

SWC'

(ppb)

(ppb)

7Q10

coc
(ppb)

Days of
7
Exceedance

(days/yr)

EFAST2
52,000

0

3
788
52,000
3
788
52,000

35
0
0
17
0
0
1
0
0
9
0
0

OES: Other Industrial Uses

250

1.553

1.63

9.03

NPDES: IN00033I 0

20

19.410

20.47

113.09

Oxy Vinyls LP - Deer Park
Pvc,
Deer Park, TX
NPDES: Tx:0007412

250

0.148

0.13

0.49

20

1.854

1.58

5.98

250

0.032

125

7.53

20

0.399

15.62

94.12

250

0.022

0.000566

0.00262

20

0.274

0.00695

0.0322

250

0.019

0.16

0.71

Eli Lilly And CompanyLilly Tech Ctr,
Indianapolis, IN

Surface
Water

NPDES
IN0003310

Surface
water

3

Surface
Water

WashingtonPenn Plastics,
Frankfort,KY
NPDES: KY0097497

Surface
Water

Natrium Plant.
New Martinsville,WV
NPDES: WV0004359

Surface
Water

Leroy Quarry,
Leroy, NY
NPDES: NY0247189

Surface
Water

NPDES
TX0007412

Surface
water

Surrogate
NPDES
KY0028410

Surface
water

NPDES
WV0004359

Surface
water

Surrogate
NPDES
NY0030546

Surface
water
20

Page 504 of 691

0.242

2.05

8.91

788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000

22
0
0
13
0
0
0
0
0
0
0
0
0
0
0
3

0
0

 INTERAGENCYDRAFT - DO NOT CITE OR Ql O TF
Modeled
Name, Location,and lD of
Active ReleaserFacility

Release
Media1

Facilityor
Industry
Sector in
EFAST2

EFAST
Waterbody
Type3

Days of
Release•

Release5
(kg/day)

250

0.010

Harmonic

7Q10

MeanSWC
(ppb)

SWC6
(ppb)

coc
(ppb)

3
George C Marshall Space
Flight Center,
Huntsville, AL
NPDES: AL0000221

Whelan Energy Center Power
Plant,
Hastings, NE
NPDES: NE0113506

Surface
Water

Surface
Water

ArmyCold Regions Research
& Engineering Lab,
Hanover, NH
NPDES: NH0001619

Surface
Water

Corning - Canton Plant,
Canton, NY
NPDES: NY0085006

Surface
Water

Ames Rubber Corp Plant #1,
Hamburg Boro, NJ
NPDES: NJ0OOOI41

Surface
Water

Surrogate
NPDES
AL0025585

NPDES
NE0113506

Surrogate
NPDES
NH0100099

Surface
Water

20

0.1:U

0.96

2.63

250

0.009

0.67

2.92

Surface
water
20

0.118

8.95

38.96

250

0.0002

0.0000266

0.00010
3

20

0.0029

0.000398

0.00154

250

0.0002

0.000101

0.00034
0

Surface
water

Surrogate
NPDES
NY0034762

Surface
water

Surrogate
NPDES
NJ0000141i

Surface
water

POTW(Ind.)

0.20

Surface
water

Surface
water

788

52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3

20

0.0028

0.00152

0.00510

250

O.OOOH

0.00258

0.0149

20
Gorham,
Providence, RI

0.0738

250

Page 505 of 691

0.00133
0.0001

0.0304
0.00253

0.18
0.0129

788
52,000
3
788
52,000

Days of
Exceedance7
(days/yr)
0
0
0
8
0
0
JO
0
0
13

0
0
0
0
0
0
0
0
0
0
0
0
0
0
S31

501
s01

3

6

788
52,000
3
788

4
4

0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active Releaser Facility

Release
Media1

Modeled
FacUityor
Industry
Sector in
EFAST2

EFAST
Waterbody
~

Release5
(kg/day)

Days of
Release4

liarmonic
MeanSWC

swc6

(ppb)

(ppb)

7Ql0

NPDES: RIG85E004

coc
(ppb)
52,000
3

20

0.0012

0.0253

0.13

788
52,000
3

Solvay - Houston Plant,
Houston, TX
NPDES: TX0007072

350
Surface
Water

NPDES
TX0007072

20

350

Akzo Nobel Surface

Chemistry LLC,
Morris,IL
NPDES: IL0026069

Solutia Nitro Site,
Nitro, WV
NPDES: WV0116181

Amphenol Corporation Columbia,
Columbia, SC
NPDES: SC0046264
Keeshan and Bost Chemical
Co., Inc.,
Manve~ TX
NPDES: TX0072168

0.024

Surface
Water

NPDES
IL0026069

0.414

0.000329

Surface
Water

Surface

Water

Surface
Water

Organic
Chemicals
Manufacture

NPDES
TX0072168

3.72

0.000300

Surface
water
20

Surrogate
NPDES
WV0023229

0.22

4.44

Surface
water

350

0.006

0.000318

0.00546

0.0000214

Surface
water
20

0.006

0.000401

75.93
0.00068
8
0.0125
0.00009
41
0.00176

350

0.000202

0.00395

0.037

20

0.004

0.0791

0.74

Surface
water

Still body

350

0.000095

20

Page 506 of 691

0.002

n/a
n/a

Days of
Exceedance7
(days/yr)
0
0
0
0
3

788

0

52,000
3

0
5

788

0

52,000
3

0

52,000
3

0
0
0
0
0
0
0
0
0
0

788

0

52,000
3
788
52,000
3
788
52,000
3

0
0
0
0
1
0
0

788
52,000
3

788
52,000
3

788

9.50

788

200.00

52,000
3

350

0
0
20

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID of
Active Releaser Facility

Release
Media 1

EFAST

Facility or
Industry

Waterbody

Sectorin

Type3

Days of
Release'

Releaw
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

coc
(ppb)

Days of
Exceedance7
(days/yr)

EFAST2

788
52,000
Chemtura North and South
Plants,
Morgantown, WV
NPDES: WV0004740
Indorama Ventures Olefins,
LLC,
Sulphur, LA
NPDES: LA0069850
Emerson Power Transmission,
Ithaca, NY

NPDES:NY0002933
William E. Warne Power
Plant,
Los Angeles County, CA
NPDES: CA0059188
Raytheon Aircraft Co(Was
Beech Aircraft)
, Boulder, CO
NPDES: COG3l 5I 76

0
0

Surface
Water

Annual releases estimated to be <0.02 kg/year werenot modeled, as they were determined to be unlikely to
exteed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Water
Surface

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to

Water

exceed the most sensitive COC using the most conservative input asswnptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

OES: Other CommercialUses
Coming Hospital,
Coming.NY
NPDES: NY0246701

Surface
Water

Surrogate
NPDES
NY0025721

250

0.013

0.00597

0.0271

Surface
water

3

0

788

0

52,000

0
0
0
0

3

20

0.159

0.0735

0.33

788
52,000
3

Water Street Commercial
Bldg,
Dayton, OH
NPDES: OH0141496

Surface
Water

Surrogate
NPDES
OH0009521

250

0.003

0.00131

0.00564

788
52,000

Surface
water

3

20

0.035

0.0153

0.0658

250

0.00040

0.0196

0.0881

Page 507 of 691

788
52,000
3

0

0
0
0
0
0
213i

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location.and ID of
Active Releaser Facility

Union Station North Wing
Office Building, Denver, CO
NPDES: COG315293

Confluence Park Apartments,
Denver, CO
NPDES: COG315339

Park Place Mixed Use
Development,
Annapolis, MD
NPDES: MD0068861
Tree Top Inc Wenatchee
Plant,
Wenatchee, WA
NPDES: WA0051527
Wynkoop Denver LLCP St,
Denver,CO
NPDES: COG603115
Greer Family Lie,
South Burlington, VT
NPDES: VTO00l3 76
John Marshall m Site,
Mclean, VA
NPDES: VA0090093

Release
Medla1

Modeled
Facility or
Industry
Sector in
EFAST2

Surface
Water

Surrogate
NPDES
C0002009Si

Surface
Water

Surrogate
NPDES
C0002009Si

Surface
Water

Surrogate
NPDES
MD0052868

EFAST
Waterbody
Type 3

Surface
water

Days of
Release4

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Ql0
SWC'
(ppb)

coc
(ppb)

788

213i
21Ji

20

0.00499

0.24

1.10

52,000
3
788
52,000
3

250

0.00028

0.0137

0.0617

788

Surface
wat.er

52,000
3
20

250

0.00354

0.00027

Still body

0.17

n/a

0.77

9.00

20

0.00334

788

110.00

18
17
17
2131
2131
21Ji
l7

17
17

52,000
3

250

788

0

52,000
3

n/a

Days of
Exceedaoce7
(days/yr)

0

788

20
0

52,000

0

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
_exceedthe most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to
exceed the most sensitive COC using the most conservative input assumptions.

OES: N/A (WWTP)
New Rochelle STP,
New Rochelle, NY
NPDES: NY0026697

Surface
Water

NPDES
NY0026697

3
Still body

365

0.043

n/a

0.70

788
52,000

Page 508 of 691

0
0
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name,Location, and ID of
Active Releaser Facility

Release
Media 1

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Type3

Harmonic
MeanSWC
(ppb)

7Q10

Days of
Release'

Release5

20

0.786

n/a

12.79

365

0.016

0.13

0.17

20

0.299

2.37

3.11

365

0.010

0.16

0.61

20

0.176

2.81

10.97

365

0.005

0.00146

0.00673

20

0.083

0.0242

0.110

365

0.002

0.0505

0.26

(kg/day)

SWC6
(ppb)

coc
(ppb)

Days of

Exceedance7
(days/yr)

EFASP

Everett Water Pollution
Control Facility,
Everett, WA
NPDES: WA0024490

Sullivan WWTP,
Sullivan, MO
NPDES: MO0104736

Sunnyside STP,
Sunnyside, WA
NPDES: WA0020991

Surface
Water

Surface
Water

Surface

Water

Port Of Sunnyside Industrial
WWTF ,
Sunnyside, WA
NPDES: WA0052426

Surface
Water

U .S. Air Force Shaw AFB SC,
Shaw AFB, SC
NPDES : SC0024970

Surface
Water

NPDES
WA0024490

NPDES
M00104736

NPDES
WA0020991

POTW(Ind.)

POTW(Ind.)

Surface
water

Surface
water

Surface
water

Surface
water

Sur face
water

20

0.035

0.88

4.51

365

0.002

0.0505

0.26

20

0.032

0.81

4.12

Page 509 of 691

3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788
52,000
3
788

20
0
0
0
0
0
7
0
0

2
0
0
7
0
0
0
0
0
0
0
0
0
0

0
5
0
0
0
0
0
4
0

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility

Release
Media1

Modeled
Facility or
Industry
Sector in
EFAST2

EFAST
Waterbody
Type-1

365

Gnf-A Wilmington-Castle
Hayne WWTP ,

Wilmington,NC
NPDES: NC0001228

Days of
Release4

Surface
Water

NPDES
NC0001228

Release'
(kg/day)

0.0004

Harmonic
MeanSWC
(ppb)

0.000304

Surface
water

7Q10
SWC6
(ppb)

coc
(ppb)

52,000
3
0.00194
788
52,000

Days of
Exceedance7·
(days/yr)
0
0
0
0

0
0
0
3
0
365
0.0003
0.00758
0.0387
788
0
Cameron Trading Post
52,000
0
WWTP ,
Surface
Surface
POTW(Ind.)
Cameron,AZ
Water
water
3
0
NPDES: NN0021610
20
0.0047
0.64
788
0
0.13
52,000
0
3
0
0.00001
788
365
0.0002
0.00000250
0
27
Coal Grove WWTP,
52000
0
Surface
NPDES
Surface
Coal Grove, OH
Water
OH0029432
water
3
0
NPDES: 080104558
20
0.0031
0.0000375
0.00019
788
0
52,000
0
1
Release media are either direct (release from facility directly to surface water) or indirect (transfer of wastewaterfrom active facility to a receiving POTW or
non-POTWWWTP facility). A wastewatertreatment removalrate of 81% is appliedto all indirectreleases.
2 If a valid NPDES of facilitywas not available in EFAST, the release was modeled using either a surrogaterepresentativefacility in EFAST(based on
location discharginginto the same water body) or a representativegeneric industrysector.
3
EFAST uses ether the "surface water" model, for rivers and streams, or the "stilJ water,.model, for lakes, bays, and oceans.
4
Modelingwas conductedwith the maximumdays of release per year expected. For direct releasingfacilities, a minimumof20 days was also modeled.
5
The daily release amount was calculated from the reportedannual release amount divided by the number of release days per year.
6
For releases dischargingto lakes, bays, estuaries,and oceans, the acute scenariomixing zone water concentrationwas reported in place of the 7Q l O SWC.
7
To detennine the PDM days of exceedancefor still bodies of water, the release days providedby the EPA Engineersis equal to the days of exceedanceonly
if the predicted surface water concentrationexceeds the COC. Otherwise, the days of exceedancecan be assumedto be zero.
8
Predicted water releases for the indicatedsites changed slightly betweenmodeling and publicationof the draft risk evaluation.For the 440 unknownsites in
the Processingas a Reactant OES changed from 1.75 kg/yr to 2.2 kg.lyr.For the sites listed under the Adhesives,Sealants, Paints, and CoatingsOBS, annual
release oredictionschan2.edfrom 3.25 kl!/vr to 4.4 ka/vr. These sli11htdifferences <i.e. between 0.5 to 1.2ksz/vr)are unlikely to imcact risk characterization.
3

20

Page 510 of 691

0.0067

0.00533

0.0340

788
52,000

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,and ID of
Active ReleaserFacility

Release
Media1

Modeled
Facility or
Industry
Sector in

EFAST
Waterbocly

Type3

Days of
Release'

2

Release5
(kg/day)

Harmonic
MeanSWC
(ppb)

7Q10
SWC6
(ppb)

coc
(ppb)

Days of
Exceedance7
(days/yr)

EFAST
!I The ,predicteddays of exceedanceate presentedalthoughthe estimated7Q 10 never approachesthe lowest COC due to the fact that the EFAST databasehas
minimumstream flow of0 MLD and a mean stream flow of2.69 MLD for this site. Therefore,these days of exceedanceswere not consideredin
environmentalrisk charactemation.
10 The predicteddays of exceedanceare presented althoughthe estimated7QIOnever approachesthe lowestCOC due to the fact that the EFAST databasehas
minimumstream flow of 0 MLD and a mean stream flow of 0 MLD for this site. Therefore,these days of exceedanceswere not consideredin enviromnental
risk characterization.

121
122
123
124

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
125

Appendix D

CONSUMER EXPOSURES
----------------------------Model Sensitivity

126
127
128
129

D.l
--The CEM developers

conducted a detailed sensitivity analysis for CEM, as described in Appendix C of
the CEM User Guide (U.S. EPA. 2019b). The CEM developers included results of model corroboration
analysis in Appendix D of the CEM User Guide (U.S. EPA. 2019b ).

130
131
132
133
134
135
136
137
138
139
140
141
142
143
144

145
146
147
148

In brief, the analysis was conducted on continuous variables and categorical variables that were used in
CEM emission or dermal models. A base run of different CEM models using various product or article
categories, along with CEM defaults, was used. Individual variables were modified, one at a time, and
the resulting Acute Dose Rate (ADR) and Chronic Average Daily Dose (CADD) were compared to the
corresponding results for the base run. Benzyl alcohol, a VOC, was used as an example for product
models such as those applied in this evaluation ofTCE.
The tested model parameters were increased by 10%. The measure of sensitivity for continuous
variables such as mass of product used, weight fraction, and air exchange rate was "elasticity,'' defined
as the ratio of percent change in each result to the corresponding percent change in model input. A
positive elasticity indicates that an increase in the model parameter resulted in an increase in the model
output, whereas a parameter with negative elasticity is associated with a decrease in the model output.
For categorical variables such as receptor activity pattern (i.e., work schedule) and room of use, the
percent difference in model outputs for different category pairs was used as the measure of sensitivity.
The results are summarizedbelow for the inhalation and dermal models used to evaluate conswner
exposures to TCE (i.e., emission models El and E3 and the dermal permeability model P_DER2b. For
full results and additional background, refer to Appendix C of the CEM User Guide (U.S. EPA. 201 7b).

149
150
151
152
153
154
155
156
157

D.1.1

Continuous Variables

-

For acute exposures generated from emission model El, WF (weight fraction) and M_acute (mass of
product used) have the greatest positive elasticities of the tested parameters (see Figure_ Apx D-1 ). The
next most sensitive parameters demonstrate negative elasticity and include: Vol_Building (building
volume); AER_Zone2 (air exchange rate in Zone 2); AER_Zonel (air exchange rate in Zone l);
Vol_Zonel (room of use, or Zone 1 volume). Inhalation exposures from liquid consumer product
formulations were modeled using El and the two most sensitive variables identified in this analysis were
varied to estimate a range of exposures.

Page 512 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
El Elasticity for ADRand CAOD

-=

WF

VP

Vol_Zonel

Vol
Q...z12
MW

-=

MW

M..., wte

_ .onel

Elasticity (%chan ge in dose/ % change in variable)
■ \OR

■

.ORPositiv

■

158

159

Figure_Apx D-1. Elasticities (2 0.05) for Parameters Applied in El

160
161
162
163
164
165
166
167
168

For acute exposures generated from emission model E3, WF (weight fraction) and M_acute (mass of
product used) have the greatest positive elasticities of the tested parameters (see Figure_Apx D-2). The
next most sensitive parameters demonstrate negative elasticity and include: Vol_Building (building
volume); AER_Zone2 (air exchange rate in Zone 2); MW (molecular weight); VP (vapor pressure);
AER_Zonel (air exchange rate in Zone 1); Vol_Zonel (room of use, or Zone 1 volume) . Inhalation
exposuresfrom aerosol or spray consumer product formulations were modeled using E3 and the two
most sensitive variables identified in this analysis were varied to estimate a range of exposures.

Page 513 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

E3 Elasticity for ADR and CADD

-

NF
V )'

VP
Vol_Zor•~
Vol_Bui l 1 ,g

=

--

-0.1

-0.4

-0.1

Q_l12
MVI'
MW
M _Chronic
M_Acute
Duration_Chronic
Duration _Acute

CSATA
Aerosol Fraction
AER_Zone.2

AER_Z
onel
0.2

0.5

0.8

1.1

Elasticity (% change in dose/% change in variable)
■ ~OR Neg..i ve

■ .\L I? Positive

□ ( ADD Negativt'

0 (ADO Positive

169
170
171
172
173
174
175
176
177
178

Figure_Apx D-2. Elasticities(?: 0.0S) for Parameters Applied in E3

For acute exposures generated from emission model P_DER2b, the chemical properties that inform
absorption rate, or absorption rate estimates, have the greatest elasticities (see Figure_Apx D-3). Dermal
exposures from consumer product formulations were modeled using P_ DER2B with a measured Kp
(permeability coefficient). Therefore, LogKow(octanol/water partition coefficient) and MW (molecular
weight) were not used to estimate skin penetration.

Page 514 of691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
P_DERlb Elasticity for AORand CAOD

MW'

loelCow'

l.J)

0

0

ElaUlclty
(% chllnaeIn close/%cnange in va,tablel

179
~

0.05) for Parameters Applied in P_DER2b

180

Figure_ Apx D-3 . .Elasticities

181
182
183
184
185
186
187
188

D.1.2 Categorical Variables
--For categorical variables there were multiple parameters that affected other model inputs . For example ,
varying the room type changed the ventilation rates, volume size and the amount of time per day that a
person spent in the room. Thus, each modeling result was calculated as the percent difference from the
base run. For continuous variables, each modeling result was calculated as elasticity.

189
190
191
192
193
194
195
196

Among the categorical variables, the most sensitive parameters included receptor type (adult vs. child),
room of use (Zone 1) selection, and application of the near-field bubble within Zone 1. However, these
types of variables were held constant within a given product modeling scenario and were applied using
consistent assumptions across all modeling scenarios.

D.2
D.l.1

Monitoring Data
Indoor Air Monitoring

.

___

____

_

_

197

Systematic review identified indoor air monitoring studies reporting levels of TCE in residential indoor
air samp les. The air concentrations reported in these studies are not used to evaluate risk to consumers
since measurements are not attributable to consumer conditions of use. The full suite of extracted data
(including residential , commercial) and associated data evaluation forms are found in [DataExtraction

198

Tab/es/or EnvironmentalMonitoringData. Docket: EPA-HQ-OPPT-2019-0500].

199
200
201
202

203
204
205
206
207

Concentrations ofTCE in residential indoor air in the United States and Canada collected from nine
studies identified during Systematic Review are summarized in Table_Apx D-1. Overall, more than
1,800 samples were collected between 1986 and 2010 in eleven US states (CA, CO, IL, IN, MA, MI,
MN, NJ, NY, OH, and TX) and Canada (exact location not reported). Concentrations ranged from nondetect (detection limits varied) to 42 µg/m 3 • The highest concentrations were observed in residential
garages and apartment hallways. Measures of central tendency (mean or median) across all studies were
generally less than 1 µg/m 3, with a couple central tendency measurements above 3 µg/m 3•
Page 515 of 691

 208
209
210
211

2019-0500].

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INTERAGENCYDRAFT - DO N01 CITE OR QUOTE
Data extracted for residential indoor air samples from studies conductedoutside of North America, as
well as studies conductedin schools and commercialestablishmentsin the US and other countries, are
provided in [DataExtractionTablesfor EnvironmentalMonitoringData. Docket: EPA-HQ-OPPTTable Apx:D-1. TCE ResidentialIndoor Air Concentrations(usz/m31) in the United States and Canada
Study Info

LOQ Min.

Site Description

(Chin et al., 2014)
US, 2009-2010 (n=126; DF =
0.06)

Detroit, MI area; Homes
(n=I26) with children
with asthma

0.09

(Dodson et al., 2008)'
US, 2004-2005 (n=83; DF =
0.93)

Boston, MA; Interior
room of residences

(Dodson et al., 2008)'
US, 2004-2005 (n=52; DF =
0.75)
(Dodson et al., 2008)'
US, 2004-2005 (n- IO;DF =
0.9)
(Dodson et al., 2008)'
US, 2004-2005 (n=l6; DF =
0.63)

Mean Median

Data Eval.

Max.

Variance

High

Score

ND

0.07

0.04

1.48

0.14 (SD)

0.04

ND

0.6

0.2

2.2 (95th)

1.7 (SD)

High

Boston, MA; Basement
of residences

0.04

ND

0.4

0.1

1.4 (95th)

1.1 (SD)

High

Boston, MA; Apartment
hallway of residences

0.04

ND

3.7

0.3

23 (95th)

7.3 (SD)

High

Boston, MA; Garage of
residences

0.04

ND

3.3

0.1

42 (95th)

lO(SD)

High

Ann Arbor, Ypsilanti,
0.008
and Dearborn MI;
(n=252; DF = 0.56)
Residences (n=l59) in
industrial, urban, and
suburban cities over two
seasons
Minneapolis, MN;
(Adeate et aL 2004)
US, 2000 {n=l 13; DF = 0.828) Inside home, during the
winter. Sampling from
room where child spent
the most time.
..
Minneapolis, MN;
(Adgate et al., 2004)
US, 2000 (n=l 13; DF = 0.737) Inside home, during the
spring. Sampling from
room where child spent
the most time.
Los Angeles, CA;
0.13
(Saxet al., 2004)
Homes (n=35) in innerUS, 2000 {n=32; DF = 0.47)
city neighborhood,
sampled in the fall
Los Angeles, CA;
0.13
(Sax etal.. 2004)
Homes (n=40) in innerUS, 2000 (n=40; DF = 0.68)
city neighborhood,
samoled in the winter
New
York, NY; Homes 0.13
(Sax et al., 2004)
(n=38) in inner-city
US, 1999 (n=36; DF = 0.92)
neighborhood, sampled
in the winter
(Saxet al., 2004)
New York, NY; Homes 0.13
(n=41) in inner-city
US, 1999 {n=30; DF = 0.44)

ND

0.06

0.03

2.01

--

Medium

ND
(10th
0.1)

--

0.3

·-

·-

Medium

ND

-

0.2

-·

-·

Medium

ND

0.2

0.1

0.8

0.2(SD)

High

ND

0.2

0.2

1.2

03 (SD)

High

ND

1.1

0.4

19

3.2 (SD)

High

ND

0.3

0.1

2.6

0.5 (SD)

High

(Jia et al., 2008a)

us, 2004-2005

-

(10th

0.1)

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 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Study Info

Site Description

Mean Median

DataEvaL
Score

WQ

Min.

--

--

0.99

0.22

1.74 (95th) 7.29(SD)

--

ND

3.84

0.56

2.28(90th)

--

High

0.12

ND

0.64

0:61

--

0 .66(SD)

Medium

Max.

Variance

neighborhood, sampled
in the summer
(Su et al.. 2013)b
US, 1999-2001 (n=539; DF =

(Lindstrom et al., 1995)
US, 1994 (n=9; DF = 0.56)

Elizabeth, NJ; Houston,
TX; and Los Angeles,
CA; Non-smo}cing
households<n=3 l 0)
IL, IN, OH,MI, MN,
WI (Great Lakes
Region);Noninstitutionalizedpersons
residing in households
in six states
Denver, Co ;·Homes,
occupied (n=9)

(Chan et al., 1990)
CA, 1987 (n-6 ; DF = 0.83)

Homes (n=6), main
floor

--

ND

1.6

--

5

--

Medium

(Chan et al., 1990)
CA, 1986 (n= 12; DF = 0.42)

Homes (n=l2), main
floor

--

ND

0.5

-

2

-

Medium

NR)

(Clayton et al., 1999)e
US, I995-1997(n4~;DF
0.361)

=

Medium

Study Info: The information provided includes the citation; cowmy and year samples collected; number of samples and detection
frequency.
Abbreviations: If a value was not reported, it is shown in this table as "--". ND = not detected at the reported detection limit. GSD =
geometric standard deviation. DF = detection frequency. NR-= Not reported. US = United States. CA = Canada
Parameters: All statistics are shown as reported in the study. Some reported statisticsmay be less than the detection limit; the method
of handling non-detects varied by study. All minimum values determined to be less than the detection limit are shown in th.istable as
"ND." Ifa maximum value was not provided, the highest percentile available is shown (as indicated in parentheses); if a minimum
value was not provided, the lowest percentile available is shown (as indicated in parentheses).
• Samples from this study were collected as part of the BEAMS study.
b Samples from this study were collected as part of the RIOPA study.
c Samples from this study were collected as part of the NHEXAS Phase 1 field studv.

214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230

D.2.2
Personal breathing Zone Monitoring Data
Concentrations ofTCE (TCE) in 1he personal breathing zones of residents in the United States collected
from seven studies identified during Systematic Review are summarized in Table _Apx D-2. Overall, the
measured concentration dataset contains approximately 2,750 samples that were collected between 1981
and 2001, and represents time spent in various microenvironments (i.e., home, school, work, transit)
during the monitoring period. Only the 3-hr samples from Heavner et al. ( 1995) represent time inside the
home only . Concentrations ranged from non-detect (1imits varied) to 327.3 µg/m 3 • The highest
concentration was observed in samples collected in 2000 as part of the NHANES 1999-2000 study (Jia
et al., 2008b ). The study states that the top ten highest concentrations exceeded 300 µg/m 3, which they
suggest may indicate exposure from immediate contact with solvents. The 95 th percentile concentration
in this study is 7.4 µg/m3.All other studies showed maximum concentrations less than 10 µg/m 3•
Median concentrations ranged from ND to 1.05 µg/m 3; and average concentrations ranged from 0.66 to
13 µg/m 3•
Data extracted for residential/general personal breathing zones studies conducted outside of North
America, as well as studies conducted in schools and commercial establishments in the US and other
Page 517 of 691

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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
countries, is provided in [Data.ExtractionTablesfor EnvironmentalMonitoring Data. Docket: EPAHQ-OPPT-2019-0500].
Table_Apx D-2. Personal Breathing Zone Concentrations (µg/m3) for TCE in the United States
(Generall/Residentfal)
Study Info

(Su et al.. 2013)•
us, 1999-2001
(n=544; DF = 0.23)

Type

Site Description

WD

Min.

Mean

Median

Max

Variance

48-hr

Data
EvaL
Score

Elizabeth, NJ; Houston.
TX; and Los Angeles, CA;
2.37
10.74
1.44
0.22
Adults (n=309) and
ND
Medium
(SD)
(95th)
children (n=l 18) from 310
non-smokina households.
48-to
Nation-wide; Adults (ages
327.3
(Jia et al., 2008b)b
0.4
3.4
ND
(7.4us, 1999-2000
72-hr 20--59years) in NHANES 0.44 ND (GM)
High
(GSD)
th
95 )
{n=665: DF = 0.229)
studv
(Sexton et al., 2007)
48-hr Minneapolis -St. Paul, MN;
Adults, non-smoking
us. 1999
(n=70) living in three
(n=333; DF = 0.925)
1.8
neighborhoods: (inner-city,
ND
I
0.2
High
(90th)
blue-collar/near
manufacturing plants, and
affluent)
6-day IL, IN, OH, MI, MN, WI
(Clavton et al., 1999)e
us, 1995-1997
(Great Lakes Region);
5.98
5.27
High
ND
0.63
-(n=386; DF = 0.394)
Non-institutionalized
(90th)
oersons
3-hrs Columbus, OH; Non(Heavner et al.. 1995)
(in
smoking women (n=24)
us, 1991
ND
1.84
1.05
9.08
2.39
Medium
home with non-smoking
(n=24; DF = NR)
onlv) husbands
(He!!vner et al.. 1995)
3-hrs Columbus, OH; Non(in
smoking (n=25) women
us. 1991
ND
0.66
ND
3.41
1.04
Medium
home with smoking husbands
(n=25; DF =NR)
onlv)
(Wallace, l 987)d
12-hrs Elizabeth and Bayonne, NJ,
Los Angeles, CA, and
us, 1981-19&4
(n=772; DF = 0-0.97)
Contra Costa, CA; Adults
3.8 to
in industrial/ chemical
High
13
manufacturing and /or
petroleum refining regions
oftheUS.
Abbreviations: If a value was not reported, it is shown in this table as "-" . LOD = level of detection. ND = not detected at the
reported detection li.tnit.GM = geometric mean. GSD = geometric standard deviation. DF = detection frequency. NR = Not reported.
US = United States.
Parameters: All statistics are shown as reported in the study. Some reported statistics may be less than the detection limit; the method
of handling non-detects varied by study. All minimum values determined to be less than the detection limit are shown in this table as
"ND." If a maximum value was not provided, the highest percentile available is shown (as indicated in parentheses); if a minimum
value was not provided, the lowest percentile available is shown (as indicated in parentheses).
' Samples from this study were collected as part of the RIOPA study.
b Samples from this study were collected as part of the NHANES 1999-2000.The top ten highest concentrations exceeded 300
µg/m3 , which the authors suggest may be from immediate contact with solvents.
0
Samples from this study were collected as part of the NHEXAS Phase 1 field study.
dSamoles from this studv were collected as oart of the TEAMS study.

--

---

--

--

-- --

236

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237

E.1

238
239
240
241
242

243
244
245
246
247
248
249
250
251

ENVIRONMENTAL HAZARDS

Appendix E

Species Sensitivity Distribution (SSD) Methodology

The SSD Toolbox is a resource created by EPA's Office of Research and Development (ORD) that can
fit SSDs to environmental hazard data (Etterson . 2019). It runs on Matlab 2018b (9.5) for Windows 64
bit. For thi?TCE Risk Evaluation, EPA created two SSDs with the SSD Toolbox, one using only algae
haz.ard data and the other using acute hazard data for all other aquatic species. This appendix outlines the
methodology used to create each.
For the algae SSD, algae hazard data were curated to prioritize study quality and to assure comparability
between toxicity values (e.g., comparingECsosto ECsos).The dataset included both saltwater and
freshwater species, because the only saltwater species value was within the range of values reported for
freshwater species. With this dataset, the Toolbox was used to apply a variety of algorithms to fit and
visualize SSDs with different distributions. Figure_Apx E-1 shows the Toolbox interface after each
distribution and fitting method was fit to the data. A hazardous concentration for 5% of species {HCos) is
calculated for each.

252
253
254

Figure_Apx E-1. SSD Toolbox interface and Hst ofHCoss for each distribution and fitting method
~!in_~ TCE's algae hazard data (Etterson , 2019)
D

• SSOToolboa
Fite

Plot

-

C:\Users'IKKoehm'IDoeuments\RAD\TtE\SSO_TCE_a
ttJle_ffles\Algae_revised.xls,c

·Status: Ready

Fit Oistribul!Dn

~ -~e!h~
, Imetmpo!is-nsstin~
-~

...,

._

,

Metti°-~- ...!..-~-OS
ML
10.7294

!normal

MO

65 .4397

,_ 3 _ 1ntlfflllll

GR

48.742'7

4 normal
5 l~iStic
6
____,

MH
UL

foglstic

MH

1

-'ion;
"-·--

lburr

V

~aling paramete~ --·-

ldinnu scaling '

i"."
1s-

Targetwelgllt :- ~ g
Ooodness -1>ffi: - -

Iteration(_ _1000-,
Units:

Resuls:

mgll

, z

1,~istic

rttiiist
ic
·L

trian;ulM
_]! trianguJar
11 tr.ngu lar
triangula r

o.mo
1
0.9950

S0.1369

().0985
0.9820

Ml

61.3196
6US f9
43.9740
40...8568
$6.6386

MO

63.8997

GR

51.9060

MH

MO
GR

0.9300
0.9141
-0.1075
1

1

14

gumbel

MO

S7.8295
81.3972
86.<W67

n

llf,lmbel

GR

67.3150

1
-0.1375
1
1
0.9990

MH

65.1299

0 .1639

MH

121.5295

o.ono

f"ii

_iJ;uime
1

-

_!L

UL

~

16 gumbel

255
256

p

burr

Page 519 of 691

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257
258
259
260
261
262
263
264
265
266
267

The SSD Toolbox's output contained several methods for choosing an appropriate distribution and
fitting method, including goodness-of-fit and standard error among others. However, choosing the
distribution with the best fit was challenging with a small dataset (e.g., hazard data for 9 algae species).
P values for goodness-of-fit were all above 0.05, showing no evidence for lack of fit, and providing no
help in discriminating among distributions (Figure_Apx E-1). Standard error was lowest across fitting
methods for the gumbel and burr distributions (Table_ Apx E-1 ). Because the ability for these measures
to distinguish between distributions was limited, visual inspection of the distributions was used. For
example, visual inspection showed burr was not a good fit (Figure_Apx E-2).
Table_Apx E-1. Standard Error for all dsitributions and fitting methods from the SSD Toolbox
usin2 TCE's ah!ae hazard data (Etterson . 2019)
Nonnal Distribution LogisticDistribution TriangularDistribution GumbelDistribution Burr
Distribution
StandardError
forHCos

268
269
270

ML MO GR MH ML MO GR MH ML MO GR MH

ML

35.7 33.9 26.1 27.9 36.4 33.7 26.1 29.2 34.0 33.4 265 28.9

26.6 28.6 26.2 23.9 20.9

MO GR MH MH

Figure_Apx E-2. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard
data (Etterson , 2019 )
1
-

0.9
0.8

triangular distributions
normal distributions
logistic distributions
gumbel distributions
burr distribution

Desmodesmussubspicat

0.7

~

:a
_! 0.6

£ 0.5
~

'i
10.4
:,
u
0.3
0.2
0.1

0

1

1.5

2

2.5

3

Toxicity Valu·e (Log 10[EC501)mg/L

271
272

Page 520 of 691

3.5

4

4.5

 273
274
275
276
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INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Using standard error and visualinspection,the distributionswith the best fit for the most sensitive algae
species included triangular and gumbel.The triangulardistribution with graphical methods fitting was
the most protective, and was used as a line of evidencefor assessingalgae in this assessment
(Figure_Apx E-3). The resulting SSD calculatedan HCosof 52 mg/L or 52,000 µg/L.
Figure_Apx E-3. TCE algae data fit with triangular distribution and graphical methods fitting
method (Etterson , 2019)

1
-AlgaeSSO
♦

0.9

HC05

Synechococcuselongalus •

0.8

Desmodesmus
subspicetus •

~0.7

:a

~ 0.6

e

a.
<l> 0.5
>

•

ChJorellalcessleri

ii

"9 0.4

E
:J
U 0.3

0.2
0.1
0

•
i.....:::....._ ____

1.5

280

281
282
283
284
285
286

287
288
289
290

Rephidocelissubcspltala

....._ _____

2

___.._

_____

2.5

....__ ___

3

__

__,

3.5

Toxicity Value (Log 10[EC50]) mg/L

For the acute SSD, acute hazard data for :fis~ amphibians,and invertebrateswere curated to prioritize
study quality and to assure comparabilitybetween toxicity values. For example,the dataset included
only LCsosfor fish and amphibians,and ECsosor LCsosthat measuredimmobilizationand mortality for
aquatic invertebrates.The dataset includedboth saltwater and freshwaterspecies, because the toxicity
values for saltwater species value were within the range of values reported for freshwater species in the
same taxonomic group. Additionally,for fish and invertebrates,the mode of action for freshwater and
saltwater species expected to be the same. With this dataset, the Toolbox was used to apply a variety of
algorithms to fit and visualizeSSDs with differentdistributions. Figure_Apx E-4 shows the Toolbox
interface after each distributionand fitting method was fit to the data. An HCosis calculated for each.

291

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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Figure_Apx E-4. SSD Toolbox interface showing HCossand P values for each distribution and
t!_ttingmethod using TCE's acute hazard data (Etterson, 2019)
SSOTool>ox

#

Filt

D

X

Plot
C:\Users\KKoe!lm\Oocuments\RAO\TCf\SSO
_TCE_arge
e_files\AU_specieS.xlsx
Status:

Fl Oistr!bution

Ready

Fating
Method:

lli
Resuls:

metroµoli$-h
astlngs

V

'

Dmribution Mdhod

Distribution
burr

Mineau
sealt\g
Targetweo!lt

ML

7.1130

0.3182

Ormll

MO

6.3275

0.7173

3

norm6I

GR

4.1033

0.4266

4

normal

MH
ML

le>giStic

1.1S

100

Iteration

1000

8

r 9

4.1173

0.3908

6.9555
6 ....792

0.4785

3.5349

0.1319

3.8530

0.3991

0.5215

logiatic

MO
GR
MH

triangt1lar

ML

7.2234

1

MO

6.1216

0.9880

IOOistie

g

logistic

Go0dne$$ of Ft

p

IKll'f7lll

V

Scalingps,ramflters

HC.,,~~

~triangular

11 tr11ngt1lar

GR

4.-4886

0.8122

12 tril ngLtlar

UH

3.9220

0.4"58

13 gumbel

Ml

11.9649

0.6783

g!Jl'l'D
III

MO
OR

9.1953

0.4106

6.3906

0.1379

MH
MH

8.7641

0.3586

26.6552L_

0.90~~

14

15 gumbel
16 gumbel
• ..11..Jburr

294
295
296

297
298
299
300
301
302
303
304
305
306

Again the SSD Toolbox's output contained several methods for choosing an appropriate distribution and
fitting method, including goodne ss-of-fit, standard error, and sample- size corrected Akaike Information
Criterion (AICc, [Burnham and Anderson, 2002]). P values for goodnes s-of-fit were all above 0.05,
showin g no evidence for lack of fit, and providing no help in discriminating among distr ibutions
(Figure _ Apx E-4). Standard error was mixed acro ss fitting methods for some distributions but generally
the lowe st for the burr distribution (Table_Apx E-2). Figure _Apx E-5 shows that the gumbel distribution
has the lowest AI Cc, indica ting it may be the be st distributi on for 1his data though the relative AIC
support compared to other distributions is weak. Because the ability for th ese measures to distinguish
between distribution s was limited , visual inspection of the distributio ns was also used . For example,
visual inspection showed burr was not a good fit (Figure_Apx E-6).

Page S22 of 691

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308

INTERAGENCYDRAFT - DO NOT CI fl· OR QUOTE
Table_Apx E-2. Standard Error for all distributions and fitting methods from the SSD Toolbox
usine. TCE's acute hazard data (Etterson . 2019)
NormalDistribution Logistic Distribution Triangular Distnoution Gumbel Distnoution Burr
Distribution
ML MO GR MH ML MO GR MH ML MO GR MH
Standard Error
forHC05

309
310
311

5.8

5.2

3.7 3.7 4.8 5.9 3.4 3.8 6.9 5.0 3.9 4.I

ML MO GR MH MH

4.1

4.6 3.6 3.9 2.9

Figure_Apx E-5. AICe for the four distribution options in the SSD Toolbox for TCE's acute
hazard data (Etterson, 2019)
7. AlC
Percentite of interest

5
C

ltlod&l-averaed ttep:
t._!·~ ~

MOdel-averaged
SE Of HCp.• 4.1586

l

CV of HCp:l_~.42011

AICc Table
1

Dist ribut io n

AICc

delta AlCc_.L_ Wei~

__ HCp
....____._
__SEHCp

aumbel

84.9297

0

11.96-49

3.3637

~ jlngisni;

87 .319 0

2.3892

<>.
1747

6 .9S55

3.735 1

87 .9162

2.9865
3.1608

0.1296
0.1188

7.2234

2.0728
3.9783

_!J

3

tri&nglll&r

~nomll

88.0905

<>.S76'9

312
313
314

Page 523 of 691

7.1130

 315
316

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Figure_ApxE-Ci.All distributionsand fitting methods in the SSD Toolbox for TCE's acute hazard
data (Etterson, 2019)
1

0.9
0.8

-

normal distributions
logistic distributions
triangular distribuUons
gumbel distributions .

-

burr distribution

Gyprmolfon 11a
t1egstus (srteeµshead) •

£ 0.7

:.c
~ 0.6
a..
Q,)

>

i

Lepomis macrcchirus (bluegill}

Pimephalas f)l'OOJ
elas /f,;ll>BIKImmno

0.5

:;:I

0.4

8 0.3
0.2

0.1
0 L_ _

-0.5

317
318
319
320

321
322

___.!!!
!!!!!!!!!
~

0

!l!!!!!!!!l!!!~
~

0.5

~

-

~

_j_--_.l__---'---___J~----1.-

1
1.5
2
2.5
Toxicity Value (Log 10[EC50])

-__J

3

3.5

4

EPA useda model average of the gumbel, logistic,triangular,and normal distributions,because it was
not clear which distribution had the best fit after considering standard error , AIC , and visual inspection.
The model-averaged HCosfrom all four distributions was 9.9 mg/Lor 9,900 µg/L, and the SSDs showed
aquatic invertebrates were the most sensitive species (Figure_Apx E-7).

323

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
324
325

Figure_Apx E~7. TCE's acute hazard data fit with the normal, logistic, triangular, and gumbel
distributions using maximum likelihood fitting method in the SSD Toolbox for (Etterson , 2019)
1

0.9
0.8

-

normal distribution
logistic distribution
triangulardistribution
gumbelC1i5tribulion

♦

HC05

Cyprinoclon v11riega1us (sheepstleild)

•

2;-0.7

=
15

~ 0.6

e

Pimephales prome/Rs (fathead mrnno

Q.

OJ 0.5

>

~

a,

"5 0.4

E
:::,

U 0.3

0.2
0.1

oL0

326
327
328

- ~ ~=-----~ -----1..---.i.__--....1....----"
0.5

1
1.5
2
ToxicityValue (Log 10[EC50])

Page 525 of 691

2.5

3

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
329

330
331
332

E.2

Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as Modeled
in E-FAST

Table Apx E-3. Environmental ROs bv Facilitv (with R( 1s > 1 in bold)
Name, Location, and ID
of Active Releaser
Facility•

Release
Media 11

Modeled
Facility or
Industry
Sector in
EFASTC

Waterbody

Surface
Water

NPDES
LA0007129

Surface
water

EFAST
Typed

7Q10

Acute
RQs
(using
COCof
3,100
ppb)

Chronic
RQs (using AlgaeRQs
AlgaeRQs
(using COC of (using COC of
fishCOC
52,000 ppb)
of788
3ppb)
ppb)

Release e

Release
(kg/day)

350

1.266

0.0051

0.00

0.00

0.00

0.00

20

22.15

0.0897

0.00

0.00

0.03

0.00

350

0.069

2.42

0.00

0.00

0.81

0.00

20

1.2

42.14

0.01

0.05

14.05

0.00

350

0.015

0.53

0.00

0.00

0.18

0.00

20

0.265

9.48

0.00

O.ot

3.16

0.00

350

0.015

2.77

0.00

0.00

0.92

0.00

20

0.265

49.91

0.02

0.06

16.64

0.00

350

0.015

0.07

0.00

0.00

0.02

0.00

20

0.265

1.33

0.00

0.00

0.44

0.00

350

0.015

0.53

0.00

0.00

0.18

0.00

20

0.265

9.48

0.00

0.01

3.16

0.00

Days of

1

swc
(ppb) e

OES: Manufacturing
Axiall Corporation,

Westlake, LA
NPDES : LA0007129
Olin Blue Cube,

Off•site

Freeport,TX

Wastewater

NPDES : Not available

Treatment

Solvents & Chemicals ,

Pearland, TX
NPDES : Not available

Occidental Chemical Corp
Wichita,
Wichita, KS
NPDES: KS0096903 and
Organ ic Chem MFG SIC

Organic
Chemicals

Manuf.

Surface
water

Off-site
Wastewater
Treatment

Organic
Chemicals
Manuf.

Surface
water

Surface
Water

Organic
Chemicals
Manuf.

Surface
water

Surface
Water

Surrogate
NPDES
KS0043036

Surface
water

Off-site
Wastewater
Treatment

Organic
Chemicals

Manuf.

Surface
water

OES: Processingas a Reactant

Page 526 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID
of Active Releaser

Facility•

Release
Media b

Facility or

EFAST

Industry

Waterbody

Sector in

Typed

Days of
Releasee

Release
(kg/day)'

7Q10

swc
(ppb) c

EFAST"

440 unknown sites
NPDES: Not applicable

Off-site
Organic
WasteChemicals
water
Manufacture
Treatment
Surface
Water

Arkema Inc.
Calvert City, KY
NPDES: KY0003603
US DOE Paducah Site,
Kevil, KY
NPDES: KY0102083

Surface
Water

Organic

Chemicals
Manufacture
NPDES
KY0003603

Acute
RQs
(using
COCof
3,200

ppb)
Surface
water

Surface

water

Surface
water

Chronic
RQs(using

flSbCOC
of788

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

ppb)

350

0.005

0.18

0.00

0.00

0.06

0.00

20

0.089

3.13

0.00

0.00

1.04

0.00

350

0.005

0.92

0.00

0.00

0.31

0.00

20

0.089

16.45

0.01

0.02

5.48

0.00

350

0.017

0.000737

0.00

0.00

0.00

0.00

20

0.295

0.128

0.00

0.00

0.04

0.00

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

GNF-AWilmington-Castle
Hayne,
Surface
WilmingtonNC
Water
NPDES: NC0001228

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Honeywell International Geismar Complex,
Surface
Geismar, LA
Water
NPDBS: LA0006181

NPDES
LA0006181

Surface

US DOE Paducah Site,
Kevil. KY

Surface

Water
Surface
Water

NPDES
NY0000281

0.0128

0.0000518 0.00

0.00

0.00

0.00

20

0.224

0.000907

0.00

0.00

0.00

0.00

350

0.00169

169

0.05

0.21

56.33

0.00

20

0.03

3000

0.94

3.81

1000.00

0.06

water

Praxair Technology Center,

Tonawanda, NY
NPDES: NY0000281

350

Still body

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Page 527 of 691

 TNTERAGENCY
DRAFT- DO NOT CITE OR QUOTE
Name, Location,and ID
of Active Releaser
Facility•

Release
Media b

Modeled
Facilityor
Industry
Sector in
EFASTC

EFAST

Days of
Waterbody
Release
e
Typed

Release
(kg/day)f

7Q10

swc
(ppb) a

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs(uslog AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC of
of788
3ppb)
52,000 ppb)
ppb)

NPDES: KY0102083
GNF-A Wilmington-Castle
Hayne,
Surface
WilmingtonNC
NPDES: NC0001228 Water

Annual releases estimated to be <0.02 kg/yearwere not modeled, as they were detennined to be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

O.ES: Repackaging

Hubbard-Hall Inc,
Waterbury,CT
NPDES: Unknown

Receiving
Facility:
Recycle Inc.;
POTW(Ind.)

Surface
water

Water

Surrogate
NPDES
TX0065943

Surface
water

Surface
Water

NPDES
LA0005487

Surface
water

Vopak Tenninal Westwego
Inc,
Surface
Westwego,LA
Water
NPDES: LA0124583

Surrogate
NPDES
LA0042064

Surface
water

OiltankingHouston Inc,
Houston, TX
NPDES: TX0091855
St. GabrielTerminal,
Saint Gabriel, LA
NPDES: LA0005487

Research SolutionsGroup
Inc,
Pelham,AL

NPDES:AL0074276

Off-site
Wastewater
Treatment
Surface

Surface
Water

250

1.108

27.18

0.01

0.03

9.06

0.00

20

13.85

339.11

0.11

0.43

113.04

0.01

250

0.003

6.52

0.00

0.01

2.17

0.00

20

0.041

89.13

0.03

0.11

29.71

0.00

250

0.0055

0.0000223 0.00

0.00

0.00

0.00

20

0.069

0.000279

0.00

0.00

0.00

0.00

250

0.00468

0.0000189 0.00

0.00

0.00

0.00

20

0.058

0.000235

0.00

0.00

0.00

0.00

Annualreleases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikelyto exceed the most
sensitiveCOC using the most conservativeinput assumptions.

