EPA Document #740R18008
November 2020
Office of Chemical Safety and
Pollution Prevention
Risk Evaluation for
T richloroethylene
CASRN: 79-01-6
CI H
CI CI
United States
Jr Lai Environmental Protection Agency
November 2020
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l TABLE OF CONTENTS
2 ACKNOWLEDGEMENTS 25
3 ABBREVIATIONS 26
4 EXECUTIVE SUMMARY 30
5 1 INTRODUCTION 44
6 1.1 Physical and Chemical Properties 45
7 1.2 Uses and Production Volume 46
8 1.2.1 Data and Information Sources 46
9 1.2.2 Domestic Manufacture of Trichloroethylene 47
10 1.3 Regulatory and Assessment History 50
11 1.4 Scope of the Evaluation 52
12 1.4.1 Conditions of Use Included in the Risk Evaluation 52
13 1.4.2 Exposure Pathways and Risks Addressed by Other EPA-Administered Statutes 62
14 1.4.3 Conceptual Models 69
15 1.5 Systematic Review 73
16 1.5.1 Data and Information Collection 73
17 1.5.2 Data Evaluation 79
18 1.5.3 Data Integration 80
19 2 EXPOSURES 81
20 2.1 Fate and Transport 81
21 2.1.1 Fate and Transport Approach and Methodology 82
22 2.1.2 Summary of Fate and Transport 82
23 2.1.3 Assumptions and Key Sources of Uncertainty for Fate and Transport 85
24 2.2 Environmental Exposures 87
25 2.2.1 Environmental Exposures Overview 87
26 2.2.2 Environmental Releases to Water 87
27 2.2,2.1 Results for Daily Release Estimate 88
28 2.2,2,2 Approach and Methodology 89
29 2.2.2.2.1 Water Release Estimates 89
30 2.2.2,2.2 Estimates of Number of Facilities 90
31 2,2,2,2,3 Estimates of Release Days 92
32 2.2.2.3 Assumptions and Key Sources of Uncertainty for Environmental Releases 92
33 2.2,2,3,1 Summary of Overall Confidence in Release Estimates 93
34 2.2.3 Aquatic Exposure Modeling Approach 100
35 2,2,3,1 E-FAST 2014 Equations and Inputs 100
36 2.2.4 Surface Water Monitoring Data Gathering Approach 103
37 2,2,4,1 Systematic Review of Surface Water Monitoring Data 103
38 2,2.4.2 Surface Water Monitoring Data from WQX/WQP 104
39 2.2.5 Geospatial Analysis Approach 104
40 2.2.6 Environmental Exposure Results 105
41 2,2,6,1 Terrestrial Environmental Exposures 105
42 2,2.6,2 Aquatic Environmental Exposures 105
43 2,2,6.2,1 Predicted Surface Water Concentrations: E-FAST 2014 Modeling 105
44 2.2.6.2.2 Measured Surface Water Concentrations 108
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2.2.6,2.3 Geospatial Analysis Comparing Predicted and Measured Surface Water
Concentrations
2.2.6.3 Assumptions and Key Sources of Uncertainty for Environmental Exposures
2.2.6.4 Confidence in Aquatic Exposure Scenarios
2.3 Human Exposures
2.3.1 Occupational Exposures
2.3.1.1 Results for Occupational Assessment
2.3.1.2 Approach and Methodology
2.3.1.2.1 General
2.3.1.2.2 Inhalation Exposure Monitoring Data
2.3.1.2.3 Inhalation Exposure Modeling
2.3.1.2.4 Acute and Chronic Inhalation Exposure Estimates
2.3.1.2.5 Dermal Exposure Modeling
2.3.1.2.6 Consideration of Engineering Controls and Personal Protective Equipment.
2.3.1.2.7 Number of Workers and Occupational Non-Users Exposed
2.3.1.3 Assumptions and Key Sources of Uncertainty for Occupational Exposures
2.3.1.3.1 Number of Workers
2.3.1.3.2 Analysis of Exposure Monitoring Data
2.3.1.3.3 Near-Field/Far-Field Model Framework
2.3.1.3.4 Modeled Dermal Exposures
2.3.1.3.5 Summary of Overall Confidence in Inhalation Exposure Estimates
2.3.2 Consumer Exposures
2.3.2.1 Consumer Conditions of Use Evaluated
2.3.2.2 Consumer Exposure Routes Evaluated
2.3.2.2.1 Inhalation
2.3.2.2.2 Dermal
2.3.2.3 Consumer Exposures Approach and Methodology
2.3.2.3.1 Modeling Approach
2.3.2.4 Consumer Exposure Scenarios and Modeling Inputs
2.3.2.4.1 Consumer Exposure Model Inputs
2.3.2.5 Consumer Exposure Results
2.3.2.5.1 Characterization of Exposure Results
2.3.2.5.2 Consumer Exposure Estimates
2.3.2.5.3 Summary of Consumer Exposure Assessment
2.3.2.6 Assumptions and Key Sources of Uncertainty for Consumer Exposures
2.3.2.6.1 Modeling Approach Uncertainties
2.3.2.6.2 Data Uncertainties
2.3.2.7 Confidence in Consumer Exposure Scenarios
2.3.3 Potentially Exposed or Susceptible Subpopulations
HAZARDS.
3.1 Environmental Hazards
3.1.1 Approach and Methodology
3.1.2 Hazard Identification
3.1.3 Species Sensitivity Distributions (SSDs).
3.1.4 Weight of the Scientific Evidence
3.1.5 Concentrations of Concern
3.1.6 Summary of Environmental Hazard
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3.1.7 Assumptions and Key Uncertainties for Environmental Hazard Data
3.2 Human Health Hazards
3.2.1 Approach and Methodology
3.2.2 Toxicokinetics
3.2.2.1 Absorption
3.2.2.2 Distribution
3.2.2.3 Metabolism
3.2.2.4 Elimination
3.2.2.5 Physiologically-Based Pharmacokinetic (PBPK) Modeling Approach.
3.2.3 Hazard Identification
3.2.3.1 Non-Cancer Hazards
3.2.3.1.1 Liver toxicity
3.2.3.1.2 Kidney toxicity
3.2.3.1.3 Neurotoxicity
3.2.3.1.4 Immunotoxicity
3.2.3.1.5 Reproductive toxi city
3.2.3.1.6 Developmental Toxicity
3.2.3.1.7 Overt Toxicity Following Acute/Short Term Exposure
3.2.3.2 Genotoxicity and Cancer Hazards
3.2.3.2.1 Genotoxicity
3.2.3.2.2 Kidney cancer
3.2.3.2.3 Liver cancer
3.2.3.2.4 Cancer of the immune system
3.2.3.2.5 Other cancers
3.2.4 Weight of Scientific Evidence
3.2.4.1 Non-Cancer Hazards
3.2.4.1.1 Liver toxicity
3.2.4.1.2 Kidney toxicity
3.2.4.1.3 Neurotoxicity
3.2.4.1.4 Immunotoxicity
3.2.4.1.5 Reproductive toxicity
3.2.4.1.6 Developmental Toxicity
3.2.4.1.7 Overt Toxicity Following Acute/Short Term Exposure
3.2.4.2 Cancer Hazards
3.2.4.2.1 Meta-Analysis Results
3.2.4.2.2 Mode of Action
3.2.5 Dose-Response Assessment
3.2.5.1 Selection of Studies for Dose-Response Assessment
3.2.5.1.1 Liver toxicity
3.2.5.1.2 Kidney toxicity
3.2.5.1.3 Neurotoxicity
3.2.5.1.4 Immunotoxicity
3.2.5.1.5 Reproductive toxicity
3.2.5.1.6 Developmental toxicity
3.2.5.1.7 Cancer
3.2.5.2 Potentially Exposed and Susceptible Subpopulations (PESS)
3.2.5.3 Derivation of Points of Departure (PODs)
3.2,5,3,1 Non-Cancer PODs for Acute Exposure
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3.2.5.3.2 Non-Cancer PODs for Chronic Exposures 265
3.2.5.3.3 Cancer POD for Lifetime Exposures 275
3.2.5.4 Selected PODs for Human Health Hazard Domains 277
3.2.5.4.1 Best Overall Non-Cancer Endpoints for Risk Conclusions 280
3.2.6 Assumptions and Key Sources of Uncertainty for Human Health Hazard 282
3.2.6.1 Confidence in Hazard Identification and Weight of Evidence 282
3.2.6.1.1 Uncertainties in Dose-Response Analysis for Select Endpoints 282
3.2.6.2 Derivation of PODs, UFs, and PBPK Results 283
3.2.6.3 Cancer Dose Response 284
3.2.6.4 Confidence in Human Health Hazard Data Integration and Best Overall Endpoints ... 285
4 RISK CHARACTERIZATION 287
4.1 Environmental Risks 287
4.1.1 Risk Estimation Approach 287
4.1.2 Risk Estimation for Aquatic Organisms 288
4.1.3 Risk Estimation for Sediment-dwelling Organisms 297
4.1.4 Risk Estimation for Terrestrial Organisms 300
4.2 Human Health Risks 301
4.2.1 Risk Estimation Approach 301
4.2.1.1 Points of Departure Used in Risk Estimation 304
4.2.2 Risk Estimation for Occupational Exposures by Exposure Scenario 305
4.2.3 Risk Estimation for Consumer Exposures by Exposure Scenario 348
4.3 Assumptions and Key Sources of Uncertainty for Risk Characterization 374
4.3.1 Environmental Risk Characterization 374
4.3.2 Human Health Risk Characterization 375
4.3.2.1 Occupational Exposure Considerations 375
4.3.2.2 Consumer/Bystander Exposure Considerations 376
4.3.2.3 Dermal Absorption Considerations 377
4.3.2.4 Confidence in Risk Estimates 377
4.4 Other Risk Related Considerations 380
4.4.1 Potentially Exposed or Susceptible Populations 380
4.4.2 Aggregate and Sentinel Exposures 381
4.5 Risk Conclusions 383
4.5.1 Environmental Risk Conclusions 383
4.5.2 Human Health Risk Conclusions 387
4.5.2.1 Summary of Risk Estimates for Workers and ONUs 387
4.5.2.2 Summary of Risk Estimates for Consumers and Bystanders 402
5 UNREASONABLE RISK DETERMINATION 407
5.1 Overview 407
5.1.1 Human Health 407
5.1.1.1 Non-Cancer Risks Estimates 408
5.1.1.2 Cancer Risks Estimates 408
5.1.1.3 Determining Unreasonable Risk of Injury to Health 409
5.1.2 Environment 410
5.1.2.1 Determining Unreasonable Risk of Injury to the Environment 410
5.2 Detailed Unreasonable Risk Determination by Condition of Use 411
5.2.1 Human Health 415
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5.2.1.1 Manufacture - Domestic manufacture (Domestic manufacture) 415
5.2.1.2 Manufacture - Import (Import) 416
5.2.1.3 Processing - Processing as a reactant/intermediate - Intermediate in industrial gas
manufacturing (e.g., manufacture of fluorinated gases used as refrigerants, foam blowing
agents and solvents) (Processing as a reactant/intermediate) 417
5.2.1.4 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) (Processing into a formulation, mixture, or reaction product) 418
5.2.1.5 Processing - Incorporation into articles - Solvents (becomes an integral component of
articles) (Processing into articles) 419
5.2.1.6 Processing - Repackaging - Solvents (for cleaning or degreasing) (Repackaging) 420
5.2.1.7 Processing - Recycling - Recycling (Recycling) 420
5.2.1.8 Distribution in Commerce- Distribution (Distribution in commerce) 421
5.2.1.9 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - Batch vapor
degreaser (open-top) (Solvent for open-top batch vapor degreasing) 422
5.2.1.10 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - Batch vapor
degreaser (closed-loop) (Solvent for closed-loop batch vapor degreasing) 423
5.2.1.11 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - In-line vapor
degreaser (conveyorized) (Solvent for in-line conveyorized vapor degreasing) 424
5.2.1.12 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - In-line vapor
degreaser (web cleaner) (Solvent for in-line web cleaner vapor degreasing) 425
5.2.1.13 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - Cold cleaners
(Solvent for cold cleaning) 426
5.2.1.14 Industrial/Commercial Use - Solvent (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner; mold release (Solvent for aerosol spray degreaser/cleaner and mold
release) 427
5.2.1.15 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant additives -
Tap and die fluid (Tap and die fluid) 428
5.2.1.16 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant additives -
Penetrating lubricant (Penetrating lubricant) 428
5.2.1.17 Industrial/Commercial Use - Adhesives and sealants - Solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant (Adhesives and sealants) 429
5.2.1.18 Industrial/Commercial Use - Functional fluids (closed systems) - Heat exchange fluid
(Functional fluids) 430
5.2.1.19 Industrial/Commercial Use - Paints and coatings - Diluent in solvent-based paints and
coatings (Paints and coatings diluent) 431
5.2.1.20 Industrial/Commercial Use - Cleaning and furniture care products - Carpet cleaner;
wipe cleaning (Carpet cleaner and wipe cleaning) 432
5.2.1.21 Industrial/Commercial Use - Laundry and dishwashing products - Spot remover (Spot
remover) 433
5.2.1.22 Industrial/Commercial Use - Arts, crafts and hobby materials - Fixatives and finishing
spray coatings (Fixatives and finishing spray coatings) 434
5.2.1.23 Industrial/Commercial Use - Corrosion inhibitors and anti-scaling agents (Corrosion
inhibitors and anti-scaling agents) 434
5.2.1.24 Industrial/Commercial Use - Processing aids - Process solvent used in battery
manufacture; process solvent used in polymer fiber spinning, fluoroelastomer
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manufacture, and Alcantara manufacture; extraction solvent used in caprolactam
manufacture; precipitant used in beta-cyclodextrin manufacture (Processing aids) 435
5.2.1.25 Industrial/Commercial Use - Ink, toner, and colorant products - Toner aid (Toner aid)
436
5.2.1.26 Industrial/Commercial Use - Automotive care products - Brake and parts cleaners
(Brake and parts cleaners) 437
5.2.1.27 Industrial/Commercial Use - Apparel and footwear care products - Shoe polish (Shoe
polish) 438
5.2.1.28 Industrial/Commercial Use - Hoof polishes; gun scrubber; pepper spray; other
miscellaneous industrial and commercial uses (Other industrial and commercial uses)
439
5.2.1.29 Consumer Use - Solvents (for cleaning or degreasing) - Brake and parts cleaner
(Solvent in brake and parts cleaner) 440
5.2.1.30 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol electronic
degreaser/cleaner (Solvent in aerosol electronic degreaser/cleaner) 440
5.2.1.31 Consumer Use - Solvents (for cleaning or degreasing) - Liquid electronic
degreaser/cleaner (Solvent in liquid electronic degreaser/cleaner) 441
5.2.1.32 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol spray degreaser/cleaner
(Solvent in aerosol spray degreaser/cleaner) 442
5.2.1.33 Consumer Use - Solvents (for cleaning or degreasing) - Liquid degreaser/cleaner
(Solvent in liquid degreaser/cleaner) 443
5.2.1.34 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol gun scrubber (Solvent
in aerosol gun scrubber) 443
5.2.1.35 Consumer Use - Solvents (for cleaning or degreasing) - Liquid gun scrubber (Solvent in
liquid gun scrubber) 444
5.2.1.36 Consumer Use - Solvents (for cleaning or degreasing) - Mold release (Solvent in mold
release) 445
5.2.1.37 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire cleaner (Solvent in
aerosol tire cleaner) 446
5.2.1.38 Consumer Use - Solvents (for cleaning or degreasing) - Liquid tire cleaner (Solvent in
liquid tire cleaner) 446
5.2.1.39 Consumer Use - Lubricants and greases - Tap and die fluid (Tap and die fluid) 447
5.2.1.40 Consumer Use - Lubricants and greases - Penetrating lubricant (Penetrating lubricant)
448
5.2.1.41 Consumer Use - Adhesives and sealants - Solvent-based adhesives and sealants
(Solvent-based adhesives and sealants) 449
5.2.1.42 Consumer Use - Adhesives and sealants - Mirror edge sealant (Mirror edge sealant) 449
5.2.1.43 Consumer Use - Adhesives and sealants - Tire repair cement/sealer (Tire repair
cement/sealer) 450
5.2.1.44 Consumer Use - Cleaning and furniture care products - Carpet cleaner (Carpet cleaner)
451
5.2.1.45 Consumer Use - Cleaning and furniture care products - Aerosol spot remover (Aerosol
spot remover) 452
5.2.1.46 Consumer Use - Cleaning and furniture care products - Liquid spot remover (Liquid
spot remover) 452
5.2.1.47 Consumer Use - Arts, crafts, and hobby materials - Fixatives and finishing spray
coatings (Fixatives and finishing spray coatings) 453
5.2.1.48 Consumer Use - Apparel and footwear care products - Shoe polish (Shoe polish) 454
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281 5.2.1.49 Consumer Use - Other consumer uses - Fabric spray (Fabric spray) 455
282 5.2.1.50 Consumer Use - Other consumer uses - Film cleaner (Film cleaner) 455
283 5.2.1.51 Consumer Use - Other consumer uses - Hoof polish (hoof polish) 456
284 5.2.1.52 Consumer Use - Other consumer uses - Pepper spray (Pepper spray) 457
285 5.2.1.53 Consumer Use - Other consumer uses - Toner aid (Toner aid) 457
286 5.2.1.54 Disposal - Disposal - Industrial pre-treatment; Industrial wastewater treatment; Publicly
287 owned treatment works (POTW) (Disposal) 458
288 5.2.2 Environment 459
289 5.3 Unreasonable Risk Determination Conclusion 460
290 5.3.1 No Unreasonable Risk Determinations 460
291 5.3.2 Unreasonable Risk Determinations 461
292 REFERENCES 463
293 APPENDICES 490
294 Appendix A REGULATORY HISTORY 490
295 A.l Federal Laws and Regulations 490
296 A.2 Slate Laws and Regulations ..497
297 A.3 International Laws and Regulations.... ...498
298 Appendix B LIST OF SUPPLEMENTAL DOCUMENTS 500
299 Appendix C ENVIRONMENTAL EXPOSURES 503
300 Appendix D CONSUMER EXPOSURES 556
301 D.l Consumer Inhalation Exposure ....556
302 D.2 Consumer Dermal Exposure .........557
303 D.3 Model Sensitivity 559
304 D.3.1 Continuous Variables 560
305 D.3.2 Categorical Variables 562
306 D.4 Monitoring Data.... 562
307 D. 4.1 Indoor Air Monitoring 5 62
308 D.4.2 Personal breathing Zone Monitoring Data 564
309 Appendix E ENVIRONMENTAL HAZARDS 566
310 E.l Species Sensitivity Distribution (SSD) Methodology ...................566
311 E.2 Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as
312 Modeled in E-FAST ...581
313 Appendix F WEIGHT OF SCIENTIFIC EVIDENCE FOR CONGENITAL HEART
314 DEFECTS 628
315 F.l Background ....628
316 F.l.l (Johnson et al., 2003) and (Dawson et al., 1993) 628
317 F. 1.2 Updates to the original publications 628
318 F.2 EPA Review of the Charles River (2019) Study... .629
319 F.2.1 Study Methodology and Results 629
320 F.2.2 EPA Review 631
321 F.2.2.1 Comparing Results Between Charles River and Johnson Studies 631
322 F.2.2.2 Differences in Types of Malformations Observed 632
323 F.2.2.3 Methodology Differences 639
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F.2.2.4 Adversity of Small VSDs 641
F.2.2.5 Conclusions 642
F.3 WOE Analysis for Congenital Cardiac Defects ,.,..643
F.3.1 Methodology 643
F.3.2 WOE Results By Study Type 646
F.3.3 Mode of Action Discussion 654
Appendix G CONSIDERATIONS FOR BMD MODELING AND APPLICATION OF
UNCERTAINTY FACTORS 656
G. 1 Selecting the BMD model to use for POD computation .........656
G.2 Uncertainty Factor Selection............................ ..657
Appendix H BENCHMARK DOSE ANALYSIS FOR (Selgrade and Gilmour, 2010) 659
H.l Applied Dose/Concentration.... .........659
H. 1.1 BMDS Wizard Output Report - Mortality 659
H.l. 1.1 BMDS Summary of Mortality - BMR 10% 659
H. 1.1.2 BMDS Summary of Mortality - BMR: 5% 662
H. 1.1.3 BMDS Summary of Mortality - BMR: 1% 664
H. 1.2 BMDS Wizard Output Report - Number of Mice Infected 667
H. 1.2.1 BMDS Summary of Infected at 72 hours - BMR - 10% 667
H.2 Internal Dose (TotOxMetabBW34) ....668
H.2.1 BMDS Wizard Output Summary - Mortality 668
H.2.1.1 BMDS Summary of Mortality - BMR 10% 669
H.2.1.2 BMDS Summary of Mortality - BMR 5% 670
H.2.1.3 BMDS Summary of Mortality - BMR 1% 671
Appendix I BENCHMARK DOSE MODELING UPDATE FOR NESTED FETAL DATA
FROM (Johnson et al., 2003) 673
Appendix J PBPK MODELING UPDATES FOR REPRESENTATIVE ACUTE AND
CHRONIC ENDPOINTS 675
J.l Derivation of Internal Dose Metric Results for (Selgrade and Gilmour, 2010)... 675
J. 1.1 Methods 675
J. 1.2 Results 675
J.2 Derivation of Human Equivalent Concentrations/Doses for Best Overall Acute and Chronic
Non-Cancer Endpoints 6 76
J.2.1 Methods 676
J.2.2 Results 677
Appendix K META-ANALYSIS FOR CANCER 679
K.l Study Screening and Selection, 679
K. 1.1 Data Quality and Inclusion/Exclusion Criteria Screening 679
K. 1.2 Screening results 680
K. 1.3 Pooled Cohorts 681
K.2 Meta-Analysis Methods and Results......... ........682
K.2.1 Methods 682
K.2.2 Results 684
K.2.2.1 Initial Meta-Analyses 684
K.2.2.2 Sensitivity analyses 690
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368 K.2.3 Selected RR estimates and confidence intervals by study and cancer type 698
369 K.2.4 Sample Stata commands for meta-analysis 704
370 Appendix L APPROACH FOR ESTIMATING WATER RELEASES FROM
371 MANUFACTURING SITES USING EFFLUENT GUIDELINES 705
372 Appendix M SAMPLE CALCULATIONS FOR CALCULATING ACUTE AND CHRONIC
373 (NON-CANCER AND CANCER) INHALATION EXPOSURE 709
374 M. 1 Example High-End AC, ADC, and LADC 709
375 M.2 Example Central Tendency AEC, ADC, and LADC 710
376 Appendix N VAPOR DEGREASING AND COLD CLEANING NEAR-FIELD/FAR-FIELD
377 INHALATION EXPOSURE MODELS APPROACH AND PARAMETERS .... 711
378 N.l Model Design Equations 712
379 N.2 Model Parameters.,.. ..716
380 N.2.1 Far-Field Volume 721
381 N.2.2 Air Exchange Rate 721
382 N.2.3 Near-Field Indoor Air Speed 721
383 N.2.4 Near-Field Volume 722
384 N.2.5 Exposure Duration 722
385 N.2.6 Averaging Time 722
386 N.2.7 Vapor Generation Rate 722
387 N.2.8 Operating Hours 725
388 Appendix O BRAKE SERVICING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
389 MODEL APPROACH AND PARAMETERS 727
390 O.l Model Design Equations.......... 727
391 0.2 Model Parameters.................................................. 732
392 0.2.1 Far-Field Volume 735
393 0.2.2 Air Exchange Rate 735
394 0.2.3 Near-Field Indoor Air Speed 735
395 0.2.4 Near-Field Volume 736
396 0.2.5 Application Time 736
397 0.2.6 Averaging Time 736
398 0.2.7 Trichloroethylene Weight Fraction 736
399 0.2.8 Volume of Degreaser Used per Brake Job 737
400 0.2.9 Number of Applications per Brake Job 737
401 0.2.10 Amount of Trichloroethylene Used per Application 738
402 0.2.11 Operating Hours per Week 738
403 0.2.12 Number of Brake Jobs per Work Shift 738
404 Appendix P SPOT CLEANING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
405 MODEL APPROACH AND PARAMETERS 739
406 P.l Model Design Equations 739
407 P.2 Model Parameters...................................... ...743
408 P.2.1 Far-Field Volume 747
409 P.2.2 Near-Field Volume 747
410 P.2.3 Air Exchange Rate 747
411 P.2.4 Near-Field Indoor Wind Speed 747
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.748
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750
,750
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.750
.753
.753
.754
.754
.754
.755
.755
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.757
.757
.760
.764
.764
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.767
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.776
.776
.777
.781
.781
P.2.5 Averaging Time
P.2.6 Use Rate
P.2.7 Vapor Generation Rate
P.2.8 Operating Hours
P.2.9 Operating Days
P.2.10 Fractional Number of Operating Days that a Worker Works
Appendix Q OCCUPATIONAL INHALATION EXPOSURE AND WATER RELEASE
ASSESSMENT
Q. 1 Manufacturing
Q. 1.1 Exposure Assessment
Q. 1.2 Water Release Assessment
Q.2 Processing as a Reactant
Q.2.1 Exposure Assessment
Q.2.2 Water Release Assessment
Q.3 Formulation of Aerosol and Non-Aerosol Products
Q.3.1 Exposure Assessment
Q.3.2 Water Release Assessment
Q.4 Repackaging
Q.4.1 Exposure Assessment
Q.4.2 Water Release Assessment
Q.5 Batch Open Top Vapor Degreasing
Q.5.1 Exposure Assessment
Q.5.2 Water Release Assessment
Q.6 Batch Closed-Loop Vapor Degreasing
Q.6.1 Exposure Assessment
Q.6.2 Water Release Assessment
Q.7 Conveyorized Vapor Degreasing
Q.7.1 Exposure Assessment
Q.7.2 Water Release Assessment
Q.8 Web Vapor Degreasing......
Q.8.1 Exposure Assessment
Q.8.2 Water Release Assessment
Q.9 Cold Cleaning
Q.9.1 Exposure Assessment
Q.9.2 Water Release Assessment
Q.10 Aerosol Applications: Spray Degreasing/CIeaning, Automotive Brake and Parts Cleaners,
Penetrating Lubricants, and Mold Releases.............
Q. 10.1 Exposure Assessment
Q. 10.2 Water Release Assessment
Q.ll Metalworking Fluids..
Q. 11.1 Exposure Assessment
Q. 11.2 Water Release Assessment
Q. 12 Adhesives, Sealants, Paints, and Coatings
Q.12.1 Exposure Assessment
Q. 12.2 Water Release Assessment
Q.13 Other Industrial Uses...........
Q. 13.1 Exposure Assessment
Page 11 of 803
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459 Q. 13.2 Water Release Assessment 781
460 Q.14 Spot Cleaning, Wipe Cleaning and Carpet Cleaning.,...,, .......................783
461 Q.14.1 Exposure Assessment 783
462 Q. 14.2 Water Release Assessment 785
463 Q.15 Industrial Processing Aid 786
464 Q. 15.1 Exposure Assessment 786
465 Q. 15.2 Water Release Assessment 787
466 Q.16 Commercial Printing and Copying................. ..787
467 Q.16.1 Exposure Assessment 787
468 Q. 16.2 Water Release Assessment 788
469 Q.17 Other Commercial Uses 789
470 Q.17.1 Exposure Assessment 789
471 Q. 17.2 Water Release Assessment 789
472 Q.18 Process Solvent Recycling and Worker Handling of Wastes ...790
473 Q. 18.1 Exposure Assessment 790
474 Q. 18.2 Water Release Assessment 790
475 Q.19 Appendix Q References 790
476 Appendix R MASS BALANCE 799
477 R.l Approach for Developing the Mass Balance... 799
478 R.2 Results and Uncertainties in the Mass Balance ....800
479 Appendix S LEVEL III FUGACITY RESULTS 802
480
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LIST OF TABLES
Table 1-1. Physical and Chemical Properties of TCE 46
Table 1-2. Assessment History of TCE 51
Table 1-3. Categories and Subcategories of Occupational Conditions of Use and Corresponding
Occupational Exposure Scenario 53
Table 1-4. Categories and Subcategories of Consumer Conditions of Use 59
Table 2-1. Environmental Fate Characteristic of TCE 81
Table 2-2. Summary of EPA's daily water release estimates for each OES and also EPA's Overall
Confidence in these estimates 88
Table 2-3. Summary of EPA's estimates for the number of facilities for each OES 91
Table 2-4. Summary of EPA's estimates for release days expected for each OES 92
Table 2-5. Summary of Overall Confidence in Release Estimates by OES 94
Table 2-6. Industry Sector Modeled for Facilities without Site-Specific Flow Data in E-FAST 2014.. 103
Table 2-7. Summary of Modeled Surface Water Concentrations by OES for Maximum Days of Release
Scenario 106
Table 2-8. Summary of Modeled Surface Water Concentrations by OES for 20 Days of Release Scenario
for Direct Releases 106
Table 2-9. Summary of Modeled Surface Water Concentrations by OES for 20 Days of Release Scenario
for Indirect Releases to a non-POTW WWTP 107
Table 2-10. Measured Concentrations of TCE in Surface Water Obtained from the Water Quality Portal:
2013-2017' 108
Table 2-11. Measured Levels of TCE in U.S. Surface Water from Published Literature 109
Table 2-12. A summary for each of the 18 occupational exposure scenarios (OESs) 123
Table 2-13. Summary of inhalation exposure results for Workers based on monitoring data and exposure
modeling for each OES 124
Table 2-14. Summary of inhalation exposure results for ONUs based on monitoring data and exposure
modeling for each OES 125
Table 2-15. A summary of dermal retained dose for Workers based on exposure modeling for each OES
126
Table 2-16: Summary of the total number of workers and ONUs potentially exposed to TCE for each
OES 127
Table 2-17. Parameter Values for Calculating Inhalation Exposure Estimates 132
Table 2-18. Overview of Average Worker Tenure from U.S. Census SIPP (Age Group 50+) 134
Table 2-19. Median Year of Tenure with Current Employer by Age Group 135
Table 2-20. Glove Protection Factors for Different Dermal Protection Strategies 137
Table 2-21. EPA grouped dermal exposures associated with the various OESs into four bins 138
Table 2-22. Assigned Protection Factors for Respirators in OSHA Standard 29 CFR § 1910.134 140
Table 2-23. SOCs with Worker and ONU Designations for All Conditions of Use Except 142
Table 2-24. SOCs with Worker and ONU Designations for Dry Cleaning Facilities 143
Table2-25. Estimated Number of Potentially Exposed Workers and ONUs under NAICS 812320.... 144
Table 2-26. Summary of overall confidence in inhalation exposure estimates by OES 149
Table 2-27. Evaluated Consumer Conditions of Use and Products for TCE 155
Table 2-28. Default Modeling Input Parameters 163
Table 2-29. Consumer Product Modeling Scenarios and Varied Input Parameters 165
Table 2-30. Consumer Product Modeling Scenarios and Additional Scenario-Specific Input Parameters
169
Table 2-31. Surface Area and Body Weight Values for Different Consumer and Bystander
Subpopulations 172
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Table 2-32. Acute Inhalation Exposure Summary: Brake & Parts Cleaner
Table 2-33. Acute Dermal Exposure Summary: Brake & Parts Cleaner
Table 2-34. Acute Inhalation Exposure Summary: Aerosol Electronic Degreaser/Cleaner
Table 2-35. Acute Dermal Exposure Summary: Aerosol Electronic Degreaser
Table 2-36. Acute Inhalation Exposure Summary: Liquid Electronic Degreaser/Cleaner
Table 2-37. Acute Dermal Exposure Summary: Liquid Electronic Degreaser/Cleaner
Table 2-38. Acute Inhalation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Table 2-39. 2014 Acute Inhalation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Table 2-40. Acute Dermal Exposure Summary: Aerosol Spray Degreaser/Cleaner
Table 2-41. Acute Inhalation Exposure Summary: Liquid Degreaser/Cleaner
Table 2-42. Acute Dermal Exposure Summary: Liquid Degreaser/Cleaner
Table 2-43. Acute Inhalation Exposure Summary: Aerosol Gun Scrubber
Table 2-44. Acute Dermal Exposure Summary: Aerosol Gun Scrubber
Table 2-45. Acute Inhalation Exposure Summary: Liquid Gun Scrubber
Table 2-46. Acute Dermal Exposure Summary: Liquid Gun Scrubber
Table 2-47. Acute Inhalation Exposure Summary: Mold Release
Table 2-48. Acute Dermal Exposure Summary: Mold Release
Table 2-49. Acute Inhalation Exposure Summary: Aerosol Tire Cleaner
Table 2-50. Acute Dermal Exposure Summary: Aerosol Tire Cleaner
Table 2-51. Acute Inhalation Exposure Summary: Liquid Tire Cleaner
Table 2-52. Acute Dermal Exposure Summary: Liquid Tire Cleaner
Table 2-53. Acute Inhalation Exposure Summary: Tap & Die Fluid
Table 2-54. Acute Dermal Exposure Summary: Tap & Die Fluid
Table 2-55. Acute Inhalation Exposure Summary: Penetrating Lubricant
Table 2-56. Acute Dermal Exposure Summary: Penetrating Lubricant
Table 2-57. Acute Inhalation Exposure Summary: Solvent-based Adhesive & Sealant
Table 2-58. Acute Dermal Exposure Summary: Solvent-based Adhesive & Sealant
Table 2-59. Acute Inhalation Exposure Summary: Mirror-Edge Sealant
Table 2-60. Acute Dermal Exposure Summary: Mirror-Edge Sealant
Table 2-61. Acute Inhalation Exposure Summary: Tire Repair Cement/Sealer
Table 2-62. Acute Dermal Exposure Summary: Tire Repair Cement/Sealer
Table 2-63. Acute Inhalation Exposure Summary: Carpet Cleaner
Table 2-64. Acute Dermal Exposure Summary: Carpet Cleaner
Table 2-65. Acute Inhalation Exposure Summary: Aerosol Spot Remover
Table 2-66. Acute Dermal Exposure Summary: Aerosol Spot Remover
Table 2-67. Acute Inhalation Exposure Summary: Liquid Spot Remover
Table 2-68. Acute Dermal Exposure Summary: Liquid Spot Remover
Table 2-69. Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings
Table 2-70. 2014 Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings
Table 2-71. Acute Dermal Exposure Summary: Fixatives & Finishing Spray Coatings
Table 2-72. Acute Inhalation Exposure Summary: Shoe Polish
Table 2-73. Acute Dermal Exposure Summary: Shoe Polish
Table 2-74. Acute Inhalation Exposure Summary: Fabric Spray
Table 2-75. Acute Dermal Exposure Summary: Fabric Spray
Table 2-76. Acute Inhalation Exposure Summary: Film Cleaner
Table 2-77. Acute Dermal Exposure Summary: Film Cleaner
Table 2-78. Acute Inhalation Exposure Summary: Hoof Polish
Table 2-79. Acute Dermal Exposure Summary: Hoof Polish
Page 14 of 803
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Table 2-80. Acute Inhalation Exposure Summary: Pepper Spray 198
Table 2-81. Acute Dermal Exposure Summary: Pepper Spray 198
Table 2-82. Acute Inhalation Exposure Summary: Toner Aid 199
Table 2-83. Acute Dermal Exposure Summary: Toner Aid 199
Table 2-84. Evaluated Pathways for Consumer Conditions of Use 200
Table 2-85. Summary of Consumer Exposure Levels by Category 201
Table 2-86. Confidence Ratings for Acute Inhalation Consumer Exposure Modeling Scenarios 206
Table 2-87. Confidence Ratings for Acute Dermal Consumer Exposure Modeling Scenarios 208
Table 2-88. Percentage of Employed Persons by Age, Sex, and Industry Sector 211
Table 2-89. Percentage of Employed Adolescent by Detailed Industry Sector 212
Table 3-1. Ecological Hazard Data used Quantitatively to Characterize TCE Hazard for Aquatic
Organisms 217
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity 224
Table 3-3. TCE Metabolites Identified by Pathway 230
Table 3-4. Common Metabolites of TCE and Related Compounds 230
Table 3-5. List of All of the PBPK-Modeled Dose Metrics Considered in this Risk Evaluation 232
Table 3-6. Overall Summary Scores by Line of Evidence for Cardiac Defects from TCE 249
Table 3-7. Dose-response analysis of selected studies considered for acute exposure scenarios 264
Table 3-8. Dose-response analysis of selected studies considered for evaluation of liver toxicity 266
Table 3-9. Dose-response analysis of selected studies considered for evaluation of kidney toxicity .... 267
Table 3-10. Dose-response analysis of selected studies considered for evaluation of neurological effects
269
Table 3-11. Dose-response analysis of selected studies considered for evaluation of immune effects.. 271
Table 3-12. Dose-response analysis of selected studies considered for evaluation of reproductive effects
274
Table 3-13. Dose-response analysis of selected studies considered for acute exposure scenarios 278
Table 3-14. Dose-response analysis of selected studies considered for chronic exposure scenarios 279
Table 3-15. Cancer Points of Departure for Lifetime Exposure Scenarios 280
Table 3-16. Occupational PODs for Representative Non-Cancer Endpoints 282
Table 4-1. Environmental Risk Quotients for Aquatic Species for Facilities Releasing TCE to Surface
Water as Modeled in E-FAST (RQs > 1 in bold) 292
Table 4-2. RQs for Aquatic Species Calculated using Monitored Environmental Concentrations from
WQX/WQP 296
Table 4-3. RQs for Aquatic Species Calculated using Monitored Environmental Concentrations from
Published Literature 297
Table 4-4. Environmental Risk Quotients for Sediment Organisms for Facilities Releasing TCE to
Surface Water as Modeled in E-FAST (RQs > 1 in bold) 299
Table 4-5. RQs for Sediment Organisms Calculated using Monitored Environmental Concentrations
from WQX/WQP 300
Table 4-6. RQs Sediment Organisms Calculated using Monitored Environmental Concentrations from
Published Literature 300
Table 4-7. Use Scenarios, Populations of Interest and Toxicological Endpoints Used for Acute and
Chronic Exposures 301
Table 4-8. Most Sensitive Endpoints from Each Health Domain for Risk Estimation 304
Table 4-9. Inhalation Exposure Data Summary and PPE Use Determination 306
Table 4-10. Occupational Risk Estimation - Manufacturing 308
Table 4-11. Occupational Risk Estimation - Processing as a Reactant 310
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Table 4-12.
Table 4-13.
Table 4-14.
Table 4-15.
Occupational Risk Estimation
Data
Batch Open Top Vapor Degreasing
Occupational Risk Estimation - Batch Open Top Vapor Degreasing
Data
Occupational Risk Estimati
Occupational Risk Estimati
Data
Occupational Risk Estimation
Occupational Risk Estimation
Occupational Risk Estimation
Occupational Risk Estimation
Inhalation Monitoring
312
Inhalation Modeling
313
on - Batch Closed-Loop Vapor Degreasing 315
on - Conveyorized Vapor Degreasing - Inhalation Monitoring
317
Table 4-16. Occupational Risk Estimation - Conveyorized Vapor Degreasing - Inhalation Modeling
Data.
Table 4-17.
Table 4-18.
Table 4-19.
Table 4-20.
Table 4-21.
Table 4-22.
Table 4-23.
Table 4-24.
Table 4-25.
Table 4-26.
318
Web Vapor Degreasing 320
Cold Cleaning 322
Aerosol Applications 324
Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Monitoring Data 326
Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Modeling Data 327
Occupational Risk Estimation - Formulation of Aerosol and Non-Aerosol Products 329
- Repackaging 331
- Metalworking Fluids - Inhalation Monitoring Data 333
- Metalworking Fluids - Inhalation Modeling Data 334
- Adhesives, Sealants, Paints, and Coatings (Industrial
Setting) 336
Occupational Risk Estimation
Occupational Risk Estimation
Occupational Risk Estimation
Occupational Risk Estimation
Setting).
Table 4-28.
Table 4-29.
Table 4-30.
Table 4-31.
Table 4-32.
Table 4-33.
Table 4-34.
Table 4-35.
Table 4-36.
Table 4-37.
Table 4-38.
Table 4-39.
Table 4-40.
Table 4-41.
Table 4-42.
Occupational Risk Esti
Occupational Risk Esti
Occupational Risk Esti
Occupational Risk Esti
Table 4-27. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Commercial
338
mation - Industrial Processing Aid (12 hr) 340
mation - Commercial Printing and Copying 342
mation - Other Industrial Uses 344
mation - Process Solvent Recycling and Worker Handling of Wastes
346
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Brake and Parts
Cleaner 349
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Electronic
Degreaser/Cleaner 350
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
Degreaser/Cleaner 351
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
Degreaser/Cleaner 352
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid
Degreaser/Cleaner 353
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Gun Scrubber
354
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Gun Scrubber
355
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Mold Release 356
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Tire Cleaner
357
Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Tire Cleaner358
Consumer Risk Estimation - Lubricants and Greases - Tap and Die Fluid 359
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Table 4-43. Consumer Risk Estimation - Lubricants and Greases - Penetrating Lubricant 360
Table 4-44. Consumer Risk Estimation - Adhesives and Sealants - Solvent-Based Adhesive and Sealant
361
Table 4-45. Consumer Risk Estimation - Adhesives and Sealants - Mirror Edge Sealant 362
Table 4-46. Consumer Risk Estimation - Adhesives and Sealants - Tire Repair Cement / Sealer 363
Table 4-47. Consumer Risk Estimation - Cleaning and Furniture Care Products - Carpet Cleaner 364
Table 4-48. Consumer Risk Estimation - Cleaning and Furniture Care Products - Aerosol Spot Remover
365
Table 4-49. Consumer Risk Estimation - Cleaning and Furniture Care Products - Liquid Spot Remover
366
Table 4-50. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and Finishing
Spray Coatings 367
Table 4-51. Consumer Risk Estimation - Apparel and Footwear Care Products - Shoe Polish 368
Table 4-52. Consumer Risk Estimation - Other Consumer Uses - Fabric Spray 369
Table 4-53. Consumer Risk Estimation - Other Consumer Uses - Film Cleaner 370
Table 4-54. Consumer Risk Estimation - Other Consumer Uses - Hoof Polish 371
Table 4-55. Consumer Risk Estimation - Other Consumer Uses - Pepper Spray 372
Table 4-56. Consumer Risk Estimation - Other Consumer Uses - Toner Aid 373
Table 4-57. Facilities with Risk from Acute or Chronic Exposure for Aquatic Organisms (RQs > 1 in
bold) 384
Table 4-58. Facilities with Risk from Acute or Chronic Exposure for Sediment Organisms (RQs > 1 in
bold) 386
Table 4-59. Occupational Risk Summary Table 389
Table 4-60. Consumer Risk Summary Table 402
Table 5-1. Categories and Subcategories of Conditions of Use Included in the Scope of the Risk
Evaluation 411
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LIST OF FIGURES
Figure 1-1. Total Aggregate TCE Production Volume (lbs.) 2012-2015a 47
Figure 1-2. Percentage of TCE Production Volume by Use 48
Figure 1-3. TCE Life Cycle Diagram 61
Figure 1-4. TCE Conceptual Model for Industrial and Commercial Activities and Uses: Potential
Exposures and Hazards 70
Figure 1-5. TCE Conceptual Model for Consumer Activities and Uses: Potential Exposures and Hazards
71
Figure 1-6. TCE Conceptual Model for Environmental Releases and Wastes: Potential Exposures and
Hazards 72
Figure 1-7. Literature Flow Diagram for Environmental Fate and Transport 75
Figure 1-8. Literature Flow Diagram for Engineering Releases and Occupational Exposure 76
Figure 1-9. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources 77
Figure 1-10. Literature Flow Diagram for Environmental Hazard 78
Figure 1-11. Literature Flow Diagram for Human Health Hazard 79
Figure 2-1. Environmental transport, partitioning and degradation processes for TCE 85
Figure 2-2. An overview of how EPA estimated daily water releases for each OES 88
Figure 2-3. Distribution of Active Facility Releases Modeled 107
Figure 2-4. TCE Modeled Concentrations from Releasing Facilities (250-365 Days of Release) and
Measured Concentrations from WQP: Eastern U.S., 2016 Ill
Figure 2-5. TCE Modeled Concentrations from Releasing Facilities (250-365 Days of Release) and
Measured Concentrations from WQP: Western U.S., 2016 112
Figure 2-6. TCE Modeled Concentrations from Releasing Facilities (20 Days of Release) and Measured
Concentrations from WQP: Eastern U.S., 2016 113
Figure 2-7. TCE Modeled Concentrations from Releasing Facilities (20 Days of Release) and Measured
Concentrations from WQP: Western U.S., 2016 114
Figure 2-8. Co-Location of Modeled Concentrations from Releasing Facilities and Measured
Concentrations from WQP (HUC-8) in North Carolina 115
Figure 2-9. Co-Location of Modeled Concentrations from Releasing Facilities and Measured
Concentrations from WQP (HUC-8) in New Mexico 116
Figure 2-10. Components of an occupational assessment for each OES 120
Figure 2-11. Illustrative applications of the NF/FF model to various exposure scenarios 130
Figure 3-1. Species Sensitivity Distributions (SSDs) for Acute Hazard Data Using LCsos or ECsos
(Etterson, 2020) 219
Figure 3-2. Species Sensitivity Distribution (SSD) for Algae Species Using ECsos (Etterson, 2020)... 220
Figure 3-3. EPA Approach to Hazard Identification, Data Integration, and Dose-Response Analysis for
TCE 226
Figure 3-4. Dose-Response Analyses of Rodent Non-Cancer Effects Using 234
Figure 3-5. Example of HEC99 Estimation through Interpecies, Intraspecies and 234
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LIST OF APPENDIX TABLES
Table_Apx A-l. Federal Laws and Regulations 490
Table_Apx A-2. State Laws and Regulations 497
Table_Apx A-3. Regulatory Actions by Other Governments and Tribes 498
Table_Apx C-l. Facility-Specific Aquatic Exposure Modeling Results 503
TableApx D-l. TCE Residential Indoor Air Concentrations (|ig/m3) in the United States and Canada
563
Table Apx D-2. Personal Breathing Zone Concentrations (|ig/m3) for TCE in the United States
(General/Residential) 565
Table Apx E-l. Acute Toxicity Data for Aquatic Organisms used in SSD 567
Table Apx E-2. Standard Error for all distributions and fitting methods using TCE's acute hazard data
(Etterson, 2020) 570
Table_Apx E-3. Algae Toxicity Data used in SSD 573
Table Apx E-4. Standard Error for all distributions and fitting methods using TCE's algae hazard data
(Etterson, 2020) 578
Table Apx E-5. Environmental RQs by Facility (with RQs > 1 in bold) 581
Table_Apx F-l. Strengths and Limitations of (Johnson et al., 2003) 629
Table Apx F-2. Experimental Design of (Charles River Laboratories, 2019) 630
Table_Apx F-3. Summary of Observed Interventricular Defects 630
Table Apx F-4. Incidence of total heart malformations in Johnson and Charles River studies 631
Table Apx F-5. Incidence of VSDs in Johnson and Charles River studies 632
Table Apx F-6. Heart and Cardiovascular Defects Observed in Select Oral TCE studies 633
Table_Apx F-7. Cardiac Defects Observed in Literature 635
Table Apx F-8. List of RA Studies Identified in the Literature Search and Observed Defects in Each 635
Table Apx F-9. Cardiac Defects Observed After Exposure to RA or TCE 638
Table_Apx F-10. Weight-of-Evidence Table for Epidemiology Studies 647
Table Apx F-l 1. Weight-of-Evidence Table for In Vivo Animal Toxicity Studies 649
Table_Apx F-12. Weight-of-Evidence Table for Mechanistic Studies 652
Table Apx F-13. Overall Weight-of-Evidence Table and Summary Scores 654
Table Apx H-l. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade and
Gilmour 2010; BMR = 10% Extra Risk 659
Table Apx H-2. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade and
Gilmour 2010; BMR = 5% Extra Risk 662
Table Apx H-3. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade and
Gilmour 2010; BMR = 1% Extra Risk 664
Table Apx H-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 667
Table Apx H-5. Study incidence data based on median internal dose metric 669
Table Apx H-6. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade and
Gilmour 2010; BMR = 10% Extra Risk 669
Table Apx H-7. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade and
Gilmour 2010; BMR = 5% Extra Risk 670
Table Apx H-8. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade and
Gilmour 2010; BMR = 1% Extra Risk 671
Table Apx 1-1. Results for Best-Fitting Model in Comparison to Results 674
Table Apx J-l. Selected percentiles for TotMetabBW34 and AUCCBld for female mouse simulations
676
Page 19 of 803
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833
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836
TableApx J-2. Human equivalent concentrations and human equivalent doses for the Selgrade and Keil
endpoints under both default and occupational respiratory conditions 678
Table Apx K-l. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies Identified
in EPA's Literature Search 679
Table Apx K-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based on
Inclusion/Exclusion Criteria 680
Table Apx K-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the 2011
IRIS Assessment or EPA Literature Search 681
Table_Apx K-4. Analysis of influential studies: NHL 690
Table_Apx K-5. Analysis of influential studies: Kidney cancer 690
Table_Apx K-6. Analysis of influential studies: Liver cancer 691
Table Apx K-7. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
cohort studies published after U.S. EPA (2011) 698
Table Apx K-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
case-control studies 699
Table Apx K-9. Selected RR estimates for NHL associated with TCE exposure (effect in the highest
exposure group) studies 699
Table Apx K-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from cohort studies 700
Table Apx K-l 1. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from case-control studies published after U.S. EPA (2011) 701
Table Apx K-12. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from cohort studies 702
Table Apx K-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from case-control studies published after U.S. EPA (2011) 703
Table Apx L-l. Summary of OCPSF Effluent Guidelines for Trichloroethylene 705
Table Apx L-2. Default Parameters for Estimating Water Releases of Trichloroethylene from
Manufacturing Sites 706
TableApx L-3. Summary of Facility Trichloroethylene Production Volumes and Wastewater Flow
Rates 707
Table Apx N-l. Summary of Parameter Values and Distributions Used in the Open-Top Vapor
Degreasing Near-Field/Far-Field Inhalation Exposure Model 717
Table Apx N-2. Summary of Parameter Values and Distributions Used in the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model 718
TableApx N-3. Summary of Parameter Values and Distributions Used in the Web Degreasing Near-
Field/Far-Field Inhalation Exposure Model 719
Table Apx N-4. Summary of Parameter Values and Distributions Used in the Cold Cleaning Near-
Field/Far-Field Inhalation Exposure Model 720
Table Apx N-5. Summary of Trichloroethylene Vapor Degreasing and Cold Cleaning Data from the
2014 NEI 722
Table Apx N-6. Distribution of Trichloroethylene Open-Top Vapor Degreasing Unit Emissions 723
Table Apx N-7. Distribution of Trichloroethylene Conveyorized Degreasing Unit Emissions 724
Table_Apx N-8. Distribution of Trichloroethylene Web Degreasing Unit Emissions 725
Table Apx N-9. Distribution of Trichloroethylene Cold Cleaning Unit Emissions 725
Table Apx N-10. Distribution of Trichloroethylene Open-Top Vapor Degreasing Operating Hours... 725
Table Apx N-l 1. Distribution of Trichloroethylene Conveyorized Degreasing Operating Hours 725
Table Apx N-12. Distribution of Trichloroethylene Web Degreasing Operating Hours 726
Table Apx N-13. Distribution of Trichloroethylene Cold Cleaning Operating Hours 726
Page 20 of 803
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875
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877
878
879
880
881
882
883
884
TableApx 0-1. Summary of Parameter Values and Distributions Used in the Brake Servicing Near-
Field/Far-Field Inhalation Exposure Model 733
Table Apx 0-2. Summary of Trichloroethylene-Based Aerosol Degreaser Formulations 737
TableApx P-l. Summary of Parameter Values and Distributions Used in the Spot Cleaning 744
Table Apx P-2. Composite Distribution of Dry Cleaning Facility Floor Areas 747
Table Apx Q-l. Summary of Worker Inhalation Exposure Monitoring Data from TCE Manufacturing
750
Table Apx Q-2. Summary of OCPSF Effluent Limitations for Trichloroethylene 751
Table Apx Q-3. Reported Water Releases of Trichloroethylene from Manufacturing Sites Reporting to
2016 TRI 752
Table Apx Q-4. Estimated Water Releases of Trichloroethylene from Manufacturing Sites Not
Reporting to 2016 TRI 753
TableApx Q-5. Summary of Worker Inhalation Exposure Surrogate Monitoring Data from TCE Use as
a Reactant 754
Table Apx Q-6. Water Release Estimates for Sites Using TCE as a Reactant 754
Table Apx Q-7. Summary of Worker Inhalation Exposure Monitoring Data for Unloading TCE During
Formulation of Aerosol and Non-Aerosol Products 755
Table Apx Q-8. Summary of Worker Inhalation Exposure Monitoring Data for Unloading/Loading TCE
from Bulk Containers 756
Table Apx Q-9. Reported Water Releases of Trichloroethylene from Sites Repackaging TCE 757
Table Apx Q-10. Summary of Worker Inhalation Exposure Monitoring Data for Batch Open-Top Vapor
Degreasing 758
Table Apx Q-l 1. Summary of Exposure Modeling Results for TCE Degreasing in OTVDs 760
Table Apx Q-12. Reported Water Releases of Trichloroethylene from Sites Using TCE in Open-Top
Vapor Degreasing 760
TableApx Q-13. Summary of Worker Inhalation Exposure Monitoring Data for Batch Closed-Loop
Vapor Degreasing 764
Table Apx Q-14. Summary of Worker Inhalation Exposure Monitoring Data for Conveyorized Vapor
Degreasing 765
Table Apx Q-15. Summary of Exposure Modeling Results for TCE Degreasing in Conveyorized
Degreasers 767
Table Apx Q-16. Summary of Exposure Modeling Results for TCE Degreasing in Web Degreasers. 769
Table Apx Q-17. Summary of Exposure Modeling Results for Use of Trichloroethylene in Cold
Cleaning 771
Table Apx Q-18. Summary of Worker and Occupational Non-User Inhalation Exposure Modeling
Results for Aerosol Degreasing 773
Table Apx Q-19. Summary of Worker Inhalation Exposure Monitoring Data for TCE Use in
Metalworking Fluids 774
Table Apx Q-20. ESD Exposure Estimates for Metalworking Fluids Based on Monitoring Data 775
Table Apx Q-21. Summary of Exposure Results for Use of TCE in Metalworking Fluids Based on ESD
Estimates 775
Table Apx Q-22. Summary of Worker Inhalation Exposure Monitoring Data for
Adhesives/Paints/Coatings 776
Table Apx Q-23. Reported Water Releases of Trichloroethylene from Sites Using TCE in Adhesives,
Sealants, Paints and Coatings 777
Table Apx Q-24. Summary of Occupational Exposure Surrogate Monitoring Data for Unloading TCE
During Other Industrial Uses 781
Table Apx Q-25. Reported Water Releases of Trichloroethylene from Other Industrial Uses 782
Page 21 of 803
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903
TableApx Q-26. Summary of Worker Inhalation Exposure Monitoring Data for Spot Cleaning Using
TCE 783
Table Apx Q-27. Summary of Exposure Modeling Results for Spot Cleaning Using TCE 785
Table Apx Q-28. Reported Water Releases of Trichloroethylene from Sites Using TCE Spot Cleaning
785
Table Apx Q-29. Summary of Exposure Monitoring Data for Use as a Processing Aid 786
Table Apx Q-30. Reported Water Releases of Trichloroethylene from Industrial Processing Aid Sites
Using TCE 787
Table Apx Q-31. Summary of Worker Inhalation Exposure Monitoring Data for High Speed Printing
Presses 788
Table Apx Q-32. Reported Water Releases of Trichloroethylene from Commercial Printing and
Copying 788
Table Apx Q-33. Reported Water Releases of Trichloroethylene from Other Commercial Uses in the
2016 DMR 789
Table Apx Q-34. Estimated Water Releases of Trichloroethylene from Disposal/Recycling of TCE.. 790
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LIST OF APPENDIX FIGURES
Figure_Apx D-l. Elasticities (> 0.05) for Parameters Applied in El 560
Figure_Apx D-2. Elasticities (> 0.05) for Parameters Applied in E3 561
FigureApx D-3. Elasticities (> 0.05) for Parameters Applied in P_DER2b 562
FigureApx E-l. SSD Toolbox interface showing HCoss and P values for each distribution and fitting
method using TCE's acute hazard data (Etterson, 2020) 569
Figure Apx E-2. AICc for the five distribution options in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2020) 570
Figure Apx E-3. All distributions and fitting methods in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2020) 571
Figure Apx E-4. TCE's acute hazard data fit with the normal, logistic, triangular, Gumbel, and Burr
distributions fit with maximum likelihood in the SSD Toolbox (Etterson, 2020) 572
Figure Apx E-5. SSD Toolbox interface and list of HCoss for each distribution and fitting method using
TCE's algae hazard data (Etterson, 2020) 577
Figure_Apx E-6. AICc Table for algae hazard data (Etterson, 2020) 578
Figure Apx E-7. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard data
(Etterson, 2020) 579
Figure Apx E-8. TCE algae data fit with all distributions using the maximum likelihood fitting method
(Etterson, 2020) 580
Figure Apx H-l. Plot of Incidence by Applied 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 660
Figure Apx H-2. Plot of Incidence by Applied 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 662
Figure Apx H-3. Plot of Incidence by Applied 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 665
Figure Apx H-4. 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 668
Figure Apx H-5. Plot of Incidence by Internal Dose with Fitted Curve for Log-Probit Model for
Mortality from Selgrade and Gilmour 2010; BMR = 10% Extra Risk 670
Figure Apx H-6. Plot of Incidence by Internal Dose with Fitted Curve for Log-Probit Model for
Mortality from Selgrade and Gilmour 2010; BMR = 5% Extra Risk 671
Figure Apx H-7. Plot of Incidence by Internal Dose with Fitted Curve for Log-Probit Model for
Mortality from Selgrade and Gilmour 2010; BMR = 1% Extra Risk 672
Figure Apx J-l. Distribution of default (resting) respiration rates compared to occupational respiratory
rate 677
FigureApx K-l. Fixed-effects model, overall association of NHL and exposure to TCE 684
Figure Apx K-2. Random-effects model, overall association of NHL and exposure to TCE 685
Figure Apx K-3. Fixed-effects model, association of NHL and high exposure to TCE 685
Figure Apx K-4. Random-effects model, association of NHL and high exposure to TCE 686
Figure Apx K-5. Fixed-effects model, overall association of kidney cancer and 687
Figure Apx K-6. Random-effects model, overall association of kidney cancer and 687
Figure Apx K-7. Fixed-effects model, overall association of liver cancer and 688
Figure Apx K-8. Random-effects model, overall association of liver cancer and 689
Page 23 of 803
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FigureApx K-9. Fixed-effects model, overall association of NHL and exposure to TCE, study of
Vlaanderen et al. (2013) omitted 692
Figure Apx K-10. Fixed-effects model, association of NHL and high exposure to TCE, study of
Vlaanderen et al. (2013) omitted 692
Figure Apx K-l 1. Fixed-effects model, overall association of kidney cancer and 693
Figure Apx K-12. Fixed-effects model, overall association of liver cancer and 693
FigureApx K-13. Fixed-effects model, overall association of NHL and 694
Figure Apx K-14. Fixed-effects model, overall association of kidney cancer and 695
Figure Apx K-l 5. Fixed-effects model, overall association of liver cancer and 695
Figure_Apx K-l6. Funnel plots for publication bias 697
Figure Apx N-l. 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 712
Figure_Apx N-2. The Near-Field/Far-Field Model as Applied to the Conveyorized Degreasing Near-
Field/Far-Field Inhalation Exposure Model 713
Figure_Apx N-3. The Near-Field/Far-Field Model as Applied to the Web Degreasing Near-Field/Far-
Field Inhalation Exposure Model 713
Figure Apx O-l. The Near-Field/Far-Field Model as Applied to the Brake Servicing Near-Field/Far-
Field Inhalation Exposure Model 728
Figure Apx P-l. The Near-Field/Far-Field Model as Applied to the Spot Cleaning Near-Field/Far-Field
Inhalation Exposure Model 740
FigureApx Q-l. Schematic of the Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation
Exposure Model 759
Figure Apx Q-2. Belt/Strip Conveyorized Vapor Degreasing Schematic of the Conveyorized
Degreasing Near-Field/Far-Field Inhalation Exposure Model 766
Figure Apx Q-3. Schematic of the Web Degreasing Near-Field/Far-Field Inhalation Exposure Model
768
FigureApx Q-4. Schematic of the Cold Cleaning Near-Field/Far-Field Inhalation Exposure Model.. 770
Figure_Apx Q-5. Schematic of the Near-Field/Far-Field Model for Aerosol Degreasing 773
Figure_Apx Q-6. Schematic of the Near-Field/Far-Field Model for Spot Cleaning 784
Figure_Apx R-l. Mass Balance for Trichloroethylene 801
Page 24 of 803
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ACKNOWLEDGEMENTS
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).
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).
Special acknowledgement is given for PBPK modeling support from EPA Office of Research and
Development (ORD), especially Todd Zurlinden for his derivation of internal doses and Human
Equivalent Concentrations/Doses for (Setgrade and Gilmour. 2.010) and (Keil et al. 2009). Additional
acknowledgements are provided to Amanda Persad, Audrey Galizia, Barbara Glenn, Channa Keshava,
Nagalakshmi Keshava, Rebecca Nachman and Suryanarayana Vulimiri for study quality evaluation and
systematic review support; George Woodall for exposure response array; and Thomas Bateson for the
cancer meta-analysis.
EPA also acknowledges the contributions of Masashi 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.
Authors/Contributors
Sheila Canavan (Division Director), Stan Barone (Deputy Division Director), Nhan Nguyen
(Management Lead), Yvette Selby-Mohamadu (Management Lead), Keith Jacobs (Staff Lead), Rehan
Choudhary (prior Staff co-Lead), Heidi Bethel (prior Staff co-Lead), Stephanie Sarraino, Kara Koehrn,
Sheila Xiah Kragie, Franklyn Hall, Wen-Hsiung Lee, Toni Krasnic, Katie McNamara, Niva Kramek,
Lynne Blake-Hedges, Shannon Rebersak, Caitlin Briere, Sue Makris, Sue Euling, Zaida Figueroa, Bryan
Lobar, Matt Etterson, Dave Lynch, Ryan Sullivan, Laura Krnavek, Yashfin Mahid, Mitchell Sumner
Page 25 of 803
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1027
1028
1029
1030
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1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
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1057
1058
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ABBREVIATIONS
°c
Degrees Celsius
Ł0
Vacuum Permittivity
ACGIH
American Conference of Governmental Industrial Hygienists
AEGL
Acute Exposure Guideline Level
ADD
Average Daily Dose
AF
Assessment Factor
APF
Assigned Protection Factor
AQS
Air Quality System
ATCM
Airborne Toxic Control Measure
atm
Atmosphere(s)
AT SDR
Agency for Toxic Substances and Disease Registries
BAF
Bioaccumulation Factor
BCF
Bioconcentration Factor
BIOWIN
The EPI Suite™ module that predicts biodegradation rates
bw3/4
body weight374
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
CEPA
Canadian Environmental Protection Act
CERCLA
Comprehensive Environmental Response, Compensation, and Liability Act
CFC
Chi orofluorocarb on
CFR
Code of Federal Regulations
CH
Chloral Hydrate
CHD
Congenital Heart Defects
CHIRP
Chemical Risk Information Platform
ChV
Chronic Value
cm3
Cubic Centimeter(s)
CNS
Central Nervous System
coc
Concentration of Concern
cou
Conditions of Use
CPCat
Chemical and Product Categories
CSCL
Chemical Substances Control Law
CWA
Clean Water Act
CYP
Cytochrome P450
DCA
Dichloroacetic acid
DCE
Di chl oroethy 1 ene
DCVC
S-dichlorovinyl-L-cysteine
DCVG
S-dichlorovinyl-glutathione
DEVL
Dermal Exposure to Volatile Liquids
DIY
Do-It-Yourself
DMR
Discharge Monitoring Report
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1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
ECso
Effect concentration at which 50% of test organisms exhibit an effect
ECCC
Environment and Climate Change Canada
ECHA
European Chemicals Agency
EDC
Ethylene Dichloride
E-FAST
Exposure and Fate Assessment Screening Tool
EG
Effluent Guidelines
EPA
Environmental Protection Agency
EPCRA
Emergency Planning and Community Right-to-Know Act
EPI Suite™
Estimation Program Interface Suite™
ESD
Emission Scenario Document
EU
European Union
FDA
Food and Drug Administration
FFDCA
Federal Food, Drug, and Cosmetic Act
FIFRA
Federal Insecticide, Fungicide, and Rodenticide Act
FR
Federal Register
g
Gram(s)
GACT
Generally Available Control Technology
GS
Generic Scenario
GSH
Glutathione
GST
Glutathione-S-transferase
HAP
Hazardous Air Pollutant
HCFC
Hy drochl orofluorocarb on
HC1
Hydrochloric Acid
HCos
Hazardous Concentration threshold for 5% of species in a Species Sensitivity Distribution
HEC
Human Equivalent Concentration
HED
Human Equivalent Dose
HFC
Hydrofluorocarbon
HHE
Health Hazard Evaluation
HPV
High Production Volume
Hr
Hour
IARC
International Agency for Research on Cancer
ICIS
Integrated Compliance Information System
IDLH
Immediately Dangerous to Life and Health
IMIS
Integrated Management Information System
IRIS
Integrated Risk Information System
ISHA
Industrial Safety and Health Act
ISOR
Initial Statement of Reasons
IUR
Inhalation Unit Risk
Koc
Soil Organic Carbon-Water Partitioning Coefficient
Kow
Octanol/Water Partition Coefficient
kg
Kilogram(s)
L
Liter(s)
lb
Pound(s)
LCso
Lethal Concentration at which 50% of test organisms die
LOAEL
Lowest-observed-adverse-effect-level
LOEC
Lowest-observable-effect Concentration
m3
Cubic Meter(s)
MACT
Maximum Achievable Control Technology
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1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
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1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
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1164
1165
1166
1167
MATC Maximum Acceptable Toxicant Concentration
MCCEM Multi-Chamber Concentration and Exposure Model
MCL Maximum Contaminant Level
MCLG Maximum Contaminant Level Goal
mg Milligram(s)
mmHg Millimeter(s) of Mercury
MOA Mode of Action
mPas Millipascal(s)-Second
MSDS Material Safety Data Sheet
MSW Municipal Solid Waste
NAICS North American Industry Classification System
NATA National Scale Air-Toxics Assessment
NCEA National Center for Environmental Assessment
NICNAS Australia National Industrial Chemicals Notification and Assessment Scheme
NCI National Cancer Institute
NCP National Contingency Plan
NEI National Emissions Inventory
NESHAP National Emission Standards for Hazardous Air Pollutants
NHANES National Health and Nutrition Examination Survey
NICNAS National Industrial Chemicals Notification and Assessment Scheme
NIH National Institute of Health
NICNAS National Industrial Chemicals Notification and Assessment Scheme
NIOSH National Institute for Occupational Safety and Health
NITE National Institute of Technology and Evaluation
NOAEL No-Observed-Adverse-Effect-Level
NOEC No-observable-effect Concentration
NPDES National Pollutant Discharge Elimination System
NPDWR National Primary Drinking Water Regulation
NRC National Research Council
NTP Nati onal Toxi col ogy Program
NWIS National Water Information System
OCPSF Organic Chemicals, Plastics and Synthetic Fibers
OCSPP Office of Chemical Safety and Pollution Prevention
OECD Organization for Economic Co-operation and Development
OEHHA Office of Environmental Health Hazard Assessment
OES Occupational Exposure Scenario
OEL Occupational Exposure Limits
ONU Occupational Non-User
OPPT Office of Pollution Prevention and Toxics
OR Odds Ratio
OSH(A) Occupational Safety and Health (Administration)
OSF Oral Slope Factor
OST Office of Science and Technology
OTVD Open-Top Vapor Degreaser
OW Offi ce of Water
PBPK Physiologically-Based Pharmacokinetic
PBZ Personal Breathing Zone
PCE Tetrachloroethylene
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1184
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1186
1187
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1190
1191
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1193
1194
1195
1196
1197
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1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
PF
Protection Factor (for gloves)
PECO
Population, Exposure, Comparator, and Outcome
PEL
Permissible Exposure Limit
PESS
Potentially Exposed or Susceptible Subpopulations
POD
Point of Departure
POTW
Publicly Owned Treatment Works
ppb
Part(s) per Billion
PPE
Personal Protective Equipment
ppm
Part(s) per Million
PSD
Particle Size Distribution
PV
Production Volume
QC
Quality Control
QSAR
Quantitative Structure Activity Relationship
RCRA
Resource Conservation and Recovery Act
REACH
Registration, Evaluation, Authorisation and Restriction of Chemicals
REL
Relative Exposure Limit
RR
Relative Risk
RTR
Risk and Technology Review
SDS
Safety Data Sheet
SDWA
Safe Drinking Water Act
SIDS
Screening Information Dataset
SNUN
Significant New Use Notice
SNUR
Significant New Use Rule
SOCMI
Synthetic Organic Chemical Manufacturing Industry
SPARC
SPARC Performs Automated Reasoning in Chemistry
SpERC
Specific Environmental Release Categories
STEL
Short-Term Exposure Limit
STP model
Sewage Treatment Plant model
STORET
STOrage and RETrieval
SSD
Species Sensitivity Distribution
TCCR
Transparent, clear, consistent, and reasonable
TCA
Trichloroacetic acid
TCE
T ri chl oroethy 1 ene
TCOH
Trichloroethanol
TCOG
Trichloroethanol, gluuronide conjugate
TNSSS
Targeted National Sewage Sludge Survey
TLV
Threshold Limit Value
TRI
Toxics Release Inventory
TSCA
Toxic Substances Control Act
TWA
Time Weighted Average
UIC
Underground Injection Control
U.S.
United States
UV
Ultraviolet
USGS
United States Geological Survey
VOC
Volatile Organic Compound
VP
Vapor Pressure
Yr
Year(s)
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EXECUTIVE SUMMARY
This Risk Evaluation for trichloroethylene was performed in accordance with the Frank R. Lautenberg
Chemical Safety for the 21st Century Act. 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. Under the amended statute, EPA is required, under TSCA Section
6(b), to conduct Risk Evaluations to determine whether a chemical substance presents unreasonable
risk of injury to health or the environment, under the conditions of use, without consideration of costs
or other non-risk factors, including an unreasonable risk to potentially exposed or susceptible
subpopulations, identified as relevant to the Risk Evaluation. Also, as required by TSCA Section
(6)(b), EPA established, by rule, a process to conduct these Risk Evaluations: Procedures for
Chemical Risk Evaluation Under the Amended Toxic Substances Control Act (82 FR 33726V the "Risk
Evaluation Rule." This Risk Evaluation is in conformance with TSCA Section 6(b), and the Risk
Evaluation Rule, and is to be used to inform risk management decisions under TSCA. In accordance
with TSCA Section 6(b), if EPA finds unreasonable risk from a chemical substance under its
conditions of use in any final Risk Evaluation, the Agency will propose actions to address those risks
within the timeframe required by TSCA. However, any proposed or final determination that a chemical
substance presents unreasonable risk under TSCA Section 6(b) is not the same as a finding that a
chemical substance is "imminently hazardous" under TSCA Section 7. The conclusions, findings, and
determinations in this final 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 Section 26(h) and (i) require EPA to use scientific information, technical procedures, measures,
methods, protocols, methodologies and models consistent with the best available science and to base
its decisions on the weight of the scientific evidence (also referred to as WOE).1 To meet these TSCA
Section 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 systematic review process are used to develop the
exposure, fate, and hazard assessments for Risk Evaluations under TSCA.
Trichloroethylene has a wide-range of uses in consumer and commercial products and in industry. An
estimated 83.6% of TCE'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% of TCE 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 171.9 million pounds
between 2012 and 2015.
EPA evaluated TCE's occupational conditions of use (COUs), including the following categories:
manufacture; import; processing as a reactant/intermediate; incorporation into formulation; mixture or
reaction product; incorporated into articles; repackaging; recycling; distribution; solvents for cleaning
and degreasing; lubricants and greases; adhesives and sealants; functional fluids in a closed system;
paints and coatings; cleaning and furniture care products; laundry and dishwashing products; arts, crafts
1 Weight of the scientific evidence means 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 and evaluate each stream of evidence, including strengths, limitations, and relevance of each study and to integrate
evidence as necessary and appropriate based upon strengths, limitations, and relevance.
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and hobby materials; corrosion inhibitors and anto-scaling agents; processing aids; ink, toner, and
colorant products; automotive care products; apparel and footwear care products; other uses; and
disposal. Consumer COU categories are the following: solvents for cleaning and degreasing; lubricants
and greases; adhesives and sealants; cleaning and furniture care products; arts, crafts, and hobby
materials; apparel and footwear care products; and other consumer uses. Consistent with the decision at
the Problem Formulation stage (l__S ;_0jSd), EPA has excluded consumer uses of paint and
coatings from the scope of the evaluation. 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-Know Act (EPCRA)
since 1987. It is designated as a Hazardous Air Pollutant (HAP) under the Clean Air Act (CAA), is a
hazardous substance under the Comprehensive Environmental Response, Compensation and Liability
Act (CERCLA), and is regulated as a hazardous waste under the Resource Conservation and Recovery
Act (RCRA). It is subject to National Primary Drinking Water Regulations (NPDWR) under the Safe
Drinking Water Act (SDWA) 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). In this final Risk Evaluation, EPA evaluated the following categories of conditions
of use: manufacturing, processing, distribution in commerce, industrial, commercial and consumer uses
and disposal.2
Approach
EPA used reasonably available information (defined in 40 CFR 702.33 in part as "information that
EPA possesses, or can reasonably obtain and synthesize for use in Risk 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 assessments, for example EPA's IRIS assessment, 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
reasonably available information and evaluated the quality of the methods and reporting of results of
the individual studies using the evaluation strategies described in Application of Systematic Review in
TSCA Risk Evaluations ( 318b). To satisfy requirements in TSCA section 26(j)(4) and 40
CFR 702.51(e), EPA has provided a list of studies considered in carrying out the Risk Evaluation and
the results of those studies in several supplemental files (Appendix B).
In the Problem Formulation ( !018d). EPA identified the conditions of use within the scope
of the Risk Evaluation and presented three conceptual models and an analysis plan. These have been
carried into the final 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 Risk Evaluation).3 EPA quantitatively evaluated the risk to aquatic species from
2 Although EPA has identified both industrial and commercial uses here, for purposes of distinguishing scenarios in this
analysis, the Agency interprets the authority over "any manner or method of commercial use" under TSCA section 6(a)(5) to
reach both.
3 EPA did not identify any "legacy uses" (i.e., circumstances associated with activities that do not reflect ongoing or
prospective manufacturing, processing, or distribution) or "associated disposal" (i.e., future disposal from legacy uses) of
TCE, as those terms are described in EPA's Risk Evaluation Rule, 82 FR 33726, 33729 (July 20, 2017). Therefore, no such
uses or disposals were added to the scope of the Risk Evaluation for TCE following the issuance of the opinion in Safer
Chemicals, Healthy Families v. EPA, 943 F.3d 397 (9th Cir. 2019). EPA did not evaluate "legacy disposal" (i.e., disposals
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exposure to surface water as a result of the manufacturing, processing, use, or disposal of
trichloroethylene. EPA evaluated the risk to workers from inhalation and dermal exposures, and
occupational non-users (ONUs)4 from inhalation exposures, by comparing the estimated exposures to
acute and chronic human health hazards {i.e., liver effects, kidney effects, neurological effects,
immunological effects, reproductive effects, developmental effects, and acute overt toxicity). 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 {i.e., immunological
effects and developmental effects).
In this final Risk Evaluation, consistent with the analysis plan from the Problem Formulation, EPA
conducted quantitative analyses for exposure pathways to aquatic organisms via surface water;
sediment-dwelling organisms via sediment; workers and ONUs from industrial/commercial activities;
consumers and bystanders from consumer activities; and workers and ONUs from waste handling,
treatment, and disposal. During Problem Formulation, EPA conducted a qualitative screening-level
analysis for other exposure pathways that were within the scope of the Risk Evaluation, including
exposures to terrestrial and aquatic organisms exposed via soil, and land-applied biosolid pathways and
exposures to terrestrial organisms exposed via surface water. EPA excluded ambient air, drinking water,
land disposal, ambient water, and waste incineration pathways leading to exposures to the general
population and terrestrial organisms from Risk Evaluation since those pathways are under the
jurisdiction of other environmental statutes administered by EPA.
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.
2.018b). As stated in Section 3.1, the reasonably available environmental hazard data indicate that TCE
presents hazard to aquatic organisms. For acute exposures, aquatic invertebrates are the most sensitive
species with toxicity values ranging from 7.8 mg/L to 33.85 mg/L. For chronic exposures, toxicity
values for fish and aquatic invertebrates are as low as 7.88 mg/L and 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. 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 rather than being categorized as acute or chronic. 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 Evaluation, listed in Section 1.4 (Scope of the Evaluation). 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 that met data evaluation criteria, where reasonably available. EPA also used modeling
approaches, where reasonably available, to estimate potential inhalation exposures. Dermal doses for
workers were estimated in occupational exposure scenarios since dermal monitoring data were not
reasonably available. In consumer settings, EPA evaluated acute inhalation exposures to both consumers
and bystanders, and acute dermal exposures to consumers. Inhalation exposures and dermal 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 Risk Evaluation.
that have already occurred) in the Risk Evaluation, because legacy disposal is not a "condition of use" under Safer
Chemicals, 943 F.3d 397.
4 ONUs are workers who do not directly handle trichloroethylene but perform work in an area where trichloroethylene is
present.
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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 Framework for Human Health Risk Assessment to Inform Decision Making (U.S.
EPA. 2014a) to evaluate, extract, and integrate trichloroethylene's human health hazard and dose-
response information. EPA reviewed key and supporting information from previous hazard assessments
ITSCA Work Plan Chemical Risk Assessment ij icliloroethylene: Degreasing. Spot Cleaning ami \ns &
C'jitts I S'* (I. v i P \ 2014b). Toxicological Review of Trichloroethyleiv (1 1 t !' 1 v), and other
national and international assessments listed in Table 1-2], however all data sources from prior
assessments were independently reviewed for this Risk Evaluation. EPA also screened and evaluated
relevant studies that were published since these reviews {i.e., from 2010 - 2017, in addition to select
studies published after completion of the literature search). Selected key and supporting studies from
these prior assessments [List of Key and Supporting Studies for Human Health Hazard. Docket # EPA-
HQ-OPPT-2019-0500/ were considered together with newer literature for characterization of human
health hazard.