Page 528 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•
Carlisle Engineered
Products Inc, Middlefield,

OH
NPDES: 080052370

Release
Media'

Surface
Water

Modeled
Facility or
Industry

Sector in
EFASTC

Acute

EFAST
of
Waterbody Days
Releasee
Typed

Release
(kg/day)f

7Q10

swc
(ppb)I

RQs
(using
COCof
3.200
ppb)

Chronic

RQs(using AlgaeRQs
fishCOC

of788
ppb)

AlgaeRQs

(using COC of (using COC of
3ppb)
52,000 ppb)

'Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

OES: OTVD(Includesreleasesfor Closed-LoopDegreasing,Conveyoriz.edDegreasing,Web Degreasing,and MetalworkingFluids)
Texas Instruments,Inc.,
Attleboro,MA
NPDES: MA0001791
Accellent Inc/Collegeville
Microcoax,Collegeville,

PA
NPDES:PA0042617

Surface
Water

NPDES
MA0001791

Surface
water

Surface
Water

NPDES
PA0042617

Surface
water

Ametek Inc. U.S. Gauge
Div.,
Surface
Sellersville,PA
Water
NPDES: PA0056014

Surrogate
NPDES
PA0020460

Surface
water

Atlc-AlleganyBallistics
Lab (Nirop),
Keyser, WV
NPDES: WV0020371

NPDES
WV0020371

Surface
water

Handy & Harman Tube
Co/East Norriton,
Norristown,PA
NPDES: PAOOl
1436

Surface

Water

Surface
Water

260

0.005

0.0188

0.00

0.00

0.01

0.00

20

0.067

0.25

0.00

0.00

0.08

0.00

260

0.002

0.0425

0.00

0.00

0.01

0.00

20

0.029

0.62

0.00

0.00

0.21

0.00

260

0.001

0.0619

0.00

0.00

0.02

0.00

20

0.011

0.68

0.00

0.00

0.23

0.00

260

0.0005

0.00311

0.00

0.00

0.00

0.00

20

0.0061

0.0373

0.00

0.00

0.01

0.00

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

Page 529 of 691

 rNTERAGENCYDRAFT- DO NOT CfTE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody

Typed

7Q10

Surface
Water

GM ComponentsHoldings
LLC,
Surface
Lockport, NY
Water
NPDES:NY0000558
Akebono Elizabethtown
Plant,
Surface
Elizabethtown,KY
Water
NPDES: KY0089672
Delphi Harrison Tbennal
Systems,
Dayton. OH
NPDES: OH000943l
Chemours Company Fe
LLC,
Washington,WV
NPDES: WV0001279

Surface
Water

Surface
Water

Surrogate
NPDES
LA0003280

Stilt body

NPDES
NY0000558

Surface
water

Surrogate
NPDES
KY0022039

Surface
water

NPDES
OH0009431

Surface
water

NPDES
WV0OOI279

Surface
water

Chronic
RQs(uslng AlgaeRQs
AlgaeRQs
fishCOC (using COC of (using COC of
of788
3 ppb)
52,000 ppb)
ppb)

Days of
Release e

Release
(kg/day)'

260

1.96

765.63

0.24

0.97

255.21

0.01

20

25.44

9937.5

3.11

12.61

3312.50

0.19

260

0.13

10.97

0.00

o.oi

3.66

0.00

20

1.71

144.47

0.05

0.18

48.16

0.00

260

0.07

4.87

0.00

0.ot

1.62

0.00

20

0.897

62.38

0.02

0.08

20.79

0.00

260

0.04

0.0752

0.00

0.00

0.03

0.00

20

0.465

0.87

0.00

0.00

0.29

0.00

260

0.03

0.00301

0.00

0.00

0.00

0.00

20

0.334

0.0335

0.00

0.00

0.01

0.00

260

0.02

2.22

0.00

0.00

0.74

0.00

20

0.218

24.44

0.ot

0.03

8.15

0.00

260

0.01

0.0425

0.00

0.00

0.ot

0.00

swc
(ppb)I

EFASTC
US Nasa Michoud
Assembly Facility,
New Orleans, LA
NPDES: LA0052256

Acute
RQs
(using
COCof
3,200
p_pb)

~

Equistar Chemicals Lp,
La Porte, TX
NPDES: TXOI19792
GE Aviation,
Lynn,MA

Surface
Water
Surface
Water

Primary Metal
Fonning

Manuf.
NPDES
MA0003905

Surface
water

StiJIwater

Page 530 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

Modeled
Facility or
Industry
Sector lo

EFAST

Daysof
Waterbody
Release•
Typed

Surface
Water

GM ComponentsHoldings
LLC Kokomo Ops,
Surface
Kokomo. IN
Water
NPDES: IN0001830
AmphenolCorp-Aerospace
Operations,
Surface
Sidney,NY
Water
NPDES:NY0003824
EmersonPower Trans
Corp,
Maysville,KY
NPDES: KY0100196

Surface
Water

Olean AdvancedProducts,
Olean, NY
Surface
NPDES:NY0073547
Water
HollingsworthSaco
Lowell,

Easley,SC
NPDES: SC0046396

swc
(ppb)I

EFASTC

Surface
Water

Primary Metal
Surface
Forming
water
Manuf.

NPDES
IN0001830

NPDES
NY0003824

Surface
water

Surface
water

Surrogate
NPDES
KY0020257

Surface
water

Surrogate
NPDES
NY0027162

Surface
water

Primary Metal
Fonning

Manuf.

3,200

ppb)

NPDES: MA0003905
Certa VandaliaLLC,
Vandalia,OH
NPDES: OH0122751

Release
(kg/day)'

7Q10

Acute
RQs
(using
COCof

Surface
water

Chronic
RQs(using AlgaeRQs
AlgaeRQs
fisbCOC (using COC of (using COC of
of788
3ppb)
52,000 ppb)
ppb)

20

0.128

0.54

0.00

0.00

0.18

0.00

260

0.01

I.It

0.00

0.00

0.37

0.00

20

0.107

11.89

0.00

0.02

3.96

0.00

260

0.01

0.2

0.00

0.00

0.07

0.00

20

0.086

1.73

0.00

0.00

0.58

0.00

260

0.01

0.0486

0.00

0.00

0.02

0.00

20

0.082

0.4

0.00

0.00

0.13

0.00

260

0.01

0.0004

0.00

0.00

0.00

0.00

20

0.081

0.00522

0.00

0.00

0.00

0.00

260

0.01

0.0188

0.00

0.00

0.01

0.00

20

0.068

0.13

0.00

0.00

0.04

0.00

260

0.00469

0.52

0.00

0.00

0.17

0.00

20

0.061

6.78

0.00

0.01

.2.26

0.00

Page 531 of 691

 INTERAGENCYDRAF1 - DO NOT CI I I OR QUOTE
Name., Location, and ID
of Active Releaser
Facility•

Trelleborg YSH
Incorporated Sandusky
Plant,
Sandusky, MI
NPDES: MI0028142
Timken Us Corp Honea
Path,
Honea Path, SC
NPDES: SC0047520
Johnson Controls
Incorporated,
Wichita, KS
NPDES: KS0000850
National Railroad
Passenger Corporation
(Amtrak) Wilmington
MaintenanceFacility,
Wilmington,DE
NPDES: DE0050962

Release
Media b

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Electrolux Home Products
(Formerly Frigidaire),
Surface
Greenville, MI
Water
NPDES: MI0002135
Rex Heat Treat Lansdale
Inc,
Surface
Lansdale, PA
Water
NPDES: PA0052965

Modeled
Facility or
Industry
Sector in
EFASTC

NPDES
MI0028142

EFAST
Waterbody

Type'

swc
(ppb) e

Chronic
RQs(using
fish COC
of788

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

ppb)

260

0.0036

1.76

0.00

0.00

0.59

0.00

20

0.047

23.04

0.01

0.03

7.68

0.00

260

0.00355

1.06

0.00

0.00

0.35

0.00

20

0.0462

13.77

0.00

0.02

4.59

0.00

260

0.00228

0.0548

0.00

0.00

0.02

0.00

20

0.0296

0.72

0.00

0.00

0.24

0.00

260

0.00203

0.23

0.00

0.00

0.08

0.00

20

0.026

2.89

0.00

0.00

0.96

0.00

260

0.00201

0.0171

0.00

0.00

0.01

0.00

20

0.026

0.22

0.00

0.00

0.07

0.00

260

0.00194

0.0523

0.00

0.00

0.02

0.00

20

0.025

0.67

0.00

0.00

0.22

0.00

Surfilce
water

Surface
water

NPDES
KS0000850

Surface
water

Primary Metal
Surface
Forming
water
Manuf.

Surrogate
NPDES
PA0026182

Release '

Release
(kg/day) r

ppb)

Surrogate
NPDES
SC0000698

NPDES
MI0002135

Days of

7Q10

Acute
RQs
(using
COCof
3,200

Surface
water

Surface
water

Page 532 of691

 INTERAGENCYDRAFT - DO NOT (
Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

Modeled
Facility or

EFAST

Industry

Waterbody

Sector in

Typed

Days of

Release

Releue e

(kg/day)r

rn

OR QUOTE

7Q10

swc
(ppb) g

EFASTC

Carrier Corporation,
Syracuse , NY
NPDES : NY000I 163
Cascade Corp
(0812100207 ),
Springfield, OH
NPDES: OH0085715
USAF-Wurtsmith Atb,
Oscoda, MI
NPDES: MI0042285

Surface
Water

Surface
Water

NPDBS
NY0001163

PrimaryMetal
Forming
Manuf.

Surface
Water

Surrogate
NPDES
MI0020257

Surface
water

Surface
Water

Surrogate
NPDES
NE0052647

Still water

Surface
Water

NPDES
PA0042617

Surface
water

Motor Components L L C, Surface
Elmira, NY
Water

NPDES
NY0004081

Surface
water

Surface
water

Lake Region Medical,

Trappe
, PA
NPDES : PA0042617

COCof
3,200
ppb)

ppb)

260

0 .00177

0.22

0.00

0.00

0.07

0.00

20

0.023 .

2.84

0.00

0.00

0.95

0.00

260

0 .00117

0.13

0.00

0.00

0.04

0.00

20

0.015

1.67

0.00

0.00

0.56

0.00

260

0.00115

0.000753

0.00

0.00

0.00

0.00

20

0.015

0.00983

0.00

0.00

0.00

0.00

260

0.00112

0.00916

0.00

0.00

0.00

0.00

20

0.014

0.11

0.00

0.00

0.04

0.00

260

0.0010 7

0.13

0 .00

0 .00

0 .04

0.00

20

0.014

1.69

0.00

0.00

0.56

0.00

260

0.0005

0.0106

0.00

0.00

0.00

0.00

20

0.007

0.15

0.00

0.00

0.05

0.00

260

0.00096

0.0618

0.00

0.00

0.02

0.00

Surface
water

Surrogate
NPDES
MI0028282

Eaton Mdh Company Inc,
Keamey,NE
NPDES:NEOI14405

(using

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fisbCOC
(using COC of (using COC of
of788
3ppb)
52,000 ppb)

Still water

Surface
Water

AAR Mobility Systems ,
Cadillac , MI
NPDES: MI0002640

Acute
RQs

Page 533 of 691

 TNTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release

Media b

Modeled
Facility or
Industry
Sector in
EFASTe

EFAST
Waterbody

Typed

NPDES: NY0004081
Salem TubeMfg,
Greenville, PA
NPDES: PA0221244
GE (Greenville)Gas
TurbinesLLC,
Greenville, SC
NPDES:SC0003484
Parker Hannifin
Corporation,
Waverly, OH
NPDES: 090104132

Surface
Water

Surface
Water

Surface
Water

Mahle Engine Components
Usa Inc,
Surface
Muskegon, MI
Water
NPDES: MI0004057
General Electric Company
- Waynesboro,
Surface
Waynesboro,VA
Water
NPDES: VA0002402
Globe EngineeringCo Inc,
Wichita,KS
Surface
NPDES: KS-0086703 Water

Primary Metal
Surface
Forming
water
Manuf.

NPDES
SC0003484

Surface
water

Primary Metal
Surface
Forming
water
Manuf.

NPDES
MI0004057

water

NPDES
VA0002402

Surface
water

Surrogate
NPDES
KS-0043036

Days of

7Ql0

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs (using AlgaeRQs
AJgaeRQs
fishCOC
(using COC of (using COC of
of788
Jppb)
52,000 ppb)
ppb)

Release e

Release
(kg/day) r

20

0.0125

0.83

0.00

0.00

0.28

0.00

260

0.000897

0.0997

0.00

0.00

0.03

0.00

20

0.012

1.33

0.00

0.00

0.44

0.00

260

0.000806

0.0821

0.00

0.00

0.03

0.00

20

O.oI

1.02

0.00

0.00

0.34

0.00

260

0.000747

0.083

0.00

0.00

0.03

0.00

20

O.oI

1.11

0.00

0.00

0.37

0.00

260

0.000742

0.0336

0.00

0.00

0.01

0.00

20

0.01

0.45

0.00

0.00

0.15

0.00

260

0.000733

0.00705

0.00

0.00

0.00

0.00

20

0.01

0.0962

0.00

0.00

0.03

0.00

260

0.00173

0.00853

0.00

0.00

0.00

0.00

20

0.023

0.11

0.00

0.00

0.04

0.00

swc
(ppb) g

Surface

Surface
water

Page 534 of 691

 INTERAGENCYDRAFT - DO NOT CI1r OR QUOTE
Nam~ Location, and ID
of Active Releaser
Facility•

R~lease

Media b

Modeled
Facility or
Industry
Sector in

EFAST

Days of
Waterbody
Releasee
Typed

Release

(kg/day) 1

EFASTC
Gayston Corp,
Dayton, OH
NPDES: OH0I27043

Surface
Water

StyrolutionAmerica LLC,
Channahon,IL
Surface
NPDES: IL000I619
Water
RemingtonArms Co Inc,
Ilion,NY
Surface
NPDES: NYOOOS282 Water
United Technologies
Corporation,Pratt And
WhitneyDivision,
East Hartford, CT
NPDES: CT0001376

Surface
Water

Surface
water

NPDES
IL0001619

Surface
water

NPDES
Surface
NYOOOS282 water

Keyser, WV
NPDES: WV0020371

Surface
Water

Sperry& Rice
ManufacturingCo LLC,
Brookville,IN
NPDES: IN0001473

Surface
Water

Owt Industries,

NPDES
WV0020371

NPDES
IN0001473

swc
(ppb)

C

(uslnii
COCof
3,.200

Chronic

RQs(using AlgaeRQs
AlgaeRQs
tlsbCOC
(using COC of (using COC of
of788
3ppb)
52,000ppb)
ppb)

260

0.000643

0.00121

0.00

0.00

0.00

0.00

20

0.008

0.015

0.00

0.00

0.01

0.00

260

0.000637

0.000221

0.00

0.00

0.00

0.00

20

0.008

0.00278

0.00

0.00

0.00

0.00

260

0.000612

0.000799

0.00

0.00

0.00

0.00

20

0.008

0.0104

0.00

0.00

0.00

0.00

260

0.00048

0.0000822 0.00

0.00

0.00

0.00

20

0.006

0.00103

0.00

0.00

0.00

0.00

260

0.00047

0.00292

0.00

0.00

0.00

0.00

20

0.006

0.0373

0.00

0.00

0.01

0.00

260

0.000328

0.00569

0.00

0.00

0.00

0.00

20

0.004

0.0694

0.00

0.00

0.02

0.00

260

0.000314

0.00213

0.00

0.00

0.00

0.00

Surface
water

Atlc-AlleganyBallistics
Lab (Nirop ),

7Ql0

ppb)

Surrogate
NPDES
OH0024881

NPDES
CT000I376

Acute
RQs

Surface
water

Surface
water

Page 535 of 691

 INTERAGENCYDRAFT- DO NOT CI I'E OR QUOTE
Name, Location,and ID
of Active Releaser
Facility•

Pickens, SC
NPDES: SC0026492
Boler Company,
Hillsdale, Ml
NPDES: MI00536Sl
Mccanna lnc.,
Carpentersville, IL
NPDES: 110071340
Cutler Hammer,
Horseheads, NY
NPDES: NY0246174
US Air Force Offutt Atb
Ne,
Offutt A F B, NE
NPDES: NE0121789
Troxel Company,
Moscow, TN
NPDES: TN0000451
Austin Tube Prod,
Baldwin, MI
NPDES: MI0054224
LS Starrett Precision
Tools,

Modeled
Facility or
Industry
Sector in
EFASTe

EFAST
Days of
Waterbody Releasee
Typed

Surface
Water

NPDES
SC0026492

Surface
water

Surface
Water

Surrogate
NPDES
MI0022136

Surface
water

Surrogate
NPDES
110027944

Surface
water

Surrogate
NPDES
NY0004081

Surface
water

Re!ease
Media b

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surface
Water
Surf.ace
Water

Primary Metal

Forming
Manuf.

NPDES
TN0000451

PrimaryMetal
Forming
Manuf.

NPDES
MA0001350

Surface
water

Surface
water

Surface
water
Surface
water

Re.lease
(kg/day) 1

7Q10

swc
(ppb)C

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs(using AlgaeRQs
AJgaeRQs
fashCOC (using COC of (using COC of
of788
3ppb)
52,000 ppb)
ppb)

20

0.004

0.0272

0.00

0.00

0.01

0.00

260

0.000269

0.0204

0.00

0.00

0.01

0.00

20

0.003

0.23

0.00

0.00

0.08

0.00

260

0.000268

0.000911

0.00

0.00

0.00

0.00

20

0.003

0.0102

0.00

0.00

0.00

0.00

260

0.000238

0.0153

0.00 ·

0.00

0.01

0.00

20

0.003

0.19

0.00

0.00

0.06

0.00

260

0.000159

0.0177

0.00

0.00

0.01

0.00

20

0.002

0.22

0.00

0.00

0.07

0.00

260

0.000134

0.000741

0.00

0.00

0.00

0.00

20

0.002

0.0111

0.00

0.00

0.00

0.00

260

O.OOOII4 0.0127

0.00

0.00

0.00

0.00

20

0.001

0.11

0.00

0.00

0.04

0.00

260

0.000102

0.00153

0.00

0.00

0.00

0.00

Page 53(; of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release
Media 1>

Modeled
Facility or
Industry
Sector in

Acute
EFAST
Waterbody

Typed

Days of
Releasee

Release
(kg/day) r

7Q10

swc
(ppb)I

EFASTC
Athol, MA
NPDES:MAOO01350
AvxCorp,
Raleigh,NC
NPDES: NC0089494

Indian Head Division,
Naval Surface Warfare
Center,
IndianHead, MD
NPDES:MD0003158

Surface
Water

PrimaryMetal Surface
Forming
water
Manut:

RQs
(using
COCof
3,200

ppb)

Chronic
RQs(using

AlgaeRQs

fashCOC
of788

(using COC of (using COC of
3ppb)
52,000 ppb)

AlpeRQs

ppb)

20

0.001

0.015

0.00

0.00

0.01

0.00

260

0.0000883

0.00981

0.00

0.00

0.00

0.00

20

0.001

O.ll

0.00

0.00

0.04

0.00

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled,as they were determinedto be unlikely to exceedthe most
sensitiveCOC using the most conservative input assumptions.

Surface

Water

Annual releasesestimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

Surface
Water

Annualreleases estimatedto be <0.02 kg/yearwere not modeled, as they were detennined to be unlikely to exceedthe most
sensitiveCOC using the most conservativeinput asswnptions.

LexmarkInternationalInc.,
Lexington,KY
Surface
NPDES:KY0097624
Water

Annual releases estimatedto be <0.02 kg/year were not modeled,as they were determinedto be unlikely to exceedthe most
sensitiveCOC using 1hemost conservativeinput assumptions.

General Dynamics
OrdnanceTactical
Systems,
RedLion,PA
NPDES:PA0043672
Trane Residential
Solutions- Fort Smith,
Fort Smith, AR
NPDES:AR00524n

Alliant Techsystems
OperationsLLC,
Elkton,MD

Surface
Water

Annual releasesestimatedto be <0.02 kg/yearwere not modeled, as they were detennined to be unlikely to exceedthe most
sensitiveCOC usingthe most conservativeinput assumptions.

Page 537 of 691

 INII·R \GLNCY DRAFT-DO NOT CITE OR QUOTE
Nam~ Location, and ID
of Active Releaser
Facility•

Release
Media 1>

Modeled
Facility or
Industry
Sector in

EFAST

Waterbody
Typed

Days of

Release•

Release
(kg/day) t

7Q10

swc
(ppb) a

EFASTC

Acute
RQs

(using
COCof
3,200
ppb)

Chronic
RQs(using

f15hCOC
of788

AlgaeRQs
AJgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

ppb)

NPDES: MD0000078
Daikin Applied America,
Inc. (FormallyMcquay
International),
Surface
Scottsboro, AL
Water
NPDES: AL0069701

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

Beechcraft Corporation,
Wichita, KS
NPDES: KS0000I83

Surface
Water

Annual releasesestimatedto be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most

Federal-MogulCorp,
Scottsville, KY
NPDES: KY0106585

Surface
Water

Annual releasesestimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

Cessna Aircraft Co
(Pawnee Facility),
Wichita,KS
NPDES: K.$0000647

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

N.G.I,
Parkersburg, WV
NPDES: WV0003204

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput asswnptions.

Hyster-YaleGroup, Inc,
Sulligent,AL
NPDES: AL0069787

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

sensitive COC using the most conservativeinput assumptions.

Page 538 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID
of Active Releaser

Facility•

Release
Media•

Facility or

EFAST

Industry
Sector in

Waterbody
Typed

Days of

Releasee

Release
(kg/day) r

7Q10

swc
(ppb)

II

EFASTC

Hitachi ElectronicDevices
(Usa), Inc.,
Surface
Greenville,SC
Water
NPDES:SC00484l l

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs(using
fisbCOC
of788
ppb)

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

Annual.releases estimatedto be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservativeinput assumptions.

OES:Adhesives,Sealants,Paints,and Coatings
Able ElectropolishlngCo
Inc,
Chicago,IL
POTW
NPDES:Not available

Adhesivesand

Garlock Sealing
Technologies,Palmyra,
NY, NPDES:NY0000078

Surface
Water

NPDES

Surface
Water

Annual releases estimatedto be <0.02 kg/year were not modeled,as they were.determinedto be unlikely to exceedthe most
sensitive COC using the most conservativeinput assumptions.

Ls StarrettCo,
Athol.MA
NPDES:MAROSB615

Aerojet Rocketdyne,Inc.,
Surface
East Camden,AR
Water
NPDES: AR.0051071,

Sealants

Manuf.

NY0000078

Adhesives and
Sealants

Manuf.

ARROOA521, ARROOA5ZO

Surface
water

Surface
water

Surface
water

POTW
Best One Tire & Service, Surface
Nashville,1N
Water
NPDES:Not available
POTW

Adhesives and
Surface
Sealants
water
Manuf.

250

0.298

7.28

0.00

O.ot

2.43

0.00

250

0.00033

0.00716

0.00

0.00

0.00

0.00

20

0.00407

0.0889

0.00

0.00

0.03

0.00

250

0.013

1.67

0.00

0.00

O.S6

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

l.67

0 .00

0.00

0.56

0.00

20

0. 16

20 .57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

Page 539 of 691

 INTERAGENCYDRAFT - DO NO I CI I l· OR QUOTE
----~
----Name, Location, and ID
of Active Releaser

Facility•

Release
Media

b

Acute

Modeled
Facility or

EFAST

Industry
Sector in

Waterbody
Typed

Days of

Release

Release e

(kg/day) t

7Ql0

swc
(ppb)'

EFAST0
Bridgestone Aircraft Tire
(Usa), Inc.,
Mayodan,NC
NPDES: Not available

Surface
Water

Clayton Homes Inc,
Oxford, NC
NPDES: Not available

Surface
Water

POTW

POTW
Cmh Manufacturing, Inc.
Oba Schult Homes - Plant
958,

Richfield, NC
NPDES: Not available

Surface
Water

POTW

Adhesives and
Sealants
Manuf.

Surface
water

Adhesives and
Sealants
Manuf.

Surface
water

Adhesives and
Sealants
Manuf.

Surface
Water

NPDES
NY0000558

POTW

No info on
receiving
facility;
Adhesives and
Sealants
Manuf.

Green Bay Packaging Inc - Surface
Coon Rapids,
Water
Coon Rapids, MN
NPDES: Not available
POTW

Adhesives and
Sealants

Delphi Thermal Systems,
Lockport, NY
NPDES: NY0000558

Mastercraft Boat
Company,

Surface
Water

Manuf.

Surface
water

RQs
(using

COCof
3,200
ppb)

Chronic

RQs(using AlgaeRQs
AlgaeRQs
fishCOC (using COC or (using COC or
of788
3ppb)
52,000 ppb)
ppb)

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.1

0.00

0.00

0.37

0.00

20

0.16

13.5

0.00

0.02

4.50

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

Surface
water

Surface
water

Surface
water

Page 540 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Name, Location, and ID
of Active Releaser
Facility•

Release

Mediab

Facility or

Industry
Sector in

EFAST
Days of
Waterbody Releasee
Typed

Release
(kg/day)'

7Q10

swc
(ppb) g

EFASTC
Vonore, TN
NPDES: Not available

POTW

Acute
RQs

(using
COCof
3,200

ppb)

Adhesives and
Sealants

Chronic
RQs(using
ftSbCOC

of788
ppb)

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

l.67

0.00

0.00

·o.S6

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

0.18

0.00

0.00

0.06

0.00

20

0.16

226

0.00

0.00

0.75

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

Manuf.
Michelin Aircraft Tire
Company,
Norwood,NC
Ni>DES:Not available
M-Tek, Inc,
Manchester, TN
NPDES: Not available

Surface
Water
POTW
Surface
Water
POTW
Surface
Water

Olin Corp,
East Alton, IL
NPDES: II..0000230

Adhesives and
Surface
Sealants
water
Manuf.

Adhesives and
Surface
Sealants
water
Manuf.
NPDES
II..0000230
No info on
receiving

POTW

facility;

Surface
water

Adhesives and
Sealants

Manuf.
Parker Hannifm Corp Paraflex Division,
Manitowoc, WI
NPDES: Not available

Surface
Water

Parrish Tire Company,
Yadkinville,NC
NPDES: Not available

Surface
Water

POTW

POTW

Adhesives and
Sealants

Manuf.

Surface
water

Adhesives and
Surface
Sealants
water
Manuf.

Page S41 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facllity •

RepublicDoors And
Frames,
Mckenzie, TN
NPDES: Not available

Release
Mediab

Modeled
Facility or
Industry
Sector in
EFASTC

EFAST
Days of
Waterbody Release e
Typed

Surface

Water

POTW

Adhesivesand
Surface
Sealants
water
Manuf.

Ro-Lab Rubber Company Surface
Inc.,
Water
Tracy,CA
NPDES: Not available
POTW

Adhesivesand
Surface
Sealants
water
Manuf.

Royale Comfort Seating, Surface
Inc. - Plant No. l,
Water
Taylorsville,NC
NPDES: Not available
POTW

Surface
Adhesivesand water
Sealants
Manuf.

Snider Tire, Inc.,
Statesville,NC
NPDES: Not available

Surface
Water
POTW

Snyder Paper Corporation, Surface
Hickory,NC
Water
NPDES: Not available
POTW
StellanaUs,
Lake Geneva, WI
NPDES:Not available

Surface
Water
POTW

Thomas Built Buses CourtesyRoad.
High Point, NC
NPDES: Not available

Surface
Water
POTW

Adhesivesand
Surface
Sealants
water
Manuf.
Adhesivesand
Surface
Sealants
water
Manuf.
Adhesivesand
Surface
Sealants
water
Manuf.
Adhesivesand
Surface
Sealants
water
Manuf.

Release
(kg/day) f

7Q10

swc
(ppb)I

Acute
RQs

(using
COCof
3,200
ppb)

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC or
of788
3 ppb)
!2,000 ppb)
ppb)

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0. 11

0.00

250

0.013

l.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

l.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

· 0.00

0.11

0.00

250

0.013

l.67

0.00

0.00

0.56

0.00

20

0.16

20.57

O.ot

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

Page 542 of 691

 INTERAGENCYDRAFT · DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Unicel Corp,
Escondido,CA
NPDES: Not available

Release
Media b

Surface
Water
POTW

Acme Finishing Co Lie,
Elk Grove Vitlage, IL
NPDES: Not available

Surface
Water
POTW

AlleghenyCnty Airport
Auth/
Pgh Intl Airport,
Coroapolis
Pittsburgh,PA
NPDES: Not available

AmphenolCorpAerospace Operations,
Sidney, NY
NPDES: NY0003824

Acute

EFAST
Waterbody
Type•

Adhesivesand
Surface
Sealants
water
Manuf.
Adhesives and
Surface
Sealants
water
Mamif.

Surface
Water

NPDES
CA000411l

POTW

No info on
Surface
receiving
water
facility;
Adhesivesand
Sealants
Manuf.

Aerojet Rocketdyne,Inc.,
Rancho Cordova, CA
NPDES: CA000411l

'

Modeled
Facility or
Industry
Sector in
EFAST"

Surface
Water

POTW

Adhesives and
Surface
Sealants
water
Manuf.

Surface
Water

NPDES
NY0003824

POTW

No info on
Surface
receiving
water
facility;
Adhesives and
Seal.ants
Manuf.

Days of
Release•

Release
(kg/day) 1

7Q10

swc
(ppb) I

RQs
(using
COCof

3,200

Cllronic
RQ• (osio1

fuhCOC

of788

ppb)

ppb)

AlgaeR.Qs
AlgaeRQs
(usin1 COC of (using COC of
3ppb)
S~OOOppb)

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

O.Ql

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

0.000818

0.00

0.00

0.00

0.00

20

0.16

0.0101

0.00

0.00

0.00

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.1 l

0.00

250

0.013

0.0631

0.00

0.00

0.02

0.00

20

0.16

0.78

0.00

0.00

0.26

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

Page 543 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
or Active Releaser
FacUity •

Release
Media b

Aprotech Powertrain,
Asheville,NC
NPDES: Not available

Surface
Water
POTW

Coating & Converting
Tech Corp/
Adhesive Coatings,
Philadelphia,PA
NPDES: Not available
Corpus Christi Army
Depot,
Corpus Christi, TX
NPDES: Not available

Surface
Water
POTW

Surface
Water
POTW

Electronic Data Systems Surface
Camp Pendleton,Camp Water
Pendleton, CA
NPDES:Not available
POTW

Florida Production
Engineering, Inc.,
Ormond Beach, FL
NPDES: Not available
GoodrichCorporation,
Jacksonville,FL
NPDES: Not available

Surface
Water
POTW

Surface
Water
POTW

Kasai North America Inc,

Surface
Water

Modeled
Facility or
Industry
Sectorio
EFASTC

EFAST
Waterbody
Typed

Adhesivesand
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.

Adhesives and
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.
Surface
water

Acute
RQs

Chronic
RQs (using AJgaeRQs
AlgaeRQs
(using coc or (using COC or
fish COC
of788
3ppb)
52,000ppb)
ppb)

Days of
Releasee

Release
(kg/day)'

7Q10
swc
(ppb)'

250

0.013

l.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

O.ot

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

l.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

Page 544 of 691

(using

cocor
3,200
ppb)

 INTERAGENCYDRAFT - DO 1\iOTCITE OR QUOTE
Modeled

Name, Location, and JD
of Active Releaser
Facility•

Release
Media b

MadisonPlant, Madison,
MS
NPDES: Not available POTW
KirtlandAir Force Base,
Albuquerque,NM
NPDES: Not available

Surface
Water
POTW

Marvin Windows& Doors, Surface
Warroad,MN
Water
NPDES:Not available
POTW

McneilusTruck &
ManufacturingInc,
Dodge Center, MN
NPDES: Not available

Surface
Water
POTW

Metal Finishing Co. Surface
Wichita (S Mclean Blvd), Water
Wichita,KS
NPDES: Not available
POTW

Facility or
Industry
Sector in
EFASTC

EFAST

Days of
Waterbody
Release e
Typed

Adhesives and
Sealants
Manuf.
Adhesives and
Surface
Sealants
water
Manuf.
Adhesives and Surface
Sealants
water
Manuf.
Adhesivesand
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.

Murakami Manufacturing

Surface
Usa Inc, Campbellsville, Water
KY
NPDES:Not available
POTW

Adhesivesand
Surface
Sealants
water
Manuf.

PeterbiltMotors Denton
Facility,
Denton, TX
NPDES: Not available

Adhesives and
Surface
Sealants
water
Manuf.

Surface
Water
POTW

Release

(kg/day) r

7Q10

swc
(ppb)I

Acute
RQs
(using

COCof
3,200
ppb)

Chronic
RQs (usin& AlgaeRQs
AlgaeRQs
fasbCOC (using COC of (usingCOC of
of788
3ppb)
52,000 ppb)
ppb)

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

o.o·o

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

. 0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.0)

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

Page 545 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled

Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

PortsmouthNaval
Shipyard,
Kittery, ME
NPDES: Not available

Surface
Water

R.D. Henry & Co.,
Wichita,KS
NPDES: Not available

Surface
Water

POTW

POTW

Raytheon Company,
Portsmouth, RI
NPDES: RI000028 I

Rehau Inc,
Culhnan,AL
NPDES: Not available

Adhesives and
Surface
Sealants
water
Manuf.

POTW

No info on
receiving
facility;
Adhesives and
Sealants
Manuf.

Surface
Water

Surface
Water

Surface
Water
POTW

EFAST
Days or
Waterbody Release e
Typed

Adhesives and
Surface
Sealants
water
Mamif.

NPDES
Rl0000281

POTW
Rubber Applications,
Mulberry, FL
NPDES: Not available

Sector in
EFASTC

Surface
Water

POTW
Rotochopper Inc,
Saint Martin, MN
NPDES: Not available

Facility or
Industry

Release
(kg/day)'

7Q10

swc
(ppb)

II

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs(using
fisbCOC
of788
ppb)

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000ppb)

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

10.83

0.00

0,01

3.61

0.00

20

0.16

133.33

0.04

0.17

44.44

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

Still body

Adhesives and
Surface
Sealants
water
Manuf.
Adhesives and
Surface
Sealants
water
Manuf.
Adhesives and
Surface
Sealants
water
Manuf.

Page 546 of 691

 INTERAGENCYDRAFT - DO NOT Cllr OR QUOTE
Name, Location, and ID
of Active Releaser

Facility•

Release
Media 11

Modeled
Facility or

Industry
Sector in

EFAST
Waterbody
Typed

Days of
Releasee

Release
(kg/day)

7Q10
1

swc
(ppb) c

EFASTC

Sapa Precision Tubing
Rockledge, Llc,
Rockledge,FL
NPDES:Not available

Surface
Water

Thomas& Betts,
Albuquerque,NM
NPDES:Not available

Surface
Water

POTW

POTW

ThomasBuilt Buses Fairfield Road,
High Point, NC
NPDES: Not available

Surface
Water

Timco,
Oba HaecoAmericas
AirframeServices,
Greensboro,NC
NPDES:Not available

Surface
Water

POTW

POTW

TrelleborgCoatedSystems Surface
Us, Inc Water
Grace AdvancedMaterials,
Rutherfordton, NC
NPDES:Not available POTW
U.S. Coast Guard Yard-

CUrtisBay,
Curtis Bay, MD
NPDES:Not available

Surface
Water

POTW

Adhesivesand
Sealants
Manuf.

Surface

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fisbCOC
(using COC of (using COC of

of788
ppb)

3 ppb)

S2,000ppb)

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0. 16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0 .32

0.00

0.00

0.11

0.00

water

Manuf.

Adhesives and
Surface
Sealants
water
Manuf.

Adhesivesand
Surface
Sealants
water
Manuf.

Surface
water

Adhesivesand
Surface
Sealants
Manuf.

3,200

ppb)

Adhesives and
Surface
Sealants
water

Adhesivesand
Sealants
Manuf.

Acute
RQs
(using
COCof

water

Page 547 of 691

 INTERAGENCYDRAFT- DO NOT CI IF OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release
Mediab

Modeled
Facility ol'
Industry
Sector in

Acute

EFAST

Dayso(
Waterbody Release e
Type d

Release
(kg/day)'

(ppb)g

RQs
(using
cocor
3,200
ppb)

7Q10

swc

EFASTC
Viracon Inc,
Owatonna, MN
NPDES: Not available

Surface
Water
POTW

Adhesives and
Surface
Sealants
water
Manuf.

Chronic
RQs(using AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC or
of788
3ppb)
52,000 ppb)
ppb}

250

0.013

1.67

0.00

0.00

0.56

0.00

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

O.ll

0.00

250

1.553

9.03

0.00

0.01

3.01

0.00

20

19.41

113.09

0.04

0.14

37.70

0.00

250

0.148

0.49

0.00

0.00

0.16

0.00

20

1.854

.5.98

0.00

O.ot

1.99

0.00

250

0.032

7.53

0.00

0.01

2.51

0.00

20

0.399

94.12

0.03

0.12

31.37

0.00

350

0.024

4.44

0.00

0.01

1.48

0.00

20

0.414

75.93

0.02

0.10

25.31

0.00

250

0.022

0.00262

0.00

0.00

0.00

0.00

20

0.274

0.0322

0.00

0.00

0.01

0.00

250

0.019

0.71

0.00

0.00

0.24

0.00

20

0.242

8.91

0.00

o.oi

2.97

0.00

OES: Ofher Industrial Uses
Eli Lilly And CompanyLilly Tech Ctr,
Indianapolis,IN
NPDES: IN0003310

Surface
Water

Oxy Vinyls LP - Deer Park
Pvc,
Surface
Deer Park, TX
Water
NPDES: TX0007412
Washington Penn Plastics,
Frankfort, KY
Surface
NPDES: KY0097497
Water
Solvay - Houston Plant,
Houston, TX
NPDES: TX0007072
Natrium Plant.
New Martinsville, WV
NPDES: WV0004359

NPDES: NY0247189

NPDES
TX0007412

Surface
water

Surface
water

Surrogate
NPDES
KY0028410

Surface
water

Surface
Water

NPDES
TX0007072

Surface
water

Surface

NPDES
WV0004359

Surface
water

Surrogate
NPDES
NY0030546

Surface
water

Water

Leroy Quarry,

Leroy,NY

NPDES
IN0003310

Surface
Water

Page 548 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
or Active Releaser
Facility•

Release

Mediab

George C Marshall Space
Flight Center,
Surface
Huntsville, AL
Water
NPDES: AL0000221
Whelan Energy Center
Power Plant,

Modeled
Facility or
Industry
Sector In
EFAS'fC

Surface
water

Swface
Water

SolutiaNitro Site,
Nitro, WV
NPDES: WVOl16181

Surface
Water

Surrogate
NPDES
WV0023229

Surface
Water

Organic
Chemicals
Manufacture

NPDES
IL0026069

Surrogate
NPDES
NH0100099

Days of
Relea,ee

Release

(kg/day)'

7Q10

swc
(ppb) a

(using

COCor
3,200

ppb)

NPDES
NE0113506

Akz.oNobel Surface
ChemistryLLC,
Morris, n.
NPDES: IL0026069

Anny Cold Regions
Research & Engineering
Lab,
Surface
Hanover.NH
Water
NPDES: NH0001619

Typed

Surface
water

NPDES: NEOI13506

AmphenolCorporationColumbia,
Columbia, SC
NPDES: SC0046264

Waterbody

Surrogate
NPDES
AL0025585

Surface
Water

Hastings,NE

EFAST

Acute
RQs

Chronic
RQs(usiog AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC of
of788
ppb)

Jppb)

52,000 ppb)

250

0.01

0.2

0.00

0.00

0.07

0.00

20

0.128

2.63

0.00

0.00

0.88

0.00

250

0.009

2.92

0.00

0.00

0.97

0.00

20

0.118

38.96

0.01

0.05

12.99

0.00

350

0.000329

0.000688

0.00

0.00

0.00

0.00

20

0.006

0.0125

0.00

0.00

0.00

0.00

350

0.000318

0.0000941 0.00

0.00

0.00

0.00

20

0.006

0.00176

0.00

0.00

0.00

0.00

350

0.000202

0.037

0.00

0.00

0.01

0.00

20

0.004

0.74

0.00

0.00

0.25

0.00

250

0.0002

0.000103

0.00

0.00

0.00

0.00

20

0.0029

0.00154

0.00

0.00

0.00

0.00

Surface
water

Surface
water

Surface
water

Surface
water

Page 549 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

Acute

Modeled
FacUity or
Industry
Sector in
EFAST C

EFAST
Waterbody
Type•

Surface
Water

Surrogate
NPDES
NY0034762

Surface
water

Keeshan and Bost
Chemical Co., Inc.,
Manvel, TX
NPDES: TX0072168

Surface
Water

NPDES
TX0072168

Still body

Ames Rubber Corp Plant
Hamburg Boro, NJ
NPDES: NJG000141

Surface
Water

Gorham,
Providence, RI
NPDES: RIG85E004

Surface
Water

Chemtura North and South
Plants,
Surface
Morgantown,WV
Water
NPDES: WV0004740
Indorama Ventures
Olefins, LLC,
Sulphur, LA
NPDES: LA0069850
Emerson Power
Transmission,
Ithaca, NY

Release
(kg/day) r

swc
(ppb)I

ppb)

Coming - Canton Plant,
Canton, NY
NPDES: NY0085006

#1,

Days of
Releasee

7Q10

RQs
(using
COCof
3,lOO

Surrogate
NPDES
NJ0000141

Surface
water

POTW(lnd.)

Surface
water

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC of
of788
3 ppb)
52,000ppb)
ppb)

250

0.0002

0.00034

0.00

0.00

0.00

0.00

20

0.0028

0.0051

0.00

0.00

0.00

0.00

350

0.000095

9.5

0.00

0.01

3.17

0.00

20

0.002

200

0.06

0.25

66.67

0.00

250

0.00011

0.0149

0.00

0.00

0.00

0.00

20

0.00133

0.18

0.00

0.00

0.06

0.00

250

0.0001

0.0129

0.00

0.00

0.00

0.00

20

0.0012

0.13

0.00

0.00

0.04

0.00

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to beunlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Page 550 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOIB
Modeled
Name, Location, and ID
of Active Releaser
Facility•

Release
Media b

Facility or

EFAST

Industry
Sector in
EFAST"

Waterbody

Typed

Days of
Releasee

Release
(kg/day)'

7Q10

swc
(ppb) I

Acute
RQs

(usin&
COCof
3,200
ppb)

Chronic
RQs(using
fishCOC

of788
ppb)

AlgaeRQs
AlgaeRQs
(using COC of (using COC of
3ppb)
52,000 ppb)

NPDES: NY0002933
William E. Warne Power
Plant,
Los Angeles County, CA Surface
Water
NPDES: CA0059188
RaytheonAircraft Co(Was
Beech Aircraft), Boulder,

co

NPDES: COG315176

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

OES: Spot Cleaning and Carpet Cleaning
Boise State University,
Boise, ID
NPDES: IDG91l006
Venetian Hotel And
Casino,
Las Vegas, NV
NPDES: NV0022888
63,746 unknown sites
NPDES: All POTW SIC

300

0.00008

0.00388

0.00

0.00

0.00

0.00

20

0.001

0.0485

0.00

0.00

0.02

0.00

Surface
Water

Surrogate
NPDES
ID0023981

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determinedto be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Surface
Water or
POTW

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

Surface

water

OES: IndustrialProcessingAid
OCcidentalChemical C0tp
Niagara Plant, .
Surfac.e
Niagara Falls, NY
Water
NPDES: NY0003336

NPDES
NY0003336

300

0.019

0.14

0.00

0.00

0.05

0.00

20

0.292

2.2

0.00

0.00

0.73

0.00

Still body

Page 551 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
-

Name, Location, and ID
of Active Releaser
Facility•

Release
Media I>

Stepan Co MillsdaleRoad,
Elwood, IL
Surface
NPDES:IL0002453
Water
Entek InternationalLLC, Off-site
Lebanon,OR
WasteNPDES:N/A
water
Treatment
National ElectricalCarbon
Products
Dba Morgan Adv
Materials,
Fostoria,OH
NPDES: OH0052744
PPG IndustriesInc
Barberton,
Barberton, OH
NPDES: OH0024007
Daramic LLC,
Corydon,IN
NPDES: IN0020893

Modeled
Faclllty or
Industry
Sector in
EFASTC

EFAST
Days of
Waterbody
Releasee
Typed

NPDES
IL0002453

Surface
water

Release
(kg/day)'

7Q10

swc
(ppb)I

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fish COC
(using COC of (using COC of
of788
3 ppb)
52,000 ppb)
ppb)

300

0.001

0.000419

0.00

0.00

0.00

0.00

20

0.008

0.00335

0.00

0.00

0.00

0.00

No info on
receiving

300

0.38

9.3

0.00

0.01

3.10

0.00

(Ind)

20

5.65

138.34

0.04

0.18

46.11

0.00

300

0.008

0.15

0.00

0.00

0.05

0.00

20

O.Il5

2.32

0.00

· 0.00

0.77

0.00

300

0.005

0.0141

0.00

0.00

0.00

0.00

20

0.07

0.2

0.00

0.00

0.07

0.00

300

0.008

0.0206

0.00

0.00

0.01

0.00

20

0.114

0.29

0.00

0.00

0.10

0.00

250

0.0002

0.00292

0.00

0.00

0.00

0.00

20

0.0025

0.0365

0.00

0.00

0.01

0.00

Surf.ace
facility; POTW water

Receiving
Off-site
Facility: City
Wasteof Fostoria;
water
NPDES
Treatment
OH0052744

Surface
water

Receiving
Off-site
Facility: City
Wasteof Barberton;
water
NPDES
Treatment
080024007

Surface
water

Surface
Water

Surf.ace
water

NPDES
IN0020893

OES: CommercialPrinting and Copying

Printing And Pub Sys Div,
Weatherford,OK
Surface
NPDES: OK0041785 Water

Printing

Surface
water

Page 552 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

--

Name, Location,and ID
of Active Releaser
Facility•

Release
Media b

Modeled
Facilityor
Industry
Sector in
EFASTC

EFAST
Days of
Waterbody
Releasee
Typed

Surrogate
NPDES

Surface

----

Release
(kg/day)'

----

7Q10

swc
(ppb)I

Acute
RQs
(using
COCof
3,200
ppb)

Chronic
RQs(using AlgaeRQs
AlgaeRQs
fish COC (using COC of (using COC of
of788
3ppb)
Sl,000 ppb)
ppb)

OES: Other Comnercial Uses

Coming Hospital,
Coming, NY
NPDES: NY024670l

Surface
Water

Water Street Commercial
Bldg,
Surface
Dayton, OH
Water
NPDES: OH0141496
Union StationNorth Wing
Office Building,Denver,

co
NPDES: COG315293
ConfluencePark
Apartments,
Denver,CO
NPDES:COG315339
Park Place Mixed Use
Development,
Annapolis,MD
NPDES: MD0068861

NY0025721

Surrogate

NPDES
OH0009521

water

Surface
water

Surrogate
NPDES
C00020095

Surface
water

Surface
Water

Surrogate
NPDES
CO0020095

Surface
water

Surface
Water

Swrogate
NPDES
MD0052868

Surface
Water

Tree Top Inc Wenatchee
Plant,
Surface
Wenatchee,WA
Water
NPDES: WA0051527

250

0.013

0.0271

0.00

0.00

0.01

0.00

20

0.159

0.33

0.00

0.00

0.11

0.00

250

0.003

0.00564

0.00

0.00

0.00

0.00

20

0.035

0.0658

0.00

0.00

0.02

0.00

250

0.0004

0.0881

0 .00

0.00

0.03

0.00

20

0.00499

1.1

0.00

0.00

0.37

0.00

250

0.00028

0 .0617

0.00

0.00

0.02

0.00

20

0.00354

0.77

0.00

0.00

0.26

0.00

250

0.00027

9

0.00

0.01

3.00

0.00

20

0.00334

110

0.03

0.14

36.67

0.00

Still body

Annual releases estimat¢ to be <0.02 kg/year were not modeled. as they were determinedto be unlikelyto exceed lbe most
sensitive COC using the most conservativeinput assumptions.

Page553 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
-

Modeled
Name, Location, and ID
of Active Releaser

Facility•

Release
Media b

Facility or
Industry
Sector in
EFASTC

EFAST
Waterbody
Typed

Days of
Releasee

Release
(kg/day)'

7Q10

swc
(ppb)&

Acute
RQs
(using
COCof

3,200
ppb)

Chronic
RQs (using AlgaeRQs
AlgaeRQs
fish COC
(using COC of (using COC of
of7S8
3 ppb)
52,000ppb)

ppb)

WynkoopDenverLLCP
St,

Denver, CO
NPDES: COG603115

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to exceed the most
sensitive COC using the most conservative input asswnptions.

Greer Family Lie,
South Burlington, VT
NPDES: VTOOOI376

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.

John Marshall Ill Site,
Mclean, VA
NPDES: VA0090093

Surface
Water

Annual releases estimated to be <0.02 kg/year were not modeled, as they were detennined to be unlikely to exceed the most
sensitive COC using the most conservative input asswnptions.

OES: Process Solvent Recyclingand Worker Handling of Wastes
Clean Water Ot'New York
Inc,
Surface
Staten Island, NY
Water
NPDES: NY0200484

Reserve Environmental
Services,
Ashtabula, OH
NPDES: OH0098540

Veolia Es Technical
Solutions LLC,
Middlesex, NJ
NPDES: NJ0020141

Surrogate
NPDES
NJ0000019

Still body

250

0.004

11.76

0.00

O.Ql

3.92

0.00

20

0.047

13824

0.04

0.18

46.08

0.00

0.00

0.00

0.00

0.00

Surface
Water

Off-site
Wastewater
Treatment

Receiving
Facility:
Middlesex
CntyUA;

250

24.1

2.85

0.00

0.00

0.95

0.00

20

301.78

35.72

0.01

0.05

11.91

0.00

Still body

Page 554 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR Ql 01 I
Name, Location, and ID
of Active Releasel'
Facility •

Release

MedJab

Modtled
Facllity or
Industry

Sectorin
EFASfC

EFAST
Days of
Waterbody Releasee
Type•

Release

7Q10

Acute
'RQs
(using
COCof
3,200
ppb)

Chronic
RQs(usin1 AlgaeRQs
AlgaeRQs
flShCOC (using COC of (using COC of
of788
3ppb)
52,000 ppb)
ppb)

(kg/day) 1

swc

250

0.35

8.57

0.00

0.01

2.86

0.00

20

4.36

106.75

0.03

0.14

35.58

0.00

250

0.04

0.98

0.00

0.00

0.33

0.00

20

0.455

11.26

0.00

0.01

3.75

0.00

365

0.043

0.7

0.00

0.00

0.23

0.00

20

0.786

12.79

0.00

0.02

4.26

0.00

365

0.016

0.17

0.00

0.00

0.06

0.00

20

0299

3.11

0.00

0.00

1.04

0.00

365

0.01

0.61

0.00

0.00

0.20

0.00

20

0.176

10.97

0.00

0.01

3.66

0.00

365

0.005

0.00673

0.00

0.00

0.00

o.oo
.

20

0.083

0.11

0.00

0.00

0.04

0.00

(ppb)&

NPDES
NJ0020141
Clean Harbors Deer Parle
Off-site
LLC,
WastePOTW(lnd.)
LaPorte, TX
water
NPDES: TX0005941
Treatment

Surface
water

Clean Harbors El Dorado
Off-site
LLC,
WastePOTW(Ind .)
El Dorado, AR
water
NPDES: AR0037800
Treatment

Surface
water

OES: Wastewater Treatment Plant (WWTP)

New Rochelle STP,
New Rochelle, NY
NPDES: NY0026697
Everett Water Pollution
Control Facility,
Everett, WA
NPDES: WA0024490

Surface
Water

NPDES
NY0026697

Still body

Surface
Water

NPDES
WA0024490

Surface
water

Sullivan WWTP,
Sullivan, MO
NPDES: M00104736

Surface
Water

NPDES
M00104736

Surface
water

Sunnyside STP,
Sunnyside, WA
NPDES: WA0020991

Surface
Water

NPDES
WA0020991

Surface
water

Page 555 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility•

Release

Media 11

Modeled
Facility or

Industry
Sector in

EFAST
Waterbody
Typed

Days of
Releasee

Release
(kg/day)'

Acute
RQs

7Ql0

(using

swc

cocor
3,200

(ppb)I

EFASTC
Port Of Sunnyside
IndustrialWWTF,
Sunnyside, WA
NPDES: WA0052426

Surface
Water

U.S. Air Force Shaw AFB
SC,
Surface
ShawAFB,SC
Water
NPDES: SC0024970
Gnf-A Wilmington-Castle
HayneWWTP,
Surface
Wilmington, NC
Water
NPDES: NC0001228
Cameron Trading Post
WWTP,
Cameron,AZ
NPDES: NN0021610
Coal Grove WWTP,
Coal Grove, OH
NPDES: OH0104558

Surface
Water

Surface
Water

POTW(Ind.)

POTW(Ind.)

NPDES
NC0001228

POTW(Ind .)

NPDES
OH0029432

ppb)

Surface
water

Chronic
RQs(using
fish COC

of788
ppb)

AlgaeRQs
AlgaeRQs
(using COC of (using coc or
3ppb)
52,000 ppb)

365

0.002

0 .26

0.00

0.00

0.09

0.00

20

0.035

4.51

0 .00

0.01

1.50

0.00

365

0.002

0.26

0.00

0.00

0.09

0.00

20

0.032

4.12

0.00

0.01

1.37

0.00

365

0.0004

0.00194

0.00

0.00

0.00

0.00

20

0.0067

0.034

0.00

0 .00

0.01

0.00

365

0.0003

0.0387

0.00

0.00

0.01

0.00

20

0.0047

0.64

0.00

0.00

0.2 1

0.00

365

0.0002

0.0000127

0.00

0.00

0.00

0.00

20

0.0031

0.00019

0.00

0.00

0.00

0.00

Surface
water

Surface
water

Surface
water

Surface
water

333
Name, Location,
and ID of Active
Releaser Facility •

Release
Media b

Modeled
FacJUtyor
EFAST
Days of
Industry
IWaterbody Release e
4

Sector in
EFAST C
OES: Adbesi"es , Sealants . Paints, and CoatinPS

Type

Release
(kg/day)'

Page 556 of 691

7Ql0

swc
(ppb) e

AcuteRQs

Chronic

(using

RQs(asing
fish COC of
788 ppb)

COCof
3,200 ppb)

AJgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(usingCOC
ofS2,000
ppb)

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and ID of Active

ReleaserFacility•
Able
ElectropolishingCo
Inc,
Chicago, IL
NPDES:Not
available

Garlock Sealing
Technologies,
Palmyra, NY,
NPDES: NY0000078

Release
Media b

POTW

Surface
Water

Modeled
Facility or

Industry
Sector in
EFASTe
Adhesives
and Sealants
Manuf.

NPDES
NY0000078

EFAST

Daysof
IWaterbody
Releasee
Type41

Surface

water

Release
(kg/day)'

7Q10

AcuteRQs

Chronic

(using

RQs(usillg

(ppb)'

COCof
3,200 ppb)

fisbCOCof
788 ppb)

swc

AlgaeRQs

AlgaeRQs

(using COC
of3 ppb)

(usingCOC
of52,000
ppb)

250

0.298

7.28

0.00

0.01

2.43

0.00

250

0.00033

0.00716

0.00

0.00

0.00

0.00

20

0.00407

0.0889

0.00

0.00

0.03

0.00

Surface
water

Ls Starrett Co,

Athol,MA
NPDES:
MAR05B615
AerojetRocketdyne,
Inc.,
East Camden,AR
NPDES:
AR0051071,
ARROOA521,
ARROOA520

Surface
Water

Best One Tire &
Service,
Nashvtlle, TN
NPDES:Not
available
BridgestoneAircraft
Tire (Usa). Inc..

Surface
Water

Not assessed (below the min risk level).

Surface
Water

POTW

POTW
Surface
Water

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water
Surface
water

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

2so ·
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

Page 557 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Mayodan,NC
NPDES: Not
available
Clayton Homes Inc,
Oxford,NC
NPDES:Not
available
CmhManufacturing,
Inc.
Dba Schult Homes Plant 958,
Richfield, NC
NPDES:Not
available

Delphi Thermal
Systems,
Lockport,NY
NPDES: NY0000558

Release
Media •

POTW
Surface
Water
POTW

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Typed

EFASTC
Adhesives
and Sealants
Manuf.
Adhesives
and Sealants
Manuf.

Surface
water

Surface
Water

POTW

Surface
Water

POTW

Green Bay Packaging
Inc - Coon Rapids,
Coon Rapids, MN
NPDES:Not
available

Surface
Water

Mastercraft Boat
Company,
Vonore, lN
NPDES:Not
available
Michelin Aircraft
Tire Company,
Norwood,NC

Surface
Water

POTW

Adhesives
and Sealants
Manuf.

NPDES
NY0000558
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
Adhesives
and Sealants
Manuf.

POTW

Adhesives
and Sealants
Manuf.

Surface
Water
POTW

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water

Surface
water

Surface
water

Surface
water

Days of

Releasee

Release
(kg/day)'

AcuteRQs
(using

Chronic
RQs(uslng

(ppb)'

COCof
3,200ppb)

fish COC of

7Ql0

swc

788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(using COC

of5Z,0OO
ppb)

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.1

13.5

0.00
0.00

0.00
0.02

0.37
4.SO

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0. 11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20
250

0.013
0.16
0.013

1.67
20.57
0.32

0.00
0.01
0.00

0.00
0.03
0.00

0.56
(i.86

0.00
0.00
0.00

Page 558 of 691

0.11

 INTERAGENCYDRAFT- DO '\,ffl CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility•

Release
Media b

Modeled
Facility or
Industry
Sectcn in
EFASTC

EFAST
Days of
Waterbody Release~
Typed

Release
(kg/day)'

7Q10

swc
(ppb)I

AcuteRQs
(using
COCof
3,lOOppb)

Chronic
RQs(using
fishCOCof
788 ppb)

AigaeRQs
(usingCOC
of3ppb)

AlgaeltQs
(uslngCOC
ofSl,000
ppb)

NPDES:Not
available
M-Tek, Inc.
Manchester, TN
NPDES:Not
available

Surface
Water
POTW
Surface
Water

Olin Corp,
East Alton,IL
NPDES: IL0000230

POTW

Parker Hannifin Corp

Surface
Water

Paraflex Division,
Manitowoc,WI
NPDES:Not
available
Parrish Tire
Company,
Yadkinville,NC
NPDES: Not
available
Republic Doors And
Frames, .
Mckenzie, 1N
NPDES:Not
available
Ro-Lab Rubber
Company Inc.,
Tracy,CA
NPDES:Not
available

POTW
Surface
Water
POTW
Surface
Water
POTW
Surface
Water
POTW

Adhesives
and Sealants
Manuf.
NPDES
IL0000230
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.
Adhesives
and Sealants
Manuf.

Adhesives
and Sealants

Manuf.

Surface
water

Swface
water

Swface
water

Surface
water

Surface
water

Surface
water

250
20

0.013
0.16

1.67
20.57

0.00

0 .00

0.01

0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

0.18
2.26

0.00
0.00

0.00
0.00

0.06
0.75

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56

0.00

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

Page 559 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled
Nam~ 1.Mation,
and ID of Active
Releaser Facility •

Release
Media b

Facility or
Industry
Sector in

EFAST

Days or

Waterbody
Release•
Typed

Release
(kg/day)

7Q10

f

swc
(ppb)

EFASTC
Royale Comfort
Seating, Inc. - Plant
No. 1,
Taylorsville, NC
NPDES:Not
available

Surface
Water

Snider Tire, Inc.,
Statesville, NC
NPDES: Not
available

Surface
Water

Snyder Paper
Corporation,
Hickory, NC
NPDES :N ot
available

Surface
Water

Stellana Us,
Lake Geneva, WI
NPDES:Not
available

Surface
Water

Thomas Built Buses Courtesy Road,
High Point, NC
NPDES:Not
available

Surface
Water

Unicel Corp,
Escondido, CA
NPDES:Not
available

Surface
Water

Acme Finishing Co

Surface
Water

Llc,

POTW

POTW

POTW

POTW

POTW

POTW

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water

Surface
water

Swface
water

Surface
water

Surface
water

Surface
water

I

AcuteRQs

Chronic

(using
COCof
3,lO0ppb)

RQs(uslog
fish COCof
788ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlaaeRQs
(ustngCOC
or52,000
ppb)

250
20

0.013
0.16

l.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.1 1

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

l.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00

0.00
0.03

0.56
6.86

0.00
0.00

Page 560 of 691

O.ot

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled

Name,Locatioa,
and ID of Active
Releaser Fadlity •

Release
Media b

Facility or
Industry
Sector in

EFAST
Waterbody
Type•

Days of
Release•

Release
(kg/day)'

7Q10

swc

(ppb)I

EFASTC
Elk Grove Village, IL
NPDES:Not
available

Aerojet Rocketdyne,
Inc.,
Rancho Cordova, CA
NPDES: CA00041l l

Allegheny Coty
Airport Auth/
Pgh Intl Airport,
Coroapolis
Pittsburgh, PA
NPDES:Not
available
Amphenol Corp Aerospace
Operations,
Sidney,NY
NPDES: NY0003824

3,200ppb)

788 ppb)

ofJ ppb)

0.00

0.00

0.11

0.00

AlgaeRQs
(osingCOC
I

AlgaeRQs
(usinaCOC
0(52,000
ppb)

and Sealants

250

0.013

250

0.013

20

0.32

Manuf.
Surface
Water

NPDES
CA0004lll

POTW

No info on
receiving
facility;
Adhesives
and Sealants
Mamlf.

Water
Adhesives
POTW
Surface
Water

POTW

Coating&
Converting Tech

Surface
Water

POTW

POTW

and Sealants
Manuf.
NPDES
NY0003824
No info on
receiving
facility;
Adhesives
and Sealan.ts
Mamlf.
Adhesives
and Sealants
Manuf.
Adhesives
and Sealants

Manuf.

0.00

0.00

0.00

0.00

0.16

0.00081
8
0.0101

0.00

0.00

0.00

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

0.0631
0.78

0.00
0.00

0.00
0.00

0.02
0.26

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.0 13
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0;03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.1I

0.00

Surface
water

Surface

Surface
Water

Adhesive Coatings_

cocor

Chronic
RQs(usina
fish COCof

(using

Adhesives

POTW

Aprote<:hPowertrain,
Asheville, NG
NPDES:Not
available

Corp/

AcuteRQs

Smface
water

Surface
water

Surface

water

Surface
water

Page 561 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and IO of Active
Releaser Facility •

Release
Media t,

Modeled
Facility or
Industry
Sector in

EFAST
Waterbody
Typed

AcuteRQs
(using
COCof
3,200 ppb)

Chronic
RQs(using
fasbCOCof
788ppb)

1.67
20.57

0.00
0.01

0.00
0.03

6.86

0.00
0.00

0.013

0.32

0.00

0.00

0.11

0.00

20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

I.67
20.57

0.00

0.00
0.03

0.56

o.oi

6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56

6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20
250

0.013
0.16
0.013

1.67

0.00
0.01

0.00

20 ..57

0.03

0.56
6.86

0.32

0.00

0.00

0.11

Days of

Release

Releasee

(kg/day)'

250
20

0.013
0.16

250
250

7Ql0

swc
(ppb) C

EFASTC
Philadelphia, PA
NPDES:Not
available
Corpus Christi Anny
Depot,
Corpus Christi, TX
NPDES:Not
available
ElectronicData
Systems
Camp Pendleton,
Camp Pendleton, CA
NPDES:Not
available

Surface
Water
POTW

Surface
water

Surface
Water
POTW

Florida Production
Engineering,Inc.,
Ormond Beach, FL
NPDES:Not
available

Surface
Water

Goodrich
Corporation,
Jacksonville, FL
NPDES:Not
available

Surface
Water

Kasai North America
Inc,
Madison Plant,
Madison,MS
NPDES:Not
available
Kirtland Air Force

Surface
Water

Base,
Albuquerque,NM

Adhesives
and Sealants
Manuf.

POTW

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants

POTW

Manuf.

Adhesives
and Sealants

POTW

Manuf.

Surface
Water
POTW

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water

AlgaeRQs
(usingCOC
of3ppb)

0.56

AlgaeRQs
(using COC
of52,000
ppb)

Surface

water

Surface
water

Surface
water

Page 562 of 691

0.00

0.00
0.00

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
and ID of Active

Releaser FacOlty•

Release
Media b

Modeled
Facility or
Industry
Sector in

EFAST
IWaterbody
Typed

(usina
COCof
3,200ppb)

Chronic
RQs(asing
fish COCof
788 ppb)

1.67
20 .57

0.00
0.01

0.00
0 .03

0.56
6.86

0.00
0.00

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0 .013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.0 13

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20 .57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.0 13
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0 .00

250

0.013

0.32

0 .00

0.00

0 .11

0.00

250

0.013
0.16

1.67
20.57

0.00
o.oi

0.00
0.03

0.56
6.86

0.00
0.00

Days of
Release•

Release
(kg/day) r

250
20

0.013
0.16

250

7Q10

swc
(ppb)I

EFASTC

AcuteRQs

AlgaeRQs
(nsingCOC

of3ppb)

AlgaeRQs
(usingCOC
ofSl,000
ppb)

NPDES:Not
available
Marvin Wmdows&

Doors,
Warroad,MN
NPDES:Not
available

Surface
Water
POTW

McneilusTruck &
ManufacturingInc,
Dodge Center, MN
NPDES:Not
available

Surface
Water

Metal FinishingCo. Wichita(S Mclean
Blvd),
Wichita,KS
NPDES: Not
available
Murakami
ManufacturingUsa
Inc, Campbellsville,
KY
NPDES:Not
available
PeterbiltMotors
Denton Facility,
Denton, TX
NPDES:Not
available

Surface
Water

PortsmouthNaval
Shinvard,

Surface
Water

POTW

POTW
Surface
Water
POTW

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water

Surface
water

Surface
water

Surface
Water

POTW

Adhesives
and Sealants
Manuf.

Surface
water

Surface
water

20

Page 563 of 691

 INTERAGENCYDRA.FT- DO "\JOTCITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Kittery, ME
NPDES:Not
available
R.D. Henry & Co.,
Wichita, KS
NPDES:Not
available

Release
Media

b

POTW

EFAST

Adhesives
and Sealants
Manuf.

Surface
Water

POTW

Rehaulnc,
Cullman,AL
NPDES:Not
available

Surface
Water

Rotochopper Inc,
Saint Martin, MN
NPDES: Not
available
Rubber Applications,
Mulberry, FL
NPDES:Not
available

Surface
Water

POTW

POTW
Surface
Water
POTW

Adhesives
and Sealants
Manuf.

NPDES
RI0000281
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.

Surface
water

Release

(kg/day) 1

7Q10

swc
(ppb) I

AcuteRQs
(using
COCof
3,200 ppb)

Chronic
RQs(using
fish COC of
788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AI1aeRQs
(nsingCOC
ofS2 ,000

ppb)

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00

0.00
0.03

0.56

O.ot

6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

10.83
133.33

0.00
0.04

0.0 1
0.17

3.61
44.44

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

l.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00

0.00

0.56

0,01

0.03

6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013

1.67

0.00

0.00

0.56

0.00

Still body

Adhesives
and Sealants
Manuf.

Surface
water

Adhesives
and Sealants
Manuf.

Surface
water

Adhesives
and Sealants
Manut:

Days of

Waterbody Release e
Typed

Surface
Water

POTW

Raytheon Company,
Portsmouth, RI
NPDES: RI000028l

Modeled
Facility or
Industry
Sector in
EFAST C

Surface
water

Page 564 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Modeled

Name,Location,
and ID of Active
Releaser Faclllty •

Release
Media

b

Facility or
Industry
Sector in

EFAST

Days of
IWaterbody
Releasee
Typed

Release
(q/day) f

EFASTC
Sapa Precision
Tubing Rockledge,
Llc,
Rockledge, FL
NPDES:Not
available

Surface
Water

Thomas & Betts,
Albuquerque, NM
NPDES: Not
available

Surface
Water

Thomas Built Buses Fairfield Road,
High Point, NC
NPDES: Not
available

Surface
Water

Timco,
Dba Haeco Americas
Airframe Services,
Greensboro, NC
NPDES:Not
available

Surface
Water

Trelleborg Coated
Systems Us, Inc Grace Advanced
Materials,
Rutherfordton, NC
NPDES: Not
available

Surface
Water

U.S. Coast Guard
Yard - Curti s Bay.
CurtisBay, MD

Surface
Water
-POTW

POTW

POTW

POTW

POTW

POTW

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants
Manuf.

Adhesives
and Sealants

Manuf.

Surface
water

Surface
water

Surface
water

Surface
water

7Q10

swc
(ppb)

I

AcuteRQs
(using
COCof
3,200ppb)

Chronic
RQs(using
fish COC of
788ppb)

AlgaeRQs
(osingCOC
of3 ppb)

AlpeRQs
(usingCOC
of52,000
ppb)

20

0.16

20.57

0.01

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0 .013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0. 16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0. 11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00
0.03

0.56
6.86

0.00
0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250
20

0.013
0.16

1.67
20.57

0.00
0.01

0.00

0.56

0.00

0.03

6.86

0.00

250

0.013

0.32

0.00

0.00

0.11

0.00

250

0.013
0.16
0.013

1.67
20.57
0.32

0.00
0.01
0.00

0.00
0.03
0.00

0.56
6.86
O.ll

0.00
0.00
0 .00

Surface

water

Surface
water

20

250

Page 565 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Release
Media ~

Modeled
Facility or
Industry
Sector in
EFASTC

EFAST
Days of
Waterbody
.
R
elease•
Typed

7Q10

Release
(kg/day)'

(ppb) I

20

0.013
0.16

250

AcuteRQs

Chronic
RQs(using

AlgaeRQs

AJgaeRQs
(usingCOC

(using
COCof
3,200 ppb)

f11hCOCof
788ppb)

1.67
20.57

0.00
0.01

0.00
0.03

6.86

0.00
0.00

0.013

0.32

0.00

0.00

0.11

0.00

250

0.0002

0.00292

0.00

0.00

0.00

0.00

20

0.0025

0.0365

0.00

0.00

0.01

0.00

300

0.019

0.14

0.00

0.00

0.05

0.00

20

0.292

2.2

0.00

0.00

0.73

0.00

300

0.001

0.00

0.00

0.00

0.00

20

0.008

0.00335

0.00

0.00

0.00

0.00

300

0.38

9.3

0.00

0.01

3.10

0.00

20

5.65

138.34

0.04

0.18

46.11

0.00

300

0.008

0.15

0.00

0.00

0.05

0.00

20

0.115

2.32

0.00

0.00

0.77

0.00

300

0.005

0.0141

0.00

0.00

0.00

0.00

20

0.07

0.2

0.00

0.00

0.07

0.00

swc

(usingCOC

of3 ppb)

of52,000
ppb)

NPDES: Not
available
Viracon Inc,
Owatonna, MN
NPDES:Not
available

Surface
Water
POTW

2 50

Adhesives
and Sealants
Manuf.

Surface
water

0.56

OES: Commercial Printin g and Coovina
Prinung And Pub Sys

Div,
Surface
Weatherford, OK
Water
NPDES : OK0041785
OES: IndustrialProcessin2Aid

Printing

Surface
water

Occidental Chemical

Corp Niagara Plant,
Niagara Falls, NY
NPDES: NY0003336
Stepan Co Millsdale
Road,
Elwood, IL
NPDES: IL0002453
Entek International

LLC,
L~banon, OR
NPDES:N/A
National Electrical
Carbon Products
Dba Morgan Adv
Materials,
Fostoria, OH
NPDES: OH0052744
PPG Industries Inc
Barberton,
Barberton, OH

Surface
Water

NPDES
NY0003336

Still body

Surface
Water

NPDES
IL0002453

Surface
water

Off-site
Wastewater
Treatment

No info on
receiving
facility;
POTW (Ind.)

Off-site
Wastewater
Treatment

Receiving
Facility: City
of Fostoria;
NPDES
OH0052744

SUrface
water

Off-site
Waste-

Receiving
Facility: City
of Barberton·

Surface
water

Smface
water

Page 566 of 691

0.00041
9

 INTERAGENCYDRAFT - DO NOT CITt OR QUOTE
Modeled

Name,Location,
and lD of Active
Releaser Facility •
NPDES : 080024007
Daramic LLC,
Corydon, IN
NPDES: IN0020893

Release
Media

11

Facility or

Industry
Sector in

EFAST

Days of

Waterbody Releasee
Typed

water
Treatment

EFASTC
NPDES
OH0024007

Surface
Water

NPDES
IN0020893

Surface
water

Surface
Water

NPDES
LA0007129

Surface
water

7Q10

Release
(kg/day)

1

swc

(ppb)

g

AcuteR.Qs
(using

Chronk
RQs(uslng

COCof
3,200 ppb)

fish COCof

788 ppb)

AlgaeRQs
(usingCOC

of3 ppb)

AJgaeRQs
(usingCOC
ofSl,000

ppb)

300

0.008

0.0206

0.00

0.00

0.01

0.00

20

0.114

0.29

0.00

0.00

0.10

0.00

350

1.266

0.0051

0.00

0.00

(}.00

0.00

20

22.15

0.0897

0.00

0.00

0.03

0.00

350

0.069

2.42

0.00

0.00

0.81

0.00

20

1.2

42.14

0.01

0.05

14.0S

0 .00

350

O.oIS

0.53

0.00

0.00

0.18

0.00

20

0.265

9.48

0 .00

0.01

3.16

0.00

350

0 .015

2.77

0.00

0.00

0.92

0.00

20

0.265

49 .91

0.02

0.06

16.64

0.00

350

0.015

0.07

0.00

0.00

0.02

0.00

20

0.265

1.33

0.00

0 .00

0.44

0.00

350

0.015

0.53

0.00

0 .00

0.18

0.00

20

0.265

9.48

0.00

0.01

3.16

0.00

365

0.043

0.7

0.00

0.00

0.23

0.00

20

0.786

12.79

0.00

0.02

4.26

0.00

365

0.016

0.17

0.00

0.00

0.06

0.00

20

0.299

3.11

0.00

0.00

1.04

0.00

OES:Manufactarine
Axiall Corporation,
Westlake, LA
NPDES: LA0007129
Olin Blue Cube,
Freeport, TX
NPDES:N ot
available
Solvents &
Chemicals ,
Pearland, TX
NPDES : N ot
available

Occidental Chemical
Corp Wichita,
Wichita, KS
NPDES: KS0096903
and Organic Chem

MFG SIC

Off-site
Wastewater
Treatment
Off-site
Wastewater
Treatment
Surface
Water
Surface
Water
Off-site
Waste-

water
Tr~atmcnt

Organic
Chemicals

Manuf.
Organic
Chemicals

Manuf.
Organic
Chemicals
Manuf.
Surrogate
NPDES
KS0043036
Organic
Chemicals

Manuf.

Surface
water

Surface
water
Surface
water
Surface
water
Surface
water

OES:WasteWaterTreatmeatPlant(WWTP)
New Rochelle STP,
New Rochelle, NY
NPDES : NY0026697
Everett Water
Pollution Control
Facilitv

Surface
Water

NPDES
NY0026697

Still body

Surface
Water

NPDES
WA0024490

Surface
water

Page 567 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR Ql TOIi;
Name, Location,
and ID of Active
Releaser Facility •

Release
Medla •

Modeled
Facility or

Industry
Sector in

EFAST
Daysof
Waterbody
Typed

Release

7Q10

swc

AcuteRQs
(using
COCof
3,200ppb)

Chronic
RQs(using
fish COCof
788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(usingCOC
ofS2,000
ppb)

Release e

(kg/day)'

365

o.oi

0.61

0.00

0.00

0.20

0,00

20

0.176

10.97

0.00

0.01

3.66

0.00

365

0.005

0.00673

0.00

0.00

0.00

0.00

20

0.083

0.11

0.00

0.00

0.04

0.00

365

0.002

0.26

0.00

0.00

0.09

0.00

20

0.035

4.51

0.00

O.ol

1.50

0.00

365

0.002

0.26

0.00

0.00

0.09

0.00

20

0.032

4.12

0.00

0.01

1.37

0.00

365

0.0004

0.00194

0.00

0.00

0.00

0.00

20

0.0067

0.034

0.00

0.00

0.01

0.00

365

0.0003

0.0387

0.00

0.00

0.01

0.00

20

0.0047

0.64

0.00

0.00

0.21

0.00

(ppb) g

EFASTe
Everett, WA
NPDES:
WA0024490
Sullivan WWTP,
Sullivan, MO
NPDES:
MO0104736
SunnysideSTP,
Sunnyside,WA
NPDES:
WA0020991
Port Of Sunnyside
Industrial WWTF,
Sunnyside,WA
NPDES:
WA0052426
U.S. Air Force Shaw
AFB SC,
Shaw AFB, SC
NPDES: SC0024970

Surface
Water

Surface
Water

Surface
Water

Surface
Water

NPDES
MO0104736

NPDES
WA0020991

POTW(Ind.)

POTW(Ind.)

Surface
water

Surface
water

Surface
water

Surface
water

Gnf-A WilmingtonCastle Hayne
WWTP,

Wilmington,NC
NPDES:NC0001228
Cameron Trading
PostWWTP,
Cameron,AZ
NPDES: NN0021610

Surface
Water

Surface
Water

NPDES
NC0001228

POTW(Ind.)

Surface
water

Surface
water

Page 568 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name,Location,
and ID of Active
ReleaserFacility•
Coal Grove WWfP ,
Coal Grove, OH
NPDES: OH0104558

Release
Media"

Surface
Water

Modeled
Facilityor
Industry
Sector in
EIF'ASTC

EFAST Days of
Waterbody
Release•
Typed

NPDES
080029432

Surface
water

Surface
Water

Surrogate
NPDES
NY0025721

Surface
water

Surface
Water

Surrogate
NPDES
OH0009521

Release
(kg/day)'

7Q10

swc
(ppb)•

AcuteRQs

Chronic

(osiog

RQs(usiog

cocor

fish COCof

3,200 ppb)

788 ppb)

AlgaeRQs
(osingCOC
of3ppb)

AiaaeRQs
(asingCOC

of52,000
ppb)

365

0.0002

0.00001
27

0.00

0.00

0.00

0.00

20

0.0031

0.00019

0.00

0.00

0.00

0.00

250

0.013

0.0271

0.00

0.00

0.01

0.00

20

0.159

0.33

0.00

0.00

0.11

0.00

250

0.003

0.00564·

0.00

0.00

0.00

0.00

20

0.035

0.0658

0.00

0.00

0.02

0.00

250

0.0004

0.0881

0.00

0.00

0.03

0.00

20

0.00499

1.1

0.00

0.00

0.37

0,00

250

0.00028

0.0617

0.00

0.00

0.02

0.00

20

0.00354

0.77

0.00

0.00

0.26

0.00

250

0.00027

9

0.00

0.01

3.00

0.00

20

0.00334

110

0.03

0.14

36.67

0.00

OES:OtherCommercialUses
Corning Hospital,
Coming.NY
NPDES: NY0246701
Water Street
Commercial Bldg,
Dayton, OH
NPDES: 080141496
Union Station North
Wing Office
Building. Denver,

co

I

NPDES:
COG315293
Confluence Park
Apartments,
Denver,CO
NPDES:.
COG315339

ParkPlaceMixed
Use Development,
Annapolis, MD
NPDES:
MD0068861
Tree Top Inc
Wenatchee Plant,
Wenatchee, WA
NPDES:
WA0051527

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
CO0020095

Surrogate
NPDES
CO0020095

Surrogate
NPDBS
MD0052868

Surface
water

Surface
water

Surface
water

Still body

Not assessed (below the min risk level).

Page 569 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Release
Media

1>

Modeled
Facility or
Industry

Waterbody

Sector In

Typed

EFAST

Days of
Releasee

Release
(kg/day)'

7Ql0

swc
(ppb) a

.EFAST C
Wynkoop Denver
LLCP St,
Denver,CO
NPDES:
COG603115
Greer Family Llc,
South Burlington, VT
NPDES: VT000 1376
John Marshall m
Site,
Mclean, VA

WashingtonPenn
Plastics,
Frankfort, KY
NPDES: KY0097497

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed {belowthe min risk level).

Surface
Water

Surface
Water

Surface
Water

Solvay - Houston
Plant,
Houston, TX
Surface
NPDES: Tx:0007072 Water

Natrium Plant,
New Martinsville,
WV

3,200ppb)

Chronic
RQs(osin e
fish COC of
788 ppb)

Surface
Water

NPDES: VA0090093
OES: Other lndustriai Uses
Eli Lilly And

CompanyLilly Tech Ctr,
Indianapolis,IN
NPDES: IN00033I0
Oxy Vinyls LP Deer Park Pvc,
Deer Parle,TX
NPDES: TX0007412

AcuteRQs
(using
cocor

Surface
Water

NPDES
IN0003310

NPDES
TX0007412

Surrogate
NPDES
KY00284IO

NPDES
TX0007072

NPDES
WV0004359

Surface
water

Surface
water

Surface
water

Surface
water

Surface
water

AJgaeRQs

AiaaeRQs

(usingCOC
of3 ppb)

(u ingCOC
of52 ,000
ppb)

250

1.553

9.03

0.00

0.01

3.01

0.00

20

19.41

113.09

0.04

0.14

37.70

0.00

250

0.148

0.49

0.00

0.00

0.16

0.00

20

1.854

5.98

0.00

0.01

1.99

0.00

250

0.032

7.53

0.00

0.01

2.51

0.00

20

0.399

94.12

0.03

0.12

31.37

0.00

350

0.024

4.44

0.00

0.01

1.48

0.00

20

0.414

75.93

0.02

0.10

25.31

0.00

250

0.022

0.00262

0.00

0.00

0.00

0.00

20

0.274

0.0322

0.00

0.00

0.01

0.00

Page 570 of 691

 INTERAGENCYDRAFT- DO NOT CITEOR QUOTE
Name, Location,
and ID of Active
Rdeaser Facility •

Release
Media b

Modeled
Facility or
Industry
Sector lo

EFAST
Waterbody
Typed

Days of

Release•

Release
(kg/day) f

7Q10

swc
(ppb)I

EFASTC

AcuteRQs
(using
COCof
3,200 ppb)

Chronic
RQs(using
fish COC of
788 ppb)

AlgaeRQs
(uslngCOC
o!3 ppb)

AlgaeRQs
(usingCOC

ofSl,000
ppb)

NPDES:
WV0004359
Leroy Quarry,
Leroy, NY
NPDES: NY0247189
GeorgeC Marshall
Space Flight Center,
Huntsville,AL
NPDES: AL0000221
Whelan Energy
CenterPower Plant,
Hastings,NE
NPDES:NEOI13506
Anny Cold Regions
Research&
EngineeringLab,
Hanover,NH
NPDES:NH0001619
Coming - Canton
Plant,
Canton,NY
NPDES:NY0085006
Ames Rubber Corp
Plant #1,
HamburgBoro,NJ
NPDES:NJG000141
Gorham,
Providence,RI
NPDES: RIG85E004

Surface
Water

Surrogate
NPDES
NY0030546

Surface
water

Surface
Water

Surrogate
NPDES
AL0025585

Surface
water

Surface
Water

NPDES
NE0113506

Surface
water

Surface
Water

Surrogate
NPDES
NH0100099

Surface
water

Surface
Water

Surrogate
NPDES
NY0034762

Surface
water

Surface
Water

Surrogate
NPDES
NJ0000141

Surface
water

POTW(lnd.)

Surface
water

Surface
Water

Abo Nobel Surface
ChemistryLLC,
Surface
Morris,IL
Water
NPDES: IL0026069

NPDES
II..0026069

Surface
water

250

0.019

0.71

0.00

0.00

0.24

0.00

20

0.242

8.91

0.00

0.01

2.97

0.00

250

0.01

0.2

0.00

0.00

0.07

0.00

20

0.128

2.63

0.00

0.00

0.88

0.00

250

0.009

2.92

0.00

0.00

0.97

0.00

20

0.118

38.96

0.01

0.05

12.99

0.00

250

0.0002

0.00

0.00

0.00

0.00

20

0.0029

0.00154

0.00

0.00

0.00

0.00

250

0.0002

0.00034

0.00

0.00

0.00

0.00

20

0.0028

0.0051

0.00

0.00

0.00

0.00

250

0.00011

0.0149

0.00

0.00

0.00

0.00

20

0.00133

0.18

0.00

0.00

0.06

0.00

250

0.0001

0.0129

0.00

0.00

0.00

0.00

20

0.0012

0.13

0.00

0.00

0.04

0.00

350

0.000329

0.00068
8

0.00

0.00

0.00

0.00

20

0.006

0.0125

0.00

0.00

0.00

0.00

Page 571 of 691

0.00010
3

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
aad ID of Active
Releaser Facility •

Release

Media"

Modeled
Facility or

Industry
Sector in

--

-

EFAST
Days of
Waterbody Release e
Typed

Release
(kg/day)'

--

7Q10

swc
(ppb) a

EFASTC
SolutiaNitro Site,
Nitro, WV
NPDES:
WV011618l

Surface
Water

Amphenol
CorporationColumbia,
Surface
Columbia,SC
Water
NPDES: SC0046264
Keeshan and Bost
ChemicalCo., Inc.,
Surface
Manvel, TX
Water
NPDES: TX0072168
ChemturaNorth and
South Plants,
Morgantown,WV
NPDES:
WV0004740
Indorama Ventures
Olefins, LLC,
Sulphur,LA

NPDES: LA0069850
EmersonPower
Transmission,
Ithaca, NY
NPDES: NY0002933
WilliamE. Warne
Power Plant,
Los Angeles County,
CA
NPDES:CA0059188

Surrogate
NPDES
WV0023229

Organic
Chemicals
Manufacture

NPDES
TX0072168

Surface
water

Surface
water

Still body

AcuteRQs
(using
3,200 ppb)

Chronic
RQs(using
fish COCof
788ppb)

AlgaeRQs
(usiDgCOC
of3 ppb)

COCof

AlgaeRQs
(uslngCOC

of52,000
ppb)

350

0.000318

0.00009
41

0.00

0.00

0.00

0.00

20

0.006

0.00176

0.00

0.00

0.00

0.00

350

0.000202

0.037

0.00

0.00

o.oi

0.00

20

0.004

0.74

0.00

0.00

0.25

0.00

350

0.000095

9.5

0.00

0.01

3.17

0.00

20

0.002

200

0.06

0.25

66.67

0.00

Surface
Water

Not assessed(below the min risk level).

Surface
Water

Not assessed(below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Page 572 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •
Raytheon Aircraft
Co(Was Beech
Aircraft),Boulder,

co

Release
Media b

Modeled
Facility or
Industry
Seetorin
EFASTC

EFAST
Days of
Waterbody
Releasee
Typed

Surface
Water

Release
(kg/day)'

7Ql0

swc

(ppb)'

AcuteRQs
(using
COCof

Chronic
RQs(using
fish COC of

3,200 ppb)

788ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(usingCOC
of52,000

ppb)

Not assessed (below the min risk level).

NPDES:
COG31Sl16

OES: OTVD (Includes releases for Closed-Loop Deereasin2,ConveyorizedDeereasin2. Web Dee:reasinl!.and Metalworkin2Fluids)
260
0.005
0.0188
0.00
0.00
Texas Instruments.
0.01

0.00

Inc.,
Attleboro, MA
NPDES:
MAOOOI791
Accellent
Inc/Collegeville
Microcoax,
Collegeville, PA
NPDES: PA0042617
Ametek Inc. U.S.
Gauge Div.,
Sellersville,PA
NPDES: PA0056014

Atk-Allegany
Ballistics Lab
(Nirop),
Keyser, WV
NPDES:
WV0020371
Handy & Harm.an
Tube Co/East
Norriton, Norristown,

PA

Surface
Water

NPDES
MA0001791

Surface
Water

NPDES
PA0042617

Surface
Water

Surrogate
NPDES
PA0020460

Surface
Water

NPDES
WV0020371

Surface
water

Surface
water

Surface
water

Surface
water

20

0.067

0.25

0.00

0.00

0.08

0.00

260

0.002

0.0425

0.00

0.00

0.01

0.00

20

0.029

0.62

0.00

0.00

021

0.00

260

0.001

0.0619

0.00

0.00

0.02

0.00

20

0.011

0.68

0.00

0.00

0.23

0.00

260

0.0005

0.00311

0.00

0.00

0.00

0.00

20

0.0061

0.0373

0.00

0.00

0.01

0.00

0.97

255.21

0.01

Surface

Not assessed (below the min risk level).

Water

NPDES: PAOOl1436
Still bodv

260

1.96

Page 573 of 691

765.63

0.24

 INTERAGENCYDRAFT- DO NOT en F OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Release

Media b

Modeled
Facility or
Industry

Sector In

EFAST

Days of
Waterbod.y Release e

Typed

Release
(kg/day)'

7Ql0

swc
(ppb) I

EFASTC
US Nasa Michoud
Assembly Facility,
New Orleans, LA
NPDES: LA0052256
GM Components
Holdings LLC,
Lockport, NY
NPDES: NY0000558
Akebono
Elizabethtown Plant,
Elizabethtown.KY
NPDES: KY0089672
Delphi Harrison
Thermal Systems,
Dayton, OH
NPDES: OH000943l
Chemours Company
FcLLC,
Washington, WV
NPDES:
WV000l279
Equistar Chemicals
Lp,
LaPorte, TX
NPDES: TX0l 19792

GE Aviation,
Lynn,MA
NPDES:
MA0003905
Certa Vandalia LLC,
Vandalia, OH

Surface
Water

Surrogate
NPDES
LA0003280

Surface
Water

NPDES
NY0000558

Surface
Water

Surrogate
NPDES
KY0022039

Surface
Water

Surface
Water

Surface
Water

NPDES
OH0009431

NPDES
WV000J279

Primary
Metal
Forming

Surface
water

Surface
water

Surface
water

Surface
water

Surface
water

AcuteRQs
(using
COCof
3,200 ppb)

Chronic
RQs(osing
fish COC of
788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AJgaeRQs

(usingCOC
of52,000
ppb)

20

25.44

9937.5

3.11

12.61

3312.50

0.19

260

0.13

10.97

0.00

0.01

3.66

0.00

20

1.71

144.47

0.05

0.18

48.16

0.00

260

0.07

4.87

0.00

0.01

1.62

0.00

20

0.897

62.38

0.02

0.08

20.79

0.00

260

0.04

0.0752

0.00

0.00

0.03

0.00

20

0.465

0.87

0.00

0.00

0.29

0.00

260

0.03

0.00301

0.00

0.00

0.00

0.00

20

0.334

0.0335

0.00

0.00

0.01

0.00

260

0.02

2.22

0.00

0.00

0.74

0.00

20

0.218

24.44

0.01

0.03

8.15

0.00

260

0.01

0.0425

0.00

0.00

0.01

0.00

0.54

0.00

0.00

0.18

0.00

0.00
0.00

0.00
0.02

0.37

0.00
0.00

Manuf.
Surface
Water

NPDES
MA0003905

Still
water

20

0.128

Surface
Water

Primary
Metal

Surface
water

260
20

O.ot

1.11

0.107

11.89

Page 574 of 691

3.96

 INTERAGENCYDRAFT - DO NOT en I OR QUOTE
Name, Location,
and ID of Active
ReleaserFacility•

Release
Media 11

Modeled
Facility OI'
Industry
Sector in

EFAST
Dayso(
Waterbody Release•
Typed

7Q10

Release
(kg/day)

1

swc
(ppb)'

EFASTC
NPDES: OH0122751
GM Components
HoldingsLLC
Kokomo Ops,
Kokomo, IN
NPDES: IN0001830
AmphenolCorpAerospace
Operations,
Sidney,NY

AcuteRQs
(using

Cllroalc
RQs(using

COCoC
3,200ppb)

fisllCOCoC
788 ppb)

AlgaeRQs
(usin&COC
of3 ppb)

AlgaeR.Qs
(usingCOC
o(S2,000
ppb)

Fonning
MamJf.
Surface

Water

NPDES
IN0001830

Surface
water

Surface
Water

NPDES
NY0003824

Surface
water

Surface
Water

SWTogate
NPDES
KY0020257

Surface
water

Surface
Water

Surrogate
NPDES
NY0027162

Surface
water

260

0.01

0.2

0.00

0.00

0.07

0.00

20

0.086

1.73

0.00

0.00

0.58

0.00

260

0.01

0.0486

0.00

0.00

0.02

0.00

20

0.082

0.4

0.00

0.00

0.13

0.00

260

0.01

0.0004

0.00

0.00

0.00

0.00

20

0.081

0.00522

0.00

0.00

0.00

0.00

260

0.01

0.0188

0.00

0.00

0.01

0.00

20

0.068

0.13

0.00

0.00

0.04

0.00

260

0.00469

0.52

0.00

0.00

0.17

0.00

20

0.061

6.78

0.00

0.01

2.26

0.00

260

0.0036

1.76

0.00

0.00

0.59

0.00

20

0.047

23.04

0.01

0.03

7.68

0.00

260

0.00355

1.06

0.00

0.00

0.35

0.00

20

0.0462

13.77

0.00

0.02

4.59

0.00

-

NPDES: NY0003824
Emerson Power
Trans Corp,
Maysville,KY
NPDES:KY0100196
Olean Advanced
Products,
Olean,NY
NPDES: NY0073547
HollingsworthSaco
Lowell,

Easley, SC
NPDES: SC0046396
TrelleborgYSH
Incorporated
SanduskyPlant,
Sandusky, MI
NPDES:MI0028142
TimkenUs Corp
Honea Path,
Honea Path, SC
NPDES: SC0047520

Primary

Surface
Water

Metal
Forming
Manuf.

Surface
water

Surface
Water

NPDES
Mr0028142

Surface
water

Surface
Water

Surrogate
NPDES
SC0000698

Surface
water

.

Page 575 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser FaclHty•

Release
Media b

Modeled
Facility or
Industry
Sector in

EFAST

Daysof

:Waterbody Release e
Typed

Release
(kg/day),

7Q10

swc
(ppb) C

EFASTC
Johnson Controls
Incorporated,
Wichita,KS
NPDES: KS0000850

Surface
Water

NationalRailroad
Passenger
Corporation
(Amtrak)

Wilmington
MaintenanceFacility,
Wilmington,DE
NPDES: DE0050962
Electrolux Home
Products (Formerly
Frigidaire),
Greenville,MI
NPDES: MI0002135
Rex Heat Treat
Lansdale Inc,
Lansdale, PA
NPDES: PA0052965
Carrier Corporation,
Syracuse,NY
NPDES: NY0001163

Surface
Water

Springfield,OH
NPDES: OH0085715
USAF-Wurtsmith
Afb,
Oscoda,MI
NPDES: MI0042285

Primary
Metal
Forming
Manuf.

Surface
water

Surface
water

Surface
Water

NPDES
MI0002135

Surface
Water

Surrogate
NPDES
PA0026182

Surface
water

Surface
Water

NPDES
NY000I163

Still
water

Surface
Water

Primary
Metal
Fonning
Manuf.