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 (IUR) and oral slope factors (OSF) for cancer risk
estimates. Health hazards of TCE described and reviewed in this Risk Evaluation include: acute overt
toxicity, liver toxicity, kidney toxicity, neurotoxicity, immunotoxicity (including 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. From among these PODs, acute immunosuppression and chronic autoimmunity were identified
as the best overall endpoints for establishing risk conclusions under TSCA in Section 4.5.2. While some
other endpoints present lower PODs (developmental neurotoxicity from Fredriksson et a 5;
congenital heart malformations from Johnson et al. 2003). there is lower confidence in the dose-
response and extrapolation of results from those studies (Section 3.2.6.1.1) resulting in increased
uncertainty surrounding the precision of the derived PODs for those endpoints. Therefore, EPA
concluded that acute immunosuppression and chronic autoimmunity were the best overall non-cancer
endpoints for use in Risk Evaluation under TSCA, based on the best available science and weight of the
scientific evidence. The selection of these endpoints for use in risk conclusions was supported by the
SACC peer review panel (https://www.re gulations.gov/document?D=I \ ; IQ-QPPT-2019-0500 0 III).
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 heterogeneity and study quality. See Appendix
J for full details and results. The 2019 meta-analysis of all relevant studies examining kidney cancer,
liver cancer, or NHL (Appendix J) concluded that there is a statistical significant association between
TCE exposure and increased incidence of all three cancers. In accordance with EPA Guidelines for
Carcinogen Risk Assessment (U.S. EPA. 2005). EPA determined that TCE is "Carcinogenic to
Humans." For context, this was the same conclusion as the previous EPA meta-analysis in the 2011 IRIS
Assessment ( ), which evaluated older literature than the current assessment. Therefore,
EPA utilized the same inhalation unit risk and oral slope factor estimates as were derived in (
2i ) and cited in the 2014 TSCA Work Plan Chemical Risk Assessment ( 014b). A linear
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non-threshold assumption was applied to the TCE cancer dose-response analysis because there is
sufficient evidence that TCE-induced kidney cancer likely operates primarily through a mutagenic mode
of action while it cannot be ruled out for the other two cancer types and positive associations were
observed via meta-analysis for all three cancers in epidemiological studies based on low-level,
environmental exposure levels.
Risk Characterization
Environmental Risk: For environmental risk, EPA utilized a risk quotient (RQ) to compare the
environmental concentration to the effect level to characterize the risk to aquatic and sediment-dwelling
organisms. EPA included a qualitive assessment describing trichloroethylene exposure from land-
applied biosolids 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 two tables (Table 4-1 and Table 4-4) that
summarize the RQs for acute and chronic risks. Surface water concentrations of TCE were modeled for
214 releases.
EPA identified the expected environmental exposures for aquatic species and sediment-dwelling
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 acute, chronic, and/ or algae concentrations of concern (COC) for the following
conditions of use in various locations (see Table 4-1 and Table 4-4): 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 and 4.1.3.
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 (Section 4.1.4).
Therefore EPA did not find risks for terrestrial organisms.
Human Health Risks: Risks were estimated following both acute and chronic exposure for the most
sensitive and robust endpoints from every hazard domain.
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 (Table 4-9). 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 the majority of exposure scenarios, risks to workers were identified for multiple endpoints in both
acute and chronic exposure scenarios. Based on the most robust and sensitive acute and chronic
endpoints from each hazard domain, acute and chronic non-cancer and cancer risks were indicated for
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all exposure scenarios and occupational conditions of use under high-end5 inhalation exposure levels.
Non-cancer risks following chronic exposure were also identified for all exposure scenarios at high-end
exposure levels with expected use of respiratory protection up to Assigned Protection Factor (APF) =
50. When only considering the central tendency6 inhalation 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 identified for all exposure scenarios (at high-end
exposure levels following acute exposure and at both exposure levels following chronic exposure) when
gloves are worn even when assuming the maximum applicable glove protection factor (either PF 10 or
20).
ONUs are expected to have lower exposure levels than workers in most instances but exposures could
not always be quantified based on reasonably available data and risk estimates for ONUs may be similar
to workers in some settings. Therefore, for those instances where monitoring data or modeling did not
distinguish between worker and far-field ONU inhalation exposure estimates, EPA considered the
worker exposure and risk estimates when determining far-field ONU risk. There is significant
uncertainty in these ONU inhalation risk estimates. While the difference between the exposures of
ONUs and the exposures of workers directly handling TCE generally cannot be quantified, ONU
inhalation exposures are expected to be lower than inhalation exposures for workers directly handling
the chemical. In these instances, EPA considered the ONU exposures to be equal to the central tendency
risk estimates for workers when determining ONU risk attributable to inhalation. While this is likely
health protective as it assumes ONU exposure is as high as it is for the majority of workers (greater
numbers are likely to be exposed near the middle of the distribution), this is uncertain. Dermal exposures
are not expected because ONUs do not typically directly handle TCE, nor are they in the immediate
proximity of TCE.
Based on central-tendency exposure levels, acute and chronic non-cancer risks to ONUs were indicated
for the majority of exposure scenarios. ONUs are not assumed 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 4-59 in Section 4.5.2.1.
For consumers and 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 (Section
2.3.2.2).
For consumers, risks were identified for multiple acute endpoints at multiple user intensity levels for
5 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. EPA provided results at the 95th percentile when
available.
6 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.
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all consumer conditions of use except Pepper Spray, which did not indicate risk for the best overall
acute endpoint (immunosuppression). Acute risks were also indicated for most conditions of use for
bystanders at both medium and high-intensity acute inhalation levels. 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 4-60 in Section 4.5.2.2.
Uncertainties: Key assumptions and uncertainties in the environmental risk estimation include
uncertainties regarding the hazard data for aquatic and sediment-dwelling species and surface water
concentrations. Additionally the reasonably available environmental monitoring data were 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 were not reasonably available. Additional
sources of uncertainty related to human health hazard include selection of the appropriate
Physiologically-Based Pharmacokinetic (PBPK) dose-metric for each endpoint, the dose-response and
POD derivation for the congenital heart defects (Johnson et ai. 2003) and developmental neurotoxicity
( Iriksson et ai. 1993) endpoints, 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.
EPA's assessments, risk estimations, and risk determinations accounted for uncertainties throughout the
Risk Evaluation. EPA used reasonably available information, 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. For instance, systematic review was conducted to identify reasonably available information
related to TCE hazards and exposures. If no applicable monitoring data were identified, exposure
scenarios were assessed using a modeling approach that requires the input of various chemical
parameters and exposure factors. When possible, default model input parameters were modified based
on chemical-specific inputs available in literature databases. The consideration of uncertainties supports
the Agency's risk determinations, each of which is supported by substantial evidence, as set forth in
detail in later sections of this final Risk Evaluation.
Potentially Exposed or Susceptible Subpopulations (PESS): TSCA Section 6(b)(4) requires that EPA
conduct a Risk Evaluation to "determine whether a 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 Section 3(12) states that "the term
'potentially exposed or susceptible subpopulation' means a 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."
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 and consumer users
using trichloroethylene along with ONUs and consumer bystanders in the vicinity of trichloroethylene
use compared to general population (Section 2.3.3). Risk estimates were also provided separately for
ONUs when sufficient data were reasonably available. EPA was unable to provide separate risk
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estimates when insufficient information was reasonably 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. See additional discussions in Section 4.4.1. EPA's determinations for unreasonable risk are based
on high-end exposure estimates for workers and high intensity use scenarios for consumers and
bystanders in order to capture individuals who are PESS.
Factors affecting susceptibility examined in the available studies on TCE include lifestage, sex, genetic
polymorphisms, race/ethnicity, preexisting health status, lifestyle factors, and nutrition status. Groups of
individuals for which one or several of these factors apply may be considered PESS (Section 3.2.5.2).
Additionally, based on the hazards identified from the available information, individuals that either have
or are susceptible to kidney, liver, neurological, reproductive, or cancer health conditions are PESS. The
use of the 99th percentile Human Equivalent Concentration/Dose (HEC/HED)99 POD values derived
from relevant (PBPK) dose metrics also account for the vast majority of toxicokinetic 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. While there may not be a risk for all endpoints
to all 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. Inclusion of risk estimates for cardiac malformations accounts
for susceptible mothers (Jenkins et at... 2007) and their offspring in addition to PESS groups with other
susceptibilities including metabolic sensitivity due to increased enzymatic activity of cytochrome P450
2E1 (CYP2E1) (Cichocki etal. 201 / > M' \ j'lle).
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
combined exposures to an individual from a single chemical substance across multiple routes and
across multiple pathways (40 CFR Section 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 consumers. 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. Without a PBPK model containing a dermal compartment to account
for toxicokinetic processes the true internal dose for any given exposure cannot be determined, and
aggregating exposures by simply adding exposures from multiple routes could inappropriately
overestimate total exposure. Conversely, not aggregating exposures in any manner may potentially
underestimate total exposure for a given individual.
The EPA defines sentinel exposure as "the exposure to a single chemical substance that represents the
plausible upper bound of exposure relative to all other exposures within a broad category of similar or
related exposures (40 CFR Section 702.33)." In this Risk Evaluation, the EPA considered sentinel
exposure the highest exposure given the details of the conditions of use and the potential exposure
scenarios. Sentinel exposures for workers are the high-end no PPE within each OES. EPA considered
sentinel exposures in this Risk Evaluation by considering risks to populations who may have upper
bound (e.g., high-end, high intensities of use) exposures. In cases where sentinel exposures result in
MOEs greater than the benchmark or cancer risk lower than the benchmark (i.e., risks were not
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identified), EPA did no further analysis because sentinel exposures represent the worst-case scenario.
EPA's decision for unreasonable risk are based on high-end exposure estimates to capture individuals
with sentinel exposure.
Additional details on how aggregate and sentinel exposures were considered in this Risk Evaluation are
provided in Section 4.4.2.
Unreasonable 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 non-
cancer 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, as determined by EPA); the severity of hazard (including the nature of the hazard, the
irreversibility of the hazard); and uncertainties. EPA also takes into consideration the Agency's
confidence in the data used in the risk estimate. This includes an evaluation of 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 5.2. The Agency's risk
determinations are supported by substantial evidence, as set forth in detail in later sections of this final
Risk Evaluation.
Unreasonable Risk of Injury to the Environment: EPA used a screening-level approach to integrate relevant
pathways of environmental exposure with available environmental hazard data to evaluate unreasonable risk to
relevant environmental receptors. EPA assessed environmental exposures derived from predicted and measured
concentrations of TCE in surface water in the U.S. Specifically, the aquatic exposures associated with the
industrial and commercial conditions of use were predicted through modeling, and the aquatic exposure
assessment also includes an analysis of collected measured surface water concentrations from monitoring data.
EPA considered the biological relevance of the species to determine the concentrations of concern for the
location of surface water concentration data to produce risk quotients, as well as frequency and duration of the
exposure. EPA determined that the evaluation does not support an unreasonable risk determination to aquatic
organisms. For sediment-dwelling invertebrates, the toxicity of TCE is similar to the toxicity to aquatic
invertebrates. Therefore, for sediment dwelling organisms the risk estimates, based on the highest ambient
surface water concentration, do not support an unreasonable risk determination to sediment-dwelling organisms
from acute or chronic exposures. TCE 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 risk estimates, the environmental effects of TCE, the exposures, physical chemical properties of TCE, and
consideration of uncertainties support EPA's determination that there is no unreasonable risk to the environment
from all conditions of use of TCE.
Unreasonable 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, occupational non-users,
consumers, or bystanders from consumer use. 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. For acute exposures, EPA evaluated unreasonable risks of non-cancer effects
(developmental toxicity and immunosuppression). For chronic exposures, EPA evaluated unreasonable
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risks of non-cancer effects (liver toxicity, kidney toxicity, neurotoxicity, autoimmunity, reproductive
toxicity, and developmental toxicity) as well as cancer (liver, kidney, and non-Hodgkin Lymphoma).
The drivers for EPA's determination of unreasonable risk are non-cancer effects (immunosuppression)
from acute inhalation and dermal exposures, non-cancer effects (autoimmunity) from chronic inhalation
and dermal exposures, and cancer from chronic inhalation and dermal exposures.
Unreasonable Risk of Injury to Health of the General Population: General population exposures to TCE
may occur from all conditions of use via releases to air, water or land. During the course of the Risk
Evaluation process for TCE, OPPT worked closely with the offices within EPA that administer and
implement regulatory programs under the Clean Air Act (CAA), the Safe Drinking Water Act (SDWA),
the Clean Water Act (CWA), the Comprehensive Environmental Response, Compensation, and Liability
Act (CERCLA), and the Resource Conservation and Recovery Act (RCRA)). Through intra-agency
coordination, EPA found exposures to the general population via surface water, drinking water, ambient
air and sediment pathways are covered under the jurisdiction of other environmental statutes,
administered by EPA, i.e., CAA, SDWA, CWA, CERCLA, and RCRA. As explained in more detail in
Section 1.4.2, EPA believes it is both reasonable and prudent to tailor TSCA Risk Evaluations when
other EPA offices have expertise and experience to address specific environmental media, rather than
attempt to evaluate and regulate potential exposures and risks from those media under TSCA. EPA
believes that coordinated action on exposure pathways and risks addressed by other EPA-administered
statutes and regulatory programs is consistent with the statutory text and legislative history, particularly
as they pertain to TSCA's function as a "gap-filling" statute, and also furthers EPA aims to efficiently
use Agency resources, avoid duplicating efforts taken pursuant to other Agency programs, and meet the
statutory deadlines for completing Risk Evaluations. EPA has therefore tailored the scope of the Risk
Evaluations for TCE using authorities in TSCA sections 6(b) and 9(b)(1). EPA did not evaluate risk to
the general population from ambient air, water and disposal and pathways for any condition of use, and
the unreasonable risk determinations do not account for exposures to the general population.
Unreasonable Risk of Injury to Health of Workers: EPA evaluated non-cancer effects from acute and
chronic inhalation and dermal occupational exposures and cancer from chronic inhalation and dermal
occupational exposures to determine if there was unreasonable risk of injury to workers' health. The
drivers for EPA's determination of unreasonable risk for workers are non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation and dermal exposures, and cancer from
chronic inhalation and dermal exposures.
EPA generally assumes compliance with OSHA requirements for protection of workers including the
implementation of the hierarchy of controls. In support of this assumption, EPA used reasonably
available information indicating that some employers, particularly in the industrial setting, are providing
appropriate engineering, administrative controls, or PPE to their employees consistent with OSHA
requirements. EPA does not have reasonable available information to support this assumption for each
condition of use; however, EPA does not believe that the Agency must presume, in the absence of such
information, a lack of compliance with existing regulatory programs and practices. Rather, EPA assumes
there is compliance with 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 in a manner that achieves the stated APF or PF. EPA's decisions for unreasonable
risk to workers are based on high-end exposure estimates, in order to account for the uncertainties
related to whether or not workers are using PPE. Therefore, EPA's approach for evaluating risk to
workers and ONUs is to use the reasonably available information and professional judgement to
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construct exposure scenarios that reflect the workplace practices involved in the conditions of use of the
chemicals and address uncertainties regarding availability and use of PPE.
For each condition of use of TCE with an identified risk for workers, EPA assumes, as a baseline, the
use of a respirator with an APF of 10 to 50. Similarly, EPA assumes the use of gloves with PF of 10 to
20. However, EPA assumes that for some conditions of use, the use of appropriate respirators is not a
standard industry practice, based on best professional judgement given the burden associated with the
use of respirators, including the expense of the equipment and the necessity of fit-testing and training for
proper use. Similarly, EPA does not assume that it is a standard industry practice that workers in some
small commercial facilities (e.g., those performing spot cleaning, wipe cleaning, shoe polishing, or hoof
polishing; commercial printing and copying) have a respiratory protection program or regularly employ
dermal protection. Therefore, the use of respirators and gloves is unlikely for workers in these facilities.
The unreasonable risk determinations reflect other risk factors, such as the severity of the effects
associated with the occupational exposures to TCE and incorporate consideration of the PPE that EPA
assumes. A full description of EPA's unreasonable risk determination for each condition of use is in
Section 5.2.
Unreasonable Risk of Injury to Health of Occupational Non-Users (ONUs): ONUs are workers who do
not directly handle TCE but perform work in the area where TCE is present. EPA evaluated non-cancer
effects to ONUs from acute and chronic inhalation occupational exposures and cancer from chronic
inhalation occupational exposures to determine if there was unreasonable risk of injury to ONU's health.
The unreasonable risk determinations reflect the severity of the effects associated with occupational
exposures to TCE and the assumed absence of PPE for ONUs, since ONUs do not directly handle the
chemical and are instead doing other tasks in the vicinity. Non-cancer effects and cancer from dermal
occupational exposures to ONUs were not evaluated because ONUs are not dermally exposed to TCE.
For inhalation exposures, when there was reasonably available information, EPA estimated ONUs'
exposures and described the risks separately from workers directly exposed. When the difference
between ONUs' exposures and workers' exposures cannot be quantified, EPA assumed that ONUs'
inhalation exposures are lower than inhalation exposures for workers directly handling the chemical
substance, and EPA considered the central tendency risk estimates when determining ONU risk. A full
description of EPA's unreasonable risk determination for each condition of us is in Section 5.2.
Unreasonable Risk of Injury to Health of Consumers: EPA evaluated non-cancer effects to consumers
from acute inhalation and dermal exposures to determine if there was unreasonable risk of injury to
consumers' health. A full description of EPA's unreasonable risk determination for each condition of
use is in Section 5.2.
Unreasonable Risk of Injury to Health of Bystanders (from Consumer Uses): EPA evaluated non-cancer
effects to bystanders from acute inhalation exposures to determine if there was unreasonable risk of
injury to bystanders' health. EPA did not evaluate non-cancer effects from dermal exposures to
bystanders because bystanders are not dermally exposed to TCE. A full description of EPA's
unreasonable risk determination for each condition of use is in Section 5.2.
Summary of Unreasonable Risk Determinations: In conducting Risk Evaluations, "EPA will determine
whether the chemical substance presents an unreasonable risk of injury to health or the environment
under each condition of use within the scope of the Risk Evaluation..." 40 CFR 702.47. Pursuant to
TSCA section 6(i)(l), a determination of "no unreasonable risk" shall be issued by order and considered
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to be final agency action. Under EPA's implementing regulations, "[a] determination by EPA that the
chemical substance, under one or more of the conditions of use within the scope of the Risk Evaluation,
does not present an unreasonable risk of injury to health or the environment will be issued by order and
considered to be a final Agency action, effective on the date of issuance of the order." 40 CFR
702.49(d).
EPA has determined that the following conditions of use of TCE do not present an unreasonable risk of
injury to health or the environment. These determinations are considered final agency action and are
being issued by order pursuant to TSCA section 6(i)(l). The details of these determinations are in
Section 5.2, and the TSCA section 6(i)(l) order is contained in Section 5.3.1 of this final Risk
Evaluation.
Conditions of I so tlisit Do Not Present sin I nreiisonsihle Risk
• Distribution in commerce
• Consumer use in pepper spray
EPA has determined that the following conditions of use of TCE present an unreasonable risk of injury.
EPA will initiate TSCA section 6(a) risk management actions on these conditions of use as required
under TSCA section 6(c)(1). Pursuant to TSCA section 6(i)(2), the unreasonable risk determinations for
these conditions of use are not considered final agency action. The details of these determinations are in
Section 5.2
Msiniirsu'liiring (lint Presents sin I nresisonsihle Risk
• Manufacturing: domestic manufacture
• Manufacturing: import
Processing llisit Presents 2111 I nrcsisonsihlc Risk
• Processing: processing as a reactant/intermediate
• Processing: incorporation into a formulation, mixture or reaction product
• Processing: incorporation into articles
• Processing: repackaging
• Processing: recycling
Imliistrinl siihI ( oininerciiil I scs (lint Present :i 11 I nresisonsihle Risk
• Industrial and commercial use as a solvent for open-top batch vapor degreasing
• Industrial and commercial use as a solvent for closed-loop batch vapor degreasing
• Industrial and commercial use as a solvent for in-line conveyorized vapor degreasing
• Industrial and commercial use as a solvent for in-line web cleaner vapor degreasing
• Industrial and commercial use as a solvent for cold cleaning
• Industrial and commercial use as a solvent for aerosol spray degreaser/cleaner and mold release
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Indlistri;il siihI (oinincrchil I sos llinl Present ;i 11 Inrcnsonnhlc Kisk
• Industrial and commercial use as a lubricant and grease in tap and die fluid
• Industrial and commercial use as a lubricant and grease in penetrating lubricant
• Industrial and commercial use as an adhesive and sealant in solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant
• Industrial and commercial use as a functional fluid in heat exchange fluid
• Industrial and commercial use in paints and coatings as a diluent in solvent-based paints and
coatings
• Industrial and commercial use in cleaning and furniture care products in carpet cleaner and
wipe cleaning
• Industrial and commercial use in laundry and dishwashing products in spot remover
• Industrial and commercial use in arts, crafts, and hobby materials in fixatives and finishing
spray coatings
• Industrial and commercial use in corrosion inhibitors and anti-scaling agents.
• Industrial and commercial use as processing aids in 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
beta-cyclodextrin manufacture
• Industrial and commercial use as ink, toner and colorant products in toner aid
• Industrial and commercial use in automotive care products in brake parts cleaner
• Industrial and commercial use in apparel and footwear care products in shoe polish
• Industrial and commercial use in hoof polish; gun scrubber; pepper spray; other miscellaneous
industrial and commercial uses
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Consumer I scs llisil Proscnl ;i 11 I nrcnsoniihlc Kisk
• Consumer use as a solvent in brake and parts cleaner
• Consumer use as a solvent in aerosol electronic degreaser/cleaner
• Consumer use as a solvent in liquid electronic degreaser/cleaner
• Consumer use as a solvent in aerosol spray degreaser/cleaner
• Consumer use as a solvent in liquid degreaser/cleaner
• Consumer use as a solvent in aerosol gun scrubber
• Consumer use as a solvent in liquid gun scrubber
• Consumer use as a solvent in mold release
• Consumer use as a solvent in aerosol tire cleaner
• Consumer use as a solvent in liquid tire cleaner
• Consumer use as a lubricant and grease in tap and die fluid
• Consumer use as a lubricant and grease in penetrating lubricant
• Consumer use as an adhesive and sealant in solvent-based adhesives and sealants
• Consumer use as an adhesive and sealant in mirror edge sealant
• Consumer use as an adhesive and sealant in tire repair cement/sealer
• Consumer use as a cleaning and furniture care product in carpet cleaner
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Consumer I scs llinl Present sin I nresisonsihle Kisk
•
Consumer use as a cleaning and furniture care product in aerosol spot remover
•
Consumer use as a cleaning and furniture care product in liquid spot remover
•
Consumer use in arts, crafts, and hobby materials in fixative and finishing spray coatings
•
Consumer use in apparel and footwear products in shoe polish
•
Consumer use in fabric spray
•
Consumer use in film cleaner
•
Consumer use in hoof polish
•
Consumer use in toner aid
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Disposal llisil Presents :i 11 lnre;ison;ihle Kisk
• Disposal
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1 INTRODUCTION
This document represents the final Risk Evaluation for trichloroethylene (TCE) 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 Environmental Protection Agency (EPA) published the scope of the Risk Evaluation for TCE (U.S.
EPA. 20171) 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 for Human Health Risk Assessment to Inform Decision
Making. In this final Risk Evaluation, consistent with the analysis plan from the Problem Formulation,
EPA conducted quantitative analyses for exposure pathways to aquatic organisms via surface water;
sediment-dwelling organisms via sediment; workers and ONUs from industrial/commercial activities;
consumers and bystanders from consumer activities; and workers and ONUs from waste handling,
treatment, and disposal. During Problem Formulation, EPA conducted a qualitative screening-level
analysis for other exposure pathways that were within the scope of the Risk Evaluation, including
exposures to terrestrial and aquatic organisms exposed via soil, and land-applied biosolid pathways and
exposures to terrestrial organisms exposed via surface water. EPA excluded ambient air, drinking water,
land disposal, ambient water, and waste incineration pathways leading to exposures to the general
population and terrestrial organisms from Risk Evaluation since those pathways are under the
jurisdiction of other environmental statutes administered by EPA. The conclusions, findings, and
determinations in this final 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.
As per EPA's final rule, Procedures for Chemical Risk Evaluation Under the Amended Toxic
Substances Control Act (82 FR 33726 (July 20, 2017)), this Risk Evaluation was subject to both public
comment and peer review, which are distinct but related processes. EPA provided 60 days for public
comment on any and all aspects of this Risk Evaluation, including the submission of any additional
information that might be relevant to the science underlying the Risk Evaluation and the outcome of the
systematic review 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.
Peer review was conducted in accordance with EPA's regulatory procedures for chemical Risk
Evaluations, including using the EPA. Peer Review Handbook and other methods consistent with the
science standards laid out in Section 26 of TSCA {See 40 CFR 702.45). As explained in the Risk
Evaluation Rule (82 FR 33726 (July 20, 2017)), the purpose of peer review is for the independent
review of the science underlying the risk assessment. As such, peer review addressed 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.
As EPA explained in the Risk Evaluation Rule (82 FR 33726 (July 20, 2017)), it is important for peer
reviewers to consider how the underlying Risk Evaluation analyses fit together to produce an integrated
risk characterization, which forms the basis of an unreasonable risk determination. EPA believed peer
reviewers were most effective in this role if they received the benefit of public comments on draft Risk
Evaluations prior to peer review. For this reason, and consistent with standard Agency practice, the
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public comment period preceded peer review. The final Risk Evaluation changed 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. EPA responded to public and peer review comments received on the
draft Risk Evaluation and explained changes made in response to those comments in this final Risk
Evaluation and the associated response to comments document.
In this final Risk Evaluation, Section 1.1 presents the basic physical-chemical characteristics 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 draft Risk Evaluation. This
section also includes a discussion of the systematic review process utilized in this final Risk Evaluation.
Section 2 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 of trichloroethylene. Section 4 presents the risk characterization, where EPA integrates
and assesses reasonably available information on health and environmental hazards 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 determination of
whether the chemical presents an unreasonable risk under the conditions of use, as required under TSCA
(15 U.S.C. 2605(b)(4)).
EPA also solicited input on the first 10 chemicals as it developed use documents, scope documents, and
Problem Formulations. At each step, EPA has received information and comments specific to individual
chemicals and of a more general nature relating to various aspects of the Risk Evaluation process,
technical issues, and the regulatory and statutory requirements. 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 comments on the published Problem Formulation of
tri chl oroethy 1 ene.
1.1 Physical and Chemical Properties
Physical-chemical properties influence the environmental behavior and the toxic properties of a
chemical, thereby informing the potential conditions of use, exposure pathways and routes and hazards
that EPA considered. For scope development, EPA considered the measured or estimated physical-
chemical properties set forth in Table 1-1 and found no additional information during Problem
Formulation or the draft Risk Evaluation that would change these values.
TCE is a colorless liquid with a pleasant, sweet odor resembling that 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 moderately water soluble (1.280 g/L at 25°C) and has a log
octanol/water partition coefficient (Kow) of 2.42. The density of TCE, 1.46 g/m3 at 20°C, is greater than
that of water.
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Table 1-1. Physical and Chemical Pro
perties of TCE
Property
Value a
References
Molecular Formula
C2HCI3
Molecular Weight
131.39 g/mole
Physical Form
Colorless, liquid, sweet, pleasant
odor, resembles chloroform
(O'Neil et al.. 2006)
Melting Point
-84.7°C
aide. 2.007)
Boiling Point
87.2°C
aide. 2.007)
Density
1.46 g/cm3 at 20°C
(ECB. 2000)
Vapor Pressure
73.72 mmHg at 25°Cb
(O^abert and Danricr. 1 (X»5)
Vapor Density
4.53
( )
Water Solubility
1,280 mg/L at 25°C
(Horvath et al.. 1999)
Octanol/Water Partition Coefficient
(Log Kow)
2.42
( leriee et al. 1980)
Henry's Law Constant
9.85E-03 atmm3/mole
(Leighton and Calo. 1981)
Flash Point
90°C (closed cup)
(ECB. 2.000)
Auto Flammability
410°C (Estimated)
(Wl !5)
Viscosity
0.545 rnPas at 25°C
aide. 2007)
Refractive Index
1.4775 at 20°C
( :ooi)
Dielectric Constant
3.4 80 at 16°C
(Weast and Selbv. 1966)
Aqueous Permeability Coefficient (Kp)
0.019 cm/hr
(Poet et al... 2000)
Neat Dermal Flux (Jskin) c
430 nmol/cm2-min
(5.65E-02 mg/cm2-min)
(Kezic et al. 2001)
a Measured unless otherwise noted
b This value was updated based on systematic review re-analysis of original values. The original value of 73.46 mmHg, from
(Daubert and Banner. 1989"). was used for occupational and consumer modelins of inhalation exposures. The effect of this
small difference is expected to be negligible for associated exposure estimates.
0 EPA calculated neat Kp as 0.00232 cm/hr from Jskin based on the density of TCE.
1.2 Uses and Production Volume
This section contains use and production volume information for TCE.
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 Formulation Document Trichloroethylene (I ) 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 Market Profile for
Trichloroethylene, (EPA-HQ-OPPT>6), 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.
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1.2.2 Domestic Manufacture of Trichloroethylene
A life cycle diagram is provided (Figure 1-3) 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.
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 information (CBI). The 2016 CDR reporting data for TCE are provided in
Figure 1-1 for TCE from the EPA's CDR database (U.S. EPA. 2016d). For the 2016 CDR reporting
period, non-confidential data indicate a total of 13 manufacturers and importers of TCE in the United
States.
Total Aggregate TCE Production Volume (lbs.) by
Year
250,000,000
221M
200,000,000
Jg 150,000,000
C
3
S. 100,000,000
50,000,000
199M 192M
172M
2012
2013
2014
2015
I Total Aggregate Production Volume (lbs.]
Figure 1-1. Total Aggregate TCE Production Volume (lbs.) 2012-2015a
aThe CDR data for the 2016 reporting period is available via ChemView (https://iava.epa.gov/chemview). Because of an
ongoing CBI substantiation process required by amended TSCA, the CDR data available in the Risk Evaluation is more
specific than currently in ChemView. M = millions of pounds (lbs).
As reported in the Use Document IEPA-HQ-OPPT-2016-0737-0003 (U.S. EPA. 2017c)l. as well as in
the 2014 TCE risk assessment (U.S. EPA. 2014b). an estimated 83.6% of TCE's annual production
Page 47 of 803
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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% of TCE production volume is
used as a degreasing solvent, leaving approximately 1.7% for other uses (Figure 1-2). The current status
of the volume of TCE used as an intermediate in the manufacture of HFC-134a, is complicated by
regulatory activity affecting hydrofluorocarbons (HFCs) in general. In 2015, EPA issued a rule under its
Significant New Alternatives Policy (SNAP) program that changed the listings for certain HFCs in
various end-uses in the aerosol, refrigeration and air conditioning, and foam blowing sectors from
acceptable, or acceptable subject to use conditions, to unacceptable, or acceptable subject to narrowed
use limits. The listings were to become effective generally starting in 2016 through 2022, depending on
the use. The SNAP rules, as originally written, would control specific uses of HFCs or HFC blends,
rather than production. SNAP continues to list as acceptable several blends of HFCs with other
compounds with lower environmental impact and other small exemptions. Under these listings, a decline
in the use of TCE as an intermedi ate in the manufacture of HFCs might be expected along with the use
of the HFCs. However, the potential effect is less than clear due to a decision to vacate EPA's rule by
the Court of Appeals for the District of Columbia "to the extent it requires manufacturers to replace
HFCs with a substitute substance." Based on the court's partial vacatur, EPA did not apply the HFC
listings in the 2015 Rule and plans to address the court's remand in a rulemaking which has not yet
occurred. Meanwhile, several states have adopted or are in the process of adopting laws similar to the
2015 SNAP rule and a similar SNAP rule issued in 2016 that also changed the status of certain HFCs
and HFC blends from acceptable to unacceptable. It is important to note that the SNAP rules, as
originally written, would control specific uses of HFCs or HFC blends, rather than production. SNAP
continues to list as acceptable several blends of HFCs with other compounds with lower environmental
impact and other small exemptions. Because of uncertainty surrounding the response to EPA's
regulatory activity and the regulatory activity of States with respect to HFCs for certain uses, EPA does
not have a reasonable basis to make assumptions about what the current distribution might be. 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.
TCE Uses
(% Production Volume)
1.70%
14.70%
¦ Intermediate (Manufacture of HFC-134a) ¦ Degreasing Solvent -Other
Figure 1-2. Percentage of TCE Production Volume by Use
Descriptions of the industrial, commercial and consumer use categories identified from the 2016 CDR
and included in the life cycle diagram (Figure 1-3) are summarized below ( J.S. EPA. 2016d). The
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descriptions provide a brief overview of the use category; the [.Environmental Releases and
Occupational Exposure Assessment. Docket: EPA-HQ-QPPT-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 industrial/commercial CDR use categories where TCE has been used;
the [Environmental Releases and Occupational Exposure Assessment. Docket: EPA-
0500) I provides additional process-related 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 of substrates including metal surfaces,
glassware and textiles. This category includes the use of TCE 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. This 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 of TCE in mirror-
edge sealants 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 of TCE in solvent-based coatings. This category covers industrial,
commercial and consumer uses 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 furniture and furnishings,
or to cleanse, sanitize, bleach, scour, polish, protect or improve the appearance of surfaces. This
category includes the use of TCE 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.
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.
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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.
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 Treaties 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.
EPA has identified assessments conducted by other agency programs and organizations (see Table 1-2).
Depending on the source, these assessments may include information on conditions of use, hazards,
exposures, and potentially exposed or susceptible subpopulations (PESS)—information useful to the
EPA in preparing this Risk Evaluation. Table 1-2 shows the assessments that 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-HQ-OPPT-2016-073 7) using the literature search strategy (see Strategy for Conducting Literature
Searches for Trichloroethylene: Supplemental File for the TSCA Scope Document, EP A-HQ-OPPT -
2016-07371 to ensure that the EPA is considering information that has been made available since these
assessments were conducted.
In EPA's previous TCE Workplan Risk Assessment (U.S. EPA. 2014b). risks from use of TCE 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 was used to support two proposed rules under TSCA section
6 ( ; December 12, 2016; , January 19, 2017) to address risks from use of TCE.
Along with other reasonably available information, the EPA used the existing TSCA risk assessments to
inform its development of the TCE Risk Evaluation.
Page 50 of 803
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1992
1993 Table 1-2. 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 Trichloroethvlene: Degreasing.
Soot Cleaning and Arts & Crafts Use (U.S. EPA, 2014b)
OCSPP/OPPT
SuDDlemental Occupational Exposure and Risk Reduction Technical Report in
Support of Risk Management Options for Trichloroethvlene (TCE) Use in
Aerosol Deereasins (U.S. EPA, 2016f)
OCSPP/OPPT
Supplemental Exposure and Risk Reduction Technical Report in Support of
Risk Management Options for Trichloroethvlene (T< : in Consumer
Aerosol Degreasing (U.S. EPA, 2016e)
OCSPP/OPPT
Supplemental Occupational Exposure and Risk Reduction Technical Report in
Support of Risk Management Options for Trichloroethvlene (TCE) Use in Spot
Cleaning ("U.S. EPA. 2016s)
OCSPP/OPPT
Supplemental Occupational Exposure and Risk Reduction Technical Report in
Support of Risk Management Options for Trichloroethvlene (TCE) Use in
Vapor Degreasing I'RIN 2070 <\k 1 1 i (» J ! • • s .01 oiO
Integrated Risk Information System
(IRIS)
Toxicologic^! Review of Trichloroethvlene (U.S. EPA,; )
National Center for Environmental
Assessment (NCEA)
Sources, Emission and Exposure for Trichloroethvlene (TCE) and Related
Chemicals (U.S. EPA. 2001)
Office of Water (OW)/ Office of
Science and Technology (OST)
Update of Human Health Ambient Water Oualitv Criteria: Trichloroethvlene
(TCE) 75 ( A 2015b)
Other U.S.-Based Organizations
Agency for Toxic Substances and
Disease Registries (ATSDR)
Final Toxicoloaical Profile for Trichloroethvlene
( .2019)
National Research Council (NRC)
Assessing the Human Health Risks of Trichloroethvlene: Kev Scientific Issues
(NRC. 2006)
Office of Environmental Health
Hazard Assessment (OEHHA),
Pesticide and Environmental
Toxicology Section
Public Heath Goal for Trichloroethvlene in Drinking Water (CalEPA, 2009)
International
Institute for Health and Consumer
Protection, European Chemicals
Bureau
European Union Risk Assessment Report. Trichloroethvlene (ECB. 2004)
Australia National Industrial
Chemicals Notification and
Assessment Scheme (NICNAS)
Trichloroethvlene: Priority Existing Chemical Assessment Report No. 8
(NICNAS. 2000)
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1.4 Scope of the Evaluation
1.4.1 Conditions of Use Included in the Risk Evaluation
TSCA Section 3(4) defines the conditions of use (COUs) as "the circumstances, as determined by 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 1-3 and Table 1-4. No additional information was received by the EPA
following the publication of the Problem Formulation ( 2018d) that would update or otherwise
require changes to the life cycle diagram (Figure 1-3) as presented in the Problem Formulation (U.S.
EPA. 2018d). Nonetheless, EPA decided to reorganize the conditions of use for this Risk Evaluation. In
this Risk Evaluation, the COUs as described in (U.S. EPA. 2018d) were evaluated for occupational
scenarios based on corresponding occupational exposure scenarios (OES) (Table 1-3). The occupational
COUs are also applicable to environmental receptors based on water releases from these activities.
"Lace wig and hair extension glues" have been eliminated as a COU since the publication of the
Problem Formulation (U.S. EPA. 2018d). EPA, after consultation with the FDA, has determined that
this use, previously identified in the Problem Formulation as a conditions of use, is not a condition of
use because it falls outside the scope of EPA's jurisdiction. TSCA sec. 3(2) excludes from the definition
of "chemical substance" cosmetics as they are defined in the Federal Food, Drug and Cosmetic Act
(FFDCA) when manufactured, processed, or distributed in commerce for use as a cosmetic. Because the
glue for lace wigs and hair extensions is a cosmetic within section 201(i) of the FFDCA, any TCE used
for these purposes is outside the scope of TSCA.
Consumer scenarios were evaluated separately from occupational scenarios, and EPA re-categorized
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 recategorization in order to improve
clarity. Additionally, subcategories were added based on availability of differing forms of a product
(e.g., aerosol vs liquid). The updated consumer conditions of use and explanations for the changes are
presented in Table 1-4.
Page 52 of 803
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2023 Table 1-3. Categories and Subcategories of Occupational Conditions of Use and Corresponding Occupational Exposure Scenario
Life Cycle
Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Manufacture
Domestic
manufacture
Domestic manufacture
Manufacturing
2016d)
Import
Import
Repackaging
2016d)
Processing
Processing as a
reactant/
intermediate
Intermediate in industrial gas
manufacturing (e.g., manufacture of
fluorinated gases used as refrigerants,
foam blowing agents and solvents)
Processing as a reactant
2.016d); EPA-HO-
OPPT-2016-073 7-0013; EPA-
HO~« M i \ r ooi .
EP A-HO-OPPX-2016-073 7-
002.6. 1 V \ ^ M'PT-2016-
0" j" -oo:v
Processing -
Incorporation into
formulation,
mixture or
reaction product
Solvents (for cleaning or degreasing)
Formulation of Aerosol
and Non- Aerosol
Products
a
I.S. EPA. 20
16d)
Adhesives and sealant chemicals
(i
I.S. EPA. 20
16d)
Solvents (which become part of
product formulation or mixture) (e.g.,
lubricants and greases, paints and
coatings, other uses)
(i
H
J.J
PPT-2016-0"
O-OPPT-
16d); EPA-HO-
07-0003; EPA-
6-0737-0056
Processing -
incorporated into
articles
Solvents (becomes an integral
components of articles)
2016d)
Repackaging
Solvents (for cleaning or degreasing)
Repackaging
2016d)
Recycling
Recycling
Process Solvent
Recycling and Worker
Handling of Wastes
2017f)
Page 53 of 803
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Life Cycle
Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Distribution in
commerce
Distribution
Distribution
[Distribution in
commerce of TCE is the
transportation associated
with the moving of TCE
in commerce. Exposures
and emissions are not
expected.]
EP A-HO-OPPT-2016-073 7-0003
Industrial/
commercial use
Solvents (for
cleaning or
degreasing)
Batch vapor degreaser (e.g., open-top,
closed-loop)c
Batch Open-Top Vapor
Degreasing;
Batch Closed-Loop
Vapor Degreasing
EP A-HO-OPPT-2016-073 7-
s { r\ Mi
-------
Life Cycle
Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Penetrating lubricant
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases;
Metalworking Fluids
2016d). EPA-HO-
v M n Mi o . { [¦ \
IKJM M 3 i Jo i O
EPA-HO-OPPT-201 (>-073 7-0028
Adhesives and
sealants
Solvent-based adhesives and sealants
Adhesives, Sealants,
Paints, and Coatings
2016d). EPA-HO-
OilN-jM o- l-r \
HO-< )3
Tire repair cement/sealer
2.016d). EPA-HO-
^11! • o 5 iMV-, i \> \
HO-OPPT-2016-073 7-0003
Mirror edge sealant
EPA-HO-OPPT-2016-073 7-
0003; (U.S. EPA. 2014b). EPA-
HO-< 56
Functional fluids
(closed systems)
Heat exchange fluid
Other Industrial Uses
2017h)
Paints and
coatings
Diluent in solvent-based paints and
coatings
Adhesives, Sealants,
Paints, and Coatings
2016d). EPA-HO-
OITi • O *
* >1 o OOOl,
EPA-HO-OPPT-2016-073 7-
0010; EPA-HO-OPPT-2016-
0 * i«0l.\ i\> \ UO-OPPT-
2016-0737-0027;
Cleaning and
furniture care
products
Carpet cleaner
Spot Cleaning, Wipe
Cleaning and Carpet
Cleaning
EPA-HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-2016-
0737-0003
Page 55 of 803
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Life Cycle
Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Wipe cleaning d
EP A-HO-OPPT-2016-073 7-
0056; EPA-HO-OF
03
Laundry and
dishwashing
products
Spot removerc
EP A-HO-OPPT-2016-073 7-
oooJ, > n \ H-), o v
il EPA-HO-OPPT-
2.016-0737-0056
Arts, crafts and
hobby materials
Fixatives and finishing spray coatings
C
Adhesives, Sealants,
Paints, and Coatings
2014b)
Corrosion
inhibitors and
anti-scaling
agents
Corrosion inhibitors and anti-scaling
agents
Industrial Processing Aid
2016d)
Processing aids
Process solvent used in battery
manufacture
2017h)
Process solvent used in polymer fiber
spinning, fluoroelastomer manufacture
and Alcantara manufacture
Extraction solvent used in caprolactam
manufacture
2017h)
Precipitant used in beta-cyclodextrin
manufacture
Ink, toner and
colorant products
Toner aid
Commercial Printing and
Copying
EP A-HO-OPPT-2016-073 7-
0056. J f i M'PT-2016-
03
Page 56 of 803
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Life Cycle
Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Automotive care
products
Brake and parts cleaner
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases
EP A-HO-OPPT-2016-073 7-
0056; EPA-HO-OF
03
Apparel and
footwear care
products
Shoe polish
Other Commercial Uses
2017h)
Other uses
Hoof polishes e
EP A-HO-OPPT-2016-073 7-
0056. J f i M'PT-2016-
03
Pepper spray
EP A-HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-2016-
03
Gun scrubber
EP A-HO-OPPT-2016-073 7-
0056. t T \ U<* OPPT-2016-
03
Other miscellaneous industrial and
commercial uses
Disposal
Disposal
Industrial pre-treatment
Process Solvent
Recycling and Worker
Handling of Wastes
2.017f)
Industrial wastewater treatment
Publicly owned treatment works
(POTW)
Page 57 of 803
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Life Cycle
Stage
Category
Subcategory 1
Occupational Exposure
Scenario (OES)
References
2024
2025
a These categories of conditions of use appear in the Life Cycle Diagram, reflect CDR codes, and broadly represent conditions of use of TCE in industrial and/or
commercial settings.
b These subcategories reflect more specific uses of TCE.
0 This includes uses assessed in the (U.S. EPA. 20.1.4b') risk assessment.
d This condition of use involves wipe cleaning. Note that the Problem Formulation described "cleaning wipes" as a condition of use. This referred to the application of
a
product that is then wiped off, rather than a pre-wet towelette.
e "Hoof polish" would remain within EPA's jurisdiction unless the article in question was also intended for the diagnosis, cure, mitigation, treatment, of disease or
intended to affect the structure or function of the body of animals, as described in the FFDCA. EPA identified a single product for hoof polish containing TCE (U.S.
EPA. 2017fa). and this product is intended for only cosmetic and not medical use. Therefore, "hoof polish" was evaluated as a COU, applicable only to products
restricted to cosmetic function.
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Table 1-4. Categories and Subcategories of Consumer Conditions of Use
Life Cycle Stage
Category
Subcategory
Use
Solvents for Cleaning and
Brake & Parts Cleaner2
Degreasing
Aerosol Electronic Degreaser/Cleaner1
Liquid Electronic Degreaser/Cleaner1
Aerosol Spray Degreaser/Cleaner1
Liquid Degreaser/Cleaner1
Aerosol Gun Scrubber1,3
Liquid Gun Scrubber1-3
Mold Release
Aerosol Tire Cleaner1,4
Liquid Tire Cleaner1,4
Lubricants and Greases
Tap & Die Fluid
Penetrating Lubricant5
Adhesives and Sealants
Solvent-based Adhesive & Sealant
Mirror-edge Sealant
Tire Repair Cement/Sealer
Cleaning and Furniture Care
Products10
Carpet Cleaner
Aerosol Spot Remover1,6
Liquid Spot Remover1,6
Arts, Crafts, and Hobby
Materials
Fixatives & Finishing Spray Coatings7
Apparel and Footwear Care
Products
Shoe Polish
Other Consumer Uses
Fabric Spray8
Film Cleaner
Hoof Polish
Pepper Spray
Toner Aid9
Page 59 of 803
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2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
Life Cycle Stage
Category
Subcategory
1 Form was determined based on the specific products identified as representative of the associated product
subcategories. Distinct subcategories based on differing forms (aerosol and liquid) were not specifically defined in the
Problem Formulation. They were added due to product availability based on additional research that helped to
differentiate specific product forms (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
evaluated product 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 Formulation 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.
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 (U.S. EPA. 20.1.7:0 and Preliminary Information on
Manufacturing, Processing, Distribution, Use, and Disposal: TCE (U.S. EPA. 2017c) and fit within the broader
Solvents for Cleaning and Degreasing category.
5 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.
6 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.
7 This subcategory is referred to as "clear protective coating spray" in U.S. EPA (20.1.4b) 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 Formulation based on identification in the
final 2014 TCE Work Plan Chemical Risk Assessment (U.S. EPA. 2014b).
9 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.
10 Problem Formulation 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 2.3.2.5.2 as leading to dermal contact with impeded
evaporation.
To help characterize the life cycle of TCE, EPA developed a national mass balance to evaluate how
much of the volume of TCE can be accounted for from cradle-to-grave. The inputs into the mass balance
included date from the 2016 CDR, 2017 NEI, 2017 TRI, and available market data. The result of the
mass balance is provided in Appendix R. The total mass accounted for at the end-of-life stage, which
includes wastes from manufacturing, processing, use, waste treatment and disposal facilities, is
approximately 101% of the 2015 production volume. The over-accounting of volume is most likely due
to incomplete reporting data and comparison of data from different years. There is additional uncertainty
arising from the potential to double count TRI volumes reported as transferred off-site for energy
recovery, treatment, and recycling that are then received by another TRI site that reports this volume in
its on-site waste management activities. Finally, the true export volume is higher than presented in the
mass balance as multiple sites reporting to 2016 CDR claimed their export volume as CBI. Additional
details on the development of the mass balance can be found in Appendix R.
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T
Manufacture
(Includes Import)
(171.9 million lbs.)
Processing as a
Reactant/lntermediate
(Volume CBI)
e.g., intermediate for
refrigerant manufacture
Incorporated into
Formulation, Mixture,
or Reaction Products
(Volume CBI)
Repackaging
(Volume CBI)
Recycling
Solvents for Cleaning and Oegreasing
(Volume CBI)
e.g., vapor degreasing, cold cleaning,
aerosol degreasing, mold release
Lubricants and Greases
(185,000 lbs.)
e.g., lubricant, tap and die fluid
Adhesives and Sealants
(Volume CBI)
e.g., mirror-edge sealant
Functional Fluids (closed system)
(Volume CBI)
e.g., refrigerant
Paints and Coatings
(Volume CBI)
Cleaning and Furniture Care Products
(Volume CBI)
e.g., carpet cleaner
Laundry and Dishwashing Products
e.g., spot remover
Arts, Crafts, and Hobby Materials
e.g., spray-applied protective coating
Apparel and Footwear Care Products
e.g., shoe polish
Other Uses, Ind. Corrosion Inhibitors and
Anti-Scaling Agents (Volume CBi);
Processing Aids; Ink, Toner and Colorant
Products; Automotive Care Products;
Miscellaneous (e.g., hoof polish, pepper
spray)
Disposal
2041
2042
2043
2044
2045
2046
~ Manufacture (Includes Import) ~ Processing ~
Category of Conditions of Use. The majority of conditions of use were evaluated for both occupational and
consumer scenarios, however there are some differences based on re-categorization of consumer uses.
Distribution
Figure 1-3. TCE Life Cycle Diagram
The life cycle diagram depicts the conditions of use that are within the scope of the Risk Evaluation during vari ous 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 ( J.S. EPA. 2016d). A mass balance of TCE throughout the life cycle can be found
in Appendix R.
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1.4.2 Exposure Pathways and Risks Addressed by Other EPA-Administered Statutes
In its TSCA section 6(b) Risk Evaluations, EPA is coordinating action on certain exposure pathways and
risks falling under the jurisdiction of other EPA-administered statutes or regulatory programs. More
specifically, EPA is exercising its TSCA authorities to tailor the scope of its Risk Evaluations, rather
than focusing on environmental exposure pathways addressed under other EPA-administered statutes or
regulatory programs or risks that could be eliminated or reduced to a sufficient extent by actions taken
under other EPA-administered laws. EPA considers this approach to be a reasonable exercise of the
Agency's TSCA authorities, which include:
• TSCA section 6(b)(4)(D): "The Administrator shall, not later than 6 months after the initiation of
a Risk Evaluation, publish the scope of the Risk Evaluation to be conducted, including the
hazards, exposures, conditions of use, and the potentially exposed or susceptible subpopulations
the Administrator expects to consider... "
• TSCA section 9(b)(1): "The Administrator shall coordinate actions taken under this chapter with
actions taken under other Federal laws administered in whole or in part by the Administrator. If
the Administrator determines that a risk to health or the environment associated with a chemical
substance or mixture could be eliminated or reduced to a sufficient extent by actions taken under
the authorities contained in such other Federal laws, the Administrator shall use such authorities
to protect against such risk unless the Administrator determines, in the Administrator's
discretion, that it is in the public interest to protect against such risk by actions taken under this
chapter."
• TSCA section 9(e): "... [I]f the Administrator obtains information related to exposures or releases
of a chemical substance or mixture that may be prevented or reduced under another Federal law,
including a law not administered by the Administrator, the Administrator shall make such
information available to the relevant Federal agency or office of the Environmental Protection
Agency."
• TSCA section 2(c): "It is the intent of Congress that the Administrator shall carry out this chapter
in a reasonable and prudent manner, and that the Administrator shall consider the environmental,
economic, and social impact of any action the Administrator takes or proposes as provided under
this chapter."
• TSCA section 18(d)(1): "Nothing in this chapter, nor any amendment made by the Frank R.
Lautenberg Chemical Safety for the 21st Century Act, nor any rule, standard of performance,
Risk Evaluation, or scientific assessment implemented pursuant to this chapter, shall affect the
right of a State or a political subdivision of a State to adopt or enforce any rule, standard of
performance, Risk Evaluation, scientific assessment, or any other protection for public health or
the environment that— (i) is adopted or authorized under the authority of any other Federal law
or adopted to satisfy or obtain authorization or approval under any other Federal law...."
TSCA authorities supporting tailored Risk Evaluations and intra-agencv referrals
TSCA section 6(b)(4)(D) requires EPA, in developing the scope of a Risk Evaluation, to identify the
hazards, exposures, conditions of use, and potentially exposed or susceptible subpopulations the Agency
"expects to consider" in a Risk Evaluation. This language suggests that EPA is not required to consider
all conditions of use, hazards, or exposure pathways in Risk Evaluations.
In the Problem Formulation documents for many of the first 10 chemicals undergoing Risk Evaluation,
EPA applied this authority and rationale to certain exposure pathways, explaining that "EPA is planning
to exercise its discretion under TSCA 6(b)(4)(D) to focus its analytical efforts on exposures that are
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likely to present the greatest concern and consequently merit a Risk Evaluation under TSCA, by
excluding, on a case-by-case basis, certain exposure pathways that fall under the jurisdiction of other
EPA-administered statutes." This approach is informed by the legislative history of the amended TSCA,
which supports the Agency's exercise of discretion to focus the Risk Evaluation on areas that raise the
greatest potential for risk. See June 7, 2016 Cong. Rec., S3519-S3520. Consistent with the approach
articulated in the Problem Formulation documents, and as described in more detail below, EPA is
exercising its authority under TSCA to tailor the scope of exposures evaluated in TSCA Risk
Evaluations, rather than focusing on environmental exposure pathways addressed under other EPA-
administered, mediaspecific statutes and regulatory programs.
TSCA section 9(b)(1)
In addition to TSCA section 6(b)(4)(D), the Agency also has discretionary authority under the first
sentence of TSCA section 9(b)(1) to "coordinate actions taken under [TSCA] with actions taken under
other Federal laws administered in whole or in part by the Administrator." This broad, freestanding
authority provides for intra-agency coordination and cooperation on a range of "actions." In EPA's
view, the phrase "actions taken under [TSCA]" in the first sentence of section 9(b)(1) is reasonably read
to encompass more than just risk management actions, and to include actions taken during Risk
Evaluation as well. More specifically, the authority to coordinate intra-agency actions exists regardless
of whether the Administrator has first made a definitive finding of risk, formally determined that such
risk could be eliminated or reduced to a sufficient extent by actions taken under authorities in other
EPA-administered Federal laws, and/or made any associated finding as to whether it is in the public
interest to protect against such risk by actions taken under TSCA. TSCA section 9(b)(1) therefore
provides EPA authority to coordinate actions with other EPA offices without ever making a risk finding,
or following an identification of risk. This includes coordination on tailoring the scope of TSCA Risk
Evaluations to focus on areas of greatest concern rather than exposure pathways addressed by other
EPA- administered statutes and regulatory programs, which does not involve a risk determination or
public interest finding under TSCA section 9(b)(2).
In a narrower application of the broad authority provided by the first sentence of TSCA section 9(b)(1),
the remaining provisions of section 9(b)(1) provide EPA authority to identify risks and refer certain of
those risks for action by other EPA offices. Under the second sentence of section 9(b)(1), "[i]f the
Administrator determines that a risk to health or the environment associated with a chemical substance
or mixture could be eliminated or reduced to a sufficient extent by actions taken under the authorities
contained in such other Federal laws, the Administrator shall use such authorities to protect against such
risk unless the Administrator determines, in the Administrator's discretion, that it is in the public interest
to protect against such risk by actions taken under [TSCA]." Coordination of intra-agency action on
risks under TSCA section 9(b)(1) therefore entails both an identification of risk, and a referral of any
risk that could be eliminated or reduced to a sufficient extent under other EPA-administered laws to the
EPA office(s) responsible for implementing those laws (absent a finding that it is in the public interest to
protect against the risk by actions taken under TSCA).
Risk may be identified by OPPT or another EPA office, and the form of the identification may vary. For
instance, OPPT may find that one or more conditions of use for a chemical substance present(s) a risk to
human or ecological receptors through specific exposure routes and/or pathways. This could involve a
quantitative or qualitative assessment of risk based on reasonably available information (which might
include, e.g., findings or statements by other EPA offices or other federal agencies). Alternatively, risk
could be identified by another EPA office. For example, another EPA office administering non-TSCA
authorities may have sufficient monitoring or modeling data to indicate that a particular condition of use
presents risk to certain human or ecological receptors, based on expected hazards and exposures. This
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risk finding could be informed by information made available to the relevant office under TSCA section
9(e), which supports cooperative actions through coordinated information-sharing.
Following an identification of risk, EPA would determine if that risk could be eliminated or reduced to a
sufficient extent by actions taken under authorities in other EPA-administered laws. If so, TSCA
requires EPA to "use such authorities to protect against such risk," unless EPA determines that it is in
the public interest to protect against that risk by actions taken under TSCA. In some instances, EPA may
find that a risk could be sufficiently reduced or eliminated by future action taken under non-TSCA
authority. This might include, e.g., action taken under the authority of the Safe Drinking Water Act to
address risk to the general population from a chemical substance in drinking water. This sort of risk
finding and referral could occur during the Risk Evaluation process, thereby enabling EPA to use more a
relevant and appropriate authority administered by another EPA office to protect against hazards or
exposures to affected receptors.
Legislative history on TSCA section 9(b)(1) supports both broad coordination on current intraagency
actions, and narrower coordination when risk is identified and referred to another EPA office for action.
A Conference Report from the time of TSCA's passage explained that section 9 is intended "to assure
that overlapping or duplicative regulation is avoided while attempting to provide for the greatest
possible measure of protection to health and the environment." S. Rep. No. 94-1302 at 84. See also H.
Rep. No. 114-176 at 28 (stating that the 2016 TSCA amendments "reinforce TSCA's original purpose of
filling gaps in Federal law," and citing new language in section 9(b)(2) intended "to focus the
Administrator's exercise of discretion regarding which statute to apply and to encourage decisions that
avoid confusion, complication, and duplication"). Exercising TSCA section 9(b)(1) authority to
coordinate on tailoring TSCA Risk Evaluations is consistent with this expression of Congressional
intent.
Legislative history also supports a reading of section 9(b)(1) under which EPA coordinates intraagency
action, including information-sharing under TSCA section 9(e), and the appropriatelypositioned EPA
office is responsible for the identification of risk and actions to protect against such risks. See, e.g.,
Senate Report 114-67, 2016 Cong. Rec. S3522 (under TSCA section 9, "if the Administrator finds that
disposal of a chemical substance may pose risks that could be prevented or reduced under the Solid
Waste Disposal Act, the Administrator should ensure that the relevant office of the EPA receives that
information"); H. Rep. No. 114-176 at 28, 2016 Cong. Rec. S3522 (under section 9, "if the
Administrator determines that a risk to health or the environment associated with disposal of a chemical
substance could be eliminated or reduced to a sufficient extent under the Solid Waste Disposal Act, the
Administrator should use those authorities to protect against the risk"). Legislative history on section
9(b)(1) therefore supports coordination with and referral of action to other EPA offices, especially when
statutes and associated regulatory programs administered by those offices could address exposure
pathways or risks associated with conditions of use, hazards, and/or exposure pathways that may
otherwise be within the scope of TSCA Risk Evaluations.
TSCA sections 2(c) & 18(d)(1)
Finally, TSCA sections 2(c) and 18(d) support coordinated action on exposure pathways and risks
addressed by other EPA-administered statutes and regulatory programs. Section 2(c) directs EPA to
carry out TSCA in a "reasonable and prudent manner" and to consider "the environmental, economic,
and social impact" of its actions under TSCA. Legislative history from around the time of TSCA's
passage indicates that Congress intended EPA to consider the context and take into account the impacts
of each action under TSCA. S. Rep. No. 94-698 at 14 ("the intent of Congress as stated in this
subsection should guide each action the Administrator takes under other sections of the bill").
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Section 18(d)(1) specifies that state actions adopted or authorized under any Federal law are not
preempted by an order of no unreasonable risk issued pursuant to TSCA section 6(i)(l) or a rule to
address unreasonable risk issued under TSCA section 6(a). Thus, even if a Risk Evaluation were to
address exposures or risks that are otherwise addressed by other federal laws and, for example,
implemented by states, the state laws implementing those federal requirements would not be preempted.
In such a case, both the other federal and state laws, as well as any TSCA section 6(i)(l) order or TSCA
section 6(a) rule, would apply to the same issue area. See also TSCA section 18(d)(l)(A)(iii). In
legislative history on amended TSCA pertaining to section 18(d), Congress opined that "[t]his approach
is appropriate for the considerable body of law regulating chemical releases to the environment, such as
air and water quality, where the states have traditionally had a significant regulatory role and often have
a uniquely local concern." Sen. Rep. 114-67 at 26.
EPA's careful consideration of whether other EPA-administered authorities are available and more
appropriate for addressing certain exposures and risks is consistent with Congress' intent to maintain
existing federal requirements and the state actions adopted to locally and more specifically implement
those federal requirements, and to carry out TSCA in a reasonable and prudent manner. EPA believes it
is both reasonable and prudent to tailor TSCA Risk Evaluations in a manner reflective of expertise and
experience exercised by other EPA and State offices to address specific environmental media, rather
than attempt to evaluate and regulate potential exposures and risks from those media under TSCA. This
approach furthers Congressional direction and EPA aims to efficiently use Agency resources, avoid
duplicating efforts taken pursuant to other Agency and State programs, and meet the statutory deadline
for completing Risk Evaluations.
EPA-administered statutes and regulatory programs that address specific exposure pathways and/or risks
During the course of the Risk Evaluation process for trichloroethylene, OPPT worked closely with the
offices within EPA that administer and implement regulatory programs under the Clean Air Act (CAA),
the Safe Drinking Water Act (SDWA), the Clean Water Act (CWA), the Comprehensive Environmental
Response, Compensation, and Liability Act (CERCLA), and the Resource Conservation and Recovery
Act (RCRA). Through this intra-agency coordination, EPA determined that specific exposure pathways
are well-regulated by the EPA statutes and regulations described in the following paragraphs.
Ambient air pathway
The CAA contains a list of hazardous air pollutants (HAP) and provides EPA with the authority to add
to that list pollutants that present, or may present, a threat of adverse human health effects or adverse
environmental effects. For stationary source categories emitting HAP, the CAA requires issuance of
technology-based standards and, if necessary, additions or revisions to address developments in
practices, processes, and control technologies, and to ensure the standards adequately protect public
health and the environment. The CAA thereby provides EPA with comprehensive authority to regulate
emissions to ambient air of any hazardous air pollutant.
Trichloroethylene is a HAP. See 42 U.S.C. 7412. EPA has issued a number of technologybased
standards for source categories that emit trichloroethylene to ambient air and, as appropriate, has
reviewed, or is in the process of reviewing remaining risks. See 40 CFR part 63; Appendix A. Because
stationary source releases of trichloroethylene to ambient air are addressed under the CAA, EPA is not
evaluating emissions to ambient air from commercial and industrial stationary sources or associated
inhalation exposure of the general population or terrestrial species in this TSCA Risk Evaluation.
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Drinking water pathway
EPA has regular analytical processes to identify and evaluate drinking water contaminants of potential
regulatory concern for public water systems under the Safe Drinking Water Act (SDWA). Under
SDWA, EPA must also review and revise "as appropriate" existing drinking water regulations every 6
years.
EPA has promulgated National Primary Drinking Water Regulations (NPDWRs) for trichloroethylene
under SDWA. See 40 CFR part 151; Appendix A. EPA has set an enforceable Maximum Contaminant
Level (MCL) as close as feasible to a health based, non-enforceable Maximum Contaminant Level Goal
(MCLG). Feasibility refers to both the ability to treat water to meet the MCL and the ability to monitor
water quality at the MCL, SDWA Section 1412(b)(4)(D), and public water systems are required to
monitor for the regulated chemical based on a standardized monitoring schedule to ensure compliance
with the maximum contaminant level (MCL). Hence, because the drinking water exposure pathway for
trichloroethylene is currently addressed in the SDWA regulatory analytical process for public water
systems, EPA is not evaluating exposures to the general population from the drinking water exposure
pathway in the Risk Evaluation for trichloroethylene under TSCA.
Ambient water pathway
EPA develops recommended water quality criteria under section 304(a) of the CWA for pollutants in
surface water that are protective of aquatic life or human health designated uses. EPA develops and
publishes water quality criteria based on priorities of states and others that reflect the latest scientific
knowledge. A subset of these chemicals are identified as "priority pollutants" (103 human health and 27
aquatic life). The CWA requires states adopt numeric criteria for priority pollutants for which EPA has
published recommended criteria under section 304(a), the discharge or presence of which in the affected
waters could reasonably be expected to interfere with designated uses adopted by the state. When states
adopt criteria that EPA approves as part of state's regulatory water quality standards, exposure is
considered when state permit writers determine if permit limits are needed and at what level for a
specific discharger of a pollutant to ensure protection of the designated uses of the receiving water. Once
states adopt criteria as water quality standards, the CWA requires that National Pollutant Discharge
Elimination System (NPDES) discharge permits include effluent limits as stringent as necessary to meet
standards. CWA section 301(b)(1)(C). This is the process used under the CWA to address risk to human
health and aquatic life from exposure to a pollutant in ambient waters.
EPA has identified trichloroethylene as a priority pollutant and has developed recommended water
quality criteria for protection of human health for trichloroethylene which are available for adoption into
state water quality standards for the protection of human health and are available for use by NPDES
permitting authorities in deriving effluent limits to meet state criteria.7 See, e.g., 40 CFR part 423,
Appendix A; 40 CFR 131.11(b)(1); 40 CFR 122.44(d)(l)(vi). As such, EPA is not evaluating exposures
to the general population from the surface water exposure pathway in the Risk Evaluation under TSCA.
Land application of biosolids and general population exposure
As wastewater undergoes treatment, some wastewater treatment facilities such as publicly-owned
treatment works (POTWs) use the remaining sludge as biosolids for land application. These biosolids
could have residual trichloroethylene. Trichloroethylene in biosolids that are land applied could be
transported via runoff from rainwater to surface waters. However, surface waters drawn for drinking
water are treated, tested and under the Safe Drinking Water Act, regulated via NPDWRs. EPA
promulgates NPDWRs under SDWA when the Agency concludes a contaminant may have adverse
7 See https://www.epa.gov/wqc/ambient-water-quality-criteria-trichloroethylene.
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health effects, occurs or is substantially likely to occur in public water systems at a level of concern and
that regulation, in the sole judgement of the Administrator, presents a meaningful opportunity for health
risk reduction. For each contaminant with NPDWRs, EPA sets an enforceable MCL as close as feasible
to a health based, non-enforceable MCLG or establishes a treatment technique. The MCL for any
residual levels of trichloroethylene that could result in exposure to the general population is 0.005mg/L.
Residual concentrations of trichloroethylene in surface waters not used for drinking water are covered
by the CWA Ambient Water Quality Criteria for human health consumption of water and organisms (0.4
|ig/L), CWA Section 304(a)(1). States and tribal governments may adopt the EPA Clean Water Act
Section 304(a) recommended criteria or may adopt their own criteria that differ from EPA's
recommendations, subject to EPA's approval, using scientifically defensible methods. States are
required to adopt and implement EPA-approved criteria as part of their regulatory water quality
standards, and compliance with these criteria is considered by states in permits and water quality
assessment decisions. Thus, general population exposure via the biosolid pathway is not evaluated under
any of the conditions of use in the final Risk Evaluation.
Onsite Releases to Land Pathway
The Comprehensive Environmental Response, Compensation, and Liability Act - otherwise known as
CERCLA or Superfund - provides EPA with broad authority to address uncontrolled or abandoned
hazardous-waste sites as well as accidents, spills, and other releases of hazardous substances, pollutants
and contaminants into the environment. Through CERCLA, EPA is provided authority to conduct a
response action and seek reimbursement of cleanup costs from potentially responsible parties, or in
certain circumstances, order a potentially responsible party to conduct a cleanup.
CERCLA Section 101(14) defines "hazardous substance" by referencing other environmental statutes,
including toxic pollutants listed under CWA Section 307(a); hazardous substances designated pursuant
to CWA Section 311(b)(2)(A); hazardous air pollutants listed under CAA Section 112; imminently
hazardous substances with respect to which EPA has taken action pursuant to TSCA Section 7; and
hazardous wastes having characteristics identified under or listed pursuant to RCRA Section 3001. See
40 CFR 302.4. CERCLA Section 102(a) also authorizes EPA to promulgate regulations designating as
hazardous substances those substances which, when released into the environment, may present
substantial danger to the public health or welfare or the environment. EPA must also promulgate
regulations establishing the quantity of any hazardous substance the release of which must be reported
under Section 103. 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.
Trichloroethylene is a hazardous substance under CERCLA. Releases of trichloroethylene in excess of
10 pounds within a 24-hour period must be reported (40 CFR 302.4, 302.6). The scope of this EPA
TSCA Risk Evaluation does not include on-site releases to the environment of trichloroethylene at
Superfund sites and subsequent exposure of the general population or non-human species.
Disposal Pathways
Trichloroethylene is included on the list of hazardous wastes pursuant to RCRA section 3001 (40 CFR
§§ 261.33) as a listed waste on the F001, F002, K030, and U228 lists. The general standard in RCRA
section 3004(a) for the technical criteria that govern the management (treatment, storage, and disposal)
of hazardous waste are those "necessary to protect human health and the environment," RCRA 3004(a).
The regulatory criteria for identifying "characteristic" hazardous wastes and for "listing" a waste as
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hazardous also relate solely to the potential risks to human health or the environment. 40 C.F.R. §§
261.11, 261.21-261.24. RCRA statutory criteria for identifying hazardous wastes require EPA to "tak[e]
into account toxicity, persistence, and degradability in nature, potential for accumulation in tissue, and
other related factors such as flammability, corrosiveness, and other hazardous characteristics." Subtitle
C controls cover not only hazardous wastes that are landfilled, but also hazardous wastes that are
incinerated (subject to joint control under RCRA Subtitle C and the CAA hazardous waste combustion
MACT) or injected into UIC Class I hazardous waste wells (subject to joint control under Subtitle C and
SDWA).
EPA is not evaluating on-site releases to land from RCRA Subtitle C hazardous waste landfills or
exposures of the general population or terrestrial species from such releases in the TSCA evaluation.
Design standards for Subtitle C landfills require double liner, double leachate collection and removal
systems, leak detection system, run on, runoff, and wind dispersal controls, and a construction quality
assurance program. They are also subject to closure and postclosure care requirements including
installing and maintaining a final cover, continuing operation of the leachate collection and removal
system until leachate is no longer detected, maintaining and monitoring the leak detection and
groundwater monitoring system. Bulk liquids may not be disposed in Subtitle C landfills. Subtitle C
landfill operators are required to implement an analysis and testing program to ensure adequate
knowledge of waste being managed, and to train personnel on routine and emergency operations at the
facility. Hazardous waste being disposed in Subtitle C landfills, including TCE (listed as a hazardous
waste in 40 CFR 261.31, 261.33), must also meet RCRA waste treatment standards before disposal. See
40 CFR part 264; Appendix A.
EPA is not evaluating on-site releases to land from RCRA Subtitle D municipal solid waste (MSW)
landfills or exposures of the general population or terrestrial species from such releases in the TSCA
evaluation. While permitted and managed by the individual states, municipal solid waste landfills are
required by federal regulations to implement some of the same requirements as Subtitle C landfills.
MSW landfills generally must have a liner system with leachate collection and conduct groundwater
monitoring and corrective action when releases are detected. MSW landfills are also subject to closure
and post-closure care requirements, and must have financial assurance for funding of any needed
corrective actions. MSW landfills have also been designed to allow for the small amounts of hazardous
waste generated by households and very small quantity waste generators (less than 220 lbs per month).
Bulk liquids, such as free solvent, may not be disposed of at MSW landfills. See 40 CFR part 258.
EPA is not evaluating on-site releases to land from industrial non-hazardous waste and
construction/demolition waste landfills or associated exposures to the general population or terrestrial
species in the trichloroethylene Risk Evaluation. Industrial non-hazardous and construction/demolition
waste landfills are primarily regulated under authorized state regulatory programs. States must also
implement limited federal regulatory requirements for siting, groundwater monitoring and corrective
action and a prohibition on open dumping and disposal of bulk liquids. States may also establish
additional requirements such as for liners, post-closure and financial assurance, but are not required to
do so. See, e.g., RCRA section 3004(c), 4007; 40 CFR part 257.
EPA is not evaluating emissions to ambient air from municipal and industrial waste incineration and
energy recovery units or associated exposures to the general population or terrestrial species in the Risk
Evaluation, as these emissions are regulated under Section 129 of the Clean Air Act. CAA Section 129
requires EPA to review and, if necessary, add provisions to ensure the standards adequately protect
public health and the environment. Thus, combustion by-products from incineration treatment of
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trichloroethylene wastes would be subject to these regulations, as would trichloroethylene burned for
energy recovery. See 40 CFR part 60.
EPA is not evaluating on-site releases to land that go to underground injection or associated exposures to
the general population or terrestrial species in its Risk Evaluation. Environmental disposal of
trichloroethylene injected into Class I hazardous well types are covered under the jurisdiction of RCRA
and SDWA and disposal of trichloroethylene via underground injection is not likely to result in
environmental and general population exposures under any of the conditions of use in this final Risk
Evaluation. See 40 CFR part 144.
1.4.3 Conceptual Models
The conceptual models for this final Risk Evaluation are shown in Figure 1-4, Figure 1-5, and Figure
1-6. The EPA considered the potential for hazards to human health and the environment resulting from
exposure pathways outlined in the preliminary conceptual models of the TCE scope document (U.S.
). These conceptual models considered potential exposures resulting from consumer
activities and uses, industrial/ commercial activities, and environmental releases and wastes. The
Problem Formulation documents refined the initial conceptual models and analysis plans that were
provided in the scope documents ( ).
For the purpose of this evaluation, EPA considered workers and occupational non-users, which includes
men and women of reproductive age (Figure 1-4). Consumer exposure was assessed for various
pathways for users age 11 and older along with bystanders of all ages (Figure 1-5).
The pathways that were determined to be included in the Risk Evaluation but did not warrant further
analysis in this Risk Evaluation were: exposure to both humans and ecological organisms due to land
application of biosolids following wastewater treatment and exposure to terrestrial organisms. In the
Problem Formulation, the EPA determined that no further evaluation of these pathways is needed due to
the physical/chemical properties associated with TCE (high vapor pressure) and its rapid volatilization
to air from soil and water or rapid migration through soil into groundwater. Due to TCE's fate
properties, a significant portion of TCE would not be available to enter the sediment compartment.
The pathways that were determined to be included in the Risk Evaluation and further analyzed include:
• Exposure to aquatic species {i.e., aquatic plants) via contaminated surface water.
• Exposure to sediment-dwelling species via sediment.
• Inhalation and dermal exposures to workers and consumers, and inhalation exposures to ONUs
and bystanders, from industrial/commercial activities and consumer activities.
• Inhalation and dermal exposures to workers and inhalation exposures to ONUs from waste
handling, treatment and disposal.
Review and evaluation of reasonably available information on TCE confirmed the preliminary
conclusions in the Problem Formulation ( ). The conceptual models from the Problem
Formulation are shown below in Figure 1-4, Figure 1-5, and Figure 1-6.
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INDUSTRIAL AND COMMERCIAL
ACTIVITIES/USES EXPOSURE PATHWAY EXPOSURE ROUTE RECEPTORS c HAZARDS
Manufacturing
Processing:
• Processing as a
reactant/intermediate
• Incorporated into
formulations, mixtures, or
reaction products
• Repackaging
• Non-incorporative
activities
Workers d
Liquid Contact
Dermal
Hazards Potentially Associated
with Acute and/or Chronic
Exposures
Occupational
Non-Users
Vapor/ Mist
Inhalation
Fugitive
Emissionsb
Recycling
Solvents for Cleaning and
Degreasing
Lubricants and Greases
Adhesives and Sealants
Functional Fluids
Paints and Coatings
Cleaning and Furniture Care
Products
KEY:
Pathways and receptors that were not
further analyzed
^ Pathways that were not further analyzed.
Pathways that were not further analyzed.
Other Industrial or
Commercial Uses a
Laundry and Dishwashing
Products
Waste Handling,
Treatment and Disposal
Wastewater and Liquid Wastes
Figure 1-4. TCE Conceptual Model for Industrial and Commercial Activities and Uses: Potential Exposures and Hazards
The conceptual model presents the exposure pathways, exposure routes and hazards to human receptors from industrial and commercial
activities and uses of TCE.
a Some products are used in both commercial and consumer applications. Additional uses of TCE are included in Table 1-3.
b Fugitive air emissions are those that are not stack emissions, and include fugitive equipment leaks from valves, pump seals, flanges,
compressors, sampling connections and open-ended lines; evaporative losses from surface impoundment and spills; and releases from
building ventilation systems.
c Receptors include Potentially Exposed or Susceptible Subpopulations (PESS) including women of childbearing age and their children and
genetically susceptible populations.
d When data and information are reasonably available to support the analysis, EPA also considers the effect that engineering controls and/or
personal protective equipment have on occupational exposure levels.
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CONSUMER
ACTIVITIES/USES
EXPOSURE PATHWAY
EXPOSURE ROUTE
RECEPTORS c
HAZARDS
Solvents for Cleaning and
Degreasing
Lubricants and Greases
Adhesives and Sealants
Cleaning and Furniture Care
Products
Arts, Crafts, and Hobby
Materials
Apparel and Footwear Care
Products
Other Consumer Uses *
Dermalb
Consumers
Liquid Contact
Vapor/Mist
nhalation
Bystanders
Hazards Potentially
Associated
with Acute and/or
Chronic
Exposures
Consumer Handling and
Disposal of Waste
- L
KEY:
Pathways and receptors that were not
further analyzed
^ Pathways that were not further analyzed.
Pathways that were notfurther analyzed.
Figure 1-5. 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 1-3.
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 physical-
chemical properties, oral exposure may also occur from incidental ingestion of residue on hand/body.
c Receptors include Potentially Exposed or Susceptible Subpopulations (PESS).
Page 71 of 803
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Wastewater or
Liquid Wastes a
Industrial Pre-
Treatment or
Industrial WWT
2450
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Indirect
POTW
Direct
Discharge
Discharge
Water
Sediment
Bioso ids
Aquatic
Species
Terrestrial
Species
Hazards Potentially Associated with Acute
and/or Chronic Exposures:
KEY:
Grey Text
~
Pathways and receptors that were not
further analyzed
Pathways that were not further analyzed.
Pathways that were notfurther analyzed.
Figure 1-6. TCE Conceptual Model for Environmental Releases and Wastes: Potential Exposures and Hazards
The conceptual model presents the exposure pathways, exposure routes and hazards to human and environmental receptors from
environmental releases and wastes of TCE.
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).
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1.5 Systematic Review
TSCA requires the EPA to use scientific information, technical procedures, measures, 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-established protocol to comprehensively, objectively,
transparently, and consistently identify and evaluate each stream of evidence, including strengths,
limitations, and relevance of each study and to integrate evidence 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 Systematic Review in TSCA Risk Evaluations document ( >). 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 Strategy for Conducting Literature Searches for Trichloroethylene (TCE): Supplemental File for the
TSCA Scope Document ( ) and the results of the title and abstract screening process
were published in the [Trichloroethylene (CASRN 79-01-6) Bibliography: Supplemental File for the
TSCA Scope Document; (U.S. EPA. 20171)1.
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 framework.8 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 Formulation of the Risk Evaluation
for Trichloroethylene (U.S. EPA. 2018d)
8 A PESO statement was used during the full text screening of environmental fate and transport data sources. PESO stands
for Pathways and Processes, Exposure, Setting or Scenario, and Outcomes. A RESO statement was used during the full text
screening of the engineering and occupational exposure literature. RESO stands for Receptors, Exposure, Setting or
Scenario, and Outcomes.