Surface
water

Surface
Water

Surrogate
NPDES
MI0028282

Surface
water

Cascade Corp
(0812100207),

NPDES
KS0000850

Acute RQs

Chronic

(usiag
COCof
3,200 ppb)

RQs(using
fish COCof
788 ppb)

AlgaeRQs
(usingCOC
of3ppb)

AJgaeRQs

(usingCOC
ofS2,000
ppb)

260

0.00228

0.0548

0.00

0.00

0.02

0.00

20

0.0296

0.72

0.00

0.00

0.24

0.00

260

0.00203

0.23

0.00

0.00

0.08

0.00

20

0.026

2.89

0.00

0.00

0.96

0.00

260

0.00201

0.0171

0.00

0.00

0.01

0.00

20

0.026

0.22

0.00

0.00

0.07

0.00

260

0.00194

0.0523

0.00

0.00

0.02

0.00

20

0.025

0.67

0.00

0.00

0.22

0.00

260

0.00177

0.22

0.00

0.00

0.07

0.00

20

0.023

2.84

0.00

0.00

0.95

0.00

260

0.00117

0.13

0.00

0.00

0.04

0.00

20

0,015

l.67

0.00

0.00

0.56

0.00

260

0.00115

0.00075
3

0.00

0.00

0.00

0.00

20

0.015

0.00983

0.00

0.00

0.00

0.00

Surface

water

Page 576 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser FaclUty •

Releaie
Media b

Modeled
Facility or
Industry
Sector in

EFAST

Days or
IWaterbody Release •
Typed

Release
(kg/day) 1

7Q10

swc

(ppb) C

EFASTC
AARMobility

Systems,
Cadillac, MI
NPDES: MI0002640
Eaton Mdh Company
Inc,
Kearney, NE
NPDES: NEOl14405
Lake Region
Medical,
Trappe, PA
NPDES: PA00426 17
Motor Components L
LC,

Elmira, NY
NPDES: NY0004081
Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
MI0020257

Surrogate

NPDES
NE0052647

NPDES
PA0042617

NPDES
NY0004081

Primary
Metal
Forming

Surface
water

AcuteRQs
(using
COCof
3,200 ppb)

Chronic
RQs(usiog
Jis•COCof
788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(usingCOC
of52,000
ppb)

260

0.00112

0.00916

0.00

0.00

0.00

0.00

20

0.014

0.11

0.00

0.00

0.04

0.00

260

0.00107

0.13

0.00

0.00

0.04

0.00

20

0.014

l.69

0 .00

0.00

0.56

0.00

260

0.0005

0.0106

0 .00

0 .00

0.00

0.00

20

0.007

0.15

0 .00

0.00

0.05

0.00

260

0.00096

0.0618

0.00

0.00

0.02

0.00

20

0.0125

0.83

0.00

0.00

0.28

0.00

260

0.000897

0.0997

0.00

0.00

0.03

0.00

20

0.012

1.33

0 .00

0.00

0.44

0.00

260

0.000806

0.0821

0 .00

0.00

0.03

0.00

20

0.01

1.02

0.00

0.00

0.34

0.00

260

0.000747

0.083

0.00

0.00

0.03

0.00

20

0.01

1.11

0.00

0.00

0.37

0.00

260

0.000742

0.0336

0.00

0.00

0.01

0.00

20

0.01

0.45

0.00

0.00

0.15

0.00

Still

water

Surface
water

Surface
water

Surface
water

Manuf.
GE (Greenville) Gas
Turbines LLC,
Greenville, SC
NPDES:SC0003484
Parker Hannifin
Corporation,
Waverly, OH
NPDES: OH0104132
Mahle Engine
Components Usa Inc,
Muskeg-on.. Ml

Surface
Water

NPDES
SC0003484

Surface
water

Primary
Surface
Water

Metal

Forming

Surface
water

Manuf.
Surface

Water

NPDES
Ml0004057

Surface

water

Page 577 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled

Name, Locatioa,
and ID of Active
Releaser Facility •
NPDES: MI0004057
General Electric
CompanyWaynesboro,
Waynesboro, VA
NPDES: VA0002402
Globe Engineering
Co Inc,
Wichita, KS
NPDES: KS0086703
Gayston Corp,
Dayton, OH
NPDES : OH0127043
Styrolution America
LLC,
Channahon, IL
NPDES: IL000 1619
Remington Anns Co
Inc,
Ilion, NY
NPDES : NY0005282
United Technologies
Corporation, Pratt
And Whitney
Division,
East Hartford, CT
NPDES: CT0001376
Atk-Allegany
Ballistics Lab
(Nirop),
Keyser, WV
NPDES:
WV0020371

Release
Media b

Facility or
Industry
Sector in
EFASTe

EFAST
Days of
Waterbody
Typed

Surface
Wat.er

NPDES
VA0002402

Surface
Wat.er

Surrogate
NPDES
KS0043036

Surface
water

Surface
Water

Surrogate
NPDES
080024881

Surface
water

Surface
Wat.er

NPDES
IL0001619

Surface
water

Surface
Water

Surface
Water

Surface
Wat.er

NPDES
NY0005282

NPDES
CT0001376

NPDES
WV0020371

Surface
wat.er

Surface
water

Surface
water

7Ql0

AcuteRQs
(uiDg
COCof
3,200 ppb)

Chronic
RQs(using
fishCOCof
788ppb)

Algae RQs
(usingCOC
(usinacoc of52,000
of3 ppb)
ppb)
AlgaeRQs

Releaset

Release
(kg/day) r

260

0.000733

0.00705

0.00

0.00

0.00

0.00

20

0,01

0.0962

0.00

0.00

0.03

0.00

260

0.00173

0.00853

0.00

0.00

0.00

0.00

20

0.023

0.11

0.00

0.00

0.04

0.00

260

0.000643

0.00121

0.00

0.00

0.00

0.00

20

0.008

0.015

0.00

0.00

0.01

0.00

260

0.000637

0.00022
1

0.00

0.00

0.00

0.00

20

0.008

0.00278

0.00

0.00

0.00

0.00

260

0.000612

0.00079
9

0.00

0.00

0.00

0.00

20

0.008

0.0104

0.00

0.00

0.00

0.00

260

0.00048

22

0.00

0.00

0.00

0.00

20

0.006

0.00103

0.00

0.00

0.00

0.00

260

0.00047

0.00292

0.00

0.00

0.00

0.00

20

0 .006

0.0373

0.00

0.00

0.01

0.00

swc
(ppb)I

0.00008

Surface

water

Page 578 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Modeled

Name, Location,
and ID of Active

ReleaserFacility•

Release

Media

b

FaciHtyor
Industry
Sector in
EFASTC

EFAST

Waterbocly Releasee
Typed

Sperry & Rice
Manufacturing Co

LLC,

Surface
Water

NPDES
IN0001473

Surface
water

Surface
Water

NPDES
SC0026492

Surface
water

NPDES: MI005 3651

Surface
Water

Surrogate
NPDES
MI0022l36

Surfa ce
water

Mccanna Inc.,
Carpentersville, IL
NPD BS: II..0071 340

Surface
Water

Surrogate
NPDES
IL0027944

Surface
water

Brookville, IN
NPDES: IN0001473
Owt Industries,
Pickens , SC
NPDES: SC0026492
Boler Company ,

Hillsdale, MI

Cutler Hammer,
Horseheads , NY
NPDES: NY0246174

Surface
Water

US Air Force Offutt

AtbNc ,
Offutt A F B, NE
NPD ES: NE01 2 1789
Troxel Company,
Moscow, TN
NPDES : TN0000451

AustinTubeProd,
Baldwin. MI
NPDES: MIOOS4224

Surface
Water

Surrogate
NPDES
NY0004081

Primary
Metal
Fonning

Days of

Surface
water

Surface
water

Release
(kg/day)'

7Q10

swc

AcuteRQs
(using

(ppb)I

COCof
3,200 ppb)

Chronic
RQs (using
fish COCof

788 ppb)

AlgaeRQs
(usingCOC
of3 ppb)

AlgaeRQs
(usingCOC
of52,000

ppb)

260

0.000 328

0.00 569

0.00

0.00

0.00

0.00

20

0 .004

0.0694

0.00

0.00

0.02

0.00

260

0.000 3 14

0.0021 3

0.00

0.00

0.00

0.00

20

0.004

0.0272

0.00

0.00

0.01

0.00

260

0.000269

0.0204

0.00

0.00

o.oi

0.00

20

0.003

0.23

0.00

0.00

0.08

0.00

260

0.000268

0.00091
1

0.00

0.00

0.00

0.00

20

0 .003

0.0102

0.00

0.00

0.00

0.00

260

0 .0002 38

0.01 53

0.00

0.00

0.01

0.00

20

0.003

0.19

0.00

0.00

0.06

0.00

260

0.000159

0.0177

0.00

0.00

o.oi

0.00

20

0.002

0.22

0.00

0.00

0.07

0.00

260

0.0001 34

0 .000 74
1

0 .00

0.00

0.00

0.00

20

0.002

0.0111

0.00

0.00

0.00

0.00

260

0.000 114

0.0 127

0.00

0.00

0.00

0.00

20

0.001

0.11

0.00

0.00

0.04

0.00

Manuf.

Surface
Water

Surface
Water

NPDES
TN0000451

Primary
Metal
Fonning
Manuf .

Surface
water

Surface
water

Page 579 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

LS Starrett Precision
Tools,
Athol,MA
NPDES:
MAOOOI350
Avx Corp,
Raleigh, NC
NPDES: NC0089494
Indian Head
Division, Naval
Surface Warfare
Center,

Indian Head, MD
NPDES:
MD0003158
General Dynamics
Ordnance Tactical
Systems,
RedLion , PA
NPDES: PA0043672
Trane Residential
Solutions - Fort
Smith,

Fort Smith, AR
NPDES:AR0052477
Lexmark
Jntemational Inc.,
Lexington, KY
NPDES: KY0097624
Alliant Techsystems
Operations LLC,
Elkton, MD

NPDES:

Re1ease

Media b

Modeled
Facility or

Industry
Sector in
EFAST"

Surface
Water

NPDES
MA0001350

Surface
Water

Primary
Metal
Forming
Manuf.

EFAST

IWaterbody
Typed

(using
COCof
3,200ppb)

Chronic
RQs(using
fis• COCof
788 ppb)

0.00153

0.00

0.001

0.015

260

0.0000883

20

0.001

7Q10

AcuteRQs

AlgaeRQs

AlgaeRQs

(nsingCOC
of3 ppb)

(usingCOC
of52,000
ppb)

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00981

0.00

0.00

0.00

0.00

0.11

0.00

0.00

0.04

0.00

Days or

Release

Release"

(kg/day)'

(ppb)I

260

0.000102

20

swc

Surface

water

Surface
water

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

MD0000078

Page 580 of 691

 INTERAGENCYDRAFT - DO NO I CITE OR QUOTE
Name, Location,
andID of Active
Releaser FaciHty •

Release
Media b

Modeled
Facility or
Industry

EFAST
Waterbody

Sectorin

Typed

Days of
Releasec

Release

(kg/day)'

7Q10

swc

(ppb)'

EFASTC

Daikin Applied
America, Inc.
(Fonnally Mcquay
International),
Scottsboro,AL
NPDES: AL0069701
Beechcraft
Corporation,
Wichita, KS
NPDES: KS0000183
Federal-MogulCorp,
Scottsville,KY
NPDES:KY0106585
Cessna Aircraft Co
(Pawnee Facility),
Wichita, KS
NPDES: KS0000647
N.G.I,
Parkersburg, WV
NPDES:
WV0003204
Hyster-YaleGroup,
Inc,
Sulligent, AL
NPDES: AL0069787
Hitachi Electronic
Devices (Usa}, Inc.,
Greenville, SC
NPDES: SC00484l l

AcuteRQs
(using
COCof
3~00 ppb)

Chronic
RQs(using
fish COC of
788ppb)

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface

AlgaeRQs
(usingCOC
of3ppb)

AlgaeRQs
(usingCOC
ofS2,000
ppb)

Not assessed (below the min risk level).

Water
Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

OES: Process Solvent Recyclln2 and Worker Handlint of Wastes
Clean Water Of New

York Inc,
Staten Island, NY
NPDES: NY0200484
Reserve
Environmental
Services,

Surface
Water

Surrogate
NPDES
NJ0000019

Still

body

250

0.004

11.76

0.00

O.ot

3.92

0.00

20

0.047

138.24

0.04

0.18

46.08

0.00

0.00

0.00

0.00

0.00

Surface
Water

Page 581 of 691

 INTERAGENCYDRAFT - DO NOT CITF OR QUOTE
Name, Location,
and lb of Active
ReleaserFacility•

Release
Media b

Modeled
Facility or
Industry

SectorIn
EFASTC

EFAST

Days or

Waterbody Releasee
Typed

Release
(kg/day)f

7QI0

swc
(ppb)

I

AcuteRQs

Chrooic

(using
COCof
3,l00ppb)

RQs(uslng
fishCOCof
788ppb)

AJgaeRQs
(uslngCOC
of3 ppb)

AlgaeRQs
(using COC
of52,000
ppb)

Ashtabula,OH
NPDES: OH0098540
Veolia Es Techbical
SolutionsLLC,
Middlesex,NJ
NPDES:NJ0020141
Clean Harbors Deer
Park LLC,
LaPorte, TX
NPDES: TX0005941
CleanHarbors El
DoradoLLC,
El Dorado, AR
NPDES: AR0037800

Off-site
Wastewater
Treatment
Off-site
Wastewater
Treatment
Off-site
Wastewater
Treatment

Receiving
Facility:
Middlesex
CntyUA;
NPDES
NJ0020141
POTW(Ind.)

Still body

Surface
water

POTW(Ind.)

Surface
water

Organic
Chemicals
Manufacture

Surface
water

Surface
Water

Organic
Chemicals
Manufacture

Surface
water

Surface
Water

NPDES
KY0003603

Surface
water

250

24.1

2.85

0.00

0.00

0.95

0.00

20

301.78

35.72

0.01

0.05

11.91

0.00

250

0.35

8.57

0.00

0.01

2.86

0.00

20

4.36

106.75

0.03

0.14

35.58

0.00

250

0.04

0.98

0.00

0.00

0.33

0.00

20

0.455

11.26

0.00

0.01

3.75

0.00

350

0.005

0.18

0.00

0 .00

0.06

0.00

20

0.089

3.13

0.00

0.00

1.04

0.00

350

0.005

0.92

0.00

0.00

0.31

0.00

20

0.089

16.45

0.01

0.02

5.48

0.00

350

0.017

0.00073
7

0.00

0.00

0.00

0.00

20

0.295

0.128

0.00

0.00

0.04

0.00

OES: Processine as a Reactant
Off-:sitt:

440 unknownsites
NPDES:Not
applicable
Arkema Inc.
CalvertCity, KY
NPDES: KY0003603
US DOE Paducah
Site,
Kevil, KY
NPDES: KY0I02083
GNF-A WilmingtonCastle Hayne,
Wilmin2ton NC

Wastewater
Treatment

Surface
Water

Not assessed(below the min risk level).

Surface
Water

Not assessed(below the min risk level).

Page 582 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name, Location,
and ID of Active

Releaser Facility•
NPDES: NC0001228
Solvay - Houston
Plant,
Houston.TX
NPDES: TX0007072
Honeywell
Jntemational Geismar Complex.
Geismar, LA
NPDES: LA0006181
Praxair Technology
Center,
Tonawanda, NY
NPDES: NY0000281
US DOE Paducah
Site,
Kevil,KY
NPDES: KY0102083
GNF-A WilmingtonCastle Hayne,
WilmingtonNC
NPDES: NC0001228

Release
Media b

Surface
Water

Surface
Water

Surface
Water

Modeled
Facility or

Industry
Sector in
EFASTC

NPDES
TX0007072

NPDES
LA0006181

NPDES
NY0000281

EFAST

Days of

Waterbody
Release•
Type•

Surface
water

Surface
water

Still body

Release
(kg/day)'

7Ql0

swc
(ppb)I

AcoteRQs

Chronic

(usin1
COCof
3,200ppb)

RQs(using
fish COC of
788ppb)

AlgaeRQs
(uslngCOC
of3 ppb)

AlgaeRQs
(usingCOC
of52,000

ppb)

350

0.024

4.44

0.00

0.01

1.48

0.00

20

0.414

75.93

0.02

0.10

25.31

0.00

350

0.0128

0.00005
18

0.00

0.00

0.00

0.00

20

0.224

0.00

0.00

0.00

0.00

350

0.00169

169

0.05

0.21

56.33

0.00

20

0.03

3000

0.94

3.81

1000.00

0.06

0.03

~.06

0.00

0.00090
7

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

OES:Repacka1dn2
Hubbard-Hall Inc,

250

1.108

Page 583 of 691

27.18

0.01

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Name,Location,
and ID of Active
Releaser Facility•

Waterbury,CT
NPDES: Unknown

Release
Media 11

Off-site
Wastewater
Treatment

Modeled
Facility or
Industry
Sector lb
EFAST •

EFAST

Days of
Waterbody Releasee
Typed

Release

(kl/day)'

7Q10

swc

(ppb) '

AcoteRQs
(usini
COCof
3,200 ppb)

Chronic
RQs(ustng
rishCOCof
788 ppb)

AlgaeRQs
(ustngCOC
of3 ppb)

AlgaeRQs
(usingCOC
of5~000
ppb)

Receiving
Facility:
Recycle Inc.;

Surface
water

20

13.85

339.11

0.11

0.43

113.04

0.01

250

0.003

6.52

0.00

0.01

2.1 7

0.00

20

0.041

89.13

0.03

0.11

29.71

0.00

250

0.0055

0.00002
23

0.00

0.00

0.00

0.00

20

0.069

0.00027
9

0.00

0.00

0.00

0.00

250

0.00468

0.00

0.00

0.00

0.00

20

0.058

0.00

0.00

0.00

0.00

POTW(Ind .)

Oiltanking Ho11Ston

Inc,
Houston, TX
NPDES: TX0091855

St Gabriel Tenninal,
Saint Gabriel, LA
NPDES: LA0005487

Vopak Terminal
Westwego Inc,
Westwego, LA
NPDES: LA0124583

Research Solutions
Group Inc,
Pelham,AL
NPDES: AL0074276
Carlisle Engineered
Products Inc,
Middlefield,OH

Surface
Water

Surface
Water

Surface
Water

Surrogate
NPDES
TX0065943

NPDES
LA0005487

Surrogate
NPDES
LA0042064

Surface
water

Surface
water

Surface
water

0.00001

89
0.00023

s

Surface
Water

Not assessed (below the min risk level).

Surface
Water

Not assessed (below the min risk level).

NPDES: OH0052370

OES: Snot Cleanin2 and Carpet Cleanine
Boise State
Surrogate
Surface
University,
NPDES
Water
Boi~ ID
ID0023981

Surface
water

300

0.00008

0.00388

0.00

0.00

0.00

0.00

20

0.001

0.0485

0 .00

0.00

0.02

0.00

Page 584 of 691

 lNTERAGENCYDRAFT- DO NOT CIT1' OR QUOTE
Name, Location,
and ID of Active
Releaser Facility •

Release
Media •

Modeled
Facility or
Industry
Sector in

EFAST

Days of
Waterbody
Release•
Typed

Release
(kg/day)'

EFASTC

7Q10

swc

(ppb)I

AcuteRQs
(asing

COCor
3,200 ppb)

Chronic
RQs(asina
fisbCOCof
788ppb)

AlgaeRQs
(uslngCOC
oUppb)

AlgaeRQs
(aslngCOC
ofSl,000
ppb)

NPDES: 1DG91W06
'

Venetian Hotel And
Casino,
Surface
Not assessed (below the min risk level).
Water
Las Vegas, NV
NPDES: NV0022888
63,746 unknown sites
Surface
NPDES: All POTW
Water or
Not assessed (below the min risk level).
SIC ·
POTW
a. Facilities activelv releasine.trichloroethvlene were identified via DMR TRI and CDR databases for the 2016 renortim.?.
vear.
b. Release media are either direct (release from active facility directly to surface water) or indirect (transfer of wastewater from active facility to a receiving POTW
or non-POTW WWfP facility). A wastewatertreatment removal rate of81% is aoolied to all indirect releases, as well as direct releases from WWTPs.
c. If a valid NPDES of the direct or indirect releaser was not available in EFAST, the release was modeled using either a surrogate representative facility in EFAST
(based on location) or a representativee.eneric industrv sector. The name of the indirectreleaser is provided, as reported in TRI.
d. EFAST uses ether the "surface water" model for rivers and streams, or the "still water'' modeL for lakes, bays and oceans.
e. Modeling was conducted with the maximumdays of release per year exoected. For directreleasin.2facilities, a minimum of20 days was also modeled.
f. The daily release amount was calculated from the reoorted annual release amount divided by the number of release davs per year.
g. For releases dischar2ing to lakes bays estuaries, and oceans the acute scenario mixine:zone water concentrationwas reoorted in olace of the 7Q 10 SWC.
h. To detennine the PDM days of exceedance for still bodies of water, the release days provided by the EPA Engineers should become the days of exceedance only if
the predicted surface water concentrationexceeds the COC. Otherwise the days of exceedance can be assumed to be zero.

334

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335

Appendix F

336
337

338
339
340
341

342
343
344
345
346
347
348

349
350
351
352
353
354
355

356
357
358
359
360
361
362
363

F.1

BENC .HMARK DOSE ANALYSIS FOR (Selgrade and
Gilmour, 2010)

BMDS Wizard Output Report - Mortality

The benchmark dose (BMD) modeling of dichotomous data was conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version 1.11). All available dichotomous models
(Gamma, Logistic, Dichotomous-Hill,Logistic, Log-Logistic, Probit, Log-Probit, Weibull, Multistage,
and Quantal Linear) were fit to the incidence data for mortality due to introduced infection in mice
following inhalation exposure to TCE. BMR.sof 1%, 5%, and 10% extra risk were used in the BMD
modeling, per technical direction. Adequacy of model fit wasjudged based on the goodness-of-fitpvalue (p > 0.1 ), magnitude of scaled residuals, and visual inspection of the model fit.

r

All models except for the Probit and Logistic provided adequate overall fit to the data, based on the 1).
goodness-of-fit p-value (p > 0.1). Among the remaining models, the Quantal Linear, Multistage,
Weibull, Gamma and Log-Logisticmodels all showed poor fit at the 25 ppm data point, based on scaled
residuals ranging from> t .S to >
This was the data point closest to the BMD for the Quantal
Linear at BMR = 10% and for the rest of these models at BMR = 5%. Regardless of whether the models
with poor fit at 25 ppm are included or not, the BMDLs at BMR = 10% or 5% are sufficiently close
(within 3-fold), so that the model with the lowest AIC was selected; this is the Log-Probit. At BMR =
1%, however, the BMDLs are no longer within 3-fold; the results at this BMR show model-dependence.
This reflects the lack of information available for the models to use in the data for the low-dose region
of the dose-response curve (responses were similar in the control, 5, 10 and 25 ppm groups) and
signifies increased uncertainty in selecting an appropriate B:MDLat this BMR Excluding the models
with high scaled residuals at 25 ppm as less reliable leaves the Log-Probit and Dichotomous-Hill
models. BMDLs for these models are sufficiently close, so the model with the lower AIC, the LogProbit, was selected.

! I

F.1.1

I21.

BMDS Summary of Mortality- BMR 10%

Table_Apx F-1. Summary ofBMD Modeling Results for Mortality from Introduced Infection in
Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra
Risk
Model•

Goodness of fit

BMD10Prt

BMDLtOPct

(ppm)

(ppm)

p-value

AIC

Gamma

0.292

342.35

43.5

31.2

Dichotomous-Hill

0.563

340.91

44.7

36.2

Logistic

0.0074

351.35

66.2

57.6

LogLogistic

0.370

341.62

43.3

31.6

Probit

0.0211

348.55

61.l

53.3

LogProbit

0.582

338.72

46.6

39.6

Weibull

0.259

342.81

42.5

30.3

0.177

344.14

39.9

27.9

0.177

344.14

39.9

27.9

Multistage 2ob
00

Multistage 3
Multistage 4 od

Page 586 of 691

Basis for model selection

All models provided adequate
overall fit to the data except for
the Probit and Logistic models
(based on the xJ,goodness-of-fit
p-value). Although the Quantal
Linear model provided adequate
overall flt, the scaled residual
nearest the BMD was > 21,
indicating poor fit in that part of
the curve. With or without the
Quanta) Linear, the BMDLs are
sufficiently close (< 3 fold), so the
model with the lowest AIC was
selected (Log-Probit).

I

 INTERAGENCYDRAFT- DO NOT CITEOR QUOTE
Multistage500
Multistage6°f
Quantal-Linear

0.230

343.25

26.6

33.0

• Selected model in bold; scaled residualsfor selected model for doses 0, S, 10, 25, SO,l 00, and 200 ppm were 0.38, -0.0S, 0.18, -1.16, 1.08, 0.22, -1.02, respectively.
b The Multistage
2° model may appear equivalentto the Multistage3° model, howeverdifferencesexist in digits not displayed
in the table. This also applies to the Multistage4° model. This also appliesto the Multistage 5° model. This also applies to the
Multistage 6° model.
• The Multistage 3° modelmay appear equivalent to the Multistage2° model, howeverdifferences exist in digits not displayed
in the table.
d For the Multistage 4° model, the beta coefficientestimates were 0 (boundary of parametersspace). The models in this row
reduced to the Multistage 3° model.
• For the Multistage 5° model, the beta coefficientestimateswere O(boundaryof parametersspace). The models in this row
reduced to the Multistage 4° model.
r For the Multistage 6° model, the beta coefficientestimates were O(boundary of parameters
space). The models in this row
reduced to the Multistage 5° model.

364
t.ogPtobil: Mocftl. with SMR of 10% EX"C
r• Ftit k fOf th• BMC amt 0.95 t..OY..etCCnfi4,h¢e

uf

---

o.e
o.a

.

o.•

<
2

0 .3

-g
'G

l..imit f Of' IM B M OL

LogPTobit -

~

~

.:
0.%

T-r-

o. 1
0

1L~ 1
0

365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384

eo

100

%00

......
Figure_Apx F-1. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
Model for Mortality from Introduced Infection in Mice Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra Risk

Probit Model. (Version: 3.4; Date: 5/21/2017)
The form of the probability function is: P[response] =Background + (I-Background)*
CumNorm(lntercept+Slope*Log(Dose)),where CumNorm(.) is the cumulative normal distribution
function
Slope parameter is restricted as slope >= l
Benchmark Dose Computation.
BMR = 10% Extra risk
BMD = 46.6299
BMDL at the 95% confidence level = 39.5537

Parameter Estimates

Page 587 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Default Initial
Parameter Values

0.0281182

0.0338983

-5.1238E+o0

-5.2930E+OO

I

1

background
intercept
slope

385
386

Analvsis of Deviance Table
Test d.f.

p-value

2

4 .00401

5

0.55

1

86.5627

6

<.0001

# Param's

Full model

-165.36

7

Fitted model

-167.36

Reduced model

-208 .64

AIC: = 338.719

Goodness of Fit Table
Est. Prob.

Expected

Observed

Size

Scaled Resid

0

0.0281

3.318

4

118

0.38

5

0.0283

I.077

I

38

--0.08

10

0.0304

l.187

1

39

-0.18

25

0.0557

4 .346

2

78

-1.16

50

0.1377

15.979

20

116

1.08

100

0.32 16

25.088

26

78

0.22

200

0.5814

22.093

19

38

- 1.02

Dose

391
392
393

Deviance

Log(likelihood)

Model

387
388
389
390

-

Estimate

Variable

ChiA2= 3.78 d.f = 5 P-value = 0.5818

394

Page 588 of 691

 395
396
397
398

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
F.1.2
BMDS Summary of Mortality-BMR: 5%
__
_
Table_Apx F-2. Summary ofBMD Modeling Results for Mortality from Introduced Infection in
Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra
Risk
Model•

Goodness

orfit

B.MDI,g,ct
(ppm)

Basis for model selection

(ppm)

All models provided adequate
overall fit to the data except for
the Probit and Logistic models
(based on the i}. goodness-of-fit
p-value). However, The Quanta!
Linear, Multistage, Weibull,
Gammaand Log-Logisticmodels
all showedpoor fit at the 25 ppm
data point, based on scaled
residualsranging from > 1.s to
> 21 . Th.iswas the data point
closest to the BMD for all of these
modelsexcept the Quanta! Linear.
With or without these models, the
BMDLs are sufficientlyclose (< 3
fold), so the model with the
lowestAIC was selected (LogProbit).

BMDSPct

p-vatue

AIC

Gamma

0.292

342.35

26.2

15.7

Dichotomous-Hill

0.563

340.91

33.9

22.5

Logistic

0.0074

351.35

40.3

34.4

LogLogistic

0.370

341.62

26.8

17.0

Probit

0.0211

348.55

36.6

31.4

LogProbit

0.582

338.72

32.4

27.S

Weibull

0.259

342.81

24.5

14.9

Multistage 2°
Multistage 3ot.
Multistage 4oc
Multistage 5oc1
Multistage6°c

0.177

344.14

20.6

13.6

0.230

343.25

16.0

12.9

Quantal -Linear
I

I I

I

• Selectedmodel in bold; scaledresidualsfor selectedmodelfor doses0, 5, 10,25, SO, 100,and 200 ppm were 0.38, -0.08, 0.18, -1.16, 1.08,0.22, -1.02, respectively.
b For the Multistage3° model, the beta coefficientestimateswere O (boundaryof parametersspace). The models in this row
reducedto the Multistage 2° model.
c For the Multistage4° model, the beta coefficientestimateswere O(boundaryof parameters space). The models in this row
reduced to the Multistage 3° model.
d For the Multistage5° model, the beta coefficientestimateswere O(boundaryof parametersspace). The models in this row
reduced to the Multistage4° model.
e For the Multistage6° model, the beta coefficientestimateswere O(boundaryof parametersspace). The models in this row
reduced to the Multistage 5° model.
0 .1

~ogP ro t it -

--

0 .0

o.&

1

11

0 .4

..I

0.3

i

o.2

....

0. 1
___..f

0

.f ..:_ l

.i
SM

0

399
400

401
402
403

,0

,oo

11/10

zoo

....
..
Figure_Apx F-2. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
Model for Mortality from Introduced Infection in Mice Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra Risk
Probit Model (Version: 3.4; Date: 5/21/2017)

Page 589 of691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
404
405
406
407
408
409
410
411
412
413
414

The form of the probability function is: P[response] =Background+ (1-Background) *
CumNorm(lntercept+Slope*Log(Dose)),where CumNorm(.) is the cumulative normal distribution
function
Slope parameter is restricted as slope >= 1
Benchmark Dose Computation.
BMR = 5% Extra risk
BMD = 32.4253
BMDL at the 95% confidence level= 27.5047
Parameter Estimates
Variable

Estimate

DefaultInitial
ParamewrValues

0.0281182

0.0338983

-5.1238E+OO

-5.2930E+o0

1

1

background

intercept
slope

415
416

Analvsis of Deviance Table
Model

Log(likelihood

#Param's

Deviance

Test d.f.

p-value

)

417
418
419
420

Full model

-165.36

7

Fitted model

-167.36

2

4.00401

5

0.55

Reduced model

-208.64

l

86.5627

6

<.0001

Est. Prob.

Expected

Observed

Size

Scaled Resid

0

0.0281

3.318

4

118

0.38

5

0.0283

1.077

1

38

-0.08

10

0.0304

1.187

1

39

-0.18

25

0.0557

4.346

2

78

-1.16

50

0.1377

15.979

20

116

1.08

100

0.3216

25.088

26

78

0.22

200

0.5814

22.093

19

38

-1.02

AIC:

= 338.719

Goodness of Fit Table
Dose

421
422
423
424

Chi"2 = 3.78

d.f = 5

P-value

= 0.5818

Page 590 of 691

 425
426
427
428

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
F .1~
BMDS Summary of Mortality- BMR: 1%_
Table_Apx F-3. Summary of BMD Modeling Results for Mortality from Introduced Infection in
Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 1% Extra
Risk
Model•

Goodness of fit
[ p-value

AIC

BMD1Pct
(ppm)

BMDL1Pet

Basis for model selection

(ppm)

Gamma

0.292

342.35

8.52

3.22

Dichotomous-Hill

0.563

340.91

19.l

7.62

Logistic

0.0074

351.35

10.2

8.35

LogLogistic

0.370

341.62

9.29

4.17

Probit

0.0211

348.55

9.14

7.52

LogProbit

0.582

338.72

16.4

13.9

Weibull

0.259

342.81

7.05

2.93

Multistage 2ob

0.177

344.14

4.27

2.66

Multistage 3oc
Multistage 4oc1
Multistage soe
Multistage 6°f

0.177

344.14

4.27

2.66

Quantal-Linear

0.230

343.25

3. 14

2.53

All models provided adequate
overall fit to the data except for
the Probit and Logistic models
(based on the x2 goodness-of-fit
p-value). However, The Quantal
Linear, Multistage, Weibull,
Gamma and Log-Logistic models
all showed poor fit at the25 ppm
data point, based on scaled
residuals ranging from> l.5 j to
> 2 If all models are
included, the BMDLs are not
sufficiently close(> 3-fold). For
this reason,the BMDS Wizard
recommended selection of the
Quantal Linear model, which had
the lowest BMDL. The > 3-fold
range ofBMDLs is indicative of
model dependence and signifies
. increased uncertainty in selecting
• an appropriate BMDL at this
BMR. Excluding the models with
high scaled re_siduals at 25 ppm as
less reliable leaves the Log-Probit
and Dichotomous-Hillmodels.
BMDLs for these models are
sufficiently close, so the model
with the lower AIC, the LogProbit, was selected.

I I.

I

• Selectedmodel in bold; scaled residualsfor selectedmodelfor doses 0, S, 10, 25, SO,100,and 200 ppm were 0.38, -0.08, 0.18, -1.16, 1.08, 0.22,-1.02, respectively.
1>The Multistage2° model may appear equivalent
to the Multistage3° model, howeverdifferences existin digitsnot displayed
in the table.This also appliesto the Multistage4° model. Thisalso appliesto the Multistage 5° model.This also appliesto the

Multistage6° model.
"The Multistage3° model may appear equivalent to the Multistage2° model, howeverdifferences exist in digits not displayed
in the table.

the Multistage4° model, the beta coefficientestimateswere O(boundary of parametersspace).The modelsin this row
reducedto the Multistage3° model.
e For the Multistage5° model, the beta coefficientestimateswereO(boundaryof parametersspace). The models in this row
reducedto the Multistage4° model.
r For the Multistage6° model, the beta coefficientestimates were O(boundaryof parametersspace). The models in this row
reduced to the Multistage5° model.
d For

429

Page 591 of691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
07

Loo,?robtt

-

0.0

..!
~

'..!
i

00

0♦

03

02

0 I

0

440
441

200

Figure _Apx F-3. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
Model for Mortality from Introduced Infection in Mice Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 1% Extra Risk

Probit Model. (Version: 3.4; Date : 5/21/2017)
The form of the probability function is: P[response] =Background+ (I-Background)*
CumNonn(Intercept+Slope*Log(Dose)) ,where CumNorm( .) is the cwnulative normal distribution
function
Slope parameter is restricted as slope >= 1
Benchmark Dose Computation.

444

BMR = 1% Extra risk

447

,ec

dCHIO

442
443
445
446

,oo

10

0

430
431
432
433
434
435
436
437
438
439

BMD = 16.4027
BMDL at the 95% confidence level= 13.9135
Parameter Estimates
Variable

Estimate

Default Initial
ParameterValues

0.0281182

0.0338983

-5.1238E+OO

-5.2930E+OO

I

1

background
intercept

slope

448
449

Analys is of Deviance Table
Model

450
451
452

Deviance

Test d.f.

p-value

2

4.00401

s

0.55

1

86.5627

6

<.0001

Log(likelibood
)

# Param's

Full model

-165.36

7

Fitted model

-167 .36

Reducedmodel

-208.64

AIC:

= 338.719
Page 592 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
453

Goodness of Fit Table
Est. Prob.

Expected

Observed

Size

Scaled Resid

0

0.0281

3.318

4

118

0.38

5

0.0283

1.077

1

38

-0 .08

10

0.0304

1.187

1

39

..().18

25

0.0557

4.346

2

78

-1.16

50

0.1377

15.979

20

116

1.08

100

0.3216

25.088

26

78

0.22

200

0.5814

22.093

19

38

-1.02

Dose

454
455
456
457

Chi"2

= 3.78

d.f= 5 P-value = 0.5818

Page 593 of691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
458
459
460
461
462
463

464

F.2

BMDS Wizard Output Report- Number of Mice Infected

The benchmark dose (BMD) modeling of dichotomous data was conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version LI 1). All available dichotomous models
(Gamma, Logistic, Dichotomous-Hill, Logistic, Log-Logistic, Probit, Log-Probit, Weibull, Multistage,
and Quantal Linear) were fit to the incidence data for mortality due to introduced infection in mice
following inhalation exposure to TCE. BMRs of 1%, 5%, and 100/oextra risk were used in the BMD
modeling, per technical direction. Adequacy of model fit was judged based on the goodness-of-fit pvalue (p > 0.1 ), magnitude of scaled residuals, and visual inspection of the model fit.

r

465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481

All models except for the Probit and Logistic provided adequate overall fit to the data, based on the xJ.
goodness-of-fit p-value (p > 0.1). Among the remaining models, the Quanta! Linear, Multistage,
Weibull, Gamma and Log-Logistic models all showed poor fit at the 25 ppm data point, based on scaled
This was the data point closest to the BMD for the Quantal
residuals ranging from> j I.5 j to> j 2
Linear at BMR = 10% and for the rest of these models at BMR = 5%. Regardless of whether the models
with poor fit at 25 ppm are included or not, the BMDLs at BMR = 100/oor 5% are sufficiently close
(within 3-fold), so that the model with the lowest AIC was selected; this is the Log-Probit. At BMR =
1%, however, the BMDLs are no longer within 3-fold; the results at this BMR show model-dependence.
This reflects the lack of information available for the models to use in the data for the low-dose region
of the dose-response curve (responses were similar in the control, 5, 10 and 25 ppm groups) and
signifies increased uncertainty in selecting an appropriate BMDL at this BMR. Excluding the models
with high scaled residuals at 25 ppm as less reliable leaves the Log-Probit and Dichotomous-Hill
models. BMDLs for these models are sufficiently close , so the model with the lower AIC, the LogProbit, was selected.

482
483
484
485

F.2.1
BMD~Summary of Infected at 72 hours - BMR-10%
Table_Apx F-4. Summary of BMD Modeling Results for Number of Mice Infected at 72 Hours
after Infection Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10%
Extra Risk

I.

Modef&

Gamma
· Dichotomous-Hill

Goodness of fit

BMD10Pct

BMDL10Pct

(ppm)

(ppm)

p-value

AIC

0.190

23.637

9-;R

~

23.965

~

~

0.164
~

Logistic

0.428

21.584

~

&¾

LogLogistic

0.164

23.965

,R.:-1

+:--8

Probit

0.448

21.445

¼➔.-+

9:-1-1-

LogProbit

0.383

21.877

-1-➔.6

6.86

Weibull

0.189

23.606

~

~

Multistage 2°

0202

23.480

H-,6

~

Multistage 3°

0.228

23.267

~

4.43

Quantal-Linear

0.425

21.639

~

~

a Selected model in bold; scaled residualsfor selectedmodel for doses 0, 50,

Basis for model selection

All models provided adequate fit
to the data (based on the "fl,
goodness-of-fitp-value). although
a BMDL could not be calculated
for the Dichotomous-Hillmodel.
The BMDS Wizard recommended
the Probit model because it had
the lowest AlC. BMDs and
BMDLs from all models are well
below the lowest data point and
cannot be considered reliable.

100, and 200 ppm were -0.23, 0.86, -0.82, 0.38,

respectively.
b BMD or BMDL computation failed for this model.

Page 594 of 691

 INTERAGENCYDR.<\FT- DO NOT CI IE OR QUOTE
486

i---------

0.8

.

...

:

0.0

C

i..

0 ,4

0.2

0

1
BMD
0

487
488
489
490
491
492

0
IIO

,oo

,eo

200

-.
Table_Apx F-5. Plot of Incidence by Dose (ppm) with Fitted Curve for Probit Model
for Number of Mice Infected at 72 Hours after Infection Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra Risk

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493

Appendix G

494
495
496

497
498
499

500
501
502
503
504
505
506
507
508

G.1

511

512
513
514

515
516
517
518
519
520

521
522
523
524

525
526

EPA Review of the Charles River (2019) Study

G.1.!_ Study Methodologyand Resu_!ts
In a study sponsored by the Halogenated Solvents Industry Alliance {HSIA), Charles River Laboratories
Ashland, LLC performed "An Oral (Drinking Water) Study of the Effects ofTrichloroethylene (TCE)
on Fetal Heart Development in Sprague Dawley Rats". The study was based on general accordance with
OPPTS 870.3700 and OECD Test Guideline 414 with the stated purpose of replicating the findings of
(Dawson et al.. 1993) and (Johnson et al.. 2003), which observed increased cardiac malformations in the
fetuses of pregnant female Sprague Dawley rats administered TCE in drinking water.

The study utili7.ed6 test groups, including negative and positive controls. Retinoic acid (RA) served as a
positive control and was administered daily via gavage. TCE was administered via drinking water. See
details in Table_Apx G-1, which is adapted from Text Table 4 in the study.

.

.
Ta ble A,PX G-1 E xoenmen tal Desum
Group

Treatment

Target
Concentration

Route of
Administration

Number of
Females (Dams)

1

Vehicle (water)

0ppm

Drinking Water

25

2

Retinoic Acid

3 mg/ml

Gavage

25

3

TCE

0.25 ppm

Drinking Water

25

4

TCE

1.5ppm

Drinking Water

25

5

TCE
TCE

500ppm

Drinking Water

25

1000 ppm

Drinking Water

25

6

509
510

WEIGHT OF EVIDENCEFOR CONGENITAL HEART
DEFECTS

In order to reduce TCE loss due to evaporation, drinking water formulations were prepared at volumes
large enough to minimize headspace and a connected nitrogen source was used to backfill headspace
during dosing. Despite this effort, 24-hour loss monitoring indicated that 30% to 49% of average
measured TCE concentration was lost over the course of a day.
Interventricular septal defects (VSDs) were the only cardiac malformation observed in TCE-treated
groups. Additional types of defects were observed in the positive control RA-treated group, including
malfonnations of the aorta and arteries, small ventricle, and situs inversus (transposition of the heart and
great/major vessels). Situs inversus was also observed in a single vehicle control fetus. The study
authors did not observe a statistically significant increase in VSDs among TCE-treated fetuses compared
to vehicle. Additionally, all VSDs observed in TCE-exposed fetuses were smaller than 1mm, in contrast
with vehicle and RA-treated groups. Results are shown in Table_Apx G-2 below, which is adapted from
Text Table 14 in the study, with a few small edits. The Charles River study described the statistical
estimate used as "summation per group(%)", which appears to be the sum of viable fetuses affected per
litter(%)/ number of litters per group". EPA determined that while this method is appropriate, the
description is unclear and would be better described as ''Mean% Affected/ Litter per Group". EPA
therefore replaced the descriptor "% per litter" with the above descriptor. BPA also identified that the
Page 596 of 691

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RA-treated group actuallyhad 41.2% affected, as opposedto 42.2% as was presented in Text Table 14
of the study.

527

528
529

.

530

'II'a ble A,PX G-2 Summaryo fOb serve di nterventricu
'I ar Dfits
e ec
Oppm 15 mg/kg-day 0.25ppm
1.5ppm
·Dosage:
(Vehicle)
RA
TCE
TCE

#Affected
Fetuses (Litters)

7 (5)

Mean % Affected
/ Litter per Group

2.4%

Size of Opening
(Number of
Fetuses)

112 (23)
41.2%
(p<0.01)
< lmm (103)
lmm(8)

<lmm(6)
Imm (1)

500ppm
TCE

1000ppm
TCE

4(4)

5 (3)

13 (8)

12(6)

1.4%

1.5%

3.8%

3.7%

<lrmn (All)

<Imm (All)

< Imm (All)

<Imm (All)

>2mm (1)

Membranous
Membranous

Defect Location

Membranous Membranous Membranous Membranous

(111);

Muscular( 1)

531
532
533
534
535
536

VSDs were not statisticallysignificantlyincreased in TCE-treatedgroups compared to vehicle control,
while RA treatment resulted in a substantiallyincreased incidenceof cardiac defects. The authors
additionallyhighlightedthe fact that all identifiedVSDs in TCE-treatedgroups were smaller than 1mm.
The study claims that these would be expectedto resolve postnatallyand are therefore unlikely to be
adverse.

537

G.1.2

EPA Review

538
539
540

Comparing Results Between Charles River and Johnson Studies
The Charles River study calculated observed defects differentlythan was done for the Dawson and
Johnson studies. The calculation for mean % affected / litter per group results in different values than the

541
542

''% fetuses affected" and ''% litters affected" metrics used in the Dawson and Johnson studies, which
simply divided the amount of affected fetuses or litters by the total (multiplied by 100 to create a
percentage). For comparison, Table_Apx G-3 below presentsthe data from both the Johnson and
Charles River studies calculated as the % fetuses and % litters affected.

G.1.2.1

543
544
545

Table A•PX G-3 • Inc1.dence ofttlh
oa

546

Dose
Oppm

% fetuses
affected
13/606
(22%)

. Joh nsonan d Ch ares
I Riv ers tud·ies.
eart ma If◄orma ti onsm
Charles River 2019
Johnson 2003
% litters

% fetuses

% Utters

affected

Source

affected

affected

9155

(Johnson et
al., 2003)

8/308
(2.5%)

(25.0%)

(16.4%)

6/24

Source/Notes
(Charles River
Laboratories. 2019),
Table 15

(soft tissue), p. 86

I

2.Sppb

0.25:ppm

0/44(0.0%)

5/110
(4.5%)

0/12

(0.00/o)
419(44 .4)

(Johnson et
al., 2003)

NIA

NIA

NIA

(Johnson et
al., 2003)

4/275
(1.4%)

4/22
(18.2%)

(Charles River
Laboratories, 2019),
Table 15
(soft tissue), o. 86

Page 597 of691

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1.Spp.m

9/181
(5 .0%)

500 ppm

1000 (Charles River) or
1100 (Johnson) ppm

547
548
549
550

551
552
553
554

555

556
557
558

5/13
(38.5%)

(Johnson et
al.. 2003 )

5/321
(1.5%)

3/24
(12.5%)

NIA

NIA

NIA

13/330
(3.9%)

8/24
(33.3%)

11/105
(10.5%)

619
(66.7%)

(Johnson et
al.,2003 )

12/342
(3 .5%)

6/24
(25 .0%)

(Charles River
Laboratories, 2019 ),
Table 15
(soft tissue ) , p. 86
(Charles River
Laboratories. 20 l9),
Table 15
(soft tissue) , 'P, 86
( Charles River
Laboratories . 2019),
Table 15
(soft tissue). o. 86

The Johnson study clearly shows greater incidences of cardiac defects at 0.25 ppm, 1.5 ppm, and 1100
ppm compared to the same or similar doses (1000 ppm in Charles River). Of note however, VSDs, and
specifically only membranous VSDs, were the only type of heart malformation identified by the Charles
River study in TCE-treated fetuses. In contrast,the Johnson study identified a broad variety of defects in
exposed fetuses. The Johnson study observed VSDs at only a slightly greater incidence per fetus than by
Charles River at higher doses, while (peri)membranous VSDs were observed at a similar or lower
incidence than by Charles River. Additionally, Charles River observed substantially higher incidences of
VSDs in the control and 0.25 ppm groups. The data comparing the incidence ofVSDs only is presented
in Table_Apx G-4. with the incidence of membranous VSDs displayed in parentheses.

.

T a ble A,PX G-4 In c1.d ence ofVSD sm. J 0 h nson and Ch ares
I Ri ver stu 1es.

Dose

Johnson 2003
% fetuses
affected
(mem. only)
Source

Charles River 2019

%
fetuses
affected

Source/Notes

Oppm

0.66%
(0.33%)

Johnson et al., 2003 ),
Table2

2.5%

(Charles River
Laboratories, 20 19),
Table 15
(soft tissue). p. 86

2.Sppb

0%

Johnson et al. 2003 )
Table2

NIA

NIA

0.25ppm

0%

Johnson et al., 2003 ),
Table2

1.4%

(Charles River
Laboratorie~, 2019 ),
Table 15
(soft tissue). n. 86

1.S ppm

2.21%
(1.66%)

Johnson et al. 2003 ),
Table 2

1.5%

500ppm

NIA

NIA

3.9%

3.81%
(2 .86%)

{Johnson et !!l.,2003 ),
Table2

3.5%

1000 (Charles River) or
1100 (Johnson) ppm

559
Page 598 of 691

(Charles River
Laboratories, 2019 ),
Table 15
(soft tissue), p. 86
(Charles Rivslr
Laboratories, 2Ql2 ),
Table 15
(soft tissue) , P . 86
(Charle~ River
Laboratori~s. 20 I9),
Table 15
(soft tissue), p. 86

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE

560

G.1.2.2

Differences in Types of Malformations Obsenred

561 The majority of cardiac malformations observed in the Johnson study were not VSDs (see Table 2 in
562 (Johnson et al., 2003). while the Charles River study only identified VSDs in controls and TCE-treated
563 offspring. Of note, two major categories of heart malformations identified in the Johnson study that are
564 absent from even the positive control group of the Charles River study are atrial septal defects and valve
565 defects. The Charles River study methodology appeared to be focused primarily on identification ofVSDs
566 over other heart defects, which may explain the observed positive bias toward detection ofVSDs in
567 vehicle control and low-dose fetuses as compared to both the Johnson study and historical control data.
568 Table_Apx G-5 compares the heart defects observed across all in vivo oral studies. Fisher at aJ. (2001), a
569 gavage study that also did not find a statistically significant association ofTCE exposure with congenital
570 cardiac defects, is also included for comparison. Of note, the (Fisher et al., 200 1) study utilized the same
571 dissection and evaluation methodology as the (Johnson et al .• 2003) studies. There is substantial overlap in
572 the many type of defects identified in the three studies, while only membranous VSDs were observed in
573 TCE-treated animals in (Charles River Laboratories. 2019) (great blood vessel variation was identified in a
574 few TCE-treated pups but was considered incidentalby the study authors).
575
. 0 ra I TCE stud·1es
Tabl e A.px G 5. Hea rt and Car d.10vascula
. r D efiec ts Ob senred m
576

-

Cardiac Malformations Observed Across Select Oral TCE and Retinoic Acid (RA) Developmental Toxicity Studies in Rats

Tricllloroetllyleae(l'CE)
Johnson et aL (2003)"'

Charles River (2019)

Retinoic Acid (RA)
Fisher et al. (2001)

Charles River (2019)

Fisher et al. (2001)

Septa! defects
Ventricular septal defect
(VSD) (perimembranous,
subaortic, muscular)
Atrial septal defect (ASD)

Ventricular septal
defect (VSD)
(membranous,
subaortic, muscular)

Ventricular septal
defect (VSD)
(membranous)
Atrial septal defect (ASD)

Ventricular septal defect
(VSD) (membranous,
aortic, muscular)
Atrial septal defect (ASD)

Valve defects
Mitral valve defect

Mitral valve defect

Mitra] valve defect

Tricuspid valve defect

Tricuspld valve defect

Tricuspid valve defect

Pulmonaryvalve defect

Pulmonary valve defect
Aortic stenosis

Aortic valve defects

Aortic stenosis

(multiple)

Atrium,ventricle, and m~eUaneous structuralabnormalities

Atrioventricularseptal
defect (endocardial
cushion defects)

Endocardialcushion
defects
Right ventricle enlarged

Right ventricle enlarged

Left ventricleaneurysm
dissecting

Heart ventricle, small

Left atrial hypertrophy
Cleft, apex of heart

Great vessel structuralabnormalities

Page 599 of 691

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Cardiac Malformations Observed Across Select Oral TCE and Retinoic Acid (RA) Developmental Toxicity Studies in Rats

Transposition of the
great vessels

Transposition of the
great vessels

Aortic arch effects

Aortic arch effects

Major blood vessel
variation

Major blood ,·essel
variation

Pulmonaryartery
hypoplasia

Pulmonaryartery
bypoplasia

Aortic hypoplasia
Innominate artery short

Coronary artery/sinus

Innominate artery effect

Stenotic carotid

Truncus dilated

Positional abnormalities of the heart and great vessels
Situs inversus

Situs invenus

Abnormal looping

Dextrocardia
Overriding aorta

• Includes data from Dawson et al. (1993).
Bold text indicates defects observed across multiple studies (both TCE and RA treatment).
Red bold text indicates defects only observed with RA treatment across multiple studies.