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Although EPA conducted a comprehensive search and screening process as described above, EPA made
the decision to leverage the literature published in previous assessments9 when identifying relevant key
and supporting data10 and information for developing the TCE Risk Evaluation. This is discussed in the
Strategy for Conducting Literature Searches for Trichloroethylene: Supplemental Document to the
TSCA Scope Document (U.S. EPA. 2.01 Te). In general, many of the key and supporting data sources
were identified in the comprehensive Trichloroethylene (CASRN 79-01-6) Bibliography: Supplemental
File for the TSCA Scope Document; ( ). However, there were instances in which EPA
missed relevant references that were not captured in the initial categorization of 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 of Systematic 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 Conducting Literature Searches for Trichloroethylene: Supplemental Document 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. 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 sources that 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.
9 Examples of existing assessments are EPA's chemical assessments (e.g., previous work plan risk assessments, Problem
Formulation documents), ATSDR's Toxicological Profiles, EPA's IRIS assessments and ECHA's dossiers. This is
described in more detail in the Strategy for Conducting Literature Searches for Trichloroethylene: Supplemental Document
to the TSCA Scope Document (U.S. EPA. 20176).
10 Key and supporting data and information are those that support key analyses, arguments, and/or conclusions in the risk
evaluation.
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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 final 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
final 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 1-8).
Data Extraction/Data Integration (n=52)
Data Search Results (n=10,040)
Data Screening (n=10,039)
Data Evaluation (n=61)
* Key/Supporting
Data Sources (n=l)
Excluded References
(n=9,979)
Excluded: Ref that are
unacceptable based on the
evaluation criteria (n=9)
This is a key and supporting source from existing assessments, the EPI Suite™ set of models, that was highly relevant
for the TSCA risk evaluation. This source bypassed the data screening step and moved directly to the data evaluation
step.
Figure 1-7. Literature Flow Diagram for Environmental Fate and Transport
Note: Literature search results for the environmental fate and transport of TCE yielded 10,040 studies. During Problem
Formulation, following data screening, most environmental exposure pathways were 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 enviromnental exposure pathways retained in the conceptual models entered data evaluation, where 9
studies were deemed unacceptable and 52 moved into data extraction and integration. Note: Data sources identified relevant
to physical-chemical properties were not included in this literature flow diagram. The data quality evaluation of physical-
chemical properties studies can be found in the supplemental document, [Data Quality Evaluation of Physical-Chemical
Properties Studies. Docket: EPA-H0-OPPT-2019-050Q\ and the extracted data are presented in Table 1-1.
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(1=144
Key/supporting
data sources
modeling > occupational exposure limits or release limits). If warranted, EPA may use data/information
of lower rated quality as supportive evidence in the environmental release and occupational exposure assessments.
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Excluded References (n = 998)
Data Evaluation (n = 151)
Data Extraction/Data Integration (n = 79)
Data Screening (n = 1149)
Data Search Results (n = 1149)
"Excluded References (n = 72)
Unacceptable based on data evaluation criteria (n = 14)
Not primary source, not extractable or
not most relevant (n = 53)
'The quality of data in these sources were acceptable for risk assessment purposes and considered for
integration. The sources; however, were not extracted for a variety of reasons, such as they contained only
secondary source data, duplicate data, or n on-extractable data (i.e., charts or figures). Additionally, some
data sources were not as relevant to the PECO as other data sources which were extracted.
Figure 1-9. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources
EPA conducted a literature search to determine relevant data sources for assessing exposures for trichloroethylene within the
scope of the Risk Evaluation. This search identified 1149 data sources including relevant supplemental documents. Of these,
998 were excluded during the screening of the title, abstract, and/or full text and 151 data sources were recommended for
data evaluation across up to five major study types in accordance with Appendix E:Data Quality Criteria for Studies on
Consumer, General Population and Environmental Exposure of the Application of Systematic Review for TSCA Risk
Evaluations document (U.S. EPA. 2018b). Following the evaluation process, 79 references were forwarded for further
extraction and data integration. EPA has not developed data quality criteria for all types of exposure information, some of
which may be relevant when estimating consumer exposures. This is the case for absorption and permeability data and some
product-specific data such as density and weight fraction often reported in Safety Data Sheets. As appropriate, EPA evaluated
and summarized these data to determine their utility with supporting the Risk Evaluation.
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Key/Sup porting
Studies
(n = 2)
Excluded References due to
ECOTOX Criteria
(n = 8144)
Excluded References due to
ECOTOX Criteria
(n =350)
Data ExtractionData Integration (n - 25}
Data Evaluation (n - 71)
Full Text Screening (n = 419)
Excluded References that are
unacceptable Based
on evaluation criteria and;or are
out of scope
(n = 45)
Data Search Results {n - 85S5)
Title/Abstract Screening (n = 8563)
Figure 1-10. Literature Flow Diagram for Environmental Hazard
The environmental hazard data sources were identified through literature searches and screening strategies using the
ECOTOXicology Knowledgebase System (ECOTOX) Standing Operating Procedures. For studies determined to be on-topic
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 as documented in the ECOTOX User Guide
(U.S. EPA. 2018c). Additional details can be found in the Strategy for Conducting Literature Searches for Trichloroethvlene
Supplemental Document to the TSCA Scope Document (U.S. EPA. 2017e).
The "Key/Supporting Studies" box represents data sources cited in an existing assessment (Environment Canada and Health
Canada. 1993) that were considered highly relevant for the TSCA Risk Evaluation because they were used as key and
supporting information by another regulatory organization to support their chemical hazard and risk assessment. These
citations were found independently from the ECOTOX process. These studies bypassed the data screening step and moved
directly to the data evaluation step. These two studies were ultimately excluded because they examined hazard to terrestrial
species and the relevant exposure pathway of air releases lias since been determined to be out of scope.
The literature search process for environmental hazard data found 8,565 citations for TCE. At the title and abstract screening
phase, 8,144 citations were excluded as off-topic using ECOTOXicology knowledgebase criteria. The remaining 419
citations underwent a more thorough full text screening using the same criteria to determine which citations should undergo
data evaluation. For data evaluation, EPA developed data quality evaluation (DQE) criteria to evaluate the data under TSCA,
based on a combination of EPA's ECOTOXicology knowledgebase (ECOTOX) criteria and the Criteria for Reporting and
Evaluating ecotoxicity Data (CRED). There were 71 citations that went to data evaluation for TCE, which included the
above-mentioned two additional citations gathered from (Enviromnent Canada and Health Canada. 1993) that were later
excluded as out of scope. EPA analyzed each of these studies using the DQE results to determine overall study quality.
Twenty-five studies were considered acceptable and were rated high, medium, or low quality during this analysis. The
extracted data from these 25 studies were used during data integration for TCE.
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n=S5
Key/supporting
data sources
(n = 95)
Excluded References {n = 5859)
Data Search Results (r = 6,049)
Excluded: Refthat are
unacceptable based on
evaluation criteria (n = 10)
Data Evaluation (n = 180)
Data Screening (n = 5954)
Data Extraction/Data Integration (n = 170)
Figure 1-11. Literature Flow Diagram for Human Health Hazard
The literature search results for human health hazard of TCE yielded 6,049 studies. This included 95 key and supporting
studies identified from previous EPA assessments11. 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 supporting studies entered data evaluation. Ten studies
were deemed unacceptable based on the evaluation criteria for human health hazard data sources and the remaining 170
studies were carried forward to data extraction/data integration. Additional details can be found in the Strategy for
Conducting Literature Searches for Trichloroethylene Supplemental Document to the TSCA Scope Document (U.S. EPA.
2017e).
The "Key/Supporting Studies" box represents data sources cited in an existing assessment (U.S. EPA. 201 le) that were
considered highly relevant for the TSCA Risk Evaluation because they were used as key and supporting information by
another regulatory organization to support their chemical hazard and risk assessment. For a list of the key and supporting
studies, see [List of Key and Supporting Studies for Human Health Hazard. Docket # EPA -HO-OPP T-2019-05001.
1.5.2 Data Evaluation
During the data evaluation stage, the EPA assesses the quality of the methods and reporting of results of
the individual studies identified during Problem Formulation using the evaluation strategies described in
Application of Systematic Review in TSCA Risk Evaluations (U.S. EPA, 2018b). The EPA evaluated the
quality of the on-topic TCE study reports identified in [Trichloroethylene (CASRN 79-01-6)
Bibliography: Supplemental File for the TSCA Scope Document; (U.S. EPA, 2017iYI, 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 Error! Reference source not found. (Releases to the Environment), Section 2.2.6
(Environmental Exposures), Section 2.3 (Human Exposures), Section 3.1 (Environmental Hazards) and
11 "Key and supporting studies" for human health are those deemed suitable for consideration for dose-response analysis.
This does not include mechanistic or qualitative data, including genotoxicity studies. Data extraction and evaluation results
for all relevant genotoxicity studies are presented in [Data Extraction and Evaluation Tables for Genotoxicity Studies.
Docket: EPA-HQ-QPPT-2019-0500].
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Section 3.2 (Human Health Hazards). Supplemental files12 also provide details of the data evaluations
including individual metric scores and the overall study score for each data source (Docket: EPA-HQ-
QPPT-2019-0500).
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 Systematic Review in TSCA Risk Evaluations ( 018b). 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 (\___S .LlA,
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 (Procedures for Chemical Risk Evaluation Under the Amended Toxic Substances Control
Act (82 FR 33726).
EPA used previous assessments (see Table 1-2) 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.
The exposures and hazards sections 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.
12 See Appendix B for the list of all supplemental files.
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i 2 EXPOSURES
2 For TSCA exposure assessments, EPA evaluated exposures and releases to the environment resulting
3 from the conditions of use applicable to TCE. Post-release pathways and routes were described to
4 characterize the relationship or connection between the conditions of use for TCE (Section 1.4.1) and
5 the exposure to human receptors, including potentially exposed or susceptible subpopulations (PESS)
6 and ecological receptors. EPA considered, where relevant, the duration, intensity (concentration),
7 frequency and number of exposures in characterizing exposures to TCE.
8
9 2.1 Fate and Transport
10 Environmental fate includes both transport and transformation processes. Environmental transport is the
11 movement of the chemical within and between environmental media. Transformation occurs through the
12 degradation or reaction of the chemical with other species in the environment. Hence, knowledge of the
13 environmental fate of the chemical informs the determination of the specific exposure pathways and
14 potential human and environmental receptors EPA expects to consider in the Risk Evaluation. Table 2-1
15 presents environmental fate data that EPA identified and considered in the Scoping and Problem
16 Formulation documents as well as additional data extracted from the systematic review process.
17
18 Table 2-1. Environmental Fate Characteristic of TCE
Property or
Endpoint
Value a
References
Data Quality
Rating
Indirect
photodegradati on
1-11 days (atmospheric oxidation based on
measured hydroxyl radical oxidation)
( 2014b")
High
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)
CDilline et aL 1975}
High
Biodegradation
0% after 3 months (aerobic groundwater)
38.9% after 28 days (aerobic OECD 302B
Inherent biodegradability test)
100% degradation after 20 days (anaerobic
serum bottle test with added glucose, phenol,
benzoate, acetate, and methanol on incubated
shaker table)
0%> degradation after 40 days (anaerobic
groundwater in untreated wells)
100%) degradation after 40 days (anaerobic
groundwater microcosms with added
hydrogen/acetate)
(Nielsen et aL 1996)
High
High
High
High
High
(Tobaias et aL 2016)
(Long et aL 1993)
(Schmidt and Tiehm.
2008)
(Schmidt and Tiehm.
2008)
Page 81 of 803
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Property or
Endpoint
Value a
References
Data Quality
Rating
TCE removed slowly with a reduction of 40%
after 8 weeks (TCE (200 (J,g/L) incubated with
batch bacterial cultures under methanogenic
conditions)
(Bouwer and
McCartv. 1983)
High
100% degradation after 20 days (aerobic with
Methane culture, aerobic with phenol culture)
(Long et al.„ 1993)
High
B i oconcentrati on
factor (BCF)
17 (Bluegill)
18.4 (estimated)
(Barrows et al.„ 1980)
( 2012b)
High
High
Bioaccumulation
factor (BAF)
24 (estimated)
(U.S. EPA... 2012b)
High
Organic
carbon: water
partition
coefficient (Log
Koc)
1.8 (estimated by MCI method)
2.1 (estimated by Kow method)
( 2012b)
High
a Measured unless otherwise noted
19
20 2.1.1 Fate and Transport Approach and Methodology
21 EPA gathered and evaluated environmental fate information according to the process described in the
22 Application of Systematic Review in TSCA Risk Evaluations ( 318b). Reasonable available
23 environmental fate data, including biotic and abiotic degradation rates, removal during wastewater
24 treatment, volatilization from lakes and rivers, and organic carbon:water partition coefficient (Koc) were
25 selected for use in this assessment document.
26
27 Other fate estimates were based on modeling results from EPI (Estimation Programs Interface) Suite™
28 ( 012b; https://www.epa.gov/tsca-screening4ools/epi-siiitetm-estimation-proeram-interface).
29 a predictive tool for physical/chemical and environmental fate properties. EPI Suite™ was reviewed by
30 the EPA Science Advisory Board
31 (https://vosemite.epa.gov/sab/sabproduct.nsf/02ad90bl36fc21e1 ;ba0043 6459/CCF982B A.9F 9CF
32 CFA8525735200739805/$File/ 0 and the individual models have been peer reviewed in
33 numerous articles published in technical journals. Citations for such articles are available in the EPI
34 Suite™ help files. Table 2-1 provides environmental fate data that EPA considered while assessing the
35 fateofTCE.
36 2.1.2 Summary of Fate and Transport
37 The EPI Suite™ ( ) STP model was run using default settings (set biodegradation half-
38 life to 10,000 hours) to evaluate the potential for TCE to volatilize to air or adsorb to sludge during
39 wastewater treatment. In order to improve the accuracy of the EPI Suite™ estimations, physical and
40 chemical properties (Log Kow, Boiling point, Melting point, Vapor Pressure, Water solubility, Henry's
41 Law Constant) from Table 1-1 were entered into EPI Suite along with TCE's SMILES notation entry
42 (C(=CCL)(CL)CL) before running the module.
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If TCE is released to the air, TCE does not absorb radiation well at wavelengths that are present in the
lower atmosphere (>290 nm) 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.
If TCE is released to water, sediment or soil, the fate of TCE 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 of TCE 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. Although the log Koc indicates that TCE will
partition to sediment organic carbon, organic matter typically comprises 25% or less of sediment
composition (e.g., https://piibs.iisgs.gov/of/2006/1053/downloads/pdf/of-2006-lQ53.pdf) of which
approximately 40-60% is organic carbon (Schwarzenbach et ai. 2003). Based on these values, and the
range of Koc of 1.8 to 2.1 the sediment-water Kd (where Kd = Koc *foc) is expected to be equal to or
less than 9.5 to 19, indicating that at equilibrium, concentrations in sediment would be expected to be
less than 19 times higher than in porewater. So, TCE is expected to be present in sediment pore water
with concentrations similar to or less than the overlying water. This is due to partitioning to organic
matter in sediment and relatively more rapid biodegradation in anaerobic and methanogenic
environments compared to aerobic conditions assumed closer to the surface of the water column. In the
case of spills or leaks of TCE directly to soil or surface water, TCE may sink as a dense non-aqueous
phase liquid (DNAPL). However, such spills and leaks are not considered conditions of use within the
scope of the Risk Evaluation.
If TCE is released to wastewater treatment, the removal percentage of TCE is estimated by using the
STP model in EPI Suite™ as 81%, including 80% removal via volatilization and 1% removal via
adsorption. This value (81%) is used for the calculation of exposure assement in this document. TCE
present in the solids and water portion of biosolids 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.
The partitioning of TCE released to air, water and soil is informed by the use of the level III fugacity
model in EPI Suite™. The fugacity model in EPI Suite™ is a level III multimedia fate model which
uses environmental parameters and computations identical to those used in (Mackay et ai. 1992). The
model environment consists of four main compartments: air, water sediment and soil. Mass transport
between the compartments via volatilization, diffusion, deposition and runoff are modeled. The level III
fugacity model in EPI Suite™ was not used to determine any specific environmental concentrations of
TCE. The model was used to qualitatively assess how TCE will behave in specific media (i.e., setting
the model to 100% emission to a single medium) in order to inform development of Figure 2-1. EPA
also ran the level III fugacity model using emissions from a mass balance developed to account for the
amount of TCE entering and leaving all facilities in the United States. For the mass balance EPA
attempted to quantify the amount of trichloroethylene associated with each of its life cycle stages from
introduction into commerce in the U.S. (from both domestic manufacture and import), processing, use,
release, and disposal. The mass balance development and uncertainties are detailed in Appendix R.
Physical chemical and environmental fate properties used as input to the model were taken from Table
1-1 and Table 2-1, respectively. The model was run using annual emissions to air and water from the
mass balance converted to kilograms per hour. Land disposal, energy recovery and treatment, and off-
site recycling were not considered as environmental releases.
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The emissions to air from the mass balance comprise >99% of the total emissions with less than one
percent released to water. The model estimates 99.2 percent of TCE will remain in air when release
estimates from the mass balance are used. TCE was predicted to continue to partition to air based on its
greater fugacities in water and sediment compared to its fugacity in air. The details of the model results
are given in Appendix S.
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 BIOWIN module in the
EPI Suite™ was run using default settings to estimate biodegradation rates of TCE in soil and sediment.
Three out of the four models built in the BIOWIN module (BIOWIN 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 the scientific evidence from these estimates
suggests that TCE does not biodegrade quickly under aerobic condition. This conclusion is supported by
test results in a frequently cited publication (Rott et at.. 1982) which indicates 19% aerobic
biodegradation in 28 days (OECD 301D) and 2.4% aerobic biodegradation in 14 days (OECD 301C),
respectively. The data were also cited in the 2004 Ell TCE Risk Assessment (ECB. 2004).
During the systematic review process, a high-quality aerobic serum bottle biodegradation study reported
that 100%) degradation occurred in 20 days in methane and phenol cultures. The result indicates that the
aerobic degradation rate with either methane or phenol culture is "fast" and is different from the
BIOWIN predictions. 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 ( ,018c). The added anaerobic biodegradation data suggest that the TCE anaerobic
biodegradation rate ranges from slow to rapid and may be dependent on presence of electron donating
co-metabolites.
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 (TJ.S.
EPA. 2014b). These log Koc values (1.8-2.1) suggest that the sorption of TCE to soil and sediment is
low and TCE is mobile in soil and sediment.
The systematic review identified a high quality bioconcentration data with low BCF ( BCF=17;
Barrows. 1980). The BAF of TCE is also low (BAF=24) based on EPI Suite™ estimation. Therefore,
TCE is not expected to accumulate in aquatic organisms due to low BCF and BAF.
Figure 2-1 summarizes the overall partitioning and degradation expected for TCE.
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162
TCE
Land-applied biosolids
Photolysis
Constant = 0.00985 alff m3/mole tl/2 111 da^s
Hydrolysis
ty, = 10.7 mo
Surface Water
log Koi = 1.8 - 2.1 log Koc = 1.8 - 2.1 ^
Anaerobic
Groundwater Biodegradation Sediment
Rate = slow to rapid
-J7? Aerobic
Biodegradation
Rate = slow
Photolysis
t1/2 I'll days
onstant = 0.00985 atm-m3/m
Rate = slow
log Kqc = 1.8 - 2.1 Bioconcentration
Hydrolysis
t,/, = 10.7 months
Land-applied biosolids
Soil a Aerobic Biodegradation < '???* Surface Water
log Koc = 1-8 - 2.1
Groundwater ??? ~ Anaerobic Biodegradation Sediment
Rate = slow to rapid
Figure 2-1. Environmental transport, partitioning and degradation processes for TCE
In Figure 2-1, transport and partitioning are indicated by green arrows and degradation is indicated by
orange arrows. The width of the arrow is a qualitative indication of the likelihood that the indicated
partitioning will occur or the rate at which the indicated degradation will occur (i.e., wider arrows
indicate more likely partitioning or more rapid degradation). Because transport and partitioning
processes (green arrows) can occur in both directions across an interface, the transport and partitioning
pathways are illustrated with arrows pointing in both directions. For interfaces where one direction of
transport and partitioning is expected to prevail based on release rates and partition coefficients, the
primary direction of transport is indicated by a wider arrow. However, the direction of transport in a
given locality depends on the site-specific properties of environmental media, weather conditi ons, TCE
release rates, degradation and transformation rates, and TCE concentrations within environmental
compartments. The question marks over the aerobic and anaerobic biodegradation arrows indicate
uncertainty regarding how quickly TCE will biodegrade. Figure 2-1 considers only transport,
partitioning, and degradation within and among environmental media; sources to the environment such
as discharge and disposal are not illustrated.
2._1.3 Assumptions and Key Sources of Uncertainty for Fate and Transport
A range of biodegradation rates have been reported for TCE. The range of degradation rates reported
were measured in laboratory studies for biodegradation in water, soil and sediment. These studies are
subject to several sources of variability including variability inherent in the methodology, inter-
laboratory variability and variability due to factors such as the specific microbial populations used,
water, soil and sediment chemi stry, oxygen concentration/redox potential, of the collected samples used
in the study, temperature and test substance concentration. No single value is universally applicable as it
is influenced by these variables and possibly others. However, the weight of evidence shows the aerobic
biodegradation of TCE is slow and the anerobic biodegradation in anaerobic condition ranges from slow
to rapid. Anaerboic biodegredation results in formation of dichloroethylene (DCE) and which is
subsequently degredaded to vinyl chloride monomer (VCM) in the same conditions (vogel and
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McCartv. 1985). But the portion of TCE that is anaerobically biodegraded, thereby forming DCE and
VCM, is unknown.
The range of Log Koc values (1.8-2.1) is supported by the basic principles of environmental chemistry
which states that the Koc is typically within one order of magnitude (one log unit) of the octanol: water
partition coefficient (K ow).
The density of TCE relative to water may result in the formation of free product, or (dense non-aqeuous
phase liquid) DNAPL under certain conditions. However, under the conditions of use for TCE examined
under this Risk Evaluation, it is not expected that TCE DNAPL would be found where disolved
concentrations are less than 1% of its aqueous solubility, or 12,800 ug/L at 25°C (Horvath etai. 1999).
Under conditions in which TCE is present in surface water at concentrations of less than 1% of its
solubility, the physical and chemical properties of TCE that lead to TCE's classification as a DNAPL
are not likely to increase the residence time in surface water. DNAPL formation in benthic sediments
and in subsurface soils and aquifers is not likely to result from the conditions of use described in this
final Risk Evaluation.
The Volatilization from Water (WVol) model in EPI SuiteTM is a screening level model that estimates
the rate of volatilization of a chemical from a model river and lake. The estimation method follows a
two-film concept for estimating the flux of volatiles across the air-water interface. The program's default
parameters for a model river were selected to yield a half-life that may be indicative of relatively fast
volatilization from environmental waters due to default current velocity, river depth and wind velocity.
The default parameters for the lake yield a much slower volatilization rate. The low wind velocity and
current speed are indicative of a pond (or very shallow lake) under relatively calm conditions. These
default parameters were selected to specifically model a body of water under calm conditions. Although
physical chemical properties of the modeled substance and wind speed, water flow velocity and water
depth can be modified by the user, the model does not employ all site specific environmental parameters
that effect the rates of volatilization. Therefore, rates of volatilization at a specific location under
specific environmental conditions could be over or under estimated by the model.
Accurate inputs are critical for fugacity modeling. Inputs to the level III fugacity model include half-
lives in various media, physical chemical properties, and emissions to air, water and soil. As
demonstrated by the change in predicted mass of TCE in each compartment when assumptions regarding
emissions (mass released to each environmental compartment) are varied, model results are significantly
impacted by emissions assumptions. Thus, for optimal use of the model, complete emissions inventories
are needed. EPA developed a mass balance for TCE, however, the uncertainty associated with the mass
balance and associated releases to the environment carries over to uncertainty in the results of the
fugacity modeling. The results of level III fugacity modeling indicate that TCE released to water will
partition to air. However, as noted in the SACC review of the TCE draft Risk Evaluation, release
scenarios could exist that, when modeled, indicate movement of TCE from air to water. Under that
scenario estimated surface water concentrations could be underpredicted if only direct releases to water
are considered.
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2.2 Environmental Exposures
2.2.1 Environmental Exposures Overview
In this section, EPA presents environmental exposures to TCE for aquatic organisms. 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. To characterize environmental exposure,
EPA assessed exposures derived from both predicted and measured concentrations of TCE in surface
water in the U.S.
Aquatic exposures associated with the industrial and commercial conditions of use evaluated were
predicted through modeling. Predicted surface water concentrations resulting from facility releases in
the EPA Lifecycle Release Analysis were generated for reporting year 2016. Release estimates were
based on loading and/or production volume information obtained from TRI, DMR, and CDR (See
Section Error! Reference source not found.). The surface water modeling was conducted with EPA's
Exposure and Fate Assessment Screening Tool, version 2014 (U.S. EPA. ), using reported annual
release/loading amounts (kg/yr) and estimates of the number of days per year that the annual load is
released. The Probabilistic Dilution Model (PDM), a module of E-FAST 2014, was run to predict the
number of days per year predicted stream concentrations are expected to exceed the designated chronic
aquatic COC value.
The aquatic exposure assessment also includes an analysis of collected measured surface water
concentrations from monitoring data in EPA's Water Quality Exchange (WQX) using the online Water
Quality Portal (WQP) tool and published literature obtained and evaluated through a systematic review
process. 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-reviewed or gray sources of measured surface
water concentrations in the US. The measured concentrations reflect ambient surface water
concentrations at the monitoring sites but cannot be directly attributed to specific industrial or
commercial conditions of use. A geospatial analysis at the watershed level was conducted to compare
the measured and predicted surface water concentrations and investigate whether modeled facility
releases may be located within the same watershed as observed concentrations in surface water.
2.2.2 Environmental Releases to Water
EPA categorized COUs listed in Table 1-3 into 18 OESs. For each OES, a daily water release was
estimated based on annual releases, release days, and the number of facilities (Figure 2-2). 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 Appendix Q of this document and the "Water Release Assessment" section for
each OES in [.Environmental Releases and Occupational Exposure Assessment. Docket: EPA-HQ-
OPPT-2019-0S00n.
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258
ESD,
Assumptions
TRI, CDR, DMR,
NEI, Census,
Market Reports
TRI, DMR, ELG
OES
Daily Release
Estimate
Release
Days
Number of
Facilities
Annual
Releases
Figure 2-2. An overview of how EPA estimated daily water releases for each OES.13
2.2.2.1 Results for Daily Release Estimate
EPA combined its estimates for annual water 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 on deriving the overall confidence score for each OES. For
some OES, EPA was not able to estimate or did not expect water releases. For example:
• OES Aerosol Application: Water releases were not expected due to the volatile nature of TCE;
releases from this OES are expected to be to air.
• OES Formulation of Aerosol and Non-Aerosol Products: All releases reported in TRI were
to off-site land, incineration, or recycling.
Table 2-2. Summary of EPA's daily water release estimates for each OES and also EPA's Overall
Confidence in these estimates.
Occupational Exposure
Scenario (OES)
Estimat
Water Rel
Acros
(kg/sil
ed Daily
ease Range
s Sites
e-day)
Overall
Confidence
Source and Notes
Minimum
Maximum
Manufacturing
0
1.27
M
From TRI, DMR
Processing as a Reactant
1.7E-03
0.02
M
From TRI, DMR
Formulation of Aerosol and
Non-Aerosol Products
No information
identified to estimate
water releases
Repackaging
6.8E-06
1.1
M
From TRI, DMR
Batch Open-Top Vapor
Degreasing
2.53E-07
1.96
M
From TRI, DMR
Batch Closed-Loop Vapor
Degreasing
2.53E-07
1.96
M
Same as Batch Open-
Top Vapor Degreasing3
13 TRI = Toxics Release Inventory; DMR = Discharge Monitoring Report; NEI = National Emissions Inventory; CDR =
Chemical Data Reporting; ELG = Effluent Limitation Guidelines; ESD = Emission Scenario Document
Page 88 of 803
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Occ ii pa 1 io n a 1 K x pos u re
Scenario (OKS)
Kslimated Daily
W ater Release Uange
Across Sites
(k«/site-da\)
Overall
Confidence
Source and Notes
Minimum
Maximum
Com c\ onzcd Vapor
Degreasing
2.53L-07
i.yo
M
Same as Batch Open-
Top Vapor Degreasing3
Web Vapor Degreasing
2.53E-07
1.96
M
Same as Batch Open-
Top Vapor Degreasing3
Cold Cleaning
2.53E-07
1.96
M
Same as Batch Open-
Top Vapor Degreasing3
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
H
EPA expects releases of
TCE to be to air for this
OES
Metalworking Fluids
2.53E-07
1.96
M
Same as Batch Open-
Top Vapor Degreasing3
Adhesives, Sealants, Paints,
and Coatings
3.68E-06
0.30
M
From TRI, DMR
Other Industrial Uses
9.2E-06
1.6
M
From DMR
Spot Cleaning and Wipe
Cleaning
2.9E-05
8.0E-05
M
From DMR
Industrial Processing Aid
5.5E-04
0.4
M
From TRI, DMR
Commercial Printing and
Copying
2.0E-04
2.0E-04
-
Based on only one
reported release in DMR
Other Commercial Uses
1.9E-06
0.013
M
From DMR
Process Solvent Recycling and
Worker Handling of Wastes
1.6E-06
24.1
M
From TRI, DMR
259 a Water releases from OTVD were repeated for other degreasing operations and for MWF because the releases were
260 estimated using TRI and DMR data. Due to the limited information in these reporting programs, these sites may in fact not
261 operate OTVDs, but may operate other solvent cleaning machines or perform metalworking activities (e.g., closed-loop
262 degreasing, conveyorized degreasing, web cleaning, or cold cleaning) or use of TCE as a metalworking fluid. They are
263 included in the OTVD assessment as EPA expects OTVDs to be the most likely condition of use. EPA assessed annual
264 releases as reported in the 2016 TRI or 2016 DMR and assessed daily releases by assuming 260 days of operation per year, as
265 recommended in the 2017 ESD on Use of Vapor Degreasers, and averaging the annual releases over the operating days.
266 2J.2.2 Approach and Methodology
267 2,2,2,2,1 Water Release Estimates
268 Where available, EPA used 2016 TRI ( ) and 2016 DMR ( ) data to
269 provide a basis for estimating releases. Facilities are only required to report to TRI if the facility has 10
270 or more full-time employees, is included in an applicable NAICS code, and manufactures, processes, or
271 uses the chemical in quantities greater than a certain threshold (25,000 pounds for manufacturers and
272 processors of TCE and 10,000 pounds for users of TCE). Due to these limitations, some sites that
273 manufacture, process, or use TCE may not report to TRI and are therefore not included in these datasets.
274
275 For the 2016 DMR (U.S. EPA. 2016al EPA used the Water Pollutant Loading Tool within EPA's
276 Enforcement and Compliance History Online (ECHO) to query all TCE point source water discharges in
277 2016. DMR data are submitted by National Pollutant Discharge Elimination System (NPDES) permit
278 holders to states or directly to the EPA according to the monitoring requirements of the facility's permit.
279 States are only required to load major discharger data into DMR and may or may not load minor
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283
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287
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293
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295
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298
299
300
301
302
303
304
305
306
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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.
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 of TCE 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 L.
2.2.2.2.2 Estimates of Number of Facilities
Where available, EPA used 2016 CDR ( • ), 2016 TRI ( ), 2016
Discharge Monitoring Report (DMR) (U.S. EPA. 2016a) 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 site's condition of use. However, information for determining the condition
of use for reporting sites in TRI and DMR is typically more limited.
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 R and 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.
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).
The National Emissions Inventory (NEI) is a comprehensive and detailed estimate of air emissions of
criteria pollutants, criteria precursors, and hazardous air pollutants from air emissions sources. The NEI
is released every three years based primarily upon data provided by State, Local, and Tribal air agencies
for sources in their jurisdictions and supplemented by data developed by the US EPA. The inventory
includes emissions estimates for larger sources that are located at a fixed, stationary location (point
sources) and emissions estimates for sources which individually are too small in magnitude to report as
point sources (nonpoint sources). In NEI, facilities report on the equipment or process sources for their
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329
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331
332
333
334
335
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337
338
339
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341
342
343
344
facility emissions. Based on these reported point sources for TCE emissions, EPA could generally
determine which condition of use the facility fell in.
Where the number of sites could not be determined using CDR/TRI/DMR/NEI or where these data
sources were determined to insufficiently capture the number of sites within a condition of use, EPA
supplemented the reasonably available information with U.S. economic data using the following
method:
• Identify the 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 data generated in Steps 1 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. Summary of EPA's estimates for
the number of facilities for each OES.
Occupational Exposure
Scenario (OES)
N il in her of
l-'acilities
Notes
Manufacturing
5
Based on CDR reporting
Processing as a Reactant
5 to 440a
Based on TRI and DMR reporting, and Census data for
NAICS 325120 (Industrial Gas Manufacturing)
Formulation of Aerosol and
19
Based on TRI reporting
Non-Aerosol Products
Repackaging
22
Based on TRI and DMR reporting
Batch Open-Top Vapor
Degreasing
194
Based on NEI and TRI reporting
Batch Closed-Loop Vapor
Degreasing
4
Based on NEI reporting
Conveyorized Vapor
Degreasing
8
Based on NEI reporting
Web Vapor Degreasing
1
Based on NEI reporting
Cold Cleaning
13
Based on NEI reporting
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
4,366
Based on Census data and market penetration estimates
based on California Air Resources Board (CARB) survey
of automotive maintenance and repair facilities
Cleaners, Penetrating
Lubricants, and Mold Releases
Metalworking Fluids
-
No information identified to estimate number of facilities
Adhesives, Sealants, Paints, and
70
Based on NEI, TRI, and DMR reporting
Coatings
Other Industrial Uses
49
Based on TRI and DMR reporting
Spot Cleaning and Wipe
Cleaning
63,748
Based on Census data for NAICS codes 812300, 812320,
561740; assumed 100% market penetration for TCE.
Industrial Processing Aid
18
Based on TRI and DMR reporting
Commercial Printing and
-
No information identified to estimate number of facilities
Copying
Other Commercial Uses
-
No information identified to estimate number of facilities
Process Solvent Recycling and
Worker Handling of Wastes
30
Based on TRI and DMR reporting
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349
350
351
352
353
354
355
356
357
358
359
a The range provided for the number of sites is a function of known sites for this OES from TRI and DMR data and
integrating it with sites reporting NAICS codes for this type of use.
2 2 2 2 ^ Estimates of Release Days
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.
Table 2-4. Summary of EPA's estimates for release days expected for each OES.
Occ ii pa 1 io n a 1 K x pos u re
Scenario (OKS)
Release
Davs
Notes
Manufacturing
350
Assumed seven days per week and 50 weeks per year with
two weeks per year for shutdown activities.
Processing as a Reactant
350
Assumed seven days per week and 50 weeks per year with
two weeks per year for shutdown activities.
Formulation of Aerosol and
Non-Aerosol Products
-
Water releases not estimated for this OES.
Repackaging
250
Assumed 5 days per week and 50 weeks per year.
Batch Open-Top Vapor
Degreasing
260
2017 ESD on Use of Vapor Degreasing
Batch Closed-Loop Vapor
Degreasing
260
2017 ESD on Use of Vapor Degreasing
Conveyorized Vapor
Degreasing
260
2017 ESD on Use of Vapor Degreasing
Web Vapor Degreasing
260
2017 ESD on Use of Vapor Degreasing
Cold Cleaning
260
2017 ESD on Use of Vapor Degreasing
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
Water releases not expected from this OES.
Metalworking Fluids
260
2017 ESD on Use of Vapor Degreasing
Adhesives, Sealants, Paints, and
Coatings
250
2011 ESD on the Application of Radiation Curable
Coatings, Inks, and Adhesives via Spray, Vacuum, Roll
and Curtain Coating
Other Industrial Uses
250
Assumed 5 days per week and 50 weeks per year.
Spot Cleaning and Wipe
Cleaning
300
Assumed 6 days per week and 50 weeks per year.
Industrial Processing Aid
300
Assumed 6 days per week and 50 weeks per year.
Commercial Printing and
Copying
250
Assumed 5 days per week and 50 weeks per year.
Other Commercial Uses
250
Assumed 5 days per week and 50 weeks per year.
Process Solvent Recycling and
Worker Handling of Wastes
250
Assumed 5 days per week and 50 weeks per year.
2,2,2,3 Assumptions and Key Sources of Uncertainty for Environmental
Releases
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" confidence rating through EPA's systematic review
process. Due to reporting requirements for TRI and DMR, the number of sites for a given OES may be
underestimated. It is uncertain, the extent to which, sites not captured in these databases discharge
wastewater containing TCE and whether any such discharges would be to surface water, POTW, or non-
POTW WWT.
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382
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384
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402
403
In addition, information on the use of TCE at facilities in TRI and DMR is 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, reasonably available data are often used by facilities for TRI reporting purposes, as they
represent the best available data. When monitoring or direct measurement data are not reasonably
available, or are known to be non-representative for 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-balance calculations, the use of emission factors, and
engineering calculations. There may be greater uncertainty in data resulting from estimates compared to
monitoring measurements. However, available monitored data that showed ambient water
concentrations were not useful in corroborating the modeling approach because most of them were far
downstream from the near-facility modeled concentration estimates.
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 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 calculated from sites reporting to TRI or DMR was 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 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 uncertain how 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 of EPA's overall confidence in its release estimates for each of the
Occupational Exposure Scenarios assessed.
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404 Table 2-5. Summary of Overall Confidence in Release Estimates by PES
Occupational Kxposure
Scenario (OKS)
Overall Confidence in Release Kslimales
Manufacturing
Wastewater discharges are assessed using reported discharges from the 2016
TRI for four sites. TRI data were determined to have a "medium" confidence
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 uncertainty that 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-day such that on any given day the actual daily
discharges may be higher or lower than the estimated average daily discharge.
One of the four 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 information, EPA has a medium
confidence in the wastewater discharge estimates for the four sites in the 2016
TRI and 2016 DMR.
Water discharges from the remaining site was estimated using the maximum
daily and monthly discharge limits in the OCPSF EG and the estimated
volume of wastewater produced per pound of TCE production from the
Specific Environmental Release Category (SpERC) developed by the
European Solvent Industry Group for the manufacture of a substance. The
estimates assume the site operates 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 information EPA has a
medium confidence in the wastewater discharge estimates for this site.
Processing as a Reactant
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE 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 reporting to 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,
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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.
Formulation of Aerosol and
Non-Aerosol Products
All sites reporting in TRI show zero water releases; EPA does not expect
water releases from this OES.
Repackaging
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as to whether all the
sites assessed in this section are performing repackaging 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 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 repackaging TCE 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 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.
Batch Open-Top Vapor
Degreasing
Wastewater discharges 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, 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 wastewater containing TCE and
whether any such discharges would be to surface water, POTW, or non-POTW
WWT; however, the sites may be required to comply with an EG depending
on the industry in which the OTVD is being used. Additionally, information
on the conditions of use of TCE at facilities in TRI and DMR is limited;
therefore, there is some uncertainty as 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 operations and use of TCE in
metalworking fluids). If the sites were categorized under a different OES, the
annual wastewater discharges for each site would remain unchanged; however,
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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 260 days/yr of operation and 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
average daily discharges may be higher if sites operate for fewer than 260
days/yr or lower if they operate for greater than 260 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 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.
Batch Closed-Loop Vapor
Degreasing
Same as the Open-Top Vapor Degreasing (OTVD) OES.
Conveyorized Vapor
Degreasing
Same as the Open-Top Vapor Degreasing (OTVD) OES.
Web Vapor Degreasing
Same as the Open-Top Vapor Degreasing (OTVD) OES.
Cold Cleaning
Same as the Open-Top Vapor Degreasing (OTVD) OES.
Aerosol Applications:
Spray Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners, Penetrating
Lubricants, and Mold
Releases
EPA assessed no wastewater discharges for this OES. There is some
uncertainty as 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 surface water, POTW, or non-POTW WWT from this source.
Based on this information, EPA has a high confidence in the release
assessment.
Metalworking Fluids
Same as the Open-Top Vapor Degreasing (OTVD) OES.
Adhesives, Sealants, Paints,
and Coatings
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as 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 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
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annual discharges over the operating days. There is some uncertainty that all
sites using TCE in adhesives, sealants, paints and coatings 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 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.
There is further uncertainty that the number of sites obtained from the 2014
NEI represent the total number of sites using adhesives, sealants, paints or
coatings containing TCE. NEI data only covers specific industries which may
not capture the entirety of industries using these products and NEI does not
include operations that are classified as area sources because area sources are
reported at the county level and do not include site-specific information. It is
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 non-POTW WWT. Also, NEI do 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 uncertain how 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 Industrial Uses
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as to whether all the
sites assessed in this section are performing other industrial uses 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 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 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 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.
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Spot Cleaning and Wipe
Cleaning
Wastewater discharges from spot cleaning facilities at industrial launderers are
assessed using reported discharges from the 2016 DMR. DMR data were
determined to have a "medium" confidence rating through EPA's systematic
review process. DMR only contains information for 2 sites. Additional sites
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, EPA assumed annual days of operation and
averaged the annual discharges over the operating days. There is some
uncertainty that 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. 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 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 uncertainty that 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 rcDortcd 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 non-POTW WWT. Based on
this information, EPA has a medium confidence in the wastewater discharge
estimates.
Industrial Processing Aid
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE 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
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 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
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Omi|);ilion;il Kxposurc
Sronsirio (OKS)
Ovorsill ( onlldciKT in Uolcsisc Kslimnlcs
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
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.
Commercial Printing and
Copying
Wastewater discharges from one commercial printing and copying site was
found in the 2016 DMR. DMR data were determined to have a "medium"
confidence rating through EPA's systematic review process. However, EPA
acknowledges this site does not represent the entirety of commercial printing
and copying sites using TCE; data were not reasonably available to estimate
water releases from additional sites.
Other Commercial Uses
Wastewater discharges are assessed using reported discharges from the 2016
DMR. DMR data were determined to have a "medium" confidence rating
through EPA's systematic review process. Due to reporting requirements for
DMR, these sites are not expected to capture the entirety of water releases
from this OES. It is uncertain the extent that sites not captured in DMR
discharge wastewater containing TCE and whether any such discharges would
be to surface water, POTW, or non-POTW WWT. Additionally, information
on the conditions of use of TCE at facilities in DMR is limited; therefore, there
is some uncertainty as to whether all the sites assessed in this section are
performing other commercial uses 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 reporting to 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
using TCE in other commercial 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 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.
Process Solvent Recycling
and Worker Handling of
Wastes
Wastewater discharges 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
databases discharge wastewater containing TCE and whether any such
discharges would be to surface water, POTW, or non-POTW WWT.
Additionally, information on the conditions of use of TCE at facilities in TRI
and DMR is limited; therefore, there is some uncertainty as to whether all the
sites assessed in this section are recycling/disposing of TCE rather than a
different OES. If the sites were categorized under a different OES, the annual
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Occupational Kxposure
Scenario (OKS)
Overall Confidence in Release Kslimalcs
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 250 days/yr of operation and averaged the
annual discharges over the operating days. There is some uncertainty that all
sites recycling/disposing of TCE 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 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.
2,2.3 Aquatic Exposure Modeling Approach
Surface water concentrations resulting from wastewater releases of TCE from facilities that use,
manufacture, or process TCE related to the evaluated industrial and commercial conditions of use were
modeled using EPA's Exposure and Fate Assessment Screening Tool, Version 2014 ( ).
E-FAST 2014 estimates chemical concentrations in surface water resulting from releases to surface
water, resulting in exposure estimates at the point of release. Advantages to this model are that it
requires minimal input parameters and it has undergone extensive peer review by experts outside of
EPA. A brief description of the calculations performed within the tool, as well as a description of
required inputs and the methodology to obtain and use inputs specific to this assessment is described
below. To obtain more detailed information on the E-FAST 2014 tool from the model documentation
(U.S. EPA. 20071 as well as to download the tool, visit this web address: https://www. epa. gov/tsca-
screening-tools/e-fast-exposure-and-fate-assessment-screening-tool-version-2014/.
E-FAST 2014 provides estimates of surface water concentration for multiple stream flow parameters. The
concentrations reflect predicted levels of TCE in the receiving water body at the point of release and do not
incorporate downstream transport or post-release chemical fate processes. For this aquatic exposure
assessment, site-specific surface water concentration estimates for free-flowing water bodies are reported for
both the 7Q10 and harmonic mean stream flows. The 7Q10 stream flow is the lowest consecutive 7-day
average flow during any 10-year period. The harmonic mean stream flow is the inverse mean of
reciprocal daily arithmetic mean flow values. Site-specific surface water concentration estimates for still
water bodies are reported for calculations using the acute dilution factors. In cases where site-specific
flow/dilution data were not reasonably 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. Estimates from this calculation method are reported for the 10th percentile harmonic mean
and 10th percentile 7Q10 stream flows.
2.2_.3.J_ E-FAST 2014 Equations and Inputs
Estimating Surface Water Concentrations
E-FAST 2014 estimates site-specific surface water concentrations for discharges to both free-flowing
water bodies {i.e., rivers and streams) and for still water bodies {i.e., bays, lakes, and estuaries).
For free-flowing water body assessments, E-FAST 2014 can calculate surface water concentrations for
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four streamflow conditions using the following equation:
where:
SWC
WWR
WWT
SF
CF1
CF2
SWC =
/ WWT \
WWRxCFl x (l—^-)
SF xCF2
(Eq. 1)
Surface water concentration (parts per billion (ppb) or |ig/L)
Chemical release to wastewater (kg/day)
Removal from wastewater treatment (%)
Estimated flow of the receiving stream (MLD)
9
Conversion factor (10 |ig/kg)
6
Conversion factor (10 L/day/MLD)
The streamflow conditions used to estimate stream concentrations within the model include a mean flow
{i.e., the harmonic mean flow) and low flows (30Q5, 7Q10, and 1Q10 flows). The harmonic mean flow
is the inverse mean of reciprocal daily arithmetic mean flow values. The 30Q5 flow reflects 30
consecutive days of lowest flow over a five-year period. The 7Q10 flow reflects seven consecutive days
of lowest flow over a 10-year period. The 1Q10 flow reflects the single day of lowest flow over a 10-
year period.
For still water body assessments, no simple streamflow value represents dilution in these types of water
bodies. As such, E-FAST 2014 accounts for dilution by incorporating an 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:
SWC =
where:
SWC
WWR
WWT
PF
DF
CF1
CF2
PFxCF2xDF
(Eq. 2)
Surface water concentration (ppb or |ig/L)
Chemical release to wastewater (kg/day)
Removal from wastewater treatment (%)
Effluent flow of the discharging facility (MLD)
Acute or chronic dilution factor used for the water body (typically between 1 and 200)
9
Conversion factor (10 |ig/kg)
6
Conversion factor (10 L/day/MLD)
Estimating Days of COC Exceedance
The Probabilistic Dilution Model (PDM) portion of E-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 (Pi Toro. 1984) that uses probability distributions as inputs to reflect that streams follow a highly
variable seasonal flow pattern and there are numerous variables in a manufacturing process that can
affect the chemical concentration and 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 assumed to be zero unless the predicted surface water concentration exceeds the COC. In
these cases, the days of exceedance is set to the number of release days per year (see required inputs
below).
Modeling Inputs
Chemical release to wastewater (WWR)
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Annual wastewater loading estimates (kg/site/year or lb/site/year) were predicted in Section Error! Reference
source not found, and based on reported production loading or production volume estimates. To model these
releases within Exposure and Fate Assessment Screening Tool 2014, the annual release is converted to a daily
release using an estimated days of release per year, e.g., WWR (kg/site/day) = Annual loading (kg/site/year) /
Days released per year (days/year). In cases where the total annual release amount from one facility is
discharged via multiple mechanisms (i.e., direct to surface water and/or indirectly through one or more
WWTPs), the annual release amount was divided accordingly based on reported information in TRI (Form R).
Release Days (days/year)
The number of days per year that the chemical is discharged is used to calculate a daily release amount from
annual loading estimates (see Eq. 3). Current regulations do not require facilities to report the number of days
associated with reported releases. Therefore, two release scenarios were modeled for direct discharging facilities
to provide a range of surface water concentrations predicted by E-FAST 2014. The two scenarios modeled are a
higher release frequency (200 to 365 days) based on release estimates in Section Error! Reference source not
found, and a low-end release frequency of 20 days of release per year as an estimate of releases that could lead
to chronic risk for aquatic organisms. 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. For discharges from water treatment facilities (e.g., POTWs, STPs, WWTPs), only the higher release
frequency was modeled because such treatment sites are anticipated to discharge more frequently than non-
treatment facilities.
Removal from wastewater treatment (WWR%)
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 off-
site 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
scenarios were modeled.
Concentration of Concern
Concentrations of Concern (COCs) are threshold concentrations below 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 output the number of days per year that the selected COCs are exceeded. The COCs used in
the PDM module of E-FAST 2014 for TCE were 3, 788, 920, and 14,400 |ig/L.
Facility or Industry Sector
The required site-specific stream flow or dilution factor information is contained in the E-FAST 2014
database, which is accessed by querying a facility National Pollutant Discharge Elimination System
(NPDES) number, facility name, or reach code. For facilities that directly discharge to surface water (i.e.,
"direct dischargers"), the NPDES of the direct discharger is selected from the database. For facilities that
indirectly discharge to surface water (i.e., "indirect dischargers" because the release is sent to a water treatment
facility prior to discharge to surface water), the NPDES of the receiving treatment facility is selected. The
receiving facility name and location was obtained from the TRI database (Form R), if available. As TRI does not
contain the NPDES of receiving facilities, the NPDES was obtained using EPA's Envirofacts search tool. If a
facility NPDES was not available in the E-FAST-2014 database, the release was modeled using water body data
for a surrogate NPDES (preferred) or an industry sector, as described below.
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Surrogate 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.
Industry Sector (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.
Table 2-6. Industry Sector Modeled for Facilities w ithout Site-Specific Flow Data in F-FAST 2014
Condition of I so
Imliisln Sector in I'.-I- AS 1 2014 for Siivsim
How Diilii1
OES: Adhesives, Sealants, Paints, and Coatings
Adhesives and Sealants Manufacture
OES: Commercial Printing and Copying
Printing
OES: Industrial Processing Aid
POTW2 (Industrial)
OES: Manufacturing
Organic Chemicals Manufacture
OES: N/A Water Treatment Facility
POTW2 (Industrial)
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)
Primary Metal 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
OES: Spot Cleaning and Carpet Cleaning
n/a
1 n/a = Not applicable because a NPDES or surrogate NPDES was available in E-FAST 2014 to obtain a site-specific stream
flow for all facilities within the OES.
2 POTW = Publicly Owned Treatment Works
2.2.4 Surface Water Monitoring Data Gathering Approach
To characterize environmental exposure in ambient water from TCE, EPA used two approaches to
obtain measured surface water concentrations.
2.2.4.1
Systematic Review of Surface Water Monitoring Data
EPA conducted a full systematic review of published literature to identify studies reporting
concentrations of TCE in surface water in the United States. Studies clearly associated with 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 systematic review process is described
in detail in Section 1.5. A total of 28 surface water studies were extracted and the results are summarized
in Section 2.2.6.2.2.
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2.2.4.2 Surface Water Monitoring Data from WQX/WQP
For this aquatic exposure assessment, the primary source for data on the occurrence of TCE in surface
water is monitoring data retrieved from the Water Quality Portal (WQP), which integrates publicly
available US water quality data from multiple databases: 1) the United States Geological Survey
National Water Information System (USGS NWIS); 2) EPA's STOrage and RETrieval (STORET); and
3) the United States Department of Agriculture Agricultural Research Service (USDA ARS) Sustaining
The Earth's Watersheds - Agricultural Research Database System (STEWARDS). 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 (NAWQA). STORET refers to an electronic data system
originally created by EPA in the 1960s 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.waterqiialitvdata.iis/portal.isp).
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. One query obtained
STORET data using the Characteristics parameter (selected "Trichl or ethylene (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, unfiltered, recoverable,
micrograms per liter"). Parameters codes were obtained from the USGS website
https://nwis.waterdata.iisgs.eov/usa/nwis/pmcodes 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.
The "Site data only" and "Sample results (physical/chemical metadata)" files were linked using the
common field "Monitoring Location Identifier" and then filtered to eliminate records not relevant to the
scope of the environmental evaluation. Specifically, filtering was applied to select the media of interest
{i.e., surface water), eliminate records that were quality control samples {i.e., field blanks) or identified
as having analytical quality concerns {i.e., quality control issues, sample contamination, or estimated
values), and eliminate records associated with contaminated sites {i.e., Superfund).
Following filtering to obtain the final dataset, the domains "ResultDetectionConditionText,"
"ResultCommentText," and "MeasureQualifierCode" were examined to identify samples with non-
detect concentrations. All non-detect samples were tagged and the concentrations were converted to V2
the reported detection limit for summary calculation purposes. If a detection limit was not provided,
calculations were performed using the average of the reported detection limits in all samples (calculated
as 0.3 |ig/L).
2.2.5 Geospatial Analysis Approach
Using 2016 data, the measured surface water concentrations from the WQP and predicted concentrations
from the modeled facility releases were mapped in ArcGIS Version 10.6 to conduct a watershed analysis
at the Hydrologic Unit Code (HUC)-8 and HUC-12 level. The purpose of the analysis is to identify
whether 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. A US-level map was developed to provide a spatial representation of the
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measured and predicted concentrations. HUCs with co-located monitoring stations and facility releases
were identified and examined further.
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 database in Envirofacts.
2.2.6 Environmental Exposure Results
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, biosolids, and soil pathways to these organisms. The partition of
TCE into sediments is very low. Furthermore, the primary fate of TCE released to surface waters or
surface soils is volatilization.
2.2.6.2 Aquatic Environmental Exposures
To characterize environmental exposure, EPA assessed surface water concentrations derived from both
predicted concentrations of TCE in surface water (using E-FAST modeling results) and measured
concentrations (using monitored data from WQP and the published literature). Generally, the modeled
concentrations reflect near-site estimates at the point of release, and the measured concentrations reflect
localized ambient water concentrations at the monitoring sites. However, there were several sources in
the published literature that represent near facility concentrations and are labeled as such. Facility
release data is summarized in Section Error! Reference source not found, and full details are provided
in Appendix Q.
2.2.6.2.1 Predicted Surface Water Concentrations: E-FAST 2014 Modeling
A summary of the surface water concentration estimates modeled using E-FAST 2014, based on the
lifecycle release analysis for the year 2016, is summarized by OES 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 facility-
specific 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 7Q10-flow conditions ranged from 1.27E-5 to 765.63 |ig/L (Table 2-7). For the 20-
day release/year scenario assumed for direct dischargers, predicted surface water concentrations under
7Q10 flow conditions ranged from 0.00019 to 9,937.5 |ig/L (Table 2-8). For comparison purposes,
indirect releases to non-POTW WWTPs were also modeled assuming 20 days of release, resulting in
surface water concentrations of 0.2 to 339.11 |ig/L (Table 2-9.).
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644 Table 2-7. Summary of Modeled Surface Water Concentrations by OES for Maximum Days of
645 Release Scenario
OES
No. of Releases
Modeled
Surface Water Concentration
(7Q10) Oig/L)
Min
Max
Manufacturing
6
0.00514
2.77
Processing as a Reactant (low-end # of sites)
3
0.0000518
169
Processing as a Reactant
4
0.18
0.92
Repackaging
4
0.0000189
27.18
OTVD
51
0.0000822
765.63
Adhesives, Sealants, Paints, and Coatings
104
0.000818
10.83
Other Industrial Uses
16
0.0000941
9.5
Spot Cleaning and Carpet Cleaning
1
0.00388
0.00388
Industrial Processing Aid
6
0.000419
9.3
Commercial Printing and Copying
1
0.00292
0.00292
Other Commercial Uses
5
0.00564
9
Process Solvent Recycling and Worker Handling of Wastes
4
0.98
11.76
N/A (WWTP)
9
0.0000127
0.7
Grand Total
214
1.27E-5
765.63
646
647 Table 2-8. Summary of Modeled Surface Water Concentrations by OES for 20 Days of Release
648 Scenario for Direct Releases
OES
No. of Releases
Modeled
Surface Water Concentration
(7Q10) Oig/L)
Min
Max
Manufacturing
3
0.0897
49.91
Processing as a Reactant (low-end # of sites)
3
0.000907
3000
Processing as a Reactant
2
16.45
16.45
Repackaging
3
0.000235
89.13
OTVD
51
0.00103
9937.5
Adhesives, Sealants, Paints, and Coatings
52
0.0101
133.33
Other Industrial Uses
16
0.00154
200
Spot Cleaning and Carpet Cleaning
1
0.0485
0.0485
Industrial Processing Aid
3
0.00335
2.2
Commercial Printing and Copying
1
0.0365
0.0365
Other Commercial Uses
5
0.0658
110
Process Solvent Recycling and Worker Handling of Wastes
1
138.24
138.24
N/A (WWTP)
9
0.00019
12.79
Grand Total
150
0.00019
9,937.5
649
650
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Table 2-9. Summary of Modeled Surface Water Concentrations by OES for 20 Days of Release
Scenario for Indirect Releases to a non-POTW WWTP
OES
No. of Releases
Modeled
Surface Water Concentration
(7Q10) Oig/L)
Min
Max
Manufacturing
3
9.48
42.14
Processing as a Reactant
1
3.13
3.13
Repackaging
1
339.11
339.11
Industrial Processing Aid
3
0.2
138.34
Process Solvent Recycling and Worker Handling of Wastes
3
11.26
106.75
Grand Total
11
0.2
339.11
On a site-specific basis, the modeled surface water concentrations did not exceed the highest COC
(14,400 |ig/L) for any facility and only exceeded the COCs of 788 |ig/L and 920 |ig/L for two releasing
facilities (US Nasa Michoud Assembly Facility in New Orleans, LA and Praxair Technology Center in
Tonawanda, 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 Error! Reference source not found., releases of TCE were estimated for use in
modeling based on data from TRI, DMRs, and CDR (primarily TRI and DMR) for the 2016 calendar
year. Release estimates were generally facility-specific 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.
ny ^¦
TX
OH
IL, Ml, PA
SC
KS, KY, LA, MN, TN
CA, FL, IN, WV
MA, NE, WA
AL, AR, CO, CT, MD, NJ, N M, Rl, Wl
AZ, DE, ID, ME, MO, MS, NH, OK, OR, VA
0 2 4 6 8 10 12 14 16 18
Number of Unique Facilities Releasing TRICHLOROETHYLENE Per State
Figure 2-3. Distribution of Active Facility Releases Modeled
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
Page 107 of 803
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675 the majority of the releases (58%); surrogate waterbody flow/dilution data were used in only 15% of the
676 cases, with the remaining cases (26%) run using a representative industry sector SIC code. For releases
677 modeled with site-specific receiving waterbody flows or dilution factors, 86% were associated with free-
678 flowing water bodies and 14% were associated with still water bodies such as lakes, bays, or estuaries.
679 2 2 6,2,2 Measured Surface Water Concentrations
680 Measured Concentrations of TCE from WQP
681 A summary of the monitoring data obtained from the WQP is provided in Table 2-10. for the years
682 2013-2017. Per year, the filtered datasets evaluated contained between 46 and 793 surface water samples
683 collected from 89 to 193 unique monitoring stations. Detection frequencies were low, ranging from 0 to
684 8.7%). Concentrations ranged from not detected (ND; <0.022-5) to 0.11 |ig/L in 2013, ND (<0.022-5) to
685 1.86 |ig/L in 2014, ND (<0.025-2.4) to 0.011 |ig/L in 2015, all ND (<0.025-5) in 2016, and ND (<0.025-
686 5) to 2.0 |ig/L in 2017. Peaks were observed in 2014 and 2017; however, caution should be used in
687 interpreting trends with these data due to the small number of samples and the lack of samples collected
688 from the same sites over multiple years. The quantitative environmental assessment used the 2016 data
689 set only. For the 2016 data, concentrations in all samples were non-detect. No samples in the 2013-2017
690 dataset had concentrations exceeding the lowest COC of 3 |ig/L.
691
692 Table 2-10. Measured Concentrations of TCE in Surface Water Obtained from the Water Quality
Pori
tal: 2013-20
171
Year
Detection
Frequency
Concentration (jig/L) in all samples
Concentrations (ju.g/L) in only samples above the
detection limit
No. of
Samples
(No. of
Unique
Stations)
Range2
Average
(Standard
Deviation)3
No. of Samples
(No. of Unique
Stations)
Range
Average
(Standard
Deviation)3
2013
4.67%
793 (164)
ND (<0.022-<5) to
0.11
0.21 (0.26)
37 (22)
0.008 to 0.11
0.051 (0.016)
2014
3.78%
609 (155)
ND (<0.022-<5) to
1.86
0.33 (0.31)
23 (13)
0.0055 to 1.86
0.17 (0.41)
2015
1.42%
352 (91)
ND (<0.025-<2.4)
to 0.011
0.42 (0.16)
5(2)
0.0075 to 0.011
0.009 (0.001)
2016
0.0%
473 (109)
ND (<0.025-<5)
0.44 (0.27)
0(0)
NA
NA
2017
8.70%
46 (25)
ND (<0.025-<5) to
2.0
0.47 (0.53)
4(1)
1.0 to 2.0
1.5 (0.71)
All
Years
3.04%
2273 (384)
ND (<0.022-<5) to
2.0
0.33 (0.29)
69 (39)
0.0055 to 2.0
0.13 (0.35)
1 Data were downloaded from the Water Oualitv Portal (www.wateroiialitvdata.iis) on 10/3/2018. STORET surface water data
were 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
reviewed and cleansed (i.e., samples/sites were eliminated if identified as estimated, quality control, media type other than
surface water, Superfund, landfill, failed laboratory quality control, etc.).
2ND = Not Detected. Reported detection limits in all samples ranged from 0.022 to 5 |ig/L.
Calculations were performed using !/2 the reported detection limit when results were reported as not detected. If a detection limit
was not provided, calculations were performed using the average of the reported detection limits in all samples (0.65 ng/L).
694
695 The original dataset downloaded contained 31,456 samples for years 2013 through 2017. Following
696 filtering for relevant media and eliminating records with quality assurance issues or those associated
697 with superfund sites, only 2,273 (7%) of the samples were retained. The majority of the samples were
698 excluded because they were an off-topic media (i.e., groundwater, artificial, bulk deposition, leachate,
699 municipal waste, or stormwater) or location type (i.e., landfill, spring, or well). A smaller number of
Page 108 of 803
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700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
samples were excluded because they were quality control samples, estimated values, or had other quality
control issues. Samples associated with one Superfund site (Palermo Wellfield Superfund Site) were
also excluded. For the 2016 WQP dataset (473 samples) that is compared with modeled surface water
concentrations in the GIS analysis, observations were made across 10 states (AZ, KS, MN, MO, NJ,
NM, NC, PA, TN, and TX) at 109 unique monitoring sites, with 1 to 13 samples collected per sampling
site.
Measured Concentrations of TCE from Published Literature
Systematic review of published literature yielded a limited amount of surface water monitoring data for
TCE (Table 2-11.). In six U.S. studies encompassing 1,177 surface water samples collected from
between 1979 and 2001, reported concentrations of TCE ranged from below the detection limit (0.0001
to 0.08) to 17.3 |ig/L, with reported central tendency values ranging from 0.0002 to 1.17 |ig/L. The
maximum concentration was collected from the Charles River in Boston, Massachusetts between 1998
and 2000 (Robinson et al. 2004). The next highest TCE concentration was 2.0 |ig/L, collected during a
large nationwide survey of surface water for drinking water sources (rivers and reservoirs) between 1999
and 2000 (TJSGS. 2003). Robinson et al. (2004) reported the results of sampling conducted between
1996 and 2000 from 26 urban sites nationwide (n=711 samples), as part of the National Water-Quality
Assessment (NAWQA) Program; the median TCE concentration was only 0.09 |ig/L (detection
frequency of 41%). One US study (U.S. EPA. 1977) reported much higher concentrations of TCE in
surface water, up to 447 |ig/L. These samples were collected in 1976-1977 near 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 reasonably 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 for Environmental Monitoring Data. Docket: t ' i ( \J < ' i' -2019-0500]).
Table 2-11
. Measured Levels of TCE in U.S. Surface Water from Publishec
Literature
Location
Type
Site Information
Dates
Sampled
N
(Det.
Freq.)
Concentration (jig/L)
Source
Data
Quality
Score
Range
Central
Tendency
(Standard
Deviation)
Ambient
Anchorage, AK; Chester
Creek (6 urban sampling
sites)
1998-2001
11(0)
All samples ND (<0.08)
OJSGS. 2006)
Medium
Nation-wide; Surface water
for drinking water sources
(rivers and reservoirs)
1999-2000
375
(0.008)
ND (<0.2)
-2.0
NR
(tJSGS.
2003)
Medium
Nation-wide; Urban Rivers
(26 sites, as part of the
NAWQA Program)
1996-2000
711 (0.41)
NR
Median:
0.09
(Robinson et
al. 2004)
Medium
Boston, MA; Charles
Rivers
1998-2000
29(1)
NR - 17.3
Median:
1.17
(Robinson et
al. 2004)
Medium
Gulf of Mexico, near mouth
of the Mississippi River and
on the Louisiana Shelf (11
stations in the open ocean
and coast representing both
1980
11 (0.27)
ND - 0.05
NR
(Saner. 1981)
Medium
Page 109 of 803
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N
(Det.
Freq.)
Concentration (jig/L)
Data
Quality
Score
Location
Type
Site Information
Dates
Sampled
Range
Central
Tendency
(Standard
Deviation)
Source
unpolluted and
anthropogenic influences)
Two Bridges, NJ; Passaic
River
1996-1998
10 (0.4)
NR
Median:
0.1
(Robinson ef
al. 2004)
Medium
Eastern Pacific Ocean
ND
Mean: 0.3
(0.002);
Median:
0.0002
(California, US to
Valparaiso, Chile)
1979-1981
30 (0.9)
(<0.0001)
- 0.0007
(Singh el al.,
1983)
Medium
Baton Rouge, LA (Ethyl
Corporation); Stream
samples (surface) collected
upstream and downstream
of the outfall.
1976
2(1.0)
0.4 - 37
NR
(U.S. EPA.
1977)
High
Freeport, TX (Dow
Chemical Plant); Stream
samples (bottom and
surface) collected from the
1976
6(1.0)
0.9 - 126
NR
(U.S. EPA.
High
receiving stream at the plant
outfall and upstream and
downstream of the outfall.
1977)
Near
Geismar, LA (Vulcan
Materials Plant); 3 surface
Facility
(methyl-
chloroform
water samples collected
from the receiving stream at
the plant outfall and
1976
3 (1.0)
5-74
NR
(U.S. EPA.
.1.977)
High
producer
or user)
upstream and downstream
of the outfall.
Lake Charles, LA (PPG
Industries); Stream samples
(bottom and surface)
collected from the receiving
stream at the plant outfall
1976
5 (1.0)
29 - 447
Mean: 282
(156);
Median:
353
(U.S. EPA.
.1.977)
High
and upstream and
downstream of the outfall.
Auburn, WA (Boeing
Company); Stream samples
(surface) collected from the
receiving stream at outfalls
and/or upstream and
downstream of the outfall.
1977
5 (1.0)
5-30
NR
(U.S. EPA.
.1.977)
High
NR = Not reported
ND = Not detected; detection limit reported in parethesis if reasonably available
728 2 2 6,2,3 Geospatial Analysis Comparing Predicted and Measured Surface Water Concentrations
729 A geospatial analysis at the watershed level (HUC-8 and HUC-12) was conducted to compare the
730 measured and predicted surface water concentrations in 2016 and investigate whether any the facility
731 releases may be associated with the measured concentrations in surface water. A geographic distribution
732 of the concentrations is shown in Figure 2-4 and Figure 2-5 for the maximum days of release scenario,
733 and Figure 2-6 and Figure 2-7 for the 20-day reelease scenario. The surface water concentrations
734 associated with the monitoring stations and facility releases are denoted on the maps using COCs to
Page 110 of 803
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735
736
737
738
739
740
741
742
743
744
745
determine the concentration thresholds. 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. The monitored data, which represents
localized concentrations of TCE in ambient water, generally show lower concentrations than the
modeled surface water concentrations from E-FAST, which represents concentrations near facilities
releasing TCE.
300
Miles
Concentration Levels
Concentration Type
~ Modeled - Direct Release (250 - 365 days/yr)
m <3 (jg/L (below all COCs) A Modeled - Indirect Release (250 - 365 days/yr)
¦ Not detected Measured - NWIS/STORET Monitoring Sites
0 A Days of exceedance 2 20 days
States with no modeled or measured
concentrations
¦ 3-787 pg/L
Figure 2-4. TCE Modeled Concentrations from Releasing Facilities (250-365 Days of Release) and
Measured Concentrations from WQP: Eastern U.S., 2016
Page 111 of 803
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746
747
748
749
Figure 2-5. TCE Modeled Concentrations from Releasing Facilities (250-365 Days of Release) and
Measured Concentrations from WQP: Western U.S., 2016
300
I Miles
Concentration Levels
Concentration Type
3 - 787 pg/L ~ Modeled - Direct Release (250 - 365 days/yr)
< 3 |jg/L (below all COCs) A Modeled - Indirect Release (250 - 365 days/yr)
Not detected Measured - NWIS/STORET Monitoring Sites
~ A Days of exceedance > 20 days
States with no modeled or measured
concentrations
Page 112 of 803
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750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
Miles
Concentration Levels Concentration Type
¦ 920 - 14,399 pg/L ~ Modeled - Direct Release (20 days/yr)
¦ 3 - 787 |jg/L Measured - NWIS/STORET Monitoring Sites
¦ < 3 |jg/L (below all COCs) IZ Days of exceedance > 20 days
Not detected States with no modeled or measured
concentrations
Figure 2-6. TCE Modeled Concentrations from Releasing Facilities (20 Days of Release) and
Measured Concentrations from WQP: Eastern U.S., 2016
Page 113 of 803
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766
767
768
769
770
771
772
773
774
775
776
111
778
779
780
781
Concentration Levels Concentration Type
¦ 3 - 787 |jg/L ~ Modeled - Direct Release (250 - 365 days/yr)
< 3 |jg/L (below all COCs) Measured - NWIS/STORET Monitoring Sites
¦ Not detected 0 Days of exceedance > 20 days
States with no modeled or measured
concentrations
Figure 2-7. TCE Modeled Concentrations from Releasing Facilities (20 Days of Release) and
Measured Concentrations from WQP: Western U.S., 2016
Co-location of releasing facilities and monitoring sampling locations was examined for their presence in
the same watershed (HUC-8 and HUC-12). Co-location does not necessarily indicate there is an
upstream/downstream connection between release and sampling sites. The monitoring stations co-
located with facilities in the same HUC in the 2016 set were also examined for proximity to Superfund
sites, however no Superfund sites were identified within five miles of these sites. The co-ocurrence of
TCE releasing facilities and monitoring sites is shown in Figure 2-8 and Figure 2-9. These HUC-level
maps are only focused on NC and NM states, as those were the only two states with co-located WQP
detects and modeled surface water concentrations.
Page 114 of 803
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782
783 Figure 2-8. Co-Location of Modeled Concentrations from Releasing Facilities and Measured
784 Concentrations from WQP (HUC-8) in North Carolina
785
786
Upper Dan
03010103
110000900446
110031424233
110001501492
Snutli
Mountain
ua» s
Haw
03030002
Upper Yadkin
03040101
110000345779
Upper Catawba
03050101
Upper Tar
03020101
110001504747
110031398707
Jouqlas
Luke ¦
Upper Neuse
03020201
Normi
NC 0089494
rRiver,
Deep
03030003
Upper French Broad
06010105
Lower Yadkin
03040103
110007119974
110000345939
Concentrations
Measured - NWIS/STORET Monitoring Sites
® Not detected
Modeled - Indirect Release (250 - 365 days/yr)
~ Below all COC
Modeled - Direct Release (250 - 365 days/yr)
¦ Below all COC
HUC-8 boundary
Northeast Cape Fear!/
03030007 : Green
r. y si SttTfmn,
NC 0001228
100 \:
,.«V, \
SGS The National Map National Hydrography Dataset Data refreshed October. 2018.
Page 115 of 803
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787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
CO
KS
AZ
TX
| U.S. Location
Concentrations
Measured - NWIS/STORET Monitoring Sites
® Not detected
Modeled - Indirect Release (250 - 365 days/yr)
a Below all COC
Modeled - Direct Release (250 - 3G5 daystyr)
¦ Below all COC
E^HUC-8 boundary
Miles
Figure 2-9. Co-Location of Modeled Concentrations from Releasing Facilities and Measured
Concentrations from WQP (HUC-8) in New Mexico
2.2,6.3 Assumptions and Key Sources of Uncertainty for Environmental
Exposures
E-FAST 2014 estimates surface water concentrations at the point of release, without post-release
accounting for environmental fate or degradation such as volatilization, biodegradation, 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. Section 4.3.1 discusses the EPISuite
modeling done to inform the degree to which volatilization may impact the modeled stream
concentrations estimated in E-FAST. Parameters (wind speed, current speed, and water depth) reflective
of two releasing sites with the highest predicted surface water concentrations (Praxair Technology
Center in Tonawanda, NY and NASA Michoud in New Orleans, LA; see Table 4-1) were used to
estimate TCE volatilization half-lives, which varied from one day to more than 10 years. The effect of
volatility on estimating instream concentrations is expected to be highly variable and site-specific
depending on stream flow and environmental conditions. For discharges to still, shallow water bodies,
E-FAST estimates are less likely to overestimate surface water concentrations, as TCE is predicted to
have a long half-life in such still water bodies. Despite some sites discharging to or near still water
bodies such as lakes or bays, E-FAST does not consider aggregation or accumulation of undegraded
Page 116 of 803
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810
811
812
813
814
815
816
817
818
819
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
852
853
854
855
856
857
858
chemical. For discharges to faster-flowing, deeper water bodies, E-FAST estimates may inadequately
reflect instream volatile losses expected within the timeframe of one day. Given this variation and the
predicted half-life of TCE in flowing water bodies, E-FAST surface water concentrations may best
represent concentrations found at the point of discharge. Despite these uncertainties, E-FAST is
considered an appropriate screening model for near-field environmental concentrations.
Releases modeled using E-FAST 2014 were predicted based on engineering site-specific estimates, as
based on DMR, TRI, 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 of release days assumed, the more the per-day release is diluted (assuming 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.
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 facility-
specific NPDES code in E-FAST. When site-specific or surrogate site-specific stream flow data were
not reasonably 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 would
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, the 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, address matching, and GPS. These are considered "Best Pick"
coordinates. While EPA does assign accuracy values for each record based on the method used, the true
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 co-
located 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
Page 117 of 803
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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
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
interpretation is not always reasonably available. For example, 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. Furthermore, with the high
fraction of non-detect (ND) levels, the average may be an overestimate of central tendancy while the
standard deviation may underestimate variability in the dataset.
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 reasonably 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 of E-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-FAST
database), the applied stream flow distribution. Of note, as stated on the EPA website, "modeled
estimates of concentrations and doses are designed to reasonably overestimate exposures, for use in an
exposure assessment in the absence of or with reliable monitoring data."
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 surface water ranged from non-detect (ND) to 17.3 |ig/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
1976 and 1977 show levels of TCE ranging from 0.4 to 447 |ig/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 [.Aquatic Exposure Modeling Outputs from E-FAST. Docket: EPA-HQ-
QPPT-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 overall moderate
confidence.
Page 118 of 803
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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
937
2.3 Human Exposures
2.3.1 Occupational Exposures
EPA categorized the conditions of use (COUs) listed in Table 1-3 into 18 Occupational Exposure
Scenarios (OES). In this section, EPA describes its approach and 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 2-10).14
For the purpose of the Risk Evaluation, EPA defines ONUs as employees who do not directly handle the
chemical but perform work in an area where the chemical is present. The size of this area can vary for
each exposure scenario and condition of use, depending on the facility configuration, building and room
sizes, presence of vapor barrier, and worker activity pattern. For example, an ONU can be a production
employee whose workstation is located on the factory floor where a degreasing unit is installed. Absence
of any vapor barrier (e.g., walls) between the degreaser and the rest of the factory, this "area" can be an
entire factory floor. Alternatively, the area can be in a specific room of a building where a chemical is
handled (e.g., a room in a dry cleaning shop where the dry cleaning machine is installed and where dry
cleaned loads are unloaded, pressed, and finished). For detailed occupational exposure results, see
Appendix Q of this document and the (i) "Exposure Assessment" section for each OES and (ii) "Dermal
Exposure Assessment" section in [Environmental Releases and Occupational Exposure Assessment.
Docket: EPa 019-0500]. An occupational exposure assessment includes the following
components:
• Inhalation Exposure: Central tendency and high-end estimates of inhalation exposure to
workers and occupational non-users by OES.
• Dermal Exposure: Occupational exposure scenarios were 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.
EPA generally does not evaluate occupational exposures through the oral route. Workers may
inadvertently transfer chemicals from their hands to their mouths or ingest inhaled particles that deposit
in the upper respiratory tract. The frequency and significance of this exposure route are dependent on
several factors including the physical-chemical properties of the substance during worker activities, the
visibility of the chemicals on the hands while working, workplace training and practices, and personal
hygiene that is difficult to predict ("Chetrie et at. 2006). Therefore, it can be difficult to quantitatively
evaluate the oral route for occupational exposure scenarios.
14 Occupational exposures from distribution are considered within each condition of use.
Page 119 of 803
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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
Modeling
NF/FF, ESD
Monitoring
Data
Number of
facilities
HSIA, Reports,
NIOSH, OSHA
DEVL model
BLS, Census,
ESD
Census, NEI,
TRI, DMR, CDR
# Workers or
ONUs per site
Modeling
Dermal Exposure
# ofWorkers,
ONUs Exposed
Inhalation
Exposure
OES
Occupational
Assessment
Figure 2-10. Components of an occupational assessment for each OES15.
Please refer to Section 2.2.2.2.2 for additional details on the approach and methodology for estimating
number of facilities.
2.3.1.1 Results for Occupational Assessment
In some cases, EPA identified relevant inhalation exposure monitoring data for a given OES. The
quality of the monitoring data was assessed and EPA established an overall confidence for the data when
integrated into the occupational exposure assessment.
Where monitoring data were reasonably available, EPA used this data to characterize central tendency
and high end inhalation exposures. Where no inhalation monitoring data were identified, but inhalation
exposure models were reasonably available, EPA estimated central tendency and high end exposures
using only modeling approaches. If both, inhalation monitoring data and exposure models were
reasonably 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 OES by indicating whether monitoring data
were reasonably available, how many data points were identified, the quality of the data, EPA's overall
confidence in the data, whether the data were 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 do
15 TRI = Toxics Release Inventory; DMR = Discharge Monitoring Report; NEI = National Emissions Inventory; CDR =
Chemical Data Reporting; ELG = Effluent Limitation Guidelines; ESD = Emission Scenario Document; BLS = Bureau of
Labor Statistics; NIOSH = National Institute of Occupational Safey and Health; OSHA = Occupational Safety and Health
Administration; HSIA = Hallogenated Solvent Industry Alliance; NF/FF = Near-Field/Far-Field; DEVL = Dermal Exposure
to Volatile Liquids.