577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603

EPA's conclusion that the Charles River study insufficiently sensitive tonon-VSD defects was supported
by the limited variety of malformations observed in the RA positive control based on a compiled literature
search:
1. EPA searched HERO and PubMed for studies investigating heart defects and malformations that
occur during prenatal exposure to all-trans retinoic acid (RA). Of the 37 studies reviewed, 12
studies were excluded from analysis because they were abstracts, book chapters, reviews, or
studies that did not expose animals to all-trans RA. Thus, EPA reviewed 25 studies and
compared the results of these studies to those reported by the Charles River and Johnson studies.
2. In all species examined, a total of 35 heart defects were associated with prenatal exposure to RA
in the cardiac toxicity literature.
3. The Charles River study reported 10 types of heart defects in animals exposed to RA.
4. Heart defects associated with TCE exposure partially overlap defects associated with RA
exposure. The Johnson study identified 10 types of cardiac defects in TCE-exposed fetuses .
Charles River only identified one defect (membranous VSDs) associated with TCE exposure
(major blood vessel variation was observed in 1-2 TCE-treated fetuses, but this effect was not
considered treatment-related).
5. All 35 defects associated with RA exposure were observed in rodents in the literature review . If
we limit the analysis to studies examining only rats, 31 of the total 35 defects were observed.
Only 6 of the 35 defects were noted in chickens, and 2 of the 35 were noted in zebrafish.
Therefore, the differences between defects captured in the Charles River study and the general
literature cannot be explained simply by inclusion of additional experimental species in the
general literature.
EPA therefore concludes that Charles River did not capture the entirety of cardiac defects that were
expected upon exposure to RA.
Page 600 of 691

 604
605
606
607
608
609
610
611

612

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
EPA searched HERO using the followingkeywords:
• Retinoic Acid
• Retinoic Acid + Cardiac
EPA also searched PubMed using the following keywords:
• retinoic acid (RA)-inducedcardiac defects
• retinoic acid AND (cardiac defects OR cardiac malfonnationsOR heart defects OR heart
malfonnations OR cardiac teratogenesis OR aorta OR ventricle OR endocardial cushion OR
pulmonary valve OR mitral valve OR aortic valve OR ventricularseptum OR atrial septum OR
tricuspid valve OR aneurysm). .

613

614
615
616
617

618
619

620
621

Table_ Apx G-6 presents all of the cardiac defects found in the literature search.

Table_ Apx G-7 comparesthe types of defects observed across the Johnson and Charles _River studies
with those identified in the literaturesearch. Several defects associatedwith TCE exposure as well as
several RA-induced defects in the Charles River study were not associatedwith RA exposure in the
literature. Overall, the spectrum of heart defects observed upon RA exposure in the literature largely, but
not entirely, overlaps with heart defects associatedwith TCE exposure. Of note, atrial septal defects,
which were the most common type of malformation identifiedin the Johnson study, were identified in 5
other RA studies but not in the Charles River study.

622
623

-

Tabl e A,J)X G 6. C ard'1ac Detiects Observed ID
· L't1 era ture
Cardiac Defect
VSD
ASD
Tetraloe:v Fallot
Hvooplastic Left Heart Svndrome
Tricusoid Atresia
Aortic Valve Stenosis
Pulmomuv Trunk Stenosis
Right Ventricular Hvoertroohv
Left Ventricular Hypertrophy
Right Atria] Hvoertroohv
Left Atrial Hypertrophy
CAVC
Situs Inversus
Dextrocardia
d Transposition
I Transoosition
Cleft Apex
CoA

ARSA
IAA
Left CirCUlltfl.exAorta
Right aortic arch defect (RAA)
Double Aortic Arch
Cervical Aortic Arch
Hvooolastic Aortic Arch
Truncus Arteriosus
PDA
Innominate Artery Absent
Innominate Artery Sh.ort

Page 601 of 691

Numberof
Studies
12
5
1
1
1
1
3

2
1
2
1
1

2
5
12
1
1

1
2
1
1
4

1
1

1
7
l

1
1

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Ri~ht Carotid Off Aorta
Ri2.ht SUbclavian Arte rv Absent

1

1

D ORY
Endocarclial Cushion Defect
Abnonn al Heart Looping
Other*

624
625

10
3
7

14

Tab le A x G-7. Cardiac Defects Observed After Ex

Malformation

Class

Malformation

Name

C harle s
River

2019
Atrium , Ven tric le and
Defects
Atrium , Ventricle and
Defects
Atrium, Ventricle and
Defects
Atrium, Ventricle and
Defects
Atrium, Ventricle and
Defects
Atrium , Ventricle and
Defects
Atrium , Ventricle and
Defects
Atrium , Ventricle and
Defects
Atrium, Ventricle a nd
Defects
Atrium, Ventricle and
Defects
Atrium , Ventricle and
Defects
Atrium, Ventricle and
Defects
Atrium, Ventricle and
Defects
Atrium, Ventricle and
Defects

s

Valve

✓

VSDs2

Valve
Valve

Atrial Se ta l Defect
Double outlet ventricle
ORV

Valve

✓

2019
✓

Other
Literature
(No.
Studies
✓ 12

Other
Literature
Species 1
C, H.M,

R

✓

R
✓ 10

Tetralo of Fallot
Hypop lastic Left Heart
S ndrome

Valve

C,H. M,

R

✓

M

✓

R

Tricus id defects

✓

✓ 1

H

Aortic valve defects

✓3

✓

R

Mitra! valve defects

✓

Ri t ventricular h

✓ 2

R

Left ventriclu lar h

✓ 1

R

✓ 2

R

✓ 1

R

✓ 1

R

✓ 2

CR

✓ 5

M R
C, H, M,
R

Valve
Valve
Valv e
Valve
Valve
Valve

Valve
Valve

Small ventricle
Compl ete Atri oventricular
Can al defect CA VC

✓
✓

✓

Situs Inve rsus

Aort ic Arch Defects

Cleft a x of heart
Coarctation of the Aorta
CoA
Left aorti c arch with aberrant
right subclav ian artery
ARS A

Aortic Arch Defects

left circumflex aorta

Aorti c Arch Defects

2003

Charles
River

Valve

Dextroc ardia
d-Tran spositi on of the great
arterie s
I-Transpositi on of the Great
Arteries

s

Johnson

Page 602 of 691

✓

✓ 12

✓

R

✓ 1

R

✓

✓

R

✓4

✓ 2

R

✓ 1

M

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
TCE

Chemical:

Malformation Class

MaHormatiooName

(RAA )

Other vessel defec1s
Other vessel defec1s:incomplete
postnatal development
Other vessel defects
Other vessel defects
Other ves.1eldefects
Other vessel defects
Other vessel defects
Other ves.seldefects
Other vessel defects
Other early developmental
defect
Other early developmental
defect

RA

RA

H.A

Other
Charles
Charles
Other
Johnson
Literature
River
River
Literature
2003
(No.
2019
2019
Species1
Studies)

Right aortic arch defects
Aortic A,rcbDefects
Aortic Arch Defects
Aortic Arch Defects
Aortic Arch Defects
Aortic Arch Defects
Aortic Arch Defects
Other vessel defects

TCE

✓

✓ (4 )

Double aortic arch

✓ <n

Cervical aortic arch

✓ (1)

lntenu otion of the aortic arch
H vuoplastic aortic arch

✓

Stenotic aortic arch
Pu.lmonarv trunk stenosis
Truncus Arteriosus (dilated
truncus)

✓

H.M,R

✓ o)

R
R
M
R

✓ (3 )

HR

✓ ( 7)

HMR

✓ en

Patent Ductus Arteriosus

✓ <n

Innominate artery absent

✓ <n

R
R

Innominate arterv short

✓ (I)

R

Ri2:ht carotid off aorta

✓ ( 1)

R

✓ en

R

✓ (3)

M.R

✓ ( 7)

C H,RZ
C,H, M,

✓

Stenotic carotid .

Rieht subclavian arterv absent
Pulmonary arterv hvnoolasia
Coronary artery/sinus defects

✓
✓

Endocardial cushion defects
Abnormal heart loooing

✓

..J7

Otller5

✓ (14 )

RZ

1 Chicken (C),

Hamster, (H), Mouse(M), Rat (R), Zebrafish (Z).
2 Most studiesrevieweddid not specifyamongperimembranous,muscularor subarterialVSDs, so these were included all as "VSDs"for the
literaturereview comparison.
3 Aonic valve defects includedaortic valve defect with fenestratedleafletsand aortic valve stenosis describedas aortic valve defect with fused
leafletscreatingaortic valvularstenosis.
4 Chicken (C), Hamster,{H), Mouse (M), Rat (R), Zebrafish(Z).
4
Retroesopbagealaortic arch describedin Charles River study was tagged as ARSA defect.
s Major blood vessel variation (right carotid and subclavianarteriesarose independentlyfrom the aortic arch [no brachiocephalictrunk) or right
subclavianartery coursed retroesophagealandjoined the aortic arch adjacentto ductus arteriosus[no brachioce-phalic
trunk]) tagged to RAA
defects.
as "other".
s If EPA wasunsure of the general malfonnationclass. the defect was cat.cgor.ized
6 .0ther" defect in HSIA study (RA exposure groups)was a major blood vessel variation(an elongatedbrachiocephalictrunk or a missing
brachiocephalictrunk due to right carotid and right subclavianarising independentlyfromthe aortic arch, or due to a retroesophageaJright
subclavian;or {right carotid and subdavian arteriesarose independentlyfrom the aortic arch [no brachiocepbalictrunk) or right subclavian
artery coursed retroesophageal and joined the aortic arch adjacentto ductus arteriosus{nobrachiocephalictrunk]).

626
627
628
629
630
631

G.1.2.3

MethodologyDifferences

There are likely several contributing factors explaining why the Charles River study failed to identify
atrial or valve defects. In the Johnson study, the materials and methods section described examination of
the internal structure of the heart for all fetuses. The dissection methodology allows detailed
examination of the atrial septum. In contrast, the Charles River study states that the fetal evaluation
methods were conducted according to Stuckhardt and Poppe (1984), which does not include

Page 603 of 691

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632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654

655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678

examination of atrial septal defects. Therefore, the methodology used by the Charles River study was
likely to miss this important category of cardiac malformations. As shown in
Table_Apx G-7, five other studies were identified in the literature that observed atrial septal defects
following RA exposure, while none were observed in the Charles River study.
The Stuckhardt and Poppe method (1984) does includes visualization of the valves (the tricuspid, mitral,
aortic, and pulmonary valves) but the methods as described in the Johnson study and supporting
information are more likely to reveal valvular defects as compared to the Stuckhardt and Poppe
methodology. The Stuckhardt and Poppe method specifies that two cuts are made in the fresh fetal heart.
This allows visualization of the tricuspid valve, between the right atrium and right ventricle, the three
cusps of the semilunar valve of the pulmonary artery, and the interventricular septum. In comparison,
the Johnson study clearly specified that the fetal hearts were to be examined in situ for external defects
and then excised, preserved with glutaraldehyde, and dissected. The examination of the internal structure
of the heart for all fetuses specifically included removing tissue to expose the pulmonary, aortic,
tricuspid, and mitral valves . The location of the coronary ostium was noted, each valve was probed for
patency, and the formation of each valve leaflet was examined.
EPA believes that there is a certain amount of tissue elasticity in fresh fetal hearts that can obscure the
detection of valvular defects during fetal morphological evaluation. Because the Johnson study evaluated
the internal structure of the fetal hearts post-fixation, examination of the valvular structures would have
been facilitated. Additionally, valve defects may be overlooked during examination unless the technician
is directly focusing on evaluating the cardiac valves in all fetuses (not just those, for example, in which
external cardiac morphological differences, such as a collapsed ventricle, might suggest a potential valve
problem). No indication is given in the Charles River report whether a directed effort was made to
identify valvular abnormalities .
Other identified differences and uncertainties in the methodology between the two studies may or may
not have contributed to the differences in results. These factors could potentially make either the Johnson
or the Charles River data more precise . These include the following:
I. Variations in TCE loss over time. While the Charles River study made extensive efforts to
minimize TCE loss, the 24-hour loss monitoring indicated that average loss across all
measurements was actually greater than that in the Johnson study (42% vs 35%). The Johnson
study did not provide analytical measurements for close comparison, but it is possible that on
average the delivered dose was greater in the Johnson study.
2. Possible differences in criteria for fetuses selected for examination. In the Johnson study, it is not
explicitly stated whether all or only viable fetuses were examined. The Charles River study
indicates that only viable fetuses were examined. For the Charles River study, this is a moot
point as there were no dead fetuses in the entire study. However, this aspect of study design is
not documented in the Dawson or Johnson studies.
3. Randomization methods. Differences in incidences at the litter level could potentially result from
non-randomized groups of animals at different dose levels. Different randomization strategies
were used in Johnson 2003 compared to the HSIA study. Dam assignments to exposure groups
was randomized in Johnson 2003, whereas the HSIA study used stratified randomization. Details
of the stratified randomization strategy were not presented, except to indicate that the goal was to
achieve similar group mean body weights. Given that there were six treatment groups and many
racks have six cages per row, it raises the possibility that treatment group was confounded with
cage position, i.e., Group l in one column, Group 2 in the next column, etc. The Dawson and

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Johnson methods of randomizationdid not include considerationof, or stratification by, body
weight.
4. Husbandry differences.the Charles River study individuallyhoused the pregnant females,
whereas the Dawson and Johnson studies gr9up-housedthe females, so several dams were
conswning treated drinking water from the same bottle. Thus, there would be greater precision in
the Charles River dose calculations.
5. Source and strain of rats. The rats used in all the studies conducted as part of the TCE research
program at the University of Arizona that included (Dawson et al.. 1993) and (Johnson et al..
2003) were Harlan Sprague-Dawleyrats purchased from Harlan Laboratories Inc., Indianapolis,
IN. The Charles River rats were Crl:CD(SD) Sprague-Dawleyrats from Charles River
Laboratories in Raleigh, NC. It is unknown what influence the source or strain differences might
have bad on the response to treatment with TCE. Additional information from both groups of
researchers would be needed to ascertain whether the source, sub strain or genetic drift of the test
animals influenced the incidencesof cardiac malformations.
6. Technical con:finnationof diagnosis.The Charles River report did not specify whether cardiac
abnormalities were confirmedby other technical staff or the Study Director. There is no
opportunity to re-examine fetuses because the report states that all carcasses were discarded
following completion of the internal examinationof the fetuses. In comparison, the three
principle authors of the Dawson and Johnson studies (P. Johnson, S. Goldberg, and B. Dawson),
each examined every identified fetal cardiac anomaly, and they only included findings for which
there was unanimous agreement on diagnosis (as described in (Makris et al.. 2016)). Therefore,
there is high confidence in the determinationof observed defects in the Dawson and Johnson
studies. Of note, neither study was designed to confirm diagnoses of normal fetal morphology.
G.1.2.4 Adversity of Small VSDs
In addition to the lack of a statistically significantincrease in cardiac defects, the Charles River study
claims that the <lmm VSDs induced by TCE are non-adverse because '' ..... similar to humans, small
spontaneous interventricular septal defects in rats close postnatally and hence should not be considered
adverse. Based on these data, the interventricularseptal defects observed in the TCE-treated groups were
considered to be spontaneous backgroundoccurrences and unrelated to TCE exposure." This claim is
confounding and internally inconsistent however,becausethe vast majority (92%) of VSDs observed in
the RA-treated positive control group were also <Imm. IfVSDs <Imm are truly non-adverse, then this
positive control data provides additional indication that the study is insufficientlysensitive for detecting
adverse cardiac defects.

The Charles River study cites (Fleeman et al.. 2004), which based on results of trimethadione exposure
concluded:" ... some treatment-induced membranous VSD will close during postnatal development
similar to spontaneously occurring membranousVSD." The authors then state that "small, isolated VSD
do not seem to impact postnatal viability and growth; however, large VSD are likely to affect postnatal
survival." Importantly, the presence of a VSD was associated with reduced survival, so observing
reduced incidence of VSDs postnatally may be selecting for those pups that were less adversely affected.
Nonetheless, the data does demonstrate that some, but not all, VSDs are compatible with postnatal life.
However, as there is no information provided in this paper to characterizethe size range ofVSD in those
pups that died compared to the size of the VSD in those that survive, one cannot rule out the possibility
that any VSD may be a potential adverse effect of chemical exposure.
A review of the literature on spontaneousclosure ofVSDs (Zhang, 2015) swnmarized that both defect
size and location can influence the likelihoodof postnatal closure. The author reports that studies have
found defects <3-6mm.are more likely to close but acknowledgesthe controversy over the significance
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of defect size. ·More significantly, the study concluded that muscular VSDs are much more likely to
close spontaneously than membranous VSDs (which were the only VSD type associated with TCE
exposure in the Charles River study). The incidence in humans of spontaneous closure in cited studies
examining only muscular VSDs ranges from 22% to 84%, while for studies examining only
membranous or perimembranous VSDs the incidence ranges from only 4% to 47%. Additionally, the
morphological characterization of closure of the membranous VSD seems to most commonly involve
the use of a leaflet of the tricuspid valve, which would be expected to impact the functional ability of
that heart valve. Therefore, even if a membranous VSD is able to spontaneously close, there are likely
functional impacts of that closer, resulting in an adverse health effect.
Overall, it is impossible to speculate whether the specific VSDs identified in these studies Wouldhave
closed during lactation. Congenital heart defects of any kind are considered to be an adverse medical
event in humans, whether they eventually close naturally or need to be surgically repaired. When
considering the uncertainty over the likelihood of VSD closure and the preponderance of additional
types of defects observed in other studies, this consideration is not relevant to the significance of this
endpoint.

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G.2.1 Methodology
_
1) EPA identified, collected and reviewed a sampling of recent literature on systematic approaches
to performing weight-of-evidenceevaluation. Relevant articles were identified by simple Google
searches and by tree searching references listed in these publications. References included the
following:
a. Weed. 2005. Weight of Evidence: A Review of Concept and Methods. Risk Anal 25(6):
1545-1557 (Weed. 2005).
b. Gough. 2007. Weight of Evidence: A Framework for the Appraisal of the Quality and
Relevance of Evidence. Research Papers in Education 22(2): 213-228 (Goue:h, 2007).
c. Rhomberg et al. 2013. A survey of frameworks for best practices in weight-of-evidence
analyses. CritRev Toxicol 43(9): 753-784 (Rhomberg et al .. 2013).
d. Rooney et al. 2014. Systematic Review and Evidence Integration for Literature-Based
Environmental Health Science Assessments. Env Health Perspect 122 (7): 711-718
(Roonev et al.. 2014).
e. NTP. 2015. Handbook for Conducting a Literature-BasedHealth Assessment Using
OHAT Approach for Systematic Review and Evidence Integration (NTP. 2015).
f. EPA. 2016. Weight of Evidence in Ecological Assessment. Risk Assessment Forum.
EPA/100/R16/001(U.S. EPA 2016i).
g. EPA. 2015. EDSP: Weight of Evidence Analysis of Potential Interaction with the
Estrogen, Androgen or Thyroid Pathways. Chemical: Glyphosate. Office of Pesticide
Programs(U.S. EPA 2015a).
h. US Army Corps of Engineers. 2018. Weight-of-EvidenceConcepts: Introduction
and Application to Sediment Management(Engineers. 2018).
1. European Commission. 2018. Memorandum on weight of evidence and uncertainties.
Revision 2018. Scientific Committee on Health, Environmental and Emerging Risks
(SCHEER) (EC. 2018).
j. EFSA. 2017. Guidance on the use of the weight of evidence approach in scientific
assessments. EFSA Journal 15(8): 4971 (l-69)( EFSA. 2017).

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k. Link.ov et al. 2015. From "Weight of Evidence" to Quantitative Data Integration using
Multicriteria Decision Analysis and Bayesian Methods . Altex 32(1): 3-8 {Linkov et al..
2015).
1. Smith et al. 2002. Weight of Evidence (WOE): Quantitative Estimation of Probability of
Impact. Manuscript {Smith et al.. 2002).
m. Bridges et al. 2017. Framework for the quantitative weight-of-evidence analysis of
'omics data for regulatory purposes. Reg Tox Phann 91: S46-S60 @ ridges et al.. 2017).
n. De.leantand Bridges. 2016 . Assessment of reproductive and developmental effects of
DINP, DnHP and DCHP using quantitative weight of evidence. Reg Tox Phann 81: 397406 (Dekant and Bridges. 2016).
o. Bridges and Solomon . 2016. Quantitative weight-of-evidence analysis of the persistence,
bioaccumulation, toxicity, and potential for long-range transport of the cyclic volatile
methyl siloxanes. J Toxicol Environ Health Part B 19(8): 345-379 (Brid ges and Solomon .
2016 ).
p. Gangwal et al. 2012. Incorporating exposure information into the toxicological
prioritization index decision support framework. Sci Total Environ 435-436 : 316-325
(Gan gwal et al.. 2012).
q. Reif et al. 2013 . ToxPi GUI: an interactive visualization tool for transparent integration
of data from diverse sources of evidence. Bioinformatics 29(3): 402-403 {Beif et al ..
2013 ).
r. Klim.isch et al. 1997. A Systematic Approach for Evaluating the Quality of Experimental
Toxicological and Ecotoxicological Data_Reg Tox Phann 25: 1-5 (Klimisch et al .. 1997).

2) Upon review of the various weight-of-evidence approaches that have been propo~ EPA chose
to adopt the method presented by EPA Risk Assessment Forum {U.S. EPA. 2016i). This method
was originally designed for ecological assessment and offers some flexibility in its
recommendations, so it has been adapted as fit-for-purpose to perform the weight-of-evidence
analysis for TCE cardiac defects. Benefits of this method are as follows:
a. The distinguishing feature of this method is that pieces of evidence are scored not just for
reliability (quality) and relevance, as in most methods reviewed, but also strength of the
evidence. EPA concurs with (Ll.S. EPA. 2016i) that explicitly scoring the strength of the
individual pieces of evidence (e.g." magnitude, dose-response, .etc .) is crucial to
performing a weight-of-evidence assessment
b. The scoring system presented is qualitative and uses intuitive and easily understood
symbols to convey both the implication of a piece of evidence (+, -, 0 for positive,
negative , none, or supports, weakens, neutral/ambiguous) and the weight attached to it(+,
++, +++ or -, --, --- for low, medium and high). EPA believes that symbols are preferable
to numerical scores because their use correctly implies that they cannot be numerically
combined. They simply signify semi-quantitative levels of confidence, strength, and
directionality of the results for the different qualitative properties .
c. Assessment results are presented as weight-of evidence tables that show a visual picture
of the findings. The tables capture nuances in the evidence being weighed and yet remain
understandable. Seeing patterns in the frequencies of+, - and O symbols that indicate the
weight of evidence is easier than if words or numbers are used to score evidence .
d. The method is flexible . Although developed for use in ecological assessment, it is easily
adaptable to use in human health assessment and to different approaches (e.g.,, individual
pieces of evidence can be assessed and weighed for a line or type of evidence based on
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source, such as inhalation toxicity studies, or for a line of evidence for a particular
property (e.g.,, temporal association or other Hill consideration).
3) For our implementation of the (U.S. EPA 2016i) weight-of-evidence method, EPA developed an
Excel spreadsheet [EPA, 2019. Data Tablefor DevelopmentalCardiac Toxicity Weight of
Evidence Analysis. Docket: EPA-HQ-OPPT-2019-0500],as follows:
a. The pieces of evidence are studies (or distinct experiments within studies). They are
organized into lines of evidence based on study type: epidemiological, in vivo animal),
and mechanistic. Within each line of evidence, pieces of evidence are further organized
into subsets based on route of exposure (oral, inhalation, other) and test material (TCE or
metabolite) for toxicological studies or vertebrate class of tissue, embryo or animal
studied (mammalian, avian, fish) for mechanistic studies. WOE determinations are made
in succession, first for subsets of a line of evidence, then for the full lines of evidence,
and then for the overall database, each building on the assessments that came before.
b. Each piece of evidence (study) was graded in 3 areas: reliability (quality),
outcome/strength, and relevance . The rationale for each grade was recorded.
i. Reliability is defined in (U.S. EPA, 2016i) as inherent properties that make
evidence convincing. For our implementation, because each piece of evidence is
a study, this refers primarily to aspects of study design, execution, and
transparency.
1. Possible scores for reliability were 0, +,++,or+++ for unusable, low,
medium and high.
2. In contrast to the study quality evaluations performed in Distiller, which
included >20 specific quality criteria for each study, here each study was
given only a single overall grade. We considered the same issues, but we
did not formally go through and assign grades on each one individually.
Instead, focus was on key attributes. Noteworthy deficiencies were
recorded and grades were assigned based on the number and nature of the
specific deficiencies identified.
11. Outcome/strength is defined in (U.S. EPA. 2016i) as degree of differentiation
from control, reference, or randomness. This is based on study results and may be
influenced by magnitude, dose-response, number of related elements changed
(e.g.,, consistent changes in histopathology and serum chemistry), temporal
concordance, etc.
1. Possible scores for outcome/strength were---, --, -, 0, +, ++, or+++ for
results ranging from strongly negative to no effect/ambiguous to strongly
positive.
111. Relevance is defined in (U.S. EPA. 2016i) as degree of correspondence between
the evidence and the assessment endpoint. This can be thought of as the degree of
extrapolation that would be needed to use the data in question for developing a
toxicity value.
1. Possible scores for relevance were 0, +, ++, or +++ for none, low, medium
and high.
2. Maximum values based on study type were +++ for epidemiology studies,
++ for in vivo animal studies by natural route of exposure, and + for in
vivo animal studies by other route of exposure and in vitro studies.
Starting from these maximum scores, deductions were made for issues
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such as testing of TCE metabolitesrather than TCE for in vivo animal
studies and poorly defined exposures in epidemiology studies.
iv. The grades for reliability, outcome/strength, and relevance for each piece of
evidence (study) were integrated across each area (horiz.ontally)into an overall
grade for that study. In deriving the overall grade, low area scores were
considered to have more weight than higher scores, as per (U.S. EPA. 2016i). In
other words, if any one of the three grading areas was low, then even if other
aspects of the s1:Udy
were rated highly, the study still contributed lower weight
overall to the WOE analysis (e.g.,, a great study with a compelling result
performed using DCA rather than TCE). Based on this methodology, overall
grades for each study were always in the same direction as the strength score (i.e.
+vs-) ata value defined by the lowest amplitude(+ vs ++ vs+++) of the three
factors. Rationale for the overall grade was provided, as it was for the individual
area grades.
c. When integrating overall study scores from all studies within a line of evidence (or subset
of a line of evidence) or across lines of evidence (vertically), overall·summary scores
were determined as a the best semi-quantitativerepresentation of all overall study grades
within that line of evidence, with considerationsgiven to both the amplitude of the
overall study grades along with the consistency of the strength direction across studies.
When results were mix~ overall summary scores for a line of evidence gave greater
weight to overall study grades of greater amplitude (e.g., ++ vs +). Similarly, studies with
non-ambiguousresults (not a strength score of 0) were considered more informative than
ambiguous studies. Additionally, consistent overall study grades oflower amplitude (e.g.,
all +) may have resulted in a summary score of a higher amplitude(+ +). In this way,
WOE detennination was most influenced by studies with the strongest, clearest effects
and/or lines of evidence with the most consistent results. This differs from how the
individual area grades were combined into overall study grades (See Section b(iv),
above), where the lowest amplitude value determined the overall weight.
d. Evidence areas were also integrated as a mathematical average (e.g.,++= 2, 0/- = -0.5),
in order to summarizethe evidence areas for all studies. In contrast with the overall
summary score however, for individual evidence areas, the integrated area scores
represented a true average and were not adjusted upward for consistency or in order to
favor non-ambiguous results (which was specific to strength score). Of note, these are
included for presentation purposes only and were not used to determine the overall
summary score for a line of evidence. The overall swnmary scores were determined by
integrating the overall grades for each study, in the manner as described in Section c.
Because of these different methodologiesand the fact that overall study grades are
defined by the lowest amplitude evidence area, the overall summary score may differ
from the integrated area scores.
Note: 1bis analysis was performed in parallel with the systematic review data evaluation of the
individual studies. The WOE analysis had a greater focus on relevance to the specific endpoint while the
data evaluation metrics aimed to evaluate the utility of a study for dose-response analysis. Therefore, the

conclusions of the WOE analysis for individual studies occasionally differed from the results of the
systematic review data evaluation. The results of both are presented together in [EPA, 2019. Data Table
for Weight-Of-Evidence(WOE)Assessmentfor DevelopmentalCardiac Toxicity.fromTCE. Docket:
EPA-HQ-OPPT-2019-0500.].Of note, studies that scored Unacceptable in data quality evaluation were
not considered in the WOE analysis. Their evaluation is included for reference, but their scores had no
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impact on the overall grades for each line of evidence or subset. Unacceptable studies are indicated by
red text in the below tables and the supplemental data table. Studies that were not rated (NR) because
EPA determined that they were not pertinent are indicated by blue text in the supplemental data table,
however they are not included in the tables below.
WOE Results By Study Type
Data evaluated to assess the weight-of-evidencefor developmental cardiac toxicity from exposure to
TCE include studies from three lines of evidence: epidemiology studies, in vivo animal toxicity studies,
G.2.2

and mechanistic studies. For this analysis, the three lines of evidence will be considered both
individually and collectively.
Table_Apx G-8 shows the weight-of-evidencefor the various epidemiology studies that were considered
in this review. Ruckart et al. (2013) was identified in previous reviews but was graded as NR (not
relevant) and dropped from the analysis because the study did not include cardiac defects as an assessed
endpoint. All of the other TCE studies were considered to be relevant, but only with medium(++)
relevance scores because quantitative exposure to TCE was assessed indirectly in all of them. One study
that examined exposure to TCE degradants (Wright et al., 2017) scored only(+) for relevance because
the degradants may also have originated :froma different source. The high potential for misclassification
of exposure was a limiting factor for all of these studies, which were otherwise generally adequate
ecological or case-control studies (reliability rated as+ for all studies). Of the relevant studies, four
reported results suggestive of a positive association between maternal TCE exposure and congenital
cardiac defects in offspring, one reported a lack of an association, and two reported ambiguous results.
Of the three studies with a positive association, (Goldbere et al.. 1990) was rated Unacceptable in data
quality evaluation and therefore did not contribute to the WOE. Toe Bove reports ( 1996; 1995)
(considered here as a single study because the two papers contain the same cardiac toxicity data)
reported elevated but nonsigni:ficantincreases in odds ratios. Yauck et al. (2004) reported a positive
association between developmental cardiac toxicity and TCE exposure only in older mothers, while
younger mothers and the overall population had a null association. The finding of a negative association
in the study by (Lagakos et al.. 1986) has some ambiguity because it was based on a very small number
of cases, exposure was not classified based on TCE specifically, and there was atypical directionality of
confounder effects. Gilboa et al. (2012) did not find any positive association with TCE exposure in a
large but limited study. Three studies showing positive associations of varying strength (Brender et al..
2014; Forand et al., 2012: Wright et aL 2017) also had some limitations but collectively provide
suggestive evidence for an association between maternal TCE exposure and cardiac defects in offspring.
In evaluating all studies and giving greater weight to studies with non-ambiguous results, the resulting
overall summary score for epidemiology is (+), indicating a positive association between TCE exposure
and congenital cardiac defects.
Table Aox G-8. Wei mt-of-Evidence Table for Eoidemiolo~ Studies
Evidence Area

Reliability

Strength

Relevance

Overall Grade

++

0/-

TCE
( l.ii 0 akQset iii- 1986)

+

OJ-

(Bove. 1996; Bove et al.,
1995)

+

0

-H-

0

(Yayck et al .. 2004 )

+

0/+

++

0/+

(Forand et al,. 2012 )

+

++

++

+

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Evidence Area

Reliability

(Gilboa et al., 2012)

+

(Brender et al., 20 14)

+

+

++

+

(Golgll~rg et i!l,. 1990)

0

+

++

0

++

+

+

+

Of+

++

Strength

Relevance

Overall Grade

++

METABOLITES (TCA, DCA)
(Wright et al., 2017)

Integrated Area Scores
(all epidemiology)

Summary Score (all epidemiology)
Possible scores for reliabilityand relevancewere 0, +, ++, or+++ for unusable,low, medium and high.
Possible scores for strengthand overall weight were -·, •·, •, 0, +, -1+, or+++, with ranges inbetwcen,for results ranging from
strongly negativeto ambiguousto stronglypositive.
Red text identifiesstudies that scored Unacceptable in data quality evaluationand a 0 for reliability.The WOE scores are
provided for reference but were not incorporatedinto the overall score for the line of evidence.

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Table_Apx G-9 shows the weight-of-evidence for the various in vivo animal studies that were
con sidered in this review. The four TCE oral studies were considered of good relevance(++) because
they used a natural route of exposure (drinking water or gavage ). Dawson et al. (1993) and the Charles
River Laboratories study (2019) were rated as (++) reliability, while Fisher et al. (2001 ) and Johnson et
al. (2003) were rated as(+) reliability . The score was downgraded for (Fisher et al., 2001 ) because only
a single dose group was used and the negative control for TCE demonstrated a very elevated prevalence
of heart and cardiovascular defects. Johnson et al. (2003 ) was rated as lower reliability due to the -small
group sizes, poor data reporting (somewhat mitigated by subsequent errata and personal
communications), and the pooling of data from multiple trials into a single experiment. Increased
inc idence of cardiac defects were observed in pups from the (Dawson et al .. 1993) and (Johnson et al.,
200 3) studies. The Strength scores for these studies were characterized as(++) for (Johnson et al., 2003 )
and (+) for (Dawson et al .• 1993), influenced by the low magnitude of effect in the high dose groups and
uncertainty surrounding the precision of estimated doses. The incidence of cardiac defects were not
increased by TCE oral gavage in the (Fisher et al., 2001 ) study; however, this study used only a single
dose group and the incidence of heart defects was elevated in the soybean oil contr.ols compared to
drinking water controls, therefore the strength score was (0/-). The recent study by Charles River
Laboratories (2019 ) also did not find any statistically significant increase in developmental cardiac
defects following TCE administration in drinking water, however this study focused only on a small
subset of cardiac defects (see Appendix G.l). It therefore also scored (0/-) for Strength. The overall
summary for the TCE oral studies was characterized as ambiguous to weakly positive (0/+) due to
conflicting study results , with a lean toward positive based on the ambiguity of the negative studies.

Six oral experiments using TCE metabolites (TCA or DCA) were rated as lower relevance (+), because
a metabolite was administered (not TCE) and the relevance of these effects to humans likely dependent
upon individual toxicokinetic variability and 1he administered dose. These studies were considered
mostly reliable with ratings of (-t-++)(Smith et al., 1989) and(++) (Fisher et al., 2001 ; Epstein et al.,
1992). Only (Johnson et al ., 1998) received a lower reliabil ity score (0/+) due to concerns about source
of the test substance and sharing of bottles among animals. Both TCA and DCA were convincingly
shown to produce strong dose-related cardiac defects (strength score of++) in the (Smith et al.~1992,
1989) studies (downgraded for use of relatively high doses that produced other embryo /fetotoxic effects
or even maternal effects), with weaker posit ive strength scores( +) in the (Johnson et al., 1998) and
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(Epstei n et al., 1992) studies. The (Fisher et al., 2001) study (also reviewed separately for TCE
administration) only showed a small, non-statistically significant increase in cardiac defects for both
TCA and DCA, but the single dose level used in these studies was too low to rule out effects at higher
doses based on results of the other studies . Toe overall summary score for the oral metabolite studies
was(+).
Three inhalation studies using TCE were considered relevant (natural exposure route) and reliable.
Reliability ratings were reduced for studies with a single exposure group and poor reporting(+,
(Schwetz et al., 1975)) in addition to small group sizes and high negative control responses with a lack
of dose-responsiveness (0/+, (Dorfmueller et al.. 1979)). These studies were also reduced in relevancy
score(+) because they were general teratology studies and the focus on cardiac effects was unclear. Two
studies scored an Unacceptable in data quality and a Oin reliability for limited reporting of study details
(Hardin et al. . 1981) and use of a nonstandard exposure duration with insufficient details on exposure
method (Heal v et al .. 1982). These studies did not contribute to the WOE. Among acceptable inhalation
studies, the results were consistently negative, however with varying scores in the three evidence areas.
Carney et al. (2006 ) was the best inhalation study, scoring the maximum(+++) for reliability and
showing a strong negative response (- ). Based on these results, the summary score for the inhalation
studies was(-), primarily driven by the weight of the (Carne \ et al .. 2006 ) data but reduced by the
weaknesses of the other studies and the limited number of acceptable studies with non-ambiguous
results.

004

005
006
007
008
009

010
011
012
013
014
015
016

017

As for other exposure routes, Dawson et al. (1990 ) administered TCE via intrauterine instillation in rats.
This relevance of this study was rated as lower (+) due to the unnatural exposure route and the study
reliability was low (0/+), because of sampling inadequacy, small group sizes, and poor reporting. The
strength of this study was(+) due to several factors, including the use of fetuses (not litters) as the
experimental unit, the small magnitude of the response seen in the high dose group only (which was a
very high dose considering the exposure route). The overall summary score for animal studies across all
exposure routes suggests an unclear/ambiguous relationship between TCE exposure during gestation and
the incidence of cardiac defects in offspring. This ambiguity is based on weakly positive evidence from
oral or intrauterine TCE administration, positive evidence from oral TCE metabolites, and a negative
evidencewith TCE inhalation. The WOE from in vivo animal toxicity studies therefore does not either
support or refute the association ofTCE exposure with developmental cardiac defects.

Table A x G-9. Wei t-of-Evidence Table for In VivoAnimal Toxici Studies
Reliability
Strength
Relevance
Overall Grade
Evidence Area
ORAL

TCE
(DawsQDcl al.• 1223)

++

+

++

+

(Jolln~on et al., 2003 )

+

++

++

+

(Eisher et al., 2001 )

+

0/-

++

0/-

(Charles River
Laboratorie s, iQI9 )

++

0/-

++

0/-

0/+

++

Integrated Area Scores

+I++
Summary Score (TCE)

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Evidence Area

Reliability

Strength

Relevance

Overall Grade

+
+

METABOLITES(TCA, DCA)
(Smith et al. , 1989)

+H-

-I+

(Smith et al. , 1992)

+++

-I+

(Johnson et al., 1998)

Of+

+

(Fisher et al.. 200 1)

++

(E12steinet al.. 1992)

++

+

+
+
+
+
+

Integrated Area Scores

++

+

+

Of+

+

SummaryScore(Metabolites)

Integrated Area Scores
(all oral studies)

++

+

++

Summary Score (all oral studies)
INHALATION

TCE
+

Of-

+

01-

(Dorfmueller et al. ,
1979)

Of+

01-

+

Of-

(Carne v et al.. 2006)

+++

-I+

(Hardin et al.. 198l)

0

++

0

(Heal\ et al.. 1982)

0

t+

0

+f++

+f++

(Schwetz et al. 1975)

Integrated Area ~ores
(all inhalation studies)

Summary Score (all inhalation studies)
OTHER ROUTES (Uterine Infusion)
(Dawson et al. , 1990)

Integrated Area Scores
(in vivo - all routes)

01+

+

+

+f++

0/+

+/++

Suamary Score (In

mo- aD routes)

Possible scores for reliabilityand relevancewere 0, +.++, or +++, with ranges inbetween,for unusable, low, mediumand high.
Possible scores for strength ood overallweight were-,- , -, 0, +,++, or +++, with ranges inbetween,for results ranging from
strongly negative to ambiguous to strongly positive.
Red text identifiesstudies that scored Unacceptablein data quality evaluation.The WOE scores are provided for referencebut
were not incorporatedinto the overall score for the line of evidence.

018
019
020
021
022
023
024

Mechanistic studies that inform the weight-of-evidencefor developmentalheart defects include
evaluations of cardiac structure and function in chick and rodent embryos and mode-of-action or key
event data focused on processes and pathways that contributeto the observed valvulo-septaldefects
(e.g.,, altered calcium flux, inhibition of stem cell differentiationand endothelial cell proliferation) as
well a.;;altered expression of oxidative metabolismenzymes.A mechanisticstudy from Palbykin et al.
(2011) was graded as not relevant and was droppedfrom the analysis because it merely examined
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026
027
028
029
030
031
032
033
034
035
036

037
038
039
040
041
042
043
044
045
046

047
048
049
050
051
052
053
054
055
056
057
058
059
060
061
062
063
064
065
066
067
068
069
070

071

molecular mechanisms underlying the results observed in (Caldwell et al.. 2008 ) without contributing
any additional WOE to the endpoint. The remaining mechanistic studies in mammalian cells/tissues,
chick embryos and zebrafish embryos were generally rated as lower relevance in comparison to human
studies and in vivo animal studies using a natural route of administration except for studies on ex vivo
whole rat embryos or in vivo data from rodents or humans, which were assigned a relevance score of
(+/++-). All other studies were rated as( +) relevance.
Mechanistic studies in mammalian systems included an occupational worker study (Green et al.. 2004 ),
in vivo rat studies (Collier et al.. 2003 ; Dow and Green . 2000 ), studies using rat and mouse whole
embryo cultures (Hunter et al.. 1996 ; Saillenfait et al.. 1995) and in vitro studies using cell lines ( Jiang et
al .• 2015 ; Caldwell et al., 2008 ; Selrnin et al.. 2008 ; Ou et al.. 2003 ). Ou et al. (2003 ) and Jiang et al.
(2015 ) were rated as highly reliable(+++) because they were well-designed and well-conducted studies
with a full reporting of the results. Most of the remaining mammalian studies were rated as(++) for
reliability, because there were minor deficiencies noted in study design, performance or reporting. Dow
and Green (2000 ) was rated as low (0/+) for reliability, with flaws including pooling of experiments,
poor data reporting, and insufficient justification of dose selection. In mammalian systems, higher
strength(++-) was ascribed to studies that demonstrated structural changes in the embryonic heart
(Hunter et al., 1996), suppression of endothelial cell proliferation in cell culture (Ou et al .. 2003 ), and
inhibition of cardiac differentiation from embryonic stem cells (Jiang et al .. 2015 ). Studies that
demonstrated precursor events that contribute to altered cardiac development (i.e., changes in gene
expression, altered calcium flux, folate deficiency) were rated as weakly positive(+) for strength . These
included changes in gene expression relating to cardiac development and calcium flux (Jianl! et al ..
2015 ; Caldwell et al.. 2008 ; Selmin et al., 2008 ; Collier et al.. 2003) and in vivo folate deficiency (Green
et al .. 2004 ; Dow and Green. 2000 ) (which has been associated with congenital heart defects in humans
(Mao et al.. 2017 ) ). Saillenfait et al. (1995) did not observe morphological cardiac changes in whole rat
embryos exposed to TCE in culture, although only morphological features were examined and the
results were not explicitly discussed in the text. This study was rated as moderately negative(-/--) for
strength.
With the exception of the Saillenfait study (which did not describe its procedure for evaluation of
malformations in whole rat embryos), the other mammalian mechanistic studies all reported positive
results. Several of these studies demonstrated a clear dose-response, although in others the results were
less clear (e.g.,, suggestive of a biphasic dose-response, with change at the lower doses but not the
higher doses, see discussion in Section 3.2.4.1.6). Toe overall summary score for mammalian
mechanistic studies was ( +).
The chick embryo is a valid model system for studying embryonic development, and in particular,
cardiac development. Eight studies investigated development of cardiac defects and associated effects
in chick embryos exposed to TCE and metabolites. These were all generally well-designed , conducted
and reported. All chick embryo studies received a(++-) rating for reliability except for (Loeber et al..
1988 ), which was downgraded slightly to ( +/++-) due to missing reporting details and a potentially
insensitive evaluation procedure. Two studies reported significant increases in incidences of a variety of
cardiac defects (Rufer et al. . 201 O; Loeber et al.. 1988 ), resulting in a a strength rating of ( ++). The
remaining studies showed various mechanistic changes thought to be involved in cardiac development
or function and scored less positive for strength, ( +). The only study that did not produce a clear
positive result featured an earlier exposure window than the others and obtained ambiguous results with
mixed results on endocardiocyte proliferation and no changes in cardiac output was rated as (0) for

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073
074
075
076
077
078
079
080

081
082
083
084
085

086
087

strength (Drake et aL 2006b ). The overall summary score for chick embryo studies was (++) based on
the relatively large number of studies demonstrating consistently positive effects.
The zebrafish embryo is also a valid model for evaluating cardiac development. Two of the three
zebrafi sh embryo studies were well designed and well documented with few notable limitations (rated as
highly reliable,++ +). The reliability rating for (Williams et al.. 2006) was reduced to(++) due to the use
of a single exposure level. All three studies gave positive results indicating the potential for TCE (or its
metabolite DCA) to effect cardiac development in zebra.fish. The study by Wirbisky et al. (2016 ) was
the most comprehensive study of the three (rated as +++ for strength), identifying multiple dose ..
responsive cardiovascular effects as well as associated gene changes . The other two studies received a
(++) for strength because of observed severe changes in heart rate but at concentrations associated with
other toxicities (Hassoun et al.. 2005) or because only a single, elevated dose was used (Williams et al..
2006 ). The overall swnmary score for zebrafish embryo studies was (+). Toe overall summary score for
mechanistic studies across all species and study designs was (++) due to consistent positive outcomes
observed in all study types. The WOE from mechanistic studies therefore provides stronger positive
evidence of an association between TCE exposure and congenital cardiac defects.

088
089

Table Apx G-10. Wei2bt-of-Evidence Table for Mechanistic Studies
Relevance
Reliability
Strength
Evidence Area

Overall Grade

MAMMALIANCELLSffISSUE
TCE
(Saillenfait et al., 1995)

++

-/--

+/++

-/--

(C2llier et al., 2003 )

++

+

+

+

(Selmin et al., 2008 )

++

+

+

+

(Caldwell et al., 2008)

++

+

+

+

(Ou et al., 2003)

+++

++

+

+

(Jiang et al., 2015)

+++

++

+

+

(Dow and Green , 2000)

0/+

+

+/++

01+

(Green et al., 2004)

++

+

+I++

+

METABOLITES (TCA, DCA, Trichloroethanol,Chloral)
(Saillenfait et al.. 1995)

++

-/--

+/++

-/--

(Collier et al., 2003)

++

+

+/++

+

(1lunter et al.. 1996)

++

++

+!++

+

(Seim in et al., 2008)

++

+

+

+

(Dow and Green . 2000)

++

+

+

+

IntegratedArea Scores

++

+

+

SummaryScore (all mammaliantissue studies)
CIDCKEMBRYO

TCE

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Reliability

Strength

Relevance

Overall Grade

(Loeber ~t al., 1988)

+/++

++

+

+

(Bo-,,er et al. , 2000 )

++

+

+

+

(Mi.shima~t a!.. 2Q06)

++

+

+

+

(Drake et al. , 2006a )

++

+

+

+

(Drake et al. , 2006b )

++

0

+

0

(Rufer et al., 2010)

++

++

+

+

(Makwan!} et al. , 2010 )

++

+

+

(Makwana et al. , 2013 )

++

+

+
+

(Harris et al., 2018)

++

+

+

+

(Drake et al., 2006a )

++

+

+

+

(Drake et al., 2006b )

++

0

+

0

Integrated Area Scores

++

+

+

Evidence Area

+

METABOLITES (TCA)

SummaryScore (all chick studies)
ZEBRAFlSH EMBRYO
TCE

+++

+++

+

+

(Hassoun et al., 2005 )

+++

++

+

+

(William§ ~ al., 2006 )

++

++

+

+

+++

++/+++

+

(Wirbisk ~ et al .. 20 l 6)

METABOLITES(DCA)

Integrated Area Scores

SummaryScore (all zebrafishstudies)
IntegratedArea Scores
(all mechanisticstudies)

+++

+/++

+

Summary Score (all mechanisticstadies)
Possible scores for reliability and relevance were 0, +, ++, or +++, with ranges inbetween, for unusable, low, medium and high.
Possible scores for strength and overall weight were -· , - , •, 0, +, ++, or+++, with ranges inbetween, for results ranging from
strongly negativeto ambiguous to strongly positive.

090
091
092
093
094
095

096
097
098

In summary, the database contains a large and diverse set of studies pertinent to assessing cardiac
toxicity from TCE exposure (overall relevance was rated as ++). Well-design~ conducted and reported
studies were located for all categories, although the epidemiology studies were limited to ecological or
case-control study designs with high potential for misclassification of exposure and the many of the in
vivo animal studies contained at least one major limitation (overall reliability rating of +/++). The
integrated strength area score was (+), indicating a suggestive positive association of TCE with
congenital cardiac defects. The epidemiologystudies as a group provide suggestive evidence for an
effect ofTC E on cardiac defects in humans (summary score of+) . Oral in vivo studies provided

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ambiguous to weakly positive (0/+) results for TCE itself, but suggestivepositive results for its TCA and
DCA metabolites(+), while inhalation studies contributed suggestive negative evidence( +).
Mechanistic studies provided solid, consistent supporting infonnation for effects of TCE and
metabolites on cardiac development and precursor effects (summary score of++). Overall, the database
is both reliable and relevant and provides suggestive overall evidence that TCE can produce cardiac
defects in humans based on suggestivepositive evidence from epidemiology studies, mixed evidence
from animal toxicity studies, and stronger positive evidence from mechanistic studies.

.

Tabl e A,px G-11 0v eraIIW.e12.ht-o f.E
- vid ence Tablean dS ummary scores
Evidence Area

Reliability

Strength

Relevance

Summary
Score

+

+

++

+

In vivo animal toxicity studies

+/++

0/+

+/++

0

Mechanistic studies

+++

+/++

+

++

IntegratedArea Scores

++

+

++

+

Epidemiology studies

Possiblescoresfor reliabilityand relevancewere0, +, ++, or +++, withrangesinbctween,for unusable,low, mediumand high.
Possiblescoresfor strengthand overallweight were-, - , -, 0, +, ++,or +++, with ranges inbetween,for resultsranging from
stronglyneglltiveto ambiguousto stronglypositive.