Page 120 of 803
-------
963
964
965
966
967
968
969
970
971
972
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
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
not contain inhalation exposure estimates for ONUs. In addition, EPA expects ONU exposures to be less
than worker exposures. Dermal exposure for ONUs was not evaluated because these employees are not
expected to be in direct contact with TCE.
A summary 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 health effects. 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.
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 of liquid 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 percent16 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.
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-type systems (e.g., during connection/
disconnection of hoses used in loading of bulk containers in manufacturing).
Dermal exposure estimates are provided for each OES, where the OESs are "binned" based on the
maximum possible exposure concentration (Yderm), the likely level of exposure, and potential for
occlusion. The exposure concentration is determined based on EPA's review of currently available
products and formulations containing TCE. For example, EPA found that TCE concentration in
degreasing formulations such as C-60 Solvent Degreaser can be as high as 100 percent. The calculated
absorbed dose is low for all non-occluded scenarios since TCE evaporates quickly after exposure.
Dermal exposure to liquid is not expected for occupational non-users, since they do not directly handle
TCE. Additional details on EPA's approach and methodology for estimating dermal exposures for
workers can be found in section 2.3.1.2.5.
Table 2-16 provides a summary of EPA's estimates for the total exposed workers and ONUs for each
OES. In order to prepare these estimates, EPA first attempted to identify NAICS codes associated with
each OES. For these NAICS codes, EPA then reviewed Standard Occupational Classification (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.
16 The absorbed fraction is a function of indoor air speed, which differs for industrial and commercial settings.
Page 121 of 803
-------
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
Based on this combination of NAICS 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 methodology for 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.
Page 122 of 803
-------
Table 2-12. A summary for each of the 18 occupational exposure scenarios (OESs).
Where EPA was not able to estimate ONU inhalation exposure from monitoring data or models, this was assumed equivalent to the central
tendency experienced by workers for the corresponding OES; dermal exposure for ONUs was not evaluated because they are not expected to
1
L
Inhalation Exposure
1
|
Dermal Exposure
Modeling0
r
Occupational Exposure j
Scenario TOES) L
Monitoring
Modeling
Ovei
Confic
'all |
ence 1
«
Monitoring
Data
# Data
Points
Data Quality
Rating
Worker
ONU
Worker
ONU
Worker
1
ONU |
¦
Worker
ONU
Manufacturing |
V
50
H
S
X
X
X
M to H
L I
S
-
Processing as a Reactant '
S
50
M
S
X
X
X
L to M
L '
S
-
Formulation of Aerosol and Non- |
Aerosol Products I
S
33
H
S
X
X
X
M
L j
S
-
Repackaging 1
S
33
H
S
X
X
X
M to H
L |
S
-
Batch Open-Top Vapor Degreasing ¦
S
123
M
S
S
S
S
M
M |
S
-
Batch Closed-Loop Vapor 1
Degreasing \
S
19
H
s
X
X
X
M to H
L |
S
-
Conveyorized Vapor Degreasing .
S
18
M
s
X
S
S
L to M
Lto m|
S
-
Web Vapor Degreasing |
X
-
-
X
X
S
S
L to M
LtoMl
S
-
Cold Cleaning !
X
-
-
X
X
S
S
L to M
Lto Mj
S
-
Aerosol Applications3 |
X
-
-
X
X
S
S
M
M 1
S
-
Metalworking Fluids !
s
3
H
S
X
S
X
L to M
L !
s
-
Adhesives, Sealants, Paints, and |
Coatings 1
s
24
M to H; Mb
S
s
X
X
L to M
L to M J
s
-
Other Industrial Uses j
s
50
M
S
X
X
X
L to M
L |
s
-
Spot Cleaning and Wipe Cleaning j
s
8
M
S
X
S
S
M
M |
s
-
Industrial Processing Aid '
s
34
H
S
S
X
X
M to H
Lto m[
s
-
Commercial Printing and Copying j
s
20
M
S
X
X
X
L to M
L |
s
-
Other Commercial Uses '
|
s
8
M
S
X
S
S
M
M |
s
-
Process Solvent Recycling and j
Worker Handling of Wastes j
¦
33
H
S
X
X
X
M to H
L j
¦
s
-
1029 a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and Mold Releases
1030 b. For Workers, data quality is M to H; For ONUs, data quality is is M.
1031 c. EPA has a medium level of confidence in its dermal exposure estimates which are based on high-end/central tendency parameters and commercial/industrial settings.
Page 123 of 803
-------
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
Table 2-13. Summary of inhalation exposure results for Workers based on monitoring data and exposure modeling for each PES.
Occupational Exposure
Inhalation Monitoring (Worker, ppm)
Inhalation Modeling (Worker, ppm)
Scenario (OES)
! TWA
AC
ADC
LADC
TWA
AC
ADC
LA
DC
1 HE
CT
HE
CT
HE
CT
HE
CT
HE
CT
HE
CT
HE
CT
HE
CT
Manufacturing
1 2.5
0.12
0.82
3.8E-02
0.56
2.6E-02
0.29
1.0E-02I -
-
-
-
-
-
-
-
Processing as a Reactant
" 2 5
0.12
0.82
3.8E-02
0.56
2.6E-02
0.29
1.0E-02J -
-
-
-
-
-
-
-
Formulation of Aerosol and Non-
Aerosol Products
j 1.14
4.9E-04
0.38
1.6E-04
0.26
1.1E-04
0.13
4.5E-05| -
1
-
-
-
-
-
-
-
Repackaging
11.14
4.9E-04
0.38
1.6E-04
0.26
1.1E-04
0.13
4.5E-05
-
-
-
-
-
-
-
-
Batch Open-Top Vapor Degreasing
177.8
13.8
25.9
4.6
17.8
3.2
9.1
1.3
388.0
34.8
129.3
11.6
88.5
8.0
35.3
3.0
Batch Closed-Loop Vapor Degreasing
j 1.45
0.46
0.48
0.15
0.33
0.10
0.17
4.2E-02
-
-
-
-
-
-
-
-
Conveyorized Vapor Degreasing
¦ 48.3
32.4
16.1
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 j -
-
-
-
-
-
-
- 1 57.2
3.3
19.1
1.1
13.1
0.76
5.2
0.28
Aerosol Applications3
¦
-
-
-
-
-
-
-
24.0
7.6
8.0
2.5
5.5
1.7
2.2
0.65
Metalworking Fluids
175.4
69.7
25.1
23.2
17.2
15.9
OO
*00
6.3
0.26
0.07
0.09
0.02
0.06
0.02
0.03
0.01
Adhesives, Sealants, Paints, and
Coatings
139.5
4.6
13.2
1.5
9.0
1.1
4.6
0.42 | -
-
-
-
-
-
-
-
Other Industrial Uses
I 2.5
0.12
0.82
3.8E-02
0.56
2.6E-02
0.29
1.0E-02J -
-
-
-
-
-
-
-
Spot Cleaning 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
1128
4.3
6.4
2.13
4.39
1.5
2.2
0.58 j -
-
-
-
-
-
-
-
Commercial Printing and Copying
1 2.1
8.5E-02
0.70
0.03
0.48
0.02
0.25
7.7E-03I -
-
-
-
-
-
-
-
Other Commercial Uses
j 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
8.4E-
02
Process Solvent Recycling and Worker
Handling of Wastes
1 11
4.9E-04
0.38
1.6E-04
0.26
1.1E-04
0.13
4.5E-05j -
1
-
-
-
-
-
-
-
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and Mold Releases
b. Exposure for this OES is based on a 12 lir TWA; all other exposures based on 8 lir TWAs
Page 124 of 803
-------
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
Table 2-14. Summary of inhalation exposure results for ONUs based on monitoring data and exposure modeling for each OES.
[For many cases EPA was not able to estimate inhalation exposure for ONUs, but EPA expects these to be lower than inhalation exposure for
Workers. 1
Occupational Exposure J
Inhalation Monitoring (ONU, ppm) |
Inhalation Modeling (ONU, ppm)
Scenario (OES) i
TWA
AC
ADC
LADC |
TWA
AC
ADC
LADC
1 HE
CT
HE
CT
HE
CT
HE
CT 1
HE
CT
HE
CT
HE
CT
HE
CT
Manufacturing i -
-
-
-
-
-
-
1
-
-
-
-
-
-
-
Processing as a Reactant j -
-
-
-
-
-
-
-
-
-
-
-
-
-
Formulation of Aerosol and Non- | -
Aerosol Products 1
-
-
-
-
-
-
"
-
-
-
-
-
-
Repackaging \
-
-
-
-
-
-
-
i
-
-
-
-
-
-
-
-
Batch Open-Top Vapor Degreasing !
9.1
1.1
3.0
0.37
2.1
0.25
1.06
0.10 !
237.0
18.1
79.0
6.0
54.0
4.1
21.1
1.5
Batch Closed-Loop Vapor Degreasing | -
-
-
-
-
-
-
¦
-
-
-
-
-
-
-
Conveyorized Vapor Degreasing | -
1
-
-
-
-
-
-
- |1878.0
1
23.3
626.0
7.8
428.8
5.3
168.
3
3.6
Web Vapor Degreasing I -
-
-
-
-
-
-
- 1 9.6
3.1
3.2
1.0
2.2
0.71
0.87
0.27
Cold Cleaning \ -
-
-
-
-
-
-
- ! 34.7
1.8
11.6
0.61
7.9
0.42
3.1
0.15
Aerosol Applications3 i -
-
-
-
-
-
-
- j 1.0
0.14
0.35
4.7E-02
0.24
3.2E-02
0.09
1.2E-02
Metalworking Fluids |
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Adhesives, Sealants, Paints, and |
Coatings 1
1.0
0.94
0.33
0.31
0.23
0.21
0.12
8.5E-02I -
-
-
-
-
-
-
-
Other Industrial Uses ! -
-
-
-
-
-
-
-
-
-
-
-
-
-
Spot Cleaning and Wipe Cleaning .
-
-
-
-
-
-
-
J
1.8
0.48
0.58
0.16
0.41
0.11
0.16
4.2E-02
Industrial Processing Aidb ¦
2.9
1.3
1.5
0.66
0.99
0.45
0.51
0.18 j -
-
-
-
-
-
-
-
Commercial Printing and Copying | -
-
-
-
-
-
-
1
-
-
-
-
-
-
-
Other Commercial Uses 1 -
-
-
-
-
-
-
- I 1.8
0.48
0.58
0.16
0.41
0.11
0.16
4.2E-02
¦
Process Solvent Recycling and Worker ¦ -
Handling of Wastes !
-
-
-
-
-
-
1
¦
-
-
-
-
-
-
-
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and Mold Releases
b. Exposure for this OES is based on a 12 lir TWA; all other exposures based on 8 lir TWAs
Page 125 of 803
-------
1055
1056
1057
1058
Table 2-15. A summary of dermal retained dose for Workers based on exposure modeling for each OES
[An explanation of 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
Max TCE
' Non-Occluded Worker Dermal Retained Dose '
, (mg/day) ,
Occluded Worker
Dermal Retained
Occupational Exposure
Scenario (OES)
Bin
Weight
Fraction
I No
1 Gloves
Protective
Gloves
Protective
Gloves
Protective |
Gloves 1
Dose
(mg/day)
(Max Yderm)
1 (PF
= D
(PF
= 5)
(PF =
10)
(PF
= 20) 1
j HE
CT
HE
CT
HE
CT
HE
CT j
HE j
CT
Manufacturing
1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 1 - 1 -
Processing as a Reactant
1
1.0
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 ! - ! -
Formulation of Aerosol and Non-
1
1.0
184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
1
¦
-
Aerosol Products
1
¦
Repackaging
1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 |
-
Batch Open-Top Vapor Degreasing
2
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 |
2,247 |
749
Batch Closed-Loop Vapor Degreasing
2
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 1
2,247 1
749
Conveyorized Vapor Degreasing
2
1.0
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
2,247 j
749
Web Vapor Degreasing
2
1.0
184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07
2,247 j
749
Cold Cleaning
2
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 |
2,247 |
749
Aerosol Applications3
3
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
-
|
|
-
Metalworking Fluids
4
0.8
1147.49
49.16
29.50
9.83
14.75
4.92
-
1
1,798 1
599
Adhesives, Sealants,
Industrial
3
0.9
j 165.92
55.31
33.18
11.06
16.59
5.53
-
Paints, and Coatings
Commercial
3
0.9
260.50
86.83
52.10
17.37
26.05
8.68
-
1 1
Other Industrial Uses
1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
¦
-
Spot Cleaning and Wipe Cleaning
4
1.0
|289.44
96.48
57.89
19.30
28.94
9.65
-
|
2,247 |
749
Industrial Processing Aid
1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 1
1
-
Commercial Printing and Copying
4
0.35
j 101.30
33.77
20.26
6.75
10.13
3.38
-
1
786 1
262
Other Commercial Uses
4
1.0
289.44
96.48
57.89
19.30
28.94
9.65
-
J
2,247 !
749
Process Solvent Recycling and Worker
1
1.0
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
1
-
Handling of Wastes
1
1059
1060
1061
1062
1063
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and Mold Releases
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Table 2-16: Summary of the total number of workers and ONUs potentially exposed to TCE for each OES
[EPA's approach and methodology for estimating the number of facilities using TCE and the number of workers and ONUs potentially
Occupational Exposure ¦
Scenario (OES) |
Total |
Exposed 1
Workers !
Total
Exposed
ONUs
j Total
| Exposed
¦ Number of
| Facilitiesb
1 Notes
Manufacturing j
350 |
170
| 530
i
Processing as a Reactant |
120 to 6.100 |
55 to 2,900
| 180 to 9.000
| 5 to 440
Formulation of Aerosol and Non- 1
306 1
99
1 405
1 19
Aerosol Products J
1
1
1
Repackaging |
36 j
12
j 48
j 22
Batch Open-Top Vapor Degreasing ¦
4.922 !
2,889
j 7.810
! 194
Batch Closed-Loop Vapor Degreasing |
50 |
18
| 68
1 4
Conveyorized Vapor Degreasing I
92 |
32
I 130
1 8
Web Vapor Degreasing 1
1
-
1
1 1
1
1 EPA does not have data to estimate the total
J workers and ONUs exposed to TCE.
Cold Cleaning i
660 j
400
j 1,100
i 13
Aerosol Applications3 ¦
14.200 j
1,690
| 15.900
| 4,366
Metalworking Fluids |
1
1
1
1
1
1
1
1
1
1
1
1
| Based on ESD on the Use of Metalworking
1 Fluids, EPA estimates 46 Workers and 2 ONUs
1 per site; the number of sites that use TCE-based
J metalworking fluids is unknown to EPA.
Adhesives, Sealants, Paints, and j
3,000 '
1,400
' 4.400
! 70
Coatings I
1
|
1
|
1
1
Other Industrial Uses |
2,300 |
1,000
| 3,300
I 49
Spot Cleaning and Wipe Cleaning I
1
244.000 |
1
25,300
I 269.000
1
I 63,748
1
1 Based on assumption of 100% market
1 penetration.
Industrial Processing Aid !
310 *
140
J 450
! 18
Commercial Printing and Copying .
1
1
1
1
1
1
1
1
1
1
1
• Based on NIOSH HHE, EPA estimates 44
¦ Workers and 74 ONUs per site; EPA does not
j have data to estimate total number of sites
Other Commercial Uses I
1
1
-
1
1
I EPA does not have data to estimate the total
1 workers and ONUs exposed to TCE
Process Solvent Recycling and ¦
380 j
140
J 520
¦ 30
Worker Handling of Wastes \
1
¦
1
¦
1
¦
¦
1067
1068
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts
b. Please refer to Table 2-3 for notes related to estimates for Number of Facilities
Cleaners, Penetrating Lubricants, and Mold Releases
using TCE.
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2,3,1,2 Approach and Methodology
2,3.1,2.1 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
reasonably 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 reasonably 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: EPA 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 deterministic and probabilistic calculations: 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.
EPA follows the following hierarchy in selecting data and approaches for assessing inhalation
exposures:
1. Monitoring data:
a. Personal and directly applicable
b. Area and directly applicable
c. Personal and potentially applicable or similar
d. Area and potentially applicable or similar
2. Modeling approaches:
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a. Surrogate monitoring data
b. Fundamental modeling approaches
c. Statistical regression modeling approaches
3. Occupational exposure limits:
a. Company-specific OELs (for site-specific exposure assessments, e.g., there is only one
manufacturer who provides to EPA their internal OEL but does not provide monitoring data)
b. OSHA PEL
c. Voluntary limits (ACGIH TLV, NIOSH REL, Occupational Alliance for Risk Science
(OARS) workplace environmental exposure level (WEEL) [formerly by AIHA])
EPA assessed TCE occupational exposure of the following two receptor categories: male or female
workers who are >16 years or older; and, female workers of reproductive age (>16 years to less than 50
years).
2.3.1.2.2 Inhalation Exposure Monitoring Data
EPA reviewed workplace inhalation monitoring data collected by government agencies such as OSHA
and NIOSH, monitoring data found in published literature {i.e., personal exposure monitoring data and
area monitoring data), and monitoring data submitted via public comments. Studies were evaluated
using the evaluation strategies laid out in the Application of Systematic Review in TSCA Risk
Evaluations (U.S. EPA. 2018b).
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 estimated the exposure concentrations for these data,
following EPA's Guidelines for Statistical Analysis of Occupational Exposure Data (U.S. EPA. 1994a)
which recommends using the LOD/V2 if the geometric standard 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-field approach (NF/FF) (Nicas. 2009).
where a vapor generation source located inside the near-field diffuses into the surrounding environment.
The NF/FF model has been extensively peer-reviewed, it is extensively used, and results of the model
have been compared with measured data. The comparison indicated that the model and measured values
agreed to within a factor of about three (U.S. EPA. 2014b).
EPA considers workers at the facility who neither directly perform activities near the TCE source area
nor regularly handle TCE to be occupational non-users (ONU). Workers that are directly handling TCE
and/or perform activities near sources of TCE are in the near field and are called workers throughout this
report. The near-field is reported to be conceptualized as a volume of air within one-meter in any
direction of the worker's head and the far-field comprised the remainder of the room (Tielemans et al...
2008). The source area/exposure zone could be judged by several factors such as the chemical inventory,
ventilation of the facility, vapor pressure and emission potential of the chemical, process temperature,
size of the room, job tasks, and modes of chemical dispersal from activities (Leblanc et al.. 2018).
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Esmen et al. (1979) indicated that the assignment of zones is a professional judgment and not a scientific
exercise. Applications of the NF/FF model are illustrated in Figure 2-11.
Open-Top Vapor Degreasing and Cold Cleaning
Far-Field
Brake Servicing
- Near-Field -
I®"
Ts) c,
%
w>
C'onveyorized Degreasing
\
cff
i" /-
VcNk
* Volatile Source \<^ Qff
1
^m /*Qh'
MM
Near-Fie Id
Q„, ~
Spot Cleaning
¦ Far-Field ¦
Web Degreasing
Far-Field
Near-Field
roiatite Source
"> Qnf
Figure 2-11. 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,
(denoted by G in Figure 2-11), 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 near-field 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 far-
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field, resulting in occupational non-user exposures to TCE at a concentration Cff. Vff 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.
Near-Field Mass Balance
Kvf ^ = CffQnf — CnfQnf + G
Far-Field Mass Balance
dCFF
Vff ^ = CnfQnf ~ CffQnf ~ CffQff
Where:
Vnf =
near-field volume;
Vff =
far-field volume;
Qnf =
near-field ventilation rate;
Qff =
far-field ventilation rate;
Cnf =
average near-field concentration;
Cff =
average far-field concentration;
G
average vapor generation rate; and
t
elapsed time.
For details on the modeling approach and model equations, please refer to Appendix N; Appendix O;
and Appendix P.
2.3.1.2.4 Acute and Chronic Inhalation Exposure Estimates
This report assesses TCE exposures to workers in occupational settings, presented as time weighted
average (TWA). The TWA exposures are then used to calculate acute exposure (AC), average daily
concentration (ADC) for chronic, non-cancer risks, and lifetime average daily concentration (LADC) for
chronic, cancer risks.
Acute workplace exposures are assumed to be equal to the contaminant concentration in air (TWA):
C x ED
AC =
AT
acute
Where:
AC = acute exposure concentration
C = contaminant concentration in air (TWA)
ED = exposure duration (hr/day)
ATacute = acute averaging time (24 hrs)
ADC and LADC are used to estimate workplace exposures for non-cancer and cancer risks, respectively.
These exposures are estimated as follows:
CxEDxEFxWY
ADC or LADC = — —
AT or ATC
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day hr
AT = WYx 365 —x 24—
yr day
day hr
ATC = LT x 365— x 24 —
yr day
Where:
ADC
= Average daily concentration used for chronic non-cancer risk calculations
LADC
= Lifetime average daily concentration used for chronic cancer risk calculations
ED
= Exposure duration (hr/day)
EF
= Exposure frequency (day/yr)
WY
= Working years per lifetime (yr)
AT
= Averaging time (hr) for chronic, non-cancer risk
ATc
= Averaging time (hr) for cancer risk
AWD
= Annual working days (day/yr)
f
= Fractional working days with exposure (unitless)
LT
= 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 Appendix M for example
calculations).
Table 2-17. Parameter Values for Calculating Inhalation Exposure Estimates
Parameter Name
Svm hoi
Value
I nil
Exposure Duration
ED
8 or 24
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, non-
cancer
AT
271,560 (central tendency)21
350,400 (high-end)b
hr
Averaging Time, cancer
ATc
683,280
hr
11 Calculated using the 50th percentile value for working years (WY)
b Calculated using the 95th percentile value for working years (WY)
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)
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 of 250 to 312 annual working days per year (AWD). Taking into
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account fractional days exposed (f) resulted in an exposure frequency (EF) of 249 at the 50th percentile
and 313 at the 95th percentile.
EF is expressed as the number of days per year a worker is exposed to the chemical being assessed. In
some cases, it may be reasonable to assume a worker is exposed to the chemical on each working day. In
other cases, it may be more appropriate to estimate a worker's exposure to the chemical occurs during a
subset of the worker's annual working days. The relationship between exposure frequency and annual
working days can be described mathematically as follows:
EF = fx AWD
Where:
EF = exposure frequency, the number of days per year a worker is exposed to the chemical
(day/yr)
f = fractional number of annual working 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)
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-digitNAICS level (where 3-digit
NAICS are less granular and 6-digitNAICS 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 approximately 140 NAICS codes applicable to 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 employee per year at the most granular NAICS level available (i.e., 4-digit, 5-
digit, or 6-digit). EPA converted the working hours per employee to 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 AWD for all
4-digit 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 approximately the 75th percentile. In the absence of industry- and TCE-
specific data, EPA 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:
• Minimum value: BLS CPS tenure data with current employer as a low-end estimate of the
number of lifetime working years: 10.4 years;
• Mode value: The 50th percentile tenure data with all employers from SIPP as a mode value for
the number of lifetime working years: 31 years; and
• Maximum value: The maximum average tenure data with all employers from SIPP as a high-end
estimate on the number of lifetime working years: 40 years.
This triangular distribution has a 50th percentile value 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.
The BLS ( >. 2014) provides information on employee tenure with current employer obtained
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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 NAICS codes.
The U.S. Census' (U.S. Census Bureau. 2019) Survey of Income 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 (
Census Bureau. ). 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
(TJBIND1), worker age (TAGE), and years of work experience with all employers over the surveyed
individual's lifetime.17 Census household surveys use different industry codes than the NAICS 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 the 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 Average Worker Tenure from F.S. Census STPP (Age Croup 50+)
| Working Years
Industry Sectors
Average
50"' Percentile
95"' 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. 20.1.9')
Note: Industries where sample size is less than five are excluded from 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
17 To calculate the number of years of work experience EPA took the difference between the year first worked
(TMAKMNYR) and the current data year (i.e., 2008). EPA then subtracted any intervening months when not working
(ETIMEOFF).
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recent (2014) CPS data for workers age 55 to 64 years, which indicates a median tenure of 10.4 years
with their current employer. The use of this low-end value 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 throughout their career.
Table 2-19. Median Year of Tenure with Current Employer by Age Group.
Age
January 2008
January 2010
January 2012
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
18 to 19 years
0.8
1.0
0.8
0.8
20 to 24 years
1.3
1.5
1.3
1.3
25 years and over
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
7.6
7.8
7.8
7.9
55 to 64 years
9.9
10.0
10.3
10.4
65 years and over
10.2
9.9
10.3
10.3
Source: (MAIiyLJOM)-
Lifetime Years (LT)
EPA assumes a lifetime of 78 years for all worker demographics.
2,3.1,2,5 Dermal Exposure Modeling
Dermal exposure data were not reasonably available for the OESs in the assessment. Because TCE is a
volatile liquid that readily evaporates from the skin, EPA estimated dermal exposures using the Dermal
Exposure to Volatile Liquids (DEVL) Model. See Appendix H of the [.Environmental Releases and
Occupational Exposure Assessment. Docket: EPA-HO-QPPT-2019-0500)1 for the development and
underlying research of this 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-state fractional
absorption for TCE based on a theoretical framework provided 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
worker is exposed.
The DEVL is used to assess occupational dermal exposure scenarios because the exposure duration is
typically not known across a wide variety of worker activities, and the model's event-based approach
allows exposure estimation using the number of exposure events, rather than exposure duration. Further,
the model can account for the impact of glove use in occupational settings.
EPA estimated workers' dermal exposure to TCE for the industrial and commercial occupational
exposure scenarios (OESs) considering evaporation of liquid from the surface of the hands and use with
and without gloves. The OSHA recommends employers utilize the hierarchy of controls for reducing or
removing hazardous exposures. 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
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considered when process design and engineering controls cannot reduce workplace exposure to an
acceptable level.
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 dermal absorption of TCE depends on the type and duration of exposure.
Where exposure is not occluded, only a fraction of TCE 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 of TCE, the following OESs are expected;
Manufacture of TCE, 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 Degreasing.
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 (U.S. EPA. 2017c).
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 ™ gloves. Two gun wipe cleaning
agent manufacturers A.V.W. Inc. and G.B. Distributors recommend Viton or Neoprene gloves and
polyethylene, neoprene, or PVA gloves, respectively ( ).
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 PVA gloves. An SDS for a 15-90% TCE adhesive from Rema Tip Top recommended using
Neoprene, Butyl-rubber, or nitrile rubber (U.S. EPA. 2017c).
EPA did not find any SDSs with applicable use in commercial printing and copying applications.
To assess exposure, EPA used the Dermal Exposure to Volatile Liquids 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)" parameter 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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( Qu Xfabs)
n c y v x a j nub s y pj,
uexp ° pp 'derm ^ ri
Where:
• S is the surface area of contact: 535 cm2 (central tendency) and 1,070 cm2 (high end),
representing the total surface area of one and two hands, respectively. Note: EPA has no data on
actual surface area of contact with liquid and that the value is assumed to represent an adequate
proxy for a high-end surface area of contact with liquid that may sometimes include exposures to
much of the hands and also beyond the hands, such as wrists, forearms, neck, or other parts of
the body, for some scenarios.
• Qu is the quantity remaining on the skin: 1.4 mg/cm2-event (central tendency) and 2.1 mg/cm2-
event (high-end). This is the high-end default value used in the EPA dermal models ((
20jl3a).
• Yderm is the weight fraction of the chemical of interest in the liquid (0 < Yderm < 1)
• FT is the frequency of events (1 event per day)
• fabs is the fraction of applied mass that is absorbed (Default for TCE: 0.08 for industrial facilities
and 0.13 for commercial facilities). Note: this value represents the proportion of TCE that
remains on the skin after evaporation.
• PF is the glove protection factor (Table 2-20)
The steady state fractional absorption (fabs) for TCE is estimated to be 0.08 in industrial facilities with
higher indoor wind flows or 0.13 in commercial facilities with lower indoor wind speeds based on a
theoretical framework provided by Kasting and Miller (2006) (Kasting and Miller. 2.006). meaning
approximately 8 or 13 percent of the applied dose is absorbed through the skin following exposure, from
industrial and commercial settings, respectively. However, there is a large standard deviation in the
experimental measurement, which is indicative of the difficulty in spreading a small, rapidly evaporating
dose of TCE evenly over the skin surface.
Table 2-20. Glove Protection Factors for Different Dermal Protection Strategies.
Dermal Protection Characteristics
Setting
Protection
Kactor. PI*"
a. No gloves used, or any glove / gauntlet without permeation data
and without employee training
Industrial and
Commercial
Uses
1
b. Gloves with available permeation data indicating that the
material of construction offers good protection for the substance
5
c. Chemically resistant gloves (i.e., as b above) with "basic"
employee training
10
d. Chemically resistant gloves in combination with specific
activity training (e.g., procedure for glove removal and disposal)
for tasks where dermal exposure can be expected to occur
Industrial
Uses Only
20
Source: (Marquart et at. 2017)
To streamline the dermal exposure assessment, EPA grouped the various OESs based on characteristics
known to effect dermal exposure such as the maximum weight fraction of TCE could be present in that
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1451 scenario, open or closed system use of TCE, and large or small-scale use. Four different groups or
1452 "bins" were created based on this analysis (Table 2-21).
1453
1454 Table 2-21. EPA grouped dermal exposures associated with the various OESs into four bins.
ISin #
Description
1
Bin 1 covers industrial uses that generally occur in closed systems. For these uses, dermal exposure is
likely limited to chemical loading/unloading activities (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 for 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 employee training 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 permeation 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 permeation 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 dermal 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 training or be
accompanied by basic employee training, but not activity-specific training. EPA does not assess
occlusion for aerosol applications because TCE formulations are often supplied in an aerosol spray can
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ISin #
Description
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
4
Bin 4 covers commercial activities 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 scenarios for 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: Workers may wear chemical-resistant gloves when
charging and draining solvent to/from machines, removing and 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, removing and 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-resistant gloves used in these industries would either not be accompanied by training or be
accompanied by basic employee training, but not activity-specific training.
2.3.1.2.6 Consideration of Engineering Controls and Personal Protective Equipment
OSHA requires and NIOSH recommends that employers utilize the hierarchy of controls to address
hazardous exposures in the workplace (OSHA. 2016. NIOSH. 2018). 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. 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
(NIOSH. 2001). For additional information, please also refer to [Memorandum NIOSH BLS Respirator
Usage in Private Sector Firms. Docket # EPA-HQ-OPPT-2019-0500].
Respiratory Protection
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.18 Respirator
selection provisions are provided in § 1910.134(d) and require that appropriate respirators are selected
18 OSHA does not require controls to be used unless a hazard assessment determines that the hazard is significant enough to
require mitigation.
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based on the respiratory hazard(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 (OSHA. 2019). a NIOSH Recommended Exposure Limit (REL) of 2 ppm (as a
60-minute ceiling for TCE usage as an anesthetic) and 25 ppm (as a 10-hour TWA for other exposures)
(NIOSH. 2019) and an American Conference of Government Industrial Hygienists (ACGIH) 8-hour
TLV of 10 ppm and a short-term limit of 25 ppm (ATSDR. 2019). 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, when workers and occupational non-users are
using respiratory protection.
The respirators should be used when effective engineering controls are not feasible as per OSHA's 29
CFR § 1910.134. 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. Assigned Proleclion Factors for F
lespiralors in OSTTA S
nmlnI'd 29 CFR S 1910.134.
Type of Ucspiralor
Quarter
Mask
Mall'
Mask
1 nil
Facepiece
II el met/
Mood
Loose-lllling
l-accpiccc
1. Air-Purifying Respirator
5
10
50
2. Power Air-Purifying Respirator
(PAPR)
50
1,000
25/1,000
25
3. Supplied-Air Respirator (SAR) or Airline Respirator
Demand mode
10
50
Continuous flow mode
50
1,000
25/1,000
25
Pressure-demand or other positive-
pressure mode
50
1,000
4. Self-Contained Breathing Apparatus (SCBA)
Demand mode
10
50
50
Pressure-demand or other positive-
pressure mode (e.g., open/closed
circuit)
10,000
10,000
Source: 29 CFR § 1910.134(d)(3)(i)(A)
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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 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 Statistics of U.S. Businesses (SUSB) (U.S. Census
Bureau.! ) 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 of TCE 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 NAICS 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 reporting 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 2-23
shows the SOC codes EPA classified as occupations potentially exposed to TCE. These occupations are
classified into workers (W) and occupational non-users (O). All other SOC codes are assumed to
represent occupations where exposure is unlikely.
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Table 2-23. SOCs with Worker and ONU Designations for All Conditions of Use Except
SOC
Occupation
Designation
11-9020
Construction Managers
O
17-2000
Engineers
O
17-3000
Drafters, Engineering Technicians, and Mapping Technicians
0
19-2031
Chemists
0
19-4000
Life, Physical, and Social Science Technicians
0
47-1000
Supervisors of Construction and Extraction Workers
0
47-2000
Construction Trades Workers
w
49-1000
Supervisors of Installation Maintenance, and Repair Workers
0
49-2000
Electrical and Electronic Equipment Mechanics, Installers, and Repairers
w
49-3000
Vehicle and Mobile Equipment Mechanics, Installers, and Repairers
w
49-9010
Control and Valve Installers and Repairers
w
49-9020
Heating, Air Conditioning, and Refrigeration Mechanics and Installers
w
49-9040
Industrial Machinery Installation, Repair, and Maintenance Workers
w
49-9060
Precision Instrument and Equipment Repairers
w
49-9070
Maintenance and Repair Workers, General
w
49-9090
Miscellaneous Installation, Maintenance, and Repair Workers
w
51-1000
Supervisors of Production Workers
0
51-2000
Assemblers and Fabricators
w
51-4020
Forming Machine Setters, Operators, and Tenders, Metal and Plastic
w
51-6010
Laundry and Dry-Cleaning Workers
w
51-6020
Pressers, Textile, Garment, and Related Materials
w
51-6030
Sewing Machine Operators
0
51-6040
Shoe and Leather Workers
0
51-6050
Tailors, Dressmakers, and Sewers
0
51-6090
Miscellaneous Textile, Apparel, and Furnishings Workers
0
51-8020
Stationary Engineers and Boiler Operators
w
51-8090
Miscellaneous Plant and System Operators
w
51-9000
Other Production Occupations
w
W = worker designation
O = ONU designation
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 2-24 summarizes the SOC codes with worker and
ONU designations used for dry cleaning facilities.
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1593
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1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
Table 2-24. SOCs with Worker and ONU Designations for Dry Cleaning Facilities
SOC
Occupation
Designation
41-2000
Retail Sales Workers
O
49-9040
Industrial Machinery Installation, Repair, and Maintenance Workers
w
49-9070
Maintenance and Repair Workers, General
w
49-9090
Miscellaneous Installation, Maintenance, and Repair Workers
w
51-6010
Laundry and Dry-Cleaning Workers
w
51-6020
Pressers, Textile, Garment, and Related Materials
w
51-6030
Sewing Machine Operators
0
51-6040
Shoe and Leather Workers
0
51-6050
Tailors, Dressmakers, and Sewers
0
51-6090
Miscellaneous Textile, Apparel, and Furnishings Workers
0
W = worker designation
O = ONU designation
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-di git NAICS 8123 {Drycleaning and Laundry Services) and SOC
51-6010 (Laundry and Dry-Cleaning Workers).
Using a combination of NAICS 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. However, in some cases, BLS only provide employment data at the 4-
digit 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 of NAICS 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 NAIC S 8123 Drycleaning and Laundry Services, which includes the
following 6-digit NAICS:
• NAICS 812310 Coin-Operated Laundries and Dry cleaners;
• NAICS 812320 Drycleaning and Laundry Services (except Coin-Operated);
• NAICS 812331 Linen Supply; and
• NAICS 812332 Industrial Launderers.
In this example, 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.
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.
Table 2-25 illustrates this granularity adjustment for NAICS 812320.
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1618
1619
1620
1621
1622
1623
1624
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1626
1627
1628
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1631
1632
1633
1634
1635
1636
1637
Table 2-25. Estimated Number of Potentially Exposed Workers and ONUs under NAICS 812320.
NAICS
SOC
CODE
SOC Description
Occupation
Designation
Employment
by SOC at 4-
digit NAICS
level
% of Total
Employment
Estimated
Employment
by SOC at 6-
digit NAICS
level
8123
41-2000
Retail Sales Workers
O
44,500
46.0%
20,459
8123
49-9040
Industrial Machinery
Installation, Repair, and
Maintenance Workers
w
1,790
46.0%
823
8123
49-9070
Maintenance and Repair
Workers, General
w
3,260
46.0%
1,499
8123
49-9090
Miscellaneous Installation,
Maintenance, and Repair
Workers
w
1,080
46.0%
497
8123
51-6010
Laundry and Dry-Cleaning
Workers
w
110,640
46.0%
50,867
8123
51-6020
Pressers, Textile, Garment,
and Related Materials
w
40,250
46.0%
18,505
8123
51-6030
Sewing Machine Operators
0
1,660
46.0%
763
8123
51-6040
Shoe and Leather Workers
0
Not Reported for this NAICS Code
8123
51-6050
Tailors, Dressmakers, and
Sewers
0
2,890
46.0%
1,329
8123
51-6090
Miscellaneous Textile,
Apparel, and Furnishings
Workers
0
0
46.0%
0
Total Potentially Exposed Employees
206,070
94,740
Total Workers
72,190
Total Occupational Non-Users
22,551
Note: numbers may not sum exactly due to rounding.
W = worker
O = 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 5: 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 of Workers or ONUs in NAICS SOC (Step 2) x Granularity Adjustment Percentage (Step 3) =
Number of Workers or ONUs in the Industry Occupation Combination
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EPA then estimated the total number of establishments by obtaining the number of establishments
reported in the U.S. Census Bureau's SUSB ('__S I ensus Bureau. I >) 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.
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 ( 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
establishments 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 Assumptions and Key Sources of Uncertainty for Occupational
Exposures
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 of CDR data as they are reported by manufacturers and importers of TCE.
Manufacturers and importers are only required to report if they manufactured or imported TCE in excess
of 25,000 pounds at a single site during any calendar year; 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 (U.S. Census Bureau. 2015). 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.
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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 industry. 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
excluded. This would result in inaccuracy but would be unlikely to systematically either overestimate or
underestimate the count of exposed workers.
2.3.1.3.2 Analysis of Exposure Monitoring Data
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 categorizations are based on descriptions of worker job
activity as provided in literature and EPA's judgment. In general, samples for employees that are
expected to have the highest exposure from direct handling of TCE 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."
Exposures for occupational non-users can vary substantially. Most data sources do not sufficiently
describe 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 categorized as "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 following exposures
during use or if exposure monitoring results were only provided from industry. 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 monitoring data 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. For example, inhalation monitoring data from manufacturing
facilities were used as surrogate for other conditions of use. The data were chosen as TCE
concentrations for these conditions of use would be comparable to manufacturing, and TCE exposures
during unloading would be comparable in magnitude to TCE loading following manufacture. 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 reasonably available, the 95th and 50th percentile exposure concentrations
were calculated using reasonably 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 representativeness of the
reasonably available data, are not known. Where discrete data were not reasonably available, EPA used
reported statistics (e.g., median, mean, 90th percentile, etc.). Since EPA could not verify these values,
there is an added level of uncertainty.
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EPA calculated ADC and LADC values assuming workers and ONUs are regularly exposed during their
entire working lifetime, which likely results in an overestimate. Individuals may 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 reasonably available literature. Where the
distribution of the input parameter is known, a distribution is assigned to capture uncertainty in
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
approximated by a single, average concentration.
• All emissions from the facility are assumed to enter the near-field. This assumption will
overestimate exposures and risks in facilities where some emissions do not enter the airspaces
relevant to worker exposure modeling.
• The exposure models estimate airborne concentrations. Exposures are calculated by assuming
workers spend the entire activity duration in their respective exposure zones {i.e., the worker in
the near-field and the occupational non-user in the far-field). Since vapor degreasing and cold
cleaning involve automated processes, a worker may actually walk away from the near-field
during part of the process and return when it is time to unload the degreaser. As such, assuming
the worker is exposed at the near-field concentration for the entire activity duration may
overestimate exposure. Conversely, assuming the occupational non-user is exposed at the far-
field concentration for the entire work day may underestimate exposure as they may not remain
exclusively in the far-field.
• For certain TCE applications {e.g., vapor degreasing and cold cleaning), TCE vapor is assumed
to emit continuously while the equipment operates {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 exposures are calculated as a time-
weighted average.
• The exposure models represent model workplace settings for each TCE condition of use.
Each subsequent item below discusses uncertainties associated with the individual model.
Vapor Degreasing and Cold Cleaning Models
The OTVD, conveyorized vapor degreasing, and cold cleaning assessments use a near-field/far-field
approach to model worker exposure. In addition to the uncertainties described above, the vapor
degreasing and cold cleaning models have the following uncertainties:
• To estimate vapor generation rate for each equipment type, EPA used a distribution of the
emission rates reported in the 2014 NEI for each degreasing/cold cleaning equipment type. NEI
only contains information on major sources not area sources. Therefore, the emission rate
distribution used in modeling may not be representative of degreasing/cold cleaning equipment
emission rates at area sources.
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• The emission rate for conveyorized vapor degreasing is based on equipment at eight sites. It is
uncertain how representative these data are of a "typical" site.
• EPA assumes workers and occupational non-users remove themselves from the contaminated
near- and far-field zones at the conclusion of the task, such that they are no longer exposed to
any residual TCE in air.
Brake Servicing Model
The aerosol degreasing assessment also uses a near-field/far-field approach to model worker exposure.
Specific uncertainties associated with the aerosol degreasing scenario are presented below:
• The model references a CARB study (GARB. 2000) on brake servicing to estimate use rate and
application frequency of the degreasing product. The brake servicing scenario may not be
representative of the use rates for other aerosol degreasing applications involving TCE.
• The TCE Use Dossier ( ) presented 16 different aerosol degreasing formulations
containing TCE. 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
formulation is equal to that found in a survey of brake cleaning shops in California. It is
uncertain if this distribution is representative of other geographic locations within the U.S.
• Some of the aerosol formulations presented in the TCE Use Dossier (U.S. EPA.: ) were
provided as ranges. For each Monte Carlo iteration the model selects a TCE concentration within
the range of concentrations using a uniform distribution. In reality, the TCE concentration in the
formulation may be more consistent than the range provided.
Spot Cleaning Model
The multi-zone spot cleaning model also uses a near-field/far-field approach. Specific uncertainties
associated with the spot cleaning scenario are presented below:
• The model assumes a use rate based on estimates of the amount of TCE-based spot cleaner sold
in California and the number of textile cleaning facilities in California ( )07). It is
uncertain if this distribution is representative of other geographic locations in the U.S.
• The model assumes a facility floor area based on data from (CARB. 2006) and King County
(Whittaker and Johanson. ^ ). It is unknown how representative the area is of "typical" spot
cleaning facilities. Therefore, these assumptions may result in an overestimate or underestimate
of worker exposure during spot cleaning.
• Many of the model input parameters were obtained from (Von Grote et al. 2003). which is a
German study. Aspects of the U.S. spot cleaning facilities may differ from German facilities.
However, it is not known whether the use of German data will under- or over-estimate exposure.
2.3.1.3.4 Modeled Dermal Exposures
The Dermal Exposure to Volatile Liquids Model is used to estimate dermal exposure to TCE in
occupational settings. The model assumes a fixed fractional absorption of the applied dose; however,
fractional absorption may be dependent on skin loading conditions. The model also assumes a single
exposure event per day based on existing framework of the EPA/OPPT 2-Hand Dermal Exposure to
Liquids Model and does not address variability in exposure duration and frequency. Additionally, the
studies used to obtain the underlying values of the quantity remaing on the skin (Qu) did not take into
consideration the fact that liquid retention on the skin may vary with individuals and techniques of
application on and removal from the hands. Also the data used were developed from three kinds of oils;
therefore, the data may not be applicable to other liquids. Based on the uncertainties described above,
EPA has a medium level of confidence in the assessed baseline exposure. See Appendix H of the
[.Environmental Releases and Occupational Exposure Assessment. Docket: E 0)]
for the development and underlying research of this model.
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1826 2.3.1.3.5 Summary of Overall Confidence in Inhalation Exposure Estimates
1827 Table 2-26 provides a summary of EPA's overall confidence in its inhalation exposure estimates for
1828 each of the Occupational Exposure Scenarios assessed.
1829
1830 Table 2-26. Summary of overall confidence in inhalation exposure estimates by PES.
Occupational Kxposure
Scenario (OKS)
Overall Confidence in Inhalation Kxposurc Kslimalcs
Manufacturing
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 50
data points from 2 sources, and the data quality ratings from systematic review
for these data were high. The primary limitations of these data 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. 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 high.
Processing as a Reactant
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 50
data points from 2 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 representativeness of 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.
Formulation of Aerosol and
Non-Aerosol Products
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 limitations of these data include the
uncertainty of the representativeness of 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.
Repackaging
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 33
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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 representativeness of 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 high.
Batch Open-Top Vapor
Degreasing
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 high. The primary limitations of these data 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. 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.
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. 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 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 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-hr
TWA data in this scenario is medium to low.
Batch Closed-Loop Vapor
Degreasing
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 representativeness of 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 high.
Conveyorized Vapor
Degreasing
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr TWA data. For the inhalation air concentration data, the primary strengths
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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 limitations of these data 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. 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. 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 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 emissions data available in the 2014 NEI were only
found for three total units, and the underlying methodologies used 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.
Web Vapor Degreasing
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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
generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National Emissions Inventory. 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 emissions data available in the 2011 NEI were only
found for one unit, and the underlying methodologies used 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 medium to low.
Cold Cleaning
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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
generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National Emissions Inventory. The primary limitations of
the air concentration outputs from the model include the uncertainty of the
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representativeness of these data toward the true distribution of inhalation
concentrations for the industries and 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 methodologies used 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.
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 8-
hr 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 representativeness of 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 air concentrations, the
overall confidence for these 8-hr TWA data in this scenario is medium.
Metalworking Fluids
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 limitations of these data include limited dataset (3
data points from 1 site), and the uncertainty of the representativeness of 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 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 representativeness of 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 from one 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 8-
hr TWA data. For the inhalation air concentration data, the primary strengths
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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 representativeness of 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 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 1 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-hr
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 8-
hr 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 50
data points from 2 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 representativeness of 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 8-
hr 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 from 2 sources, and the data quality ratings from systematic review
for these data were high. The primary limitations of these data 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. 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
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Omi|);ilion;il Kxposurc
Sronsirio (OKS)
Ovorsill ( onlldciKT in Inhiihilion Kxposure Ksliinsilcs
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
concentrations, the overall confidence for these 8-hr TWA data in this scenario
is medium to low.
Despite these limitations, the modeling and monitoring results match each
other very closely. Therefore, the overall confidence is medium.
Industrial Processing Aid
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the
12-hr 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 30 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 representativeness of 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 12-hr TWA
data in this scenario is medium to high.
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 4 data points
from 1 source, and the data quality ratings from systematic review for the data
point was high. The primary limitations of this single data point 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. Based on these strengths and limitations of the inhalation air
concentration data, the overall confidence for these 12-hr TWA data in this
scenario is medium to low.
Commercial Printing and
Copying
EPA considered the assessment approach, the quality of the data, and
uncertainties in assessment results to determine a level of confidence for the 8-
hr 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 20
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 a
limited dataset, and the uncertainty of the representativeness of 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.
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Occupational Kxposure
Scenario (OKS)
Overall Confidence in Inhalation Kxposurc Kslimalcs
Other Commercial Uses
EPA did not identify any inhalation exposure 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 inhalation exposure monitoring data related to waste
handling/recycling. EPA assumes the exposure sources, routes, and exposure
levels are similar to those for the Repackaging OES.
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. , h).
2,3.2.1 Consumer Conditions of Use Evaluated
Conditions of use associated with consumer exposure were described in the Problem Formulation (U.S.
). 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 0 . >_A i V). Following Problem Formulation,
EPA performed targeted internet searches to confirm TCE concentrations in identified products and to
identify additional examples 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
formulation type) associated with each consumer condition of use.
Table 2-27 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
Formulation (U.S. EPA. 2018d). Any differences between the displayed categories and those presented
in the Problem Formulation are described in the footnotes.
Table 2-27. Evaluated Consumer Conditions of Use and Products for TCE
Life
Cycle
Stage
Category
Product Subcategory
Form1
No. of Products
Utilized in
Modeling1
Use
Solvents for Cleaning and
Degreasing
Brake & Parts Cleaner2
Aerosol
4
Electronic Degreaser/Cleaner3
Aerosol
9
Electronic Degreaser/Cleaner3
Liquid
1
Aerosol Spray Degreaser/Cleaner
Aerosol
8
Liquid Degreaser/Cleaner3
Liquid
2
Gun Scrubber4
Aerosol
2
Gun Scrubber4
Liquid
1
Mold Release
Aerosol
2
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Life
Cycle
Stage
Category
Product Subcategory
Form1
No. of Products
Utilized in
Modeling1
Tire Cleaner5
Aerosol
2
Tire Cleaner5
Liquid
1
Lubricants and Greases
Tap & Die Fluid
Aerosol
1
Penetrating Lubricant6
Aerosol
5
Adhesives and Sealants
Solvent-based Adhesive & Sealant
Liquid
3
Mirror-edge Sealant
Aerosol
1
Tire Repair Cement/Sealer
Liquid
5
Cleaning and Furniture Care
Products 11
Carpet Cleaner
Liquid
1
Spot Remover7
Aerosol
1
Spot Remover7
Liquid
4
Arts, Crafts, and Hobby
Materials
Fixatives & Finishing Spray Coatings8
Aerosol
1
Apparel and Footwear Care
Products
Shoe Polish
Aerosol
1
Other Consumer Uses
Fabric Spray9
Aerosol
1
Film Cleaner
Aerosol
2
Hoof Polish
Aerosol
1
Pepper Spray
Aerosol
2
Toner Aid10
Aerosol
1
1 Form was determined based on the specific products identified as representative of the associated product subcategories.
Please see Supplemental File [Consumer Exposure Assessment Model Input Parameters. Docket: EPA-HO-OPPT-2019-
05001 for the full list of representative products.
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
evaluated product scenarios are based on the brake cleaner product(s) and not a broader 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 Formulation 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 Information on Manufacturing, Processing,
Distribution. Use. and Disposal: TCE (U.S. EPA. 2017c) and fit within the broader Solvents for Cleanine and Desreasins
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 (20.1.4b) and as "spray fixative"
in the TCE Significant New Use Rule (80 FR 47441). This product subcategory is not expected to be a children's arts,
crafts, or hobby use.
9 Fabric spray (specifically an anti-fray spray) was added following Problem Formulation based on identification in the
final 2014 TCE Work Plan Chemical Risk Assessment (U.S. EPA, 2014b).
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Life
Cycle
Stage
Category
Product Subcategory
Form1
No. of Products
Utilized in
Modeling1
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 Formulation 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 2.3.2.5.2 as leading to dermal contact with
impeded evaporation.
2.3.2.2 Consumer Exposure Routes Evaluated
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
consumer products were not evaluated. In general, the frequency of product use was considered to be too
low to create chronic risk concerns. Although high-end frequencies of consumer use for a small
percentage of consumers are up to 50 times per year, reasonably available toxicological data is based on
either single or continuous TCE exposure and it is unknown whether these use patterns are expected to
be clustered (e.g., every day for several weeks) or intermittent (e.g., one time per week). There is
uncertainty regarding the extrapolation from continuous studies in animals to the case of repeated,
intermittent human exposures. Therefore, EPA cannot fully rule out that consumers at the high-end
frequency of use could possibly be at risk for chronic hazard effects, however it is expected to be unlikely
based on these considerations.
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 ( 2014b). EPA evaluated inhalation exposures for consumers and
bystanders for all consumer conditions of use.
Background levels of TCE 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 TCE-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.
2.3.2.2.2 Dermal
EPA assessed dermal exposures to TCE from consumer uses. Dermal exposure may occur via contact
with vapor or mist deposition on the skin or via direct liquid contact during use. Exposures to skin
would be expected to evaporate rapidly based on physical chemical properties. 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
estimates for instantaneous exposures are 0.8% absorption and 99.2% volatilization and are 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 has greater relative
absorption, based on the estimate from IHSkinPerm for an 8-hr exposure of 1.6% absorption and 98.4%
volatilization. Dermal exposures to liquid TCE are expected to be concurrent with inhalation exposures,
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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 (Hudson and Dotson. 2017). There may be certain scenarios with higher
dermal exposure potential - where liquid TCE is not able to evaporate readily and volatilization is
inhibited. However, dermal exposures are quantified and presented for all consumer conditions of use.
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.
2,3,2,3 Consumer Exposures Approach and Methodology
Modeling was conducted to estimate exposure from the identified consumer conditions of use.
Exposures via inhalation and dermal contact to TCE-containing consumer products were estimated using
EPA's Consumer Exposure Model (CEM) Version 2.1 ( 19a). along with consumer
behavioral pattern data (i.e., use patterns) and product-specific characteristics.
Residential indoor air and personal breathing zone data were identified and evaluated during systematic
review. However, measured levels are not attributable to specific consumer products or conditions of use
and were therefore not compared to modeled estimates. For a summary of these data, see Appendix D.4.
2,3,2,3,1 Modeling Approach
Consumer Exposure Model (CEM) Version 2.1 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-specific chamber emission
data) required to run higher-tier indoor air models. The advantages of using CEM to assess exposures to
consumers and bystanders are the following:
• CEM model has been peer-reviewed;
• CEM accommodates the distinct inputs available for the products containing TCE; and
• CEM uses the same calculation engine to compute indoor air concentrations from a source as the
higher-tier Multi-Chamber Concentration and Exposure Model (MCCEM) but does not require
measured chamber emission values.
For a characterization of model sensitivity, see Appendix D.3.
Modeling Air Concentrations and Inhalation Exposure
CEM predicts indoor air concentrations from consumer product use by implementing a deterministic,
mass-balance calculation utilizing an emission profile determined by implementing appropriate emission
scenarios. The model uses a two-zone representation of 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 simulated period. In some instances of 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 lm3, and far-field, which reflects the remainder
of Zone 1. Each zone is considered well-mixed. Product users are exposed to airborne concentrations
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estimated within the near-field during the time of use and otherwise follow their prescribed activity
pattern. Bystanders follow their prescribed activity pattern and are exposed to far-field concentrations
when they are in Zone 1. Background concentrations can be set to a non-zero concentration if desired.
For acute exposure scenarios, emissions from each incidence of product usage are estimated over a
period of 72 hours using the following approach that 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.
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 30-
second 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, 24-
hour TWAs were quantified for use in Risk Evaluation based on alignment relevant to acute human
health hazard endpoints. 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 (
E 19a). also summarized in Appendix D. 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 ( .019b).
Modeling Dermal Exposure
CEM contains dermal modeling components that estimate absorbed dermal doses resulting from dermal
contact with chemicals found in consumer products: P_DER2a: Dermal Dose from a Product Applied to
Skin, Fraction Absorbed Model and P_DER2b: Dermal Dose from Product Applied to Skin,
Permeability Model. The selection of the appropriate dermal model was based on whether an evaluated
condition of use is expected to involve dermal contact with impeded or unimpeded evaporation. For
scenarios that are more likely to involve dermal contact with impeded evaporation {e.g., wiping or
cleaning with a chemical soaked rag), the permeability model is applied. In contrast, for scenarios less
likely to involve impeded evaporation, the fraction absorbed model is applied. See Appendix D for a
more detailed comparison of these dermal models.
The permeability model estimates the mass of a chemical absorbed and dermal flux based on a
permeability coefficient (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 throughout the
exposure duration. KP is a measure of the rate of chemical flux through the skin. The parameter can
either be specified by the user (if measured data are reasonably available) or be estimated within CEM
using a chemical's molecular weight and octanol-water partition coefficient (Kow). The permeability
model does not inherently account for evaporative losses (unless the available flux or KP values are
based on non-occluded, evaporative conditions), which can be considerable for volatile chemicals in
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scenarios where evaporation is not impeded. While the permeability model does not explicitly represent
exposures involving such impeded evaporation, the model assumptions make it the preferred model for
such a scenario. For TCE, a measured dermal permeability coefficient (KP 0.0023 cm/hr) is used, based
on measured dermal flux from a human dermal absorption test with neat TCE (Kezic et al. 2001). For
additional details on this model, please see Appendix D and the CEM User Guide Section 3: Detailed
Descriptions of Models within CEM ( ).
The fraction absorbed model estimates the mass of a chemical absorbed through the applicational of a
fractional absorption factor to the mass of chemical present on or in the skin following a use event. The
initial dose or amount retained on the skin is determined using a film thickness approach. A fractional
absorption factor is then applied to the initial dose to estimate absorbed dose. The fraction absorbed is
essentially the measure of two competing processes, evaporation of the chemical from the skin surface
and penetration deeper into the skin. It can be estimated using an empirical relationship based on Frasch
and Bunge (2015). Due to the model"s consideration of evaporative processes, it was considered to be
more representative of dermal exposure under unimpeded exposure conditions. For additional details on
this model, please see Appendix D and the CEM User Guide Section 3: Detailed Descriptions of Models
within CEM (U.S. EPA. 2019a).
Variation
To capture a range of potential exposure levels associated with consumer conditions of use, three input
parameters were varied: mass of product used, weight fraction, 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 dermal 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 2-28); therefore, the
combination of high-end inputs for the three varied parameters do not reflect "worst-case" or bounding
estimates.
Varied Inputs:
Considering the model sensitivity analysis summarized in Appendix D.3 and the availability of high-
quality 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.
The low-, mid-, and/or high-end weight fractions were selected principally from MSDS/SDS forms. 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, [Consumer Exposure Assessment Model Input Parameters. Docket:
EPA-HO-OPPT-2Q19-OSOOI
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 Household Solvent Products: A
National Usage Survey ( >87). referred to as the "Westat survey" or "Westat" herein, and
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described further in section 2.3.2.5. The survey was rated as having "high" quality during the data
evaluation phase of systematic review. Weight fraction {i.e., the percentage of TCE in the product
formulation) represents the true range in the market based on manufacturer-developed Safety Data
Sheets (SDSs).
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 models.
In the model sensitivity analysis, summarized in Appendix D.3 and shown in the user guide appendices
( J019b), 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.
2,3,2,4 Consumer Exposure 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).
Consumer exposure modeling scenarios for identified conditions of use were 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 2-27 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 of EPA's 2017 Market and Use Report and
Preliminary Information on Manufacturing, Processing, Distribution, Use, and Disposal: TCE.
Following Problem Formulation, EPA performed targeted internet searches to confirm TCE
concentrations in identified products and to identify additional examples 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 [Consumer
Exposure Assessment Model Input Parameters. Docket: EPA-HQ-OPPT-2019-050Q\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 the Westat Survey (1987). Westat surveyed thousands of
Page 161 of 803
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2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
American households via questionnaire or telephone from 4,920 respondents across the United States 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
Westat Survey was rated as a high quality study during data evaluation within the systematic review
process. It forms the basis for relevant chapters of EPA's Exposure Factors Handbook (
2011c) 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; amount of product used; and room of use.
The room of use selected for this evaluation is based on the room in which the Westat Survey results
reported the highest percentage of respondents that last used a product within the room. When the
Westat Survey identified the room of use where the highest percentage of respondents last used the
product as "other inside room," the utility room was selected within CEM for modeling. The pre-defined
product scenarios within CEM were selected based on a cross-walk to similar product categories within
the Westat Survey.
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. A crosswalk between TCE consumer use scenarios and Westat Product
Categories are listed in Table 2-30 and described in more detail in Section 2.3.2.5.2.
2.3.2.4.1 Consumer Exposure Model Inputs
Chemical-specific inputs required to model consumer inhalation and dermal exposure included physical
and chemical properties (Table 1-1), as well as a chemical-specific dermal permeability coefficient,
which were held constant across all modeling scenarios and iterations.
The consumer exposure model requires product-specific data 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, interzonal ventilation rates); general use patterns defining the amount of time a receptor
is likely to be in the home; receptor characteristics (e.g., age, surface area to body weight ratios); and
emission characteristics (e.g., background air concentration, emission factor). These default inputs are
held constant for a given scenario but may vary across scenarios based on scenario-specific exposure
factors or assumptions. As such, these inputs were not altered to capture within-scenario variation. Table
2-28 shows these default parameters.
Table 2-29 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 ( I) 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 2-30 presents the consumer 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 (Table 2-28 and
Table 2-29) were utilized, along with the general chemical-specific characteristics and other model
defaults. Please see Supplemental File [Consumer Exposure Assessment Model Input Parameters.
Page 162 of 803
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2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
Docket: EPa 019-05001 for a spreadsheet summarizing all of the model inputs and product
information.
For all scenarios, the consumer user was assumed to be an adult (age 21+) and two child age groups (16-
20 years and 11-15 years), while a non-user bystander can include individuals of any age. For the TCE
products identified, younger children would not be expected to directly use these products. Inhalation
exposure results are presented as concentrations encountered by users and non-user bystanders and are
independent of age group. EPA presents all three evaluated user age groups for dermal exposures as reported
doses are age-group specific.
Table 2-28. Default Modeling Input Parameters
Parameter Type
Modeling
Parameter
Default Value
Modeled
Value
Characterization
Reference
Building
Characteristic1
Building Volume
(m3)
492
Central Tendency
(Mean)
(U.S. EPA. 20.1.1c)
Air Exchange Rate
(hr1)
0.452
Central Tendency
(Median)
(U.S. EPA. 20.1.1c)
Interzonal
Ventilation Rate
(m3/hr)3
Garage: 109
NA
Default (U.S. EPA. 2019a. b)
All other rooms
modeled: 107
Emission
Characteristics
Background Air
Concentration
(mg/m3)
0
Minimum
Gas Phase Mass
Transfer
Coefficient (m/hr)
Based on chemical properties and estimated
within CEM
Emission Factor
(ug/m2/hr)
Saturation
Concentration in
Air (mg/m3)
5.18E+05
Based on chemical
properties and
estimated within
CEM
Aerosol Fraction
(Spray Scenarios
Only)
0.06
High-end
Product Dilution
Fraction
1 (no dilution)
NA
Based on formulation and
intended use
Use Patterns and
Exposure Factors
Receptor Activity
Pattern
Stay at home4
NA
Default (U.S. EPA. 2019a. b)
Use Start Time
9 AM5
NA
NA
Frequency of Use
1 event per day
NA
Default (U.S. EPA. 2019a. b)
Acute Averaging
Time
1 day
NA
Film Thickness
(cm)
0.006556
Inside of One Hand
Page 163 of 803
-------
Parameter Type
Modeling
Parameter
Default Value
Modeled
Value
Characterization
Reference
Surface Area to
Body Weight Ratio
Adult (21+): 3.10
Children (16-20): 2.90
Children (11-15): 3.17
Central tendency
(mean)
10% of Hands
Adult (21+): 1.24
Children (16-20): 1.16
Children (11-15): 1.27
Central tendency
(mean)
1 An overall residential building volume of 492 m3 is used to calculate air concentrations in Zone 2 and room volume is
used to calculate air concentrations in Zone 1. The volume of the near-field bubble in Zone 1 was assumed to be 1 m3 in
all cases, with the remaining volume of Zone 1 comprising the far-field volume.
2Air exchange rates differed for two scenarios: pepper spray and hoof polish (see
Table 2-30).
3 The default interzonal air flows are a function of the overall air exchange rate 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, laundry rooms, and utility rooms are usually accessed through one
door and are considered more closed.
4 The activity pattern (i.e., zone location throughout the simulated exposure period) for user and bystander was the
default "stay-at-home" resident, which assumes the receptors are primarily in the home (in either Zone 1 or 2)
throughout the day. These activity patterns in CEM were developed based on Consolidated Human Activity Database
(CHAD) data of activity ratterns (Isaacs. 2014).
5 Product use was assumed to start at 9 AM in the morning; as such, the user was assumed to be in the room of use (Zone
1) at that time, regardless of the default activity pattern placement at 9 AM.
6Film thickness of water/ethanol after immersion and no wipe from Table 7-24 from the Exposure Factors Handbook
(U.S. EPA. 2011c).
2138
2139
Page 164 of 803
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Table 2-29. Consumer Product Modeling Scenarios and Varied Input Parameters
Consumer
Category
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight
Fraction
(%
TCE)2
Weight Fractions
Selected for
Modeling
(% TCE)
Selected
Westat
Survey
Scenario
Duration of Use
(min)
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of Product
Used
(g, [oz])
Min2
Mid
Max
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Solvents
for
Cleaning
and
Degreasing
Brake &
Parts
Cleaner
Aerosol
(4)
0-100
20
60
100
Brake
Quieters /
Cleaners
1
15
120
1.23-
1.62
47.9
[1]
191.6
[4]
766.5
[16]
Electronic
Degreaser/
Cleaner
Aerosol
(9)
30 - 100
30
65
100
Specialized
Electronics
Cleaners
(for TV,
VCR,
Razor, etc.)
0.17
2
30
1.25-
1.52
1.8
[0.04]
22.5
[0.5]
337.1
[7.5]
Electronic
Degreaser/
Cleaner
Liquid
(1)
100
100
Specialized
Electronics
Cleaners
(for TV,
VCR,
Razor, etc.)
0.17
2
30
1.46
1.7
[0.04]
21.6
[0.5]
323.8
[7.5]
Spray
Degreaser/
Cleaner
Aerosol
(8)
60 - 100
60
100
Engine
Degreasing5
5
15
120
1.46-
1.52
130.8
[2.91]
521.4
[11.6]
2157.4
[48]
Liquid
Degreaser/
Cleaner
Liquid
(2)
90 - 100
100
Solvent-
Type
Cleaning
Fluids or
Degreasers
2
15
120
1.456
24.1
[0.56]
139.9
[3.25]
1377.7
[32]
Gun
Scrubber
Aerosol
(2)
60 - 1006
60
100
Solvent-
Type
Cleaning
Fluids or
Degreasers7
2
15
120
1.36-
1.465
NA
0.7
[0.45
mL]8
NA
Gun
Scrubber
Liquid
(1)
1008
100
Solvent-
Type
Cleaning
Fluids or
Degreasers7
2
15
120
1.36
NA
0.6
[0.45
mL]8
NA
Page 165 of 803
-------
Consumer
Category
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight
Fraction
(%
TCE)2
Weight Fractions
Selected for
Modeling
(% TCE)
Selected
Westat
Survey
Scenario
Duration of Use
(min)
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of Product
Used
(g, [oz])
Min2
Mid
Max
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Mold
Release
Aerosol
(2)
40 - 68.9
40
68.9
Other
Lubricants
(Excluding
Automotive)
0.08
2
30
0.77-
1.44
4.3
[0.1]
23.4
[0.55]
212.9
[5]
Tire Cleaner
Aerosol
(2)
70 - 100
70
100
Tire /
Hubcap
Cleaner
5
15
60
0.67
10.5
[0.53]
52.9
[2.67]
317.0
[16]
Tire Cleaner
Liquid
(1)
80 - 100
100
Tire /
Hubcap
Cleaner
5
15
60
0.67-
1.493
23.4
[0.53]
117.9
[2.67]
706.4
[16]
Lubricants
and
Greases
Tap & Die
Fluid
Aerosol
(1)
98
98
Other
Lubricants
(Excluding
Automotive)
0.08
2
30
0.9
2.7
[0.1]
14.8
[0.55]
134.5
[5]
Penetrating
Lubricant
Aerosol
(5)
5-50
5
27.5
50
Other
Lubricants
(Excluding
Automotive)
0.08
2
30
0.636-
1.42
4.2
[0.1]
23.1
[0.55]
209.9
[5]
Adhesives
and
Sealants
Solvent-
based
Adhesive &
Sealant
Liquid
(3)
5 ->90
5
47.5
90
Contact
Cement,
Super
Glues, and
Spray
Adhesives
0.33
4.25
60
1.33-
1.45
1.3
[0.03]
10.7
[0.25]
185.2
[4.32]
Mirror-edge
Sealant
Aerosol
(1)
20-40
40
Contact
Cement,
Super
Glues, and
Spray
Adhesives
0.33
4.25
60
0.614
0.5
[0.03]
4.5
[0.25]
78.4
[4.32]
Tire Repair
Cement/
Sealer
Liquid
(5)
65-95
65
80
95
Contact
Cement,
Super
Glues, and
Spray
Adhesives
0.33
4.25
60
1.45
1.3
[0.03]
10.7
[0.25]
185.2
[4.32]
Page 166 of 803
-------
Consumer
Category
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight
Fraction
(%
TCE)2
Weight Fractions
Selected for
Modeling
(% TCE)
Selected
Westat
Survey
Scenario
Duration of Use
(min)
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of Product
Used
(g, [oz])
Min2
Mid
Max
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Cleaning
and
Furniture
Care
Products
Carpet
Cleaner
Liquid
(1)
99
99
Spot
Removers
0.25
5
30
1.6
11.8
[0.25]
62.9
[1.33]
526.6
[11.13]
Spot
Remover
Aerosol
(1)
20-30
30
Spot
Removers
0.25
5
30
1.562
11.5
[0.25]
61.4
[1.33]
514.1
[11.13]
Spot
Remover
Liquid
(4)
<50-
>75
50
75
Spot
Removers
0.25
5
30
1.25-
1.45
10.7
[0.25]
57.0
[1.33]
477.2
[11.13]
Arts,
Crafts, and
Hobby
Materials
Fixatives &
Finishing
Spray
Coatings
Aerosol
(1)
20-30
30
Aerosol
Rust
Removers9
0.25
5
60
0.704
9.4
[0.45]
45.2
[2.17]
306.0
[14.7]
Apparel
and
Footwear
Care
Products
Shoe Polish
Aerosol
(1)
10-20
20
Spray Shoe
Polish
0.5
5
30
0.512
2.9
[0.19]
15.4
[1.02]
151.4
[10]
Other
Consumer
Uses
Fabric Spray
Aerosol
(1)
20-40
40
Water
Repellents /
Protectors
(for Suede,
Leather, and
Cloth)
1.4
10
60
0.614
11.4
[0.63]
49.9
[2.75]
326.8
[18]
Film
Cleaner
Aerosol
(2)
80 - 100
100
Aerosol
Rust
Removers9
0.25
5
60
1.45-
1.456
19.4
[0.45]
93.4
[2.17]
632.9
[14.7]
Hoof Polish
Aerosol
(1)
301CI
30
Spray Shoe
Polish11
0.5
5
30
0.512-
0.704
4.0
[0.19]
21.2
[1.02]
208.2
[10]
Pepper
Spray
Aerosol
(2)
91.5
91.5
NA12
NA
0.0812
NA
1.25
4.0
[0.108
]12
7.5
[0.27]
12
15
[0.54]12
Toner Aid
Aerosol
(1)
10-20
20
Aerosol
Rust
Removers9
0.25
5
60
1
13.3
[0.45]
64.2
[2.17]
434.7
[14.7]
Page 167 of 803
-------
Consumer
Category
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight
Fraction
(%
TCE)2
Weight Fractions
Selected for
Modeling
(% TCE)
Min2
Mid
Max
Selected
Westat
Survey
Scenario
Duration of Use
(min)
10th
50th
95th
%ile3
%ile
%ile
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of Product
Used
(g, [oz])
10th
%ile
50th
%ile
95th
%ile
1 The number of products identified is based on the product lists in EPA's 2017 Market and Use Report and Preliminary Information on Manufacturing, Processing,
Distribution. Use and Disposal: TCE, as well as the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (U.S. EPA. 2017c. h). Please see Supplemental File
[Consumer Exposure Assessment Model Input Parameters. Docket: EPA -HO-QPPT-2019-0500] for the full product list utilized.
2 Weight fractions were primarily sourced from product Safety Data Sheets (SDSs) or Material Safety Data Sheets (MSDSs), unless otherwise noted. Please see
Supplemental File [Consumer Exposure Assessment Model Input Parameters. Docket: EPA -HO-QPPT-2019-0500] for more detailed information on 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 minimum.
3 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).
4 Product density ranges reflect identified products containing TCE and were sourced from product SDSs or MSDSs. The high end of the range identified was used to
convert reported ounces of product used from Westat (1987) to grams of product used, as required for model input.
5 Two Westat product categories were considered for use (engine degreasing and solvent-type cleaning fluids or degreasers); however, engine degreasing was
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 reasonably available for the aerosol and liquid gun scrubber formulations, so the weight fractions were based on the ranges reflected by the
aerosol and liquid degreasing products.
7 The solvent-type cleaning fluids or degreasers product category from Westat was used as a surrogate for gun scrubbers for the selection of use durations. Product-
specific literature was identified and applied for mass of product used.
8 Based Eezox Premium Gun Care testing results (ASTMB.1.1.7-5 Salt Spray Fog Test). 0.42-0.45 inL of the product was used to coat the firearm 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-aligned Westat product categories. Therefore, a number of Westat
product categories and use pattern data were considered for appropriateness, with a focus on primary formulation type (aerosol or liquid), duration of use, and
amount used. The rust remover product category reflects 98% aerosol products and a lower use duration and amount used than many of the other solvent degreasing-
type uses.
10 Weight fraction and density were not reasonably available, so were based on the ranges reflected by the spray fixative/coating and aerosol shoe polish products.
11 There were no reasonably available data sources for aerosol hoof polish use patterns; the Westat spray shoe polish product category was used for selection of use
duration and amount used.
12 One spray from the most common civilian canister (0.54 oz) is estimated to be approximately 0.0216-0.108 oz (https://www.sabrered.com/pepper~sprav-
frequentlv-asked-quest.ions-0). One spray was assumed for the low-intensity user scenario, while the entire keychain canister (0.54 oz) was assumed for the high-
intensity user scenario and a half canister was assumed fo rthe moderate-intensity user scenario. Spraying occurred between 3 and 5 seconds (converted to minutes
for use in modeling) before obtaining desired effect (Bertilsson et at.. 20.1.7). but use duration was rounded up to the lowest time step within CEM (30 seconds).
2141
2142
2143
2144
2145
Page 168 of 803
-------
Table 2-30. Consumer Product Modeling Scenarios and Additional Scenario-Specific Input Parameters
Consumer Category
Product Sub-
Categories
Form
(No. of
Pdts)1
Zone 1
Room of Use
(Volume m3)2
CEM
Emission
Model
Applied3
Air Exchange
Rate
(hr1)
Interzonal
Ventilation
Rate
(m3/hr)
CEM
Dermal
Exposure
Model
Applied
Dermal
Surface Area
Exposed
Solvents for Cleaning
and Degreasing
Brake & Parts
Cleaner
Aerosol (4)
Garage
(90)
E3
0.45
109
Permeability
Inside of one
hand
Electronic Degreaser/
Cleaner
Aerosol (9)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Electronic
Degreaser/Cleaner
Liquid (1)
Utility
(20)
El
0.45
107
Permeability
Inside of one
hand
Spray
Degreaser/Cleaner
Aerosol (8)
Garage
(90)
E3
0.45
109
Permeability
Inside of one
hand
Liquid
Degreaser/Cleaner
Liquid (2)
Utility
(20)
El
0.45
107
Permeability
Inside of one
hand
Gun Scrubber
Aerosol (2)
Utility
(20)
E3
0.45
107
Permeability
Inside of one
hand
Gun Scrubber
Liquid (1)
Utility
(20)
El
0.45
107
Permeability
Inside of one
hand
Mold Release
Aerosol (2)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Tire Cleaner
Aerosol (2)
Garage
(90)
E3
0.45
109
Permeability
Inside of one
hand
Tire Cleaner
Liquid (1)
Garage
(90)
El
0.45
109
Permeability
Inside of one
hand
Lubricants and
Greases
Tap & Die Fluid
Aerosol (1)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Penetrating Lubricant
Aerosol (5)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Adhesives and
Sealants
Solvent-based
Adhesive & Sealant
Liquid (3)
Utility
(20)
El
0.45
107
Fraction
Absorbed
10% of hands
Mirror-edge Sealant
Aerosol (1)
Bathroom
(15)
E3
0.45
107
Fraction
Absorbed
10% of hands
Tire Repair Cement/
Sealer
Liquid (5)
Garage
(90)
El
0.45
109
Fraction
Absorbed
Inside of one
hand
Carpet Cleaner
Liquid (1)
Bedroom
(36)
El
0.45
107
Permeability
Inside of one
hand
Page 169 of 803
-------
Consumer Category
Product Sub-
Categories
Form
(No. of
Pdts)1
Zone 1
Room of Use
(Volume m3)2
CEM
Emission
Model
Applied3
Air Exchange
Rate
(hr1)
Interzonal
Ventilation
Rate
(m3/hr)
CEM
Dermal
Exposure
Model
Applied
Dermal
Surface Area
Exposed
Cleaning and
Furniture Care
Products
Spot Remover
Aerosol (1)
Utility
(20)
E3
0.45
107
Permeability
Inside of one
hand
Spot Remover
Liquid (4)
Utility
(20)
El
0.45
107
Permeability
Inside of one
hand
Arts, Crafts, and
Hobby Materials
Fixatives & Finishing
Spray Coatings
Aerosol (1)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Apparel and
Footwear care
products
Shoe Polish
Aerosol (1)
Utility
(20)
E3
0.45
107
Permeability
Inside of one
hand
Other Consumer Uses
Fabric Spray
Aerosol (1)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Film Cleaner
Aerosol (2)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
Hoof Polish
Aerosol (1)
Barn5
E3
45
109
Fraction
Absorbed
10% of hands
Pepper Spray
Aerosol (2)
Outside6
E3
1006
0
Fraction
Absorbed
10% of hands
Toner Aid
Aerosol (1)
Utility
(20)
E3
0.45
107
Fraction
Absorbed
10% of hands
1 The number of products identified is based on the product lists in EPA's 2017 Market and Use Report and Preliminary Information on Manufacturing, Processing,
Distribution. Use and Disposal: TCE (U.S. EPA, 2017c. If), as well as the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (IIS. EPA. 2014b). It is possible
that specific products and/or formulations identified in those reports and used herein to select appropriate weight fractions, formulation types, and formulation densities
for use in modeling no longer contain TCE or are no longer reasonably available to consumers for purchase; however, they were still considered for sourcing such
information since they were identified as in these recent EPA publications and therefore represent reasonably-foreseen uses. Please see Supplemental File for the full
product list utilized.
2 The use environment (room of use) was generally based on the Westat (1987) survey of consumer behavior patterns, which reported the percentages for the location
of last use of product. In cases where the room was identified as "other inside room," the utility room was selected based on professional judgment. Additionally,
professional judgment was applied to certain uses, such as those that could reasonably be used in a garage setting.
3Emission models used for TCE include El - Emission from Product Applied to a Surface Indoors Incremental Source Model and E3 - Emission from Product Sprayed.
5For the purposed of modeling typical aerosol hoof polish consumer exposure, a barn setting was approximated by selecting the garage as the room of use and changing
the default air exchanee rate from 0.45 to 4 hr1. which more closelv aliens with recommended ventilation levels in a horse barn (Pennsylvania State University. 2016)
6The outdoor environment was approximated by selecting the garage as the room of use and increasing the air exchange rate from 0.45 to 100. The "room of use" was
also edited to reflect a 16 m3 cloud around user (roughly 6.5-foot dome or cloud surrounding user).
2147
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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. 2014b). The inputs included in the 2014 assessment differed from those used in this Risk
Evaluation for similar conditions of use, either due to updated parameter data (e.g., Zone 2 volume), or
professional judgment. The most notable difference between this Risk Evaluation and the 2014 scenarios
related to the parameter selected for mass of product used. In the 2014 assessment, aerosol degreaser
was modeled assuming 24 g (0.85 oz) and clear protecting coating spray was modeled assuming 1 lg
(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 RRisk
Evaluation.
2.3.2.5 Consumer Exposure Results
Acute inhalation and dermal exposure results are presented below for each consumer condition of use.
These conditions of use are organized by product subcategories and are also referred to 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.
2,3.2,5.1 Characterization of Exposure Results
As described in Section 2.3.2.3.1, 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 used, 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, 24-hr TWA air concentrations are provided for consumers and bystanders based on the
relevant human health hazard metrics. 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.
Dermal exposure scenarios and results are presented for children and adult age groups, with the children
(age 11-15) resulting in the highest estimates dermal exposures due to surface area to body weight ratio
differences between age groups. Results are not presented specifically for pregnant women or women of
reproductive age; however, the range of results presented for adult and child age groups are expected to
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cover dermal exposures for pregnant women as well, with the child (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 in Table 2-31 are sourced and/or derived from EPA's 2011
Exposure Factors Handbook (x " H' \ 201 k).
Table 2-31. Surface Area and Body Weight Values for Different Consumer and Bystander
Subpopulations
Parameter
Adult
Children
(16-21)
Children
(11-15)
Pregnant
Women
Women (21+)
Women (16-21)
10% of Hands Surface Area (cm2)
99
83
72
891
891
832
Body Weight (kg)
80
71.6
56.8
753
744
65.9s
SA:BW
1.24
1.16
1.27
1.19
1.20
1.26
1 Surface area based on women 21+
2Surface area based on combined male/female 16-21
3Body weight for all pregnant women
4Body weight for females 21+
5Body weight for females 16-21
2.3.2.5.2 Consumer Exposure Estimates
Solvents for Cleaning and Degreasing
Brake & Parts Cleaner
Exposure to TCE in brake & parts cleaner products was evaluated based on four aerosol products with
weight fractions ranging from 0-20% to 90-100% TCE.
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 m3)
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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: -2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-32. Acute Inha
ation Exposure Summary: Brake & Parts Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User or
Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
5.76E+01
(120)
(100)
(766.5)
Bystander
1.67E+01
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
9.06
(15)
(60)
(191.6)
Bystander
2.26
Low-Intensity User
10th %ile
Min
10th %ile
User
7.09E-01
(1)
(20)
(47.9)
Bystander
1.81E-01
Actual product weight fractions were: 0-20%; 45-55%; 97.5%; 90-100%. 60% is a mathematically-derived mid-point (i.e.,
mean) for use in modeling, based on the minimum and maximum inputs.
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2239
2240
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2247
2248
2249
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-33. Acute Dermal Exposure Summary: Brake & Parts Cleaner
Scenario Description
Duration of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(120)
Max
(100)
Adult (>21 years)
2.33E+01
Children (16-20 years)
2.18E+01
Children (11-15 years)
2.38E+01
Central Tendency
50th %ile
(15)
Mid
(60)
Adult (>21 years)
1.75
Children (16-20 years)
1.63
Children (11-15 years)
1.79
Low-Intensity User
10th %ile
(1)
Min
(20)
Adult (>21 years)
3.88E-02
Children (16-20 years)
3.63E-02
Children (11-15 years)
3.97E-02
Actual product weight fractions were: 0-20%; 45-55%; 97.5%; 90-100%. 60% is a mathematically-derived mid-point (i.e.,
mean) for use in modeling, based on the minimum and maximum inputs.
Aerosol Electronic Degreaser/Cleaner
Exposure to TCE in aerosol electronic degreasing/cleaning products was evaluated based on nine
aerosol products with weight fractions ranging from 30-100% TCE.
Westat Survey data on specialized electronics cleaners were used as the basis for duration of use and
mass of product used. Survey responses indicate 13.1% of respondents have used products in this
category; 34% reported use of aerosol formulations and 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 bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Table 2-34. Acute Inha ation Exposure Summary: Aerosol Electronic Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
3.76E+01
(30)
(100)
(337.1)
Bystander
7.56
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
1.58
(2)
(65)
(22.5)
Bystander
2.95E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
5.55E-02
(0.5)2
(30)
(1.8)
Bystander
1.08E-02
Actual product weight fractions were: 30-50%; 30-60%; 97.2%; 98%; 60-100%; and 90-100%. 65% is a mathematically-
derived mid-point (i.e., mean) for use in modeling, based on the minimum and maximum inputs.
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2266
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2282
2283
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2285
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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-35. Acute Dermal Exposure Summary: Aerosol Electronic Degreaser
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
Max
(100)
Adult (>21 years)
3.35
Children (16-20 years)
3.14
Children (11-15 years)
3.43
Central Tendency
50th %ile
(2)
Mid
(65)
Adult (>21 years)
2.85E-01
Children (16-20 years)
2.67E-01
Children (11-15 years)
2.92E-01
Low-Intensity User
10th %ile
(0.5)2
Min
(30)
Adult (>21 years)
3.44E-02
Children (16-20 years)
3.22E-02
Children (11-15 years)
3.52E-02
1 Actual product weight fractions were: 30-50%; 30-60%; 97.2%; 98%; 60-100%; and 90-100%. 65% 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.
Liquid Electronic Degreaser/Cleaner
Exposure to TCE in liquid electronic degreasing/cleaning products was evaluated based on one liquid
product with a weight fraction of 100% TCE.
Westat Survey data on specialized electronics cleaners were used as the basis for duration of use and
mass of product used. Survey responses indicate 13.1% of respondents have used products in this
category; 34% reported use of aerosol formulations and 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 bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: )5001 for the full range of results based on all
iterations of this modeling scenario.
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2301
2302
2303
2304
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2306
2307
2308
2309
2310
2311
2312
2313
Table 2-36. Acute Inhalation Exposure Summary: Liquid Electronic Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(30)
(100)
95th %ile
(337.1)
User
3.61E+01
Bystander
7.26
Moderate-Intensity User
50th %ile
(2)
(100)
50th %ile
(22.5)
User
2.33
Bystander
4.36E-01
Low-Intensity User
10th %ile
(0.5)2
(100)
10th %ile
(1.8)
User
1.74E-01
Bystander
3.41E-02
1 Single weight fraction of 100% available.
2The 10th percentile duration from Westat was < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest
timestep in the model run.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-37. Acute Dermal Exposure Summary: Liquid Electronic Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(100)
Adult (>21 years)
5.24
Children (16-20 years)
4.91
Children (11-15 years)
5.37
Moderate-Intensity User
50th %ile
(2)
(100)
Adult (>21 years)
3.50E-01
Children (16-20 years)
3.27E-01
Children (11-15 years)
3.58E-01
Low-Intensity User
10th %ile
(0.5)2
(100)
Adult (>21 years)
8.74E-02
Children (16-20 years)
8.18E-02
Children (11-15 years)
8.95E-02
1 Single weight fraction of 100% available.
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.
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.
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: -2019-050Q\ for the full range of results based on all
iterations of this modeling scenario.
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2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
Table 2-38. Acute Inha ation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.62E+02
(120)
(100)
(2157.4)
Bystander
4.71E+01
Moderate-Intensity User
50th %ile
Max
50th %ile
User
4.11E+01
(15)
(100)
(521.4)
Bystander
1.02E+01
Low-Intensity User
10th %ile
Min
10th %ile
User
6.20
(5)
(60)
(130.8)
Bystander
1.50
Actual product weight fractions were: 60-100% and 90-100%.
This condition of use was also assessed in the 2014 TSCA Work Plan Chemical Risk Assessment and
refined in the 2016 Supplemental Exposure and Risk Reduction Technical Report in Support of Risk
management Options for TCE (TCE) Use in Consumer Aerosol Degreasing. In these prior assessments,
different inputs were used for certain modeling parameters including mass 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 10th percentile input obtained from the Westat
survey engine degreasing scenario. The lower amount applied in 2014 more closely reflects an aerosol
electronic cleaning condition of use, which is characterized by a median mass used of 0.5 oz, or 22.5 g.
It is therefore unlikely that the previous assessment captured exposures for consumer involved in larger
degreasing efforts such as engine degreasing or brake cleaning. The inputs and associated 24-hr acute air
concentrations for users and bystanders from the 2014 assessment are shown below.
Table 2-39. 2014 Acute Inha
ation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction
(%)
Mass Used
(g)
Product User
or Bystander
24-hr TWA
(ppm)
2014 Work Plan Chemical Risk
Assessment
60
90
(24)1
User
2.92
Bystander
0.8
1 This conversion assumes a formulation density of 1. Actual product densities range from 1.46-1.52 g/cm3. This input is
also provided in terms of mass of TCE per use, rather than mass of product per use, which is the actual model input. 24 g of
TCE in this 90% formulation 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 2016 supplemental report, it was
corrected to 2.9 ppm due to an earlier rounding error or typo.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
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2343
2344
2345
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2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
Table 2-40. Acute Dermal Exposure Summary: Aerosol Spray Degreaser/<
Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(120)
Max
(100)
Adult (>21 years)
2.18E+01
Children (16-20 years)
2.04E+01
Children (11-15 years)
2.24E+01
Moderate-Intensity User
50th %ile
(15)
Max
(100)
Adult (>21 years)
2.73
Children (16-20 years)
2.55
Children (11-15 years)
2.79
Low-Intensity User
10th %ile
(5)
Min
(60)
Adult (>21 years)
5.46E-01
Children (16-20 years)
5.11E-01
Children (11-15 years)
5.59E-01
Actual product weight fractions were: 60-100% and 90-100%.
Liquid Degreaser/Cleaner
Exposure to TCE in liquid degreasing/cleaning products was evaluated based on two aerosol products
with weight fractions ranging from 90-100% TCE.
Westat Survey data on solvent-type cleaning fluids or degreasers were used as the basis for room of use,
duration of use, and mass of product used. Survey responses indicate that 28.1% of respondents have
used products in this category; 74.4% reported use of liquid formulations. 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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based on all
iterations of this modeling scenario.
Table 2-41. Acute Inhalation Exposure Summary: Liquid Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
(120)
(100)
95th %ile
(1337.7)
User
1.46E+02
Bystander
3.61E+01
Moderate-Intensity User
50th %ile
(15)
(100)
50th %ile
(139.9)
User
1.56E+01
Bystander
2.96
Low-Intensity User
10th %ile
(2)
(100)
10th %ile
(24.1)
User
2.60
Bystander
4.86E-01
Actual product weight fractions were: 90-100% and 100%.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
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2372
2373
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2377
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2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
Table 2-42. Acute Dermal Exposure Summary: Liquid Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(120)
(100)
Adult (>21 years)
2.09E+01
Children (16-20 years)
1.96E+01
Children (11-15 years)
2.14E+01
Moderate-Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.62
Children (16-20 years)
2.45
Children (11-15 years)
2.68
Low-Intensity User
10th %ile
(2)
(100)
Adult (>21 years)
3.49E-01
Children (16-20 years)
3.26E-01
Children (11-15 years)
3.57E-01
Actual product weight fractions were: 90-100% and 100%.
Aerosol Gun Scrubber
Exposure to TCE in aerosol gun scrubber/cleaner products was evaluated based on two aerosol products.
Only one product had a reported weight fraction (97%), so modeling was based on the full range of
aerosol degreasing formulation weight fractions (60-100%).
Westat Survey data on solvent-type cleaning fluids or degreasers were used as the basis for room of use
and duration, while manufacturer data 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. This mass input may not appropriately capture
consumers cleaning multiple guns in a day - a scenario that may require a higher mass input. 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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based on all
iterations of this modeling scenario.
Table 2-43. Acute Inhalation Exposure Summary: Aerosol Gun Scrubber
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
Max
(0.7)
User
7.44E-02
(120)
(100)
Bystander
1.83E-02
Moderate-Intensity User
50th %ile
Max
(0.7)
User
7.83E-02
(15)
(100)
Bystander
1.48E-02
Low-Intensity User
10th %ile
Min
(0.7)
User
4.55E-02
(2)
(60)
Bystander
8.47E-03
'Only one product had a reported weight fraction (97%), so modeling was based on the full range of aerosol degreasing
formulation weight fractions (60-100%).
Page 178 of 803
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2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-44. Acute Dermal Exposure Summary: Aerosol Gun Scrubber
Scenario Description
Duration of Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR
(mg/kg/day)
High-Intensity User
95th %ile
(120)
Max
(100)
Adult (>21 years)
2.11E+01
Children (16-20 years)
1.97E+01
Children (11-15 years)
2.15E+01
Moderate-Intensity User
50th %ile
(15)
Max
(100)
Adult (>21 years)
2.63
Children (16-20 years)
2.46
Children (11-15 years)
2.69
Low-Intensity User
10th %ile
(2)
Min
(60)
Adult (>21 years)
2.11E-01
Children (16-20 years)
1.97E-01
Children (11-15 years)
2.15E-01
'Only one product had a reported weight fraction (97%), so modeling was based on the full range of aerosol degreasing
formulation 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 formulation weight fractions (90-100%).
Westat Survey data on solvent-type cleaning fluids or degreasers were used as the basis for room of use
and duration, while manufacturer data 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. This mass input may not appropriately capture
consumers cleaning multiple guns in a day - a scenario that may require a higher mass input. 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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based on all
iterations of this modeling scenario.
Page 179 of 803
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2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
Table 2-45. Acute Inhalation Exposure Summary:
liquid Gun Scrubber
Scenario Description
Duration of Use
(min)
Weight
Fraction1 (%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(120)
(100)
(0.7)
User
6.37E-02
Bystander
1.57E-02
Moderate-Intensity
User
50th %ile
(15)
(100)
(0.7)
User
6.71E-02
Bystander
1.27E-02
Low-Intensity User
10th %ile
(2)
(100)
(0.7)
User
6.22E-02
Bystander
1.22E-02
Modeling was based on the upper-end of the narrow range of liquid degreasing formulation weight fractions (90-100%).
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-46. Acute Dermal Exposure Summary: Liquit
Gun Scrubber
Scenario Description
Duration of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(120)
(100)
Adult (>21 years)
1.95E+01
Children (16-20 years)
1.83E+01
Children (11-15 years)
2.00E+01
Moderate-Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.44
Children (16-20 years)
2.29
Children (11-15 years)
2.50
Low-Intensity User
10th %ile
(2)
(100)
Adult (>21 years)
3.26E-01
Children (16-20 years)
3.05E-01
Children (11-15 years)
3.33E-01
Modeling was based on the upper-end of the narrow range of liquid degreasing formulation weight fractions (90-100%).
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-type cleaning 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 1) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-H()-OPPT-2019-050Q~\ for the full range of results based
on all iterations of this modeling scenario.
Page 180 of 803
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2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
Table 2-47. Acute Inhalation Exposure Summary: Mold Release
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.64E+01
(30)
(68.9)
(212.9)
Bystander
3.29
Moderate-Intensity User
50th %ile
Max
50th %ile
User
1.75
(2)
(68.9)
(23.4)
Bystander
3.25E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
1.77E-01
(0.5)2
(40)
(4.3)
Bystander
3.45E-02
Actual product weight fractions were: 40-50% and 68.9%.
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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-48. Acute Dermal Exposure Summary: Mold Release
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
Max
(68.9)
Adult (>21 years)
2.19
Children (16-20 years)
2.05
Children (11-15 years)
2.24
Central Tendency
50th %ile
(2)
Max
(68.9)
Adult (>21 years)
2.87E-01
Children (16-20 years)
2.68E-01
Children (11-15 years)
2.93E-01
Low-Intensity User
10th %ile
(0.5)2
Min
(40)
Adult (>21 years)
4.34E-02
Children (16-20 years)
4.06E-02
Children (11-15 years)
4.44E-02
Actual product weight fractions were: 40-50% and 68.9%.
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.
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 10.5% 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 m3) 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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Page 181 of 803
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2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
Dermal Exposures. Docket: EPA- -2019-0500~\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-49. Acute Inhalation Exposure Summary: Aerosol Tire Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.57E+01
(60)
(100)
(317)
Bystander
6.84
Moderate-Intensity User
50th %ile
Max
50th %ile
User
4.17
(15)
(100)
(52.9)
Bystander
1.04
Low-Intensity User
10th %ile
Min
10th %ile
User
5.81E-01
(5)
(70)
(10.5)
Bystander
1.40E-01
Actual product weight fractions were: 70-90% and 80-100%.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-50. Acute Dermal Exposure Summary: Aerosol Tire Cleaner
Scenario Description
Duration of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
Max
(100)
Adult (>21 years)
4.81
Children (16-20 years)
4.50
Children (11-15 years)
4.93
Moderate-Intensity User
50th %ile
(15)
Max
(100)
Adult (>21 years)
1.20E+00
Children (16-20 years)
1.13E+00
Children (11-15 years)
1.23E+00
Low-Intensity User
10th %ile
(5)
Min
(70)
Adult (>21 years)
2.81E-01
Children (16-20 years)
2.63E-01
Children (11-15 years)
2.87E-01
Actual product weight fractions were: 70-90% and 80-100%.
Liquid Tire Cleaner
Exposure to TCE in liquid tire cleaning products 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. Survey responses indicate that 15.9% of respondents have used products in this category;
29.5% reported use of aerosol formulations and 10.5% 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 m3) 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 low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Page 182 of 803
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2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
Dermal Exposures. Docket: EPA- -2019-0500~\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-51. Acute Inhalation Exposure Summary: Liquid Tire Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
(60)
(100)
95th %ile
(706.4)
User
4.76E+01
Bystander
1.52E+01
Moderate-Intensity User
50th %ile
(15)
(100)
50th %ile
(117.9)
User
9.28
Bystander
2.32
Low-Intensity User
10th %ile
(5)
(100)
10th %ile
(23.4)
User
1.85
Bystander
4.47E-01
Single weight fraction of 80-100% available.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-52. Acute Dermal Exposure Summary: Liquid Tire Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR
(mg/kg/day)
High-Intensity User
95th %ile
(60)
(100)
Adult (>21 years)
1.07E+01
Children (16-20 years)
1.00E+01
Children (11-15 years)
1.10E+01
Moderate-Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.68
Children (16-20 years)
2.51
Children (11-15 years)
2.74
Low-Intensity User
10th %ile
(5)
(100)
Adult (>21 years)
8.94E-01
Children (16-20 years)
8.37E-01
Children (11-15 years)
9.15E-01
'Single weight fraction of 80-100% available.
Lubricants and Greases
Tap & Die Fluid
Exposure to TCE in tap & die fluid was evaluated based on one aerosol product with a weight fraction
of 98% TCE.
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 1) was set
to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Page 183 of 803
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2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
Table 2-53. Acute Inha
ation Exposure Summary: Tap & Die Fluid
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(30)
(98)
95th %ile
(134.5)
User
1.47E+01
Bystander
2.95
Moderate-Intensity User
50th %ile
(2)
(98)
50th %ile
(14.8)
User
1.57
Bystander
2.93E-01
Low-Intensity User
10th %ile
(0.5)2
(98)
10th %ile
(2.7)
User
2.72E-01
Bystander
5.30E-02
Single weight fraction of 98% 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-54. Acute Dermal Exposure Summary: Tap & Die Fluid
Scenario Description
Duration of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(98)
Adult (>21 years)
1.97
Children (16-20 years)
1.84
Children (11-15 years)
2.01
Central Tendency
50th %ile
(2)
(98)
Adult (>21 years)
2.58E-01
Children (16-20 years)
2.41E-01
Children (11-15 years)
2.64E-01
Low-Intensity User
10th %ile
(0.5)2
(98)
Adult (>21 years)
6.72E-02
Children (16-20 years)
6.29E-02
Children (11-15 years)
6.88E-02
1 Single weight fraction of 98% 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.
Penetrating Lubricant
Exposure to TCE in lubricant products was evaluated based on five aerosol products with weight
fractions ranging from 5-50 % 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. 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).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Page 184 of 803
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2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
Table 2-55. Acute Inha
ation Exposure Summary: Penetrating
Lubricant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
(30)
Max
(50)
95th %ile
(209.9)
User
1.17E+01
Bystander
2.35
Moderate-Intensity User
50th %ile
(2)
Mid
(27.5)
50th %ile
(23.1)
User
6.88E-01
Bystander
1.28E-01
Low-Intensity User
10th %ile
(0.5)2
Min
(5)
10th %ile
(4.2)
User
2.16E-02
Bystander
4.21E-03
Actual 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 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-56. Acute Dermal Exposure Summary: Penetrating Lubricant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
Max
(50)
Adult (>21 years)
1.57
Children (16-20 years)
1.47
Children (11-15 years)
1.60
Central Tendency
50th %ile
(2)
Mid
(27.5)
Adult (>21 years)
1.13E-01
Children (16-20 years)
1.06E-01
Children (11-15 years)
1.15E-01
Low-Intensity User
10th %ile
(0.5)2
Min
(5)
Adult (>21 years)
5.35E-03
Children (16-20 years)
5.01E-03
Children (11-15 years)
5.48E-03
'Actual 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 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.
Adhesives and Sealants
Solvent-based Adhesive & Sealant
Exposure to TCE in solvent-based adhesive & sealant products was evaluated based on three liquid
products with weight fractions ranging from 5->90% TCE.
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).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Page 185 of 803
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2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
Consumer Inhalation Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based
on all iterations of this modeling scenario.
Table 2-57. Acute Inhalation Exposure Summary: Solvent-based Adhesive & Sealant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.69E+01
(60)
(90)
(185.2)
Bystander
4.14
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
5.55E-01
(4.25)
(47.5)
(10.7)
Bystander
1.03E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
6.64E-03
(0.5)2
(5)
(1.3)
Bystander
1.30E-03
Actual 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-58. Acute Dermal Exposure Summary: Solvent-based Adhesive & Sealant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
Max
(90)
Adult (>21 years)
8.53
Children (16-20 years)
7.98
Children (11-15 years)
8.72
Central Tendency
50th %ile
(4.25)
Mid
(47.5)
Adult (>21 years)
9.93E-01
Children (16-20 years)
9.29E-01
Children (11-15 years)
1.02E+00
Low-Intensity User
10th %ile
(0.5)2
Min
(5)
Adult (>21 years)
1.37E-02
Children (16-20 years)
1.28E-02
Children (11-15 years)
1.40E-02
'Actual 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.
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.
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 of liquid 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
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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.
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-H0-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Table 2-59. Acute Inhalation Exposure Summary: Mirror-Edge Sealant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA
(ppm)
High-Intensity User
95th %ile
(60)
(40)
95th %ile
(78.4)
User
3.33
Bystander
7.84E-01
Moderate-Intensity User
50th %ile
(4.25)
(40)
50th %ile
(4.5)
User
4.98E-01
Bystander
9.07E-02
Low-Intensity User
10th %ile
(0.5)2
(40)
10th %ile
(0.5)
User
2.24E-02
Bystander
4.07E-03
Single weight fraction of 20-40% 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-60. Acute Dermal Exposure Summary: Mirror-Edge Sealant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR
(mg/kg/day)
High-Intensity User
95th %ile
(60)
(40)
Adult (>21 years)
6.42E-01
Children (16-20 years)
6.01E-01
Children (11-15 years)
6.57E-01
Central Tendency
50th %ile
(4.25)
(40)
Adult (>21 years)
1.42E-01
Children (16-20 years)
1.33E-01
Children (11-15 years)
1.45E-01
Low-Intensity User
10th %ile
(0.5)2
(40)
Adult (>21 years)
1.85E-02
Children (16-20 years)
1.73E-02
Children (11-15 years)
1.89E-02
1 Single weight fraction of 20-40% 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.
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.
Westat 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 1) was set
to the garage (90 m3) based on the product's apparent use on tires.
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Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Table 2-61. Acute Inhalation Exposure Summary: Tire Repair Cement/Sea er
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.18E+01
(60)
(95)
(185.2)
Bystander
3.80
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
6.64E-01
(4.25)
(80)
(10.7)
Bystander
1.63E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
5.97E-02
(0.5)2
(65)
(1.3)
Bystander
1.59E-02
Actual product weight fractions were: 65-80%; 70-85%; 75-90%; and 80-95%. 80% 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-62. Acute Dermal
Exposure Summary: Tire Repair Cement/Sealer
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
Max
(95)
Adult (>21 years)
9.00
Children (16-20 years)
8.42
Children (11-15 years)
9.21
Central Tendency
50th %ile
(4.25)
Mid
(80)
Adult (>21 years)
1.67
Children (16-20 years)
1.57
Children (11-15 years)
1.71
Low-Intensity User
10th %ile
(0.5)2
Min
(65)
Adult (>21 years)
1.78E-01
Children (16-20 years)
1.66E-01
Children (11-15 years)
1.82E-01
Actual product weight fractions were: 65-80%; 70-85%; 75-90%; and 80-95%. 80% 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.
Cleaning and Furniture Care Products
Carpet 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 m3) based on
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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.
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: -2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-63. Acute Inha
ation Exposure Summary: Carpet Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(99)
95th %ile
User
5.26E+01
(30)
(526.6)
Bystander
1.15E+01
Moderate-Intensity User
50th %ile
(99)
50th %ile
User
6.36
(5)
(62.9)
Bystander
1.26
Low-Intensity User
10th %ile
(99)
10th %ile
User
1.10
(0.5)2
(11.8)
Bystander
2.33E-01
1 Single weight fraction of >99% 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.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-64. Acute Dermal Exposure Summary: Carpet Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(99)
Adult (>21 years)
5.69
Children (16-20 years)
5.32
Children (11-15 years)
5.82
Central-Tendency
50th %ile
(5)
(99)
Adult (>21 years)
9.48E-01
Children (16-20 years)
8.87E-01
Children (11-15 years)
9.70E-01
Low-Intensity User
10th %ile
(0.5)2
(99)
Adult (>21 years)
9.48E-02
Children (16-20 years)
8.87E-02
Children (11-15 years)
9.70E-02
1 Single weight fraction of >99% available.
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.
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.
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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 1) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: -2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-65. Acute Inha
ation Exposure Summary: Aerosol Spot Remover
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(30)
(30)
95th %ile
(514.1)
User
1.72E+01
Bystander
3.46
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(61.4)
User
2.04
Bystander
3.76E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
10th %ile
(11.15)
User
3.55E-01
Bystander
6.92E-02
Single weight fraction of 20-30% 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.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-66. Acute Dermal Exposure Summary: Aerosol Spot Remover
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(30)
Adult (>21 years)
1.68
Children (16-20 years)
1.58
Children (11-15 years)
1.72
Moderate-Intensity User
50th %ile
(5)
(30)
Adult (>21 years)
2.81E-01
Children (16-20 years)
2.63E-01
Children (11-15 years)
2.87E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
Adult (>21 years)
2.81E-02
Children (16-20 years)
2.63E-02
Children (11-15 years)
2.87E-02
1 Single weight fraction of 20-30% available.
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.
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Liquid 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 1) was set to the utility room (20 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: EPA-H()-OPPT-2019-050Q\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-67. Acute Inha
ation Exposure Summary: Liquid Spot Remover
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
3.99E+01
(30)
(75)
(477.2)
Bystander
8.02
Moderate-Intensity User
50th %ile
Max
50th %ile
User
4.73
(5)
(75)
(57)
Bystander
8.72E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
5.47E-01
(0.5)2
(50)
(10.7)
Bystander
1.07E-01
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.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-68. Acute Dermal Exposure Summary: Liquid Spot Remover
Scenario Description
Duration of
Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR
(mg/kg/day)
High-Intensity User
95th %ile
(30)
Max
(75)
Adult (>21 years)
3.91
Children (16-20 years)
3.66
Children (11-15 years)
4.00
Moderate-Intensity User
50th %ile
(5)
Max
(75)
Adult (>21 years)
6.51E-01
Children (16-20 years)
6.09E-01
Children (11-15 years)
6.66E-01
Low-Intensity User
10th %ile
(0.5)2
Min
(50)
Adult (>21 years)
4.34E-02
Children (16-20 years)
4.06E-02
Children (11-15 years)
4.44E-02
1 Actual product weight 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
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model, as it reflects the smallest timestep in the model run.
Arts, Crafts, and Hobby Materials
Fixatives & Finishing Spray Coating
Exposure to TCE in fixatives & finishing spray coating products was evaluated based on one aerosol
product with a weight fraction of 20-30% TCE. This particular product subcategory is not expected to be
a children's artcs, crafts, or hobby use; therefore, in the dermal exposure scenarios, only children 11
years or greater are included as users, as with other evaluated consumer scenarios.
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 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).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HO-OPPT-2019-OSOQ~\ for the full range of results based
on all iterations of this modeling scenario.
Table 2-69. Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(60)
(30)
95th %ile
(306)
User
9.31
Bystander
2.28
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(45.2)
User
1.50
Bystander
2.77E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
10th %ile
(9.4)
User
2.90E-01
Bystander
5.66E-02
1 Single product weight fraction of 20-30% 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.
This condition of use was also assessed in the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
»). In the prior assessment, different inputs were used for certain modeling parameters
including mass used and duration of use. The amount of TCE used (11 g - roughly 37 g of product) in
the 2014 assessment is roughly equivalent to the 50th percentile input obtained from the Westat survey
rust remover surrogate scenario applied in this latest evaluation. These inputs and associated 24-hr acute
air concentrations for users and bystanders are included below.
Table 2-70. 2014 Acute Inha
ation Exposure Summary: Fixatives & Finishing Spray Coatings
Scenario Description
Duration
of Use
(min)
Weight
Fraction
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr TWA (ppm)
2014 Chemical Work Plan Risk
Assessment
30
30
ll1
User
0.4
Bystander
0.1
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2801
2802
2803
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2805
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2811
2812
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2820
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'Note that this conversion assumes a formulation density of 1. Actual product densities range from 1.46-1.52 g/cm3. This
input is also provided in terms of mass of TCE per use, rather than mass of product per use, which is the actual model input.
11 g of TCE 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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-71. Acute Dermal Exposure Summary: Fixatives & Finishing Spray Coatings
Scenario Description
Duration of
Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
(30)
Adult (>21 years)
5.52E-01
Children (16-20 years)
5.16E-01
Children (11-15 years)
5.65E-01
Moderate-Intensity User
50th %ile
(5)
(30)
Adult (>21 years)
1.40E-01
Children (16-20 years)
1.31E-01
Children (11-15 years)
1.44E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
Adult (>21 years)
1.59E-02
Children (16-20 years)
1.49E-02
Children (11-15 years)
1.63E-02
Single product weight fraction of 20-30% 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.
Apparel and Footwear care Products
Shoe Polish
Exposure to TCE in shoe polish products was evaluated based 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 respondents have used products in this
category; 91.1% 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 presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental Files [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures and Risk Exposure Modeling Results and Risk Estimates for Consumer
Dermal Exposures. Docket: 15001 for the full range of results based on all
iterations of this modeling scenario.
Table 2-72. Acute Inha
ation Exposure Summary: Shoe Polish
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(20)
95th %ile
User
2.77
(30)
(151.4)
Bystander
6.79E-01
Moderate-Intensity User
50th %ile
(20)
50th %ile
User
3.41E-01
(5)
(15.4)
Bystander
6.28E-02
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2833
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2835
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2850
2851
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
Low-Intensity User
10th %ile
(0.5)
(20)
10th %ile
(2.9)
User
5.96E-02
Bystander
1.16E-02
Single weight fraction of 10-20% available.
Dermal exposures for this scenario are based on CEM's permeability model (P_DER2b), as it is
assumed that the product could be applied in a manner leading to dermal contact with impeded
evaporation.
Table 2-73. Acute Dermal Exposure Summary: Shoe Polish
Scenario Description
Duration
of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(20)
Adult (>21 years)
3.68E-01
Children (16-20 years)
3.44E-01
Children (11-15 years)
3.76E-01
Moderate-Intensity User
50th %ile
(5)
(20)
Adult (>21 years)
6.13E-02
Children (16-20 years)
5.74E-02
Children (11-15 years)
6.27E-02
Low-Intensity User
10th %ile
(0.5)
(20)
Adult (>21 years)
6.13E-03
Children (16-20 years)
5.74E-03
Children (11-15 years)
6.27E-03
Single weight fraction of 10-20% 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.
Other Consumer Uses
Fabric Spray
Exposure to TCE in fabric spray products was evaluated based on one aerosol product with a weight
fraction of 20-40% TCE. This use (i.e., no-fray fabric spray) was originally identified in the 2014 TSCA
Work Plan Chemical Risk Assessment of TCE ( 2014b).
Westat Survey data on water repellents/protectors for suede, leather, and cloth were used as the basis for
room of use, duration of use, and mass of product used. Survey responses indicate that 35.5% of
respondents have used products in this category; 12.1% 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 presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HO-OPPT-2019-OSOQ~\ for the full range of results based
on all iterations of this modeling scenario.
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2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
Table 2-74. Acute Inhalation Exposure Summary: Fabric Spray
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(60)
(40)
95th %ile
(326.8)
User
1.33E+01
Bystander
3.24
Moderate-Intensity User
50th %ile
(10)
(40)
50th %ile
(49.9)
User
2.23
Bystander
4.15E-01
Low-Intensity User
10th %ile
(1.4)
(40)
10th %ile
(11.4)
User
4.66E-01
Bystander
9.18E-02
Single product weight fraction of 20-40% available.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-75. Acute Dermal Exposure Summary: Fabric Spray
Scenario Description
Duration
of Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
(40)
Adult (>21 years)
6.42E-01
Children (16-20 years)
6.01E-01
Children (11-15 years)
6.58E-01
Moderate-Intensity User
50th %ile
(10)
(40)
Adult (>21 years)
2.87E-01
Children (16-20 years)
2.68E-01
Children (11-15 years)
2.94E-01
Low-Intensity User
10th %ile
(1.4)
(40)
Adult (>21 years)
5.05E-02
Children (16-20 years)
4.73E-02
Children (11-15 years)
5.18E-02
Single product weight fraction of 20-40% available.
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 m3).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-H()-OPPT-2019-050Q~\ for the full range of results based
on all iterations of this modeling scenario.
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2888
2889
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2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
Table 2-76. Acute Inha
ation Exposure Summary: Film Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(100)
95th %ile
User
6.42E+01
(60)
(632.9)
Bystander
1.57E+01
Moderate-Intensity User
50th %ile
(100)
50th %ile
User
1.03E+01
(5)
(93.4)
Bystander
1.91
Low-Intensity User
10th %ile
(100)
10th %ile
User
1.99
(0.5)2
(19.4)
Bystander
3.89E-01
Actual product weight fractions were: 80-100% and 95%.
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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-77. Acute Dermal
Exposure Summary: Film Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
(100)
Adult (>21 years)
3.80
Children (16-20 years)
3.56
Children (11-15 years)
3.89
Moderate-Intensity User
50th %ile
(5)
(100)
Adult (>21 years)
9.68E-01
Children (16-20 years)
9.06E-01
Children (11-15 years)
9.91E-01
Low-Intensity User
10th %ile
(0.5)2
(100)
Adult (>21 years)
1.10E-01
Children (16-20 years)
1.03E-01
Children (11-15 years)
1.12E-01
Actual product weight fractions were: 80-100% and 95%.
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.
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 (Pennsylvania State University. 2016).
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
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2923
2924
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2926
2927
2928
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2930
2931
2932
2933
2934
2935
Consumer Inhalation Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based
on all iterations of this modeling scenario.
Table 2-78. Acute Inhalation Exposure Summary: Hoof Polis
l
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(30)
(30)
95th %ile
(208.2)
User
2.21
Bystander
1.10E-02
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(21.2)
User
2.16E-01
Bystander
4.76E-04
Low-Intensity User
10th %ile
(0.5)
(30)
10th %ile
(4)
User
3.08E-02
Bystander
7.79E-05
Actual 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%).
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-79. Acute Dermal Exposure Summary: Hool
F Polish
Scenario Description
Duration of
Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(30)
(30)
Adult (>21 years)
4.66E-01
Children (16-20 years)
4.36E-01
Children (11-15 years)
4.77E-01
Moderate-Intensity User
50th %ile
(5)
(30)
Adult (>21 years)
1.40E-01
Children (16-20 years)
1.31E-01
Children (11-15 years)
1.44E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
Adult (>21 years)
1.59E-02
Children (16-20 years)
1.49E-02
Children (11-15 years)
1.63E-02
Actual 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%).
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.
Pepper Spray
Exposure to TCE in pepper spray products was evaluated based on two aerosol products with a single
reported weight fraction of 91.5% TCE.
Product research was the basis for duration of use and mass of product used. One spray from the most
common civilian canister is estimated to be approximately 0.0216-0.108 ounces, based on information
on a pepper spray manufacturer's website. One spray was assumed for the low-intensity scenario, while
use of the entire key chain-sized canister (0.54 oz, 15 g) was assumed for the high-intensity user scenario
and a half canister was assumed for the moderate-use intensity scenario. Spraying occurred between 3
and 5 seconds (0.05-0.08 min) before obtaining desired effect (Bertilsson et at.. 2017). but use duration
was rounded up to the lowest time step within CEM (30 seconds). The room of use (Zone 1) was set to
approximate a "cloud" around the user (16 m3) in an outdoor environment. This was done by increasing
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2969
2970
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.
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-QPPT-2019-05001 for the full range of results based
on all iterations of this modeling scenario.
Table 2-80. Acute Inhalation Exposure Summary: Pepper Spray
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
(0.5)2
(91.5)
(15)
User
6.65E-02
Bystander
6.65E-02
Moderate -Intensity User
(0.5)2
(91.5)
(7.5)
User
3.33E-02
Bystander
3.33E-02
Low-Intensity User
(0.5)2
(91.5)
(4)
User
1.77E-02
Bystander
1.77E-02
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 smallest timestep in the model run.
3Bystander in the home not modeled due to simulated outdoor scenario - can be considered equal to user.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation. Only a single scenario is
shown for dermal, as there are only single inputs for duration and weight fraction, which are the only
two varied parameters utilized in the dermal model.
Table 2-81. Acute Dermal Exposure Summary: Pepper Spray
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR
(mg/kg/day)
Single Scenario
(0.5)2
(91.5)
Adult (>21 years)
8.62E-02
Children (16-20 years)
8.07E-02
Children (11-15 years)
8.82E-02
Single weight fraction of 91.5% available.
2The low-end duration is < 0.5 minutes, but 0.5 minutes was used in the model, as it reflects the smallest timestep in the
model run.
Toner Aid
Exposure to TCE in toner aid products was evaluated based on one aerosol product with a weight
fraction of 10-20% 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 m3).
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2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
Inhalation exposures for users and bystanders are presented below reflecting high-, moderate-, and low-
intensity user scenarios. See Supplemental File [Exposure Modeling Results and Risk Estimates for
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-050Q\ for the full range of results based
on all iterations of this modeling scenario.
Table 2-82. Acute Inhalation Exposure Summary: Toner Ait
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
24-hr Max TWA (ppm)
High-Intensity User
95th %ile
(60)
(20)
95th %ile
(434.7)
User
8.82
Bystander
2.16
Moderate-Intensity User
50th %ile
(5)
(20)
50th %ile
(64.2)
User
1.42
Bystander
2.62E-01
Low-Intensity User
10th %ile
(0.5)2
(20)
10th %ile
(13.3)
User
2.73E-01
Bystander
5.34E-02
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.
Dermal exposures for this scenario are based on CEM's fraction absorbed model (P_DER2a), as this use
pattern is not expected to involve dermal contact with impeded evaporation.
Table 2-
83. Acute Dermal Exposure Summary: Toner Aid
Scenario Description
Duration
of Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR (mg/kg/day)
High-Intensity User
95th %ile
(60)
(20)
Adult (>21 years)
5.23E-01
Children (16-20 years)
4.89E-01
Children (11-15 years)
5.35E-01
Moderate-Intensity User
50th %ile
(5)
(20)
Adult (>21 years)
1.33E-01
Children (16-20 years)
1.24E-01
Children (11-15 years)
1.36E-01
Low-Intensity User
10th %ile
(0.5)2
(20)
Adult (>21 years)
1.51E-02
Children (16-20 years)
1.41E-02
Children (11-15 years)
1.54E-02
1 Single weight fraction of 10-20% 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.
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2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
2.3.2.5.3 Summary of Consumer Exposure Assessment
Table 2-84 displays the consumer conditions of use evaluated for acute inhalation and/or dermal
exposures.
Table 2-84. Evaluated Pathways for Consumer Conditions of 1
Jse
Life
Cycle
Stage
Categories
Product Subcategories
Form
Acute
Inhalation
Exposure
Acute
Dermal
Exposure
Use
Solvents for
Cleaning and
Degreasing
Brake & Parts Cleaner
Aerosol
V
V
Electronic Degreaser/Cleaner
Aerosol
V
V
Electronic Degreaser/Cleaner
Liquid
V
V
Aerosol Spray Degreaser/Cleaner
Aerosol
V
V
Liquid Degreaser/Cleaner
Liquid
V
V
Gun Scrubber
Aerosol
V
V
Gun Scrubber
Liquid
V
V
Mold Release
Aerosol
V
V
Tire Cleaner
Aerosol
V
V
Tire Cleaner
Liquid
V
V
Lubricants and
Greases
Tap & Die Fluid
Aerosol
V
V
Penetrating Lubricant
Aerosol
V
V
Adhesives and
Sealants
Solvent-based Adhesive & Sealant
Liquid
V
V
Mirror-edge Sealant
Aerosol
V
V
Tire Repair Cement/Sealer
Liquid
V
V
Cleaning and
Furniture Care
Products
Carpet Cleaner
Liquid
V
V
Spot Remover
Aerosol
V
V
Spot Remover
Liquid
V
V
Arts, Crafts, and
Hobby Materials
Fixatives & Finishing Spray Coatings
Aerosol
V
V
Apparel and
Footwear Care
Products
Shoe Polish
Aerosol
V
V
Other Consumer
Uses
Fabric Spray
Aerosol
V
V
Film Cleaner
Aerosol
V
V
Hoof Polish
Aerosol
V
V
Pepper Spray
Aerosol
V
V
Toner Aid
Aerosol
V
V
A range in acute inhalation and acute dermal exposures is provided in Table 2-85., summarized by the
consumer category. 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 iterations of the modeling scenario, which can be seen in the Supplemental Files {Exposure Modeling
Results and Risk Estimates for Consumer Inhalation Exposures and Risk Exposure Modeling Results
and Risk Estimates for Consumer Dermal Exposures. Docket: EPa 00].
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3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
Table 2-85. Summary of Consumer Exposure Levels by Category
Consumer Category
Acute Inhalation 24-hr TWA1
(ppm)
Acute Dermal ADR2
(mg/kg/d)
Solvents for Cleaning and
Degreasing
User
4.55E-02 - 1.62E+02
3.52E-02 - 2.38E+01
Bystander
8.47E-03 -4.71E+01
Lubricants and Greases
User
2.16E-02 - 1.47E+01
5.01E-03 -2.01
Bystander
4.21E-03 - 2.95
Adhesives and Sealants
User
6.64E-03 - 1.69E+01
1.28E-02 - 9.00
Bystander
1.30E-03 - 4.14
Cleaning and Furniture Care
Products
User
3.55E-01 - 5.26E+01
2.63E-02 - 5.82
Bystander
6.92E-02 - 1.15E+01
Arts, Crafts, and Hobby Materials
User
2.90E-01 - 9.31
1.49E-02 - 5.65E-01
Bystander
5.66E-02 - 2.28
Apparel and Footwear Care Products
User
5.96E-02 - 2.77
5.74E-03 - 3.76E-01
Bystander
1.16E-02 - 6.79E-01
Other Consumer Uses
User
1.77E-02 - 6.42E+01
1.41E-02 - 3.56
Bystander
7.79E-05 - 1.57E+01
'The level of variation displayed in the ranges of consumer categories reflect multiple, specific consumer conditions of use /
subcategories and do not reflect the degree of variation present within scenario-specific results. The displayed category
ranges therefore reflect a much broader spread of exposure estimates.
2The range in acute dermal ADRs reflect all age groups modeled (children and adult).
2,3.2,6 Assumptions and Key Sources of Uncertainty for Consumer
Exposures
EPA'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.
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 or stochastic methods. 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.
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3040
3041
3042
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3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
2.3.2.6.1 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 of low- 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 fraction, 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
Dermal and inhalation exposure estimates were not aggregated due to uncertainties associated with the
absence of a dermal compartment in the PBPK model. Further, background levels of TCE 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.4 were not considered for aggregation with modeled, use-specific 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. See Section 4.4.2 for additional discussion on
EPA's decision to not incorporate aggregate exposure.
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
of TCE products, only acute exposure is applicable.
Dermal Exposure Approach
For dermal exposure scenarios using the permeability model that may involve dermal contact with
impeded evaporation based on professional considerations of the formulation type and likely use pattern,
there is uncertainty surrounding the assumption that such dermal contact with impeded evaporation
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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.
For scenarios using the absorption fraction model that are less likely to involve dermal contact with
impeded evaporation, there is uncertainty surrounding the assumption that the entire mass present in the
thin film is absorbed and retained in the stratum corneum following a use event. The fractional
absorption factor estimated based on on Frasch and Bunge (2015) is intended to be applied to the mass
retained in the stratum cornum after exposure; it does not account for evaporation from the skin surface
during the exposure event. Therefore, the assumption that the entire amount of chemical present in the
thin film on the skin surface is retained in the stratum corneum may lead to uncertainty in the absorbed
dose estimate.
Inhalation Modeling for 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
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.
Bystanders
Inhalation exposures for bystanders in the home are estimated assuming that they are not present in the
room of use {i.e., Zone 1) during the use event. This is unlike the product user or consumer, who is
assumed to be present in Zone 1 for the duration of the use event. It is possible that bystanders could be
in the room of use, in which casern their exposure levels may approach those estimated for the product
users.
2,3.2,6,2 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. h). 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 product databases such as the NIH Household Product Survey
and EPA's Chemical and Products Database (CPDat), and internet searches using CASRNs, chemical
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3137
3138
3139
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3141
3142
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3146
3147
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3149
3150
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3161
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3168
names, and trade names to identify supplier and retail sites for available products, product labels, and
safety data sheets (SDSs). EPA also makes use of communications with companies, industry groups,
environmental organizations, and public comments to supplement the information when possible.There
are limited available product databases and they are not necessarily complete nor consistently updated
and general internet searches cannot guarantee entirely comprehensive product identification. Therefore,
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 Westat Survey, 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.
A crosswalk was completed to select the most appropriate Westat 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.
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,
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.
Ease of access to products on-line or in big box stores (like home improvement stores), readily
accessible how-to videos, and a consumer movement toward more do-it-yourself projects with products
containing the chemical of concern could impact the representativeness of the consumer use patterns
described within the Westat Survey and may lead to an underestimate of overall consumer exposure. In
addition, patterns of consumer use for certain subpopulations (e.g., tribal communities) may not be
represented in the survey data. Thus, there is a some uncertainty associated with the representativeness
of the consumer use patterns described within the Westat Survey and present day consumer use patterns.
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3186
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3194
3195
3196
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3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
Emission Rate
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 reasonably available. Therefore, the model used (CEM 2.1) estimates
of emission rate based on chemical properties and emission profiles matching a spray or liquid
application.
2.3.2.7 Confidence in Consumer Exposure Scenarios
The considerations and confidence ratings for the acute inhalation consumer exposure scenarios are
displayed in Table 2-86. 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/varied modeling 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. 2011c). 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 equation19 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). The representativeness of the consumer use patterns (duration of
use, amount used, room of use, etc.) described in the Westat Survey ( 7) is believed to
remain strong when compared to present day consumer use patterns even though some aspects of the use
may have changed. There is some uncertainty associated with the representativeness of the consumer
use patterns described within the Westat Survey and present day consumer use patterns. In some cases,
professional judgment was used in selection of room of use, which sets the volume for modeling zone 1.
The considerations and confidence ratings for the acute dermal consumer exposure scenarios are
displayed in Table 2-87. Overall, there is a moderate confidence in the consumer dermal exposure
modeling approach and results. For scenarios evaluated using the permeability model, there is
uncertainty related to the potential for and duration of dermal contact with impeded evaporation {i.e.,
dermal exposure scenarios wherein volatilization from the skin surface is inhibited). For scenarios
evaluated using the fraction absorbed model, there is uncertainty related to the application of the
fractional absorption term to the amount of chemical within the thin film {i.e., amount retained). Neither
approach incorporates any losses of chemical during the exposure event. However, in doing so, the
model assumes that there are no losses throughout the entire use duration. These factors contribute to the
overall lower confidence in dermal exposure estimates.
19 The value of k is determined from an empirical relationship, developed by (Chinn. 1981). between the time required for
90% of a pure chemical film to evaporate (EvapTime) and the chemical's molecular weight (MW) and vapor pressure
(VP): EvapTime = 145 / (MW x VP) 0.9546, k = ln(10) / (EvapTime x 60), where k = first-order rate constant for emission
decline (min-1), MW = molecular weight, VP = vapor pressure.
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3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
An additional point of confidence in the consumer modeling approach related to capturing variation and
estimating results for a range of exposure levels. Although a probabilistic assessment 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 distribution of
responses. For these parameters, a low-end (10th percentile), central tendency (50th percentile), and high-
end (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 minimum and maximum 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 parameters were varied in all possible combinations, resulting in up to 27
iterations for a given modeling scenario.
Consumer exposure monitoring studies associated with conditions of use are not reasonably available
for direct comparison with modeled results. Indoor air monitoring data are available but are not
associated with specific conditions of use or TCE-containing consumer products and are therefore only
relevant for considerations of background levels of TCE in homes.
While there were certain scenarios that have moderate confidence ratings rather than high confidence for
user-selected varied inputs, there are not reasonably available alternative inputs that would serve to
increase confidence in the modeling estimates. For example, in modeling film cleaner, the alternative to
applying mass used and use duration from the rust remover Westat survey scenario is professional
judgment, which is unlikely to decrease uncertainty.
Table 2-86. Confidence Ratings for Acute Inhalation Consumer Exposure Modeling Scenarios
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Confidence in User-Selected Varied Inputs
Overall
Category
Subcategory
Form
Default
Values2
Mass
Used3
Use
Duration4
Weight
Fraction
Room of
Use5
Confidence
Solvents for
Cleaning
and
Brake &
Parts
Cleaner
Aerosol
High
High
High
High
High
High
High
Decreasing
Solvents for
Cleaning
and
Electronic
Degreaser/
Cleaner
Aerosol
High
High
High
High
High
High
High
Degreasing
Solvents for
Cleaning
and
Electronic
Degreaser/
Cleaner
Liquid
High
High
High
High
High
High
High
Degreasing
Solvents for
Cleaning
and
Spray
Degreaser/
Cleaner
Aerosol
High
High
High
High
High
High
High
Degreasing
Solvents for
Cleaning
and
Liquid
Degreaser/
Cleaner
Liquid
High
High
High
High
High
High
High
Degreasing
Solvents for
Gun
Aerosol
High
High
High
Moderate
High
Moderate
Moderate
Cleaning
and
Scrubber
to High
Degreasing
Solvents for
Gun
Liquid
High
High
High
Moderate
High
Moderate
Moderate
Cleaning
Scrubber
to High
Page 206 of 803
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Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Confidence in User-Selected Varied Inputs
Overall
Category
Subcategory
Form
Default
Values2
Mass
Used3
Use
Duration4
Weight
Fraction
Room of
Use5
Confidence
and
Decreasing
Solvents for
Mold
Aerosol
High
High
Moderate
High
High
High
Moderate
Cleaning
and
Release
to High
Degreasing
Solvents for
Cleaning
and
Tire Cleaner
Aerosol
High
High
High
High
High
High
High
Degreasing
Solvents for
Cleaning
and
Tire Cleaner
Liquid
High
High
High
High
High
High
High
Degreasing
Lubricants
and Greases
Tap & Die
Fluid
Aerosol
High
High
High
High
High
High
High
Lubricants
and Greases
Penetrating
Lubricant
Aerosol
High
High
High
High
High
High
High
Adhesives
and Sealants
Solvent-
based
Adhesive &
Sealant
Liquid
High
High
High
High
High
High
High
Adhesives
and Sealants
Mirror-edge
Sealant
Aerosol
High
High
Moderate
Moderate
High
High
High
Adhesives
and Sealants
Tire Repair
Cement/
Sealer
Liquid
High
High
High
High
High
High
High
Cleaning
and
Carpet
Cleaner
Liquid
High
High
Moderate
Moderate
High
Moderate
Moderate
to High
Furniture
Care
Products
Cleaning
and
Spot
Remover
Aerosol
High
High
High
High
High
High
High
Furniture
Care
Products
Cleaning
and
Spot
Remover
Liquid
High
High
High
High
High
High
High
Furniture
Care
Products
Arts, Crafts,
Fixatives &
Aerosol
High
High
Moderate
Moderate
High
Moderate
Moderate
and Hobby
Materials
Finishing
Spray
Coatings
to High
Apparel and
Footwear
Shoe Polish
Aerosol
High
High
High
High
High
High
High
Care
Products
Other
Consumer
Fabric Spray
Aerosol
High
High
High
High
High
High
High
Uses
Page 207 of 803
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Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in User-Selected Varied Inputs
Overall
Confidence
Category
Subcategory
Form
Mass
Used3
Use
Duration4
Weight
Fraction
Room of
Use5
Other
Consumer
Uses
Film Cleaner
Aerosol
High
High
Moderate
Moderate
High
Moderate
Moderate
to High
Other
Consumer
Uses
Hoof Polish
Aerosol
High
NA
Moderate
Moderate
High
High
Moderate
to High
Other
Consumer
Uses
Pepper
Spray
Aerosol
High
NA
High
High
High
Moderate
Moderate
to High
Other
Consumer
Uses
Toner Aid
Aerosol
High
High
Moderate
Moderate
High
Moderate
Moderate
to High
'The inhalation models within CEM 2.1 have been peer reviewed, are publicly available, and have been applied in a manner
intended - to exposures associated with uses of household products and/or articles.
2These values include inputs such as building and room volumes, interzonal ventilation rates, and air exchange rates. These default
values are all central tendency values (i.e., mean or median values) sourced from EPA's Exposure Factors Handbook (IIS. EPA.
2011c).
3Mass Used is primarily sourced from the Westat (1987) survey, which received a high-quality rating during data evaluation and
has been applied in previous agency assessments. Two conditions of use had product information that was used instead of Westat
(gun scrubber and pepper spray).
4Use Duration is primarily sourced from the Westat (.1.987) survey, which received a high-quality rating during data evaluation and
has been applied in previous agency assessments. One condition of use had product information that was used instead of Westat
(pepper spray). Relevance of these inputs from the Westat survey to the specific consumer condition of use they were applied to is
considered in the reported confidence ratings.
5Room of use (zone 1 in modeling) is informed by responses in the Westat (.1.987) survey, which received a high-quality rating
during data evaluation, although professional judgment is also applied for some scenarios. The reasonableness of these judgements
is considered in the reported confidence ratings.
3237
3238
Table 2-87. Confidence Ratings
for Acute Dermal Consumer Exposure Modeling Scenarios
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in User-Selected Inputs
Overall
Confidence
Category
Subcategory
Form
Use
Duration3
Weight
Fraction
Kp4
Solvents for
Cleaning and
Degreasing
Brake & Parts
Cleaner
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Electronic
Degreaser/
Cleaner
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Electronic
Degreaser/
Cleaner
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Spray
Degreaser/
Cleaner
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Liquid
Degreaser/
Cleaner
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Gun Scrubber
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Gun Scrubber
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Mold Release
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Page 208 of 803
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Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in User-Selected Inputs
Overall
Confidence
Category
Subcategory
Form
Use
Duration3
Weight
Fraction
Kp4
Tire Cleaner
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Tire Cleaner
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Lubricants
and Greases
Tap & Die
Fluid
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Penetrating
Lubricant
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Adhesives
and Sealants
Solvent-based
Adhesive &
Sealant
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Mirror-edge
Sealant
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Tire Repair
Cement/
Sealer
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Cleaning and
Furniture
Care
Products
Carpet
Cleaner
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Spot Remover
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Spot Remover
Liquid
Low to
Moderate
Moderate
Low
High
High
Moderate
Arts, Crafts,
and Hobby
Materials
Fixatives &
Finishing
Spray
Coatings
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Apparel and
Footwear
Care
Products
Shoe Polish
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Other
Consumer
Uses
Fabric Spray
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Film Cleaner
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Hoof Polish
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Pepper Spray
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
Toner Aid
Aerosol
Low to
Moderate
Moderate
Low
High
High
Moderate
1 The dermal models used (permeability and absorption fraction models within CEM 2.1) have been peer reviewed, are publicly
available, and have been applied in a manner intended - to estimate exposures associated with uses of household products and/or
articles. The low to moderate confidence reflects uncertainties discussed in Section 2.3.2.6.1.
2These values include inputs such as surface area to body weight ratios reflecting dermal contact area and film thickness applied
in the absorption fraction model. These values are sourced from EPA's Exposure Factors Handbook (IIS. EPA. 201 lc).
3The dermal permeability coefficient (Kp) used (0.0023 cm/hr) is derived from the measured flux for TCE (430 nmol/cm2-min
[5.65E-02 mg/cirf-min]) for neat TCE on human skin from (Kezic et al. 2001).
4The use duration is primarily sourced from the Westat (1987) survey, which received a hieh-aualitv ratine durine 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 how accurately an exposure event duration reflects dermal contact time.
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3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
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2.3.3 Potentially Exposed or Susceptible Subpopulations
TSCA requires that a Risk Evaluation "determine whether a 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 exposed or susceptible subpopulation' means a 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 susceptibility in Section 3.2.5.2.
In developing the final Risk Evaluation, EPA analyzed the 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 greater 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:
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 of TCE). Exposure estimates
were developed for employees (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 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 2.3.3. For example, higher-
intensity users {i.e., those using consumer products for longer durations and in greater amounts) were
considered and evaluated in Section 2.3.2. In addition, consumers are considered to include adults as
well as children as young as age 11. Bystanders in the home exposed via inhalation are considered to
include any age group from infant (including breast fed infants) to adult (including elderly), including
pregnant women and/or women of reproductive age. Younger lifestages are likely exposed to higher
internal dose concentrations of TCE than adults due to relative physiological differences in body weight,
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breathing rate, and other parameters. 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 2.3.2.5 (Table 2-32 through Table 2-82.).
Additionally, higher-intensity users (i.e., those using consumer products for longer durations and in
greater amounts) were considered and evaluated. Exposures and risks for these subpopulations are
considered and evaluated herein. Receptor categories overlap among highly exposed and potentially
exposed subpopulations, as individuals may belong to multiple PESS groups.
In developing dermal exposure scenarios, EPA quantified age and sex-specific differences. For TCE,
exposure scenarios that involve potentially exposed or susceptible subpopulations considered age-
specific behaviors, activity patterns, and exposure factors unique to those subpopulations. EPA used the
Exposure Factors Handbook (U.S. EPA 2011c) to inform body weights, intake rates, and body surface
areas for children and adults. Distinct dermal exposure estimates are provided for adults (including
women of reproductive age) and children (Section 2.3.2.5.1).
For occupational exposures, EPA assessed exposures to workers and ONUs from all TCE conditions of
use. Table 2-88. presents the percentage of employed workers and ONUs who may experience either
greater exposure or biological susceptibility within select industry sectors relevant to TCE conditions of
use. The percentages were calculated using Current Population Survey (CPS) data for 2017 (U.S. BLS,
2017). CPS is a monthly survey of households conducted by the Bureau of Census for the Bureau of
Labor Statistics and provides a comprehensive body of data on the labor force characteristics. Statistics
for the following subpopulations of workers and ONUs are provided: adolescents, men and women of
reproductive age, 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 2-88., men make up the majority of the workforce in manufacturing sectors. In other
sectors, women (including those of reproductive age and elderly women) make up nearly half of the
workforce. Adolescents are generally a small part of the total workforce. Table 2-89. presents further
breakdown on the percentage of employed adolescents by industry subsectors. As shown in the tables,
they comprise only 1.2% percent of the manufacturing workforce, and only as high as 3.7% for other
services such as dry cleaning that fall under a COU for TCE.
Table 2-88. Percentage of Employed Persons by Age, Sex, and Industry Sector
Age group
Sex
Manufacturing
Wholesale and
Retail Trade
Professional and
Business Services
Other Services
Adolescent
(16-19 years)
Male
0.8%
3.0%
0.7%
1.4%
Female
0.4%
3.2%
0.5%
1.7%
Reproductive age
(16-54 years)
Male
52.9%
42.8%
44.4%
35.2%
Female
22.2%
35.4%
32.8%
38.4%
Elderly (55+)
Male
17.5%
12.3%
13.4%
13.1%
Female
7.3%
9.6%
9.4%
13.3%
Source: (U.S. BLS. 2017). While statistics on pregnant women are not reasonably available, CPS provides data on the
number of employed female workers by age group, which allows for determination of the number of employed women of
reproductive age. Percentage calculated using CPS Table 14, "Employed persons in nonagricultural industries by age, sex,
race, and Hispanic or Latino ethnicity."
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Table 2-89. Percentage of Employed Adolescent by Detailed Industry Sector
Sector
Subsector
Adolescent
(16-19 years)
Manufacturing
All
1.2%
Wholesale and retail trade
Wholesale trade
1.4%
Professional and business
services
Waste management and remediation services
0.9%
Other services
Repair and maintenance
3.1%
Dry cleaning and laundry services
3.7%
Source: (U.S. BLS. 2017). Percentage of adolescent calculated using CPS table 18b, "Employed persons by detailed industry
and age."
The CPS uses 2012 Census industry classification, which was derived from the 2012 NAICS. The
Census classification uses the same basic structure as NAICS but is generally less detailed. TCE
conditions of use fall under the following Census industry sectors:
Manufacturing - The Manufacturing sector comprises establishments engaged in the mechanical,
physical, or chemical transformation of materials, substances, or components into new products.
Establishments in the sector are often described as 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-Aerosol Products, the vast majority of facilities likely
engaged in Vapor Degreasing (all degreaser types), Cold Cleaning, Metalworking Fluids, Adhesives,
Sealants, Paints and Coatings, Other Industrial Uses, Industrial Processing Aids and Printing and
Copying. This sector also covers cement manufacturing facilities that may burn waste containing TCE
for energy recovery. Printing and Copying worker information may also be captured under the
Information sector (see below).
Wholesale and retail trade - The wholesale trade sector comprises establishments engaged in
wholesaling merchandise, generally without transformation, and rendering services incidental to the sale
of merchandise. Wholesalers normally operate from a warehouse or office. This sector likely covers
facilities that are engaged in the repackaging TCE or products and formulations containing TCE. The
retail trade sector comprises establishments engaged in retailing merchandise and rendering services
incidental to the sale of merchandise.
Professional and business services - This sector comprises establishments that specialize in a
wide range of services. This sector covers waste management and remediation services, which includes
establishments that may handle, dispose, treat, and recycle wastes containing TCE.
Other services - This sector comprises establishments engaged in providing services not
specifically provided for elsewhere in the classification system. For TCE, this sector covers the vast
majority of commercial repair and maintenance facilities that are likely to use TCE for Aerosol
Applications (spray degreasing). The sector also covers the use of TCE in spot cleaning.
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3 HAZARDS
3.1 Environmental Hazards
3.1.1 Approach and Methodology
During scoping and Problem Formulation ( 018d), EPA reviewed potential environmental
health hazards associated with TCE. EPA identified the following sources of environmental hazard data:
European Chemicals Agency (ECHA) Database (EC ), European Union (EU) environmental
risk assessment on TCE (ECHA.. 2004) EPA Chemical Test Rule Data (U.S. 017a) 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 ( 0171).
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-HO-OPPT-2019-050Q~\ and indicate that most of the
acceptable studies for TCE were rated high or medium for quality. With the reasonably available data,
EPA used studies rated high or medium for quantitative analysis during data integration, and used
studies rated low qualitatively to characterize the environmental hazards of trichloroethylene. Any study
assigned an overall quality level of unacceptable was not used for data integration. Mechanistic studies
were used qualitatively, because toxicity values measuring a population-level effect (e.g., mortality,
development, growth) were available to use quantitatively.
3.1.2 Hazard Identification
Toxicity to Aquatic Organisms
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 the text below, and the data EPA used quantitatively are displayed in Table 3-1. 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
\Jordanella floridae\ bluegill [Lepomis macrochirus], and sheepshead minnow \Cyprinodon
variegatus]). In these studies, all used quantitatively in this assessment, the lethal concentrations at
which 50% of test organisms die (LCsos) ranged from 28.28 mg/L to 66.8 mg/L (Geteer et at.. 1985):
(Broderius et at... 2005: Smith et at.. 1991: \\ it... 1986: Buccafusco et at... 1981; Alexander et at..
1978). Ward et al. (1986) tested a saltwater species, sheepshead minnow, and derived an LCso of 52
mg/L. Because this value is within 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 at..
1978); (Ward et at.. 1986); (Broderius et al. 2005). freshwater and saltwater LCso values were assessed
together during data integration. EPA calculated a geometric mean of 42 mg/L using LCsos from high
and medium quality studies. Acute fish data for TCE also included a 96-hour EC so (the concentration at
which 50% of test organisms exhibit an effect) of 21.9 mg/L for loss of equilibrium in a freshwater
species, fathead minnows (Alexander et at.. 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.
(1991) established a 10-day NOEC of 5.758 mg/L and a LOEC of 21.233 mg/L resulting in a chronic
value (ChV) of 1 1 mg/L for fry survival in American flagfish {Jordanella floridae). Schell (1987)
established a 10-day LCso of 82 mg/L in 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, and EPA used the data quantitatively.
Chronic fish data for TCE were identified in two acceptable studies representing two freshwater species,
American flagfish {Jordanella floridae) 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/L and
a LOEC of 20.915 mg/L for fry survival in American flagfish. This allowed the authors to establish a
28-day ChV of 14.85 for fry survival. Broderius et al. (2005) established an EC.mi for growth of 11.8
mg/L and an EC20 for growth of 7.88 mg/L in a 32-day fathead minnow study. Both studies were rated
high for quality during data evaluation. EPA used the chronic data in these studies quantitatively.
Broderius et al. (2005) reported baseline narcosis as TCE's expected mode of action in fish. This is
corroborated by other studies, including Ward, et al. (1986). which observed signs of narcosis in
sheepshead minnows, a saltwater species, with observations of fish spinning at 357 mg/L. EPA used this
information qualitatively in this assessment. Alexander et al. ( J) reported signs of narcosis in fathead
minnows, a freshwater species, with a 96-hour EC10 of 13.7 mg/L, EC50 of 21.9 mg/L, and EC90 of 34.9
mg/L. The effect reported was loss of equilibrium. EPA used the 96-hour EC50 from Alexander et al.
(1978) quantitatively in this assessment.
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 EC50 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.
Amphibian Toxicity
For amphibians, acute data were available from three acceptable studies, representing one species,
African clawed frogs (Xenopus laevis). All three studies were rated either high or medium for quality
during data evaluation. The studies included 96-hour LC50 values ranging from 412.0 mg/L to 490.0
mg/L (McDaniel et al.. 2004; Fort et al.. 2001; Fort et at. 1993; Fort et al.. 1991). EPA used these
studies quantitatively, and during data integration, a geometric mean of all LCsos was calculated at 438
mg/L.
Sub-chronic data were also available for amphibians, from four acceptable studies representing five
different species (green frog [Lithobates clamitans, formerly Rana clamitans\ wood frog [Lithobates
sylvatica, formerly Rana sylvatica], African clawed frogs [.Xenopus laevis], American toad \Bufo
americanus], and spotted salamander \Ambystoma maculatum]). These studies reported 96-hr EC50
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values for developmental effects ranging from 22 mg/L to > 85 mg/L (McDaniel et at.. 2004; Fort et at..
200J; hct et at... 1993; Fort et at... 1991). EPA used these data quantitatively, and during data
integration, a geometric mean of all definitive ECsos 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. They could also result in premature death. Developmental effects described included
gut miscoiling and microphthalmia, muscular kinking, incomplete development of the mouth, and severe
hypognathia in African clawed frogs, and edema and dorsal flexure of the tail and notochord in tadpoles
of green frogs, wood frogs, American toads, and spotted salamanders (McDaniel et at.. 2.004; Fort et at..
1993; Fort et at.. 1991). As stated previously, McDaniel et al. (2.004) 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 American toad 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 of TCE. 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. EPA used this information qualitatively.
McDaniel et al. (2004) reported signs of narcosis in green and wood frog tadpoles.
Aquatic Invertebrate Toxicity
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 values or ECso values
measuring immobilization rated high or medium for quality; these values ranged from 7.75 mg/L to
43.14 mg/L for Daphnia magna, Ceriodaphnia dubia, andMysidopsis bahia (Dobaradaran et at.. 2012;
Niederlehner et at.. 1998; Abernethy et al.. 1986; Ward et a 5; LeBtamc. 1980). The only saltwater
species tested, Mysidopsis bahia, had an LCso of 14 mg/L, which is within the of the range of values for
freshwater species. EPA used these data quantitatively. Additionally, Ward et al. (1986) 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 EC so and LCsos from high and medium quality studies is 16 mg/L. EPA used
these data quantitatively. Another study, Sanchez-Fortun et al. (1997). rated low for quality, established
LCsos in Artemia salina larvae at three different ages; however, this study was not used quantitatively
during data integration, given that medium and high-quality studies were available for invertebrates.
One sub chronic study found an LC50 of 1.7 mg/L in planarian (Dugesia japonica) over 7 days (Yoshioka
et al.. 1986). This study was rated low for quality. Because other higher quality studies were available
for aquatic invertebrates, this study was not used quantitatively during data integration.
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 a NOEC of 7.1 mg/L and a LOEC of 12 mg/L
for reproduction in Ceriodaphnia dubia, resulting in a ChV of 9.2 mg/L. Niederlehner et al. (1998)
established a 7-day reproductive inhibitory concentration (IC50) of 11 mg/L, the concentration at which
the mean number of young decreased by 50%. EPA used these data quantitatively.
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Two studies reported baseline narcosis as the mode of action for TCE in invertebrates. Ward et al.
(1986) observed mild intoxication in Mysidopsis bahia, a saltwater species, and Niederlehner et al.
(1998) observed behavioral changes, including narcosis and abnormal movement in Ceriodaphnia
dubia, a freshwater species. EPA used this information qualitatively.
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, Corbicula fluminea. A
one-time exposure over five days resulted in a significant change in protein activity related to phase I
metabolism. Results indicated aNOEC of 1.2 mg/L and aLOEC of 3.6 mg/L for significantly increasing
cytochrome P-450 levels, and a NOEC of 3.6 mg/L and LOEC of 14 mg/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 of TCE on Daphnia magna at the cellular and life-stage levels. The authors
found a significant increase in chitinase production over 10 days, with a NOEC of 0.001 mg/L and a
LOEC of 0.01 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. Because these mechanistic data are not directly linked to a population-level response,
these data were used qualitatively.
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 rather than being categorized as acute or chronic.
There were six acceptable studies that 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
literature for algae exposed to TCE. ECsos measuring growth represent nine species and range from
26.24 mg/L to 820 mg/L (Lukavsky et al.. 2.011; Labra et al.. 2010; Tsai and Chen. 2007; An do et al..
2003; Brack and Rottler. 1994; Ward et al.. 1986). Ward et al. (1986) reported results on the only
saltwater species found in the acceptable studies, Skeletonema costatum, with an ECso of 95 mg/L. This
value is within 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 high and medium quality
EC.mis. A 72-hour ECio of 12.3 mg/L was also established by Brack and Rottler (1994) measuring
biomass (a measure of growth) in Chlamydomonas reinbardtii, a freshwater eukaryotic green algae.
Additionally, several NOECs and LOECs were established. Labra et al. Q ) found a 72-hour NOEC
of 0.02 mg/L and 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. EPA used the abovementioned algae data
quantitatively.
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 Volvulina steinii. They
found no significant change in the relative absorbance of chlorophyll a for S. capricornutum or C.
vulgaris during the 10-day test; however, they established a 10-day LOEC of 0.003 mg/L for V. steinii, a
flagellar algae. The authors attributed the variation in algal species sensitivity to TCE to V. steinii' s high
metabolism. For several reasons explained in Section 3.1.4, these data were considered less biologically
relevant than values from other studies and were not used quantitatively during data integration.
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3550 Table 3-1. Ecological Hazard Data used Quantitatively to Characterize TCE Hazard for Aquatic
Duration
Test
organism
Endpoint
Hazard
value
(mg/L)1
Geometric
Mean2
(mg/L)
Effect Endpoint
Citation
(Study Quality)
Acute3
Fish
LCso
(freshwater)
28.28-66.8
42
Mortality
(Geiger et al.. 1985) (liieh);
(Alexander et al.. 1978)
(liieh); (Smith et al.. 1991)
(liieh); (Broderius et al..
2005) (liieh); (Buccafusco et
al.. 1981) (medium)
LCso
(saltwater)
52
(Ward et al.. 1986) (medium)
ECso
(freshwater)
21.9
Immobilization
(Alexander et al.. 1978)
(high)
Amphibian
LCso
412.0-
490.0
436
Mortality
(Fort et al.. 2001) (medium);
(Fort et al.. 1991) (medium);
(Fort et al.. 1993) (high)
Aquatic
Invertebrates
EC50/LC50
(freshwater)
7.8-33.85
16
Mortality and
Immobilization
(LeBlanc. 1980) (high);
(Niederleliner et al.. 1998)
(high); (Abernethv et al..
1986) (medium);
(Dobaradaran et al.. 2012)
(medium)
LC50
(saltwater)
14
(Ward et al.. 1986) (medium)
Subchronic
/Chronic3
Fish
EC20
7.88
Growth
(Broderius et al.. 2005)
(high)
ECso
11.8
Growth
NOEC
LOEC
ChV
10.568
20.915
14.87
Fry Survival
(Smith etal.. 1991) (high)
NOEC
LOEC
ChV
(subclironic)
5.758
21.233
11
Fry Survival
LC50
(subclironic)
82
Mortality
(Schell. 1987) (high)
Amphibians
NOEC
4
Tadpole
Survival
(McDaniel et al.. 2004)
(medium)
ECso
(subclironic)
22 - >85
34
Deformities
(Fort et al.. 2001) (medium);
(Fort et al.. 1991) (medium);
(Fort et al.. 1993) (high);
(McDaniel et al.. 2004) (high
and medium)
Aquatic
invertebrates
NOEC
LOEC
ChV
7.1
12
9.2
Reproduction
(Niederleliner et al.. 1998)
(high)
ICso
11
Algae4
ECso
(freshwater)
26.24 - 820
242
Growth
(Brack and Rottler. 1994)
(high); (Tsai and Chen. 2007)
(high); (Labra et al.. 2010)
(medium); (Ando et al..
2003) (medium); (Lukavskv
et al.. 2011) (medium)
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EC50
(saltwater)
95
(Ward et al.. 1986) (medium)
EC10
12.3
Growth
(Brack and Rottler. 1994)
(high)
NOEC
0.02
LOEC
0.05
Growth
(Labra et al.. 2010) (medium)
ChV
0.03
Values in the table are presented in the number of significant figures 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 amphibian data
4 Because algae can cycle through several generations in hours to days, the data for algae was assessed together regardless of
duration (i.e., 48-hrs to 96-lirs).
Values in bold were used to derive Concentrations of Concern (COC) as described in Section 3.1.5 of this document. All
values are listed individually with study quality in [Data Quality Evaluation of Environmental Hazard Studies and Data
Extraction for Environmental Hazard Studies. Docket: EPA-HQ-QPPT-2019-0500\.
3.L3 Species Sensitivity Distributions (SSDs)
A Species Sensitivity Distribution (SSD) is a type of probability distribution of 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 test
species. This hazardous concentration is represented as an HCP, where p is the percent of species. EPA
used an HCos (a Hazardous Concentration threshold for 5% of species) to estimate a concentration that
would protect 95% of species.
EPA created SSDs using EPA's SSD Toolbox and the acute hazard data for aquatic species, including
fish, amphibians, and invertebrates (Figure 3-1) (Etterson, 2020). The input data for Figure 3-1 included
acute toxicity values measuring mortality available in the literature representing four species of fish
(LCsos), one species of amphibian (LCsos), and three species of invertebrates (LCsos/ECsos). For
invertebrates EC50s measuring immobilization were used in addition to LCsos, because it is difficult to
distinguish between death and immobilization for aquatic invertebrates. As stated previously, freshwater
and saltwater species were assessed together, because the saltwater values were within the range of
freshwater species in the same taxonomic group. Additionally, for fish and invertebrates, the mode of
action for freshwater and saltwater species is expected to be the same (Broderius et al.. 2005; Ward et
al.. 1986; Alexander et al.. 1978).
Using acute hazard data for these aquatic species, EPA derived a model-averaged HCos from the normal,
logistic, triangular, Gumbel, and Burr distributions (Figure 3-1). The model-averaged HCos from all five
distributions was 10 mg/L, which estimates a concentration that is hazardous for 5% of aquatic species.
The SSDs showed aquatic invertebrates were the most sensitive species.
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^—normal distribution
^-logistic distribution
^—triangular distribution
gumbel distribution
burr distribution
~ HC05
Cyprinodon variegatus (sheepshead)
¦8 0-6
Xenopus laevis (Afnc
ed frog)
Lepomis macrochirus (bluegill)
Pimephales promelas (fathead minnow)
anella ftondae (flagfish)
odaphnia dubia
Daphnia magna
Mysidopsis bahia
1 1.5 2 2.5
Toxicity Value (Log 10[EC50]) mg/L
Figure 3-1. Species Sensitivity Distributions (SSDs) for Acute Hazard Data Using LC50S or ECsos
(Etterson., 2020)
Note: The data in this figure includes LC50S and ECsoS measuring mortality and immobilization from medium- or high-quality
studies. A black dot indicates the toxicity value used for that species. The red diamonds indicate HCosS for the normal,
logistic, triangular, Gumbel, and Burr distributions using the maximum likelihood fitting method (Appendix E. 1).
This SSD shows that generally, invertebrates are the most sensitive taxonomic group to short-term (48-
96 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
(.Jordanellafloridae) the most sensitive fish.
A chronic SSD for aquatic species was not created due to insufficient data.
As stated previously, there was a wide range of toxicity values reported in the literature for algae
exposed to TCE. ECsos were 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 3-2 shows the
SSD for algae created using EPA's SSD Toolbox (Etterson. 2020). The data used in the SSD includes
ECsos measuring growth from freshwater species, a saltwater species, cyanobacteria, eukaryotes, a
diatom, and a colonizing species. As stated in Section 3.1.2, saltwater and freshwater species were
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assessed together, because the only saltwater species, Skeletonema costatum, had an EC so within the
range of values for freshwater species.
Using algae hazard data, EPA derived a model-averaged HCos from six distributions, the normal,
logistic, triangular, Gumbel, Weibull, and Burr distributions (Figure 3-2). The model-averaged HCos
was 72 mg/L, which estimates a concentration that is hazardous for 5% of aquatic species.