108
109

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116
117

118
119

120
121
122
123
124

125

Appendix H
H.1

MET A-ANALYSIS FOR CANCER

Study Screening and Selection

All epidemiologic studies included in the U.S. EPA 2011 IRIS assessment ofTCE (Appendix C, (1):.S.
EPA. 2011 b) were considered to be informative and carried forward for meta-analysis. Informative
epidemio\ogic studies of non-Hodgkin lymphoma (NHL), kidney cancer or liver cancer and exposure to

TCE published since the 2011 IRIS assessment were identified through a systematicliterature search.
Studies examining only.other cancer types were excluded from consideration.
H.1.1 Data Quality and Inclusion/Exclusion Criteria Screening
Relevant studies were evaluated for data quality and were additionally screened through

inclusion/exclusioncriteria developed based on the criteria established in the 2011 IRIS assessment
(Appendix C, (U.S. EPA, 201 lb)), as describedin Table_ApxH-1. Results of this criteria screening are
presented in
Table_Apx H-2.
Table_Apx H-1. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies
Identified in EPA's Literature Search
Inclusion Criteria

Exclusion Criteria
Study Design

Geographic-based,ecological, or proportionatemortality ratio
(PMR) study design.

Cohort and case control studies.

ParticipantSelection
Inadequate selection in cohort studies (exposed and control
Adequate selection in cohort studies of exposure and
groups were not similar, and differenceswere not controlled
control groups and of cases and controls in case-control for in the statistical analysis). Controls were drawn from a
studies.
very dissimilarpopulation than cases or recruited within very
different time frames (case control studies).

Exposure
TCE exposure potential inferred to each subject and
quantitative assessment of TCE exposure for each
subject by reference to industrial hygiene records
indicating a high probability of TCE use, individual
biomarlcers,job exposure matrices (JEMs), water
distribution models, or obtained from subjects using
questionnaire(case-control studies).

TCE exposure potential not assigned to individual subjects
using JEM, individualbiomarkers,water distribution models,
or industrialhygiene data indicating a high probability ofTCE
use (cohort studies).

The range and distribution of exposure are not adequate to
determine an exposure-responserelationship. No description is
provided on the levels or range of exposure.

Reports as least 2 levels of exposure (e.g.,,
exposed/unexposed).

OutcomeAssessment
Evaluation of incidence or mortality from kidney cancer, Data for non-cancer health outcomesor incidence or mortality
liver cancer, or NHL. RR estimates and corresponding reported for cancers other than kidney, liver, or NHL. All
hemato-and lymphopoieticcancer reported as broad category.
Cls (or informationto allow calculation).
Statistical Power (sensldvlty)
The number of participants or cases and controls are
adequate to detect an effect in the exposed population
and/or subgroups of the total population.

The number of participants or cases and controls are inadequate
to detect an effect in the exposed population and/or subgroups
of the total population.

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Table_Apx H-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based
on Inclusion/Exclusion Criteria
Studies recommended for inclusionJn g wmtitative meta-an alys is:
Studies
I

(Bove et al., 2014a
)
(Bove et al., 20 14b )
(Buhagen et al., 20 I 6)

I

(Christensen et al., 201 3)
(Cocco et al., 2013 )
(Hansen et al., 2013 )
(Lipworth et al., 2011)

(Purdue et al., 2016)
(Silver et al., 2014 )
(Vlaanderen et al., 2013 )

Analytical study designs of cohort or case-control; evaluation
of incidence or mortality; adequate selection in cohort studies
of exposure and control groups and of cases and controls in
case,.controlstudies; TCE exposure potential inferred to each
subject and quantitative assessment of TCE exposure
assessment for each subject by reference to industrial hygiene
records indicating a high probability of TCE use, individual
biomarkers, JEMs, water distnbution models, or obtained from
subjects using questionnaire (c:ase-control studies); RR
estimatesfor kidney cancer, liver cancer, or NHL with
confidence intervals

Studies NOT recomme nded for inclusion in quantitative meta-analys is:

I (Alane e et al.. 2015 )

Weakness with respect to analytical study design (i.e.,
aeo!!raohic-based,ecolo!!icalor PMR desiPn).

I
II (Alanee

TCE exposure potential not as.signedto individual subjects
using JEM, individual biomarkers, water distribution models,
or industrial hygiene data from other process indicating a high
probability ofTCE use (cohort studies).

I

et al., 2015 )

(Bassig et al., 20 16)
(Ruckart et al.. 2013)

I (Bahr etal.• 2011)
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145

I

Primary reason(s)

Studies

Examined noncancer health outcomes or cancer incidence or
mortality for cancers other than kidney, liver. or NHL. All
hemato-and lvmohoooietic cancer reoorted as broadcateeorv.
EPA reviewer scored the study as Unacceptable (Rationale:
Repeatedexamples ofj>oor quaJity, study design and execution
and ignorance of potential biases that went unmentionedeven
in the discussion indicate inexperience and poor quality
control).

H.1.2
Screening results
_
Data quality and inclusion/exclusion criteria screening identified ten studies suitable for use in metaanalysis . Of these, there were nine new studies with suitable informative data on the association of
exposure to TCE and NHL (Bove et al.. 2014a: Bove et al., 2014b: Christensen et al., 2013; Cocco et al..
2013: Han sen et al., 2013: Lipworth et al., 20 11: Purdue et al .• 2016; Silver et al., 2014 ; Vlaanderen et
al., 2013 ), eight new studies with infonnative data for kidney cancer (Bove et al., 2014a: Buhagen et al.,
2016; Christensen et al., 2013; Hansen et al., 2013; Lipworth et al., 2011; Purdue et al., 2016; Silver et
al., 2014: Vlaanderen et al .• 2013 t and six new studies with informative data for liver cancer @ove et
al., 2014a: Christensen et al., 2013: Hansen et al., 2013: Lipworth et al., 2011; Silver et al., 2014:
Vlaanderen et al.. 2013). All of these studies scored Acceptable for data quality except (Bahr et al.,
2011 ), which was excluded for scoring Unacceptable. Every study scored at least a Medium except for
(Buhagen et al., 2016 ), which scored a Low but was recommended for inclusion by inclusion/exclusion
criteria. The respective data quality scores were considered in sensitivity analyses of the meta-analyses
results (see Appendix H.2.2.2).
All studies from the 2011 IRIS meta-analysis were Acceptable in data quality and scored at least a
Medium. Therefore, data from the ten new studies that passed the criteria screening were extracted along

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147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163

164
165
166
167
168
169
170
171

with results from previous studies identified in the 2011 IRIS assessment (U.S. EPA, 201 le ). When
more than one report was available for a single study population, only the most recent publication or the
publication reporting the most informative data for TCE was selected for inclusion in the meta-analysis
(see Table_Apx H-3). This resulted in a smaller set of data included in the meta-analysis as compared to
the total list of studies.

H.1.3

Pooled Cohorts

Two of the new papers pooled data from earlier studies included in the 2011 IRIS meta-analysis.
(Hansen et al .. 2013 ) pooled and updated three Nordic national cohort studies of workers biologically
monitored for exposure to TCE (Anttila et al .. 1995: Axelson et al.. 1994: Hansen et aL 2001 ).
Similarly, (Cocco et al.. 2013 ) pooled earl ier case-control studies of NHL including (Cocco et al .. 2010) ,
(Miligi et al., 2006 ), and (Purdue et al .. 20 I 1). Two other new studies provided updated data on
populations included in the U.S. EPA 2011 IRIS assessment: (Lipworth et al. , 2011 ) updated a cohort
study of aircraft workers (B oice et al.. 1999) and (Christensen et al.. 2013 ) updated an earlier
population-based case-control study (Siemiat\ cki . 1991). After removing these overlapping and
superseded studies, a total of 18 studies of NHL , 18 studies of kidney cancer, and 11 studies of liver
cancer were available for meta-analysis.
Among the included studies, up to about 800 of the approximately 40,000 Danish workers studied by
(Raaschou-Nielsen et al.. 2003) may have also been included in the Nordic pooled study of 5553
biomonitored workers (Hansen et al., 2013). However, both studies were retained in the analysis because
any overlap would have bet:n minor. There was also minor overlap between the cohorts studied by
(Zhao et al.. 2005) and (Boice et al., 2006), but those papers reported data for different outcomes . These
results are summarized in Table_Apx H-3.

Table_Apx H-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the
2011 IRIS Assessment or EPA Literature Search
Study reviewed

Other assessed studies with participants from the same cohort
2011 IRIS Assessment

(Anttila et al., 1225) (Finland only)

Included in (Hansen et al., 2013)

(Axelson ~t !!l.. 1994) (Sweden only)

Included in (Hansen et al., 2013)

(Boice et al., 1299)

Updated in (Linworth et al.. 20 11)

(Boice et al., 2006)

(Zhao et al.. 2005 ) (partial)

(Bruning et al., 2003 )

None

(Charbotel~I ~I.. 2006)

None

(Cocco et al., 2010 )

Included in (~occo et al., 2013)

(Dosemeci ~I !!I., 1229)

None

(Gre~nls1nd~t al., 1994)

None

(Hans~net al., 20Ql ) (Denmarkonly)

(RaaschQu-Niel
sen et al., 2003 ) (partial); Included in (Hansen et al., 2013 )

(Hardell eUl.. 1994)

None

(Miligi ~t al.. 2006 )

Included in (Cocco et al., 2013 )

(Moore et al.. 201Q)

None

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Study reviewed

Other assessed studies with participants from the same coltort

(Mor gan et al., 1998)

None

(Nordstrom et al.. 1998)

None

(Persson and E~ddkson , 1999)

None

(Pesch et al., 2000 )

None

(Purdu~ ~

ru..2Qll )

Included in (Cocco et al.. 2013 )

(Raaschou-Nielsen et al. , 2003 )

Partial overlap with (Hansen et al. , 2001)

(Radican et al., 2008 )

None

(Siemia n cki. 1991)

Updated in (Christensen et al.. 2013)

(Wang et al., 2009 )

None

(Zhao et al., 2005)

(Boice et al.. 2006 ) (partial)

New Studies Identified in EPA Literature Search

172

173
174

175
176
177
178

179
180
181
182
183
184

185

(BQve et al. , 2014a)

None

(Bove et al.. 2014b)

None

(Bu.hagen et al., 2016 )

None

(Cocco et al., 2013 )

(Cocco et al., 2010); (Miligi et al., 2006 ); (Purdue et al.. 2011)

(Christensen et al. , 2013)

(Siemia t'vcki. I 991)

(Hansen et al., 2013 )

(Hansen et al., 2001); (Anttila et al .. 1995); (Baaschou-Nielsen ct al., 2003)
(partial)

(L i12wor!;het al., 2011 )

(Boice et al. , 1999)

(Purdue et al.. 20 I 6)

None

(Silv~ ~ al. 20.14)

None

(Vlaanderen et al., 2013 )

None

H.2
H.2.1

Meta-Analysis Methods and Results
Methods

Data abstraction
Data for each pertinent study identified, including measures of the association (including rate ratio (RR),
odds ratio (OR), hazard ratio (HR), etc .) of each cancer of interest with exposure to TCE, their
confidence intervals (CI) and if available, standard errors, identification of the type of measure (RR OR,
etc), the study design and the exposure metric (ever/never exposed, cumulative exposure, duration of
exposure, etc.) were abstracted for meta-analysis. All types of epidemiologic ratio measures of
association, including RR, OR, HR and standardizedmortality or incidence ratios (SMR SIR), were
considered to be equivalent and are collectively referred to below as RRs. The preferred estimates of
association for meta-analysis were based on contrasts within the study population and were either 1)
comparisons of groups exposed and not ~xposed to perchloroethylene or 2) comparisons of groups with
the highest and lowest level of exposure to perchloroethylene, in that order. For NHL, estimates of
association for the most highly exposed group were also abstracted, when they were available. For each

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comparison, the mo st fully adjusted risk estimate was selected. Estimates of association based on
cumulative exposure were preferred to those based on other exposure metrics.
Data for studies included in the U.S. EPA 2011 IR1S assessment (U.S. EPA. 201 le ) were abstracted
from tables in Appendix C of that assessment. The measures of association, confidence limits and
estimates of SE listed ·in those tables were utilized for consistency with the previous assessment.
For newer studies not included in the IRIS assessment, log-relative risks and their standard errors were
estimated from the extracted data; the data for the newer studies are provided in tables in Section H.2.3.
lfthe standard error (SE) of RR was reported in the publicatio~ the standard error ofln(RR) was taken
as ln(SE). If SE was not reported and the CI was reasonably symmetric arotu1d the point estimate (< 5%
difference between upper and lower half Cl), it was approximated as (ln(upper bound CI)-ln(lower
bound Cl)) /3.92. Different approaches in the event of more substantial CI asymmetry. If the measure
of RR was a SMR or SIR, SE was approximated by (1/0) 112, where O is the observed number of cases
(Greenland & O'Rourke, 2008). IfRR was 1 or > 1, SE was estimated from the upper half Cl , as
(ln(upper botu1d CI)- ln(RR))/1.96. For RR < 1, SE was estimated from the lower half CI in an
equivalent manner. Despite these varying approaches, differences in the method of estimating SE are
unlikely to substantially affect the point estimate or CI of a meta-RR .

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212

213
214
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Data analysis
Meta-analyses were performed using the metan procedure in Stata (Stata Corp, College Station TX).
The metan procedure also provides options for utilizing a user-provided estimate of SE or estimating SE
from input confidence intervals assuming approximate symmetry.
For each cancer type of interest, the initial analysis included all of the selected studies in a fixed-effects
model. Models were specified using the logs of RR and SE as input parameters, allowing the software
to estimate study-specific and overall 95% Cls . Heterogeneity was assessed using the I2 statistic
(Higgins et a1.. 2003) and visual inspection of the plots. If no important heterogeneity was indicated, the
fixed-effects meta-estimate was taken as the measure of overall association . Fixed effects models are
preferred for this purpose, as they are generally unbiased (Poole and Greenland, 1999). Where notable
heterogeneity was indicated, a random-effects model using the DerSimonian-Laird estimators was
applied to estimate the overall association. EPA ' s preferred approach is to estimate SE according to the
methods described above . With this procedure, the study-specific Cls displayed on forest plots were
estimated by the software and may differ slightly from those reported in the original publications.

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226
227
228
229
230

The influence of individual studies was assessed in a "leave one out" meta-analysis using the metaninf
procedure in Stata . Each study was omitted in tum and the meta-estimate was re-calculated without that
study to gauge its effect on the overall association. Meta-analyses stratified by the quality score
assigned in the initial reviewer were carried out to assess whether effects differed in high versus
medium- or low-quality studies.
The potential for publication bias was assessed by visual inspection of funnel plots.
Sample Stata commands are provided in Section H.2 .4.

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H.2.2

Results

H.2.2.1
lnitial Meta-Analyses
Non-Hodgkin lymphoma

In the fixed-effectsmodel for NHL (Figure_ApxH-1), the meta-RR for overall exposure to TCE was
1.02 (95% CI 0.97-1.08)with moderateheterogeneitybetween studies (12 38.4%, p 0.05). The large
study by Vlaanderen et al. (2013 ) was heavily weighted in the fixed-effectsmodel. Fitting a randomeffects model (Figure_Apx H-2) to the same set of studies reduced the weight of the (Ylaanderen et al..
2013 ) study and gave a meta-estimateof 1.14 (95% CI 1.00-1.30).
In the 2011 TCE meta-analysisof NHL, there was some indicationof heterogeneity (12-valuewas 26%,
suggesting low-to-moderateheterogeneity).Little to no heterogeneitywas found for kidney or renal
cancers. Additional analysesfocused on the studies with the highest exposure, because ifTCE exposure
increases the risk of NHL, the effects should be more apparentin the highest exposure groups. Analysis
showed that the summaryeffect estimate of the highest exposedgroups was stronger, a finding that lent
support to the conclusion that TCE exposure increased the risk of NHL. Since moderate heterogeneity
(greater than in 2011) was identifiedfor the overall set of studies,EPA additionally analyzed results
from populations identified as receiving ''high exposure"to TCE in order to parallel the analyses
performed in the 2011 IRIS Assessment.Fixed-and random-effectsmodels comparing the highest to
lowest exposure groups in each study also weightedthe (Ylaanderen et al., 2013 ) study heavily and
produced meta-RRs of 1.03 (95% CI 0.93-1.15) and 1.33 (95% CI 0.98-1.80), respectively (Figure_Apx
H-3 and Figure_Apx H-4). Extracted RR estimates and confidenceintervals from each NHL study are
presented in Table_ApxH-7, Table_ApxH-8, and Table_ApxH-9.
Figure_Apx H-1. Fixed-effects model, overall association of NHL and exposure to TCE.
'!I,

Study

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256
257

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Figure_Apx H-2. Random-effects model, overall association of Nill, and exposure to TCE.
%

Sludy

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0.76 (0.94 , 2All)

1.22

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5.74

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2.52

1.20 (0.65. 2&)

2.41

1.40 (O.t1, 2.42)

4.51

1.20 fl).15, 1 ,7'0)

8.57

1.14 (1 .00, 1.30)

100.00

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.5

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5

2

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260
261

Figure_Apx H-3. Fixed-effects model, association of NHL and high exposure to TCE .

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268
269
270
271
272
273
274
275
276

Kidney Cancer

For kidney cancer, the fixed effects model (Figure_ApxH-5) gave a meta-RR of 1.06 (95% CI 1.001.11) for overall exposure, with moderate, statistically-significant heterogeneity (12 41.1%, p 0.04). As
for NHL, the study of (Vlaanderen et al.. 2013) was heavily weighted. In the randoµi-effectsmodel
(Figure_Apx H-6), the meta-RR was 1.22 (95% CI 1.07-1.38). Extracted RR estimates and confidence
intervals from each kidney cancer study are presented in Table_ApxH-10 and Table_Apx H-11.

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Figure_ Apx H-5. Fixed-effects model, overall association of kidney cancer and
exposure to TCE.

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exposure to TCE.
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289
290
291
292
293
294
295
296

Liver cancer
Fixed- and random-effectsmodels for liver cancer showed a similar pattern of results, with meta-RRs of
1.08 (95% CI 0.99-1.18) and 1.18 (95% CI 0.98-1.43), respectively (Figure_Apx H-7 and Figure_Apx
H-8). Heterogeneitywas moderateand not statisticallysignificant(12 36.5%, p 0.107). Extracted RR
estimates and confidenceintervalsfrom each liver cancer study are presented in Table_Apx H-12 and
Table_Apx H-13.
Figure_Apx H-7. Fixed-effectsmodel, overall associationof liver cancer and
exposure to TCE.

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 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
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Figure_Apx H-8. Random-effects model, overall association of liver cancer and
exposure to TCE.

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Page 628 of 691

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

303
304
305

H.2.2.2
Sensitivity analyses
Removal ofVlaanderen et al. (2013~
In analyses of influentialobservations,the study of (Vlaanderenet al .. 2013) strongly influenced the

306

meta-RRs for all three cancers (Table_Apx H-4, Table_Apx H-5, and Table_Apx H-6). No other single
study had an appreciableimpact on the overall association.Further meta-analyseswere conductedto
characterizethe sensitivityof the resultsto the influenceof that study.

307

308
309

Table Aex H-4. Anal,rsis of influential studies: NHL
Study omitted

Estimate

95%CI

Bove et al. 2014a

1.02

0.97

1.08

Bove et al. 2014b

1.03

0.97

Hansen et al. 2013

1.02

Lipworth et al.2011

1.02

0.96
0.97

l.09
1.08
1.09

Silver et al. 2014

1.03

0.97

1.09

Vlaanderen et al. 2013

1.20

1.07

1.34

Christensen et al. 20 13

1.02

1.08

Cocco et al. 2013

1.02

0.97
0.96

Greenland et al. 1994

1.02

0.97

Morgan et al. 1998

1.02

0.97

Raaschou-Nielsen 2003

0.95

Radican et al. 2008

1.01
1.02

Zhao et at 2005

1.02

0.96

Hardell et al. 1994
Nordstrom et al. 1998

1.02
1.02
1.02
1.02
1.02

0.96

Persson and Fredrikson 1999
Purdue et al. 2011
Wang et al. 2009

0.96

0.96
0.97

1.08
1.09
1.09
1.07
1.08
1.08
1.08
1.08

0.96

1.08
1.08

0.96

1.08

310
Table A~x H-5. Analysis of influential studies: Kidne! cancer
95%CI
Study omitted
Estimate
Bove et al. 2014a

1.06

1.00

Bubagen et al. 2016

1.05

1.00

Hansen et al. 2013

1.06
1.06
1.05
1.26
1.06
1.06

1.00
1.01

1.00

Morgan et al. 1998

1.06
1.06

1.00

Radican et al. 2008

1.06

1.00

Zhao et al 2005

1.06

1.00

1.05

1.00

Lipworth et al. 2011
Silver et al. 2014
Vlaanderen et al. 2013
Christensen et al. 2013
Purdue et al. 2016
Greenland et al. 1994

BrOning et al. 2003

Page 629 of 691

1.00

1.14
1.01
1.01

1.11
1.11
1.11
1.11
1.11
1.40
1.11
l.12
1.11
1.11
1.11
1.11
1.11

 INTERAGENCYDR.A.FT- DO NOT CITE OR QUOTE
Table Apx H-5. Analysis of influential studies: Kidney cancer
Estimate
95%CI
Stud~ omitted
1.05
1.00
1.11
Charbotel et al. 2006
Dosemeci et al. 1999
I.OS
1.00
1.11
Moore et al. 2010
1.05
1.00
1.11
Pesch et al. 2000
1.04
0.99
1.10
Raaschou-Nielsen et al.
2003
l.00
LOS
1.11

311
Table A2x H-'. Analfsis of influential studies: Liver cancer
Estimate
95%CI
Stud:tomitted
Bove et al. 2014a
1.09
0.99
1.19
1.04
0.95
1.14
Hansen et al. 2013
1.09
0.99
Lipworth et al. 2011
1.19
Silver et al. 2014
1.08
0.99
1.19
Vlaanderen et al. 2013
1.34
1.13
1.59
Christensen et al. 2013
1.08
0.99
1.18
Boice et al. 2006
1.08
0.99
1.18
1.08
0.99
Greenland et al. 1994
1.19
Morgan et al. 1998
1.18
1.08
0.99
Radican et al. 2008
1.08
1.19
0.99
Raaschou-Nielsen et al.
2003
1.05
0.95
1.16
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327

Meta-R.Rs for each cancer were re-estimated by omitting that study from the fixed-effects model. For
NHL, omitting the study of (Vlaanderen et al.. 2013 ) from the analysis of overall exposure to TCE
(Figure_Apx H-9) substantially reduced between-study heterogeneity (I29.7%, p 0.34) and yielded a
meta-RR of 1.20 (95% CI 1.07-1.34). In the model for NHL using only the high exposure groups
(Figure_ Apx H- 10), no heterogeneity remained when the (Ylaanderen et al.. 2013 ) study was omitted (12
0.0%, p 0.56); the meta-RR for high exposure was 1.53 (95% Cl 1.19-1.97). Omitting the study of
(Vlaanderen et aL 2013 ) from the model for kidney cancer (Figure_ Apx H-11 ), gave a meta-RR of 1.26
(95% CI 1.14-1.40) with no indication of heterogenei ty (I20.0%, p 0.57). Dropping that study from the
analysis ofliver cancer (
Figure _Apx B-12) similarly eliminated the heterogeneity among studies (I20.0%, p 0.56) and gave a
met.a-RR of 1.34 (95% CI 1.13-1.59). Meta-RR values for all three tissues increased without the
(Vlaanderen et al .. 2013 ) study and achieved statistical significance.

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Figure_Apx H-9. Fixed-effects model, overall association of NHL and exposure to TCE, study of
Vlaanderen et aL (2013) omitted.

RR(9S'1!,CI)

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Vlaanderen et aL(2013) omitted.

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Figure_Apx H-11. Fixed-effects model, overall association of kidney cancer and
exposure to TCE, study of Vlaanderen et al. (2013) omitted.
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Figure_Apx H-12. Fixed-effects model, overall association of liver cancer and
exposure to TCE, study of Vlaanderen et al. (2013) omitted.

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Page 632 of 691

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 349
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356
357

lNTERAGENCYDRAFT- DO NOT CITE OR QUOTE
Stra,tification by Data Quality
Fixed-effectsmeta-analysesfor each cancerwere also stratifiedby the study quality score assigned in
EPA's review to assess whetherthe strengthof associationvaried between highest- and lower-quality
studies. In this manner, the meta-RRwas comparedamong studies scoring High in data quality to those
scoring Medium or Low. For NHL (Figure_ApxH-13),there was no heterogeneity among studies
scored as high quality (12 0.0%, p 0. 78) and the meta-RRwas 1.29 (95% CI 1.04-1.59), while among
studies scored medium or low the meta-RRwas 1.01 (95% CI 0.95-1 .07) with moderate heterogeneity
(12 40.0%, p 0.06). Studies of kidney cancer (

358

359
360
361
362

363
364
365
366
367
368

369
370
371

Figure_ApxH-14) that scored high for data quality gave a meta-RR of 1.14 (95% CI 0.85-1.53)with no
indicated heterogeneity(120.0% p 0.45), whereas lower-rankedstudies gave a meta-RR of 1.06 (95% CI
l.00-1.11) with significant heterogeneity(I2 50.0% p 0.02). In contrast,moderate, non-significant
heterogeneity(1236.0% p 0.21), remainedamong the three studies of liver cancer (Figure_ApxH-15)
scored high for data quality; the meta-RRamong those studieswas 1.59(95% Cl I.I 7-2.16).Lower
scoring studies showed heterogeneity(12 0.0% p 0.56) and a meta-RR of 1.04 (95% CI 0.95-1.15) .
Fitting a random-effectsmodel reducedthe meta-RRfor highly scored studies to 1.42 (95% CI 0.882.30) but did not change the estimatefor lower-scoredstudies.For all three tissues, the meta-RR was
greater among the high quaiity studies comparedto mediumor low quality studies. Statistical
significancewas not always achieveddue to the low number of studies scored High, howeverthis
stratificationdemonstratesstronger associationsof cancerwith TCE exposure among higher-quality
data.

no

372
373

374

Figure_Apx H-13. Fixed-effectsmodel, overall association of NHL and
exposure to TCE stratified by study quality score.
Sludy
10

AR (95"4Cl)

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Page 633 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

377
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379
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381

Figure_Apx H-14. Fixed-effects model, overall association of kidney cancer and
exposure to TCE stratified by study quality score.

.,_

Study
10

RA (95% Cl)

Medlvm/LOW
8ovo2014a
8uhagen2016
snver20 14
\llaanderen2013
Chfii1en,en 2013
Purdue 2016
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Morgen 1998
Radlcan2008
Brilning 2003
Oosemeci 11199
Moore2010
Pesch2000
Raascnou•Nlelsen
2003
c 50.D%
.p
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1.18 (0.47, 2.95) 0.31
2.47 (1.36, 4.49) 0.73
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2.05 (1.13, 3.73) 0.73
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Hansen2013
Upworth2011

Zhao 2005
ChllltlOlel 2006

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H111e,ogene1ty
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Overan (kQuared ,. 41.1'4 . p•0 .038)

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Page 634 of 691 ·

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
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390

Funnel plots including all studies (Figure_Apx H-16, a-c) were consistent with modest publication bias,
with a possible tendency toward omission of moderate-sized studies with weak or null associations .
With the (Vlaanderen et al .. 2013) study omitted, however, the plots became more symmetrical,
consistent with an absence of publication bias among the remaining studies (Figure_ Apx H-16 , d-f).

391
392
393
394

Figure_Apx H-16. Funnel plots for publication bias.
All studies: a. NHL; b. kidney cancer; c. liver cancer;
Omitting Vlaanderen et al. (2013): d. NHL; e. kidney cancer; f. liver cancer.

395
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 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
1405
1406
l407

H.2.3

Selected RR estimates and confidence intervals by study and cancer type

Table_Apx B-7. Selected RR estimates for NHL associatedwith TCE exposure (overall effect) from cohort studies published after
U.S. E PA (2011)
SE

RR

In
(In
Alternate RR
Study
RR LCL UCL type RR RR)
estimates (95% CI)
Comments
Bove et al. 1.15 0.56 2.34 HR 0.140 0.37 None
Adjusted hazard ratio for males and females;
(2014a)
cumulative exposure for high exposure in enlisted
(2799547)
personnel; reference group had no exposure to TCE;
10-year lag ti.me;specific ICD codes were not
reported.
Bove et al. 0.32 0.05 2.10 HR -1.l 0.45 None
Adjusted hazard ratio for males and females, Camp
(2014b)
Lejeune cohort; cumulative exposure to TCE,
(2800329)
>median vs <median (referent group); 10-year lag
time; specific ICD codes not reported.
Hansen et al. 1.21 0.83 1.71 SIR 0.191 0.18 1.11 (0.68-1.72)SIR
ICD-7 200 + 202; standard incidence ratio for males
2013)
for 20-year lag time;
and females in three populations (Denmark,
'2128005)
1.26 (0.89-1.73) SIR
Sweden, and Finland); 10-yearlag time; study also
for no lag
reports hazard rate ratios for NHL based on urinary
TCE metabolite
Lipwortb et 1.02 0.55 1.90 RR 0.020 0.32 1.10 (0.59-2.04) RR
ICD-9 200 + 202; relative risk for sex and race
for 1-4yr exposure;
al. (2011)
combined; 2'.:.5
yr exposure in workers, routine and
0.84 (0.48-1.47)RR
(1235276)
intermittentexposure; referent category was
for <l yr exposure;
nonexposed factory workers
1.31 (0.97-1.73) SMR
for routine and
intermittentexposure
for at least 1 yr
(compared with
general population)
Silver et al 0.87 0.57 1.35 HR -0.14 ~.22 None
Hazard ratio at 5 modified exposure years for males
(2014)
and females; cumulative exposure;adjusted for sex
(2799800)
and paycode; 10-year lag time; specific ICD codes
not reported.
95%

95%

Page 637 of 691

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE

SE
Study
Vlaanderen
et al (2013)
2128436

95% 95% RR
In
(In
Alternate RR
estimates (95% Cl)
Comments
RR LCL UCL type RR RR)
0.97 0.91
1.04 HR -0.030 0.034 0.95 (0.84-1.06) HR for ICD-7 200 + 202; hazard ratio for men and
men and women;
women; third tertile of cumulative exposure
(n=
1211 cases); occupationally unexposed
cumulative exposurefor
high exposure groups individuals were used as the reference group;
only (n=353 cases)
untagged exposure (up to 20 years of lag time had
a negligible impact on HR)

1408

1409
l410

Table_Apx H-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from case-control studies
publis hed after U.S. EPA (2011

Study
~hristensen
~t al. (2013)
2127914)

Cocco et al.
_2013)
'2129584)

1411
1412
1413

1414

95%
95%
RR LCL UCL
0.5
1.2
2.9

1.4

0.9

2.1

SE

lo RR

(In RR)

0.18

0.45

0.34

0.22

Alternate RR
estimates (95% Cl)
1.0 (0.3-3.5) OR for
substantial exposure

Comments
ICD-9 200 + 202; odds ratio for males and females;
any exposure; adjusted by age, census tract median
income, educational attainment (years), ethnicity,
questionnaire respondent (self vs. proxy) and,
smoking using population and cancer controls
wei~ting proportionately
1.0 (0.8-1.2); any vs no Specific ICD codes not reported; odds ratio for
exposure in all subjects males and females; all study subjects with high
probability of exposure ; adjusted by age, gender,
and contributing study (50 cases, 38 controls).

Table_Apx H-9. Selected RR estimates for NHL assoeiated with TCE exposure (effect in the highest exposure group) studies
p ublished after U.S. EPA (2011}
Study

RR

95%
LCL

95%
SE
UCL loe:RR Oo2RRl

Alternate RR
estimates (95°/. en
Cohort Studies

Page 638 of 691

Comments

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Hansen et al.
0.66 0.21
2013}
2128005)
Vlaanderen et al 0.95 0.84
,2013) 2128436
Nested Casecontrol

L415
l416
l417

~.03 HRR

·0.42

1.06

HR

0.051

Christensenet
al. (2013)
2127914}

1.0

0.3

3.5

0.00

0.63

~occo etal.
~2013)
~2129584)

Z.2 0.7

6.7

0.79

0.58

0.50

None

0.059

0.96 (0.84-1.09) HR for men and women; intensity x prevalence
for high exposure groups only (n=269 cases}; occupationally
unexposed individuals were used as the reference group; unlagged
exoosure
Case-Control Studies
NA
ICD-9 200 + 202; odds ratio for males and females; substantial
exposure; adjusted by age, census tract median income,
educational attainment (years), ethnicity, questionnaire respondent
:self vs. proxy) and, smoking using population and cancer controls
lweillhtine.orooortionatelv.
1.4 (1.0-2.1) OR for Specific ICD codes were not reported; odds ratio for males and
t> 150 ppm intensity
~emales;>75 ppm intensity level for study subjects with high
1evelamong all
probabilityof exposure (9 cases, 5 controls); adjusted by age,
subiects.
aender, and studv.

Table_Apx B-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from cohort studies
p ublished after U.S. EPA (2011)

Study
Bove etal
(2014a)
(2799547)

Buhagen et al
2016) 3502047
Hansen et al.
,2013)
:2128005)

Alternate RR
95% 95%
RR
SE (In
Comments
estimates (95% Cl)
RR LCL UCL type In RR RR)
0.419
Adjusted hazard ratio for males and females; cwnulative
~-44 None
1.52 0.64
3.61 HR
exposure for high exposure in enlisted personnel;
reference group had no exposure to TCE; 10-year lag
time
None
1.7 1.0
3.0
SIR
0.53
0.30
14cases had confinned occupationalexposure to TCE.

1.04 0.71

1.50

SIR

0.039

0.18

1.11(0.67-1.73)

SIRfor 20-year lag
time; 1.01 (0.701.42}SIR for no
lag

Page 639 of 691

Standard incidence ratio for males and females in three
populations (Denmark, Sweden, and Finland); IO-year
lag time; study also reports hazard rate ratios for kidney
cancer based on urinary TCB metabolite

 INTERAGENCYDRAFT- DO NOT CITE OR QUOTE
95%
RR
SE (In
Alternate RR
type
In RR RR)
estimates (95% Cl)
Comments
RR LCL UCL
0.42
(0.13-1.42)
Relative risk; sex and race combined; ~5 yr exposure in
2.19 RR
-0.16
0.48
0.85 0.33
RR for 1-4 yr
workers, routine and intermittent exposure; referent
exposure; 0.52
category was nonexposed factory workers
(0.21-1.30) RR for
<1 yr exposure;
0.66 (0.38-1.07)
SMR for routine
and intennittent
exposure for at
least 1 yr
(compared with
11:eneral
population)
1.24 0.87
0.215
Hazard ratio at 5 modified exposure years for males and
1.77 HR
0.18 None
females; cumulative exposure; adjusted for sex and
paycode; 10-year lag time
0.00
0.030 0.86 (0.75-0.98) HR for Hazard ratio for males and females; third tertile of
1.00 0.95
1.07 HR
men and women;
cumulative exposure (n=l372 cases); occupationally
cumulative exposure for unexposed individuals were used as the reference
high exposw-egroups group; unlagged exposure (up to 20 years of lag time
only (n=251 cases)
had a negligible impact on HR)
95%

Study

Lipworth et
al (2011)
(1235276)

Silver et al
(2014)
(2799800)
Vlaanderen et
al (2013)
(2128436)

1418
l419

(420

Table_Apx H-11. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from case-control studies
published after U.S. EPA (2011)
95%
In
Study
RR LCL UCL RR
2.4
0.11
2hristense 0.9 0.4
h et al.
2013)
2127914)
95%

SE(ln

Alternate RR estimate
RR)
(95% CI)
0.46 0.6 (0.1-2.8) OR for
substantial exposure

Comments

Odds ratio for males and females; any exposure, adjusted
by age, census tract median income, educational attainment
(years), ethnicity, questionnaire respondent (self vs. proxy),
smoking, and coffee, beer, wine, and spirit intake using
population and cancer controls wei~htin~ prooortionately

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Purdue et D.8
~I. (2016)
3482059)

0.4

1.5

0.22

0.34

OR 0.9 (0.5 - 1.9) for third
tertile of cumulative hours
~xposed,any exposure
intensity(23 cases, 19
controls).

Odds ratio for kidney cancer in group with highest
probability of exposure ~0%; 32 cases, 32 controls);
adjusted for age, sex, race, study center, education level,
smoking status, BMI and
history of hypertension

1421
1422

Table_ApxH-12. SelectedRR estimatesfor liver cancer associatedwith TCE exposure(overall effect) from cohort studies,

l423

published after U.S. EPA (2011)

Study

Bove et al
(2014a)
(2799547)

SE(ln
AlteruateRR
95¾
RR
95%
RR) estimates (95% Cl)
In RR
RR LCL UCL type
None
0.86 0.37 1.97 HR
-0.15 0.43

Hansen et
al. (2013)
(2128005)

1.83

1.24

2.56

SIR

0.604

D.177

Lipworthet
al (2011)
(1235276)

0.83

0.36

1.91

RR

-0.19

0.43

Silver et al
(2014)
(2799800)

0.99

0.50

1.95

HR

-0.010 l.35

Comments
Adjusted hazard ratio for males and females;
cumulittive exposure for high exposure in
enlisted personnel; reference group had no
exposure to TCE; 10-year Jagtime
2.09 (134-3.11) SIR for Liver and biliary passages; standard incidence
20-year lag time; 1.n ratio for males and females in three populations
( 1.24-2.45) SIR for no (Denmark, Sweden, and Finland); 10-year lag
lag
time; study also reports hazard ra1eratios for
liver and biliary passages cancer based on
urinary TCE metabolite
0.69 (0.28-1.71) RR for Liver and biliary passages; relative risk; sex and
1-4 yr exposure; 0.67
race combined; ~ yr exposure in workers,
(0.32-1.42) RR for <l yr routine and intermittent exposure; referent
exposure
category was nonexposed factory workers

0.89 (0.57-1.33) SMR
for routine and
intennittent exposure for
at least I yr (compared
with general population)
None
Liver, biliary passages, and gallbladder; baz.ard
ratio at 5 modified exposure years for males and
females; cumulative exposure; adjusted for sex
and oavcode; l 0-year lag time

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Studv
Vlaandere
net al
(2013)
2128436

1424
1425
1426

95%
RR LCL
1.00

0.90

95•1.
UCL
1.11

RR
tvoe

HR

SE(ln
In RR
0.00

Alternate RR
estimates(95% Cl)

RR)

~.054

1.02 (0.82-1.25) HR for
men and women;
cumulative exposure for
high exposure groups
only (n:::::106cases)

Comments
Hazard ratio for males and females; third
tertile of cumulative exposure (n=422 cases);
occupationally unexposed individuals were
used as the reference group; unlagged
exposure (up to 20 years oflag time had a
negligible imoact on HR)

Table_Apx H-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from case-control studies

p ublished after U.S. EPA (2011)
Alternate
95%

Studv
Christensen et
al.c2013)
2127914)

RR
1.1

95%
LCL UCL
0.1
8.5

In

SE (Jo

RR
RR)
0.095 1.1

RR estimate
(95% Cl)
Comments
2.1 (0.2-18) OR Odds ratio for males and females; any exposure, adjusted by age,
for substantial
census tract median income, educational attainment (years),
~xposure
ethnicity, questionnaire respondent (self vs. proxy), smoking, and
beer , wine, and spirit intake using population and cancer controls
weighting proportionately

1427

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1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452

H.2:4 Sample Stata commanrls for meta-analysis
Notes: the variables LnEst and SE are the natural log(RR) and its estimated standard error,
respectively; Author_date labels studies on forest plots.
Basic fixed-effects analysis with axis labels:
metan LnEst SE, eform label(namevar=Author_date) effect(RR) xlabel(0.1, 0.2, 0.5, 1.0,
2.0,5.0,10)
Basic random-effects analysis with axis labels:
metan LnEst SE random, eform label(namevar=Author_date) effect(RR) xlabel(0.l, 0.2, 0.5, 1.0,
2.0,5.0,10)
.
Basic fixed-effects model omitting one study (indicated by NAME):
metan LnEst SE if Author!="NAME", eform label(namevar=Author_date) effect(RR) xlabel(0.l,
0.2, 0.5, 1.0, 2.0,5.0,10)
Fixed-effects model stratifying by quality score (HiQ):
metan LnEst SE, efonn label(namevar=Author_date) effect(RR) xlabel(0.1, 0.2, 0.5, 1.0,
2.0,5.0, 10) by(HiQ)
Basic "leave one out" analysis of influence:
metaninf LnEst SE, eform label(namevar=Author_date) effect(RR)
Basic funnel plot:
metafunnel LnEst SE

1453

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1454

1455
1456
1457
1458
1459
1460
1461

1462

Appendix I

APPROACHFOR ESTIMATINGWATER
RELEASESFROM MANUFACTURINGSITES
USING EFFLUENTGUIDELINES

This appendix presents a methodology for estimating water releases of TCE from manufacturing
sites using effluent guidelines (EGs). Titis method uses the maximum daily and maximum

average monthly concentrations allowed under the Organic Chemicals, Plastics and Synthetic
Fibers (OCPSF) Effluent Guidelines and Standards (U.S. EPA). EOs are national regulatory
standards set forth by EPA for wastewater discharges to surface water and municipal sewage
treatment plants. The OCPSF EG applies to facilities classified under the following SIC codes:

1463

1464
1465
1466

1467
1468
1469

1470
1471
1472
1473

1474
1475
1476

1477
1478
1479

•
•
•
•

2821-Plastic Materials, Synthetic Resins, and Nonvulcanizable Elastomers;
2823--Cellulosic Man-Made Fibers;
2865-Cyclic Crudes and Intermediates,Dyes, and Organic Pigments; and
2869- Industrial Organic Chemicals, Not Elsewhere Classified.

Manufacturers of TCE would typically be classified under SIC code 2869; therefore, the
requirements of the OCPSF EG are assumed to apply to manufacturing sites. Subparts I, J, and K
of the OCPSF EO set limits for the concentration of TCE in wastewater effluent for industrial
facilities that are direct discharge point sources using end-of-pipe biological treatment. direct
discharge point sources that do not use end-of-pipe biological treatment, and indirect discharge
point sources, respectively (U.S. EPA. 2019c). Direct dischargers are facilities that discharge
effluent directly to surface waters and indirect dischargers are facilities that discharge effluent to
publicly-owned treatment works (POTW). The OCPSF limits for TCE in each of the Subparts
are provided in Table_Apx 1-1.

' bloroeth1yl:ene
of OCPSFEffluent Gw deIines fior T.nc
T able A,PX I - 1• S um1111ary
Maximum
Maximum
for Any
for Any
Monthly
OCPSF Subpart
Basis
One Day
Average
(pg/L)
(pg/L)
0

Subpart I - Direct Discharge Point Sources
That Use End-of-Pipe Biological Treatment

54

21

BAT effluent
limitations and NSPS

Subpart J - Direct Discharge Point Sources
That Do Not Use End-of-Pipe Biological
Treatment

69

26

BAT effluent
limitations and NSPS

26

Pretreatment Standards
for Existing Sources
(PSES) and
Pretreatment Standards
for New Sources
(PSNS)

Subpart K - Indirect Discharge Point
Sources

69

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1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493

BAT = Best Available Technology EconomicallyAchievable; NSPS =New Source Performance Standards; PSES =

Pretreatment Standards for Existing Sources; PSNS = Pretreatment Standards for New Sources.

Source: (U.S. EPA)

To estimate daily releases from the EG, EPA used Equation 1-1to estimate daily releases and
Equation D-2 to estimate annual releases using the parameters in Table_Apx I-2. The prevalence
of end-of-pipe biological treatment is unknown; therefore, EPA used the discharge limits for
direct discharge point sources that do not use end-of-pipebiological treatment (Subpart J) and
indirect discharge point sources (Subpart K). EPA estimated a central tendency daily release
using the limit for the maximum monthly average (26 µg/L) from Subparts J and K, a high-end
daily release using the limit for the maximumfor any one day (69 µg/L) from Subparts J and K,
and an annual release using the maximummonthly average from Subparts J andK.

Equation 1-1
DLxPWxPV
DR=-----1,000,000,000 X OD

1494
1495
1496 Equation 1-2

DLxPWxPV
AR= ---1,000,000,000

1497
1498
1499
1500

Table_Apx 1-2. Default Parameters

Default Value

Unit

Daily release rate

Calculated from
equation

kg/site-day

DL

Discharge limit:8

Max Daily: 69
Average Daily: 26
Annual: 26

µg/L

PW

Produced waterb

10

L/kg

PV

Annual TCE production volume

Site-specific

kg/site-yr

OD

Operating Days0

350

days/yr

AR

Annual release rate

Calculated from
equation

kg/site-yr

DR

I

1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511

for Estimating Water Releases ofTrichloroethylene

fr om M anuf ac tu.rme
. s·t
I es
Parameter
ParameterDescription

a Discharge limits

are based on the maximum discharge limits allowed in the OCPSF EG, which correspondto the
discharge limits for direct discharge point sourceswith no biological end-of-pipetreatment (Subpart J) and indirect
discharge points sources (Subpart K) (citation for 40 C.F.R. 414). There is no "average" daily dischargelimit set by
the EGs; therefore, EPA assumed that the average daily discharge concentrationwould be equal to the maximum
monthly average discharge limit.
"The am01mtof produced water per kilogram of TCE produced is based on the SpERC developed by the European
Soivent Industry Group for the manufactureof a substance,which estimates t Om3 of wastewatergeneratedper
metric ton of substance produced and converted to 10 L/kg(Euro pean Solvents Indusl r\ Group (ESIG).

2012).
~ Due to

large throughput.manufacturingsites are assumed to operate seven days per week and 50 weeks per year
with two weeks per year for shutdownactivities.

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1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523

1524
1525
1526
1527
1528
1529

EPA did not identify TCE-specific information on the amount of wastewater produced per day.
The Specific Environmental Release Category (SpERC) developed by the European Solvent
Industry Group for the manufacture of a substance estimates 10m3 of wastewater generated per
metric ton of substance produced (equivalent to 10 L water/kg of substance produced) (European
Solvents Industr. Group (ESIG). 2012). In lieu ofTCE-specific information, EPA estimated
wastewater flow using the SpERC specified wastewater production volume and the annual TCE
production rates for each facility.Table_Apx I-3 provides estimated daily production volume
and wastewater flow for each facility that EPA used the EG to assess water releases.
Table_Apx 1-3. Summary of Facility TrichloroethyleneProduction Volumes and
Wastewater Flow Rates
Daily
Daily
Annual Production
Annual
Production
Wastewater
Site
Volume
Operating Days
Volume
Flow
(kg/site-yr)
(days/yr)
(kg/site-day)
(L/site-day)
Solvents &
Chemicals,
Pearland, TX1

20,382,094

350

58,234

582,345

Occidental
Chemical Corp.
Wichata,KS8

20,382,094

350

58,234

582,345

• The 2015 annual production volumes in the 2016 CDR for these sites was either claimed as CBI or withheld. EPA
estimate the production volume by subtractingknown site production volumes from the national production volume
and averaging the result over all the sites with CBI or withheld production volumesand converting from poWldsto
kilograms.
b Annual production volume for this site is based on the 2015 production volume reported in the 2016 CDR and
converting from pounds to kilograms.

1530

1531
1532
1533
1534
1535
1536
1537
1538
1539

1540

EPA estimated both a maximum daily release and an average daily release using the OCPSF EG
limits for TCE for maximum on any one day and maximum for any monthly average,
respectively. Prevalence of end-of-pipebiological treatment at TCE manufacturing sites is
unknown; therefore, EPA used limits for direct discharges with no end-of-pipe biological
treatment and indirect dischargers as conservative.EPA estimated annual releases from the
average daily release and assuming 350 days/yr of operation.
Example max daily, average daily, and annual.waterrelease calculationsfor TCE at
manufacturing sites based on the estimated production volume for Solvents & Chemicals
(44,934,862lbs/yr or 20,382,094kg/yr)22:

1541

µg

69y

1542

Max DR

X

L

10 kg

X

kg

20,382,094 yr

= -------"'--------,,-_;;._

1,000 ,000 ,000 Kg
l!:B..
x 350 days
yr

22

kg

= 0.04day

This estimatedproductionvolumeisequalto the estimatedproductionvolumeassessed for all rnamfacnuingsites.

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1543

1544

µg
L
lg/_
26 T x 10 kg x 20,382,094 yr

Average DR

= -------------,,-,----

1 000 000 000 H1l
Kg x 350 days
yr
I

I

kg

= 0.015 -

day

I

1545
1546

µg
L
kg
26 T x 10 kg x 20,382,094 yr
AR=

µg

1,000,000,000 Tig

1547

Page 647 of691

kg
=5.3-

yr

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1548

Appendix J

1549
1550
1551
1552
1553
1554
1555
1556
1557

1558
1559
1560
1561

SAMPLE CALCULATIONS FOR
CALCULATING ACUTE AND CHRONIC (NONCANCER AND CANCER) INHALATION
EXPOSURES

Sample calculations for high-end and central tendency acute and chronic exposure
concentrations for one setting, Manufacturing,are demonstratedbelow. The explanation of the
equations and parameters used is provided in [EnvironmentalReleasesand Occupational
ExposureAssessmenJ.Docket: EPA-HQ-OPPT-2019-0500].
The final values will have two
significant figures since they are based on values from modeling.

J.1

Example High-End AC, ADC , and LADC

Calculate ACHE:

CHEX ED
ACHE= AT

1562

acute

1563
1564
1565
1566

--------

ACnE

=

2.6ppm x 8 hr/day
24 hr I day

= 0.87 ppm

Calculate ADCHE:

ADCnE =

1567

CHE X

ED

X EF
AT

X

EWY

1568
2. 6 ppm

1569

ADCnE =

hr

days

x 8 day x 250 year x 40 years
d

h

ays
ours)
(40 years x 365 year x 24 day

= 0.59ppm

1570
1571

1572
1573

Calculate LADCHE:
LADCHE =

CHE X

ED X EF X EWY
ATLADc

1574

hr

1575

days

2. 6 ppm x 8 day x 250 year x 40 years
LADCHE =
da
h
.