1
0.9
0.8
0.7
g
1 0.6
n
E
Q_
To
| 0.4
=J
o
0.3
0.2
0.1
0
1 1.5 2 2.5 3 3.5 4 4.5
Toxicity Value (Log 10[EC50]) mg/L
Figure 3-2. Species Sensitivity Distribution (SSD) for Algae Species Using ECsos (Etterson, 2020)
Note: The data in this figure includes EC50S measuring growth from medium- or high-quality studies. A black dot indicates
the toxicity value used for that species. The red diamonds indicate HG*s for the normal, logistic, triangular, Gumbel,
Weibull, and Burr distributions using the maximum likelihood fitting method (Appendix E. 1).
Given these data, certain algae species may be more sensitive than others; however, there is not enough
data to make definitive conclusions. The three cyanobacteria, Mycrocystis aeruginosa, Synechococcus
leopoliensis, and Synechococcus elongatus, are distributed throughout the curve and as a group do not
appear to be more or less sensitive than the eukaryotic species. 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, Mycrocystis aeruginosa, is also one of the more sensitive species represented on
the curve. However, with only one saltwater species, diatom, and colonizing species represented,
generalizations about the sensitivity of these types of algae could not be made.
It is important to note that, for consistency, this distribution only includes ECsos to compare between
studies and species. Therefore, it does not capture some of the lowest toxicity 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. To account for this uncertainty, EPA used an assessment factor (AF) of 5
when calculating the concentration of concern (COC), which is described in Section 3.1.5.
normal
logistic
distribution
distribution
Synechococcus elongatus * S
triangular distnbution
gumbel distribution
weibull distribution
t_ _»• * »• L/VSIllUUtfSrilUS SUUSUIlsCllUS — Ą *
burr distnbution Mr
~ HC05 Jfr
Desmodesmus quadricaudeM
oynecnococcus leopoaeijsis »
m; wnassitm
• Ctmmydomonas reinhartdtii
• JMycrocystis aemginosa
Mkeletonema costatum
—
Raphidocelis subcapitata
i
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3.1.4 Weight of the Scientific Evidence
During the data integration stage of systematic review EPA analyzed, synthesized, and integrated the
data/information. This involved weighing the scientific evidence for quality and relevance, using a
weight-of-evidence approach (U.S. EPA. 2018b).
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 environmental hazard data for TCE, EPA gave more
weight to relevant data/information rated high or medium for quality than to data/information rated low.
Only data/information 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
relevance in selecting data/information for this Risk Evaluation, specifically biological,
physical/chemical, and environmental relevance (U.S. EPA. 1998):
- Biological relevance: correspondence among the taxa, life stages, and processes measured or
observed and the assessment endpoint.
- Physical/chemical relevance: correspondence between the chemical or physical agent tested and
the chemical or physical agent constituting the stressor of concern.
- Environmental relevance: correspondence between test conditions and conditions in the region of
concern. (U.S. EPA. 1998)
EPA used this weight-of-evidence approach to assess hazard data and develop concentrations of concern
(COCs) and HCoss. Given the reasonably available data, EPA was able to use studies assigned an overall
quality level of high or medium to derive COCs or HCoss for each taxonomic group and could avoid
studies rated low for quality. EPA integrated data for each trophic level that had comparable toxicity
values (e.g., multiple ECsos measuring the same or comparable effects from various species within a
trophic level). EPA used probabilistic approaches (e.g., SSDs) when enough data were available and
deterministic approaches (e.g., deriving a geometric mean of several comparable values) where more
limited data were available. To calculate HCoss, EPA created SSDs for algae species using comparable
data (e.g., ECsos measuring growth) and for all other aquatic species (e.g., LCsos for fish and
amphibians, and LCsos measuring mortality and ECsos measuring immobilization for aquatic
invertebrates). Non-definitive toxicity values (e.g., ECso >85 mg/L) were not integrated with other data
to derive HCoss or geometric means.
To assess aquatic toxicity from acute exposures, data for three taxonomic groups were reasonably
available: fish, amphibians, and aquatic invertebrates. For each taxonomic group, data were available for
multiple species, and enough acute data were available to create an SSD, which showed that the three
most sensitive species in the distribution are aquatic invertebrates. EPA used the SSD to derive a model-
averaged HCos of 10 mg/L. In addition to this probabilistic approach, EPA integrated the data for each
taxonomic group by calculating geometric means 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. However, EPA has more confidence in the probabilistic approach.
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, whereas the NOEC did
not. Of the more relevant values, the most sensitive was the EC20 measuring growth in fish at 7.88 mg/L.
The EC20 was from a high-quality study, whereas the NOEC of 4 mg/L was from a medium quality
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study. Considering the relevance and the quality of each value, EPA had more confidence in the EC20
for fish than in the NOEC for tadpoles.
To assess the toxicity of TCE to algae, data for 11 species were reasonably 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 from Ando et al. (2003). rated medium for quality. However, the study did not include
critical details, such as analytical measurement of test concentrations, or chemical substance source or
purity, and the authors were not able to establish a NOEC. Therefore, these data were considered less
biologically relevant than values from other studies, and not used quantitatively during data integration.
The ChV of 0.03 from Labra et al. (2010) was the most sensitive endpoint from the more relevant
studies. Labra et al. (2010) was rated medium for quality. An EC 10 of 12.3 mg/L from a high-quality
study, Brack et al. (1994). was also available; however, taking biological relevance into consideration,
EPA used the ChV derived from Labra et al. (2010). because there was a wide range in toxicity values
reported in the literature between algae species. Therefore, EPA used the value from Raphidocelis
subcapitata (formerly known as Pseudokirchneriella subcapitata) from Labra et al. ( ) to represent
the more sensitive algae species in the COCs. (According to the algae SSD, Raphidocelis subcapitata is
generally more sensitive to TCE exposure than Chlamydomonas reinhartdtii, 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. EPA has more
confidence in the probabilistic approach.
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 (
EPA. 20161. ). 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. 20161. 2012c). The application of AFs provides a lower bound effect level that
would likely encompass more sensitive species not 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
reasonably 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. 2013. 2012c).
To derive an acute COC for TCE, EPA used acute aquatic species data representing eight species to
produce an SSD, which was used to calculate an HC05 of 10 mg/L. As stated previously, this HC05
estimates a concentration that is hazardous for 5% of species. The HC05 estimates the concentration of
TCE that is expected to protect 95% of algae species. Because the SSD was created using the limited
number of species available across multiple taxa, EPA applied an assessment factor of 5. The HC05, 10
mg/L was divided by an assessment factor of 5, and then multiplied by 1,000 to convert mg/L to |ig/L
(or ppb).
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Therefore, the acute COC derived from the HCos = (10 mg/L) / AF of 5 = 2 x 1,000 = 2,000 |ig/L or
ppb.
The acute COC derived from the HCos for TCE is 2,000 ppb.
Additionally, EPA used the geometric mean of the ECso and LCsos for aquatic invertebrates from five
different studies, all rated high or medium for quality (Dobaradaran et at.. 2012; Niederlehner et at..
1998; Abernethy et at.. 1986; Ward et at.. 1986; LeBtanc. 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. EPA derived the geometric
mean, because the hazard values for all three species were similar, and because EPA had more
confidence in a COC derived from a geometric mean for three species than a COC derived from one
value from one species. 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 |ig/L (or ppb).
Therefore, the acute COC = (16 mg/L) / AF of 5 = 3.2 x 1,000 = 3,200 |ig/L or ppb.
The acute COC derived from the geometric mean 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 at.. 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 |ig/L (or ppb).
Therefore, the chronic COC = (7.88 mg/L) / AF of 10 = 0.788 x 1,000 = 788 |ig/L or ppb.
The chronic COC for TCE is 788 ppb.
To derive an algal COC, EPA used algae data representing nine species to produce an SSD, which was
used to calculate an HC05 of 72 mg/L. As stated previously, this HC05 estimates a concentration that is
hazardous for 5% of species. The HC05 estimates the concentration of TCE that is expected to protect
95% of algae species. Because the SSD was created using ECsos rather than ECios or ChVs and because
no higher order plants were represented in the data, EPA applied an assessment factor of 5. The HC05,
72 mg/L was divided by an assessment factor of 5, and then multiplied by 1,000 to convert mg/L to |ig/L
(or ppb).
Therefore, the algal COC derived from the HC05 = (72 mg/L) / AF of 5 = 14.4 x 1,000 = 14,400 |ig/L or
ppb.
The algal COC derived from the HC05 for TCE is 14,400 ppb.
Additionally, EPA used a geometric mean of a LOEC and a NOEC for growth in Raphidocelis
subcapitata (Labra et at.. 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 |ig/L (or ppb).
Therefore, the algal COC = (0.03 mg/L) / AF of 10 = 0.003 x 1,000 = 3 |ig/L or ppb.
The algal COC derived from geometric mean of the NOEC and LOEC (ChV) for TCE is 3 ppb.
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3.1.6 Summary of Environmental Hazard
The reasonably 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 (LCsos
and ECsos 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/L (EC20 for growth) and 9.2 mg/L (ChV for
reproduction), 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 (ECsos ranging from 26.24 - 820 mg/L).
EPA calculated COCs for aquatic organisms, which are summarized in Table 3-2. EPA calculated an
acute COC from the HCos of 2,000 ppb for aquatic organisms based on the LCsos (and ECsos measuring
immobilization for aquatic invertebrates) for eight species, from studies rated medium and high for
quality. EPA also calculated an acute COC for TCE at 3,200 ppb, based on the geometric mean of LCsos
and ECsos for aquatic invertebrates, from five studies rated either high or medium for quality
(Dobaradaran et at.. 2012; Niederlehner et at.. 1998; Abernethy et at.. 1986; Ward et at.. 1986; LeBtanc.
1980). EPA calculated the chronic COC for TCE at 788 ppb, based on an EC20 for fathead minnows
from Broderius et al. (2.005). 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., 96 hours) can encompass several generations of algae.
EPA calculated a COC from the HCos of 14,400 ppb for algae based on the ECsos for nine species, from
studies rated medium and high for quality. EPA also calculated an algal COC for TCE at 3 ppb, based
on a geometric mean of a LOEC and NOEC for growth in Raphidocelis subcapitata from Labra et al.
(2010). a study rated medium for quality.
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity
l-n\iionmental Aquatic Toxicity
1 la/ard
Assessment
Concentration
Value (Liu 1.)
l actor (A1 ¦')
of Concern
(Liu 1. or pph)
Toxicity from Acute Exposure:
Probabilistic Approach (HCos from SSD)
10,000
5
2,000
Deterministic Approach (Geometric mean of
invertebrate LCsos and ECsos for immobilization)
16,000
5
3,200
Toxicity from Chronic Exposure:
Deterministic Approach (fish EC20 for growth)
7,880
10
788
Deterministic Approach (invertebrate ChV for
reproduction)
9,200
10
920
Toxicity for Algae:
Probabilistic Approach (HCos from SSD)
72,000
5
14,400
Deterministic Approach (ChV)
30
10
3
3.1.7 Assumptions and Key Uncertainties for Environmental Hazard Data
After evaluating all available environmental hazard data on TCE, EPA has high confidence in the
environmental hazard data used to assess the environmental hazard of TCE and high confidence that the
data incorporates environmentally protective acute and chronic COCs (as described above). Despite the
high confidence in the data used to assess the environmental hazard of TCE, there are sources of
uncertainty.
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First, assessment factors (AFs) were used to calculate the acute and chronic concentrations of concern
for TCE. As described in Section 3.1.5, AFs account for differences in inter- and intra-species
variability, as well as laboratory-to-field variability and are routinely used within TSCA for assessing
the hazard of new industrial chemicals. Some uncertainty may be associated with the use of the specific
AFs used in the hazard assessment.
Second, there was more acute duration data reasonably available in the literature than chronic duration
data. Therefore, EPA is less certain of chronic hazard values, which are based on a deterministic
approach using one fish species, than the acute hazard values, which are based on a probabilistic
approach using 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 on which the chronic
COC is based, is from a high-quality, relevant study. Additionally, the acute data show aquatic
invertebrates are the most sensitive taxonomic group, and they are 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 associated
with the chronic COC were mitigated.
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. ( )
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 fact a wide range in species sensitivities. Taking this
range of sensitivies into consideration, EPA used two approaches to characterize hazard 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 HCos analogous 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 HC05 is derived using data from nine species rather than just one and is therefore representative of a
larger portion of species in the environment. To account for the uncertainty, EPA used an AF of 5 to
calculate the algae COC using the HCos.
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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.
Data
Summaries for
Adverse
Endpoints
(Supplemental
Human Health
Document)
Risk Characterization
Human Health Hazard Assessment
Data Evaluation
After full-text screening,
apply pre-determined data
quality evaluation criteria
to assess the confidence of
key and supporting studies
identified from previous
assessments as well as
new studies not
considered in the previous
assessments
• Uncertainty and variability
• Data quality
• PESS
• Alternative interpretations
Risk Characterization
Analysis
Determine the qualitative
and/or quantitative human
health risks and include, as
appropriate, a discussion of:
Data Integration
Integrate hazard information by considering quality (i_e.,
strengths, limitations), consistency, relevancy, coherence and
biological plausibility
Hazard ID
Confirm potential
hazards identified
during
scoping problem
formulation and
identify new hazards
from new literature (if
applicable)
Dose-Response
Analysis
Benchmark dose
modeling for
endpoints with
adequate data;
Selection ofPODs
Output of
Systematic
Review
Stage
WOE
Narrative by
Adverse
Endpoint
(Section 3.2.4)
Summary of
Results and
PODs
(Section 3.2.5)
Risk Estimates
and
Uncertainties
(Section 4.2)
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 hazard assessments as well as
the existing body of knowledge on TCE's human health hazards. These data sources included an EPA
IRIS Assessment ( ).S EPA. 201 le) and an ATSDR Toxicological Profile (ATSDR. 2019). data sources
originally obtained from the 2014 Draft Toxicological Profile); hence, many of the hazards of TCE have
been previously compiled and systematically reviewed. Furthermore, EPA previously reviewed
data/information on health effects endpoints, identified hazards and conducted dose-response analysis in
the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (US. EPA. 2014b) but did not
exclusively rely on this assessment.
All health hazards of TCE 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 Council (NRC) risk assessment
guidance.
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The new literature was screened against inclusion criteria in the PECO statement and the relevant
studies (e.g., useful for dose-response)20 were further evaluated using the data quality criteria for human,
animal, and in vitro studies described in the Application of Systematic Review in TSCA Risk Evaluations
(U.S. EPA. 2018b) (see Section 1.5). EPA skipped the screening step (for relevance to TCE) of the key
and supporting studies [List of Key and Supporting Studies for Human Health Hazard. Docket # EPA-
HQ-OPPT-2019-0500/ identified in previous assessments and entered them directly into the data
evaluation step based on their previously identified relevance to the chemical.
EPA considered studies of low, 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 dose-
response 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.
Tables summarizing all studies considered for this assessment, including the reported no-observed- or
lowest-observed-adverse-effect levels (NOAEL and LOAEL, respectively) for non-cancer health
endpoints by target organ/system and the incidence for cancer endpoints, along with the results of the
data quality evaluation, are provided in [Data Quality Evaluation of Human Health Hazard Studies and
Data Extraction for Human Health Hazard Studies. Docket: EPA- )500J.
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-adverse-
effect 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).21 PODs were adjusted as appropriate to conform to
the specific exposure scenarios evaluated.
20 Some of the studies that were excluded based on the PECO statement were considered later during the systematic review
process as needed. For example, EPA reviewed mode of action information to qualitatively support the health hazard
assessment.
21 The benchmark dose (BMD) is a dose or concentration that produces a predetermined change in response range or rate of
an adverse effect (called the benchmark response or BMR) compared to baseline.
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Human equivalent concentrations (HECs) and human equivalent doses (HEDs) were obtained via EPA's
previously published and peer-reviewed Physiologically-Based Pharmacokinetic (PBPK) model (
), which accounts for both extrapolation from rodents to humans and human variability (see
Section 3.2.2.5 and [PBPKModelandReadMe (zipped). Docket: EPA¦ [Ł/). 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 reasonably available by the dermal route for TCE and a PBPK
model that would 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, consistent
with the analysis plan as described in the Problem Formulation (U.S. EPA. 2018d).
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 Gltroour. 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. 201 le).
3.2.2 Toxicokinetics
The toxicokinetics and PBPK modeling of TCE were thoroughly discussed in the 2014 Risk Assessment
(U.S. EPA. 2014b). This discussion is summarized below.
3.2.2.1 Absorption
TCE is fat soluble (lipophilic) and easily crosses biological membranes. Due to it's relatively low water
solubility and positive log K0W(Table 1-1), it partitions into blood through binding to soluble
components including lipids (Cichocki et al. 2016). 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 breast milk (U.S. EPA.
2.01 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, although any more specific
absorption data were incorporated into the PBPK model (Section 3.2.2.5). 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) (' r U \ i i ). Dermal exposure to TCE disrupts the stratum corneum, impacting the
barrier function of skin and promoting its own absorption. Therefore, absorption may increase at a
greater than linear rate due to increasing epidermal disruption over time (ATSDR. 2019). Based on this
information, this Risk Evaluation assumes that TCE dermal absorption under occluded (or impeded
evaporation) 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). For consumer exposures, dermal absorption was evaluated
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differently for scenarios that are expected to involve impeded evaporation and those with unimpeded
evaporation. For scenarios involving impeded evaporation, a permeability model was applied. In
contrast, for scenarios less likely to involve impeded evaporation, the fraction absorbed model was
applied (Section 2.3.2.3.1).
3.2.2.2 Distribution
Regardless of the route of exposure, TCE is widely distributed throughout the body and preferentially
partitions into lipid-containing tissues (Cichocki et al. 2016). 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 of lactating women (U.S. EPA.
201 le). Breast milk ingestion is an exposure pathway specific to infants. In one study detectable levels
of TCE were found in all eight breast milk samples of mothers living in urban areas, however,
concentrations were not provided (Pellizzari et al.. 1982). In a separate study, TCE was detected in 7 of
20 breast milk samples (35%) with a mean concentration of 1.5 ng/mL; concentrations ranged from not
detected to 6 ng/mL milk (Bearner et al.. ).
3.2.2.3 Metabolism
The metabolism of TCE has been extensively studied in humans and rodents ( ).
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
biotransformations and processing with other enzymes. Oxidative metabolism is considered to be the
major metabolic pathway relative to conjugative metabolism (Cichocki et al. 2016; Lash et al. 2014).
This is supported by data showing that production of conjugative metabolites increases in CYP2El-null
mice (Luo et al. 2018). That same data also demonstrates that there are various CYPs involved with
oxidative metabolism and some redundancy exists among them, as oxidative metabolism was only
decreased but still active in CYP2El-null mice (Luo et al. 2018).
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.
Relative metabolism of TCE differs whether absorbed via inhalation or ingestion due to the influence of
first-pass liver metabolism on gastrointestinally-absorbed xenobiotics. 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 of TCE-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 (<_ _H_ \ !_0jj_c).
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Table 3-3. TCE Metabolites Identified by Pathway
Oxidative Metabolites
GSH Conjugation Metabolites
Chloral
(metabolized to TCOHa)
DCVGe
(,metabolized to DCVCi isomers)
Trichloroethylene oxide
(re-arranged to DCACb)
Trichloroethanol or TCOH
(metabolized to TCOGc)
Trichloroacetic acid or TCA
(may lead to DC Ad)
Abbreviations: a TCOH = trichloroethanol; bDCAC= dichloroacetyl chloride; cTCOG= trichloroethanol,
glucuronide conjugate; dDCA=dichloroacetic acid; eDCVG= S-dichlorovinyl-glutathione (collectively, the 1,2-
and 2,2- isomers); fDCVC= S-dichlorovinyl-L-cysteine (collectively, the 1,2- and 2,2- isomers)
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 (U.S. EPA. 201 le). In 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. 201 le).
Table 3-4. Common Metabolites of TCE and Related Compounds
Parent
1
Metabolites
Tetrachloro-
ethylene
1,1,2,2,-
Tetrachloro-
ethane
TCE
1,1,1-
Trichloro-
ethane
1,2,-
Dichloro-
ethylene
1,2,-
Dichloro-ethane
Oxalic acid
X
X
X
Chloral
X
X
Chloral hydrate (CH)
X
X
Monochloroacetic acid
X
X
X
X
X
X
Dichloroacetic acid (DCA)
X
X
X
X
Dichloroacetic acid (TCA)
X
X
X
X
Trichloroethanol (TCOH)
X
X
X
X
T richloroethanol-
glucuronide
X
X
X
X
Note: Table is the same as Table 2-21 in (U.S. EPA. 2014b).
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.
201leV
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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. ^ ). 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 (TJ.S. EPA.
201 le). Furthermore, species-dependent enzymatic activities have been reported for the P-lyase and
FM03 enzymes ( )1 le). with contrasting evidence suggests that metabolic formation of the
reactive conjugative metabolites may be an order of magnitude greater in rats than humans (Green et al.
1997b; Lash et al. 1990) based on P-lyase-activity. Overall, the majority of evidence supports faster
metabolism through both oxidative and GSH-conjugative pathways in rodents compared to humans
(Lash et al. 2014).
3.2.2.4 Elimination
The majority of TCE absorbed into the body is eliminated by the metabolic pathways discussed above.
With the exception of unchanged TCE and CO2, which are excreted by exhalation, most TCE
metabolites {i.e., TCA, TCOH, GSH metabolites) are primarily excreted in urine and feces. Elimination
of TCE metabolites can also occur through the sweat and saliva, but these excretion routes are likely to
be relatively minor ( ).
Varying rates of TCE pulmonary excretion in humans have been observed in different studies (Chiu et
al.. 2007; Opdaro. 1989; Sato et al.. 1977). The relatively long terminal 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 Q r \ _ ). Various laboratories have studied the urinary
elimination kinetics of TCE and its major metabolites in humans and rodents. Animal studies have
shown that rodents exhibit faster urinary elimination kinetics 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).
3.2.2.5 Physiologically-Based Pharmacokinetic (PBPK) Modeling Approach
Given the complicated metabolic profile of TCE, understanding the 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 ( 306). Predictions of internal dose in chemical risk assessments for a given external
applied dose/concentration are achieved by employing PBPK modeling.
PBPK models use a series of mathematical representations to describe the absorption, distribution,
metabolism and excretion (ADME) of a chemical and its metabolites. Because PBPK modeling assumes
that the toxic effects in the target tissue are closely related to the internal dose of the biologically active
form of the chemical, knowledge about the chemical's mode of action guides the selection of the
appropriate dose metric. Traditional risk estimates based on applied dose carry higher uncertainties than
those based on PBPK-derived internal dose metrics because they do not account for the toxicokinetics of
the chemical, which are both dose and time-dependent. This reduction in uncertainty and the versatility
of PBPK approaches have resulted in a growing interest to use these models in risk assessment products
(U.S. EPA. 2006).
U.S. EPA developed a peer-reviewed comprehensive Bayesian PBPK model-based analysis of TCE and
its metabolites in mice, rats and humans (U.S. EPA. 201 le). This model is briefly discussed below to
provide clarity on how the PBPK modeling was used to estimate the PBPK-derived HECs. For all PBPK
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model files, including inputs and outputs of all model runs, see [PBPKModel andReadMe (zipped).
Docket: EPA-HQ-QPPT-2019-05001.
Physiological, chemical, in vitro and in vivo data were considered when building the PBPK model,
including many studies in animals and humans that quantified TCE levels in various tissues following
oral and inhalation exposures. Some of these studies provided key data/ parameters for the calibration of
the PBPK model used in the IRIS assessment (U.S. EPA. 201 le). All of this information was used to
build a model that was able to predict different dose metrics as measures of potential TCE toxicity. Each
dose-metric was developed to evaluate a different metabolic pathway/target organ effect based on the
dose-response analysis and understanding of metabolism (Table 3-5 and Figure 3-4).
The internal dose-metric for addressing cross-species pharmacokinetics is based on the EPA's cross-
species scaling methodology. The preferred dose-metric for the parent compound under this
methodology is equivalent to the daily AUC of the active moiety (parent compound or metabolite). For
metabolites, in cases where the rate of production, but not the rate of clearance, of the active moiety can
be estimated, the preferred dose-metric is the rate of metabolism (through the appropriate pathway)
scaled by body weight to the 3A power. If there are sufficient data to consider the active metabolite
moiety(ies) reactive and cleared through nonbiological processes, then the preferred dose-metric is the
rate of metabolism (through the appropriate pathway) scaled by the tissue mass. Finally, if local
metabolism is thought to be involved, but cannot be estimated with the available data, then the AUC of
the parent compound in blood is considered an appropriate surrogate and thus the preferred dose-metric.
In general, an attempt was made to use tissue-specific dose-metrics representing particular pathways or
metabolites identified from reasonably available data on the role of metabolism in toxicity for each
endpoint (discussed in more detail below). The selection was limited to dose metrics for which
uncertainty and variability could be adequately characterized by the PBPK model. For most endpoints,
sufficient information on the role of metabolites or mode of action was not available to identify likely
relevant dose metrics, and more upstream metrics representing either parent compound or total
metabolism had to be used. Both preferred or primary dose metrics and alternative dose metrics were
selected for each endpoint based on biological support for their involvement in TCE toxicity.
Table 3-5. List of All of the PBPK-Modeled Dose Metrics Considered in this Risk Evaluation
Dose-Metric
Identifier
Dose-Metric Definition
ABioactDCVCBW34
Amount of DCVC bioactivated in the kidney per unit adjusted body weight
ABioactDCVCKid
Amount of DCVC bioactivated in the kidney per unit kidney mass
AMetGSHBW34
Amount of TCE conjugated with GSH per unit adjusted body weight
AMetLivlBW34
Amount of TCE oxidized in liver per unit adjusted body weight
AMetLivOtherB W34
Amount of TCE oxidized to metabolites other than TCA or TCOH per unit adjusted body weight
AMetLivOtherLiv
Amount of TCE oxidized to metabolites other than TCA or TCOH per unit liver weight
AMetLngBW34
Amount of TCE oxidized in respiratory tract per unit adjusted body weight
AMetLngResp
Amount of TCE oxidized in respiratory tract per unit respiratory tract tissue
AUCCBld
Area under the curve of venous blood concentration of TCE
AUCCTCOH
Area under the curve of blood concentration of TCOH
AUCLivTCA
Area under the curve of the liver concentration of TCA
TotMetabBW34
Total amount of TCE metabolized per unit adjusted body weight
T otOxMetabB W 3 4
Total amount of TCE oxidized per unit adjusted body weight
TotTCAInBW
Total amount of TCA produced
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For developmental toxicity endpoints, the TCE PBPK model did not incorporate a pregnancy model to
estimate the internal dose of TCE in the developing fetus. In this case, the maternal dose-metric was used
as the surrogate measure of target tissue dose in the developing fetus. This was considered reasonable
because TCE and the major circulating metabolites (TCA and TCOH) appear to cross the placenta and
maternal metabolizing capacity is generally greater than that of the fetus. In the cases where exposure
continues after birth ("(Peden-Adams et at.. 2006). Section 3.2.5.1.6), no PBPK model-based internal dose
was used. Because of the complicated fetus/neonate dosing that includes transplacental, lactational, and
direct (if dosing continues postweaning) exposure, the maternal internal dose is no more accurate a
surrogate than applied dose in this case. A complete description of the TCE PBPK model, including the
rationale for parameter choices in animals and humans, choice of dose metric, and experimental
information used to calibrate and optimize the model is found in the TCE IRIS assessment (
2.01 leY
As shown in Figure 3-4 and Figure 3-5, several steps were needed to derive the PBPK-derived HECs
used in this assessment. First, the rodent PBPK model was run to estimate rodent internal doses (for
rodent toxicity studies) for the applied doses in a study based on the selected dose metric (Table 3-5).
The internal dose Point of Departure (idPOD) is then obtained either directly from the internal dose
corresponding to the applied dose LOAEL/NOAEL, or by BMD modeling of responses based on internal
doses. Separately, the human 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/day to establish the relationship between human exposure air levels
and internal dose for the same dose-metric evaluated in the rodent PBPK model. This relationship was
used to derive Human Equivalent Concentrations (HECs) and Human Equivalent Doses (HEDs)
corresponding to the idPOD by interpolation. Median values of dose metric estimates were used for
determining rodent internal doses, while both median (50th percentile) and 99th percentile values were
determined for HECs and HEDs ( ).
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Rodent
model
parameters
Human
model
parameters
Fixed
Fixed
Distribution
Distribution
Human
PBPK
model
Rodent
PBPK
w model
Human
internal
dose as
function of
applied
dose
Rodent
internal
dose
Fixed
Median
Benchmark dose-response
Modeling or LOAEL/NOAEL
Invert functions of
dose or concentration
Rodent non-
cancer study
experimental
paradigm
Rodent non-
cancer study
responses
Rodent idPOD
for any given
BMDL, LOAEL
or NOAEL
Human internal dose
at 50th percentile as
function of applied
dose
0.1 to 2000 ppm TCE in
air or 0.1 to 2000
mg/kg-bw/day
continuous exposure
Human internal dose
at 95th percentile as
function of applied
dose
Human internal dose
at 99th percentile as
function of applied
dose
hec99
Figure 3-4. Dose-Response Analyses of Rodent Non-Cancer Effects Using
the Rodent and Human PBPK Models
Figure adapted from Figure 5-2 (Chapter 5, TCE IRIS assessment) (U.S. EPA. 201 le). Square nodes indicate point values5
circle nodes indicate distributions and the inverted triangle indicates a (deterministic) functional relationship.
Rodent internal
dose
Uncerta'**8'
~ *****
Human internal
dose
Uncertainty *
variability
distribution
, Lower 99'
percentile
Human inhalation
^exposure (ppm)
=HECc
idPOD
Study dose groups
LOAEL/
NOAEL
Figure adapted from Figure 5-3 (Chapter 5,
TCE IRIS assessment)
Notes: When using benchmark dose estimates,
the idPOD is the modeled BMDL in
internal dose units.
4129
4130
m
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
(50th percentile) 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 identical and represented by the group average. In practice, the use
of median versus mean internal doses for rodents did not make a substantial difference except when the
uncertainty in the rodent dose-metric was high (U.S. EPA. 2 ).
On the other hand, the human population model characterizes toxicokinetic uncertainty and individual-
to-individual variation and used median, 95th and 99th percentile values of dose- metrics to general
human idPODs. The 50th, 95th, or 99th percentile of the combined uncertainty and variability distribution
of human internal doses was used to derive the HEC/HED50, HEC/HED95 or HEC/HED99 estimates,
respectively. The HEC95 and HEC99 were interpreted as being the concentrations of TCE 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/HED50 and
HEC/HED99 POD values.
3.2,3 Hazard Identification
3.2,3,1 Non-Cancer Hazards
EPA previously identified human health hazard for the below endpoints in (; • J • ; .1 ) and ( ;
E 14b). Key and supporting studies from those publications that were used for derivation of tissue-
specific PODs were reviewed along with any newer studies identified through EPA's updated literature
search beginning with studies published after the TCE IRIS assessment ( Olle). 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 [Data Extraction for Human Health
Hazard Studies. Docket: EPA- )5001.
3.2.3.1.1 Liver toxicity
Several studies have demonstrated liver toxicity in both animals and humans exposed to TCE. Specific
effects include the following structural changes: increased liver weight, increase in deoxyribonucleic
acid (DNA) synthesis (transient) and polyploidy, 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 determine 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 of TCA, DCA, and chloral hydrate), as well
as the parent TCE, is unclear.
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 (U.S.
EPA. 2014bY
Human Data
Several human studies (including those in TCE degreaser operations) reported an association between
TCE exposure and significant changes in serum liver function tests used in diagnosing liver disease,
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or changes in plasma or serum bile acids. There was also human evidence for hepatitis accompanying
immune-related generalized skin diseases Jaundice, hepatomegaly, hepatosplenomegaly, and liver
failure in TCE-exposed workers (U.S. EPA. ^ ). Cohort studies examining cirrhosis 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 (!. .S j J 20 Nc). A case study published after the 2011 IRIS Assessment reported
TCE hypersensitivity-induced liver damage (Jung et at. 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. ). Dose-responsive increases in relative liver weight
(compared to body weight) were observed both following administration of TCE for 6 weeks via
gavage (Buben and O'Flahertv. 1985) and for up to 120 days via inhalation (Woolhiser et at. 2006;
Kiellstrand et at. 1983). Hypertrophy, histopathology, cytotoxicity, and altered serum biochemistry
were also observed in mice in (Buben and Q'Ftat 985) with histopathology including
vacuolization and inflammatory cell infiltration observed in (Kiellstrand et at. 1983). Increased liver
weight was additionally observed in (Boverhof et at. 2013). identified in the EPA literature search,
following 6 hr/day inhalation exposure to a single concentration level (1000 ppm) of TCE for 4 weeks.
3.2.3.1.2 Kidney toxicity
Studies in both humans and animals have shown changes in the proximal tubules of the kidney
following exposure to TCE. DCVC (and to a lesser extent other metabolites) appears to be responsible
for kidney damage and kidney cancer following TCE exposure (U.S. EPA. 201 le). Toxicokinetic
data suggest that the TCE metabolites derived from GSH conjugation (in particular 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 ( 01 le).
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 (U.S.
EPA. 2014b).
Human Data
Occupational studies showed increased levels of kidney damage (proximal tubules) and end-stage
renal disease in TCE-exposed workers. Human studies 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 (\ v \ 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) identified pathological changes in the renal tubule of rats following 1-
2 years of either oral or inhalation exposure. Similar changes were also observed in a chronic gavage study
in female mice conducted by NCI, (NCI. 1976). however that study scored Unacceptable in EPA data
quality evaluation due to high mortality in control mice and rats as well as long post-exposure period prior
to sacrifice that could have allowed for recovery. 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
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100 percent of rodents 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 through conjugative metabolism (U.S. EPA. 2 ). 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 at.. 2013; Woolhiser et at.. 2.006; Kiellstrand et
at.. 19831
3.2,3.1,3 Neurotoxicity
Neurotoxicity has been demonstrated in animal and human studies under both acute and chronic
exposure conditions ( )1 le). Due to the effects on the nervous system, TCE was initially
synthesized for use as an anesthetic in humans in the early part of the 20th century.These anesthetic-like
effects occurred at high concentrations. CNS depression has been consistently observed following
acute exposure of humans to TCE (see Section 3.2.3.1.7).
Among newer studies not previously discussed in ( ), a single repeat-dose
experimental study in rats (Liu et at.. , ) 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
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-induced neurotoxic 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 (U.S. EPA.
201 le). 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 (Ruiiten et at.. 1991). Fewer and more limited epidemiological
studies are suggestive of TCE exposure being associated with delayed motor function, and changes in
auditory, visual, and cognitive function or performance, and neurodevelopmental abnormalities (U.S.
El \ i«;).
Human studies have consistently reported vestibular system-related symptoms such as headaches,
dizziness, and nausea following TCE exposure. Although these symptoms are subjective and self-
reported, these effects have been reported extensively in human chamber, occupational, and
geographic-based/drinking water studies (U.S. EPA. 201 le). Additionally, several newer
epidemiological studies have found an association between TCE exposure and neurodegenerative
disorders such as Amyotrophic Lateral Sclerosis (Bove et at.. 2014a) and Parkinson's disease (Bove et
at.. 2014b; Got dm an et at.. ).
Animal Data
The 2014 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: morphological changes in the trigeminal
nerve, disruption of the auditory system, visual changes, structural or functional changes in the
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hippocampus, sleep disturbances and changes in psychomotor effects ( ). Key and
supporting studies considered in this Risk Evaluation identified significant decreases in wakefulness
following 6 weeks of TCE inhalation exposure (Art to et at.. 1994) and demyelination of the
hippocampus following 8 weeks of drinking water exposure (Isaacson et at.. 1990) in rats. Neuronal
degeneration (Gash et at.. 2008) and diminished sciatic nerve regeneration (Kiettstrand et at.. 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
(Witmer et at.. 2014) and stimulatory effects (Shelton and Nicholson. 2014) of TCE via inhalation at
doses at or above 5000 ppm. Rats administered TCE 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 at... 2010).
3.2.3.1.4 Immunotoxicity
Immune-related effects following TCE exposures have been observed in both animal and human
studies. In general, these effects were associated with both inducing enhanced immune responses as
well as immunosuppressive effects. These effects may influence a variety of other conditions of
considerable public health importance, such as susceptibility to infection, 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 (Setgrade Ł mour. 2010). This study was discussed in the TCE
IRIS assessment ( ) but was not included in the 2014 TSCA Work Plan Chemical
Risk Assessment ( 014b). All other studies supported the hazard conclusions of the 2014
TCE Risk Assessment (U.S. EPA. 2014b). Therefore, EPA primarily relied on the previous hazard
conclusions for all other endpoints.
Human Studies
Autoimmunity/Inappropriate Immune Activation
Studies have reported a relationship between systemic autoimmune diseases, such as scleroderma, and
occupational exposure to TCE. The TCE IRIS assessment j* * P.CH.Le) performed a meta-
analysis of a number of human studies evaluating a 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 sex difference because the incidence of this
disease is very low in men (approximately one 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 sex-related
differences in exposure prevalence, the reliability of the exposure assessment, sex-related differences
in susceptibility to TCE toxicity or chance (\ ' \ JO I h').
Increased levels of human inflammatory cytokines have been observed in both workers exposed
occupationally to TCE and infants exposed to TCE via indoor air (U.S. EPA. IV N c). These findings
were supported by studies in mice (described below) in which short exposures to TCE resulted in
increased levels of inflammatory cytokines.
Immunosuppression
The epidemiological database also provides limited evidence of immunosuppression based on
reduced IgG antibody levels in TCE-exposed workers (Zhang et at.. 2013).
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Animal Data
Autoimmunity/Inappropriate Immune Activation
Numerous studies have shown increased autoimmune responses in autoimmune-prone mice, including
changes in cytokine levels similar to those reported in human studies, with more severe effects, including
autoimmune hepatitis, inflammatory skin lesions, and alopecia, manifesting at longer exposure periods
(U.S. EPA, z ). Key studies identified evidence of autoimmunity from chronic TCE exposure in both
non-autoimmune prone (Keil et at.. 2009) and autoimmune prone (Wang et al. 2012; Gilbert et al. 2006;
Griffin et at. 2000; Kaneko et al. 2000) mice.
Sensitization / Hypersensitivity
Limited epidemiological data do not support an association between TCE exposure and allergic
respiratory sensitization or asthma. However, there have been a large number of case reports and
epidemiological studies (Kane et al. 2018; Liu 2009; Xu et al. 2.009; Nakaiima et al. 2003;
Chittasobhaktra et al. 1997; Bond 1996) of TCE-exposed workers developing a severe hypersensitivity
skin disorder, distinct from contact dermatitis, and often accompanied by systemic effects (e.g., hepatitis,
lymph node changes, and other organ effects including cardiac arrest in at least one instance). These
effects appeared after inhalation exposures ranging from less than 9 to greater than 700 ppm TCE.
Similar sensitization/hypersensitivity effects have been observed in guinea pigs and mice following TCE
exposure via drinking water (x v < \ _ JO Nc), including in the autoimmune-prone MRL+/+ mouse line
(Griffin et al. 2000).
Immunosuppression
Evidence of localized immunosuppression has 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 decreased thymus weight and cellularity in the non-autoimmune prone mice following up
to 30 weeks of drinking water exposure (Keil et al.. 2009).
Inhalation exposure to TCE has been shown to suppress pulmonary host defenses and enhance
susceptibility to respiratory infection in mice co-exposed to aerosolized pathogenic bacteria. Increased
mortality was observed post-infection following exposure to TCE concentrations of 50ppm or greater,
with corresponding dose-dependent effects on bacterial clearance, percentage of infected mice, and
alveolar phagocytosis (Selgrade and Gilmour. 2010).
3.2.3.1.5 Reproductive toxicity
The epidemiological, animal, and mechanistic literature provide suggestive, but limited, evidence of
adverse outcomes to female reproductive toxicity. However, much more extensive evidence exists in
support of an association between TCE exposures and male reproductive toxicity ( >1 le).
The reasonably available human data that associate TCE with adverse effects on male reproductive
function are limited in sample size and provide little quantitative dose data. However, the animal data
provide strong and compelling evidence for TCE-related male reproductive toxicity. Strengths of the
animal database include the presence of both functional and structural outcomes, similarities in adverse
treatment-related effects observed in multiple species, and evidence that metabolism of TCE in male
reproductive tract tissues is associated with adverse effects on sperm measures in both humans 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. ).
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,
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EPA relied primarily on conclusions from the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
EPA. 2014b).
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 hyperzoospermia in 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. 2 ).
Animal Data
Laboratory animal studies provide evidence for similar effects, particularly for male reproductive
toxicity. These animal studies have reported effects on sperm, libido/copulatory behavior, and serum
hormone levels, although some studies that assessed sperm measures did not report treatment-related
alterations (U.S. EPA. ^ ). Identified key and supporting studies have observed TCE-related
histopathological lesions in the testes or epididymides, altered in vitro sperm-oocyte binding, 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 reasonably available for the female reproductive toxicity endpoint. While in
vitro oocyte fertilizability has been reported to be reduced as a result of TCE exposure in rats, a
number of other laboratory animal studies did not report adverse effects on female reproductive
function effects (U.S. EPA. 201 le). The key study Narotsky et al. (1995) observed delayed parturition
in female rats. Exposure of either males or females to TCE in feed resulted in reduced successful
copulation and an associated decrease in the number of live pups and litters (George et al.. 1986). A
recent study found that a single high dose of TCE administered orally to rats resulted in reduced fetal
weight and indicators of placental oxidative stress (Loch-Caruso et al. 2019). A series of studies have
found that the reactive conjugative metabolite DCVC induces oxidative stress and cell death in a
placental cell line (Elkin et al. 2020). although there is uncertainty relating to the relevance of DCVC
to reproductive toxicity outcomes.
3.2.3.1.6 Developmental Toxicity
Developmental toxicity refers to endpoints affecting fetal or neonatal outcomes. An evaluation of the
human and animal developmental toxicity 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 (U.S. EPA... 2014b) as the most sensitive
developmental toxicity endpoint for dose-response analysis. The developmental toxicity information is
briefly described below, including information from the 2014 TCE TSCA Work Plan Chemical Risk
Assessment and more recent studies.
For developmental toxicity other than congenital heart defects EPA did not identify any new repeat-
dose experimental studies in animals or human epidemiological studies that would contribute
significant novel information for this hazard. Therefore, EPA relied primarily on conclusions from the
2014 TCE TSCA Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) for these other endpoints.
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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 (I; I'P \ -014b) 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-based epidemiological studies have been conducted
in various parts of the United States, including Arizona (Tucson Valley), Colorado (Rocky Mountain
Arsenal), Massachusetts, New York (Endicott), Camp Lejeune, North Carolina and Milwaukee,
Wisconsin (l. -s l!P\ 20 f t c).
Perinatal death, decreased birth weight, and birth defects
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).
ATSDR has conducted studies at Camp Lejeune, North Carolina, where individuals were exposed to
VOC-contaminated drinking water (Ruckart et at. 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 null to negative 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 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.. 2.015). and both statistically significant and non-significant associations have
been observed between exposure to the TCE metabolites trichloracetic acid and trichloroethanol with
various outcomes including oral clefts, urinary tract malformations, and limb defects (Cordier et al..
2.012). In contrast, (6render et al.. ) 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 deficiency, or longitudinal or transverse limb deficiencies. The study did identify an increased risk
of septal heart defects (see below section), however.
Developmental neurotoxicity
As for human developmental neurotoxicity, the available studies collectively suggest that the
developing brain is susceptible to TCE toxicity. These studies have reported an association with TCE
exposure and CNS congenital or postnatal effects such as delayed newborn reflexes, impaired learning
or memory, aggressive behavior, hearing impairment, speech impairment, encephalopathy, impaired
executive and motor function and attention deficit (U.S. EPA. 2 ).
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Developmental immunotoxicity
There are very few studies on developmental immunotoxicity associated with human exposure to TCE.
A set of studies published by Lehman et al. (2002; 2001). cited in (U.S. EPA. 201 leV) did not find any
statistically significant association with allergic sensitization or change in cytokine-producing T cells
based on measurements of TCE air concentrations in children's bedrooms.
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; Taylor et al.. 1985). developmental immunotoxicity (Peden-Adams et al.. 2006).
and congenital heart defects anomalies ((Johnson et al.. 2003; Dawson et al.. 1993). further details
below). 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 ( ). Among newer studies identified in the EPA literature
search, developmental neurotoxicity was indicated by increased locomotor and exploratory activities were
observed following drinking water exposures to mice during nervous system development (Blossom et
al.. 2013). however these effects were not consistently dose-responsive. A follow-up study from that
laboratory (Blossom et al. 2016) reported inflammation-mediated cerebellar oxidative stress and
increased locomotor activity following gestational TCE exposure in autoimmune-prone mice, while
another study demonstrated that TCE reduces cell viability and inhibits differentiation of neural
progenitor cells in culture (Salama et al. 2018). In addition to the results from (Blossom et al. 2016).
various indicators of developmental immunotoxicity were also observed in another MRL +/+ mice study
(Gilbert et al. 2014).
Congenital Heart Defects
In vivo animal studies in rats and chicks have identified an association between TCE exposures and
cardiac defects22 in the developing embryo and/or fetus ( ). The 2014 TSCA Work
Plan Chemical Risk Assessment ( 2014b) identified congenital heart defects following TCE
exposure via drinking water as the most sensitive human health endpoint for dose-response analysis
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. Human studies have
also identified statistically significant increased risk of developmental cardiac defects following
exposure to TCE (Brender et al.. 2014; Forand et al.. 2012; Yauck et al.. 2004) or metabolites (Wright
et al.. 2017). with increased association for older mothers (Yauck et al.. 2004; Brender et al.. 2014).
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.
The scientific literature also has examples of relatively well-conducted studies in rats and mice that did
not observe an increase in 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-significant increases in combined
cardiac and cardiovascular effects, there was a very high background incidence of cardiovascular defects
22 "Cardiac" (or "heart") "defects," "malformations," and "abnormalities" are used throughout this Risk Evaluation to refer to
adverse findings in the developing heart. These terms, in addition to "congenital heart defects" (CHD), are used in
experimental animal, epidemiological, and/or clinical studies to characterize or categorize various morphological
cardiovascular outcomes in the fetus or neonate. For the purpose of this Risk Evaluation, they are used interchangeably.
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in soybean oil-control rats, and the authors did observe a 19% increase in cardiac-specific defects (per-
litter, statistical significance not calculated) following TCE treatment compared to controls. During the
development of this Risk Evaluation, a study was completed that also did not identify a statistically
significant increase in cardiac defects following TCE exposure via drinking water (Charles River
Laboratories. 2.019). Several epidemiological studies also report either negative (Lagakos et al. 1986) or
equivocal (Bove. 1996; 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.
3.2.3.1.7 Overt Toxicity Following Acute/Short Term 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). This section describes overt acute toxicity,
representing readily observable clinical effects resulting from short-term exposure (as opposed to
subclinical indications of adversity or delayed/long-term effects).
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 15,000 ppm 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/occupational literature or experimental human inhalation
studies. Lethality data in humans have been reported following accidental exposure to TCE. However,
there is insufficient information about the exposure characterization of 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 tract in the form of both localized irritation and broad fibrosis, likely
dependent on oxidative metabolism. (U.S. EPA. ).
CNS depression and effects on neurobehavioral functions were seen in human volunteers exposed to
1,000 ppm TCE for a 2-hr 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
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 (Hudson 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 reporting at most only
very mild effects. Studies on both humans and animals demonstrate that TCE is a moderate skin
sensitizer, with hypersensitivity reactions observed following exposure to both TCE and various
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metabolites.
3,2,3,2 Genotoxicity and Cancer Hazards
3.2.3.2.1 Genotoxicity
EPA extracted and all relevant genotoxicity studies for TCE and various important metabolites. Relevant
metabolites were selected based on the species most closely associated with a potential mutagenic mode
of action for cancer target sites {i.e., conjugative metabolites for kidney, CH for liver, see Section
3.2.4.2.2). Results of genotoxicity studies are presented in [Data Extraction and Evaluation Tables for
Genotoxicity Studies. Docket: EPA-HQ-OPPT-2019-050Q]. All identified relevant studies were included
in the data tables for comparison and transparency, including studies that scored Unacceptable or could
not be evaluated. Only acceptable studies were considered in the geneotoxicity weight of scientific
evidence and cancer MOA assessment (Section 3.2.4.2.2). There was no overall particular pattern of
excluded studies among positive and negative results, except for GSH conjugation metabolites where all
of the negative studies were deemed unacceptable.
Overall, TCE genotoxicity was mostly negative in bacterial and yeast systems, although metabolic
activation did induce genotoxicity in a few otherwise negative assays. Results were mixed in
mammalian systems, with positive results observed both with and without metabolic activation across
the database. The metabolite CH was mostly positive across a wide variety of assays both in vitro and in
vivo/ex vivo, however positive results were more consistently observed in in vitro systems. GSH
conjugative netabolites such as DCVC were predominantly positive in a variety of assays in both
bacteria and mammalian kidney tissue.
3.2.3.2.2 Kidney cancer
The TCE IRIS assessment concluded that TCE is "carcinogenic to 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
( 'JIM). The carcinogenic classification was based on a review of more than 30 human studies,
including studies in TCE degreasing operations, and meta-analyses of 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 countries and industries
(Appendix C, (U.S. EPA. 1 ). This strong consistency of the epidemiologic data on TCE and
kidney cancer argues against chance, bias, and confounding as explanations for the elevated kidney
cancer risks (U.S. EPA. 201 le).
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.
Kidney tumors in rats are relatively rare (U.S. EPA. ).
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 in humans (U.S. EPA. 201 le).
3.2.3.2.3 Liver cancer
U.S. EPA concluded that TCE exposure causes liver tumors in mice but not rats and the meta-analysis
of human data on liver and gallbladder/biliary passages indicated "...a small, statistically significant
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increase in risk". Multiple TCE metabolites {i.e., and thus pathways) likely contribute to TCE-induced
liver tumors (i__S J_0jj_e).
Previous meta-analyses of the cohort, case-control, and community (geographic) studies reporting liver
and biliary tract cancer, primary liver cancer, and gallbladder and extra-hepatic bile duct cancer (see
Appendix C in (U .S. EPA. )) reported a small, statistically significant summary relative risk
(RRm, overall RR from meta-analysis) for liver and gallbladder/biliary cancer with overall TCE
exposure. However, the meta-analyses reported a lower, nonstatistically significant RRm for primary
liver cancer when using the highest exposure groups (U.S. EPA. 21 ).
With respect to liver carcinogenicity, TCE and its oxidative metabolites TCA, DCA, and CH are
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 hepatocarcinogenicity of TCE and its metabolites in rats or hamsters (U.S. EPA. 2.01 le).
3.2.3.2.4 Cancer of the immune system
Human studies have reported cancers of the immune system resulting from TCE exposure. Lymphoid
tissue neoplasms arise in the immune system and result from events that occur within immature
lymphoid cells in the bone marrow or peripheral blood (leukemias), or more mature cells in the
peripheral organs (non-Hodgkin's lymphoma). The broad category of lymphomas can be divided into
specific types of cancers, including non-Hodgkin's lymphoma, Hodgkin lymphoma, multiple
myeloma, and various types of leukemia (e.g., acute and chronic forms of lymphoblastic and myeloid
leukemia). Leukemia during childhood has been observed in a number of studies in children exposed
to TCE, however this association has not been confirmed (l__S J;L\, rlli.tp)-
One of the three cancers for which the TCE IRIS assessment based its cancer findings was non-
Hodgkin's lymphoma (NHL) (the other two being kidney and liver cancer) (U.S. EPA. 2 ). The
human epidemiological database identifies a statistically significant association between TCE exposure
and NHL (Appendix C, ( ). Further support comes from animal studies reporting rates
of lymphomas and/or leukemias following TCE exposure Q v u*).
3.2.3.2.5 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. 21 ).
Other cancers
There is limited evidence of increased risk for esophageal cancer following TCE exposure in males only.
The reasonably available evidence is not statistically sensitive enough for informing 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 reasonably 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
of TCE exposure with CNS cancers in children ( )
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3.2.4 Weight of Scientific Evidence
3,2,4,1 Non-Cancer Hazards
The EPA literature search (U.S. EPA.. 20171) 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 ( 31 le) and the 2014 TSCA Work Plan
Chemical Risk Assessment ( )14b).
3.2.4.1.1 Liver toxicity
The EPA literature search (U.S. EPA. 20170 did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
Animal data demonstrating increased liver weight, cytotoxicity, hypertrophy, and peroxisome
proliferation is supported by human data demonstrating changes in plasma or bile acid liver enzyme
levels and hypersensitivity-induced liver damage (Section 3.2.3.1.1). Overall, liver toxicity following
TCE exposure is supported by the weight of evidence. Therefore, this hazard was carried forward for
dose-response analysis.
3.2.4.1.2 Kidney toxicity
The EPA literature search (U.S. EPA. 20170 did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
The kidney is one of the more sensitive targets of TCE, with toxicity resulting from conjugative
metabolites such as DCVC. Both animal and human studies have observed induction of kidney toxicity
(e.g., damage to renal tubules and nephropathy) and progression of existing kidney disease (Section
3.2.3.1.2). Overall, kidney toxicity following TCE exposure is supported by the weight of evidence.
Therefore, this hazard was carried forward for dose-response analysis.
3.2.4.1.3 Neurotoxicity
The EPA literature search (U.S. EPA. 20170 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 (Section 3.2.3.1.3). Overall,
neurotoxicity following TCE exposure is supported by the weight of evidence. Therefore, this hazard
was carried forward for dose-response analysis.
3.2.4.1.4 Immunotoxicity
The EPA literature search (U.S. EPA. 20170 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/immune
enhancement responses or immunosuppression. There is also evidence of both systemic and localized
hypersensitivity resulting in skin sensitization and autoimmune hepatitis (Section 3.2.3.1.4). Selgrade
and Gilmour (2010). which was not discussed in ( 5), demonstrated reduced response to
respiratory infection based on a well-established protocol, in agreement with data from an almost identical
study design decades earlier (however K. pneumoniae was used in that study ( ivi et al. 1986) instead
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of S. zooepidemicus). This endpoint is also consistent with other chronic data on immunosuppression.
Overall, immunotoxicity in the form of both autoimmunity and immune suppression following TCE
exposure are supported by the weight of evidence. Therefore, this hazard was carried forward for dose-
response analysis.
3.2.4.1.5 Reproductive toxicity
The EPA literature search (U.S. EPA. 201Tt) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
Both human and animal data provide consistent evidence for male reproductive effects from TCE.
Effects observed include effects on sperm, male reproductive organs, hormone levels, and sexual
behavior. There is limited evidence indicating TCE effects on female reproductive toxicity and
mechanistic support for placental effects from metabolites, although the relevance of those studies is
uncertain (Section 3.2.3.1.5). Overall, 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 (U.S. EPA. 20171) 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 hazards also 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 (Section 3.2.3.1.6).
Overall, based on suggestive epidemiologic data and fairly consistent laboratory animal data,
developmental toxicity for the above adverse outcomes following TCE exposure is supported by the
weight of evidence. Therefore, this hazard was carried forward for dose-response analysis.
Developmental toxicity endpoints were considered for both acute and chronic scenarios. Although
developmental studies typically involve multiple exposures, they are considered relevant for evaluating
single exposures because evidence indicates that certain developmental effects may result from a single
exposure during a critical window of development (Davis et ai. 2009; Van Raaii et at.. 2003). This is
consistent with EPA's Guidelines for Reproductive Toxicity Risk Assessment ( 96) and
Guidelines for Developmental Toxicity Risk Assessment ( ), which state that repeated
exposure is not a necessary prerequisite for the manifestation of developmental toxicity. This is a health
protective assumption.
Congenital Heart Defects
The congenital heart defects endpoint for TCE has been widely discussed since the release of the 2011
IRIS Assessment (< ^ I IW "II ). The primary basis for this endpoint was a developmental drinking
water study in rats, (Johnson et. ai. 2003). that has been the source of extensive controversy (see
Appendix F. 1 for more study details). During the development of this Risk Evaluation, EPA received a
study sponsored by the Halogenated Solvents Industry Alliance (HSIA) (Charles River Laboratories.
2019) that attempted to replicate the (Johnson et at.. 2003) study, examining the incidence of
developmental cardiac defects following administration of TCE to rats via drinking water (see Appendix
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F.2 for more study details and EPA review). This study was subsequently peer reviewed and published
in the scientific literature.
The results of the Charles River study (2019) appear to contradict the results observed by (Johnson et
at.. 2003) and (Dawson et at.. 1993). however EPA concluded that the Charles River study methodology
was likely of reduced sensitivity for the full array of defects observed in (Johnson et at.. 2003).
Therefore, (Charles River Laboratories. 2019) insufficiently replicates the methodology of (Johnson et
at.. 2003). and the results do not entirely contradict the conclusions of Johnson et al. While (Charles
River Laboratories. 2019) was not considered a close enough replication to (Johnson et al.. 2003) to
reduce the overall weight of evidence for the endpoint, EPA did consider (Charles River Laboratories.
2019) to be an overall well-conducted study, and it was incorporated into the WOE analysis for the
cardiac defects endpoint along with all other relevant studies identified in the literature.
WOE Analysis
EPA previously published weight of evidence (WOE) analyses on the congenital heart defects (CHD)
endpoint both as part of the 2014 TCE Work Plan Chemical Risk Assessment and as a peer-reviewed
journal article (Makris et al.. 2016). which concluded that the totality of data demonstrates congenital
heart defects as a human health hazard resulting from exposure to TCE. These WOE analyses utilized
modified Bradford-Hill criteria (Hill. 1965) to evaluate the overall evidence for causality following
study quality review. Recently though, (Wikoffet al.. 2018) published a WOE analysis focusing only on
animal and epidemiological data (excluding data from mechanistic studies and TCE metabolites) and
came to the opposite conclusion using a Risk of Bias assessment for internal study validity.
In order to address the conflicting results of the previous WOE assessments (U.S. EPA. 2014b; Makris
et al.. 2.016; Wikoff et al.. 2.018). 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 Work Plan Chemical Risk Assessment ( 14b). 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 recently 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 methodology described in [Weight of Evidence in Ecological Assessment. Risk
Assessment Forum. EPA/100/R16/00. ( _)], which advocates for presenting evidence on a
semiqualitative scale on the basis of three evidence areas: reliability, outcome/strength, and relevance
(see Appendix F.3.1 for more details on selection of approach and methodological details). Summary
scores for individual studies were integrated within each line of evidence (epidemiological, in vivo, or
mechanistic) and then finally all lines of evidence were integrated into a single overall score.
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 WOE for TCE-induced congenital cardiac defects is presented in Table 3-6. The
epidemiology studies as a group provide suggestive evidence for an effect of TCE on cardiac defects in
humans (summary score of +). Even though there are some uncertainties associated with the relevant
epidemiological literature, the observation of a positive association between TCE exposure and CHDs in
multiple exposed human populations increases the plausibility of the positive results from other lines of
evidence {i.e., in vivo animal, mechanistic). Oral in vivo studies provided ambiguous to weakly positive
(0/+) results for TCE itself, but positive results for its TCA and DCA metabolites (+). Inhalation studies
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(which may be most relevant to the majority of human exposure scenarios) contributed negative
evidence (-). Overall, the in vivo animal toxicity studies provided mixed, ambiguous evidence for an
effect of TCE (summary score of 0). Mechanistic studies provided strong and consistent supporting
information for effects of TCE and metabolites on cardiac development and precursor effects (summary
score of +/++) despite lack of support for any particular adverse outcome pathway (AOP).
The database overall was determined to be both reliable and relevant. Integration of the three lines of
evidence resulted in an overall summary score of (+), demonstrating positive overall evidence that TCE
exposure may result in congenital heart defects in humans (based on positive evidence from
epidemiology studies, mixed evidence from animal toxicity studies, and stronger positive evidence from
mechanistic studies).
See Appendix F.3 for the complete WOE narrative and methodology. The complete scoring table and
detailed evaluation of all studies is presented in [Data Table for Congenital Heart Defects Weight of
Evidence Analysis. Docket: EPA-HQ-QPPT-2019-0500J.
Table 3-6. Overall Summary Scores by Line of Evidence for Cardiac Defects
rom TCE
Evidence Area
Summary Score
Epidemiology studies
+
In vivo animal toxicity studies
0
Mechanistic studies
+/++
Overall
+
The differences in observed responses across studies may be partially attributed to experimental design
differences. These differential responses may also represent varying susceptibility among mammalian
species, strains, and populations. It is possible that animals showing a greater incidence of defects
following TCE exposure represent an especially susceptible population, and genetic drift may preclude a
true replication of previous study conditions (Makris et al.. 2016). Functionally, this WOE scoring
methodology is similar to that used by (Wikoff et al.. 2018). although that analysis focused only on data
quality and 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 review
and this analysis.
Mechanistic Evidence Mode of Action
The abundance of available mechanistic studies suggest various potential modes of action (MOAs)for
TCE-related cardiac teratogenicity, however the totality of the data does not consistently support any
single MOA or AOP. Teratogens may function through a multitude of pathways, often resulting in a
constellation of effects. Therefore, evidence of a single dominant MOA is not required in order for the
data to support a plausible mechanism of TCE-induced congenital heart defects. Existing data supports
potential mechanisms involving endothelial cushion development, alterations in cellular Ca2+ flux,
oxidative stress, epigenetic changes, impaired stem cell differentiation, suppressed endothelial cell
proliferation, and folate deficiency. Several studies demonstrate non-monotonic and even inverse dose
responses in gene activation and molecular changes, which may explain the non-monotonic
polynomial dose-response observed in (Johnson et al.. 2003). See Appendix F.3.3 for more discussion
and details on potential modes of action.
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Overall, an association between increased congenital cardiac defects and TCE exposure is supported by
the weight of evidence, in agreement with previous EPA analyses ( 14b; Makris et at..
2016). While the inconsistent observations across studies (especially in animal models) indicate that
TCE-induced CHDs may not be a common occurrence, the endpoint likely remains relevant for
susceptible populations. As described in Section 3.2.5.2, various risk factors may influence the
susceptibility to CHDs and it is possible that experiments using relatively young, healthy, and inbred
laboratory rodent strains may not capture this variability. For instance, epidemiological data indicates
that TCE is strongly associated with CHDs in older mothers (Brender et at.. 2.014; Yauck et at.. 2004).
Therefore, in order to account for PESS considerations this endpoint was carried forward for dose-
response analysis.
3X4.1,7 Overt Toxicity Following Acute/Short Term Exposure
There is strong evidence for overt toxicity in humans following acute exposure to high concentrations of
TCE. AEGL guidelines indicate the concentrations at which increasing levels of toxicity are established
following acute inhalation exposure to TCE. High concentrations of TCE 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 Gitmour. 2010).
3.2.4,2 Cancer Hazards
Meta-analyses were performed in the 2011 EPA TCE IRIS Assessment (Appendix C, (i i.s>.
21 )) in order to statistically evaluate the epidemiological data for NHL, kidney cancer, and liver
cancer. The IRIS Assessment also investigated the association of TCE with lung cancer, primarily as a
means to examine smoking as a potential confounder for the kidney cancer studies (Appendix C, (U.S.
)). 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. That 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.
The IRIS Toxicological Review of TCE (U.S. EPA. ) also cited other lines of supporting evidence
for TCE carcinogenicity in humans by all routes of exposure:
"First, multiple chronic bioassays in rats and mice have reported increased incidences of tumors with
TCE treatment via inhalation and gavage, including tumors in the kidney, liver, and lymphoid tissues -
target tissues of TCE carcinogenicity also seen in epidemiological studies. "
"A second line of supporting evidence for TCE carcinogenicity in humans consists of toxicokinetic data
indicating that TCE is well absorbed by all routes of exposure, and that TCE absorption, distribution,
metabolism, and excretion are qualitatively similar in humans and rodents. "
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"Finally, available mechanistic data do not suggest a lack of human carcinogenic hazardfrom 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.
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 Application of Systematic
Review in TSCA Risk Evaluations ( [18b). EPA utilized similar methodology as was
employed in the 2011 EPA TCE IRIS Assessment ( ) while also incorporating
consideration of data quality evaluation as described in (U.S. EPA. 2018b). Additionally, EPA included
sensitivity analyses as needed to partition the results based on both heterogeneity and data quality score.
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. While the updated meta-analysis builds off of (U.S. EPA. 201 lb), the results presented below
represent a standalone, new analysis. See Appendix J for full details and results.
1 2 4,2,1 Meta-Analysis Results
Tne initial results of meta-analyses for NHL, kidney cancer and liver cancer showed moderate
heterogeneity among studies, due largely to the influence of the study by Vlaanderen et al. ( ).
Random-effects models are consequently preferred to fixed-effects models due to the degree of
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.
The study of Vlaanderen et al. (2013) carries very large statistical weight due to its large sample size,
but its sensitivity to detect any true effect of TCE is likely to be low. The study is based on a large
general population cohort with exposures estimated by linking job titles recorded in national census data
to a job-exposure matrix. The prevalence and average intensity of TCE 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 was significantly reduced or eliminated,
demonstrating improved consistency of the data and improved reliability of the meta-analysis results.
Resulting meta-RRs for exposure to TCE were strengthened and were statistically significant for all
three cancers.
Analyses stratified by a data quality score also indicated stronger associations of all cancers with TCE
exposure in studies that scored High for data quality compared to studies that scored Medium or Low;
notably, the latter group included the influential study of (Vlaanderen et al.. 2013). Studies that scored
high showed no heterogeneity of effects for NHL and kidney cancer, but moderate heterogeneity
remained for liver cancer.
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In summary, meta-analyses accounting for between-study heterogeneity, influential observations, and
data quality consistently indicate positive associations of NHL, kidney cancer and liver cancer with
exposure to TCE. This conclusion generally agrees with that of other governmental and international
organizations. The International Agency for Research on Cancer (IARC) (IARC. 2.014) found sufficient
evidence for the carcinogenicity of TCE in humans. IARC definitively stated that TCE causes kidney
cancer and determined that a positive associated has been identified for NHL and liver cancer. Based on
the weight of evidence when accounting for both these authoritiative assessments and the results of
EPA's meta-analyses and in accordance with EPA Guidelines for Carcinogen Risk Assessment (
05), EPA determines that TCE is "Carcinogenic to Humans". Cancer was therefore carried
forward for dose-response analysis, incorporating extra cancer risk from all three cancer types.
3.2.4.2.2 Mode of Action
Kidney Cancer
Genotoxicitv
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-supported, as
toxicokinetic data indicates that these metabolites are present in both human blood and urine, and these
metabolites have been shown to be genotoxic both in vitro and in animal studies demonstrating kidney-
specific genotoxicity (Q k >. M _ < < u , Cichocki et al. 2016) and [Data Extraction and Evaluation
Tables for Genotoxicity Studies. Docket OOP. These reactive metabolites may
be formed much less in humans than rodents however (Green et al. 1997b; Lash et al. 1990; Lash et al.
2014). although in vitro data suggests that human GSH conjugation activity may actually be higher in
humans than rodents in some cases (Table 3-23 and 3-26 of ( 01 le) and (Lash et al.. 1999;
Lash et al. I' >08)). Since genotoxicity of parent TCE has not been consistently observed (Section
3.2.3.2.1 and [Data Extraction and Evaluation Tables for Genotoxicity Studies. Docket: EPA-HQ-
OPPT-2019-0S0QY). there is some uncertainty as to the true contribution of genotoxicity toward
carcinogenesis in humans.
Cytotoxicity and other mechanisms
Observed nephrotoxicity in both human 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
sufficient or rate-limiting for renal carcinogenesis. Additionally, studies have not established that TCE-
induced proliferation in renal cells is necessary for clonal expansion or cancer. Therefore, a causal or
predictive link between cytotoxicity and carcinogenicity cannot be established ( ),
however cytotoxicity is likely the dominant mechanism of kidney non-cancer toxicity (Cichocki et al.
2016). There is also inadequate experimental support for other potential MOAs such as peroxisome
proliferator activated receptor alpha (PPARa) induction, a2[j.-globulin nephropathy, and formic acid-
related nephrotoxicity ( )
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. ).
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Liver Cancer
Genotoxicitv
The strongest data supporting mutagenic potential of TCE 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 ( 01 le. Cichocki et al. 2.016. and [Data Extraction and Evaluation Tables
for Genotoxicity Studies. Docket: EPA-HO-OPPT-2019-050QY). The peak in vivo concentrations 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. 201 le). Notably, all of the CH studies performed on human
cells exposed to TCE either in vitro or in vivo demonstrated positive genotoxic activity ([Data
Extraction and Evaluation Tables for Genotoxicity Studies. Docket: EPA- 05001).
PPARq receptor activation
An MOA through PPARa is often considered to be less relevant to humans (or at least result in reduced
potency) based on reduced human sensitivity to peroxisome proliferators compared to rodents flSfRC.
2.006). While strong evidence exists for TCA-mediated PPARa 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, but the overall data suggests that PPARa activation may not be
sufficient for carcinogenesis. TCA-induced liver tumors in mice occur at lower concentrations than
peroxisome proliferation in vivo, however PPARa occurs at even lower exposure levels. For DCA-
induced tumors, tumorigenesis occurs at much lower doses than either process. Additionally, TCE
induces liver weight increases in PPARa-null mice and transgene-mediated constitutively active PPARa
did not induce liver tumors after 11 months in mice. TCE does clearly activate PPARa and the
reasonably available data supports at least some role of PPARa activation in liver tumorigenesis, but 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).
Polvploidization
TCE induces chromosome duplication in hepatocytes, or polyploidization. Increased DNA content
results in increased gene expression but are also slower dividing and more likely to undergo apoptosis.
Changes in ploidy have been observed in transgenic mouse models that are prone to develop liver
cancer, and there is biological plausibility that polyploidication can contribute to liver carcinogenesis.
However, any potential mechanism of enhancing carcinogenesis is unknown (U.S. EPA. 201 le) and
available evidence is only correlative. Therefore, it cannot be determined whether polyploidization is
actually contributing to liver tumorigenesis or is merely a biomarker.
Cytotoxicity and regenerative hyperplasia
TCE has been demonstrated to induce liver effects in the form of hypertrophy, histopathology, increased
DNA synthesis, and cirrhosis (Section 3.2.3.1.1), all of which may be indicators of cytotoxicity and
compensatory proliferation leading to hyperplasia. Broad cytotoxicity therefore may play a role in liver
tumorigenesis, however TCE doses relevant to liver carcinogenicity do not result in significant
cytotoxicity. Observed increases in DNA synthesis are likely due to both cellular proliferation and
increased ploidy. Necrosis is not prevalent and is typically minimal to mild. Therefore, it is unlikely that
cytotoxicity and reparative hyperplasia play a significant role in TCE carcinogenicity (U.S. EPA.
2 ).
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Other mechanisms
There is limited evidence for a tumorigenic role of increased liver weight, negative selection, 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 under-expression 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 in other contexts 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 ( ).
Conclusions
The reasonably available data is inadequate to support any singular MOA. The strongest evidence exists
for involvement of both genotoxicity and PPARa activation, however a causal relationship cannot be
established because the dose levels required to elicit outcomes through both MO As are higher than those
demonstrating tumorigenic activity ( ). The MOA for liver tumors is likely complex and
may involve contributions from multiple pathways, while any single mechanism may be insufficient for
tumorigenesis on it's own.
Non-Hodgkin Lymphoma
There is insufficient data reasonably available for suggesting any particular MOA for NHL.
Overall Conclusions
TCE is carcinogenic by a genotoxic mode of action at least for kidney cancer, while a predominant
mode of action cannot be determined for the other tumor types. Per EPA Guidelines for Carcinogen Risk
Assessment (U.S. EPA. 2005). overall, the totality of the reasonably available data/information and the
WOE analysis for the cancer endpoint was sufficient to support a linear non-threshold model. The
application of a linear non-threshold model is justified based on the likely genotoxic MOA for kidney
cancer, the combined relative contributions of multiple tumor types, and the positive associations
observed via meta-analysis for all three cancers in epidemiological studies based on low-level,
environmental exposure levels (as opposed to relying on extrapolation from high doses in a rodent
bioassay).
3.2.5 Dose-Response Assessment
3,2,5.1 Selection of Studies for Dose-Response Assessment
The EPA evaluated data from studies described above (Section 3.2.3.1) to characterize the dose-
response relationships of TCE and selected studies and endpoints to quantify risks for specific exposure
scenarios. One of the additional considerations was that the selected key studies had adequate
information to perform dose-response analysis for the selected PODs. The EPA defines a POD as the
dose-response point that marks the beginning of a low-dose extrapolation. This point can be the lower
bound in the dose for an estimated incidence, or a change in response level from a dose-response model
(i.e., BMD), a NOAEL or a LOAEL for an observed incidence or change in the level of response.
Based on the weight of the evidence evaluation, six health effect domains were selected for non-cancer
dose-response analysis: (1) liver; (2) kidney; (3) neurological; (4) immunological; (5) reproductive; and
(6) developmental. Additionally, dose-response analysis was performed for cancer based on observed
incidences of kidney cancer, liver cancer, and non-Hodgkin lymphoma. These hazards have been carried
forward for dose-response analysis. While there is also evidence to support overt toxicity following
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acute exposure, endpoints for these effects were not carried forward for dose-response analysis. For a
complete discussion, see Section 3.2.4.1.
Studies that evaluated each of the health effect domains were identified in Section 3.2.3, and are
considered in this section for dose-response analysis. In order to identify studies for dose-response
analysis, several attributes of the studies were reviewed. Preference was given to studies using designs
reasonably expected to detect a dose-related response. Chronic or subchronic studies are generally
preferred over studies of less-than-subchronic duration for deriving chronic and subchronic reference
values. Studies with a broad exposure range and multiple exposure levels are preferred to the extent that
they can provide information about the shape of the exposure-response relationship. Additionally, with
respect to measurement of the endpoint, studies that can reliably measure the magnitude and/or degree
of severity of the effect are preferred.
Experimental animal studies considered for each hazard and 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-of-
evidence 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 evidence indicates that certain developmental effects may result
from a single exposure during a critical window of development (Davis et at.. 2009; Van Raati et al.
2003; U.S. EPA. 1991). This is consistent with EPA's Guidelines for Reproductive Toxicity Risk
Assessment ( 96) 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. This 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 examining pulmonary immunotoxicity following 3h TCE inhalation exposure
(S el grade and Gilmour. 2010) was also considered for 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.
3.2.5.1,1 Liver toxicity
The 2014 TSCA Work Plan Chemical Risk Assessment (U.S. EPA. 2014b) determined that the studies
of (Woothiser et al.. 2.006; Buben and O'Flaheny [985; Kiellstrand et al.. 1983) were suitable for the
dose-response assessment of the liver health effects domain. These three studies reported dose-
responsive increases in liver/body weight ratios. (Buben and O'Flaherty. 1985) and (Kiellstrand et al..
1983) also reported cytotoxicity and histopathology in mice. All three of these studies scored Medium
or High in EPA's data quality evaluation [Data Quality Evaluation of Human Health Hazard Studies.
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Docket: EPA-HQ-QPPT-2019-05001 and were therefore utilized for dose-response analysis.
3.2.5.1.2 Kidney toxicity
The 2014 TSCA Work Plan Chemical Risk Assessment ( ) considered five animal
studies reporting kidney toxicity for further non-cancer dose-response analysis. (Maltoni et at.. 1986).
(NCI. 1976) and (N 18) reported histological changes in the kidney, whereas (Kiellstrand et at..
1983) and fWoolhiser et at.. 2006) reported increased kidney/body weight ratios (U.S. EPA. 201 le).
NCI (1976) scored Unacceptable in EPA's data quality evaluation [Data Quality Evaluation of Human
Health Hazard Studies. Docket: EPA-HQ-QPPT-2019-05001 and therefore was excluded from dose-
response analysis. All of the other studies scored Medium in data quality and were therefore utilized for
dose-response analysis.
3.2.5.1.3 Neurotoxicity
Among the human studies, (Ruiiten et at... 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
analysis for the neurotoxicity endpoint in the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
E ): (Artto et at.. 1994). (Isaacson et at... 1990). (Gash et at... 2008). and (Kiellstrand et at..
1987). Kj ell strand (1987) scored Unacceptable in EPA's data quality evaluation [Data Quality
Evaluation of Human Health Hazard Studies. Docket: EPA 05001 and therefore was
excluded from dose-response analysis. Gash et al. (2.008) scored a Low in data evaluation and was also
not carried forward to dose-response analysis given the other, higher quality studies available. Ruijten
et al. (1991). Arito et al. (1994). and Isaacson et al. (1990) all scored Medium or High for data quality
and were therefore utilized for dose-response analysis.
} 2 *>.1,4 Immunotoxicity
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:
(Kelt et at... 2009). (Kaneko et at... 2000). (Sanders et at... 1982). and (Woolhiser et at.. 2006). For this
Risk Evaluation, EPA also assessed the endpoint of acute immunosuppression observed in (Selgrade
and Gltmour. 2010). In Selgrade and Gilmour (2.010). 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 larger numbers of mice per exposure
group and more dose groups, however "percent of mice infected" was also considered for dose-response
analysis (Appendix H.1.2). All of these studies scored Medium or High in EPA's data quality
evaluation [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA -HO-OPP 7-
20194)5001 and were therefore utilized for dose-response analysis.
3,2.5.1.5^ Reproductive toxicity
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 (U.S. EPA. 2014b): (Kumar et at.. 2000). (Kumar et at...
2001). (Kan et at.. 2007). (Xu et at.. 2004). (Narotskv et at.. 1995). (George et at.. 1986). (Duteaux et
at.. 2004). and ( icert et at.. 2002). Forkert et al. (2002) scored Unacceptable in EPA's data quality
evaluation and therefore was excluded from dose-response analysis, however it had the same POD as
(Kan et at.. 2007). which scored Medium. Duteaux et al. (2004) scored a Low for data quality and was
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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- 05001 and were therefore utilized for dose-
response analysis.
3.2.5.1.6 Developmental toxicity
The 2014 TSCA Work Plan Chemical Risk Assessment ( ) found 5 animal studies that
were suitable for non-cancer dose- response analysis for the following developmental outcomes: pre-
and postnatal mortality; pre- and postnatal growth; developmental neurotoxicity; and congenital heart
malformations (Appendix L of that document).
Developmental Immunotoxicitv
Although the focus of the discussion below is on these 5 studies and corresponding endpoints,
developmental immunotoxicity has also been demonstrated in TCE-treated animals. The most sensitive
immune system response was reported by (Peden-Adams et at.. 2006). which observed functional
indications of both immunosuppression and autoimmunity. In this study, B6C3F1 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
concentration levels that the dams received in drinking water. Suppressed plaque-forming cell (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 hypersensitivity at 1.4 ppm 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 of 0.37 mg/kg-bw/day served as a POD for the
decreased PFC and increased delayed hypersensitivity responses (U.S. EPA.' ). While this
endpoint exhibits one of the lower PODs among developmental toxicity studies, the study scored a
"Low" in EPA's data quality evaluation [Data Quality Evaluation of Human Health Hazard Studies.
Docket: EPa 0194)5001 due to concerns over statistical reliability and dose precision.
Additionally, it could not be accurately PBPK modeled because exposure occurred in utero, through
nursing, and after weaning.
The 2011 IRIS Assessment (IS \ tP \ 101 le) also included discussion of several studies that reported
evidence of developmental toxicity in autoimmune-prone MRL +/+ mice. These studies (Blossom et
at 2008; Peden-Adams et al. 2008; Blossom and Doss 2007). Similarly to (Peden-Adams et at... 2006).
these studies demonstrated indications of both immunosuppression and autoimmunity. These studies
also involve uncertainties over dose precision due to exposure covering both pre- and postnatal periods
however, in addition to uncertainty about extrapolation of results in an auto-immune prone strain to
humans. A more recent Medium-quality study in MRL+/+ mice that examined exposure independently
during gestation and early-life periods (Gilbert et al. 2014) observed various cytokine changes,
evidence of epigenetic changes, increased T-cell activation, and varied effects on thymus cellularity.
The conflicting directionality of cytokine changes and unclear adversity of the other observations make
it difficult to identify any potential POD. Therefore, none of these studies were considered adequate for
for dose-response analysis, although developmental immunotoxicity will still be considered
qualitatively when evaluating PODs for other developmental or immune endpoints.
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.
EPA. 2014b): (Healy et al.. 1982) and (Narotsky et Ł 5). Healy et al. (1982) scored Unacceptable
in EPA's data quality evaluation [Quality Evaluation of Human Health Hazard Studies. Docket: EPA-
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HQ-OPPT-2019-05001 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 reasonably 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 (Taylor 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 05001 and was therefore
utilized for dose-response 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 effect
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 (9-
13/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 0 to 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-analyzed in (Johnson et al.. 2003).
While the WOE analysis supports a likely association of gestational TCE exposure with induction of
CHDs (Appendix F.3), there is substantial uncertainty in the quantitative dose-response from both
studies and the relevance of these results to the human general population (Appendix F.l, Section
3.2.4.1.6, Section 3.2.5.3.1, and Section 3.2.6.1). Nonetheless, this endpoint is of concern to
susceptible subpopulations (Section 3.2.5.2) and consideration of dose responses from studies that are
more sensitive than the more commonly observed responses observed among relatively young,
healthy, and inbred laboratory rodent strains is important in accounting for human susceptibility.
Therefore, the results from (Dawson et al.. 1993) and (Johnson et al.. 2003) were considered for dose-
response analysis.
Because both studies passed data evaluation with the same score (both scored Medium for data
quality) and statistics were only performed using a pup as the statistical unit for (Dawson et al.. 1993).
EPA decided to utilize the (Johnson, et al.. 2003) data for dose-response analysis, which has increased
statistical sensitivity from the additional two dose levels and allowed a nested design for BMD
modeling analysis in order to account for litter effects. Additionally, some defects originally identified
in (Dawson et al.. 1993) were later reclassified or recharacterized in (Johnson et al.. 2003). so
(Johnson et al.. 2003) contains the more updated analysis.
3.2,5.1,7 Cancer
The 2019 meta-analysis of all relevant studies examining kidney cancer, liver cancer, or NHL
(Appendix J) came to the same conclusion as the previous EPA meta-analysis in the 2011 IRIS
Assessment (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 le) and cited in the 2014 TSCA. Work Plan Chemical
Risk Assessment ( 014b). A linear non-threshold assumption was applied to the TCE cancer
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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. 2.01 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. Charbotel et al. (2006) was a case-control study with quantitative
cumulative exposure estimates based on a task-exposure matrix based on decades of measurement. The
study received a High score for data quality both overall and for the exposure domain in EPA's data
evaluation [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA -HQ-OPP T-2 019-
05001. Therefore, EPA relied on its previous dose-response analysis from this study.
3.2.5.2 Potentially Exposed and Susceptible Subpopulations (PESS)
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 exposed or susceptible subpopulation' means a 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 susceptibility. EPA addresses the
subpopulations identified as relevant based on greater exposure in Section 2.3.3.
There is some evidence that certain populations may be more biologically susceptible to exposure to
TCE. Factors affecting biological susceptibility examined in the available studies on TCE include
lifestage, sex, genetic polymorphisms, race/ethnicity, preexisting health status, 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 (a breastfeeding infant who
is nursing from a mother exposed to the occupational exposure limit for TCE could receive more than
80% of the daily lifetime advisory limit for adults (8 earner et al.. 2 )), early lifestage-specific
toxicokinetics, and early lifestage-specific health outcomes including developmental cardiac defects.
Groups of individuals for which one or several of these factors apply may be considered PESS. Sex-
specific differences also exist in toxicokinetics (e.g., cardiac outputs, percent body fat, expression of
metabolizing enzymes) and susceptibility to toxic endpoints (e.g., sex-specific effects on the
reproductive system, sex differences in baseline risks to endpoints such as scleroderma or liver cancer).
Based on the hazards identified from the available information, individuals that either have or are
susceptible to kidney, liver, neurological, reproductive, or cancer health conditions are PESS.
Genetic variation likely has an effect on the toxicokinetics of TCE. Pre-existing diminished health status
(especially diminished function in one of the health domains supported by the weight of the scientific
evidence in Section 3.2.4) may alter the response to TCE exposure. Individuals with increased body
mass or certain conditions such as non-alcoholic fatty liver disease 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
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consumption, 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 reasonably 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 at.. 2014;
Yauck et at.. 2004). Additional maternal risk factors for susceptibility to congenital cardiovascular
defects include diabetes, infection status, drug exposure, and stress, among others (Jenkins et at.. 2007).
Significant variability in human susceptibility to TCE toxicity may result from differences in
metabolic potential, given the existence of CYP isoforms and the variability in CYP-mediated TCE
oxidation. Increased enzymatic activity of cytochrome P450 2E1 (CYP2E1) and glutathione-S-
transferase (GST) polymorphisms may influence TCE susceptibility due to effects on the production
of toxic metabolites (Cichocki , ) CYP2E1 expression may be enhanced
by various health conditions including alcoholism, obesity, and diabetes (NRC. 2006). An
individual may be a member of multiple PESS groups and may exhibit multiple concurrent
susceptibilities.
Animal data show that rates of TCE GSH conjugation in male rats/mice are higher than females
(Section 3.2.2.3), suggesting potential increased susceptibility for kidney effects in males. 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.
201 le). In this Risk Evaluation, EPA performed a population analysis to systematically estimate
uncertainty and 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/HED99 values representing the most metabolically
sensitive 1% of the population, a susceptible subpopulation, were used for risk estimation.
3.2.5.3 Derivation of Points of Departure (PODs)
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
TCE metabolites in toxicity ( 01 le). Note that the effects within the same health effect
domain were generally assumed to have the same relevant internal dose-metrics, with some exceptions.
Given that the majority of the toxic and carcinogenic responses in many tissues to TCE appears to be
associated with metabolism, the primary dose-metric for systemic effects not associated with a particular
highly metabolic organ {i.e., excluding kidney and liver) or specific metabolite was total metabolism of
TCE scaled by the % power of body weight (TotMetabBW34 [mg/kgyVday]). For these endpoints, AUC
of TCE in blood (AUCBld [mg-hour/L/day]) is the alternative dose-metric. The rationale for the scaling
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5384
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5399
5400
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5402
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5404
by body weight to the 3/4 power is analogous to that for the other metabolism dose-metrics, above.
Compared to the 2014 TSCA Work Plan Chemical Risk Assessment, an additional POD from Selgrade
and Gilmour (2010) has also been added for acute exposure scenarios.
For this assessment, when an endpoint can be BMD and PBPK modeled, default cumulative acute UF =
10 (UFa and UFh both = 3 based only on toxicodynamic uncertainty (UFtd); UFs and UFl = 1) and
default cumulative chronic UF =100 (UFs = 10 if the study covers less than 10% of lifetime). See
Appendix F for details on the criteria for selection of appropriate BMD models and UFs for each
endpoint.
POD Selection Metrics
The below sections present all studies considered for dose-response analysis. From this list, the most
robust and sensitive studies were selected from each health domain /organ system that best
characterized each available endpoint. For some health domains with multiple endpoints this resulted in
multiple studies being selected for consideration in risk estimation. In selecting the most robust and
sensitive studies and PODs, EPA considered the following factors:
• Data quality evaluation score
• Species {i.e., animal or human)
• Exposure duration
• Dose range
• Cumulative uncertainty factor
• Relevance to the endpoint of interest and human exposure scenarios
Dose metric selection is based on a determination of which toxicokinetic measure is most predictive of
localized effects from TCE exposure (Section 3.2.2.5). These factors were evaluated for each
independent endpoint, and EPA considered use of the most health-protective POD only after first
considering each of the above factors. See the 2011 EPA TCE IRIS Assessment (U.S. EPA. 2.01 le) for
more details on dose-metric and benchmark response (BMR) determinations for all endpoints except acute
immunosuppression from from Selgrade and Gilmour (2010). BMD modeling results for (Selgrade and
Gilmour. 2010) are presented in Appendix F.
3,2,5,3,1 Non-Cancer PODs for Acute Exposure
Acute exposure in humans is defined for occupational settings 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 evidence indicates that certain developmental effects may result from a single exposure during
a critical window of development (Davis et ai. 2009; Van Raaii et at... 2003; ). 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. 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 and Gilmour ( ), a 3hr acute
inhalation study, was adjusted to a 24hr HEC value for occupational risk estimates due to limited
reasonably 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
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5449
5450
5451
5452
time periods.
Developmental Toxicity Endpoints
— Prenatal Mortality
(Narotsky et at.. 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 BMDLoi for
increased resorptions was 32.2 mg/kg-bw/day ( ).
— Developmental Neurotoxicity
(Fredriksson etai. 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-treated mice showed decreased rearing activity at both
dose levels on PND 60, but not PND 17, resulting in a LOAEL of 50 mg/kg-bw/day as a POD (U.S.
).
— 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 129 mg/kg-bw/day) measured on both an individual fetus basis and a litter basis. A BMDLoi
HEC99 of 0.0037 ppm and HED99 of 0.0052 mg/kg-bw/day were identified as the inhalation and oral
PODs, respectively, for heart malformations in the 2014 TSCA Work Plan Chemical Risk Assessment
(I Ł014b). EPA quantified the totality of cardiac defects instead of any particular defect, as
cardiac teratogens can result in a diverse constellation of effects (e.g., retinoic acid, see Appendix
F.2.2.2).
The BMR selection from the 2014 TSCA Work Plan Chemical Risk Assessment (U.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 assessment in (Makris et al.. 2016). which acknowledged
the uncertainty inherent in a selection of a 1% BMR:
"BMD inference at the 1% extra-risk level is highly uncertain, because BMD and BMDL values vary
by several orders of magnitude depending on the modeling assumptions. This is attributed in part to
the lack of monotonicity at the lowest dose and the apparent supralinearity of the overall exposure-
response relationship. Additional doses would be required to better specify the curve shape in the low-
dose region. More reliable inference can be made for higher BMRs...
There is substantial model and parameter uncertainty at the 1% level of extra risk, although 1% is the
appropriate BMR based on severity of the effect (i.e., cardiac malformations). These uncertainties can
be attributed primarily to having too few data points in the low-dose range, where more data would be
required to adequately characterize the dose-response shape. Uncertainty decreases for higher BMR
levels (5% and 10% extra risk), although 10% exceeds the range of the data for 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
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5499
5500
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.4.2.1 in (I S JOj 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
at.. 2003) was reduced from controls, adding uncertainty to the modeling estimate, however the
difference was not statistically significant. A larger sample size for the treated groups may have
increased the statistical sensitivity at lower doses. The BMD model actually displays better visual fit at
the lower end of the dose range, near the control, suggesting that a lower BMR may actually represent
a more accurate model estimate.
In evaluating model fit, EPA determined that the BMD:BMDL ratio was adequate (3.1), indicating
reasonably small variance. To confirm the model fit, EPA updated the BMD analysis on the nested
dataset using the latest version of the BMDS software (v3.1.1) due to limitations of the software at the
time of the original modeling for the 2011 IRIS Assessment ( Olle). These results and
discussion of the analysis compared to the 2011 analysis are provided in Appendix I. These results
demonstrate strong model fit and agree with the 2011 conclusion that the modeling results for cardiac
malformation data are appropriate for reference value derivation.
Based on the above considerations and the improved model fit from the updated BMD modeling run,
EPA determined that use of a 1% BMR is most appropriate for risk estimation. The difference
between the 1% and 5% BMR POD values is 5.2-fold. Results for both 1% and 5% extra risk BMR
options (along with 10%) are presented in Appendix I.
Immunotoxicitv
— Immunosuppression (diminished response to infection)
In addition to the previously described developmental toxicity studies, (Setgrade and Gilmour. 1\> 10)
was deemed suitable for dose-response analysis of immunotoxicity based on observed decreased
response to infection. In Selgrade and Gilmour (20101 female CD-I mice were infected via respiration
with aerosolized S. zooepidemicus bacteria following 3h exposure to 0, 5, 10, 25, 50, 100, or 200 ppm of
TCE. Mortality was assessed for all dose groups, with statistically significant and dose-responsive
increases observed at 50 ppm and above. Bacterial clearance from the lung, percent of mice infected, and
phagocytic index were also assessed for 0, 50, 100, and 200ppm dose groups. This study examined
pulmonary immunological responses to respiratory infection following inhalation of TCE and is
therefore only applicable to inhalation exposure. The inclusion of the Selgrade and Gilmour (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 ( ) but was excluded from the 2014 Risk Assessment in an
oversight.
For (Selgrade and Gilmour. 2.010). BMD modeling was performed on the endpoints of mortality and
percentage of mice infected (see [Personal Communication to OPPT. Raw Data Values from Selgrade
and Gilmour, 2010. Docket: EPA-HQ-OPPT-2019-05001). A reliable BMDL 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 (Sanders et at...
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5501 1982; Woolhiser et at.. 2006). this acute endpoint was applied to systemic exposure. The BMDLi based
5502 on applied dose is 13.9 ppm (Appendix H. 1.1.3).
5503
5504 The raw data from (Selgrade and Gilmour. ^ ) was input through the PBPK model (described in
5505 Section 3.2.2.5) to obtain internal doses based on two dose metrics, the total amount of TCE
5506 metabolized per unit adjusted body weight (TotMetabBW34) and area under the curve venous blood
5507 concentration of TCE (AUCBld). These two metrics were selected as the primary and alternative dose
5508 metrics for this endpoint under the assumption that the metabolic contribution to this endpoint matches
5509 that for other immune endpoints (see ( )1 le) and Table 3-11). The internal doses were BMD
5510 modeled, and HEC/HECso and HEC/HED99 were then derived based on default model parameters
5511 assuming continuous exposure. Full modeling runs and details for both dose metrics are provided in
5512 \PBPK Modeling Results for Representative Non-Cancer Endpoints under Continuous and
5513 Occupational Exposure Scenarios and Internal Dose BMD Modeling Results for Selgrade and Gilmour,
5514 2010. Docket: EPA-HO-OPPT-2019-0500\ BMD modeling results for applied dose and TotMetabBW34
5515 dose metric are provided in Appendix F.
5516
5517 Table 3-7. Dose-response analysis of selected studies considered for acute exposure scenarios
Target
Organ/
System
Species
Duration
POD Type1
(applied dose)
Effect
Dose
Metric
HEC50
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Rat
(female)
Gestational
days 6 to 15
BMDL01=
32.2 mg/kg -
bw/day
Increased
resorptions
TotMetab
BW34
57
23
29
28
UFs=1;UFa= 3;
UFH=3;UFL=1;
Total UF= 10
19951
High
(1.3)
Develop-
mental
Effects
Rat
(female)
22 days
throughout
gestation
(gestational
days 0 to 221
BMDL01 =
0.0207
mg/kg-
bw/day
Congenital
heart
defects
TotOx
Metab
BW34
0.012
0.0037
0.0058
0.0052
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF= 10
(Johnson et al.
20031
Medium
(1.9)
Rat
(male
pups)
Postnatal days
10 to 16
LOAEL =
50 mg/kg -
bw/day
Decreased
rearing
activity
TotMetab
BW34
4.2
4.1
UFS=1;UFA=3;
UFH=3; UFL=10:
Total UF= 100
(Fredriksson et
al.. 1.9931
Medium
(1.7)
Immune
System
Rat
(female)
3hr/day, single
dose; followed
by respiratory
infection
BMDL01 —
13.9 ppm
Mortality
due to
immuno-
suppression
TotMetab
BW34
0.973
1.36
1.34
UFS=1;UFA=3;
UFH=3;UFL=1;
Total UF=10
(Selgrade and
Gilmour. 201.0)
High
(1.6)
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 UFs=subchronic to chronic UF; UFA=interspecies UF; UFH=intraspecies UF; UFl=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0S0Q] for full evaluation by metric.
Endpoints within an organ system are separated by double-line borders (=); organ systems are separated by thicker borders (-).
5518
5519
5520
5521
5522
5523
5524
5525
5526
5527
5528
5529
5530
Table 3-7 presents the derived PODs from all studies considered for dose-response analysis of acute
exposure scenarios. EPA selected studies representative of the distinct endpoints of prenatal mortality,
congenital defects, developmental neurotoxicity, and response to infection. Most of the developmental
toxicity studies utilized the PBPK dose metric of TotMetabBW34, or the total amount TCE metabolized
per unit adjusted body weight. 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.
TotOxMetab34, or the total amount TCE oxidized per unit adjusted body weight, was used for deriving
HEC/HED values for congenital heart defects because evidence demonstrating effects from TCA and
DCA (see Section 3.2.4.1.6) suggests that oxidative metabolism is important for TCE-induced heart
malformations.
Page 264 of 803
-------
5531
5532
5533
5534
5535
5536
5537
5538
5539
5540
5541
5542
5543
5544
5545
5546
5547
5548
5549
5550
5551
5552
5553
5554
5555
5556
5557
5558
5559
5560
5561
5562
5563
5564
5565
5566
5567
5568
5569
5570
5571
5572
5573
5574
5575
5576
5577
5578
The LogProbit model was selected for BMD modeling results of (Selgrade and Gilmour. 2010) data
because it was the model with the lowest AIC, using a BMR of 1% based on the endpoint of mortality
(Appendix F). Data from (Narotsky et ai. 1995) and (Johnson et al. 2003) were also BMD modeled. A
BMR of 1% ER was selected for (Johnson, et al.. 2003) based on the severity of the effect and absence of
a strong statistical justification for raising the value (see discussion above). A BMR of 1% was also
selected for (Narotsky et al... 1995) because of the severity of the effect (full-litter resorptions) and low
background response. A LOAEL was used as a POD for (Fredriksson et al.. 1993). which was not BMD
modeled. For acute exposures, subchronic-to-chronic UF does not apply, so UFs = 1 for all studies. See
Section 3.2.2.5 and (U.S. EPA. 2.01 le) for more details on TCE PBPK modeling, dose metric selection,
and BMR selection.
Differences from standard UF values are explained below:
A UFa value of 3 was applied to (Selgrade and Gilmour. 2.010) because cross-species scaling based on
blood:air partition coefficient or allometric scaling for body weight was used to adjust the HEC/HED as
necessary. A UFh of 10 was applied to that study because the data were not subject to PBPK modeling
and therefore a HEC99/HED99 value was not applied which would have accounted for human
toxicokinetic variability.
The selected studies are bold in the table above. The endpoints were each represented by a single study.
While there are some methodological and statistical concerns about (Johnson et al.. 2.003) and
(Fredriksson et al.. 1993). based on the WOE for the endpoints and data quality scores of at least
Medium, all four of the studies will be utilized for quantitative risk estimation following acute
exposures. There is also some inherent uncertainty extrapolating from the response to pulmonary
infection observed in (Selgrade and Gilmour. 2010) to a systemic response across multiple exposure
routes, but an acute systemic response to infection is likely based on the systemic immunosuppression
observed in multiple chronic studies (Sanders et al... 1982; Woolhiser et al.. 2006).
3.2.5.3.2 Non-Cancer PODs for Chronic Exposures
Chronic exposure was defined for occupational settings as exposure reflecting a 40-hour work week.
Chronic exposure was not considered relevant to to consumers based on expected use patterns (Section
2.3.2.6.1). Non-cancer endpoints selected as most relevant for calculating risks associated with chronic
(repeated) occupational exposures to TCE included effects to the liver, kidney, nervous system, immune
system, reproductive system, and developmental outcomes, with all HECs adjusted to reflect a 24-hr
value, consistent with calculated occupational exposure values.
Liver toxicity
— Increased liver weight and cytotoxicity/hypertrophy
(Kiellstrand et al. 1983) exposed NMRI male mice (10-20/group) with up to nine different TCE
concentrations. These concentrations ranged from 37 to 3,600 ppm and included an air control group.
Exposures were conducted for various durations (1, 2, 4, 8, 16, or 24 hrs/day) and for different time
frames (from 30 to 120 days). Liver weight increased in a dose-responsive matter, with statistical
significance apparent at all exposure groups and durations. EPA calculated a benchmark concentration
lower-bound confidence limit of 21.6 ppm based on the 10% benchmark response (BMDLio) for
increased liver/body weight ratios, with histopathology including vacuolization and inflammatory cell
infiltration also observed at 150ppm and above.
(Buben and O'Ftafaerty. 1985) exposed Swiss-Cox male mice (12-15 group) to TCE by gavage. Mice
were exposed to a range of TCE doses (100 to 3,200 mg/kg-bw/day plus control) for 5 days/week for 6
weeks. A BMDLio of 82 mg/kg-bw/day was identified as the POD for increased liver/body weight
Page 265 of 803
-------
5579
5580
5581
5582
5583
5584
5585
ratios, with cytotoxicity, histopathology, and reduced glucose-6-phosphatase activity also observed.
In (Woolhiser et al. 20061 Sprague-Dawley female rats (16/group) were exposed to TCE via
inhalation at concentrations of 0, 100, 300, or 1,000 ppm for 6 hrs/day, 5 days/week for 4 weeks. A
BMDLio of 25 ppm was estimated for increased liver/body weight ratio.
Table 3-8. Dose-response analysis of selected studies considered for evaluation of liver toxicity
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
HEC50
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality3
Liver
Mouse
(male)
Continuous and
intermittent
exposures,
variable time
periods for 30-
120 days
BMDL10=
21.6
ppm
Increased
liver/body
weight ratio
and
cytotoxicity/
hypertrophy
AMetLivl
BW34
25
9.1
9.0
7.9
UFS=1;UFA=3;
UFh=3; UFL=1;
Total UF=10
(Kiellstrand
et al. 1983)
Medium
(1.8)
Mouse
(male)
6 weeks
BMDL io= 82
mg/kg-bw/day
AmetLivl
BW34
32
11
12
10
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF=10
(Buben and
'"I®)1
High
(1.3)
Rat
(female)
6 hr/day, 5
days/week for 4
weeks
BMDL io= 25
ppm
Increased
liver/body
weight ratio
AmetLivl
BW34
53
19
19
16
UFs=l; UFa= 3;
UFh=3;UFl=1;
Total UF=10
('Woolhiser
et al. 2006)
Medium
(2)*
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 UFS=subchronic to chronic UF; UFA=interspecies UF; UFLl=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Oualitv Evaluation of Human Health Hazard Studies. Docket: EPA-HO-OPPT-2019-0S00J for full evaluation by metric. * Woolhiser
et al., 2006 was downgraded from a Fligh, with calculated score =1.3.
Bold rows indicate studies selected to represent the endpoint within the organ system domain.
5586
5587
5588
5589
5590
5591
5592
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610
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 of TCE 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 (indications of liver toxicity were linearly associated with total urinary {i.e., oxidative)
metabolites in (Buben and O'Ftah >85)). Additionally, dose-response relationships using this dose
metric showed greater consistency than other considered metrics. All studies were BMDL modeled. A
BMR of 10% RD was used to represent a minimal, biologically significant amount of change in relative
liver weight. See Section 3.2.2.5 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:
All three studies were assigned UFs = 1 despite shorter exposure duration because although the studies
were subchronic, hepatomegaly (enlarged liver) occurs rapidly with TCE exposure, and no differences
were observed in severity of relative liver weight increases between 30 and 120 days in (Kiellstrand et
al. 19831
The data from (Kiellstrand et al.. 1983) was selected to represent the liver toxicity hazard. (Woolhiser et
al.. 2006) was excluded from further consideration because additional signs of toxicity were not
observed, indicating that the increased liver weight was likely merely adaptive. (Kiellstrand 5)
was selected over (Buben and O'Flahertv. 1985) because it covered up to 120 days exposure as opposed
to only 42 days. Additionally, (Kiellstrand et al... 1983) utilized the widest dose range of any study,
imparting more precision in the POD estimate.
Page 266 of 803
-------
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
5634
5635
5636
5637
5638
5639
Kidney toxicity
— Kidney Pathology
(Maltoni et al. 1986) exposed Sprague-Dawley male 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 investigators also 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. BMDLio
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) observed in male rats
(U.S. EPA. ). These changes included dose-dependent enlargement of tubuli cells (cytomegaly)
and their nuclei (karyomegaly) leading to dysplasia, which may serve as a precursor to cancer and/or
morphological indicators of damaged kidney function (Maltoni et al... 1986).
In another oral (gavage) study (NTP. 1988). the National Toxicology Program exposed Marshall female
rats (44-50/group) to TCE (i.e., 0, 500, or 1,000 mg/kg-bw/day) for 5 days/week for 104 weeks. Rats
developed toxic nephropathy following TCE exposure. A BMDLos of 9.45 mg/kg- bw/day was
calculated for the observed kidney effects Q H \ Ic).
— 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 1,000 ppm 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 BMDLio of 15.7 ppm was estimated
for these effects (l__S i0jj_c).
Increased kidney/body weight ratios were also seen in (Kieiistrand et al. 1983). NMRI male mice (10-
20/group) were exposed to a range of TCE concentrations (37 to 3,600 ppm) for 30 to 120 days on
continuous and intermittent exposure regimens. A BMDLio of 34.7 ppm was identified as the POD for
increased kidney/body weight ratios ( ).
Table 3-9. Dose-response ana
ysis of selected studies considered for evaluation of kidney toxicity
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
hec50
(ppm)
hec99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Kidney
Rat
(female)
5 days/week
for 104 weeks
BMDLos = 9.45
mg/kg-bw/day
Toxic nephropathy
ABioact
DCVC
BW34
0.042
0.0056
0.033
0.0034
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF=10
(NIP. 1988)
Medium
(2)*
Rat
(male)
- Oral
4-5 days/week
for 52 weeks
BMDL10 = 34
mg/kg-bw/day
Pathology
changes in renal
tubule
ABioact
DCVC
BW34
0.19
0.025
0.15
0.015
UFS=1;UFA=3;
UFh=3; UFl=1;
Total UF=10
(Maltoni et
al. 1986)
Medium
(2)*
Rat
(male)
- Inhal.
7 hrs/day, 5
days/week for
2 years
BMDL10= 40.2
ppm
Pathology changes
in renal tubule
ABioact
DCVC
BW34
0.28
0.038
0.22
0.023
UFs=1;UFa= 3;
UFH=3;UFL=1;
Total UF=10
(Maltoni et
Ski 986)
Medium
(2)*
Rat
(female)
6 hr/day, 5
days/week for
4 weeks
BMDL10= 15.7
ppm
Increased kidney
weight/body
weight ratio
ABioact
DCVC
BW34
0.099
0.013
0.078
0.0079
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF=10
(Woolhiser
et al.. 2006)
Medium
(2)*
Mouse
(male)
Continuous
and
intermittent
exposures for
30-120 days
BMDL10 = 34.7
ppm
Increased kidney
weight/body
weight ratio
AMet
GSH
BW34
0.88
0.12
0.69
0.07
UFs=1;UFa= 3;
UFH=3;UFL=1;
Total UF=10
elaljisi)
Medium
(1.8)
Page 267 of 803
-------
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2LTFS=subchronic to chronic UF; LTFA=interspecies UF; LTFH=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HO-QPPT-2019-0500] for full evaluation by metric. *NTP 1998 was
downgraded from a High, with calculated score =1.2; Maltoni 1986 was downgraded from a High, with calculated scores = 1.4 (oral) and 1.3 (inhalation);
Woolhiser 2006 was downgraded from a High, with calculated score =1.3.
Bold rows indicate studies selected to represent the endpoint within the organ system domain; endpoints within an organ system are separated by double-line
borders (=).
5640
5641 Table 3-9 presents the derived PODs from all studies considered for dose-response analysis. The studies
5642 considered for dose-response analysis identified either indications of kidney pathology or increase
5643 kidney/body weight ratio. All rat studies utilized ABioactDCVCBW34, or the amount of DCVC
5644 bioactivated in the kidney per unit adjusted body weight, because GSH-conjugative bioactivation of
5645 TCE into metabolites such as DCVC in the kidney is expected to be responsible for kidney toxicity,
5646 although there is some uncertainty about their direct connection to kidney toxicity (Green et at. 1997a.
5647 b). AMetGSHBW34, or the amount of TCE conjugated with GSH per unit adjusted body weight, was
5648 utilized for mice studies because PBPK information on DCVC activation in mice is not reasonably
5649 available. All studies were BMDL modeled. A BMR of 5% ER was used for (NTP. 1988) because toxic
5650 nephropathy is a severe toxic effect. (Maltoni et at.. 1986) used a BMR of 10% ER because
5651 meganuclocytosis is considered minimally adverse, while both studies examining increased relative
5652 kidney weight used a standard BMR of 10% RD. See Section 3.2.2.5 and ( 31 le) for more
5653 details on TCE PBPK modeling, dose metric selection, and BMR selection.
5654
5655 Differences from standard UF values are explained below:
5656 (Woolhiser et al.. 2006) and (Kiettstrand et at.. 1983) were assigned UFs = 1 despite shorter exposure
5657 duration because no differences were observed in severity of relative kidney weight increases between 30
5658 and 120 days in (Kiettstrand et al.. 1983).
5659
5660 EPA determined that kidney pathology was a better indicator of adverse kidney effects than increased
5661 relative organ weight and therefore only that endpoint was selected to represent kidney toxicity. While
5662 there are concerns about the procedure of continuing observation until spontaneous death in (Maltoni et
5663 a 5) due to the potential for confounding effects from autophagy or infection, there are unlikely to
5664 be significant artifacts from this methodology affecting the interpretation of kidney lesions. There was
5665 random allocation to study groups and kidney lesions were not observed in the control or lowest dose
5666 group. Therefore, background false positives were not an issue and the observed dose-response is
5667 expected to be independent of this confounder. Additionally, a 2011 review of pathology results from
5668 other cancer studies performed in this laboratory (Ramazzini Institute) by the NTP Pathology Working
5669 Group (Matarkev and Bucher. 2011) found good agreement on the interpretation of most solid tumors
5670 and only identified significant differences among inflammatory cancers of the blood and respiratory
5671 tract.
5672
5673 Both (Maltoni et at.. 1986) and (NTP. 1988) scored a Medium in data quality, however (Maltoni et al..
5674 1986) tested exposure over a sufficiently similar duration with a more appropriate dose range. The
5675 elevated doses in (NTP. 1988) resulted in massive nephrotoxicity and introduce large uncertainty in
5676 BMD modeling the effects at low doses well below the tested doses with a BMR well below the
5677 observed effect incidence in the study. Therefore, the BMDL and resulting HEC/HED from (Maltoni et
5678 al.. 1986) was considered more reliable. Among the inhalation and oral results from (Maltoni et al..
5679 1986). with few other differences among the data the lower resulting oral POD was selected to represent
5680 the endpoint in order to be health-protective. Of note, this represents a change from the 2014 TSCA Work
5681 Plan Chemical Risk Assessment ( ). which selected the POD from (NTP. 1988) to
5682 represent kidney toxicity.
Page 268 of 803
-------
5683
5684 Neurotoxicity
5685 — CNS Depression
5686 (Arito et al. 1994) exposed Wistar male rats (5/group) to TCE via inhalation to concentrations of 0,
5687 50, 100, or 300 ppm for 8 hrs/day, 5 days/week for 6 weeks. Exposure to all of the TCE concentrations
5688 significantly decreased the amount of time spent in wakefulness during the exposure period. Some
5689 carry over was observed in the 22 hr-post exposure period, with significant decreases in wakefulness
5690 seen at 100 ppm TCE. Significant changes in wakefulness- sleep elicited by the long-term exposure
5691 appeared at lower exposure levels. The LOAEL for sleep changes was 12 ppm {i.e., LOAEL, adjusted
5692 for continuous exposure) (U.S. EPA. 201 le).
5693
5694 — Trigeminal nerve effects
5695 (Ruiiten et al.. 1991) evaluated the TCE exposures and possible health effects of 31 male printing
5696 workers (mean age: 44 yrs) and 28 unexposed control subjects (mean age: 45 yrs). The exposure
5697 duration was expressed as "cumulative exposure" (concentration x time). Using historical monitoring
5698 data, mean exposures were calculated as 704 ppm x number of years worked, where the mean number
5699 of years worked was 16 (range: 160-2,150 ppm x yr) ( 01 le). The study measured the
5700 trigeminal nerve function by using the blink reflex, but no abnormal findings were observed. However,
5701 the study found a statistically significant average increase in the latency response time in TCE-exposed
5702 workers on the masseter reflex test, another test commonly used to measure the integrity of the
5703 trigeminal nerve. The POD derived from the dataset was a LOAEL of 14 ppm (U.S. EPA. 2 ).
5704
5705 — Neuronal demyelination
5706 (Isaacson et al.. 1990) dosed weanling Sprague-Dawley male rats (12/dose group) via the oral route
5707 (drinking water) in an experimental protocol for an 8-week period. The control group had unexposed
5708 rats for 8 weeks. The experimental group #1 exposed rats to 47 mg/kg-bw/day TCE for 4 weeks and
5709 then no TCE exposure for 4 weeks. The experimental group #2 exposed rats to 47 mg/kg-bw/day TCE
5710 for 4 weeks, no TCE exposure for the following 2 weeks, and then 24 mg/kg-bw/day TCE for the final
5711 2 weeks. Rats in group #2 reported a decreased latency to find the platform in the Morris water maze
5712 test. While these results actually suggest increased cognitive performance, all of the TCE-treated groups
5713 exhibited hippocampal demyelination, with effects more severe in the twice-exposed group. The
5714 LOAEL for neurodegenerative effects (i.e., demyelination in the hippocampus) was 47 mg/kg-bw/day
5715 (U.S. EPA. ).
5716
717 1
"able
5-10. Dose-response analysis of selected studies considered for evaluation of neurological e
'fects
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
HEC50
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Nervous
system
Rat
(male)
8 hrs/day, 5
days/weeks
for 6 weeks
LOAEL=
12 ppm
Significant
decreases in
wakefulness
TotMetab
BW34
13
4.8
6.6
6.5
UFS=3;UFA=3;
UFh=3; UFl=10;
Total UF=300
(Arito et al,.
1.9941
Medium
(2)"
Human
(both
sexes)
Mean of 16
years
LOAEL=
14 ppm
Trigeminal nerve
effects (increased
latency in
masseter reflex)
TotMetab
BW34
14
5.3
7.4
7.3
UFs=l; UFa= 1;
UFH=3; UFl=3;
Total UF=10
(Ruiiten et
al. 1991)
Medium
(1.7)
Rat
(male)
8 weeks
(intermittent)
LOAEL = 47
mg/kg-
bw/day
Demyelination of
hippocampus
TotMetab
BW34
18
7.1
9.4
9.2
LTFS=10; IIFA= 3;
LTFH=3; LTFL=10;
Total UF=1000
(Isaacson et
aLJ.990)
Medium
(2)*
Page 269 of 803
-------
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 LTFS=subchronic to chronic UF; LTFA=interspecies UF; LTFH=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HO-QPPT-2019-0500] for full evaluation by metric. *Arito 1994 was
downgraded from a High, with calculated score =1.6; Isaacson 1990 was downgraded from a High, with calculated score =1.6.
Bold rows indicate studies selected to represent the endpoint within the organ system domain; endpoints within an organ system are separated by double-
line borders (=).
5718
5719 Table 3-10 presents the derived PODs from all studies considered for dose-response analysis. The
5720 reasonably available datasets for considering neurotoxicity included single studies for each of the three
5721 endpoints of central nervous system (CNS) depression, trigeminal nerve effects, and neuronal
5722 demyelination. The TotMetabBW34 dose metric, or the total amount TCE metabolized per unit adjusted
5723 body weight, was used for all three studies. This dose metric was selected because for these endpoints
5724 there is insufficient information for site-specific or mechanism-specific determinations of an appropriate
5725 dose-metric, however in general TCE toxicity is associated with metabolites rather than the parent
5726 compound. LOAELs were used as PODs for all studies, and none were BMD modeled. See Section
5727 3.2.2.5 and ( ) for more details on TCE PBPK modeling and dose metric selection.
5728
5729 Differences from standard UF values are explained below:
5730 (Arito et at.. 1994) was assigned UFs = 3 (instead of 10) despite being only a 6 week study because
5731 effects observed at 6 weeks exposure were only minimally different than effects at 2 weeks (differences
5732 observed post-exposure).
5733 (Ruiiten et at.. 1991) was assigned UFs = 1 because the data were based on a mean of 16 years of human
5734 exposure. UFl = 3 (instead of 10) due to the observed effect being an early marker and representing a
5735 minimal degree of change.
5736
5737 EPA did not select (Isaacson et at.. 1990). demonstrating demyelination of the hippocampus, to
5738 represent the neurotoxicity hazard because dosing during the study was not continuous and the resulting
5739 POD was subject to a large cumulative uncertainty factor (1000). (Arito et at.. 1994) and (Ruiiten et at..
5740 1991) were both considered for use in quantitative risk estimation as they were relatively well-conducted
5741 studies examining independent endpoints within the hazard of neurological effects.
5742
5743 Immunotoxicitv
5744 (Kelt et at.. 2009) exposed B6C3F1 mice (10/group), a standard test strain not genetically prone to
5745 develop autoimmune disease, to TCE via drinking water for 27 or 30 weeks at concentrations in water
5746 of 0, 1.4, or 14 ppm (0.35 or 3.5 mg/kg-bw/day). The study reported a significant decrease in thymus
5747 weight concentrations at both doses and decreased thymic cellularity at the highest dose. Increased
5748 autoantibodies to ssDNA (single-stranded DNA) and dsDNA (double-stranded DNA) were significantly
5749 increased only at the lowest dose. Activated splenic CD4+/CD44+ T-cells (suggestive of autoimmunity)
5750 were also observed at the highest dose. A LOAEL of 0.35 mg/kg-bw/day was identified as the POD for
5751 the thymic and autoimmune effects (U.S. EPA.: ), although EPA has since determined that the
5752 thymic effects may not be a reliable indicator of autoimmunity and have ambiguous adversity. The
5753 significance of the thymic effects is therefore unclear but may be representative of other immune
5754 outcomes. Increased autoantibodies were not observed in the autoimmune-prone strain (NZBWF1)
5755 tested in parallel. While there was not a consistent dose-response for autoantibodies (responses are
5756 similar or even decreased at the higher dose), this inconsistent dose response is in agreement with
5757 results from autoimmune-prone MRL +/+ mice in (Griffin et at. 2000).
5758
5759 (Kaneko et at.. 2000) exposed auto-immune prone mice (5/group) to TCE via inhalation at
5760 concentrations of 0, 500, 1,000, or 2,000 ppm for 4 hrs/day, 6 days/week, for 8 weeks. At
5761 concentrations > 500 ppm, mice exhibited dose-related liver inflammation, splenomegaly and
Page 270 of 803
-------
5762
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
5782
hyperplasia of lymphatic follicles. Immunoblastic cell 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 (\ - Li c).
— Immunosuppression
In (Sanders et at... 19821 male and female CD-I mice (7-25/group) were given TCE in drinking water
concentrations of 0, 0.1, 1.0, 2.5, or 5.0 mg/mL (0, 18, 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/kg-
bw/day), whereas cell-mediated immunity 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 immunosuppressive effects (\ \ U* \ 201 le).
Another study that was previously discussed for liver and kidney effects (Woolfalser et ai. 2006) also
reported immunosuppressive effects. Sprague-Dawley female rats (16/group) were treated with 0, 100,
300 or 1,000 ppm 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
anti-SRBC IgM response. At 1,000 ppm, rats demonstrated a 64% decrease in the PFC assay response.
A BMDLisd of 24.9 ppm was identified for this immunosuppressive effect (I, ^ I'P \ j'l 1^).
able 3-11. Dose-response analysis of selected studies considered for evaluation of immune effects
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
HECS0
(ppm)
HEC99
(ppm)
HED50
(mg/kg) (i
HED99
mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Mouse
(female)
27-30 weeks
LOAEL=
0.35
mg/kg-
bw/day
Autoimmunity
(increased
anti-dsDNA
and ssDNA
antibodies)
TotMetab
BW34
0.092
0.033
0.049
0.048
UFS=1;UFA=3;
UFh=3; UFl=3;
Total UF=304
(Keil et at.,
2009')
High
(1.6)
Immune
System
Mouse
(males;
auto-
immune
prone
strain)
4 hrs/day, 6
days/week
for 8 weeks
LOAEL = 70
ppm
Autoimmunity
(changes in
immunoreactive
organs)
TotMetab
BW34
97
37
44
42
UFs=10; LTFa= 3;
UFH=1;UFL=10;
Total UF=300
(Kaneko et al.
20001
High
(1.5)
Mouse
(female)
16 or 24
weeks (4 or
6 months)
LOAEL =18
mg/kg-
bw/day
Immuno-
suppression
TotMetab
BW34
4.8
1.7
2.5
2.5
UFs=1;UFa=3;
UFh=3; UFl=10;
Total UF=100
(Sanders et al.
1.9821
High
(1.4)
Rat
(female)
6 hrs/day, 5
days/ week
for 4 weeks
BMDL1sd-
24.9 ppm
Immuno-
suppression
TotMetab
BW34
29
11
14
14
UFS=10; UFA= 3;
UFH=3; UFL=1;
Total UF=100
fWoothiser et
al. 20061
High
(1.1)
1 POD type can be NOAEL, LOAEL, or BMDL. The IRIS program adjusted all values to continuous exposure.
2 LTFs=subchronic to chronic UF; UFA=interspecies UF; UFH=intraspecies UF; UFl=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HO-OPPT-2019-0500] for full evaluation by metric.
4 Two different effects were reported by Keil et al, (2009): decreased thymic weight and cellularity and autoimmunity. A total UF of 100 was used for the
thymus toxicity, whereas a total UF of 30 was used for the autoimmune effects. The TCE IRIS assessment allocated different LOAEL-to-NOAEL
uncertainty factors (UFl) based on the severity of the effects, which resulted in different total UF (U.S. EPA, 201. le).
Bold rows indicate studies selected to represent the endpoint within the organ system domain; endpoints within an organ system are separated by double-
line borders (=).
5783
5784
5785
5786
5787
Table 3-11 presents the derived PODs from all studies considered for dose-response analysis. These
studies covered the endpoints of thyroid effects, autoimmunity, and immunosuppression. The
TotMetabBW34 dose 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
Page 271 of 803
-------
5788
5789
5790
5791
5792
5793
5794
5795
5796
5797
5798
5799
5800
5801
5802
5803
5804
5805
5806
5807
5808
5809
5810
5811
5812
5813
5814
5815
5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
5826
5827
5828
5829
5830
5831
5832
5833
5834
5835
5836
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 except (Woolhiser et at.. 20061 which was BMD modeled
with a BMR of 1 SD because it was unclear what should constitute the cutoff point for a minimal,
biologically significant change. See Section 3.2.2.5 and ( lie) for more details on TCE
PBPK modeling, dose metric selection, and BMR selection.
Differences from standard UF values are explained below:
(Keil et at. 2009) was assigned UFi = 3 (instead of 10). Detection of anti-nuclear antibodies (ANA) is a
long-established clinical marker of autoimmune connective tissue diseases (e.g., lupus). Specificity of
ANA for autoimmune disease states can be low, however anti-dsDNA antibodies have been shown to be
quite specific and are rarely detected at elevated levels in healthy patients (Kavanaugh et al. 2000;
Wichainun et ai. 2013). Therefore, the results from (Keil et al.. 2009) do represent an adequate
biomarker of autoimmunity, and the selection of UFl = 3 is justified due to the observed effect being
considered an early, subclinical or pre-clinical early marker of disease and the non-standard dose-
response observed in the study. An increase in activated T cells, another indicator of autoimmunity, were
observed only at the highest dose, further supporting a reduced UFl at the lowest dose.
Decreased thymus weight and eellularity as observed in (Keil et al.. 2009) was not considered for use in
dose-response analysis or risk estimation because EPA determined that this effect is insufficiently
adverse compared to the other endpoints and the effects are inconsistent with the indications of
autoimmunity. Of note, elimination of this endpoint and corresponding change in total UF (UFl =10 was
previously applied to the thymus effects) represents a change from the 2014 TSCA Work Plan Chemical
Risk Assessment ( 014b). The POD from (Keil et al.. 2009) for anti-ssDNA and dsDNA was
selected to represent autoimmunity however, because the study was of longer duration than (Kaneko et
al.. 2000) with a smaller cumulative uncertainty factor, and the data from (Kaneko et al.. 2000) was only
on autoimmune-prone mice. (Sanders 2) 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-
136.4 mg/g creatinine). In addition, 12 workers provided personal 8-hr air samples, which resulted in a
mean TCE exposure of 29.6 ppm (range: 9-131 ppm). There were no controls in the study. Males
experienced decreased percentage of normal sperm morphology and hyperzoospermia. A BMDLio of
1.4 ppm was identified as the POD for these effects ( ).
(Xu et al.. 2004) exposed male CD-I mice (27/group) to TCE at concentration of 0 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-oocyte binding 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. 2 ).
(Kumar et al.. 2000) and (Kumar et al.. 2001) exposed male Wistar rats by inhalation at concentrations
of 0 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:
Page 272 of 803
-------
5837
5838
5839
5840
5841
5842
5843
5844
5845
5846
5847
5848
5849
5850
5851
5852
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
5872
5873
5874
5875
5876
2 weeks (to observe the effect on the epididymal sperm maturation phase), 10 weeks (to observe the
effect on the entire spermatogenic 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
spermatogonia! differentiation to sperm). (Kumar et al.. 2001) exposed rats for either 12 or 24 weeks.
(Kumar et at.. 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 of TCE exposure, rats exhibited decreased number of
spermatogenic cells in the seminiferous tubules, fewer spermatids as compared to controls, and the
presence of necrotic spermatogenic cells. Following 24 weeks of exposure, male rates showed reduced
testes weights and epididymal sperm count and motility, testicular atrophy, smaller tubules,
hyperplastic Ley dig cells, and a lack of spermatocytes and spermatids in the tubules. Testicular marker
enzymes were altered at both 12 and 24 weeks of exposure. A LOAEL of 45 ppm was identified as the
POD for the sperm and male reproductive effects reported in both studies ( 1 ie).
(Kan et al.. 2007) also provided evidence for the damage to the epididymis epithelium and sperm.
CD-I male mice (4/group) were exposed via inhalation to 0 or 1,000-ppm TCE for 6 hrs/day, 5
days/week for 1 to 4 weeks. As early as 1 week after TCE exposure, exposed mice showed
degeneration and sloughing of epithelial cells. These effects increased in severity at 4 weeks of
exposure. A LOAEL of 180 ppm (adjusted for continuous 24hr exposure) was identified as a POD for
the effects in the epididymis epithelium.
— Female Reproductive Effects
(Narotsky et al.. 1995) administered TCE to F344 timed-pregnant rats (8-12 dams/group) by gavage.
Dams were exposed to TCE doses of 0, 10.1, 32, 101, 320, 475, 633, 844 or 1125 mg/kg-bw/day during
gestational days (GD) 6 to 15. The study was a prequel to a complicated protocol with other chemicals
in a mixture study. Delayed parturition was observed at >475 mg/kg- bw/day. The LOAEL for female
reproductive effects was 475 mg/kg-bw/day ( )
— Diminished Reproductive Behavior
George et al. (1986) administered TCE to both male and female F344 rats (20 each treated, 40 each
controls) in feed with estimated doses of 0, 72, 186, or 389 mg/kg-bw/day. Breeders were exposed for
one week premating and then for 13 weeks while cohabitating. Pregnant females were subsequently
exposed throughout gestation (an additional 4 weeks). Copulation was reduced equally following
either exposed males or exposed females cohabitating with control mates (only the highest dose
examined). This corresponded with a dose-responsive decrease in the number of litters produced per
breeding pair and the number of live pups per litter.
Page 273 of 803
-------
5877 Table 3-
2. Dose-response analysis of selected studies considered for evaluation of reproductive effects
Target
Organ
System
Species
Duration
POD Type1
(applied dose)
Effect
Dose
Metric
HECS0
(ppm)
HEC99
(ppm)
HED50
(mg/kg) (i
HED99
mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Human
(male)
Measured
values after an
8-hr work shift;
mean 5.1 years
on the job
BMDL10 =
1.4 ppm
Hyper-
zoospermia
TotMetab
BW34
1.4
0.5
0.74
0.73
UFS=10; UFA= 1;
UFh=3; UFL=1;
Total UF=30
(Chia_et
aL 1.9961
Medium
(1.8)
Reproductive
system
Rat
(male)
4 hrs/day, 5
days/week, 2-10
weeks exposed,
2-8 weeks
unexposed
LOAEL = 45
ppm
4 hrs/day, 5
days/week for
12 or 24 weeks
Sperm effects
and male
reproductive
tract effects
TotMetab
BW34
32
13
16
16
LTFs=10; LTFa= 3;
LTFH=3; LTFL=10;
Total UF=1000
(Kumar et
al. 2000')
Medium
(1.7)
(Kumar et
aL 200 11
High
(1.4)
Mouse
(male)
6 hrs/day, 5
days/week for 1-
4 weeks
LOAEL = 180
ppm
Effects on
epididymis
epithelium
TotMetab
BW34
190
67
73
UFS=10; UFA= 3;
UFh=3; UFl=10;
Total UF=1000
(Kan et al.
20071
Medium
(2)*
Mouse
(male)
6 hrs/day, 5
days/week for 6
weeks
LOAEL = 180
ppm
Sperm effects
(decreased in
vitro sperm-
oocyte binding
and in vivo
fertilization)
TotMetab
BW34
190
67
73
UFS=10; UFA= 3;
UFH=3; UFL=10;
Total UF=1000
(Xu et al.
20041
High
(1.4)
Rat
(female
dams)
9 days (during
gestational days
6 to 15)
LOAEL=
475 mg/kg-
bw/day
Delayed
parturition
TotMetab
BW34
98
37
47
44
UFS=1;UFA=3;
UFh=3; UFL=10;
Total UF=100
(Narotsky
l t a 1..
1.9951
High
(1.3)
Rat
(male/
female)
Breeders
exposed 1 week
premating and
then for 13
weeks
cohabitating
LOAEL = 389
mg/kg-bw/day
Decreased
copulation;
reduced numbers
of live litters/pair
and pups/litter
TotMetab
BW34
204
71
85
77
UFS=1;UFA=3;
UFH=3; UFL=10;
UFD=1
Total UF=100
(George et
al. 1.9861
High
(1.1)
1 POD type can be NOAEL, LOAEL, or BMDL. The IRIS program adjusted all values to continuous exposure.
2LTFS=subchronic to chronic UF; UFA=interspecies UF; LTFH=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HO-QPPT-2019-0500] for full evaluation by metric. *Kan 2007 was
downgraded from a High, with calculated score = 1.6.
Bold rows indicate studies selected to represent the endpoint within the organ system domain; endpoints within an organ system are separated by double-
line borders (=).
5878
5879 Table 3-12 presents the derived PODs from all studies considered for dose-response analysis. The
5880 majority of studies identified effects indicative of male reproductive toxicity, with one study
5881 demonstrating female reproductive toxicity. The TotMetabBW34 dose metric, or the total amount of
5882 TCE metabolized per unit adjusted body weight, was used for all three studies. This dose metric was
5883 selected because for these endpoints there is insufficient information for site-specific or mechanism-
5884 specific determinations of an appropriate dose-metric, however in general TCE toxicity is associated
5885 with metabolites rather than the parent compound. For (Chiaetal... 19961 the 2011 IRIS Assessment
5886 0 ^ ^ I I1') notes some additional uncertainty in the dose estimate because exposure groups were
5887 defined by ranges and exposure was estimated by conversion of urinary TCA. LOAELs were used as
5888 PODs for all studies except (Chia et al.. 1996). which was BMD modeled with a standard BMR of 10%
5889 extra risk. The 2011 IRIS Assessment ( . ) indicates some uncertainty in the biological
5890 signficance of this BMR because the study used a lower cutoff to define hyperzoospermia than other
5891 studies. See Section 3.2.2.5 and v N1 \ ) for more details on TCE PBPK modeling, dose
5892 metric selection, and BMR selection.
Page 274 of 803
-------
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
For male reproductive toxicity, (Chiaetai. 1996) was selected over the other studies because it was a
human 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 ai. 2001). Additionally, (Chia et ai. 1996) only has a
cumulative uncertainty factor of 30, compared to 1000 for the other three studies. (Narotsky et ai.
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 ai. 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 ai. 1986). Therefore, the POD for
reduced copulation was not selected to represent the reproductive toxicity hazard.
Developmental toxicity
As described above in Section 3.2.5.3.1, developmental effects may result from single as well as
repeated exposures at a developmentally critical period; therefore the same endpoints are relevant for
both acute and chronic exposure scenarios. The only difference between acute and chronic exposure
scenarios in evaluating developmental toxicity is the benchmark MOE for (Fredriksson et ai. 1993). The
subchronic-to-chronic UFs = 3 for chronic exposure, because the study only exposed pups during
postnatal days 10-16, suggesting that exposure during a longer period of development may have
exacerbated the observed effects (UFs would not = 10 because neurological development only occurs
over a portion of a lifetime). This results in a cumulative UF and benchmark MOE of 300. See Section
3.2.5.3.1 for a detailed description of the developmental toxicity endpoints.
3.2.5.3.3 Cancer POD for Lifetime Exposures
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
substantial background incidences of various cancers.
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 ai. 2005; Zeise et ai. 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).
3) The general use of linear extrapolation provides reasonable upper-bound estimates that
are believed to be health-protective (U.S. EPA. 2005) and also provides consistency
across assessments.
Page 275 of 803
-------
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
Dose-response analysis of kidney cancer utilized ABioactDCVCBW34, or the amount of DCVC
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
performed by linear regression weighted by the inverse of variances for RR estimates. Consistent with
EPA's Guidelines for Carcinogen Risk Assessment (U.S. EPA. 2005). the same data and methodology
were also used to estimate the exposure level (ECx: —effective concentration corresponding to an extra
risk of x%) and the associated 95% lower confidence limit of the effective concentration corresponding
to an extra risk of 1% (LECx [lowest effective concentration], x = 0.01). A 1% extra risk level is
commonly used for the determination of the POD for epidemiological data. Use of a 1% extra risk level
for these data is supported by the fact that, based on the actuarial program, the risk ratio {i.e., Rx/Ro) for
an extra risk of 1% for kidney cancer incidence is 1.9, which is in the range of the ORs reported by
Charbotel et al. (ORs range from 1.16-2.16 across exposure tertiles). Thus, 1% extra risk was selected
for determination of the POD, and, consistent with EPA's Guidelines for Carcinogen Risk Assessment
(I ^005). the LEC value corresponding to that risk level was used as the actual POD. For more
details, see Section 5.2.2 in the 2011 IRIS Assessment (U.S. EPA. ). Based on the results of the
meta-analysis (Section 3.2.4.2.1 and Appendix J) confirming a positive association between TCE
exposure and all three cancer sites, the derived PODs will remain the same as for ( Hie) and
a ).
The inhalation unit risk (IUR) for TCE is defined as a plausible upper bound lifetime extra risk
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"6 per [^g/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 High-
quality 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"6 to 3 x 10"5 per
^g/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 of TCE
across tumor sites. Section 5.2.2 of the 2011 IRIS Assessment ( ) describes the process
for this adjustment in more detail. 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 kidney cancer alone was 3.28 from the first calculation (using meta-analysis results) and 4.36
from the second calculation (using (Raaschou-Nielsen et al.. 2003) 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
Page 276 of 803
-------
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
6020
6021
6022
6023
6024
6025
6026
6027
6028
6029
6030
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 metrics,23 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 dose-metric, 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. The OSF was used
for evaluating dermal risk (dermal absorption was considered in the exposure estimates (Section 2.3.1
and Section 2.3.2.3.1).
EPA decided not to use the IUR or 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 of lifetime theoretical excess cancer risks to single exposures has great
uncertainties. Thus, this Risk Evaluation for TCE does not estimate excess cancer risks for acute
exposures because the relationship between a single short-term exposure to TCE and the induction of
cancer in humans has not been established in the current scientific literature. Risk estimates for cancer
will be based on lifetime exposure durations, represented as Lifetime Average Daily Concentration/Dose
(LADC/LADD).
3.2,5,4 Selected PODs for Human Health Hazard Domains
Table 3-13 and Table 3-14 list the studies and corresponding HECs, HEDs, and UFs that EPA is using
in the TCE Risk Evaluation following acute and chronic exposure. Table 3-15 provides the cancer
PODs for evaluating lifetime exposure. Key studies in Table 3-13 and Table 3-14 are briefly described
in Section 3.2.5.1. Presenting PODs for the HEC/HED50 and HEC/HED99 values is intended to provide
a sense of the difference between the median and 99% confidence bound for the combined uncertainty
and variability. Calculations of HEC50/99 and HED50/99 ratios generally showed a 2-3 fold difference
for the various studies described in Section 3.2.5.3. The exception was for studies reporting kidney
effects, which showed high HEC50/99 and HED50/99 ratios (7 to 10-fold) due to larger uncertainty in
the rodent internal dose estimates for the GSH metabolism dose metrics (e.g., ABioActDCVCBW34)
(U.S. EPA. ^ ) and greater influence of human variability. Confidence in these metrics was lower
for mouse data due to an absence of GSD-specific in vivo data, and there is some question about how
relevant DCVC formation is for renal toxicity (Green et al. 1997a. b), however sensitivity analyses
demonstrated that model uncertainty was similar as to other metrics for rat and human data (U.S. EPA.
201 le). The HEC/HED99 values represent the PODs that are expected to be protective of sensitive
subpopulations, accounting for the majority of identified toxicokinetic human variability.
23 Kidney: ABioactDCVCBW34; NHL: TotMetabBW34; Liver: AMetLivlBW34
Page 277 of 803
-------
6031 Table 2
5-13. Dose-response ana
ysis of selected stut
ies considered for acute exposure scenarios
T:ir»i-l
()r»;in/
S\ sk-in
S|K-iii-s
Duniliiin
POD T\ pi-
(iipplkililii*)
i.nw-i
Diisc
Mi-lrk
II Ms.
(ppm)
iii:c,,
(ppm)
i ii:i>5»
iii:i)....
I luiThiiim
I'liiiHls (I I s)
kll'l'IVIUl'
l);il;i
Qu;ilil\
Develop-
mental
Effects
Rat
(female)
Gestational days
6 to 15
BMDL0i= 32.2
mg/kg-bw/day
Increased
resorptions
TotMetab
BW34
57
23
29
28
UFs=l; UFA= 3;
UFh=3; UFl=1;
Total UF= 10
(Narotskv et
aL 1995)
High
Rat
(female)
22 days
throughout
gestation
(gestational days
0 to 22)
BMDLoi =
0.0207 mg/kg-
bw/day
Congenital
heart defects
TotOx
Metab
BW34
0.012
0.0037
0.0058
0.0052
UFs=l; UFA= 3;
UFH=3; UFL=1;
Total UF= 10
(Johnson et
aL2003)
Medium
Rat
(male
pups)
Postnatal days
10 to 16
LOAEL = 50
mg/kg-bw/day
Decreased
rearing activity
TotMetab
BW34
8
3
4.2
4.1
UFs=l; UFA= 3;
UFh=3; UFl=10;
Total UF= 100
(Fredriksson
et al._ 1.993)
Medium
Immune
System
Rat
(female)
3hr/day, single
dose; followed
by respiratory
infection
BMDLoi =
13.9 ppm
Mortality due
to immuno-
suppression
TotMetab
BW34
2.84
0.973
1.36
1.34
UFS=1;UFA=3;
UFh=3; UFl=1;
Total UF=10
(Belgrade and
Gilmour.
2010)
High
6032
Page 278 of 803
-------
able 3-14. Dose-response analysis of selected studies considered for
chronic exposure scenarios
()r»;in
S\sk-m
Speck's
Dumliiiii
POD T\ pi-
feipplkilriiisi')
I.H'iil
Dusi- \k-irk
UK*.
(ppm)
11 l-'C
(ppm)
1111)511
(m»/k»)
mi: IK.
I iuiTl;iinl\
I'.ulors (I I s)
kll'l'IVIHl'
l);il;i
Qu;ilil\
Liver
Mouse
(male)
Continuous and
intermittent
exposures, variable
time periods for 30-
120 days
BMDL10=
21.6 ppm
Increased liver/body
weight ratio and
cytotoxicity/
hypertrophy
AMetLivl
BW34
25
9.1
9.0
7.9
UFS=1;UFA=3;
LTFH=3; UFL=1;
Total UF=10
(Kiellstrand et
al.. 1983)
Medium
Kidney
Rat
(male)
- Oral
4-5 days/week for
52 weeks
BMDLio = 34
mg/kg-bw/day
Pathology changes in
renal tubule
ABioact
DCVCBW34
0.19
0.025
0.15
0.015
UFS=1;UFA=3;
LTFH=3; UFL=1;
Total UF=10
(Maltoni et al,
19861
Medium
Nervous
System
Rat
(male)
8 hrs/day, 5
days/weeks for 6
weeks
LOAEL =
12 ppm
Significant decreases
in wakefulness
TotMetab
BW34
13
4.8
6.6
6.5
UFS=3; UFA= 3;
UFH=3;UFL=10;
Total UF=300
(Arito et al,
1.9941
Medium
Human
(both
sexes)
Mean of 16 years
LOAEL =
14 ppm
Trigeminal nerve
effects (increased
latency in masseter
reflex)
TotMetab
BW34
14
5.3
7.4
7.3
UFs=l;UFA= l;
LTFH=3; LTFL=3;
Total UF=10
ir
Medium
Immune
System
Mouse
(female)
27-30 weeks
LOAEL = 0.35
mg/kg-bw/day
Autoimmunity
(increased anti-
dsDNA and ssDNA
antibodies)
TotMetab
BW34
0.092
0.033
0.049
0.048
UFS=1;UFA=3;
LTFH=3; LTFL=3;
Total UF=30
(Keil et al,,
2009)
High
Mouse
(female)
16 or 24 weeks
(4 or 6 months)
LOAEL = 18
mg/kg-bw/day
Immunosuppression
TotMetab
BW34
4.8
1.7
2.5
2.5
UFS=1;UFA=3;
UFH=3;UFL=10;
Total UF=100
(Sanders et al,
1982)
High
Repro-
ductive
System
Human
(male)
Measured values
after an 8-hr work
shift; mean 5.1 years
on the job
BMDLIO =
1.4 ppm
Decreased normal
sperm morphology
and hyperzoospermia
TotMetab
BW34
1.4
0.5
0.74
0.73
LTFS=10; LTFA= 1;
UFH=3;UFL=1;
Total LTF=30
fChia et al,
1996)
Medium
Rat
(female
dams)
9 days (during
gestational days 6-15)
LOAEL = 475
mg/kg-bw/day
Delayed parturition
TotMetab
BW34
98
37
47
44
UFS=1;UFA= 3;
UFH=3; UFL=10;
Total UF= 100
(Narotsky et al,
1995)
High
Develop-
mental
Effects
Rat
(female)
Gestational days 6 to
15
BMDL0i= 32.2
mg/kg-bw/day
Increased resorptions
TotMetab
BW34
57
23
29
28
UFS=1; UFA= 3;
UFh=3;UFl=1;
Total UF= 10
TNarotskv et al,
1995)
High
Rat
(female)
22 days
(gestational days
0-22)
BMDLoi =
0.0207 mg/kg-
bw/day
Congenital heart
defects
TotOxMetab
BW34
0.012
0.0037
0.0058
0.0052
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF= 10
(Johnson et al,
2003)
Medium
Rat
(male
pups)
Postnatal days
10-16
LOAEL = 50
mg/kg-bw/day
Decreased rearing
activity
TotMetab
BW34
8
3
4.2
4.1
UFS=3; UFA= 3;
UFH=3;UFL=10;
Total UF=300
(Fredriksson et
al,19931
Medium
6034
Page 279 of 803
-------
6035
6036
6037
6038
6039
6040
6041
6042
6043
6044
6045
6046
6047
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
6072
6073
6074
6075
6076
6077
Table 3-15. Cancer Points of Departure for Lifetime Exposure Scenarios
pod i\pl-
Oral Slope I'aclor
Inhalation I nil Risk
l-\lia Risk Ik-nchmai'k
pod (extra risk per
dose/concentrati on)
0.0464 per mg/kg
0.022 per ppm
1 x 10"4
As stated in Section 3.2.5.3.3, 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 ai. 2006) and the combined estimates account for the additional relative
contribution from the other two cancers.
EPA, consistent with 2016 NIOSH guidance (Whittaker et at..: ), used 1 x 10"4 as the benchmark for
the purposes of this risk determination for individuals in industrial and commercial work environments
subject to Occupational Safety and Health (OSH) Act requirements. It is important to note that lxlO"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).
3.2.5.4.1 Best Overall Non-Cancer Endpoints for Risk Conclusions
From among all the above acute and chronic endpoints presented in Table 3-13 and Table 3-14, EPA
identified the best overall non-cancer endpoints for risk characterization characterize risk for acute and
chronic exposure scenarios based on considerations of being both scientifically robust and sufficiently
sensitive. While some other endpoints present lower PODs (developmental neurotoxicity from
Fredriksson etai. 1993; congenital heart malformations from Johnson et at.. 2003). there is lower
confidence in the dose-response and extrapolation of results from those studies (Section 3.2.6.1.1)
resulting in increased uncertainty surrounding the precision of the derived PODs for those endpoints.
Therefore, EPA concluded that acute immunosuppression and chronic autoimmunity were the
best overall non-cancer endpoints for use in Risk Evaluation under TSCA, based on the best available
science and weight of the scientific evidence, and were used as the basis of risk conclusions in Section
4.5.2. The selection of these endpoints for use in risk conclusions was supported by the SACC peer
review panel (https://www.reeutations.gov/document T? < PA-HQ-OPPT-2019-0500 01 I I).
Best Overall Acute Non-Cancer Endpoint
Based on the following considerations, the POD for mortality due to immunosuppression from (Setgrade
and Gilmour. 2010) is considered to be the most robust and best overall POD for acute non-cancer
scenarios. Confidence in the use of this study for evaluating acute exposure scenarios is High.
Considerations for selection of this study and the High confidence rating include the following:
1) The study scored a High in data quality evaluation (the only other high quality study
applicable to acute exposures, (Narotsky et at.. 1995). is >20x less sensitive)
2) The study used a broad dose range, with several concentrations above and below the LOAEL
3) The response data followed a consistent dose-response curve
4) The data is based on an acute exposure study so there is no uncertainty resulting from
extrapolating from a repeated-dose study
5) The study demonstrated multiple assays supporting the apical outcome
Page 280 of 803
-------
6078
6079
6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
6092
6093
6094
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
6105
6106
6107
6108
6109
6110
6111
6112
6113
6114
6115
6116
6117
6118
6119
6120
6121
6122
6123
6) The endpoint is severe (an important consideration per the Risk Evaluation Rule (82 FR
33726)
7) The derived POD is very similar to that of the study selected to represent chronic
immunosuppression (Sanders et at.. 1982.). In contrast, there are large uncertainties associated
with the dose-response for the other sensitive acute endpoints (Johnson et at.. 2003; Fredriksson
et at.. 1993); see Section 3.2.6.1.1
Best Overall Chronic Non-Cancer Endpoint
Based on the following considerations, the POD for autoimmunity from (Kelt et at.. 2009) is considered
to be the most robust and best overall POD for chronic non-cancer scenarios. Confidence in the use of
this study for evaluating acute exposure scenarios is High. Considerations for selection of this study and
the High confidence rating include the following:
1) The study scored a High in data quality evaluation
2) The study was of chronic duration (27-30 weeks) so uncertainty is reduced by not requiring a
subchronic-to-chronic UF
3) The endpoint is associated with sensitive functional immunological markers (increased anti-
self antibodies)
4) The use of an early clinical marker as an endpoint and dose range are are expected to account
for susceptibilities of subpopulations in disease progression
5) The POD for this study is also expected to be protective of developmental immunotoxicity.
While EPA did not identify any developmental immunotoxicity studies of sufficient quality for
dose-response analysis, the LOAEL from (Kelt et at... 2009) is almost identical to and even
slightly lower than the LOAEL from (Peden-Adams et at.. 2006). which demonstrated TCE-
induced autoimmunity in neonatal mice.
Derivation of Occupational HEC/HEDs for Best Overall Endpoints
For these two endpoints, EPA performed additional PBPK modeling to present PODs specific to
occupational scenarios. All PODs (including for these two endpoints) were otherwise derived on the
basis of continuous exposure (24 hr/day, 7days/week) as presented in Section 3.2.5.3.
For deriving PODs for occupational scenarios, EPA adjusted model parameters to assume only 8hr/day
exposure (with continued metabolism throughout the day). Additionally, respiratory rate was set at 1.25
m3/hr based on light activity levels (Table 6-43 in ( : 01 I •:)), a higher rate than the default
median rate of 0.64 m3/hr used in the PBPK model (Appendix J and [PBPKModel andReadMe
(zipped). Docket: EPA-HQ-QPPT-20194)500T) based on sedentary activity levels. Occupational
HECs/HEDs based on the primary dose metric of TotMetabBW34 are presented in Table 3-16. They
will be compared to acute and chronic exposure values based on an 8hr duration of daily exposure for
risk estimation.
Page 281 of 803
-------
6124 Ta
)le 3-16. Occupational POPs for Representative Non-Cancer Endpoints
Exposure
Scenario
Species
Duration
POD Type1
(applied dose)
Effect
Dose
Metric
HECS0
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Acute
Rat
(female)
3hr/day, single
dose; followed
by respiratory
infection
BMDL01 =
13.9 ppm
Mortality due
to immuno-
suppression
TotMetab
BW34
4.464
2.344
1.38
1.34
UFS=1;UFA=3;
UFh=3;UFl=1;
Total UF=10
f Belgrade
and Gilmour.
201.0)
High
(1.6)
Chronic
Mouse
(female)
27-30 weeks
LOAEL =
0.35
mg/kg-bw/day
Autoimmunity
(increased
anti- dsDNA
and ssDNA
antibodies)
TotMetab
BW34
0.1535
0.0835
0.049
0.048
UFs=1;UFa=3;
UFH=3; UFL=3;
Total UF=304
(Keil et al.a
2009)
High
(1.6)
1 POD type can be NOAEL, LOAEL, or BMDL. Values presented are for 8hr daily exposure at occupational respiratory rates.
2LTFS=subchronic to chronic UF; LTFA=interspecies LTF; LTFH=intraspecies LTF; LTFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HO-QPPT-2019-0500] for full evaluation by metric.
4 The HECs represent 8-hr values. Adjusted 12-hr HECs for (Belgrade and Gilmour, 2010) based onHaber's rule are: HEC5o = 2.97 ppm; HEC99 =1.56
ppm.
6125 3.2.6 Assumptions and Key Sources of Uncertainty for Human Health Hazard
6126 3.2.6.1 Confidence in Hazard Identification and Weight of Evidence
6127 There is high confidence in the database for human health hazard. All studies considered for dose-
6128 response analysis scored either Medium or High in data quality evaluation and were determined to be
6129 highly relevant to the pertinent health outcome. EPA selected the most robust, sensitive, and relevant
6130 study for each identified endpoint from among a broad selection of studies, taking into account factors
6131 such as data quality evaluation score, species, exposure duration, dose range, cumulative uncertainty
6132 factor, and relevance. The only identified study that examined developmental immunotoxicity (Peden-
6133 Adams et al.. 2006) scored a Low in data evaluation and a POD could not be sufficiently derived.
6134
6135 EPA has medium to high confidence in the overall weight of scientific evidence. EPA did not identify
6136 any information that would question the previous WOE regarding the evaluation of liver, kidney,
6137 neurological, immunological, reproductive toxicity, and developmental toxicity (other than cardiac
6138 malformations). For cancer, EPA performed an updated meta-analysis that found positive statistical
6139 associations between human TCE exposure and cancer of kidney, liver, and NHL types, in agreement
6140 with the previous meta-analyses performed in 201 1 (Appendix C, (U.S. EPA. 2 ).
6141 3.2.6.1.1 Uncertainties in Dose-Response Analysis for Select Endpoints
6142 For congenital heart defects, EPA performed a thorough WOE assessment (Appendix F.3), examining
6143 all pertinent studies in the reasonably available literature. There is medium confidence in the relevance
6144 of the endpoint to human toxicity based on the results of the WOE, although uncertainty remains in the
6145 POD derivation of (Johnson et al. 2003) and the resulting POD for congenital heart defects and the
6146 weight of the scientific evidence only provided qualitative support for the CHD endpoint. Unlike the
6147 immune PODs (Section 3.2.5.4.1), the POD for cardiac defects derived from (Johnson et al.. 2003) is not
6148 corroborated by results of other animal studies with similar quantitative results. Uncertainty is further
6149 increased by the non-monotonicity of the dose-response (Makris et al.. 2016) and less than recommended
6150 sample size (Section 3.2.5.3.1). EPA does not dismiss the results of (Johnson et al.. 2003). however the
6151 aforementioned uncertainties reduce confidence in that value. Nonetheless, epidemiological, metabolic,
6152 and mechanistic data suggest that congenital heart defects may be of concern for particular biologically
6153 susceptible PESS groups such as older mothers (Section 3.2.5.2).
6154
Page 282 of 803
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6155
6156
6157
6158
6159
6160
6161
6162
6163
6164
6165
6166
6167
6168
6169
6170
6171
6172
6173
6174
6175
6176
6177
6178
6179
6180
6181
6182
6183
6184
6185
6186
6187
6188
6189
6190
6191
6192
6193
6194
6195
6196
6197
6198
6199
6200
6201
6202
There is also uncertainty in the dose-response for developmental neurotoxicity (Fredriksson et at.. 1993)
based on the study design of statistically evaluating neonatal offspring on a per-pup basis, which does
not account for litter effects. The study was also limited in that it only evaluated males instead of both
sexes, as recommended by (Hoison et at.. 2008).
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 dose-
response relationship). In fact, it is not possible to know that exact exposure level even for a laboratory
study because of experimental limitations {e.g., the ability to detect an effect, the doses used and dose
spacing, measurement errors, etc.). The application of UFs 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 of UFs is informed by reasonably available data, the true necessary
extent of adjustment most appropriate for capturing all relevant uncertainty and variability is unknown.
BMD modeling for a selected benchmark response can reduce uncertainty surrounding POD
approximations that rely on the particular doses used in the study {e.g., a NOAEL). 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 {i.e., 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. While a 1% BMR is justified for many of the PODs
derived in this assessment based on the severity of the endpoint, it can potentially amplify BMD model
and parameter uncertainty. This is especially of concern for endpoints with greater uncertainties in the
dose-response assessment such as the congenital heart defects endpoint from (Johnson et at.. 2003).
however a reanalysis of the BMR selection for this endpoint concluded that the 1% BMR was in fact
most appropriate (Section 3.2.5.3.1).
For each of these types of PODs, there are additional uncertainties pertaining 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.
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 dose-
metrics 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, and there is likely to be
remaining unaccounted uncertainties associated with route-to-route extrapolation as opposed to relying
on data from the same exposure route as is being assessed.
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 determined that the PBPK model was most reliable
for dose metrics involving oxidative metabolism flux. There remains substantial uncertainty in the
Page 283 of 803
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6203
6204
6205
6206
6207
6208
6209
6210
6211
6212
6213
6214
6215
6216
6217
6218
6219
6220
6221
6222
6223
6224
6225
6226
6227
6228
6229
6230
6231
6232
6233
6234
6235
6236
6237
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
extrapolation of GSH conjugation from mice to humans due to limitations in the reasonably 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/HED99 values (which account for
both modeling uncertainty and interspecies/intraspecies toxicokinetic variability) may potentially over-
estimate 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 uncertainty for extrapolation of
rat GSH conjugation data, which was used for the selected kidney PODs, compared to data from mice.
There is additional uncertainty in extrapolation to humans based on evidence suggesting that metabolic
formation of the reactive conjugative metabolites may be an order of magnitude greater in rats than
humans (Green et at. 1997b; Lash et al. 1990) and that renal toxicity may not be directly related to the
rate of DCVC formation (Green et al. 1997a. b). These metabolites are indeed formed in both rats and
humans however (Bemauer et al. 1996). and in vitro data suggest that human GSH conjugation activity
may actually be higher in humans than rodents in some cases (Table 3-23 and 3-26 of (
201 le) and (Lash et al.. 1999; Lash et al... 1998)). Additionally, the slow elimination kinetics of GSH
metabolites relative to oxidative species indicate that even lower relative concentrations may contribute
to sustained chronic toxicity (Bemauer et al. 1996). Uncertainty is also elevated for developmental
endpoints based on fetal effects due to the lack of a fetal compartment in the PBPK model, requiring
reliance instead on default adult female parameters.
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/HED99 values replacing default
assumptions and uncertainty factors that would have otherwise been used such as allometric scaling and
a UFtk of 3 in accounting for both interspecies and intraspecies toxicokinetic variability. Data-derived
values are always preferred to default uncertainty adjustments and improve confidence in the adjusted
PODs.
There is additional uncertainty in the precision and appropriateness of a particular POD for representing
the associated endpoint. The POD for immunosuppression in (Selgrade and Gitroour. 2010) is derived
from mortality data, which may underestimate risk by not capturing more sensitive sublethal effects.
This is likely accounted for in the BMR selection however, whereby a 1% BMR for mortality would be
expected to result in a similar POD as a more sensitive biological endpoint with a higher BMR. In
contrast, the POD for autoimmunity from (Kelt et al.. 2009) is an example of a POD based on an early
biomarker that may not be adverse itself. The use of an early biomarker is accounted for by reducing the
UFl from 10 to 3 for that endpoint. Therefore, in both instances EPA assumes that the resulting POD and
benchmark MOEs sufficiently account for the uncertainty associated with endpoint selection.
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
specific region in France), the retrospective estimation of TCE 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. (2.006) 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. , ).
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
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incidence derived from the Charbotel et al. (2.006) 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 kidney cancer risk with increasing cumulative 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 ( 005). 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 (
2.01 le). 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 and Best
Overall Endpoints
Acute Non-Cancer
There is medium 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. Two of the four studies scored Medium in data quality, while one developmental
endpoint and the acute immunotoxicity study scored High. The PODs cover several orders of magnitude,
with benchmark MOEs of either 10 or 100. 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). For developmental endpoints, there is some uncertainty extrapolating from chronic
developmental toxicity studies to acute exposure, especially in assuming a consistent dose-response.
This is a health protective assumption consistent with EPA Guidance (
1991). however this may possibly result in an overestimation of risk for some scenarios. For the acute
immunotoxicity study (Selgrade and Gilmour. 2010) there is some inherent uncertainty extrapolating
from the observed responses to pulmonary infection to a systemic response across multiple exposure
routes, however an acute systemic response to infection is likely based on the systemic
immunosuppression observed in multiple chronic studies (Sanders et a I: Woolhiser et al.. 2006).