( 78 years

ys

ours)

x 365 year x 24 day

1576
1577

Page 648 of 691

= 0.30ppm

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1578
1579
1580

J.2

Example Central Tendency AEC, ADC, and LADC

Calculate ACcr:

Ccr x ED
ACcr = AT.

1581

acute

1582
1583
1584
1585

---

ACcr =

0.03 ppm x 8 hr/day
24 hr/day
= 0.01 ppm

Calculate ADCcr:

1586

ADCcr

=

Ccr X ED x EF X WY
AT

1587
hr
days
0.03 ppm x Belayx 250 year x 31 years

1588

~

ADCcr =

hr

31 years x 365 yr x 24 day

= 0.01 ppm

1589

1590
1591

Calculate LADCcr:

LADCer

=

Ccr x ED x EF x WY
ATc

1592

1593

hr
days
0. 03 ppm x 8 day x 250 year x 31 years
LADCcr =
da
78 years x 36S ye!: x 24 hr /day

Page 649 of 691

= 2. 8 x 10- 3 ppm

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594

Appendix K

595

596
597
598

VAPOR DEGREASINGAND COLD CLEANING
NEAR-FIELD/FAR-FIELDINHALATION EXPOSURE
MODELS APPROACH AND PARAMETERS

This appendix presents the modeling approach and model equations used in the following models:

599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637

•
•
•
•

Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation Exposure Model;
Conveyorized Degreasing Near-Field/Far-Field Inhalation Exposure Model;
Web Degreasing Near-Field/Far-Field Inhalation Exposure Model ; and
Cold Cleaning Near-Field/Far-Field Inhalation Exposure Model.

The models were developed through review of the literature and consideration of existing EPA exposure
models. These models use a near-field/far-field approach (Nicas. 2009), where a vapor generation source
located inside the near-field diffuses into the surrounding environment. Workers are asswned to be
exposed to TCE vapor concentrations in the near-field, while occupational non-users are exposed at
concentrations in the far-field.
The model uses the following parameters to estimate exposure concentrations in the near-field and farfield :
•
•
•
•
•
•
•

Far-field size;
Near-field size;
Air exchange rate;
Indoor air speed;
Exposure duration;
Vapor generation rate; and
Operating hours per day.

An individual model input parameter could either have a discrete value or a distribution of values. EPA
assigned statistical distributions based on available literature data . A Monte Carlo simulation (a type of
stochastic simulation) was conducted to capture variability in the model input parameters. The
simulation was conducted using the Latin hypercube sampling method in @Risk Industrial Edition,
Version 7.0.0. The Latin hypercube sampling method is a statistical method for generating a sample of
possible values from a multi-dimensional distribution. Latin hypercube sampling is a stratified method ,
meaning it guarantees that its generated samples are representative of the probability density function
(variability) defined in the model EPA performed the model at 100,000 iterations to capture the range of
possible input values (i.e., including values with low probability of occurrence) .
Model results from the Monte Carlo simulation are presented as 95 th and 50th percentile values. The
statistics were calculated directly in @Risk. The 95th percentile value was selected to represent high-end
exposure level, whereas the 50 th percentile value was selected to represent typical exposure level. The
following subsections detail the model design equations and parameters for vapor degreasing and cold
cleaning models .

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638
639
640
641

642
643
644

645
646
647

K.1 Model Design Equations
---Figure_Apx K-1 through Figure_Apx K-3 illustrate the near-field/far-field

modeling apprm1~h as it was

applied by EPA to each vapor degreasingand cold cleaning model.As the figures show, volatile TCE
vapors evaporate into the near-field, resulting in worker exposuresat a TCE concentration CNF.The
concentrationis directly proportionalto the evaporationrate ofTCE, G, into the near-field, whose
volume is denoted by VNF.The ventilationrate for the near-field zone (QNF)determines how quickly
TCE dissipates into the far-field,resulting in occupationalnon-userexposures to TCE at a concentration
CFF.VFFdenotes the volume of the far-field space into which the TCE dissipates out of the near-field.
The ventilationrate for the surroundings,denoted by Qpp, determineshow quickly TCE dissipates out of
the surroundingspace and into the outside air.

648
Far-Field

vff

r

'-H

Near-Field
VNF

c,lf

649

650
651
652
653

Q

➔

Figure_Apx K-1. The Near-Field/Far-Field Model as Applied to the Open-Top Vapor Degreasing
Near-Field/Far-Field Inhalation Exposure Model and the Cold Cleaning Near-Field/Far-Field
Inhalation Exposure Model

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----------

far-Field

v.,

c.,
~----

►(

654
655
656
657

Near-Field-----

Figure_Apx K-2. The Near-Field/Far-Field Model as Applied to the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model
-----------Far-Field-----------

Near-Field-------.

658
659
660
661
662
663
664

Figure_Apx K-3. The Near-Field/Far-Field Model as Applied to the Web Degreasing NearField/Far-Fie ld Inhalation Expos ure Model

The model design equations are presented below in Equation K-1 through Equation K-18. Note the
design equations are the same for each of the models discussed in this appendix.

Page 6S2 of 691

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666

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Near-Field Mass Balance
Equation K-1

667
668
669

670
671
672
673
674
675
676

677
678
679

Far-Field Mass Balance
Equation K-2

Where:

VNF

=

Vpp

=

QNF =

Qpp =
CNF =
Cpp =
G
=
=
t

near-fieldvolume;
far-field volume;
near-field ventilationrate;
far-field ventilationrate;
average near-fieldconcentration;
average far-field concentration;
average vapor generationrate; and
elapsed time.

680
681
682
683

Both of the previous equations can be solved for the time-varyingconcentrationsin the near-field and
far-field as follows (Nicas. 2009):

684

Equation K-3

685
686
687

Equation K-4

688
689
690

Where :
Equation K-5

691
692
693

Equation K-6

kz

694
695
696

QNFQFFVNF0..1
- A2)
Equation K-7

k3 = QNpQpp
+ l1VNF(QNF+ Qpp)
QNFQFFVNF(J..1
- A.z)

697
698
699

Equation K-8

700

701
702
703

= QNpQpp+ l2VNp(QNF + Qpp)

Equation K-9

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

704
705
706
707
708
709

Equation K-10

A1 = 0.5 [-(QNFVFF

+ VNF(QNF+ QFF)) +
VNFVFF

Equation K-11

A. = Q.S[-(QNFVFF+ VNF(QNF
+ QpF))2
VNFVFF

710
711

712

713
714
715
716
717
718

719
720
721
722

EPA calculated the hourly TWA concentrationsin the near-field and far-field using Equation M-1221
and Equation M-13, respectively. Note that the numerator and denominator of Equation M-1221 and
Equation M-132 use two different sets of time parameters. The numerator is based on operating times
for the scenario (e.g.,, two or eight hours for OTVDs, 8 to 24 hours for conveyorized degreasers, 8 hours
for web degreasers, and 3 to 8 hours for cold cleaning, see Appendix M.2) while the denominator is
fixed to an average time span, t_avg, of eight hours (since EPA is interested in calculating 8-hr TWA
exposures). Mathematically, the numerator and denominatormust reflect the same amount of time. This
is indeed the case since the numerator assumes exposures are zero for any hours not within the operating
time. Therefore, mathematically speaking, both the numerator and the denominatorreflect eight hours
regardless of the values selected for ti and b.
Equation K-12

723
724
725
726
727

Equation K-13

728

729

730
731
732
733

734
735
736

To calculate the mass transfer to and from the near-field, the free surface area, FSA, is defined to be the
surface area through which mass transfer can occur. Note that the FSA is not equal to the surface area of
the entire near-field. EPA defined the near-field zone to be a rectangular box resting on the floor;
therefore, no mass transfer can occur through the near-field box's floor. FSA is calculated in Equation
M-23, below:
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737
738
739
740
741
742
743
744
745
746

Equation K-14

Where: LNF,WNF,and HNFare the length,width, and height of the near-field, respectively. The nearfield ventilation rate, QNF,is calculated in Equation M-154 from the near-field indoor wind speed, VNF,
and FSA, assuming half of FSA is available for mass transfer into the near-field and half of FSA is
available for mass transfer out of the near-field:
Equation K-15

747
748
749
750
751
752
753
754
755
756
757
758

The far-field volwne, VFF,and the air exchangerate, AER, is used to calculate the far-field ventilation
~~~~~~~~:
.
Equation K-16

Using the model inputs described in AppendixE.2, EPA estimatedTCE inhalation exposures for
workers in the near-field and for occupationalnon-users in the far-field. EPA then conducted the Monte
Carlo simulations using@Risk (Version 7.0.0). The simulationsapplied 100,000 iterations and the Latin
Hypercube sampling method for each model.

759
760

761
762
763

K.2

Model Parameters

Table_Apx K-1 through Table_Apx K-4 summarizethe model parameters and their values for each of
the models discussed in this Appendix.Each parameter is discussedin detail in the following
subsections.

Page 655 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
1764
l 765

l 766

Table_Apx K-1. Summaryof ParameterValues and DistributionsUsed in the Open-TopVapor DegreasingNear-Field/Far-Field
Inh aIation E xoosure M odeI
Input
Symbol Unit
Parameter ,
Far-field
vo lume

Near-field
indoor wind
speed
Near-field
lenlrth
Near-field
width
Near-field

heil!ht
Startin g
time

Exposure
Duration
Averaging

Time
Vapor
generation
rate
Operating
hours per

·- -·-

■

- ..---

.. ...

- · DistributionParameten

Value

Basis

Lower
Bound

Upper
Bound

Mode

Comments

Distribution

Tne

VFF

ft3

10,594

Midpoint

10,594

70,629

17,657

Triangular

See Section K .2.1

AER

hr-I

2

Mode

2

20

3.5

Triangu lar

See Section K.2.2

ft/hr

1,181

154

23,882

Air
exchange
rate

DeterministicVal11es

VNF

50th
percentile
50th
oercentile

-

-

-

-

-

-

-

-

-

-

0

-

-

-

-

Constant
Value
Constant
Value
Constant
Value
Constant
Value

hr

8

-

hr

8

-

mg/hr

2.34E+o7

lb/hr

51.50

hr/day

8

10

LNF

ft

10

-

-

WNF

ft

10

-

HNF

ft

6

t1

hr

ti

1.3

2

202.2

8

-

--

-

-

-

Constant
Value

Average

4.54E+02

4.67E+07

-

Discrete

Average

0.001

103.00

-

Discrete

-

Discrete

G

OH

-

-

cm.ls

tavg

-

-

day

Page 656 of 691

See Section K.2.3

See Section K.2.4

Constant.
See Section K.2 .5
See Section K.2.6
See Section K.2.7

See Section E.2.8

 1767
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INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table_Apx K-2. Summaryof Parameter Values and DistributionsUsed in the ConveyorizedDegreasingNear-Field/Far-Field
Inh a Iation
. E xoosure Model
- - -- - - - .
-

..,..

Parameter
Far-field
volume
Air
exchange
rate

..

Symbol Unit

width

Near-field
heii!ht
Starting
time
Exposure
Duration
Averaging
Time
Vapor

generation

Lower

Upper

Bound

Bound

Mode

Distribution

Comments

Twl

ft3

10,594

Midpoint

J0,594

70,629

17,657

Triangular

See Section K.2.1

AER

br·l

2

Mode

2

20

3.5

Triangular

See Section K.2.2

ft/hr

l,181

percentile

154

23,882

emfs

10

1.3

202.2

-

LNF

ft

10

-

-

-

-

WNF

ft

10

-

-

-

-

HNF

ft

6

-

-

-

t1

hr

0

-

-

-

t2

hr

24

-

24

8

-

tavg

hr

8

-

-

mg/hr

l.6E +07

Average

lb/hr

36 .6

Average

hr/day

24

VNF

G

rate

Operating
hours per
dav

Basia

VFF

Near-field
indoor
wind speed
Near-field
lenf!th
Near-field

Valae

OH

50th
50th

nercentile

-

-

3.63E+o5 3.29E+07
0.80

-

72.5

-

Page 657 of691

-

-

Constant
Value
Constant
Value
Constant
Value
Constant
Value
Constant
Value
Constant
Value
Discrete
Discrete
Constant

See Section K.2.3

See Section K.2.4

Constant.
See Section K.2.5
See Section K.2.6
See Section K.2.7

See Section E.2.-8

 1769
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INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table_Apx K-3. Summary of Parameter Values and DistributionsUsed in the Web DegreasingNear-Field/Far-FieldInhalation
Exoosure Md
o eI
. ... . . -- - ... . - -·
Input

Parameter
Far-field
volume
Air
exchange
rate
Near-field
indoor

Symbol
V pp

AER

leneth
Near-field
width
Near -field

heiaht
Starting
time
. Exposure

Duration
Averaging
Time

Vapor
generation
rate
Operating
hours per

Uait

-

•

--

Comment.,

Value

Basis

Lower
Bound

Upper
Bound

Mode I

ft3

10,594

Midpoint

10,594

70,629

17,657

Triangular

See Section K.2.1

11r-1

2

Mode

2

20

3.5

Triangular

See Section K.2.2

154

23,882

1.3

202.2

50th

I

-

ft/hr

1,181

cm/s

lO

LNF

ft

10

-

-

-

-

WNF

ft

10

-

-

-

-

HNF

ft

6

-

-

-

-

ti

hr

0

-

-

-

-

12

hr

8

-

tavg

hr

8

-

G

mg/hr

-

-

OH

hr/day

24

-

VNF

wind speed
Near-field

.

-

I

oercentile
50th
oercentile

8

-

-

1.12E+05 1.12E+05

-

-

day

Page 658 of 691

.

Tne

-

See Section K.2.3

-

Constant
Value
Constant
Value
Constant
Value
Constant
Value
Constant
Value
Constant
Value

-

Discrete

See Section K.2.7; Single Data
Point

-

Constant

See Section M.2.8

-

8

Distribution

See Section K.2.4

Constant.
See Section K.2.5
See Section K.2.6

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
l771
l772

Table_ApxK-4. Summaryof ParameterValues and DistributionsUsed in the Cold CleaningNear--Field/Far
-Field Inhalation
Exnosure M. odeI

Input
Symbol
Parameter

Starting

-

...

- - Distribution

Buis

Lower
Bound

Bound

ft3

10,594

Midpoint

10,594

70,629

17,657

Triangular

See Section K.2.1

br•I

2

Mode

2

20

3.5

Triangular

See Section K.2.2

ft/hr

1,181

154

23,882

-

-

cm/s

10

1.3

202.2

-

-

ft

10

-

-

-

-

ft

10

-

-

-

-

HNF

ft

6

-

-

-

-

t1

hr

0

-

-

-

-

Constant
Value
Constant
Value
Consta11t
Value
Constant
Value .

ti

hr

-

8

-

Discrete

tavg

hr

8

-

.mwbr

5.14E+o5

Average

Constant
Value
Discrete

lb/hr

1.13

Average

-

width

time
Exposure
Duration
Averaging
Time
Vapor
generation
rate
Operating
hours per

Unit

-

Value

Far-field
VFF
volume
Air
exchange
AER
rate
Near-field
indoor
VNF
wind i.,-peed
Near-field
LNF
leneth
Near-field
WNF
Near-field
height

-

DetenainiatieValat1

G

OH

hr/day

-

50th
oercentile
50th
percentile

-

3

-

-

-

Upper

6.28E+02 1.02£+06
0.001

-

2.26

-

dav

1773

Page 659 of 691

Mode

-

Comments

Tne

Discrete

-

See Section K.2.3

See Section K.2.4

Constant.

See Section K.2.5
See Section K.2.6
See Section K.2.7

See Section M.2.8

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790

791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807

K.2.1

Far•Field Volume

EPA used the same far-fie ld volume distribution for each of the models discussed. The far-field volume
is based on information obtained from (Yon Grote et al., 2003 ) that indicated volumes at German metal
degreasing facilities can vary from 300 to several thousand cubic meters. They noted that smaller
volumes are more typical and assumed 400 and 600 m3 (14,126 and 21,189 ft3) in their exposure models
(Yon Grote et al .• 2003). These are the highest and lowest values EPA identified in the literature;
therefore, EPA assumes a triangular distribution bound from 300 m 3 (10,594 ft3)to 2,000 m 3 (70,629 ft3)
with a mode of 500 m 3 ( the midpoint of 400 and 600 m 3) (17,657 ft3).

K.2.2

Air Exchange Rate
EPA used the same air exchange rate distribution for each of the models discussed . The air exchange
rate is based on data from {Hellweg et al.. 2009 ) and information received from a peer reviewer during
the development of the 2014 TSCA Work Plan Chemical Risk Assessment Trichloroethylene:
Degreasing, Spot Cleaning and Arts & Crafts Uses (U.S. EPA. 2013a ). (Hellweg et al., 2009 ) reported
that average air exchange rates for occupational settings using mechanical ventilation systems vary from
3 to 20 hr· 1• The risk assessment peer reviewer comments indicated that values around 2 to 5 hr 1 are
likely (U .S. EPA, 2013a ), in agreement with the low end reported by (Hellweg et al., 2009 ). Therefore,
EPA used a triangular distribution with the mode equal to 3.5 br 1, the midpoint of the range provided by
the risk assessment peer reviewer (3.5 is the midpoint of the range 2 to 5 hr· 1), with a minimum of 2 hr· 1,
per the risk assessment peer reviewer {U.S. EPA, 2013a ) and a maximum of20 hr· 1 per (Hellweg et al.,
2009 ).

K.2.3

Near-!ield Indoor Air Speed

___

_ _

(Baldwin and Maynard, 1998a) measured indoor air speeds across a variety of occupational settings in
the Un ited Kingdom. Fifty-five work areas were surveyed across a variety of workplaces.
EPA analyzed the air speed data from (Baldwin and Maynard. 1998a) and categorized the air speed
surveys into settirlgs representative of industrial facilities and representative of commercial facilities.
EPA fit separate distributions for these industrial and commercial settings and used the industrial
distribution for facilities performing vapor degreasing and/or cold cleaning.
EPA fit a lognonnal distribution for both data sets as consistent with the authors observations that the air
speed measurements within a surveyed location were lognormally distributed and the population of the
mean air speeds among all surveys were lognormally distributed. Since lognormal distributions are
bound by zero and positive infinity, EPA truncated the distribution at the largest observed value among
all of the survey mean air speeds from (Baldwin and Maynard, 19<>8a
) (1998).

808
809
810
811
812
813

EPA fit the air speed surveys representative of industrial facilities to a lognormal distribution with the
following parameter values: mean of22.414 cm/sand standard deviation of 19.958 emfs. In the model ,
the 1ognonnal distribution is truncated at a maximum aUowed value of202.2 cm/s (largest surveyed
mean air speed observe d in (Baldwin and Maynard, 1998a) (1998)) to prevent the model from sampling
values that approach infinity or are otherwise unrealistically large.

814
815
816
817
818
819

(Baldwin and Maynard, 1998a) only presented the mean air speed of each survey. The authors did not
present the individual measurements within each survey. Therefore, these distributions represent a
distribution of mean air speeds and not a distribution of spatially variable air speeds within a single
workplace setting. However, a mean air speed (averaged over a work area) is the required input for the
model.
Page 660 of691

 820
821
822

823
824
825
826
827
828
829

830
831
832
833
834
835

836
837
838
839

840
841

842
843
844
845

846
847
848
849
850
851

K.2.4

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Near-Field Volume

EPA asswned a near-field of constant dimensions of 10 ft x 10 ft x 6 ft resulting in a total volume of 600

ft' .
K.2.5

Exposure Duration
EPA assumed the m.aximwnexposure duration for each model is equal to the entire work-shift (eight
hours). Therefore, if the degreaser /cold cleaning machine operating time was greater than eight hours ,
then exposure duration was set equal to eight hours . If the operating time was less than eight hours , then
exposure duration was set equal to the degreaser/cold cleaningmachine operating time (see Appendix
E.2.8 for discussion of operating hours) .

K.2.6 Averaging Time
__
EPA was interested in estimating 8-hr TWAs for use in risk calculations; therefore, a constan t averaging
time of eight hours was used for each of the models.
K.2.7

Vapor Generation Rate

For the vapor generation rate from each machine type (OTVD, conveyorized and cold), EPA used a
discrete distribution based on the annual unit emission rates reported in the (U.S. EPA . 2018a ). No web
degreasers were reported in the 2014 NEI, therefore , (U.S. EPA. 201 la) data was used for web
degreasers. Annual unit emission rates were converted to hourly unit emission rates by dividing the
annual reported emissions by the reported annual operating hours (see Appendix E.2.8). Reported annual
emissions in NEI without accompanying reported annual operating hours were not included in the
analysis. Emission rates reported as zero were also excluded as it is unclear if this is before or after
vapor controls used by the site and if the vapor controls used would control emissions into the work area
(thus reducing exposure) or only control emissions to the environment (which would not affect worker
exposures) . Table_Apx K-S summarizesthe data available in the 2014 NEI.

Table_Apx K-5. Summary ofTrichloroethylene

Vapor Degreasing and Cold Cleaning Data from

the 2014 NEI

Unit Type

Total Units

Open-Top Vaoor Degreasers
Conveyorized Desrreasers
Web Degreasersb
Cold Cleaning Machines

149
8

1
17

Units with Zero
Emissions
29
0
0
1

Units without
Accompanying

Units Used
in

5

76
3
1

-

-·

•••
62
0
6

• - .-

10

a - Some units with zero emissions also did not include accompanyingoperating hours; therefore, subtracting the uruts with
zero emissions and the units without operating hours from the total units does not equal the units in the analysis due to double
counting.
b - Now eb degreasers reported in the 2014 NEI. Qie web degreaserreported in the (U.S. EPA . 201 la) was usedin this
analysis.
Source: cU.S. EPA . 2018 a}; (U.S. EPA . 201 l a)

852

853
854

855

Table_Apx K-6 through Table_Apx K-9 summarize the distribution of hourly unit emissions for each
machine type calculated from the annual emission in the 2014 NEI.

856

Page 661 of 691

 857

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
Table_Apx K-6. Distribution 0 fT nc. hioroe thtYIene O,pen-T op Vapor Degreasing Unit Emissions
Count
Unit
of
Emissions
Fractional
Units
(lb/unit-hr)
Probability
l
1
1
1
1
1
1
1
1
1

l
1

1
1

1
1
1

l
I

103.00

0.0132

63.95
19.04

0.0132
0.0132

13.20

0.0132

12.18

0.0132

9.47

0.0 132
0.0132

9.21

8.14
7.30
6.93
6.64
6.61
6.44
6.40

0.0132

6.32
5.10

0.0132
0.0132

5.06

0.0 132

4.89

0.0132
0.0132

0.0132
0.0132
0.0132
0.0132
0.0132
0.0132

4.85
4.14
3.96
3.82
3.77
3.68
3.66
3.64

0.0132

3.43
3.40

0.0132
0.0132

1

2.88

0.0132

1

2.79

0.0132

1

2.64

0.0132

1

2.61

0.0132

1

2.48

0.0132

1

2.37

1

2.20
1.97

0.0132
0.0132

1
1
1
1

1
2

1
1
1

1

1
1
1

1.96

0.0 132
0.0132
0.0132
0.0 132
0.0132
0.0263

0.0132

1.73

0.0132
0.0132

1.62

0.0132

Page 662 of 691

 INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Count
of

Units

Unit
Emissions
(lb/unit-hr)

1

1.59
1.44
1.33
1.22

1

1
1

1.09
0.93
0.90
0.84
0.83
0.79
0.79
0.70
0.62
0.60
0.43
0.42
0.39
0.38
038
0.35
0.23
0.18
0.15
0.15
0.14
0.11
0.10
0.10
0.07
0.03
0.001

1

2
1
2
1
1
3
1

1
1
1
1
1
1

1
1
1
1

1
1
1
1

I
2

1
1

1

858
859

Table_Apx K-7. Distribution ofTrichloroetb

0.0132
0.0132
0.0132

0.0132
0.0132
0.0263
0.0132
0.0263
0.0132
0.0132
0.0395
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0132
0.0263
0.0132
0.0132
0.0132

easing Unit Emissions

Jene Conv

Count
of Units

Unit
Emissions
lb/unit-hr

1

72.48

1

1.51
0 .80

1

Fractional
Probabilitv

860
Page 663 of 691

Fnctional
Probabili
0.3333
0.3333

0.3333

 861

862
863

INTERAGENCY DRAFT - DO NOT CITE OR QUOTE
Table_Apx K-8. Distribution ofTrichloroeth Jene Web De reasin Unit Emissions
Ullit
Emilsiom
Count
Fractional
Probabi
of Units
r
0.247
1.00
Table_Apx K-9. Distribution

0 fT

I
C 0 Id Cleanm2
. Um't Emissions
r1c
' hi oroeth yene

Coant
of Units
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

Unit
Emissions

- ·~. •
~

2.26
0.83
0.83
0.83
0.83
0.05
0.01
0.01
0.01
0.00

Fraedonal

- -~

;

0.1000
0.1000
0.1000
0.1000
0.1000
0.1000
0.1000
0.1000
0.1000
0.1000

864
865

870

K.2.8 Operating Hoon
For the operating hours of each machine type (OTVD, conveyorized, web, and cold), EPA used a
discrete distribution based on the daily operating hours reported in the 2014 NEI. It should be noted that
not all units had an accompanying reported daily operating hours; therefore, the distribution for the
operating hours per day is based on a subset of the reported units. Table_Apx K-10 through Table_Apx
K-13 summarize the distribution of operating hours per day for each machine type.

871
872

. hioroet htyIene 0 ,pen-T op Vapor De ,reasing Operating Houn
Table_Apx K-10. Distribu tion
. ofT nc

866
867
868
869

Count of

•

..

-

-

Operathag
Houn

- ·-

.
II

24

Fractional

-~
••

4

0.4048
0.0952
0.2381
0.0476
0.0714

2

0.1429

16
8
6

873

874

Table_Apx K-11. Distribution ofTrichloroeth lene Conve. orized De reasing Operating Hours
Count of
Occurrences

Operating
Houn

24
875
Page 664 of 691

Fractional

p

1.0000

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
876

Table_Apx K-12. Distribution ofTrichloroeth

Count of
Occurrences

lene Web D
Operating

Houn

Fractional

r/da

Probabili

24

877
878

1.0000

Table_Apx K-13. Distributi ODO fT nc. hi oroet hity ene CldCI.
0
ean1n2 0 ,pera ting Hours
Operating
Count of
Houn
Fractional
(hr/day )
Probability
Occurrences
0.4000
24
8
0.5000
0.1000
3

879

880

Page 665 of 691

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
881
882

Appendix L

883
884

BRAKE SERVICING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODEL APPROACH AND
PARAMETERS

885

This appendixpresents the modeling approachand model equations used in the Brake ServicingNear-

886
887

Field/Far-FieldInhalation Exposure Model. The model was developedthrough review of the literature
and considerationof existing EPA exposure models. This model uses a near-field/far-fieldapproach
(Nicas. 2009 ), where an aerosol application located inside the near-field generates a mist of droplets, and
indoor air movements lead to the convectionof the droplets between the near-field and far-field.
Workers are assumed to be exposed to TCE droplet concentrationsin the near-field, while occupational
non-users are exposed at concentrationsin the far-field.

888
889
890

891
892
893
894

The model uses the following parametersto estimate exposure concentrationsin the near-field and farfield:

895
896
897

•
•

898
899

•
•

900
901
902
903
904

•
•
•
•
•

905

•

906
907
908

909
910
911
912

913
914

915
916
917

918
919
920
921

Far-field size;
Near-field size;
Air exchange rate;
Indoor air speed;
ConcentrationofTCE in the aerosol formulation;
Amount of degreaser used per brakejob;
Number of degreaser applicationsper brakejob;
Time duration of brake job;
Operating hours per week;and
Number of jobs per work shift.

An individual model input parameter could either have a discrete value or a distribution of values. EPA

assigned statistical distributionsbased on available literature data. A Monte Carlo simulation(a type of
stochastic simulation) was conductedto capture variabilityin the model input parameters. The
simulation was conducted using the Latin hypercube samplingmethod in @Risk IndustrialEdition,
Version 7.0.0. The Latin hypercube sampling method is a statistical method for generatinga sample of
possible values from a multi-dimensionaldistribution.Latin hypercube sampling is a stratified method,
meaning it guarantees that its generated samples are representativeof the probability density function
(variability)defined in the model. EPA performedthe model at 100,000 iterations to capture the range of
possible input values (i.e., including values with low probability of occurrence).
Model results from the Monte Carlo simulationare presented as 95th and 50 t11percentile values. The
statistics were calculated directly in@Risk. The 95t1tpercentile value was selected to represent high-end
exposure level, whereas the 50th percentilevalue was selected to represent central tendency exposure
level. The following subsections detail the model design equations and parameters for the brake
servicing model.

922
923
924
925

L.1

Model Design Equations

In brake servicing,the vehicle is raised on an automobilelift to a comfortable working height to allow
the worker (mechanic) to remove the wheel and access the brake system. Brake servicingcan include
Page 666 of 691

 926
927

928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943

INTERAGENCYDRAFT - DO '\JOTCITE OR QUOTE
inspections,adjustments,brake pad replacements,and rotor resurfacing.These service types often
involve disassembly,replacementor repair, and reassemblyof the brake system. Automotive brake
cleaners are used to remove oil, grease, brake fluid, brake pad dust, or dirt. Mechanics may occasionally
use brake cleaners, engine degreasers,carburetorcleaners,and generalpurpose degreasers
interchangeably(CARB. 2000). Automotivebrake cleaners can come in aerosol or liquid form (CARB,
2000): this model estimatesexposuresfrom aerosol brake cleaners (degreasers).
Figure L-1 illustratesthe near-field/far-field modeling approachas it was applied by EPA to brake
servicing using an aerosol degreaser. The applicationof the aerosol degreaser immediately generates a
mist of droplets in the near-field,resulting in worker exposuresat a TCE concentrationCNF.The
concentrationis directly proportionalto the amount of aerosol degreaserapplied by the worker, who is
standing in the near-field-rone(i.e., the working zone). The volume of this zone is denoted by VNF,The
ventilation rate for the near-fieldzone (QNF)determineshow quicklyTCE dissipates into the far-field
(i.e., the facility space surroundingthe near-field),resulting in occupationalbystander exposures to TCE
at a concentrationCpp.V FF denotesthe volume of the far-field space into which the TCE dissipates out
of the near-field. The ventilationrate for the surroundings,denotedby QFF,determines how quickly
TCE dissipates out of the surroundingspace and into the outside air.

0

944
945
946
947
948
949
950
951
952
953
954

955
956

-►

Figure L-1. The Near-Field/Far-FieldModel as Applied to the Brake Servicing Near-Field/FarField Inhalation ExposureModel
In brake servicing using an aerosol degreaser,aerosol degreaserdroplets enter the near-field in nonsteady "bursts," where each burst results in a sudden rise in the near-field concentration.The near-field
and far-field concentrationsthen decay with time until the next burst causesa new rise in near-field
concentration.Based on site data from automotivemaintenanceand repair shops obtained by CARB
(CARB, 2000) for brake cleaning activitiesand as explainedin 'SectionsL.2.5 and L.2.9 below, the
model assumes a worker will perform an average of 11 applicationsof the degreaserproduct per brake
job with five minutes between each applicationand that a worker may perform one to four br~e jobs
per day each taldng ·one hour to complete.EPA modeledtwo scenarios:one where the brake jobs
occurred back-to-back and one where brake jobs occurred one hour apart. In both scenarios,EPA

Page 667 of 691

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958

INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
assumed the worker does not perfonn a brakejob, and does not use the aerosol degreaser,during the
first hour of the day.

959
960

961
962
963
964
965

966
967

968
969
970
971

972
973
974
975
976
977
978

979
980

981

EPA denoted the top of each five-minuteperiod for each hour of the day (e.g.,, 8:00 am, 8:05 am, 8: 10
am, etc.) as tm,n,Here, m has the values of 0, 1, 2, 3, 4, 5, 6, and 7 to indicate the top of each hour of the
day (e.g.,, 8 am, 9 am, etc.) and n has the values of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11 to indicate the

top of each five-minute period within the hour. No aerosol degreaser is used, and no exposures occur,
during the first hour of the day, to.oto to,11(e.g.,, 8 am to 9 am). Then, in both scenarios,the worker
begins the first brake job during the secondhour, t1,o(e.g.,, 9 am to 10 am). The worker applies the
aerosol degreaser at the top of the second 5-minuteperiod and each subsequent 5-minuteperiod during
the hour-long brake job (e.g.,, 9:05 am, 9:10 am, ... 9:55 am). In the first scenario,the brake jobs are
performedback-to-back, if performingmore than one brakejob on the given day. Therefore, the second
brake job begins at the top of the third hour (e.g.,, l Oam), and the worker applies the aerosol degreaser
at the top of the second 5-minute period and each subsequent5-minute period (e.g.,, 10:05 am, 10:10
am, ... 10:55 am). In the second scenario, the brakejobs are performed every other hour, if performing
more than one brake job on the given day. Therefore,the second brakejob begins at the top of the fourth
hour (e.g.,. 11 am), and the worker appliesthe aerosol degreaserat the top of the second 5-minute period
and each subsequent 5-minute period (e.g.,, 11:05 am, 11:IO am, ... 11:55 am).
In the first scenario, after the worker performsthe last brake job, the workers and occupationalnon-users
(ONUs) continue to be exposed as the airborneconcentrationsdecay during the final three to six hours
until the end of the day (e.g.,, 4 pm). In the second scenario,after the worker performs each brake job,
the workers and ONUs continue to be exposedas the airborne concentrationsdecay during the time in
which no brake jobs are occurring and then again when the next brake job is initiated. In both scenarios,
the workers and ONUs are no longer exposedonce they leave work.

982

983
984
985
986
987

Based on data from CARB (CARB , 2000), EPA assumes each brakejob requires one 14.4-oz can of
aerosol brake cleaner as described in further detail below. The model determinesthe application rate of
TCE using the weight fraction of TCE in the aerosol product. EPA uses a uniform distribution of weight
fractions for TCE based on facility data for the aerosol products in use (CARB, 2000).

988
989
990
991

The model design equations are presentedbelow.
Near-Field Mass Balance
Equation L-1

992
993
994

Far-Field Mass Balance
Equation L-2

995
996
997

998
999
000
001
002

Where:
VNF
VFF

QNF
Qpp
CNF
CFF

=
=
=

=
=
=

near-field volume;
far-field volume;
near-field ventilationrate;
far-field ventilationrate;
average near-field concentration;
average far-field concentration;and
Page 668 of 691

 003

004
005
006

007
008
009
010

011
012
013
014
015
016
017
018
019

020
021
022
023
024
025
026
027
028
029
030

=

t

INTERAGENCYDRAFT- DO NOT CITE OR Ql 10TI:,
elapsed time.

Solving the above equationsin terms of the time-varyingconcentrationsin the near-field and far-field
yields Equation L-3 and EquationL-4, which EPA appliedto each of the 12 five-minute increments
during each hour of the day. For each five-minuteincrement,EPA calculated the initial near-field
concentrationat the top of the period (tm.n),accounting for both the burst of TCE from the degreaser
application(if the five-minuteincrementis during a brake job) and the residual near-field concentration
remaining after the previousfive-minuteincrement(tm,n..1;
except during the first hour and tm,oof the first
brakejob, in which case there would be no residualTCE_from a previous application). The initial far.

field concentrationis equal to the residual far-field concentrationremaining after the previous fiveminute increment. EPA then calculatedthe decayed concentrationin the near-field and far-field at the
end of the five-minuteperiod,just before the degreaserapplicationat the top of the next period (tm,n+1).
EPA then calculated a 5-minute TWA exposurefor the near-fieldand far-field, representativeof the
worker's and ONUs' exposuresto the airborneconcentrationsduring each five-minute increment using
Equation L-13 and Equation L-14. The k coefficients(EquationL-5 through Equation L-8) are a
function of the initial near-field and far-field concentrations,and thereforeare re-calculated at the top of
each five-minuteperiod. In the equationsbelow,where the subscript "m, n-1" is used, if the value of n-1
is less than zero, the value at "m-1, 11" is used and where the subscript"m, n+ I" is used, if the value of
n+ 1 is greater than 11, the value at "m+ 1, 0" is used.
Equation L-3

Equation L-4

Where:
Equation L-5

031
032
033

Equation L-6
QNF( CNF,o(tm,n)- Cpp,o(tm,n))+ ..l1VNFCNF,o(tm,n)
k:um,n =
VNF(At- A2)

034
035
036

037
038
039
040

Equation L-7
(QNF+ l1VNF)(QNF( Cpp,o(tm,n)- CNF,o(tm,n)
)-l2VNFCNF,o(t'm,n))

k3t• m.n

=---------------------------Az)
QNFVNF(A·l
-

Equation L-8
k+t• m,n

(QNP+ l2VNF)(QNF( CNP,o(tm,n)- CFF,o(tm.n))+ iliVNPCNF,o(tm,n))
QNFVNp(A,1
- A.2)

=--------------------------

041

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
042

043
044
045
046

047
048
049
050
051

Equation L-9

i1

= o.s[-

VNFVFF

Equation L-10

A.
2

= o.s[-(QNFvFF+ VNp(QNF
+ QFF))vNFvFF

Equation L-11
CNF
,o(tm,n)

Equation L-12

052
053
054

(QNFvFF
+ vNF(QNF
+ QFF))+

={

~=
(1,000

0, m=O

:g)+ CNF{Cm.n-i),
n > 0 for all m where brake job occurs

0, m = 0
( tm,n) = {Cpp(tm,n-i), for all n where m > 0
CFF,o

Equation L-13

055
056
057

Equation L-14

058
059
060
061
062
063

064

After calculating all near-field/far-field 5-minute TWA exposures (i.e., CNF,
s-minTWA.tm.nand
CPF, s-min
TWA.tm.n) for each five-minuteperiod of the work day, EPA calculated the near-field/far-field
8-hour TWA concentration and I-hour TWA concentrations following the equations below:

Equation L-15

065

L~= OL~~o[CNF,S-minTWA,tm,n

X

0.0833 hr]

CNF,8-hrTWA

=

CNF,8-brTWA

~ =O~~o[CFF,S-minTWA,tm,n
= _______
B_h_r
_____x 0.0833 _hr]

8 hr

066

067
068
069

Equation L-16

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 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE

070

Equation L-17
l:~;o[CNF,5-m.inTWA ,tmn

071

CNF,1-hrTWA

==

CFF,1-hrTWA

=

1 hr·

072
073

074
075
076
077

x 0.0833 hr]

Equation L-18
r!;o[CFF,5-minTWA,tm,n

x 0.0833 hr]

1 hr

EPA calculated rolling 1-hour TWA's throughout the workday and the model reports the maximum
calculated I-hour TWA.

078
079
080
081
082
083
084
085
086

To calculate the mass transfer to and from the near-field, the free surface area (FSA) is defined to be the
surface area through which mass transfer can occur. The FSA is not equal to the surface area of the
entire near-field . EPA defined the near-field zone to be a hemisphere with its major axis oriented
vertically, against the vehicle, and aligned through the center of the wheel (see Figure L-1). The top half
of the circular cross-section rests against, and is blocked by, the vehicle and is not available for mass
transfer. The FSA is calculated as the entire surface area of the hemi sphere's curved surface and half of
the hemisphere's circular surface per Equation M-19, below :

087

Equation L-19

088

FSA

= (½X 4nR~p) + (½X nR~F)

089
090

091
092
093

094
095
096

Where: RNFis the radius of the near-field
The near-field ventilation rate, QNF,is calculated in Equation M-1520 from the indoor wind speed, VNF,
and FSA, assuming half of the FSA is available for mass transfer into the near-field and half of the FSA
is available for mass transfer out of the near-field:

Equation L-20

097
098

099
100

The far-field volume, VFF,and the air exchange rate, AER, is used to calculate the far-field ventilation
rate, QFF,as given by Equation M-21:

101

102
103

Equation L-21

104

105
106
107
108

Using the model inputs described in Appendix F.2, EPA estimated TCE inhalation exposures for
workers in the near-field and for occupational non-users in the far-field. EPA then conducted the Monte
Carlo simulations using@Risk (Version 7.0.0). The simulations applied 100,000 iterations and the Latin
Hypercube sampling method.

109
110
111

112

L.2

Model Parameters

Table_Apx L-1 surrnna:ri~ lht: model parameters and their values for the Drake Servicing N'?M•Field/
Far-Field Inhalation Exposure Model. Eachparameter is discussedin detail in the following subsections.
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?113
ll 14
H 15

Table_ApxL-1. Summaryof ParameterValues and DistributionsUsed in the Brake ServicingNear-Field/Far-FieldInhalation
Exposure Md
o eI
Coatlat Model

Input
Parameter

-

Symbol

Unit

-

Value

v••

Bub

...
-.,..
--

VariableModelParameterValues

I

Lower

Bound

Far-field volume

VFF

m3

-

-

206

Air exchange
rate

AER

hr'

-

-

ft/hr

-

emfs

-

-

Upper

Mode

■■-

70,679

3,769

Triangular

1

20

3.5

Triangular

0

23,882

Lognormal

0

202.2

-

Distribution based on data
collected by CARB (CARB.
2000).
(Dem.QM
~l al., 2009) identifies
typical AERs of l hr' and 3 to 20
hr-1 for occupational settings
without and with mechanical
ventilation systems, respectively.
(Hellweg et al., 2009) identifies
average AERs for occupational
settings utilizing mechanical
ventilation systems to be between
3 and 20 tu'"1• (Golsteijn et al.,
2014) indicates a characteristic
AER of 4 tu'"1• Peer reviewers of
EPA's 2013 TCE draft risk
assessment commented that
values around 2 to 5 hr 1 may be
more likely (U.S. EPA, 2013a). in
agreement with (Golsteijn et al.,
2.QM). A triangular distribution is
used with the mode equal to the
midpoint of the range provided by
the peer reviewer (3.5 is the
midooint of the range 2 to 5 br 1).
Lognormal distnbution fit to
commercial-typeworkplace data
from (Baldwin and Mamard ,
1998a).

Near-field indoor
wind speed

VNF

Near-field radius

R NF

m

1.5

-

-

-

·-

Const.ant
Value

Constant.

Starting time for
each application
oeriod

t,

hr

0

-

-

-

-

Const.ant
Value

Const.ant.

Page 672 of 691

Lognonnal

Comments

 INTERAGENCYDRAFT - DO NOT CITE OR QUOTE
lapat
Pannaeter
End time for
each application

Symbol

lJalC

---

Value

TCEweigbt
fraction

DegreaserUsed
ner Brake Job
Number of
Applications per
Job

VariableModelParameter
Valaes

..... --

Lower

.....
Upper

Mode

.......

Distributlo

Commeata

0.0833

-

-

-

-

Constant
Value

Assumes aerosol degreaser is
applied in 5-minute increments
during brake iob.

t.vg

hr

8

-

-

-

-

Constant
Value

Const.ant.

wtfrac

wtfrac

-

-

0.40

Wd

oz/ job

14.4

-

-

Applications/
job

Amt

Operating hours
perweek

Number of
Brake Jobs per
Work Shift

nerAnnlication

-- -

hr

NA

Amount Used

Model

t2

oeriod

AveragingTime

-

1.00

-

-

Discrete

-

Constant
Value

-

Const.ant
Value

37.1

-

Calculated

40

122.5

-

Lognormal

1

4

-

11

-

-

gTCFJ
annlication

-

-

14.8

OHpW

hr/week

-

-

N,

jobs/site~shift

-

-

H16

Page 673 of 691

-

-

Discrete distribution of TCEbased aerosol product
fonnulations based on products
identified in EPA' s Preliminary
Information on Manufacturing,
Processing, Distn"bution,Use, and
Disposal for TCE (U.S. EPA,
2017c). Where the weight fraction
ofTCE in the formulation was
given as a range, EPA assumed a
unifonn distribution within the
reported range for the TCE
concentration in the product.
Based on data from CARB
<CARB 2000).

Calculated from the average of
the number of applicationsper
brake and number of brakes per
iob.
Calculated from wtfrac, WcJ.and
NA.
Lognormal distribution fit to the
operating hours per week
observed in CARB (CARB.
2000) site visits.
Calculated from the average
number of brake jobs per site per
year, OHpW, and assuming 52
operating weeks per year and 8
hours oer work shift.

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117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162

L.2.1

Far-Field Volume

The far-field volume is based on information obtained from (CARB. 2000 ) from site visits of 137
automotive maintenance and repair shops in California. (CARB, 2000 ) indicated that shop volumes at
the visited sites ranged from 200 to 70,679 m 3 with an average shop volume of3,769 m 3• Based on this
data EPA assumed a triangular distribution bound from 200 m3 to 70,679 m3 with a mode of 3,769 m3
(the average of the data from (CARB, 2000 )).
CARB measured the physical d.imensions of the portion of the fadlity where brake service work was
performed at the visited facilities. CARB did not consider other areas of the facility, such as customer
waiting areas and adjacent storage rooms, if they were separated by a normally closed door. If the door
was normally open, then CARB did consider those areas as part of the measured portion where brake
servicing emissions could occur (CARB, 2000 ). CARB's methodology for measuring the physical
dimensions of the visited facilities provides the appropriate physical dimensions needed to represent the
far~field volume in EPA' s model. Therefore, CARB' s reported facility volume data are appropriate for
EPA' s modeling purposes.

L.2.2

Air Exchange Rate

The air exchange rate (AER) is based on data from (Demou et al., 2009 ), (Hellweg et al., 2009 ).
(Golsteijn et al .• 2014 ), and information received from a peer reviewer during the development of the
2014 TSCA Work Plan Chemical Risk Assessment Trichloroethylene: Degreasing, Spot Cleaning and
Arts & Crafts Uses (U.S. EPA, 2013a ). {Demou et al., 2009) identifies typical AERs of 1 hr·1 and 3 to 20
hr"1 for occupational settings without and with mechanical ventilation systems, respectively. Similarly,
(Hellweg et al. , 2009 ) identifies average AERs for occupational settings using mechanical ventilation
systems to vary from 3 to 20 br"1• (Golsteijn et al., 2014 ) indicates a characteristic AER of 4 hr"1• The
risk assessment peer reviewer comments indicated that values around 2 to 5 hr"1 are likely (U.S. EPA.
2013a ), in agreement with (Golstei jn et al., 2014 ) and the low end reported by (Demou et al., 2009 ) and
{Hellweg et al., 2009 ). Therefore, EPA used a triangular distribution with the mode equal to 3.5 hr 1, the
midpoint of the range provided by the risk assessment peer reviewer (3.5 is the midpoint of the range 2
to 5 br· 1), with a minimum of 1 hr·1, per (Pemou et al., 2009 ) and a maximum of20 hr"1 per (Demou et
al., 2009 ) and (Hellweg et al., 2009 )).

L.2.3

Near-Field Indoor Air Speed

_

____

_
(Baldwin and Maynard, 1998a) measured indoor air speeds across a variety of occupational settings in
the United Kingdom. Fifty.five work areas were surveyed across a variety of workplaces.
EPA analyzed the air speed data from (Baldwin and Maynard . 1998a) and categorized the air speed
surveys into settings representative of industrial facilities and representative of commercial facilities.
EPA fit separate distributions for these industrial and commercial settings and used the commercial
distribution for facilities performing aerosol degreasing or other aerosol applications.
EPA fit a lognonnal distribution for both data sets as consistent with the authors observations that the air
speed measurements within a surveyed location were lognormally distributed and the population of the
mean air speeds among all surveys were lognonnally distributed. Since lognormal distributions are
bound by zero and positive infinity, EPA truncated the distribution at the largest observed value among
all of the survey mean air speeds from (Baldwin and Maynard, 1998a).

BPA fit the air speed surveys representative of commercial facilities to a lognormal distribution with the
following parameter values: mean of 10.853 cm/sand standard deviation of7.883 cm/s. In the model,
Page 674 of691

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the lognormal distributionis truncated at a maximum allowedvalue of 202.2 cm/s (largest surveyed
mean air speed observed in (Baldwin and Mavnard. 1998a) to prevent the model from sampling values
that approach infinity or are otherwise unrealisticallylarge.

167 (Baldwin and
168

169
170

171
172
173

174
175

176

Ma vnard . 1998a) only presentedthe mean air speed of each survey. The authors did not
present the individual measurementswithin each survey. Therefore,these distributions represent a
distribution of mean air speeds and not a distributionof spatially-variableair speeds within a single
workplace setting. However,a mean air speed (averagedover a work area) is the required input for the
model.

L.2.4

Near-Field Volume

EPA defined the near-field zone to be a hemispherewith its major axis oriented vertically, against the
vehicle, and aligned through the center of the wheel (see Figure L-1). The near-field volume is
calculated per Equation L-22. EPA defined a near-fieldradius (RNF)of 1.5 meters, approximately4.9
feet, as an estimate of the working height of the wheel, as measuredfrom the floor to the center of the

177 wheel.
178
179

Equation L-22

180
181
182
183
184

185
186

187
188
189
190

191
192

193
194

195
196
197
198
199
200
201
202
203
204
205
206

VNF

L.2.5

1

4

3
= -x-nRNP
2 3

Application Time

EPA assumed an average of 11 brake cleaner applicationsper brakejob (see Section F .2.9). CARB
observed, from their site visits, that the visited facilitiesdid not perform more than one brake job in any
given hour (CARB . 2000). Therefore, EPA assumed a brakejob takes one hour to perform. Using an
assumed average of 11 brake cleaner applicationsper brakejob and one hour to perform a brake job,
EPA calculates an average brake cleaner applicationfrequencyof once every five minutes (0.0833 hr).
EPA models an average brake job of having no brake cleaner applicationduring its first five minutes
and then one brake cleaner application per each subsequent5-minuteperiod during the one-hour brake
job.
L.2.6

Averaging Time

EPA was interested in estimating 8-hr TWAs for use in risk calculations;therefore, a constant averaging
time of eight hours was usoo.
L.2. 7 Trichloroethylene Weight Fraction
_
EPA reviewed the PreliminaryInformationon Manufacturing,Processing,Distribution,Use, and
Disposal: Trichloroethylenereport (U.S. EPA 2017c) for aerosol degreasersthat contain TCE. EPA
(2017) identifies 16 aerosol degreaser products that overall range in TCE content from 40 to 100 weight
percent The identified aerosol degreasers include a brake cleaner as well as general purpose degreasers,
machine cleaners, electronic/electricalparts cleaners, and a mold cleaner. EPA includes all of these
aerosol degreasers in the estimation of .TCE content as: 1) automotivemaintenance and repair facilities
may use different degreaser products interchangeablyas observed by (CARB. 2000); and 2) EPA uses
this brake servicing model as an exposure scenario representativeof all commercial-typeaerpsol
degreaser applications.

EPA used a discrete distributionto model the TCE weight fractionbased on the number of occurrences
of each product type. In some instances,the concentrationof TCE was reported as a range. For these
product types, EPA used a uniform distribution to model the TCE weight fraction within the product
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type. Table_Apx L-2 provides a summary of the reported TCE content reported in the safety data sheets
identified in (U.S. EPA, 2017c), the number of occurrences of each product type, and the fractional
probability of eachproduct type .

.

.
T able A,px L-2 S ummarvo fT.r1chioroet htVIIene- BasedA erosoID el!l'easerF orm ul ations

Name of Ael'0301
Degreaser Product
ldenti6ed in {U.S. EPA,
101 Tie\

Triehloroedlylene
WeigbtPerceat

Occurrences

Fractlenal
Probability

C-60 Solvent Degreaser
Fusing Machine Cleaner
Solvent Degreaser
Electro Blast
Electro Solv
Pro Tools NF Solvent
Degreaser
Aerosolve Il
Power Solv Il
Zep45
Super Solv
Parts Cleaner
Electronic Contact Cleaner &
Protectant - Aerosol
Flash Free Electrical Degreaser
Chlorinated Brake & Parts
Cleaner - Aerosol
MR 3 51 - MoldCleaner
C-60 Solvent [TCE Cleaner]
Degreaser

90-100%
40-60%
> 90%
90-100%
90-100%

1
1
1
1
1

0.063
0.063
0.063
0.063
0.063

60-100%

1

0.063

>90%
90- 100%
40-50%
90-100%
45-55%

1
1
1
1
1

0.063
0.063
0.063
0.063
0.063

97%

1

0.063

98%

1

0.063

98%

1

0.063

69%

I

0.063

90-100%

1

0.063

16

1.000

Total

Namberof

212
213
214

L.2.8
Volume of Degreaser Used per Brake Job
(CARB , 2000) assumed that brake jobs require 14.4 oz of aerosol product. EPA did not identify other

215
216

information to estimate the volume of aerosol product per job; therefore, EPA used a constant volume of
14.4 oz per brake job based on (CARB. 2000).

217
218
219

L.2.9
Number of Applications i,er Brake Job _
_
Workers typically apply the brake cleaner before, during, and after brake disassembly. Workers may
also apply the brake cleaner after brake reassembly as a final cleaning process (CARB, 2000).
Therefore, EPA assumed a worker applies a brake cleaner three or four ti.mesper wheel. Since a brake
job can be performed on either one axle or two axles (CARB , 2000), EPA assumed a brake job may
involve either two or four wheels. Therefore, the number of brake cleaner (aerosol degreaser)
applications per brake job can range from six (3 applications/brakex 2 brakes) to 16 (4
applications/brake x 4 brakes). EPA assumed a constant number of applications per brake job based on
the midpoint of this range of 11 applications per brake job.

220
221
222

223
224

225

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227
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L.2.10 Amount ofTrichJoroethylene Used per Application _ _
EPA calculated the amount of Trichloroethylene used per application using Equation L-23. The
calculated mass of Trichloroethylene used per application ranges from 14.8to 37.1 grams.
Equation L-23

Wd X wtfrac X 28.3495 -!z
Amt= - ----- ..........

231
232
233

234
235
236
237
238
239
240
241

242
243
244

245
246

NA

Where:
Amt
Wd
Wtfrac
NA

=
=
=

Amount of TCE used per application (g/application);
Weight of degreaser used per brake job (oz/job);
Weight fraction ofTCE in aerosol degreaser (unitless); and
Nwnber of degreaser applications per brake job (applications/job).

L.2.11 Operating Hours per Week
(CARE. 2000) collected weekly operating hour data for 54 automotive maintenance and repair facilities.
The surveyed facilities included service stations (fuel retail stations), general automotive shops, car
dealerships, brake repair shops, and vehicle fleet maintenance facilities. The weekly operating hours of
the surveyed facilities ranged from 40 to 122.5 hr/week. EPA fit a lognormal distribution to the surveyed
weekly operating hour data. The resulting lognormal distribution has a mean of 16.943 and standard
deviation of 13.813, which set the shape of the lognormal distribution. EPA shifted the distribution to
the right such that its minimum value is 40 hr/week and set a truncation of 122.5 hr/week (the truncation
is set as 82.5 hr/week relative to the left shift of 40 hr/week).

252
253

L.2.12 Number of Brake Jobs per Work Shift
(CARB . 2000) visited 137 automotive maintenance and repair shops and collected data on the number of
brake jobs performed annually at each facility. CARB calculated an average of 936 brake jobs
performed per facility per year. EPA calculated the number of brake jobs per work shift using the
average number of jobs per site per year, the operating hours per week, and assuming 52 weeks of
operation per year and eight hours per work shift using Equation L-24 and rounding to the nearest
integer. The calculated number of brake jobs per work shift ranges from one to four.

254
255

Equation L-24

247

248
249
250
251

jobs

256

257
258

259

hours

93 6 site-year x 8 shift
N1 = ------=---=-ks----"--

52 wee
yr

Where:
N1
OHpW

=
=

xOH p W

Number of brake jobs per work shift (jobs/site-shift); and
Operating hours per week (hr/week).

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260

Appendix M

261
262

SPOT CLEANING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODEL APPROACH AND
PARAMETERS

263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291

This appendix presents the modeling approach and model equations used in the Spot Cleaning NearField/Far-Field Inhalation Exposure Model. The model was developed through review of relevant
literature and consideration of existing EPA exposure models. The model uses a near-field/far-field
approach (AlHA. 2009 ), where a vapor generation source located inside the near-field leads to the
evaporation of vapors into the near-field, and indoor air movements lead to the convection of vapors
between the near-field and far-field. Workers are assumed to be exposed to TCE vapor concentrations in
the near-field. while occupational non-users are exposed at concentrations in the far-field.

292
293
294
295
296

Model results from the Monte Carlo simulation are presented as 95th and 50th percentile values. The
statistics were calculated directly in @Risk. The 95 th percentile value was selected to represent a highend exposure, whereas the 50th percentile value was selected to represent a central tendency exposure
level. The following subsections detail the model design equations and parameters for the spot cleaning
model.

The model uses the following parameters to estimate exposure concentrations in the near-field and farfield:
•
•
•
•
•
•
•
•

Far-field size;
Near-field size;
Air exchange rate;
Indoor air speed;
Spot cleaner use rate;
Vapor generation rate;
Weight fraction ofTCE in the spot cleaner; and
Operating hours per day.

An individual model input parameter could either have a discrete value or a distribution of values. EPA
assigned statistical distributions based on available literature data . A Monte Carlo simulation (a type of
stochastic simulation) was conducted to capture variability in the model input parameters. The
simulation was conducted using the Latin hypercube sampling method in @Risk Industrial Edition,
Version 7.0.0 . The Latin hypercube sampling method is a statistical method for generating a sample of
possible values from a multi-dimensional distribution. Latin hypercube sampling is a stratified method,
meaning it guarantees that its generated samples are representative of the probability density function
(variability) defined in the model. EPA performed the model at 100,000 iterations to capture the range of
possible input values (i.e., including values with low probability of occurrence).

297
298
299
300
301

302
303

M.1

Model Design Equations

--=-----------------------

Figure _Apx M-1 illustrates the near-field/far-field modeling approach as it was applied by EPA to spot
cleaning facilities. As the figure shows, TCE vapors evaporate into the near-field (at evaporation rate G),
resulting in near-field exposures to workers at a concentration CNF• The concentration is directly
proportional to the amount of spot cleaner applied by the worker, who is standing in the near-field-zone
(i.e., the working zone). The volume of this zone is denoted by VNF.The ventilation rate for the nearPage 678 of 691

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field zone (QNF)determines how quickly TCE dissipates into the far-field (i.e., the facility space
surrounding the near-field), resulting in occupational non-user exposures to TCE at a concentration CFF,
VFFdenotes the volume of the far-field space into which the TCE dissipates out of the near-field. The
ventilation rate for the surroundings, denoted by Qi:F, determines how quickly TCE dissipates out of the
surrounding space and into the outdoor air.

309

---------Far-Field---------

--- ~===
- Near-Field-----

C

G

-

310
311

312

-+

Q

Volati
leSource

Q

►

Figure_Apx M-1. The Near-Field/Far-Field Model as Applied to the Spot Cleaning NearField/Far-Field Inhalation Exposure Model

313

314
315
316
317
318
319
320
321
322
323

The model design equations are presented. below in Equation M-1 through Equation M-16.
Near-Field Mass Balance
Equation M-1

Far-Field Mass Balance
Equation M-2

Where:

324

VNF

325
326
327
328

VFF

329

330
331

=

=
QNF =
QFF =
CNF =
CFF =
G
=
t
=

near-field volume;
far-field volume;
near-field ventilation rate;
far-field ventilation rate;
average near-field concentration;
average far-field concentration;
average vapor generation rate; and
elapsed time.
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332
333

334
335
336

Both of the previous equations can be solved for the time-varyingconcentrationsin the near-field and
far-field as follows (AIHA . 2009):
Equation M-3

337
338
339

Equation M-4

340

341
342

Where:
Equation M-5

343
344

345

Equation M-6

k2 = QNFQFF+ A2VNF(QNF
+ QFF)

346
347
348

QNFQFFVNp(A.1
- A.2)
Equation M-7
k3 = QNFQFF
+ A1VNF(QNF
+ QpF)
QNpQppVNF(At
- ).2)

349

350
351

Equation M-8

352
353

354

Equation M-9

355

356
357

358
359
360

361

Equation M-10

At= 0.5 [-(QNpVpp + VNp(QNF+ Qpp)) +
VNFVFF
Equation M-11

A.2= 0.5 [-(QNpVpp+ VNp(QNF
+ QFF))VNFVFF

362
363
364

EPA calculated the hourly TWA concentrationsin the near-field and far-field using the following
equations.Note that the numerator and denominatorof Equation M-12 and EquationM-1313, use two

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different sets of time parameters.The nwnerator is based on the operatinghours for the scenario while
the denominator is fixed to an averaging time span, t_avg, of8 hours (since EPA is interested in
calculating 8-hr TWA exposures).Mathematically,the nwnerator and denominator must reflect the
same amount of time. This is indeed the case: although the spot cleaning operating hours ranges from
two to five hours (as discussed in Section A.2.8), EPA assumes exposures are equal to zero outside of
the operating hours, such that the integral over the balance of the eight hours (three to six hours) is equal
to zero in the numerator. Therefore, the numerator inherentlyincludes an integral over the balance of the
eight hours.equal to zero that is swnmed to the integral from t1 to t2.

373
374

Equation M-12

365
366

367
368
369

370

375
376

377
378
379

Equation M-13

380
381

382
383

384

385
386
387
388
389
390
391

To calculate the masstransfer to and from the near-field, the Free Surface Area, FSA, is defined to be
the surface area through which mass transfer can occur. Note that the FSA is not equal to the surface
area of the entire near-field. EPAdefined the near-field zone to be a rectangular box resting on the floor;
therefore, no mass transfer can occur through the near-field box's floor. FSA is calculated in Equation ·
M-14, below:
Equation M-14

FSA = 2(LNFHNF)+ 2(WNFHNF)+ (LNFWNF)

392
393
3.94
395
396
397
398

Where: LNF,WNF,and HNFare the length, width, and height of the near-field, respectively. The nearfield ventilation rate, QNF,is calculated in Equation M-15 from the near-field indoor wind speed, VNF,
and FSA, assuming half of FSA is available for mass transfer into the near-field and half of FSA is
available for mass transfer out of the near-field:
Equation M-15

399
400

401
402

The far-field volume, VFF,and the air exchange rate, AER, is used to calculate the far-field ventilation
rate, QFF,as given by Equation M-:
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403
404

Equation M-16

Qpp= VppAER

405
406
407
408
409
410
411
412
413
414

Using the model inputs in Table H-1, EPA estimated TCE inhalation exposures for workers in the nearfield and for occupational bystanders in the far-field. EPA then conducted the Monte Carlo simulations
using@Risk (Version 7.0.0). The simulations applied 100,000 iterations and the Latin hypercube
sampling method.

M.2

Model Parameters

Table_Apx M-1 summ.arizes the model parameters and their values for the Spot Cleaning Nc::arField/Far-Field Exposure Model. Each parameter is discussed in detail in the following subsections.

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2415
Ul6
~417

Table_ApxM-1. Summaryof ParameterValues and DistributionsUsed in the Spot CleaningNear-Field/Far-FieldInhalation
E xposureModeI
Input
Parameter

Symbol

U■it

Constant
Model
Parameter
Values
Value Basis

Variable Model Parameter Values
Comments

Lower
Bound

Upper
Bound

Mode

Distributio
n'I'yp e

Floor Area

A

ft2

-

-

500

20,000

-

Beta

Far-field
volume

Vyp

ft3

-

-

6,000

240 ,000

-

-

LNF

ft

10

-

-

-

-

-

WNF

ft

10

-

-

-

-

-

HNP

ft

6

-

-

-

-

-

Near-field
length
Near-field
width
Near-field
heimt
Air exchange
rate
Near-field
indoor wind
speed
Starting time
Exposµre
Duration
Averagin2.time

AER

VNF

br·l

-

-

1

19

emfs

-

-

0

ft/hr

-

-

0

0

-

-

-

2

t1

1ir

t2

hr

tavu

hr

8

-

3.5

Triangular

202.2

-

Lo~nonnal

23,882

-

Lognonnal

-

-

5

-

Page 683 of691

-

Uniform

-

Facility floor area is based on data
from the (CARB , 2006) and King
County (Whittaker and Johanson,
2011 ) study. ERG fit a beta function
to this distribution with parameters: «1
= 6.655, 112 = 108.22, min = 500 ft2,
max = 20,000 ft2.
Floor area mul!iplied by height.
Facility height is 12 ft (median value
per (CARB 2006) study).

EPA assumed a constant near-field
volwne.
Values based on (von Grote et al.,
2006), and (U.S. EPA , 2013a). The
mode represents the midpoint of the
ran1,t
e reoorted in (U.S. EPA 2013a).
Lognonnal distributionfit to the data
nard.
presented in (Baldwin and Ma:x:

1998a).
Constant value.
Equal to operating hours per day.
Constant value.

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Input
Parameter

Symbol

Unit

Constant
Model
Parameter
Values
Value Basis

Use rate

Vapor
generation rate

UR

G

Variable Model Parameter Values
Comments
Lower
Bound

Upper
Bound

Mode

Distributio
n Type

gaVyr

8.4

-

-

-

-

mJ1/hr

-

-

2.97E+03

9.32E+04

-

Calculated

g/min

-

-

0.05

1.55

-

Calculated

-

(IRTA . 2007 ) used estimates of the
amount of TCE-based spot cleaner
sold in California and the number of
textile cleaning facilities in California
to calculate a use rate value.
G is calculated based on UR and
assumes l 000/ovolatilization and
accounts for the weight fraction of
TCE.

TCEweight
fraction

wtfrac

wt frac

-

-

0.1

1

-

Uniform

Operating
hours per day

OH

hr/day

-

-

2

5

-

Uniform

Operating days
per year

OD

days/yr

-

-

249

313

300

Triangular

Page 684 of 691

(IRTA . 2007) observed TCE-based
spotting agents contain 10% to 100%
TCE.
Detennined from a California survey
performed by (Morris and \Volf,
2005 ) and an analysis of two model
plants constructed by the researchers
Operating days/yr distribution assumed
as triangular distribution with min of
250, max of 312, and mode of 300.

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Input
Parameter

Symbol

Unit

Constant
Model
Parameter
Values
Value Basis

Variable Model ParameterValues
Comments
Lower
Bound

Upper
Bound

Mode

Distributio
nType

I

In BLS/Census data, the weighted
average worked hours per year and per
worker in the dry cleaning sector is
approximately 1,600 (i.e., 200 day/yr
at 8 hr/day).

Fractional
number of
operating days
that a worker
works

I

Dimensionles
s

1

-

0.8

1.0

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-

Uniform

The BLS/Census data weighted
average of 200 day/yr falls outside the
triangular distribution of operating
days and to account for lower exposure
frequencies and part-timeworkers,
EPA defines/ as a uniform distribution
ranging from 0.8 to 1.0. The 0.8 value
was derived from the observationthat
the weighted average of 200 day/yr
worked (from BLS/Census) is 80% of
the standard assumptionthat a fulltime worker works 250 day/yr . The
maximum of 1.0 is appropriate as dry
cleaners may be family owned and
operated and some workers may work
as much as every operating day.

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419
420

421

422
423
424
425
426
427
428
429
430
431

432
433
434

M.2.1

Far-FieldVolume

EPA calculated the far-field volume by setting a distribution for the facility floor area and multiplying
the floor area by a facility height of 12 ft (median value per (CARB, 2006) study) as discussed in more
detail below.
The 2006 CARB CaliforniaDry Cleaning Industry TechnicalAssessmentReport (CARB, 2006) and the
Local Hazardous Waste Management Program in King County A Pro.fileof the Dry Cleaning Industry in
King County, Washington(Whittaker and Johanson , 2011 ) provide survey data on dry cleaning facility
floor area The CARB (2006) study also provides survey data on facility height. Using survey results
from both studies, EPA composed the following distribution of floor area. To calculate facility volume,
EPA used the median facility heigh t from the CA.RB (2006) study. The facility height distribution in the
CARB (2006) study has a low level of variability, so the median height value of 12 ft presents a simple
but reasonable approach to calculate facility volume combined with the floor area distribution. Results
are provided in Table_Apx M-2

Table_Apx M-2. CompositeDistribution of ]l)rvCleanin2 Facilitv Fl oor Areas
Percentile

435
436

437
438

Floor Area
Valae (ft%)

Cractioa)

20,000

1

Source
King County

3,000

0.96

King County

2,000

0.84

King County

1,600

0.5

1,100

0.1

CARB2006

500

0

CARB2006

(as

CARB2006

EPA fit a beta function to this distribution with parameters: «1= 6.655, «2= 108.22, min= 500
= 20,000 ft2.

M.2.2

:ft2,max

Near-Field Volume

439
440

EPA assumed a near-field of constant dimensions of 10 ft wide by l Oft long by 6 ft high resulting in a
total volume of 600 ft3.

441

M.2.3 Air Exchange Rate
(von Grote et al .• 2006 ) indicated typical~ exchange rates (AERs) of 5 to 19 hr-1 for dry cleaning
facilities in Germany. (Klein and Kurz, 1994a) indicated AERs of I to 19 hr 1, with a mean of 8 hr·1 for
dry cleaning facilities in Gennany. During the 2013 peer reviewofEPA's 2013 draft risk assessment of
.S. EPA.
TCE, a peer reviewer indicated that air exchange rate values around 2 to 5 hr-1 are likely (LJ"
2013a ), in agreement with the low end of the ranges reported by von Grote et al. and (Klein and Kurz,J
1994a). A triangular distributiori is used with the mode equal to the midpoint of the range provided by
the peer reviewer (3.5 is the midpoint of the range 2 to 5 hr-1).

442
443

444
445
446

447
448
449
450

451
452
453

454

M.2.4 Near-Field!ndoor Wind Speed __

_

(Baldwin and Maynard, 1998a) measured indoor air speeds across a variety of occupational settings in
the United Kingdom . Fifty-five work areas were surveyed across a variety of workplaces.
EPA analyzed the air speed data from (Baldwin and Maynard. 1998a) and categorizing the air speed
surveys into settings representative of industrial facilities and representative of commercial facilities.
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455
456
457
458
459
460

461

EPA fit separate distributions for these industrial and commercial settings and used the commercial
distribution for dry cleaners (including other textile cleaning facilities that conduct spot cleaning) .
EPA fit a lognormal distribution for both data sets as consistent with the authors observations that the air
speed measurements within a surveyed location were lognormally distributed and the population of the
mean air speeds among all surveys were lognormally distributed. Since lognormal distributions are
bound by zero and positive infinity, EPA truncated the distribution at the largest observed value among
all of the survey mean air speeds from (Baldwin and Mavnard. 1998a).

462
463
464
465
466
467
468
469
470
471
4 72
473
474

(Baldwin and Ma ynard . 1998a) only pre sented the mean air speed of each survey. The authors did not
present the individual measurements within each survey . Therefore, these distributions represent a
distribution of mean air speeds and not a distribution of spatially-variable air speeds within a single
workplace setting. However, a mean air speed (averaged over a work area) is the required input for the
model.

475
476
477

M.2.5 Averaging Time
EPA is interested in estimating 8-hr TWAs for use in risk calculations; therefore, a constant averaging
time of eight hours was used.

478
479

480
481

482
483

484
485
486
487
488
489

The air speed surveys representative of commercial facilities were fit to a lognormal distribution with
the following parameter values: mean of 10.853 cm/sand standard deviation of7.883 cm/s . In the
model, the lognormal distribution is truncated at a maximum allowed value of 202 .2 cm/s (largest
surveyed mean air speed observed in (Baldwin and Ma, nard. 1998a) to prevent the model from
sampling values that approach infinity or are otherwise unrealistically large.

M.2.6 Use Rate ·
EPA used a top-down approach to estimate use rate based on the volume ofTCE-based spotting agent
sold in California and the number of textile cleaning facilities in California.
(IRTA . 2007 ) estimated 42,000 gal ofTC&based spotting agents are sold in California annually and
there are approximately 5,000 textile cleaning facilities in California. This results in an average use rate

of 8.4 gal/site-year of TCE-based spotting agents.
The study authors' review of safety data sheets identified TCE-based spotting agents contain 10% to
100% TCE.

491
492
493
494

M.2. 7 Vapor Generation Rate
EPA set the vapor generation rate for spot cleaning (G) equal to the use rate of TCE with appropriate
unit conversions. EPA multiplied the spotting agent use rate by the weight fraction of TCE (which
ranges from 0.1 to 1) and assumed all TCE applied to the garment evaporates. EPA used a density of
1.46 g/cm3 (U.S. EPA. 2018d ). To calculate an howiy vapor generation rate, EPA divided the annual use
rate by the number of operating days and the number of operating hours selected from their respective
distributions for each iteration.

495
496
497
498

M.2.8 Operating Hours
(Morris and Wo lf, 2005 ) surveyed dry cleaners in California, including their spotting labor. The authors
developed two model plants: a small PERC dry cleaner that cleans 40,000 lb of clothes annually; and a
large PERC dry cleaner that cleans 100,000lb of clothes annually. The authors modeled the small dry

490

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499

500
501
502
503
504

505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521

cleaner with a spotting labor of 2.46 hr/day and the large dry cleaner with a spotting labor of 5 hr/day.
EPA models a uniform distribution of spotting labor varying from 2 to 5 hr/day.

M.2.9 Operating Days
_
EPA modeled the operating days per year using a triangular distribution from 250 to 312 days per year
with a mode of 300 days per year23. The low-end operating days per year is based on the asswnption that
at a minimum the dry cleaner operates five days per week and 50 weeks per year. The mode of 300 days
per year is based on an assumption that most dry cleaners will operate six days per week and 50 weeks
per year. The high-end value is based on the assumption that the dry cleaner would operate at most six
days per week and 52 weeks per year, assuming the dry cleaner is open year-round .
M.2.10 Fractional Number of Operating Days that a Worker Works
To account for lower exposure frequencies and part-time workers, EPA defines a fractional days of
exposure as a unifonn distribution ranging from 0.8 to 1.0.EPA expects a worker's annual working days
may be less than the operating days based on BLS/Census data that showed the weighted average
worked hours per year and per worker in the dry cleaning sector is approximately 1,600 (i.e., 200 day/yr
at 8 hr/day) which falls outside the range of operating days per year used in the model (250 to 312
day/yr with mode of300 day/yr).
The low end of the range, 0.8, was derived from the observation that the weighted average of200 day/yr
worked (fromBLS/Census) is 80% of the standard assumption that a full-time worker works 250 day/yr.
The maximum of 1.0 is appropriate as dry cleaners may be family owned and operated and some
workers may work as much as every operating day. EPA defines the exposure frequency as the number
of operating days (250 to 312 day/yr) multiplied by the fractional days of exposure (0.8 to 1.0).

23 For modeling purposes,the minimum value was set to

249 days per year and the maximumto 313 days per year; however,
these values have a probabilityof z.ero;therefore,the true range is from 250 to 312 days per year.

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523
524
525
526
527

Appendix N

BENCHMARK DOSE MODELING UPDATE FOR
NESTED FETAL DATA FROM (Johnson et al.. 2003 )

BMD modeling of the nested fetal data for cardiac defects from (Johnson et al.. 2003) was done to verify
the BMD modeling results reported in Appendix F.4.2.1 of the EPA 2011 IRIS ToxicologicalReview
for TCE Appendices (U.S. EPA. 201 lb ).

528
529

530
531
532
533
534
535
536

537
538
539
540

3i2

543
544

545

~i~

548
549

550
551

552
553

554
555
556

33s
559

560

~82
563

3~
566

3gl
569

570

1) BMD modeling was performed using the nested logistic model in BMDS (v3.l .l) with and
without a litter specific covariate to account for intra-litter similarity (litter effects) based on pretreatment condition and with and without modeling of intra-litter correlation to account for intralitter similarity based on effects during treatment.IRIS also used the nested logistic model with
and without litter specific covariate and intra-littercorrelation.Previous modeling from (U.S.
EPA. 2011b) was performed with and without the high dose group dropped, however the model
based on dropping the highest dose was used in the assessment because it had smaller scaled
residuals and predicted expected response values were closer to observed. Therefore. current
modeling was performed without the high dose group. Modeling in (U.S. EPA. 2011b) was
performed using applied dose and two alternativeinternal dose metrics based on PBPK modeling
(avg amount of TCE metabolized by oxidation/kg314-day and AUC for TCE in blood). Toe same
3 sets of doses were modeled for the current effort. BMRs used for both the IRIS and current
modeling were 10%, 5% and 1% extra risk.
2) Total weight gain during pregnancy (TWtGn) was used as the litter specific covariate in the
modeling performed for the IRIS assessment.The individual animal data available for the current
effort included TWtGn for the treated groups, but not for the control group. Based on the data
available, litter size was used as the covariatefor the current modeling effort instead of TWtGn.
3) P-values reported by an older version of the BMDS software as presented in Table F-6 of (U.S.
EPA, 2011b) for the nested models are incorrect,apparently due to a problem with the software
used at that time, suggesting that the models did not have adequate fit to the data. The exercise
reported in Section F.4.2.1.2 of (U.S. EPA. 2011b) was performed to show that the p-values were
much higher than indicated in the raw modelingresults and that model fit was acceptable.
Calculationof p-values for the nested models in the current version of BMDS follows a
bootstrap methodology similar to that described in Section F.4.2.1.2. of the IRIS
assessment.Because the original p-values in presented in (U.S. EPA, 2011b) were incorrect,
comparisonsof current modeling results to IRIS were only made for AIC, BMD and BMDL. The
p-values from the updated BMD modeling runs are presented for context.
4) In the previous BMD modeling, the best fitting model as determined by lowest AIC was the
model without litter-specificcovariate but with intra-littercorrelation. This was true for the
current modeling as well.
5) Results from the models without litter-specificcovariate, including the best-fitting model,
closely matched the results from the IRIS assessment (see Table_ApxN-1).
6) Results for the models that included the litter-specificcovariate differed from the IRIS results,
because a different covariate was used (litter size rather than TWtGn, due to missing data).
7) Model fits (AICs) and BMD/BMDLvalues are identical (within rounding error) between the
updated modeling results and those reported in (U.S. EPA, 2011e).
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Table_Ap:xN-1. Results for Best-FittingModel in Comparison to Results
Reported in IRIS ill S. EPA 2011e) (Highli.gh.ted)
l

p-valued BMD

Model

Covariate Intra-litter
Correlation

Dose Metric

BMR

AIC

Nested

Not Used

Applied Dose3

0.10

243.81S 0.665

0.71114

0.22767S

243.81S NR

0.71114

0.227675

243.815

0.336856

0.107846

243.815 NR

0.336856

0.107846

243.81S 0.661

0.064649

0.020698

243.815 NR

0.064649

0.020698

243.816

0.642

0.489388

0.156646

243.815

NR

0.489442

0.156698

243.816

0.642

0.231816

0.074201

ND

NR

ND

ND

243.816

0.636

0.04449

0.014241

243.815

NR

0.0444948

0.0142453

243.816

0.656

0.022279

0.00713

243.816 NR

0.0222789

0.00712997

243.816

0.656

0.010553

0.003377

ND

NR

ND

ND

243.816

0.656

0.002025

0.000648

243.816

NR

0.00202535 .000648179

Modeled

BMDL

Logistic

0.0S
0.01
TotOxMetabBW34b 0.10
0.05
0.01
AUCCBldc

0.10
0.05
0.01

0.641

"O,0.00045, 0.048, 0.218 mg/kg-day
bTotaloxidative metabolism scaled by body weight to the ¾-power: 0, 0.00031, 0.033, 0.15
cAUCofTCE in blood: 0, 0.0000141,0.00150254,0.00682727
4
p-values from the 2011 IRIS Assessment are not reportedbecause the original values were incorrect.
ND = nodata
NR = not relevant; original p-values as calculated by BMDS software in 20 l l were incorrect

571

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EPASCIENTIFICADVISORYCOMMITTEEON CHEMICALS
CHARGETOTHEPANEL-TRICHLOROETHYLENE
As amended by the Frank R. Lautenberg Chemical Safety for the 21st Century Act on June 22,
2016, the Toxic Substances Control Act (TSCA), requires the U.S. Environmental Protection
Agency (EPA) to conduct risk evaluations on existing chemicals.In December of 2016, EPA
published a list of the initial ten chemical substancesthat are the subject of the Agency's chemical
risk evaluation process (81 FR 91927), as required by TSCA. Trichloroethylene (TCE) is one of
the first ten chemical substances and the ninth of the ten to undergo a peer review by the Scientific
Advisory Committee on Chemicals (SACC). In response to this requirement, EPA has prepared
and published a draft risk evaluation for TCE. The EPA has solicited comments from the public on
the draft and will incorporate them as appropriate, along with comments from peer reviewers, into
the final risk evaluation.
The focus of this meeting is to conduct the peer review of the Agency's draft risk evaluation of
TCE and associated supplemental materials. At the end of the peer review process, EPA will use
the reviewers' comments/recommendations,as well as public comment, to finalize the risk
evaluation.
1bis draft risk evaluation contains the following components:
• Discussion of chemistry and physical-chemical properties
• Character:iz.ati.on
of uses/sources
• Environmentalfate and transport assessment
• Environmentalexposure assessment
• Human health hazard assessment
• Environmentalhazard assessment
• Risk characterization
• Risk determination
• Detailed description of the systematic review process developed by the Office of Pollution
Prevention and Toxics to search, screen, and evaluate scientific literature for µse in the risk
evaluation process.

CHARGEQUESTIONS:
Systematic Review (Section 1.5 of the Draft Risk Ewuuation):.
The Toxic Substances Control Act (TSCA) requires that EPA use data.and/or information in a
manner consistent with the "best available science" and that EPA base decisions on the ''weight
of the scientific evidence". The EPA' s Final Rule, Proceduresfor ChemicalRisk Evaluation
Underthe Amended Toxic SubstancesControlAct (82 FR 33726), defines .. best available
science'' as science ithatis n::liableand unbiased. This involves the use of supporting studies
conducted in accordance with sound and objective science practices, including, when available,
peer reviewed science and supporting studies and data collected by accepted methods or best
available methods (if the reliability of the method and the nature of the decisionjustifies use of
the data). The Final Rule also defines the ''weight of the scientific evidence" as a systematic
review method, applied in a manner suited to the nature of the evidence or decision, that uses a
pre-established protocol to comprehensively,objectively,transparently, and consistently identify
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and evaluate each stream of evidence, including the strengths, limitations, and relevance of each
study and to integrate evidence as necessary and appropriate based upon strengths, limitations,
and relevance.
To meet these scientific standards, EPA applied systematic review approaches and methods to
support the TCE draft risk evaluation. Information on the approaches and/or methods is
described in the draft risk evaluation as well as the following documents:
•

Strategy for Conducting Literature Searches for Trichloroethylene: Supplemental File for
the TSCA Scope Document, (EPA-HO-OPPT-2016-0737)

•

Trichloroethylene (CASRN 79-01-6) Bibliography: SupplementalFile for the TSCA Scope
Document, (EPA-HO-OPPT-2016-0737)

•

Trichloroethylene Problem Fonnulation( EPA-HO-OPPT-2016-0737)

•

Application of Systematic Review in TSCA Risk Evaluations

EPA has solicited peer review and public feedback on systematicreview approaches and methods
for prior evaluations. A general question on these approaches is not included in this charge;
however, EPA will accept comment on the systematic review approaches used for this evaluation if
provided.

1. Environmental Fate and Exposure:
EPA qualitatively analyzed the sediment, land application, and biosolids pathways based on TCE's
physical/chemical and fate properties. Exposure estimates to the environment were developed for
the conditions of use for exposures to aquatic organisms.

1.1. Please comment on EPA's qualitative analysis of pathways based on physical/chemical and
fate properties. (Section 2.1)
1.2. Please comment on the data, approaches, and/or methods used to characterize exposure to
aquatic receptors. (Section 2.2)
1.3 Please comment on EPA's assumption that TCE concentrationsin sediment pore water are
expected to be similar to the concentrations in the overlying water or lower in the deeper
part of sediment, in which anaerobic conditions prevail. Thus, the TCE detected in
sediments is likely from the pore. (Section 4.1.3)

2. Environmental Exposure and Releases:
EPA evaluated releases to water and aquatic exposures for conditions of use in industrial and
commercial settings. EPA used Toxics Release Inventory (TRI) and Discharge Monitoring Report
(DMR) data to provide a basis for estimating releases. EPA used these releases and associated
inputs within EFAST 2014 to estimate instream chemical concentrationsand days of exceedance.
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EPA also evaluated monitoredvalues ofTCE in surface water and where possible compared those
values to estimated release concentrations.
2.1. Please comment on the approaches,models, and data used in the water release assessment
including comparisonto monitoreddata. (Section 2.2)
2.2. Please provide any specific suggestionsor recommendationsfor alternative data or
estimationmetbods, including modeling approaches,that could be considered by EPA for
conducting or refining the water release assessmentand relation to monitored data.
(Section 2.2)
3. Environmental Hazard:
EPA evaluated environmentalhazards for aquatic species from acute and chronic exposure
scenarios.

3.1. Please commenton EPA' s approachfor characterizingenvironmentalhazard for each
risk scenario (e.g., acute aquatic, chronic aquatic). What other additional information,if
any, should be considered?
3.2. Please comment on the use and interpretationof Species Sensitivity Distributions
(SSDs) and hazardous concentrations(HCoss)for ecologicalrisk characterizationand
provide any specific suggestions or recommendationsfor how this information could
inform EPA' s risk assessment for TCE or other solvents.
4. Occupational and Consumer Exposure:

OccupationalExposure
EPA evaluated acute and chronic exposuresto workers for conditionsof use in industrial and
commercial settings. For exposure via the inhalation pathway,EPA quantified occupational
exposures for both workers and occupationalnon-users based on a combination of monitoring data
and modeled exposure concentrations.For exposure via the dermal route, EPA modeled exposure
for workers. accountingfor the effect of volatilization.EPA assumed dermal contact with liquids
would not occur for occupationalnon-users.EPA assumed that workers and occupationalnonusers, exposed via the inhalation and dermal pathways, would be adolescents and adults of both
sexes (::?:16
and older, including males and females of reproductiveage, and pregnant women and
their developing embryo and fetus).
4.1. Please comment on the approachesand estimationmethods, models, and data used in the
occupationalexposure assessment.(Section 2.3.1.2)
4.2. Please provide any specific suggestionsor recommendationsfor alternativedata
(modelingor monitoring)or estimationmethods that could be considered by the Agency
for conductingthe occupationalexposure assessment.If so, please provide specific
literature,reports, or data that would help us refine the exposure assessment. (Section
2.3.1.2)

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4.3. Please comment on assumptions used in the absence of specific exposure infonnation (e.g.,
dermal surface area assumptions: high-end values, which represents two full hands in
contact with a liquid: 890 cm2 (mean for females), 1070 cm2 (mean for males); central
tendency values, which is half of two full hands (equivalent to one full hand) in contact
with a liquid and represents only the palm-side of both hands exposed to a liquid: 445 cm2
(females), 535 cm2 (males)). Please also consider these values in the context of different
lifestages and body weights. (Section 2.3.1.2)
4.4. Please comment on EPA's approach to characterizingthe strengths, limitations and overall
confidence for each occupational exposure scenariospresented in Section 2.3.1. Please
comment on the appropriatenessof these confidenceratings for each scenario. Please also
comment on EPAs approach to characterizingthe uncertainties summarized in Section
2.3.1.3.
To estimate occupational non-user (ONU) inhalation exposure,EPA reviewed personal monitoring
data, area monitoring data and modeled far-field.exposure concentrations. When EPA did not

identify personal or area data or parameters for modeling potential ONU inhalation exposures, EPA
assumed ONU inhalation exposures could be lower than worker inhalation exposures; however,
relative exposure of ONUs to workers could not be quantified. When exposures to ONUs were not
quantified, EPA considered the central tendency from worker personal breathing zones to estimate
ONU exposures.
4.5. Please comment on the adequacy, appropriateness,and transparency ofEPA's approach
and the assumptions EPA used to characterize ONU exposure via this approach. (Section
2.3.1)

4.6. Are there other approaches or methods for assessing ONU exposure for the specific
condition of use?

ConsumerExposure
Consumer exposure estimates were developed for the conditionsof use for inhalation and dermal
exposures to consumers. EPA performed systematic review, collected data from available sources
and conducted modeling for estimating consumer inhalation and dermal exposures using the
Consumer Exposure Model (CEM) model.
Product specific consumer monitoring information was not identified during the systematic review
process, therefore, model inputs related to consumeruse patterns (duration of use, mass of product
used, room of use, and similar inputs) are based on survey data found in the literature as described
and referenced within the TCE draft risk evaluation. Weight fractions of chemical within products
are based on product specific safety data sheets (SDS). Default values utilized within the models
are based on literature reviewed as part of model developmentas well as EPA's Exposure Factors
Handbook.

of

4.7. Please comment on the appropriateness the approaches, models, exposure or use
information and overall characterizationof consumer inhalation exposure for users and
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bystanders for each of the identified conditions of use. What other additional information,
or approaches,if any, should be considered? (Section2.3.2)
4.8. Please comment on the appropriatenessof the approaches,models, exposure or use
information,and overall characterizationof consumer dermal exposure for each of the
identified conditions of use. What other additional informationor modeling approaches, if
any, should be considered? (Section 2.3.2)
4.9. Dermal exposure was evaluated using the permeabilitysub-model within CEM. Please
comment on the suitability and use of this modeling approachfor this evaluation. Please
provide any suggestionsor recommendationsfor alternativeapproaches, dermal methods,
models or other information which may guide EPA in developing and re.fining the dermal
exposure estimates. (Section 2.3.2.4.1)
4.10. Please comment on EPAs approach to characterizingthe strengths, limitations and overall
confidence for each consumer exposure scenariospresented in Section 2.3.2. Please
comment on the appropriatenessof these confidenceratings for each scenario. Please also
comment on EPA' s approach for characterizingthe uncertainties summarized in Section
2.3.2.7.
5. Human Health Hazard:
For hazard identification and dose-response,EPA reviewedthe evidence for TCE toxicity
and selected liver toxicity, kidney toxicity, reproductivetoxicity, developmental toxicity,
neurotoxicity;immunotoxicity, and cancer, that taken as a whole, demonstrated the most robust,
sensitive and consistent adverse human health effects for risk characterization.EPA used
benchmark dose (BMD) modeling where practicable and, when BMD values were adequate, they
were used to generate the Point of Departure (POD) for characterizingchronic and acute exposure
scenarios.

5.1. Please comment on the appropriatenessof the.approach, including the data quality
evaluation, and the underlying assumptions, strengths and weaknesses.
5.2. Have the most scientifically supported health effects and PODs been identified for TCE?
Are there additional data regarding sensitive life stages or health effects for TCE that EPA
needs to consider? If data gaps exist in the TCE database,how could the uncertainty about
sensitive health effects and critical windows of exposure be better accounted for in the risk
characterization?

Non-Cancer
5.3. EPA performed a weight of evidence assessment for the endpoint of developmental
cardiac defects based on available epidemiological,in vivo animal, and mechanistic data.
EPA concluded that the available literature overall supporteda causal relationship between
developmental exposure to TCE or its metabolites and cardiac defects (Section 3.2.4.1.6
and Appendix G.2). Additionally, EPA detennined that the Charles River Laboratories
(2019) developmentaltoxicity study was insufficient as a replication of the Johnson et al.
(2003) study (Appendix G.l). The Charles River dissectionmethodology differed from
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Johnson et. al. (2003), resulting in reduced sensitivityto the full range of cardiac defects
comparedto Johnson et al. (2003) and other studies.Therefore, EPA concluded that the
Charles River study did not adequately recapitulatethe methodology of the Johnson et al.
(2003) study. Please comment on EPA's Weight of Evidence (WOE) analysis approach
and conclusionsfor this endpoint.
5.4. EPA did not input the data on response to pulmonaryinfection from Selgrade and Gilmour
(2010) into the TCE PBPK model due to uncertaintyover the proper dose metric to be
used. Therefore,EPA relied on standard methods for cross-speciesscaling (i.e., blood:air
partition coefficient for HEC, allometricscaling for HED) and accordingly reduced the
default I OXUFAuncertainty factor to 3 (see Section 3.2.5.3.2). Please comment on
whether this approach is appropriateand whether the UF is sufficient.
5.5. Please comment on the assumptions,strengthsand weaknessesof the non-cancer doseresponse approachesused to estimate the non-cancerand cancer risks to workers,
occupationalnon-users, and consumers. Pleasecomment on whether EPA sufficiently
justified its selectionsof BMR.sfor BMD modelingresults and uncertainty factor values in
deriving the PODs and benchmarkmargin of exposures(MOEs) (Sections 3.2.5.3.2 and
3.2.5.3.3). As part of this discussion,please comment on EPA's justification for selecting a
1% BMR for the cardiac malformationendpoint based on the severity of the endpoint {i.e.,
potential mortality).

Cancer
5.6. EPA perfonned a meta-analysison the publisheddatabase for liver cancer, kidney cancer,
and non-Hodgkinslymphoma(NHL), concludingthat there was a statistically significant
association between TCE exposure and all three cancers when accounting for various
sensitivityanalyses. Please comment on EPA's methodologyand conclusions.
5.7. For the cancer dose.response assessment,EPA derived an inhalation unit risk (IUR) and
oral cancer slope factor (OSF) based on epidemiologicalkidney cancer data from
Charbotel et al, 2006, adjusted upward to also account for the relative contributionNHL
and liver cancer. Per EPA Guidelinesfor CarcinogenRisk Assessment, overall, the totality
of the available datafmformationand the WOE analysis for the cancer endpoint was
sufficientto support a linear non-thresholdmodel (Section 3.2.4.2.2). Please comment
whether the cancer hazard assessmenthas adequatelydescribedthe methodology and
justification for the cancer dose-responseapproach,includingthe use of a linear model and
the adjustmentsmade for the other tumor sites (Section 3.2.5.3.4).
6. Risk Characterization:
EPA calculated environmentalrisk using exposure data (e.g., modeling tools and monitored
datasets) and environmentaltoxicity information,accountingfor variability within the
environment.EPA concludes that TCE poses a hazard to environmentalaquatic receptors, with
invertebratesand fish being the most sensitive tax:aidentifiedfor aquatic exposmes. Risk Quotients
{RQs)and the number of days a concentrationof concern (COC) was exceeded were used to assess
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environmental risks. The risk characterizationsection provides a discussion of the risk and
uncertainties around the risk calculations.
EPA calculated human health risks for acute and chronic exposures.For non-cancer effects EPA
used an MOE, which is the ratio of the hazard value to the exposure. EPA evaluated potential risks
for workers and ONUs, consumer users, and bystanders/non-users(e.g., childre~ women of
childbearing age). For the most sensitive endpoint of congenital heart defects, a benchmark MOE
of 10 was used for both acute and chronic risks. An IUR and OSF that account for the combined
extra risk kidney cancer, liver cancer, and NHL was used to evaluate potential chronic risks to
cancer endpoints for the worker exposure scenarios. The risk characterization also providesa
discussion of the uncertainties surroundingthe risk calculations.
After consideration of all identified information,EPA concluded that TCE presents an
unreasonable risk of injury to workers, ONUs, consumers, and bystanders by inhalation and dermal
exposure based on the potential for adverse human health effects (See Section 4.2). EPA also
concludes that TCE does not present an unreasonable risk to environmentalreceptors exposed via
surface water (see Section 4.1). EPA makes this determinationconsidering risk to potentially
exposed and susceptible subpopulationsidentified as relevant, under the conditions of use with.out
considering costs or other non-risk factors.
6.1. Please comment on whether the information presented to the panel supports the
conclusions outlined in the draft risk characterizationsection concerning TCE. If not,
please suggest alternative approaches or information that could be used to further develop
a risk estimates within the context of the requirements stated in EPA' s Final Rule,
Procedures for Chemical Risk Evaluation Under the Amended Toxic Substances Control
Act (82 FR 33726). (Section 5)
6.2. Please comment on the validity of specific confidence summaries presented in Section 4.3.
6.3. Please comment on any other aspect of the environmentalor human health risk
characterizationthat has not been mentioned above.
6.4. Please comment on the calculation of risk derived from different exposure data sources
(e.g. modeling tools and monitored datasets) and how they account f~r variability in
environmental and hwnan exposure. Please provide specific recommendations as needed
for improving the risk characterizationand references to support any recommendations.
6.5. Please comment on whether the risk evaluation document has adequately described the

uncertainties and data limitations associated with the methodologiesused to assess the
environmental and human health risks. Please comment on whether this information is
presented in a clear and transparent manner.
The Frank R Lautenberg Chemical Safety for the 21st Century Act (2016) (amended TSCA) states
that "potentially exposed or susceptible subpopulations"(PESS) be considered in the risk
evaluation process. PESS is defined in the Lautenberg Act to include populations with greater
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exposure or greater response, including due to lifestyle, dietary, and biological susceptibility
factors, than the general population.
6.6. Has a thorough and transparent review of the available information been conducted that
has led to the identification and characteriz.ationof all PESS (Sections 2.3.3, 3.2.5.2, and
4.4.1)? Do you know of additional information about PESS that EPA needs to consider?
Additionally, has the uncertainty around PESS been adequately characterized?
The EPA characteriz.ationof human health risk from inhalation exposure to workers includes
estimates of risk for respirator use. These estimates are calculated by multiplying the high end and
central tendency MOE or extra cancer risk estimates without respirator use by the respirator
assigned protection factors (APFs) of 10 or 50 and the glove protection factors of 5, 10, or 20. EPA
also characterized exposure scenarios in which respirator use was unlikely. EPA did not assume
occupational non users (ONUs) or consumers used personal protective equipment (PPE) in the risk
estimation process.
6.7. Please comment on whether EPA has adequately, clearly, and appropriately presented the
reasoning, approach, assumptions, and uncertainties for characterizing risk to workers and
ONUs using PPE.
7. Overall Content and Organization:

EPA's Final Rule, Procedmes for Chemical Risk Evaluation Under the Amended Toxic Substances
Control Act (82 FR 33726) stipulates the process by which EPA is to complete risk evaluations
under the Frank R. Lautenberg Chemical Safety for the 21st Century Act.
As part of this draft risk evaluation for TCE, EPA evaluated potential environmental, occupational

and consumer exposures. The evaluation considered reasonably available information, including
manufacture, use, and release information, and physical-chemical characteristics. It is important
that the information presented in the risk evaluation and accompanying docmnents is clear and
concise and describes the process in a scientifically credible manner.
To increase the quality and credibility of scientific information disseminated by EPA, EPA uses the
peer review process specifically as a tool for determining fitness of scientific information for the
intended purpose. The questions below are intended to guide the peer reviewers toward
determining if EPA collected, used and disseminated information that is·'fit for pmpose' based on
utility (the data's utility for its intended users and for its intended purpose), integrity (the data's
security), and objectivity (whether the disseminated information is accurate, reliable, and unbiased
as a matter of presentation and substance). The peer reviewers' critical focus should pertain to
recommendations of the technical information's usefulness for intended users and the public.
7.1. Please comment on the overall content, organization, and presentation of the NMP draft
risk evaluation. Please provide suggestions for improving the clarity of the information
presented.

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7.2. Please comment on the objectivity of the underlying data used to support the risk
characterization and the sensitivity of the agency's conclusions to analytic assumptions made.

9