Confidence is raised from the robust WOE analysis performed on the congenital heart defects endpoint
(see Appendix I), 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. As
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stated in Section 3.2.5.4.1, there is High confidence in the POD for the best overall acute endpoint of
immunosuppression from (Selgrade and Gilmour. 2010).
Chronic Non-Cancer
There is high overall confidence in the database, weight of evidence, and dose-response for chronic non-
cancer 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 benchmark MOEs 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 magnitude from each other, and the majority of PODs and have
reduced uncertainty factors due to the use of a PBPK model. As stated in Section 3.2.5.4.1, there is High
confidence in the POD for the best overall chronic endpoint of immunosuppression autoimmunity from
(Rett et ai. 2009).
Cancer
There is medium to high overall confidence in the database, weight of evidence, and dose-response for
cancer. Meta-analyses on the full database of relevant epidemiological studies 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 at. 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-response estimate in accounting
for all three cancer sites and in the GSH metabolism dose metrics but remains medium-high due to
strong evidence for a mutagenic mode of action.
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4 RISK CHARACTERIZATION
4.1 Environmental Risks
EPA took fate, exposure, and environmental hazard into consideration to characterize the environmental
risk of TCE. EPA determined that no further analysis beyond what was presented in the Problem
Formulation document would be done for environmental exposure pathways for terrestrial organisms, or
land application of biosolids, 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,
soil, 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 EPI 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 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). TCE is not expected to accumulate in
aquatic organisms due to low measured BCFs and estimated BAF.
Environmental exposure pathways for surface water for aquatic and sediment organisms are assessed
and presented in this Risk Evaluation. As stated in Section 2.2 Environmental Exposures, modeled
surface water concentrations of TCE ranged from 1.27E-5 ppb 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 17 ppb in the published literature (1996-2001).
As stated in Section 3.1 Environmental Hazards, the reasonably 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/L and 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 [Data Quality Evaluation of
Environmental Hazard Studies and Environmental Hazard Data Extraction Table. Docket: EPA-HQ-
OPPT-2019-050QJ). The {Data Quality Evaluation of Environmental Hazard Studies. Docket: EPA-HQ-
OPPT-2019-050Q~\ document presents details of the data evaluations for each study, including scores for
each metric and the overall study score.
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.
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.waterqualitvdata.iis) and reasonably available literature, to characterize the risk of TCE to
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aquatic species. Risk quotients (RQs) were calculated using modeled surface water concentrations from
E-FAST, monitored data, reasonably available literature, and the COCs calculated in the hazard section
of this document (Section 3.1.5). An RQ is defined as:
RQ = Predicted Environmental Concentration / Effect Level or COC
An RQ equal to 1 indicates that environmental exposures 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 environmental concentrations shown in Section
2.2.6.2 were used to calculate RQs. (U.S. EPA. 199fr)
EPA considered the biological relevance of the species that the COCs were based on when integrating
the COCs with surface water concentration data to produce RQs. For example, certain biological factors
affect the potential for adverse effects in aquatic organisms. Life-history and the habitat of aquatic
organisms influences the likelihood of exposure above the hazard benchmark in an aquatic environment.
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 exceedance modeled in E-FAST are not necessarily consecutive
and could occur sporadically throughout the year. For TCE, EPA assumed continuous aquatic 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 of TCE, 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.
4.1.2 Risk Estimation for Aquatic Organisms
To characterize potential risk due to TCE exposure, RQs were calculated based on modeled data from E-
FAST for sites that had surface water discharges of TCE according to TRI and DMR data (see Table
4-1). Surface water concentrations of TCE were modeled for 214 releases. 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-POTW WWTP) were modeled with a high-end days of releases scenario. As
stated in Section 2.2.3, the maximum releases frequency (200 to 365 days) is based on release estimates
specific to the facility's condition of use and the low-end releases frequency (20 days) is an estimate of
releases that could lead to chronic risk for aquatic organisms.
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 number 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
a chronic RQ > 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; therefore, EPA did not identify risks to aquatic organisms for these
conditions of use.
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 > 1, or chronic or algae RQs > 1 with 20 days or more of exceedances.
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Assuming 20 days of releases, Praxair Technology Center in Tonawanda, NY had an acute RQ of 1.50,
a chronic RQ of 3.81 with 20 days of exceedance, and an algae RQ 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 1.5 times higher than the COC for acute exposures, 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 14,400 ppb which represents nine different species of algae.
Therefore, there may be risk for some of the most sensitive species of algae at this site, but EPA did not
identify risks for algae species as a whole. Risks were identified at this site for other aquatic organisms
for acute exposures with a surface water concentration 1.50 times higher than the acute COC, and
chronic exposures, with a surface water concentration 3.81 times higher than the chronic COC and 20
days of exceedance.
Repackaging:
Of the six facilities repackaging TCE, one had algae RQs > 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 algae 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 14,400 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 EPA did
not identify risks for algae species as a whole. EPA did not identify risks for 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 > 1, or chronic or algae RQs >
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 of 4.97, a chronic RQs of 12.61 with 20 days of exceedance,
and an algae RQ representing the most sensitive species of algae of 3,312.50 with 20 days of
exceedance. Assuming 260 days of release 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 0.05 assuming 260 days of release, and 0.69 assuming 20 days of release, meaning the
concentration did not exceed the COC of 14,400 ppb which represents nine different species of algae.
Therefore, there may be risk for some of the most sensitive species of algae at this site, but EPA did not
identify risks for algae species as a whole. Risks were identified at this site for other aquatic organisms
for acute and chronic exposures, with a surface water concentration 4.97 times higher 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 exceedance, 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 assuming 260 days or release and
0.01 assuming 20 days of release for this site, meaning the concentration did not exceed the COC of
14,400 ppb which represents nine different species of algae. Therefore, there may be risk for some of the
most sensitive species of algae at this site, but EPA did not identify risks for algae species as a whole.
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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 14,400 ppb which represents nine different species of algae. Therefore, there may be risk for
some of the most sensitive species of algae at this site, but EPA did not identify risks for 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 algae 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 assuming 250 days or release and 0.01
assuming 20 days of release, meaning the concentration did not exceed the COC of 14,400 ppb which
represents nine different species of algae. Therefore, there may be risk for some of the most sensitive
species of algae at this site, but not for algae species as a whole. EPA did not identify risks for other
aquatic organisms for this condition of use.
Other Industrial Uses:
Of the 21 facilities with other industrial uses of TCE, 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 algae 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 algae 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 or 0.01, meaning the concentration did not exceed the COC of 14,400
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. EPA did not identify risks
for other aquatic organisms for this condition of 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 algae
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 or 0.01, meaning the concentration did not exceed the COC of 14,400 ppb
which represents nine different species of algae. Therefore, there may be risk for some of the most
sensitive species of algae at this site, but EPA did not identify risks for algae species as a whole. EPA
did not identify risks for other aquatic organisms for this condition of use.
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Other Commercial Uses:
Of the nine facilities with other commercial uses of TCE, one site had algae RQs > 1 with 20 days or
more of exceedances. Park Place Mixed Use Development in Annapolis, MD had an RQ for the most
sensitive species of algae 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 or 0.01, meaning the concentration did not
exceed the COC of 14,400 ppb which represents nine different species of algae. Therefore, there may be
risk for some of the most sensitive species of algae at this site, but EPA did not identify risks for algae
species as a whole. EPA did not identify risks for other aquatic organisms in this condition of use.
Process Solvent Recycling and Worker Handling of Wastes:
Of the five facilities with other commercial uses of TCE, three sites had algae RQs > 1 with 20 days or
more of exceedances. Assuming 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 algae 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 algae 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
algae of 2.86 with 110 days of exceedance. However, for algae species as a whole, RQs for at all three
facilities were 0.00 or 0.01, meaning the concentration did not exceed the COC of 14,400 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 EPA did not identify risks for algae species as a whole. EPA did not
identify risks for 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 algae 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 0 days 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, EPA did not identify risks to aquatic species for this
facility or condition of use.
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226 Table 4-1. Environmental Risk Quotients for Aquatic Species for Facilities Releasing TCE to Surface Water as Modeled in E-FAST
227 (RQs > 1 in bold)
Name. 1 .ocalion. ;md II) of
\cli\ e Releaser l'aalil>
Release
Media
Modeled 1 acilils or
IndusiiA Seclor in
i:r\sr
I I \ST
Walerhods
1 > lv
1 )a> s ol'
Release
Release
(ku da> i
"Old
S\V(
ipphi
('()(' T\ pe
('()('
(pphi
1 )a> s of
1 Aceedance
(da>s \ean
Risk
Olkilicill
OES: Processing as a Reactant
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
Surface
Water
NPDES NY0000281
Still body
350
0.00169
169
Acute (HCos)
2,000
NA
0.08
Chronic
788
0
0.21
Algae (ChV)
3
350
56.33
Algae (HCos)
14,400
0
0.01
20
0.03
3000
Acute (HCos)
2,000
NA
1.50
Chronic
788
20
3.81
Algae (ChV)
3
20
1,000.00
Algae (HCos)
14,400
0
0.21
OES: Repackaging
Hubbard-Hall Inc,
Waterbury, CT
NPDES: Unknown
Off-site
Waste-
water
Treatment
Receiving Facility:
Recycle Inc.; POTW
(Ind.)
Surface water
250
1.108
27.18
Acute (HCos)
2,000
NA
0.02
Chronic
788
0
0.03
Algae (ChV)
3
194
9.06
Algae (HCos)
14,400
0
0.00
20
13.85
339.11
Acute (HCos)
2,000
NA
0.17
Chronic
788
1
0.43
Algae (ChV)
3
20
113.04
Algae (HCos)
14,400
0
0.01
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
US Nasa Michoud Assembly
Facility,
New Orleans, LA
NPDES: LA0052256
Surface
Water
Surrogate NPDES
LA0003280
Still body
260
1.96
765.63
Acute (HCos)
2,000
NA
0.38
Chronic
788
0
0.97
Algae (ChV)
3
260
255.21
Algae (HCos)
14,400
0
0.05
20
25.44
9937.5
Acute (HCos)
2,000
NA
4.97
Chronic
788
20
12.61
Algae (ChV)
3
20
3,312.50
Algae (HCos)
14,400
0
0.69
GM Components Holdings
LLC,
Lockport, NY
NPDES: NY0000558
Surface
Water
NPDES NY0000558
Surface water
260
0.13
10.97
Acute (HCos)
2,000
NA
0.01
Chronic
788
0
0.01
Algae (ChV)
3
117
3.66
Algae (HCos)
14,400
0
0.00
20
1.71
144.47
Acute (HCos)
2,000
NA
0.07
Chronic
788
0
0.18
Algae (ChV)
3
20
48.16
Page 292 of 803
-------
Name. 1 .ocalkin. ;md II) ol'
Release
Modeled 1 acilils or
IndusiiA Seclor in
i:r\sr
I I \ST
Waleihods
T\ pe
1 )a> s of
Release
Release
"Old
S\V(
ipphi
('()(' T\ pe
('()('
1 )a> s nf
1 Aceedance
Risk
\ijlin e Releaser laalil>
Media
(ku da> i
(pphi
(da>s \eari
nikHieiii
Algae (HCos)
14,400
0
0.01
Acute (HCos)
2,000
NA
0.00
260
0.07
4.87
Chronic
788
0
0.01
Akebono Elizabethtown Plant,
Elizabethtown, KY
NPDES: KY0089672
Algae (ChV)
3
27
1.62
Surface
Surrogate NPDES
Surface water
Algae (HCos)
14,400
0
0.00
Water
KY0022039
Acute (HCos)
2,000
NA
0.03
20
0.897
62.38
Chronic
788
0
0.08
Algae (ChV)
3
16
20.79
Algae (HCos)
14,400
0
0.00
OES: Adhesives, Sealants, Paints, and Coatings
Acute (HCos)
2,000
NA
0.01
250
0.013
10.83
Chronic
788
0
0.01
Algae (ChV)
3
250
3.61
Surface
NPDES RI0000281
Algae (HCos)
14,400
0
0.00
Raytheon Company,
Portsmouth, RI
NPDES: RI0000281
Water
Acute (HCos)
2,000
NA
0.07
Still body
20
0.160
133.33
Chronic
788
0
0.17
Algae (ChV)
3
20
44.44
Algae (HCos)
14,400
0
0.01
No info on receiving
facility; Adhesives
and Sealants Manuf.
Acute (HCos)
2,000
NA
0.00
POTW
250
0.013
0.32
Chronic
788
0
0.00
Algae (ChV)
3
0
0.11
Algae (HCos)
14,400
0
0.00
OES: Other Industrial Uses
Acute (HCos)
2,000
NA
0.00
250
1.553
9.03
Chronic
788
0
0.01
Eli Lilly And Company-
Algae (ChV)
3
35
3.01
Lilly Tech Ctr,
Surface
NPDES IN0003310
Surface water
Algae (HCos)
14,400
0
0.00
Indianapolis, IN
Water
Acute (HCos)
2,000
NA
0.06
NPDES: IN0003310
20
19.410
113.09
Chronic
788
0
0.14
Algae (ChV)
3
17
37.70
Algae (HCos)
14,400
0
0.01
Acute (HCos)
2,000
NA
0.00
Washington Penn Plastics,
Frankfort, KY
NPDES: KY0097497
250
0.032
7.53
Chronic
788
0
0.01
Surface
Surrogate NPDES
Surface water
Algae (ChV)
3
22
2.51
Water
KY0028410
Algae (HCos)
14,400
0
0.00
20
0.399
94.12
Acute (HCos)
2,000
NA
0.05
Chronic
788
0
0.12
Page 293 of 803
-------
Name. 1 .oc;ilioii. ;md II) ol'
Release
Modeled l';icilil> or
IndusiiA Seclor in
i:r\sr
I I \ST
\V;iieihod>
1 > lv
1 );i> s ol'
Release
Release
"Old
S\V(
ipphi
('()(' T\ pe
('()('
1 )a> s of
1 Aceed;nice
Risk
\ijlin e Releaser l';ialil>
Media
(ku d;i\ i
(pphi
(d;i>s \e;in
Olkilicill
Algae (ChV)
3
13
31.37
Algae (HCos)
14,400
0
0.01
Acute (HCos)
2,000
NA
0.00
350
0.000095
9.50
Chronic
788
0
0.01
Keeshan and Bost Chemical
Algae (ChV)
3
350
3.17
Co., Inc.,
Surface
NPDES TX0072168
Still body
Algae (HCos)
14,400
0
0.00
Manvel, TX
Water
Acute (HCos)
2,000
NA
0.10
NPDES: TX0072168
20
0.002
200.00
Chronic
788
0
0.25
Algae (ChV)
3
20
66.67
Algae (HCos)
14,400
0
0.01
OES: Industrial Processing Aid
Acute (HCos)
2,000
NA
0.00
300
0.38
9.3
Chronic
788
0
0.01
Entek International LLC,
Lebanon, OR
NPDES: N/A
Off-site
No info on receiving
facility; POTW
(Ind.)
Algae (ChV)
3
140
3.10
Waste-
Surface water
Algae (HCos)
14,400
0
0.00
water
Acute (HCos)
2,000
0
0.07
Treatment
20
5.65
138.34
Chronic
788
0
0.18
Algae (ChV)
3
20
46.11
Algae (HCos)
14,400
0
0.01
OES: Other Commercial Uses
Acute (HCos)
2,000
NA
0.00
250
0.00027
9
Chronic
788
0
0.01
Park Place Mixed Use
Algae (ChV)
3
250
3.00
Development,
Surface
Surrogate NPDES
Still body
Algae (HCos)
14,400
0
0.00
Annapolis, MD
Water
MD0052868
Acute (HCos)
2,000
NA
0.06
NPDES: MD0068861
20
0.00334
110
Chronic
788
0
0.14
Algae (ChV)
3
20
36.67
Algae (HCos)
14,400
0
0.01
OES: Process Solvent Recycling and Worker Handling of Wastes
Acute (HCos)
2,000
NA
0.01
250
0.004
11.76
Chronic
788
0
0.01
Clean Water Of New York
Algae (ChV)
3
250
3.92
Inc,
Surface
Surrogate NPDES
Still body
Algae (HCos)
14,400
0
0.00
Staten Island, NY
Water
NJ0000019
Acute (HCos)
2,000
NA
0.07
NPDES: NY0200484
20
0.047
138.24
Chronic
788
0
0.18
Algae (ChV)
3
20
46.08
Algae (HCos)
14,400
0
0.01
Page 294 of 803
-------
Name. 1 .ocalkin. ;md II) ol'
Release
Modeled l acilils or
IikImsiia Scaur mi
i:r\sr
I I \ST
Walcrhnd>
T\ pe
1 )a> s of
Release
Release
"Old
SWC
ipphi
COC T\ pe
COC
1 )a> s nf
1 Acccdaiicc
Risk
\ijlin e Releaser laalil>
Media
(ku da> i
(pphi
(da>s scan
Oikiiienl
Acute (HCos)
2,000
NA
0.00
250
24.1
2.85
Chronic
788
0
0.00
Veolia Es Technical Solutions
Off-site
Receiving Facility:
Algae (ChV)
3
0
0.95
LLC,
Waste-
Middlesex Cnty
Still body
Algae (HCos)
14,400
0
0.00
Middlesex, NJ
water
UA; NPDES
Acute (HCos)
2,000
NA
0.02
NPDES: NJ0020141
Treatment
NJ0020141
20
301.78
35.72
Chronic
788
0
0.05
Algae (ChV)
3
20
11.91
Algae (HCos)
14,400
0
0.00
Acute (HCos)
2,000
NA
0.00
250
0.35
8.57
Chronic
788
0
0.01
Clean Harbors Deer Park
Off-site
Algae (ChV)
3
110
2.86
LLC,
Waste-
POTW (Ind.)
Surface water
Algae (HCos)
14,400
0
0.00
La Porte, TX
water
Acute (HCos)
2,000
NA
0.05
NPDES: TX0005941
Treatment
20
4.36
106.75
Chronic
788
0
0.14
Algae (ChV)
3
19
35.58
Algae (HCos)
14,400
0
0.01
OES: Wastewater Treatment Plants (WWTP)
Acute (HCos)
2,000
NA
0.00
365
0.043
0.7
Chronic
788
0
0.00
New Rochelle STP,
New Rochelle, NY
NPDES: NY0026697
Algae (ChV)
3
0
0.23
Surface
NPDES NY0026697
Still body
Algae (HCos)
14,400
0
0.00
Water
Acute (HCos)
2,000
NA
0.01
20
0.786
12.79
Chronic
788
0
0.02
Algae (ChV)
3
20
4.26
Algae (HCos)
14,400
0
0.00
a. Facilities actively releasing trichloroethylene were identified via DMR, TRI, and CDR databases for 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 wastewater treatment removal rate of 81% is applied to all indirect releases, i.e.. volumes characterized as being transferred off-site for treatment at a water
treatment facility prior to discharge to surface water,
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 representative generic industry sector. The name of the indirect releaser is provided, as reported in TRI.
d. EFAST uses ether the "surface watef' 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 of 20 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 concentration was reported in place of the 7Q10 SWC.
h. To determine 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 concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero.
228
Page 295 of 803
-------
229
230
231
232
233
234
235
236
237
238
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
EPA also used surface water monitoring data from the Water Quality Portal (WQP) and from the
published literature to characterize the risk of TCE to aquatic organisms. For the most part these
monitored surface water concentrations reflect concentrations of TCE in ambient water. There was one
U.S. study ( 7) that had measurements reflecting near-facility monitoring data. The other
monitored data collected in the US reflect ambient concentrations.
Monitored data from one U.S. study (U.S. EPA. 1977) in the published literature reporting near-facility
concentrations of TCE collected between 1976 and 1977 ranging from 0.4 to 447 |ig/L. While these data
reflect historical levels of TCE, they are helpful to compare measured near-facility concentrations to the
modeled near-facility concentrations from E-FAST. The measured concentrations in this study
encompasses 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
3rd, 2018 from the WQX/WQP. These data show an average concentration for TCE of 0.33 ± 0.29 |ig/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 |ig/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 0 to 0.67. Acute and chronic RQs for other aquatic species are all very close to 0.
Table 4-2. RQs for Aquatic Species Calculated using Monitored Environmental Concentrations
from WQX/WQP
Monitored Surface Water
Concentrations (pph) lYom
2<) I3-2D17
Algae RQ
using COC
of 3 ppb
using 1 IC"; i)f
52.i)oo pph
RQ using Acute
COC ol"Z.ooo
pph
RQ using Chronic
COC dI" 7SS pph
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 of TCE ranged from below the detection limit (0.0001 to 0.08) to 17.3 |ig/L or ppb, with
reported central tendency values ranging from 0.0002 to 1.17 |ig/L (USGS. 2006; Sauer. 1981; Singh et
ai. 1983; USGS. 2003; Robinson et ai. 2004). The maximum concentration was collected from the
Charles River in Boston, Massachusetts (an urban area) between 1998 and 2000 (Robinson et at... 2004).
The next highest TCE concentration was 2.0 |ig/L, collected during a large nationwide survey of surface
water for drinking water sources (rivers and reservoirs) between 1999 and 2000 (USGS. 2003). Table
4-3 shows that RQs for algae range from 0 to 5.77 using monitored surface water concentrations from
the published literature. Acute RQs for other aquatic organisms range from 0 to 0.01, and chronic RQs
range from 0 to 0.02.
Page 296 of 803
-------
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
292
293
294
295
296
297
298
299
300
301
302
303
Table 4-3. RQs for Aquatic Species Calculated using Monitored Environmental Concentrations
from Published Literature
Monitored Surface
Wilier Concenliiilions
(pph> from 2<)|3-2<)|7
Aluae RO
usinu COC of 3
PI1'1
usinu 1of
52.I1I1H pph
RQ usinu Acute
COC of 2.ooo
pph
RO usinu Chronic
COC of 7SS pph
Central tendency values:
0.0002- 1.17 ppb
0.00-0.39
0.00
0.00
0.00
Maximum: 17.3 ppb
5.77
0.00
0.01
0.02
To compare the modeled data with the monitored data, EPA conducted a watershed analysis by
combining monitored data from WQX/WQP with predicted concentrations from E-FAST modeled
facility releases, using the geospatial analysis outlined in Section 2.2. A geographic distribution of the
concentrations is shown in Figure 2-4 and Figure 2-5 (east and west US) for the maximum days of
release scenario, and in Figure 2-6 and Figure 2-7 (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 2-8 for
HUCs in North Carolina and in Figure 2-9 for the HUC in New Mexico. The modeled estimates are only
shown in Figure 2-8 and Figure 2-9 for the higher release frequency scenarios, which are associated with
lower predicted surface water concentrations. The surface water concentrations were compared to the
COCs in these maps.
Figure 2-4 to Figure 2-9 in Section 2.2.6 compare WQX Monitoring Stations from 2016 to TCE-
releasing facilities modeled in E-FAST. The figures show that while some facilities releasing TCE to
surface water were co-located with monitoring locations in WQX, none were downstream from
facilities. The monitored data, which represents localized concentrations of TCE in ambient water,
generally show lower concentrations than the modeled surface water concentrations from E-FAST,
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; however the monitored data were limited geographically and temporally.
4.1,3 Risk Estimation for Sediment-dwelling Organisms
EPA also quantitatively analyzed exposure to sediment organisms. While no ecotoxicity studies were
available for sediment-dwelling organisms (e.g., Lumbriculus variegatus, Hyalella azteca, Chironomus
riparius), aquatic invertebrates were used as a surrogate species. EPA is uncertain whether TCE is more
or less toxic to daphnia than sediment-dwelling species. However, because TCE is not expected to sorb
to sediment and will instead remain in pore water, daphnia which feed through the entire water column
were deemed to be an acceptable surrogate species for sediment invertebrates. EPA calculated an acute
aquatic invertebrate COC of 2,000 ppb, and a chronic aquatic invertebrate COC of 920 ppb to address
hazards to sediment organisms. TCE is expected to be in sediment and pore water with concentrations
similar to or less than the overlying water due to its water solubility (>1280 mg/L), low partitioning to
organic matter (log Koc = 1.8-2.17), and biodegradability in anaerobic environments. Thus, TCE
concentrations in sediment and pore water are expected to be similar to or less than the concentrations in
the overlying water, and concentrations of TCE in the deeper part of sediment, where anaerobic
conditions prevail, are expected to be lower.
Therefore, EPA used modeled surface water concentrations to estimate the concentration of TCE in pore
water near facilities. EPA also used monitored data to estimate the concentration of TCE in pore water
Page 297 of 803
-------
304
305
306
307
308
309
310
311
312
313
314
315
based on ambient surface water. Comparing aquatic invertebrate data to these exposure numbers, the
data showed that there is risk to sediment dwelling organisms near two facilities due to acute and
chronic exposure. Table 4-4 shows an RQ from acute exposure near Praxair Technology Center at RQ =
1.5 and an RQ from chronic exposure at 3.26 with 20 days of exceedance for aquatic invertebrates.
Table 4-4 also shows an RQ from acute exposure near US Nasa Michoud Assembly Facility at RQ =
4.97 and an RQ from chronic exposure at 10.8 with 20 days of exceedance for aquatic invertebrates
(Table 4-4).
However, in ambient surface water, for both acute and chronic exposures to TCE, the RQs are 0.00 and
0.02, based on the highest ambient surface water concentration of 17.3 ppb, indicating exposures are less
than the COC (RQs < 0) to sediment organisms from acute or chronic exposures (Table 4-5 and Table
4-6).
Page 298 of 803
-------
316 Table 4-4. Environmental Risk Quotients for Sediment Organisms for Facilities Releasing TCE to Surface Water as Modeled in E-
317 FAST (RQs > 1 in bold)
Name. 1 Avalion. and II) of
\cli\e Releaser l'aalil>
Release
Media
Modeled laalils or
liidusm Secloi'iii
i:r \sr
I I AST
Waleihods
T\ pe
1 )a> s of
Release
Release
(ku da>)
"Old
SWC
ipph)
COC l >pe
COC
(pph)
1 )a\ s nf
1 Aceedance
(da>s \eari
Risk
Onolienl
OES: Processing as a Reactant
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
Surface
Water
NPDES NY0000281
Still body
350
0.00169
169
Acute (HC05)
2,000
NA
0.08
Chronic (ChV)
920
0
0.18
20
0.03
3000
Acute (HC05)
2,000
NA
1.50
Chronic (ChV)
920
20
3.26
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
US Nasa Michoud Assembly
Facility,
New Orleans, LA
NPDES: LA0052256
Surface
Water
Surrogate NPDES
LA0003280
Still body
260
1.96
765.63
Acute (HC05)
2,000
NA
0.38
Chronic (ChV)
920
0
0.83
20
25.44
9937.5
Acute (HC05)
2,000
NA
4.97
Chronic (ChV)
920
20
10.8
a. Facilities actively releasing trichloroethylene were identified via DMR, TRI, and CDR databases for 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 wastewater treatment removal rate of 81% is applied to all indirect releases, i.e., volumes characterized as being transferred off-site for treatment at a water
treatment facility prior to discharge to surface water.
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 representative generic industry sector. The name of the indirect releaser 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 maximum days of release per year expected. For direct releasing facilities, a minimum of 20 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 concentration was reported in place of the 7Q10 SWC.
h.To determine 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 concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero
318
Page 299 of 803
-------
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
Table 4-5. RQs for Sediment Organisms Calculated using Monitored Environmental
Concentrations from WQX/WQP
Monitored Surface Water
Concentrations (pph) from 2o|3-2oi7
RO using Acute ('()(' of
2.i)oo pph
RO using Chronic ('()(' of l^2o
pph
Mean (Standard Deviation): 0.33 (0.29)
ppb
0.0
0.0
Maximum: 2 ppb
0.0
0.0
Table 4-6. RQs Sediment Organisms Calculated using Monitored Environmental Concentrations
from Published Literature
Monitored Surface \Y'liter
Concentrations (ppb) from 2o|3-2oi7
RO using Acute ('()(' of
2.000 pph
RO using Chronic COC of l^2o
pph
Central tendency values: u.uuu2 -1.17
ppb
U.UU
U.UU
Maximum: 17.3 ppb
0.01
0.02
4.1.4 Risk Estimation for Terrestrial Organisms
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, physical-
chemical properties do not support an exposure pathway through water and soil pathways to terrestrial
organisms.
For terrestrial organisms, during Problem Formulation exposure pathways to these organisms through
water and biosolids were within scope but not further analyzed, because physical chemical properties do
not support these pathways. TCE is not anticipated to partition to biosolids during wastewater treatment.
TCE has a predicted 81% wastewater treatment removal efficiency, predominately due to volatilization
during aeration. Any TCE present in the water portion of biosolids 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. And
the air exposure pathway from biosolids and surface water are insignificant. Based on the Guidance for
Ecological Soil Screening Levels (U.S. EPA. 2003a; U.S. EPA. 2003b) document, for terrestrial
wildlife, relative exposures associated with inhalation and dermal exposure pathways are insignificant,
even for volatile substances, compared to direct ingestion and ingestion of food (by approximately
1,000-fold). Therefore, volatization from surface water and biosolids to air of TCE is not a concern for
wildlife. TCE is not expected to bioaccumulate in tissues, and concentrations will not increase from prey
to predator in either aquatic or terrestrial food webs.
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 of TCE to ambient air are covered under the jurisdiction of the Clean Air Act (CAA).
Page 300 of 803
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351 4.2 Human Health Risks
352 4.2.1 Risk Estimation Approach
353 The use scenarios, populations of interest and toxicological endpoints used for acute and chronic
354 exposures are are presented in Table 4-7.
355
356 Table 4-7. Use Scenarios, Populations of Interest and Toxicological Endpoints Used for Acute and
357 Chronic Exposures
Workers: 1
Acute- Adolescent (>16 years old to <21 years old) and adult workers
exposed to TCE for a single 8-hr exposure
Chronic- Adolescent (>16 years old to <21 years old) and adult workers
exposed to TCE for the entire 8-hr workday for 260 days per year for 40
working years
Occupational Non-User:
Acute or Chronic- Adolescent (>16 years old to <21 years old) and adult
worker exposed to TCE indirectly by being in the same work area of the
building
Consumers 2
Acute- Children (>11 years old to <21 years old) and adult consumers
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):
HEC- ppm;
POD HECs represent 24hr values based on continuous exposure and
resting respiratory rate. Exposure concentrations have been adjusted to
match the time duration for inhalation exposure.
HECs for the best overall acute (immunosuppression) and chronic
(autoimmunity) non-cancer endpoints were also derived for occupational
scenarios based on 8hr daily exposure and increased respiratory rate
(Section 3.2.5.4.1).
HEP- mg/kg; for dermal risk estimates
Non-Cancer Health Effects: 4
Acute- Developmental effects and immunotoxicity
Chronic- Liver effects, kidney effects, neurological effects, immune
effects, reproductive effects, and developmental effects
Uncertainty Factors (UF) Benchmark MOEs: Vary by endpoint; Benchmark MOE = 10 for best
used in Non-Cancer Margin overall acute endpoint (immunosuppression), 30 for best overall chronic
of Exposure (MOE) endpoint (autoimmunity)
calculations Benchmark MOE = (UFS) x (UFA) x (UFH) x (UFl)5
Population of Interest and
Exposure Scenario
Health Effects,
Concentration and Time
Duration
Page 301 of 803
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362
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365
366
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371
372
373
374
375
376
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378
379
380
381
382
383
384
385
386
387
1 Adult workers (>16 years old to <21 years old) include both female and male workers.
2 EPA believes that the users of these products are generally adults, but young teenagers and even younger children may be
users or be in the same room with the user while engaging in various conditions of use. Since there are not survey data for
consumer behavior patterns or a way to create varying behavior patterns for different age groups, the indoor air concentrations
shown in Table 4-7. Use could be extended to all users.
3 EPA believes that the users of these products are generally adults, but young teenagers and even younger children may be
users or be in the same room with the user while engaging in various conditions of use. Since there are not survey data for
consumer behavior patterns or a way to create varying behavior patterns for different age groups, the indoor air concentrations
could be extended to all users.
4 Female workers of childbearing age are the population of interest for reproductive and developmental effects. For other health
effects (e.g., liver, kidney, etc.), healthy female or male workers were assumed to be the population of interest.
5UFs=subchronic to chronic UF; UFA=interspecies UF; UFH=intraspecies UF; UFl=LOAEL to NOAEL UF
The EPA uses a Margin of Exposure (MOE) approach to assess 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 MOEchronic) 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)
MOEacuteorchronic = Human Exposure
Where:
MOE
Hazard Value (POD)
Human Exposure
= Margin of exposure (unitless)
= HEC (ppm) or HED (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 dermal exposure, respectively. Average
Daily Concentrations (ADC) and non-cancer chronic ADDs were used for calculating occupational non-
cancer risks following inhalation or dermal 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 M 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
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.
Page 302 of 803
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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
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
EPA used margin of exposures (MOEs) to estimate acute or chronic risks for non-cancer based on the
following:
• the HECs/HEDs from robust and sensitive studies that best represent each endpoint;
• the endpoint/study-specific UFs applied to the HECs/HEDs per EPA RfD/RfC Guidance ("U.S. EPA.
2002'); and
• the exposure estimates calculated for TCE uses examined in this risk assessment (see Section 2.3 -
Human Exposures).
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. In general, the frequency of product use was considered to be too low to create chronic risk
concerns. Although Westat (1987) survey data indicate that use frequencies for a small percentage of
high-end product users {i.e., those reflecting 95th percentile annual use frequencies) may use products up
to 50 times per year, available toxicological data is based on either single or continuous TCE exposure
and it is unknown whether these use patterns are expected to be clustered {e.g., every day for several
weeks) or intermittent {e.g., one time per week). There is uncertainty regarding the extrapolation from
continuous studies in animals to the case of repeated intermittent human exposures. Therefore, EPA
cannot fully rule out that consumers at the high-end frequency of use could possibly be at risk for chronic
hazard effects (Section 3.2), however it is expected to be unlikely based on these considerations.
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 exposures to TCE were estimated using Equation 4-2. Estimates of extra
cancer risks should be interpreted as the incremental probability of an individual developing cancer over
a lifetime as a result of exposure to the potential carcinogen {i.e., incremental or extra individual lifetime
cancer risk). For purposes of this Risk Evaluation, EPA considers extra risk of 1 x 10"4 (or 1E-4 in
shorthand) to be the benchmark for occupational risk estimation.
Equation 4-2. Equation to Calculate Extra Cancer Risks
Risk = Human Exposure (LADC) x POD (IUR 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)
Page 303 of 803
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438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
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 reasonably available toxicokinetic data. EPA did not evaluate TCE exposure through the oral
route because the route is out of scope for this evaluation (U.S. EPA.: ). 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 exposure for most
conditions of use. Dermal exposure was considered for occupational scenarios while accounting for
evaporation according to modeling from (Kastine and Miller. 2006) (see Section 2.3.1.2.5). For
consumers, dermal exposure was only considered for scenarios resulting in dermal contact with impeded
evaporation (See Section 2.3.2.2.2).
4.2.1.1
Points of Departure Used 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 hazard 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
4-8. For complete MOE tables displaying risk estimates for all chronic endpoints, see [Risk Calculator
for Occupational Exposures. Docket: EPA-HQ-QPPT-2019-05001.
As described in Section 3.2.5.4.1, EPA considers the POD for mortality due to immunosuppression from
(Setgrade and Gitroour. 2010) (referred to as simply immunosuppression in the risk tables) to be the best
overall endpoint for acute scenarios and autoimmunity from (Keil et at.. 2009) to be the best overall non-
cancer endpoint for chronic scenarios. However, EPA presents risk estimates for all acute endpoints and
chronic health domains in Section 4.2.2 and 4.2.3 in order to more accurately describe the range of risk
associated with TCE exposure.
Table 4-8. Most Sensitive Endpoints from Each Health Domain for Risk Estimation
of Chronic Exposure Scenarios
Tiir»ii ()r»;in /
S\ sit-m
POD T\|k-
I". ITi'il
MIC,,
(ppiii)
II II)-.
(m»/k»)
I iui i l;iiiil\
I'iKliirs (I I s)
Ki'lVmuv
l);il;i
Qu;ilil\
Developmental Effects
BMDLoi —
0.0207mg/kg-
bw/day
Congenital heart defects
0.0037
0.0052
UFS=1;UFa=3;
LTFH=3;trFL=l;
Total LTF= 10
(Johnson et aL,
20031
Medium
Kidney
BMDLio = 34
mg/kg-bw/day
Pathology changes in
renal tubule
0.025
0.015
UFS=1;UFA= 3;
LTFh=3;LTFl=1;
Total UF=10
(Maltoni et aL,
1.9861
Medium
Immune System
LOAEL = 0.35
mg/kg-bw/day
Autoimmunity (increased
anti-dsDNA and -ssDNA
antibodies)
0.033
0.048
UFS=1;UFA= 3;
LTFH=3; LTFL=3;
Total UF=30
(Keil et al.
2009)
High
Reproductive System
BMDLio = 1.4
ppm
Decreased normal sperm
morphology and hyper-
zoospermia
0.5
0.73
LTFs=10; UFa= 1;
LTFh=3;LTFl=1;
Total UF=30
fChia et aL,
1996)
Medium
Page 304 of 803
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470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
Nervous System
LOAEL = 12
ppm
Significant decreases in
wakefulness
4.8
6.5
LTFs=3; UFa= 3;
UFh=3; LTFl=10;
Total UF=300
(Ari to et aL
19941
Medium
Liver
BMDLio= 21.6
ppm
Increased liver/body
weight ratio and
cytotoxicity/hypertrophy
9.1
7.9
UFs=1;UFa=3;
UFh=3;UFl=1;
Total UF=10
(Kiellstrand et
aL,19831
Medium
HEC/HED99 values will be used for risk estimation. These upper-end outputs from the PBPK model are
expected to be protective of susceptible subpopulations, accounting for 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 UFa and
UFh of 3.
4.2.2 Risk Estimation for Occupational Exposures by Exposure Scenario
Risk estimates via inhalation and dermal exposure are provided below for workers and ONUs following
acute (single day), chronic (40-year), or lifetime (78 year) TCE exposure. Inhalation risk estimates are
based on monitoring and/or modeling exposure data. Both are presented for exposure scenarios where
both data types were reasonably available. Non-cancer endpoints were applied to acute and chronic
exposures while cancer risk estimates are provided for adjusted lifetime exposure. For most endpoints,
HECs based on default PBPK parameters of continuous exposure and resting respiratory rate were used
for occupational risk estimates. For the best overall non-cancer endpoints of acute immunosuppression
and chronic autoimmunity however, risk estimates are based on derived occupational HECs (presented
in Table 3-16).
Although generally ONU exposures are expected to be less than workers, when sufficient data were not
reasonably available for quantifying ONU exposures EPA provided risk estimates for ONUs based on
assuming that ONU exposure may be comparable to worker central-tendency values. This is a health-
protective assumption. When reasonably available, inhalation risk estimates are presented based on both
monitoring and modeling data. Otherwise, risk estimates are presented for the type of inhalation
exposure data that was reasonably available. All dermal risk estimates are based on modeling data as
discussed in Section 2.3.1.2.5. 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 calculated non-cancer MOE or extra cancer risk with
respirator use is then compared to the benchmark MOE to determine the level of APF required to
mitigate risk for all health domains. EPA does not evaluate respirator use for occupational non-users
because they do not directly handle TCE and EPA assumes that they are unlikely to consistently 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 of 20 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 Calculator for Occupational Exposures. Docket: EPA-HQ-QPPT-2019-05001.
EPA considered the reasonably available data for estimating exposures for each OES. EPA also
determined whether air-supplied respirator use up to APF = 50 was plausible for those OES based on
expert judgement and reasonably available information. Table 4-9 presents this information below,
which is considered in the risk characterization for each OES in the following sections.
Page 305 of 803
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513
514
515
516
517
518
519
520
521
522
523
524
EPA did not assume respirator or glove use for the following occupational scenarios:
• Dry Cleaning; Spot Cleaner, Stain Remover. Many dry cleaning shops are small, family -owned
businesses and are unlikely to have a respiratory protection program or regularly employ dermal
protection.
• Commercial Copying and Printing: Many copying and printing shops are small, family -owned
businesses and are unlikely to have a respiratory protection program or regularly employ dermal
protection.
• Other Commercial Uses: Due to unknown facilities and operations and the likelihood that
commercial operations will be family-owned businesses, EPA believes these facilities are unlikely to
have a respiratory protection program or regularly employ dermal protection.
Table 4-9. Inhalation Exposure Data Summary and PPE Use Determinai
Occiipiilioiiiil
I'1\|)osiiiv Scenario
Inhiiliilion
l'l\|)OSIIIV
Approach
Number of
Diilii Points
Model I sod
Approach
lor OM s
Kcspii'iiloi'/
(Jo\e I so
Induslriiil or
('ommcrciiil
OI.S
Domestic
Manufacture
Monitoring
Data
50 (8-hr TWA)
\.A
monitoring data
only
None
Established
Assumed
Industrial
Processing as a
Reactant
Surrogate
Monitoring
Data
50 (8-hr TWA)
N/A-
monitoring data
only
None
Established
Assumed
Industrial
Batch Open Top
Vapor Degreasing
Monitoring
Data and
Modeling
108 (8-hr TWA),
1 (12-hr TWA)
Open-Top Vapor
Degreasing
Near-Field/Far-
Field Inhalation
Exposure Model
Monitoring
Data and
Modeling
Assumed
Industrial/
Commercial
Batch Closed-Loop
Vapor Degreasing
Monitoring
Data
19 (8-hr TWA)
N/A-
monitoring data
only
None
Established
Assumed
Industrial
Conveyorized Vapor
Degreasing
Monitoring
Data and
Modeling
18 (8-hr TWA)
Conveyorized
Vapor
Degreasing
Near-Field/Far-
Field Inhalation
Exposure Model
Far-field
model results
Assumed
Industrial
Web Vapor
Degreasing
Modeling
N/A - model
only
Web Vapor
Degreasing
Near-Field/Far-
Field Inhalation
Exposure Model
Far-field
model results
Assumed
Industrial
Cold Cleaning
Modeling
N/A - model
only
Cold Cleaning
Near-Field/Far-
Field Inhalation
Exposure Model
Far-field
model results
Assumed
Industrial
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake
and Parts Cleaners,
Penetrating
Lubricants, and Mold
Releases
Modeling
N/A - model
only
Brake Servicing
Near-field/Far-
field Exposure
Model
Far-field
model results
Assumed
Commercial
ion
Page 306 of 803
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Occiipiilioiiiil
I-'.\|)osiiiv SiTiiiirin
Inhiiliilion
l'l\|)OMIIV
Approach
Number of
l);ilii Points
Model I snl
Approiich
lor OM s
Kcspiriilor/
(Jo\c I sc
liulusli'iiil oi'
Coinniciviiil
oi:s
Spot Cleaning, Wipe
Cleaning and Carpet
Cleaning
Monitoring
Data and
Modeling
8 (8-hr TWA),
1 (12-hr TWA)
Spot Cleaning
Near-Field/Far-
Field Inhalation
Exposure Model
Far-field
model results
Not expected
Commercial
Formulation of
Aerosol and Non-
Aerosol Products
Surrogate
Monitoring
Data
33 (8-hr TWA)
N/A-
monitoring data
only
None
Established
Assumed
Industrial
Repackaging
Monitoring
Data
33 (8-hr TWA)
N/A-
monitoring data
only
None
Established
Assumed
Industrial
Metalworking Fluids
Monitoring
Data and
Modeling
3 (8-hour
TWA)
2011 ESD on
Use of
Metalworking
Fluids
None
Established
Assumed
Industrial
Adhesives, Sealants,
Paints, and Coatings
(Commercial)
Surrogate
Monitoring
Data
22 (8-hr TWA),
2 (8-hr TWA,
ONU)
N/A-
monitoring data
only
Monitoring
Data
Assumed
Commercial
Adhesives, Sealants,
Paints, and Coatings
(Industrial)
Monitoring
Data
22 (8-hr TWA),
2 (8-hr TWA,
ONU)
N/A-
monitoring data
only
Monitoring
Data
Assumed
Industrial
Industrial Processing
Aid
Monitoring
Data
30 (12-hr
TWA),
4 (12-hr TWA,
ONU)
N/A-
monitoring data
only
Monitoring
Data
Assumed
Industrial
Commercial Printing
and Copying
Monitoring
Data
20 (8-hr TWA)
N/A-
monitoring data
only
Monitoring
Data
Not expected
Commercial
Other Industrial Uses
Surrogate
Monitoring
Data
50 (8-hr TWA)
N/A-
monitoring data
only
Monitoring
Data
Assumed
Industrial
Other Commercial
Uses
Monitoring
Data and
Modeling
8 (8-hr TWA),
1 (12-hr TWA)
Spot Cleaning
Near-Field/Far-
Field Inhalation
Exposure Model
Far-field
model results
Not expected
Commercial
Process Solvent
Recycling and Worker
Handling of Wastes
Surrogate
Monitoring
Data
33 (8-hr TWA)
N/A-
monitoring data
only
None
Established
Assumed
Industrial
525
Page 307 of 803
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526 Table 4-10. Occupational Risk Estimation - Manufacturing
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE 1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.5E-03
4.5E-02
0.23
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
9.7E-02
0.97
4.8
9.7E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.7
36.6
183.0
-
1.8
8.9
17.8
35.6
Central Tendency
78.3
782.6
3,913.0
78.3
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
28.1
280.6
1,403.0
-
12.2
60.8
121.5
243.0
Central Tendency
600.0
6,000.0
30,000.0
600.0
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.95
9.5
47.6
-
0.58
2.9
5.8
11.6
Central Tendency
20.3
203.5
1,017.4
20.3
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
16.2
162.1
810.5
-
5.0
25.0
50.1
100.1
Central Tendency
346.6
3,465.9
17,329.6
346.6
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.5E-02
0.45
2.2
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.95
9.5
47.6
0.95
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.5
85.5
427.5
-
4.1
20.6
41.2
82.4
Central Tendency
182.8
1,828.2
9,140.9
182.8
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.89
8.9
44.5
-
0.46
2.3
4.6
9.2
Central Tendency
19.0
190.4
952.2
19.0
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.6E-03
6.6E-02
0.33
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
0.14
1.4
7.0
0.14
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.9E-02
0.49
2.5
-
3.0E-02
0.15
0.30
0.61
Central Tendency
1.1
10.5
52.7
1.1
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
6.3E-03
6.3E-04
1.3E-04
-
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
2.3E-04
2.3E-05
4.6E-06
2.3E-04
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink 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; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 308 of 803
-------
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
MOE results for Manufacturing utilized 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, therefore central tendency worker estimates
were applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for congenital heart defects 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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 dermal routes. EPA is unable to estimate ONU exposures separately from workers, therefore central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 309 of 803
-------
551 Table 4-11. Occupational Risk Estimation - Processing as a Reactant
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE 1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.5E-03
4.5E-02
0.23
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
9.7E-02
0.97
4.8
9.7E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.7
36.6
183.0
-
1.8
8.9
17.8
35.6
Central Tendency
78.3
782.6
3,913.0
78.3
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
28.1
280.6
1,403.0
-
12.2
60.8
121.5
243.0
Central Tendency
600.0
6,000.0
30,000.0
600.0
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.95
9.5
47.6
-
0.58
2.9
5.8
11.6
Central Tendency
20.3
203.5
1,017.4
20.3
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
16.2
162.1
810.5
-
5.0
25.0
50.1
100.1
Central Tendency
346.6
3,465.9
17,329.6
346.6
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.5E-02
0.45
2.2
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.95
9.5
47.6
0.95
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.5
85.5
427.5
-
4.1
20.6
41.2
82.4
Central Tendency
182.8
1,828.2
9,140.9
182.8
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.89
8.9
44.5
-
0.46
2.3
4.6
9.2
Central Tendency
19.0
190.4
952.2
19.0
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.6E-03
6.6E-02
0.33
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
0.14
1.4
7.0
0.14
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.9E-02
0.49
2.5
-
3.0E-02
0.15
0.30
0.61
Central Tendency
1.1
10.5
52.7
1.1
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
6.3E-03
6.3E-04
1.3E-04
-
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
2.3E-04
2.3E-05
4.6E-06
2.3E-04
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink 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; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 310 of 803
-------
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
MOE results for Processing as a Reactant utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table
4-11.
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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for congenital heart defects 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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 dermal routes. EPA is unable to estimate ONU exposures separately from workers, therefore central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 311 of 803
-------
577 Table 4-12. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Monitoring Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF= 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
1.4E-04
1.4E-03
7.1E-03
1.2E-03
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
8.0E-04
8.0E-03
4.0E-02
1.0E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
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
Developmental -
Mortality
(Narotskv et al.. 1995)
10
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
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
3.0E-02
0.30
1.5
0.26
0.58
2.9
5.8
11.6
Central Tendency
0.17
1.7
8.5
2.1
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
0.51
5.1
25.6
4.4
5.0
25.0
50.1
100.1
Central Tendency
2.9
28.9
144.4
36.0
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
1.4E-03
1.4E-02
7.0E-02
1.2E-02
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
7.9E-03
7.9E-02
0.40
9.9E-02
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.27
2.7
13.5
2.3
4.1
20.6
41.2
82.4
Central Tendency
1.5
15.2
76.2
19.0
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
2.8E-02
0.28
1.4
0.24
0.46
2.3
4.6
9.2
Central Tendency
0.16
1.6
7.9
2.0
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
2.1E-04
2.1E-03
1.0E-02
1.8E-03
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
1.2E-03
1.2E-02
5.9E-02
1.5E-02
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
1.6E-03
1.6E-02
7.8E-02
1.3E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
8.8E-03
8.8E-02
0.44
0.11
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.20
2.0E-02
4.0E-03
2.3E-02
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
2.8E-02
2.8E-03
5.5E-04
2.2E-03
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
Page 312 of 803
-------
578 Table 4-13. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Modeling Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF= 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE 1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
2.9E-05
2.9E-04
1.4E-03
4.7E-05
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.2E-04
3.2E-03
1.6E-02
6.1E-04
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
2.3E-02
0.23
1.2
3.8E-02
1.8
8.9
17.8
35.6
Central Tendency
0.26
2.6
12.9
0.50
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
0.18
1.8
8.9
0.29
12.2
60.8
121.5
243.0
Central Tendency
2.0
19.8
99.1
3.8
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
6.0E-03
6.0E-02
0.30
9.9E-03
0.58
2.9
5.8
11.6
Central Tendency
6.7E-02
0.67
3.4
0.13
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
0.10
1.0
5.1
0.17
5.0
25.0
50.1
100.1
Central Tendency
1.1
11.4
57.2
2.2
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
2.8E-04
2.8E-03
1.4E-02
4.6E-04
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
3.1E-03
3.1E-02
0.16
6.0E-03
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
5.4E-02
0.54
2.7
8.9E-02
4.1
20.6
41.2
82.4
Central Tendency
0.60
6.0
30.2
1.2
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
5.6E-03
5.6E-02
0.28
9.3E-03
0.46
2.3
4.6
9.2
Central Tendency
6.3E-02
0.63
3.1
0.12
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
4.2E-05
4.2E-04
2.1E-03
6.9E-05
3.3E-03
1.6E-02
3.3E-02
6.6E-02
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
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
3.1E-04
3.1E-03
1.6E-02
5.1E-04
3.0E-02
0.15
0.30
0.61
Central Tendency
3.5E-03
3.5E-02
0.17
6.7E-03
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.78
7.8E-02
1.6E-02
0.46
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
6.5E-02
6.5E-03
1.3E-03
3.4E-02
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are considered plausible for this exposure scenario.
579
Page 313 of 803
-------
580
581
582
583
584
585
586
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
614
615
616
617
MOE results for Batch Open Top Vapor Degreasing utilized both monitoring and modeling inhalation exposure data (with dermal modeling).
Results are presented in Table 4-12 and Table 4-13.
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 congenital heart defects 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 dermal 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 25.9 ppm to 19.2 ppm and the central tendency acute exposure estimate from 4.6 ppm
to 4.3 ppm. Chronic high-end and central tendency exposures are reduced from 17.8 ppm and 3.2 ppm to 13.17 ppm and 2.92 ppm,
respectively. Lifetime exposures are reduced from 9.1 ppm and 1.23 ppm to 6.8 ppm and 1.2 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
central tendency MOE for the acute immunosuppression endpoint (with benchmark MOE = 10) is 0.18 and the central tendency MOE for the
chronic autoimmunity endpoint (with benchmark MOE = 30) is 9.5E-03. The central tendency cancer extra risk (benchmark = 1E-04) is 2.6E-
02. Therefore, the MOEs remain orders of magnitude below the benchmark MOE (or above the benchmark for cancer risk) when using only
PEL-capped exposure estimates. Risks also remain at these endpoints for ONUs. Full details are provided in [Occupational Risk Estimate
Calculator. Docket # EPA-HQ-QPPT-2019-0500J.
Page 314 of 803
-------
618 Table 4-14. Occupational Risk Estimation - Batch Closed-Loop Vapor Degreasing
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF= 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
7.6E-03
7.6E-02
0.38
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
2.4E-02
0.24
1.2
2.4E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
6.2
61.9
309.5
-
1.8
8.9
17.8
35.6
Central Tendency
19.7
196.6
983.0
19.7
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
47.5
474.5
2,372.5
-
12.2
60.8
121.5
243.0
Central Tendency
150.7
1,507.3
7,536.5
150.7
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
1.6
16.1
80.5
-
0.58
2.9
5.8
11.6
Central Tendency
5.1
51.1
255.6
5.1
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
27.4
274.1
1,370.5
-
5.0
25.0
50.1
100.1
Central Tendency
87.1
870.7
4,353.5
87.1
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
7.5E-02
0.75
3.8
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.24
2.4
12.0
0.24
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
14.5
144.6
722.9
-
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
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
1.5
15.1
75.3
-
0.46
2.3
4.6
9.2
Central Tendency
4.8
47.8
239.2
4.8
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
1.1E-02
0.11
0.56
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
3.5E-02
0.35
1.8
3.5E-02
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
8.3E-02
0.83
4.2
-
3.0E-02
0.15
0.30
0.61
Central Tendency
0.26
2.6
13.2
0.26
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
3.7E-03
3.7E-04
7.5E-05
-
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
9.1E-04
9.1E-05
1.8E-05
9.1E-04
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 315 of 803
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619
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
MOE results for Batch Closed-Loop Vapor Degreasing utilized monitoring inhalation exposure data (with dermal modeling) and are
presented in Table 4-14.
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, central tendency worker estimates were
applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for congenital heart defects 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, central tendency worker estimates were
applied as an approximation of likely ONU exposures. 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 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 dermal routes. EPA is unable to estimate ONU exposures separately from workers, central tendency worker estimates were
applied as an approximation of likely ONU exposures. Risk estimates were not above the benchmark for high-end inhalation exposure when
assuming APF = 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 316 of 803
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646 Table 4-15. Occupational Risk Estimation - Conveyorized Vapor Degreasing - Inhalation Monitoring Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF= 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
2.3E-04
2.3E-03
1.1E-02
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.4E-04
3.4E-03
1.7E-02
3.4E-04
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.19
1.9
9.3
-
1.8
8.9
17.8
35.6
Central Tendency
0.28
2.8
13.9
0.28
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
1.4
14.3
71.4
-
12.2
60.8
121.5
243.0
Central Tendency
2.1
21.3
106.5
2.1
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
4.8E-02
0.48
2.4
-
0.58
2.9
5.8
11.6
Central Tendency
7.2E-02
0.72
3.6
7.2E-02
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
0.83
8.3
41.3
-
5.0
25.0
50.1
100.1
Central Tendency
1.2
12.3
61.5
1.2
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
2.3E-03
2.3E-02
0.11
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
3.4E-03
3.4E-02
0.17
3.4E-03
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.44
4.4
21.8
-
4.1
20.6
41.2
82.4
Central Tendency
0.65
6.5
32.5
0.65
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
4.5E-02
0.45
2.3
-
0.46
2.3
4.6
9.2
Central Tendency
6.8E-02
0.68
3.4
6.8E-02
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
3.4E-04
3.4E-03
1.7E-02
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
5.0E-04
5.0E-03
2.5E-02
5.0E-04
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
2.5E-03
2.5E-02
0.13
-
3.0E-02
0.15
0.30
0.61
Central Tendency
3.7E-03
3.7E-02
0.19
3.7E-03
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.12
1.2E-02
2.5E-03
-
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
6.5E-02
6.5E-03
1.3E-03
6.5E-02
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 317 of 803
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647 Table 4-16. Occupational Risk Estimation - Conveyorized Vapor Degreasing - Inhalation JV
odeling Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF= 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
3.6E-06
3.6E-05
1.8E-04
5.9E-06
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
2.7E-04
2.7E-03
1.4E-02
4.8E-04
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.0E-03
3.0E-02
0.15
4.8E-03
1.8
8.9
17.8
35.6
Central Tendency
0.22
2.2
11.0
0.39
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
2.3E-02
0.23
1.1
3.7E-02
12.2
60.8
121.5
243.0
Central Tendency
1.7
16.9
84.6
3.0
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
7.7E-04
7.7E-03
3.8E-02
1.2E-03
0.58
2.9
5.8
11.6
Central Tendency
5.7E-02
0.57
2.9
0.10
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
1.3E-02
0.13
0.65
2.1E-02
5.0
25.0
50.1
100.1
Central Tendency
0.98
9.8
48.8
1.7
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
3.6E-05
3.6E-04
1.8E-03
5.8E-05
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
2.7E-03
2.7E-02
0.13
4.7E-03
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
6.9E-03
6.9E-02
0.35
1.1E-02
4.1
20.6
41.2
82.4
Central Tendency
0.52
5.2
25.8
0.90
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
7.2E-04
7.2E-03
3.6E-02
1.2E-03
0.46
2.3
4.6
9.2
Central Tendency
5.4E-02
0.54
2.7
9.4E-02
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
5.3E-06
5.3E-05
2.7E-04
8.6E-06
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
4.0E-04
4.0E-03
2.0E-02
6.9E-04
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.0E-05
4.0E-04
2.0E-03
6.5E-05
3.0E-02
0.15
0.30
0.61
Central Tendency
3.0E-03
3.0E-02
0.15
5.2E-03
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
6.1
0.61
0.12
3.7
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
0.12
1.2E-02
2.3E-03
7.9E-02
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
648
Page 318 of 803
-------
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
679
680
MOE results for Conveyorized Vapor Degreasing utilized both monitoring and modeling inhalation exposure data (with dermal modeling).
Results are presented in Table 4-15 and Table 4-16.
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, central tendency worker estimates were applied as an approximation of likely ONU
exposures. 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 inhalation exposure levels even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for
congenital heart defects 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. EPA is unable to estimate ONU exposures separately from workers
based on monitoring data, central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 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 dermal routes. EPA is unable to estimate ONU exposures separately from
workers based on monitoring data, central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 inhalation
routes even when assuming the highest plausible APF and glove PF protection.
Page 319 of 803
-------
681 Table 4-17. Occupational Risk Estimation - Web Vapor Degreasing
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
7.9E-04
7.9E-03
3.9E-02
1.2E-03
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
1.9E-03
1.9E-02
9.3E-02
3.5E-03
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.64
6.4
31.8
0.94
1.8
8.9
17.8
35.6
Central Tendency
1.5
15.1
75.7
2.9
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
4.9
48.8
244.0
7.2
12.2
60.8
121.5
243.0
Central Tendency
11.6
116.1
580.4
22.1
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.17
1.7
8.3
0.24
0.58
2.9
5.8
11.6
Central Tendency
0.39
3.9
19.7
0.75
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
2.8
28.2
140.9
4.2
5.0
25.0
50.1
100.1
Central Tendency
6.7
67.1
335.3
12.7
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
7.7E-03
7.7E-02
0.39
1.1E-02
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
1.8E-02
0.18
0.92
3.5E-02
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
1.5
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
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.15
1.5
7.7
0.23
0.46
2.3
4.6
9.2
Central Tendency
0.37
3.7
18.4
0.70
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
1.1E-03
1.1E-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
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
8.6E-03
8.6E-02
0.43
1.3E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
2.0E-02
0.20
1.0
3.9E-02
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
2.9E-02
2.9E-03
5.8E-04
1.9E-02
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
1.1E-02
1.1E-03
2.3E-04
5.9E-03
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
682
Page 320 of 803
-------
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
MOE results for Web Vapor Decreasing utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-17.
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 the central tendency inhalation exposure
level. 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 congenital heart defects 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 the central tendency inhalation
exposure level. 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 the central tendency inhalation exposure
level. 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 321 of 803
-------
709 Table 4-18. Occupational Risk Estimation - Cold Cleaning
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
1.9E-04
1.9E-03
9.7E-03
3.2E-04
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.3E-03
3.3E-02
0.17
6.0E-03
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.16
1.6
7.9
0.26
1.8
8.9
17.8
35.6
Central Tendency
2.7
27.0
135.1
4.9
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
1.2
12.1
60.3
2.0
12.2
60.8
121.5
243.0
Central Tendency
20.7
207.2
1,036.0
37.5
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
4.1E-02
0.41
2.0
6.7E-02
0.58
2.9
5.8
11.6
Central Tendency
0.70
7.0
35.1
1.3
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
0.69
6.9
34.7
1.2
5.0
25.0
50.1
100.1
Central Tendency
12.0
119.7
598.7
21.7
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
1.9E-03
1.9E-02
9.5E-02
3.2E-03
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
3.3E-02
0.33
1.6
6.0E-02
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.37
3.7
18.3
0.61
4.1
20.6
41.2
82.4
Central Tendency
6.3
63.2
315.8
11.4
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
3.8E-02
0.38
1.9
6.3E-02
0.46
2.3
4.6
9.2
Central Tendency
0.66
6.6
32.9
1.2
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
2.8E-04
2.8E-03
1.4E-02
4.7E-04
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
4.9E-03
4.9E-02
0.24
8.8E-03
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
2.1E-03
2.1E-02
0.11
3.5E-03
3.0E-02
0.15
0.30
0.61
Central Tendency
3.6E-02
0.36
1.8
6.6E-02
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.11
1.1E-02
2.3E-03
6.9E-02
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
6.2E-03
6.2E-04
1.2E-04
3.3E-03
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
710
Page 322 of 803
-------
711
712
713
714
715
716
111
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
MOE results for Cold Cleaning utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-18.
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 congenital heart defects 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 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 323 of 803
-------
733 Table 4-19. Occupational Risk Estimation - Aerosol Applications
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.6E-04
4.6E-03
2.3E-02
1.1E-02
1.4E-03
7.2E-03
1.4E-02
2.9E-02
Central Tendency
1.5E-03
1.5E-02
7.3E-02
7.9E-02
4.3E-03
2.2E-02
4.3E-02
8.6E-02
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.38
3.8
18.8
8.7
1.1
5.7
11.3
22.7
Central Tendency
1.2
11.8
59.0
64.3
3.4
17.0
34.0
68.0
Developmental -
Mortality
(Narotskv et al.. 1995)
10
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
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
9.8E-02
0.98
4.9
2.3
0.37
1.9
3.7
7.4
Central Tendency
0.31
3.1
15.3
16.7
1.1
5.6
11.1
22.2
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
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
Kidney
(Maltoni et al.. 1986)
10
High End
4.6E-03
4.6E-02
0.23
0.11
6.1E-03
3.0E-02
6.1E-02
0.12
Central Tendency
1.4E-02
0.14
0.72
0.78
1.8E-02
9.1E-02
0.18
0.36
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.88
8.8
43.8
20.2
2.6
13.1
26.2
52.5
Central Tendency
2.8
27.6
137.9
150.0
7.9
39.3
78.7
157.4
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
9.1E-02
0.91
4.6
2.1
0.29
1.5
2.9
5.9
Central Tendency
0.29
2.9
14.4
15.6
0.88
4.4
8.8
17.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.8E-04
6.8E-03
3.4E-02
1.6E-02
2.1E-03
1.0E-02
2.1E-02
4.2E-02
Central Tendency
2.1E-03
2.1E-02
0.11
0.12
6.3E-03
3.1E-02
6.3E-02
0.13
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
5.1E-03
5.1E-02
0.25
0.12
1.9E-02
9.7E-02
0.19
0.39
Central Tendency
1.6E-02
0.16
0.79
0.87
5.8E-02
0.29
0.58
1.2
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
4.9E-02
4.9E-03
9.7E-04
2.0E-03
5.9E-02
1.2E-02
5.9E-03
2.9E-03
Central Tendency
1.4E-02
1.4E-03
2.9E-04
2.6E-04
1.5E-02
3.0E-03
1.5E-03
7.6E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
734
Page 324 of 803
-------
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
MOE results for Aerosol Applications utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-19.
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 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.
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 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 325 of 803
-------
756 Table 4-20. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial Uses) - Inhalation Monitoring Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
3.9E-03
3.9E-02
0.19
-
1.4E-03
7.2E-03
1.4E-02
N/A2
Central Tendency
2.9E-02
0.29
1.4
2.9E-02
4.3E-03
2.2E-02
4.3E-02
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.2
31.6
157.8
-
1.1
5.7
11.3
Central Tendency
23.5
235.1
1,175.3
23.5
3.4
17.0
34.0
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
24.2
242.0
1,210.1
-
7.7
38.7
77.4
Central Tendency
180.2
1,802.2
9,010.9
180.2
23.2
116.1
232.2
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.82
8.2
41.0
-
0.37
1.9
3.7
Central Tendency
6.1
61.1
305.6
6.1
1.1
5.6
11.1
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
13.5
135.5
677.3
-
2.7
13.6
27.2
N/A2
Central Tendency
100.9
1,008.7
5,043.7
100.9
9.3
46.3
92.7
Kidney
(Maltoni et al.. 1986)
10
High End
3.7E-02
0.37
1.9
-
5.2E-03
2.6E-02
5.2E-02
Central Tendency
0.28
2.8
13.9
0.28
1.8E-02
8.8E-02
0.18
Neurotoxicity
(Arito et al.. 1994)
300
High End
7.1
71.5
357.3
-
2.2
11.2
22.4
Central Tendency
53.2
532.1
2,660.4
53.2
7.6
38.1
76.3
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.74
7.4
37.2
-
0.25
1.3
2.5
Central Tendency
5.5
55.4
277.1
5.5
0.86
4.3
8.6
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
5.5E-03
5.5E-02
0.28
-
1.8E-03
9.0E-03
1.8E-02
Central Tendency
4.1E-02
0.41
2.1
4.1E-02
6.1E-03
3.1E-02
6.1E-02
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.1E-02
0.41
2.1
-
1.7E-02
8.3E-02
0.17
Central Tendency
0.31
3.1
15.3
0.31
5.6E-02
0.28
0.56
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
7.6E-03
7.6E-04
1.5E-04
-
6.9E-02
1.4E-02
6.9E-03
N/A2
Central Tendency
7.9E-04
7.9E-05
1.6E-05
7.9E-04
1.6E-02
3.1E-03
1.6E-03
Bold text/pink shading indicates MOE < benchmark MOE. Consistent PPE usage is not expected for this scenario and is only included as a "what-if' analysis for comparison purposes.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
2 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
Page 326 of 803
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757 Table 4-21. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial Uses) - Inhalation Modeling Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.0E-03
4.0E-02
0.20
6.3E-03
1.4E-03
7.2E-03
1.4E-02
N/A1
Central Tendency
1.2E-02
0.12
0.58
2.3E-02
4.3E-03
2.2E-02
4.3E-02
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.2
32.5
162.5
5.1
1.1
5.7
11.3
Central Tendency
9.4
93.7
468.3
18.8
3.4
17.0
34.0
Developmental -
Mortality
(Narotskv et al.. 1995)
10
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
144.1
23.2
116.1
232.2
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.85
8.5
42.5
1.3
0.37
1.9
3.7
Central Tendency
2.4
24.3
121.6
4.9
1.1
5.6
11.1
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
14.0
139.6
697.9
22.1
2.7
13.6
27.2
N/A1
Central Tendency
40.3
402.7
2,013.3
80.5
9.3
46.3
92.7
Kidney
(Maltoni et al.. 1986)
10
High End
3.8E-02
0.38
1.9
6.1E-02
5.2E-03
2.6E-02
5.2E-02
Central Tendency
0.11
1.1
5.5
0.22
1.8E-02
8.8E-02
0.18
Neurotoxicity
(Arito et al.. 1994)
300
High End
7.4
73.6
368.1
11.7
2.2
11.2
22.4
Central Tendency
21.2
212.4
1,061.9
42.5
7.6
38.1
76.3
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.77
7.7
38.3
1.2
0.25
1.3
2.5
Central Tendency
2.2
22.1
110.6
4.4
0.86
4.3
8.6
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
5.7E-03
5.7E-02
0.28
9.0E-03
1.8E-03
9.0E-03
1.8E-02
Central Tendency
1.6E-02
0.16
0.82
3.3E-02
6.1E-03
3.1E-02
6.1E-02
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.3E-02
0.43
2.1
6.7E-02
1.7E-02
8.3E-02
0.17
Central Tendency
0.12
1.2
6.1
0.25
5.6E-02
0.28
0.56
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
5.8E-03
5.8E-04
1.2E-04
3.6E-03
6.9E-02
1.4E-02
6.9E-03
N/A1
Central Tendency
1.8E-03
1.8E-04
3.7E-05
9.2E-04
1.6E-02
3.1E-03
1.6E-03
Bold text/pink shading indicates MOE < benchmark MOE. Consistent PPE usage is not expected for this scenario and is only included as a "what-if' analysis for comparison purposes.
1 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
758
Page 327 of 803
-------
759
760
761
762
763
764
765
766
767
768
769
770
111
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
MOE calculations for Spot Cleaning and Wipe Cleaning utilized both monitoring and modeling inhalation exposure data (with dermal
modeling). This data also applies to the exposure scenario of Other Commercial Uses. Results are presented in Table 4-20 and Table 4-21.
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 based on monitoring data, therefore central tendency worker estimates were applied as an approximation of likely ONU exposures.
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 congenital heart
defects at both exposure levels via inhalation and for multiple endpoints via the dermal route even when assuming the highest plausible APF
and 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. EPA is unable to estimate ONU exposures separately from workers
based on monitoring data, therefore central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 multiple endpoints at both
exposure levels via both inhalation and dermal 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 dermal routes. EPA is unable to estimate ONU exposures separately from
workers based on monitoring data, therefore central tendency worker estimates were applied as an approximation of likely ONU exposures.
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 presenting risk estimates for respiratory protection up to APF = 50 as a what-if scenario, however EPA believes that small commercial
facilities performing spot cleaning, wipe cleaning, and other related commercial uses are unlikely to have a respiratory protection program or
regularly employ dermal protection. Therefore, the use of respirators or gloves is unlikely for workers in these facilities.
Page 328 of 803
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795 Table 4-22. Occupational Risk Estimation - Formulation of Aerosol and Non-Aerosol Products
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
9.7E-03
9.7E-02
0.49
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
22.4
224.3
1,121.3
22.4
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
7.9
78.9
394.7
-
1.8
8.9
17.8
35.6
Central Tendency
18,182.7
181,827.5
909,137.3
18,182.7
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
60.5
605.3
3,026.3
-
12.2
60.8
121.5
243.0
Central Tendency
139,401.1
1,394,010.5
6,970,052.6
139,401.1
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
2.1
20.5
102.6
-
0.58
2.9
5.8
11.6
Central Tendency
4,727.5
47,275.1
236,375.7
4727.5
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
35.0
349.6
1,748.2
-
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
Kidney
(Maltoni et al.. 1986)
10
High End
9.6E-02
0.96
4.8
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
221.2
2,212.2
11,061.2
221.2
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
18.4
184.4
922.1
-
4.1
20.6
41.2
82.4
Central Tendency
42,474.9
424,748.9
2,123,744.7
42,474.9
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
1.9
19.2
96.1
-
0.46
2.3
4.6
9.2
Central Tendency
4,424.5
44,244.7
221,223.4
4,424.5
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
1.4E-02
0.14
0.71
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
32.7
327.4
1,637.1
32.7
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
0.11
1.1
5.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
244.8
2,448.2
12,241.0
244.8
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
2.9E-03
2.9E-04
5.9E-05
-
3.8E-02
7.5E-03
3.8E-03
1.9E-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 < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
796
Page 329 of 803
-------
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
826
MOE results for Formulation of Aerosol and Non-Aerosol Products utilized monitoring inhalation exposure data (with dermal modeling) and
are presented in Table 4-22.
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 central tendency worker estimates were applied as an approximation of likely ONU exposures. MOEs were below the
benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE for congenital heart
defects at high-end inhalation exposure even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for
congenital heart defects at both dermal exposure levels and for multiple endpoints at high-end dermal exposures 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. Risk estimates were above the
benchmark at both dermal exposure levels. Risk estimates were not above the benchmark for high-end 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.
Page 330 of 803
-------
827 Table 4-23. Occupational Risk Estimation - Repackaging
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
9.7E-03
9.7E-02
0.49
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
22.4
224.3
1,121.3
22.4
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
7.9
78.9
394.7
-
1.8
8.9
17.8
35.6
Central Tendency
18,182.7
181,827.5
909,137.3
18,182.7
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
60.5
605.3
3,026.3
-
12.2
60.8
121.5
243.0
Central Tendency
139,401.1
1,394,010.5
6,970,052.6
139,401.1
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
2.1
20.5
102.6
-
0.58
2.9
5.8
11.6
Central Tendency
4,727.5
47,275.1
236,375.7
4,727.5
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
35.0
349.6
1,748.2
-
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
Kidney
(Maltoni et al.. 1986)
10
High End
9.6E-02
0.96
4.8
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
221.2
2,212.2
11,061.2
221.2
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
18.4
184.4
922.1
-
4.1
20.6
41.2
82.4
Central Tendency
42,474.9
424,748.9
2,123,744.7
42,474.9
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
1.9
19.2
96.1
-
0.46
2.3
4.6
9.2
Central Tendency
4,424.5
44,244.7
221,223.4
4,424.5
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
1.4E-02
0.14
0.71
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
32.7
327.4
1,637.1
32.7
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
0.11
1.1
5.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
244.8
2,448.2
12,241.0
244.8
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
2.9E-03
2.9E-04
5.9E-05
-
3.8E-02
7.5E-03
3.8E-03
1.9E-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 < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
828
Page 331 of 803
-------
829
830
831
832
833
834
835
836
837
838
839
840
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
MOE results for Repackaging utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table 4-23.
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 central tendency worker estimates were applied as an approximation of likely ONU exposures. MOEs were below the
benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE for congenital heart
defects at high-end inhalation exposure even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for
congenital heart defects at both dermal exposure levels and for multiple endpoints at high-end dermal exposures 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. Risk estimates were above the
benchmark at both dermal exposure levels. Risk estimates were not above the benchmark for high-end 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.
Page 332 of 803
-------
866 Table 4-24. Occupational Risk Estimation - Metalworking Fluids - Inhalation Monitoring Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
1.5E-04
1.5E-03
7.4E-03
-
2.8E-03
1.4E-02
2.8E-02
5.6E-02
Central Tendency
1.6E-04
1.6E-03
8.0E-03
1.6E-04
8.5E-03
4.2E-02
8.5E-02
0.17
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.12
1.2
6.0
-
2.2
11.1
22.2
44.5
Central Tendency
0.13
1.3
6.5
0.13
6.7
33.4
66.7
133.4
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
0.92
9.2
45.8
-
15.2
75.9
151.9
303.8
Central Tendency
0.99
9.9
49.5
0.99
45.6
227.8
455.6
911.3
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
3.1E-02
0.31
1.6
-
0.73
3.6
7.3
14.5
Central Tendency
3.4E-02
0.34
1.7
3.4E-02
2.2
10.9
21.8
43.6
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
0.53
5.3
26.4
-
6.3
31.3
62.6
125.1
Central Tendency
0.57
5.7
28.6
0.57
18.8
93.8
187.7
375.4
Kidney
(Maltoni et al.. 1986)
10
High End
1.5E-03
1.5E-02
7.3E-02
-
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
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.28
2.8
13.9
-
5.1
25.7
51.5
103.0
Central Tendency
0.30
3.0
15.1
0.30
15.4
77.2
154.4
308.9
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
2.9E-02
0.29
1.5
-
0.58
2.9
5.8
11.6
Central Tendency
3.1E-02
0.31
1.6
3.1E-02
1.7
8.7
17.3
34.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
2.2E-04
2.2E-03
1.1E-02
-
4.1E-03
2.1E-02
4.1E-02
8.2E-02
Central Tendency
2.3E-04
2.3E-03
1.2E-02
2.3E-04
1.2E-02
6.2E-02
0.12
0.25
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
1.6E-03
1.6E-02
8.0E-02
-
3.8E-02
0.19
0.38
0.76
Central Tendency
1.7E-03
1.7E-02
8.7E-02
1.7E-03
0.11
0.57
1.1
2.3
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.19
1.9E-02
3.9E-03
-
3.0E-02
6.0E-03
3.0E-03
1.5E-03
Central Tendency
0.14
1.4E-02
2.8E-03
0.14
7.8E-03
1.6E-03
7.8E-04
3.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 333 of 803
-------
867 Table 4-25. Occupational Risk Estimation - Metalworking Fluids - Inhalation Modeling Data
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Modeling)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.3E-02
0.43
2.1
-
2.8E-03
1.4E-02
2.8E-02
5.6E-02
Central Tendency
0.16
1.6
7.9
0.16
8.5E-03
4.2E-02
8.5E-02
0.17
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
34.6
346.2
1,730.8
-
2.2
11.1
22.2
44.5
Central Tendency
128.6
1,285.7
6,428.6
128.6
6.7
33.4
66.7
133.4
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
265.4
2,653.8
13,269.2
-
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
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
9.0
90.0
450.0
-
0.73
3.6
7.3
14.5
Central Tendency
33.4
334.3
1,671.4
33.4
2.2
10.9
21.8
43.6
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
151.7
1,516.7
7,583.3
-
6.3
31.3
62.6
125.1
Central Tendency
568.8
5,687.5
28,437.5
568.8
18.8
93.8
187.7
375.4
Kidney
(Maltoni et al.. 1986)
10
High End
0.42
4.2
20.8
-
1.2E-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
Neurotoxicity
(Arito et al.. 1994)
300
High End
80.0
800.0
4,000.0
-
5.1
25.7
51.5
103.0
Central Tendency
300.0
3,000.0
15,000.0
300.0
15.4
77.2
154.4
308.9
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
8.3
83.3
416.7
-
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
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.2E-02
0.62
3.1
-
4.1E-03
2.1E-02
4.1E-02
8.2E-02
Central Tendency
0.23
2.3
11.6
0.23
1.2E-02
6.2E-02
0.12
0.25
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
0.47
4.7
23.3
-
3.8E-02
0.19
0.38
0.76
Central Tendency
1.7
17.3
86.6
1.7
0.11
0.57
1.1
2.3
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
6.6E-04
6.6E-05
1.3E-05
-
3.0E-02
6.0E-03
3.0E-03
1.5E-03
Central Tendency
1.3E-04
1.3E-05
2.6E-06
1.3E-04
7.8E-03
1.6E-03
7.8E-04
3.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
868
Page 334 of 803
-------
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
MOE calculations for Metalw or king Fluids utilized both monitoring and modeling inhalation exposure data (with dermal modeling). Results
are presented in Table 4-24 and Table 4-25.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints based on monitoring and for congenital heart defects 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 central tendency worker estimates were applied as an approximation of likely ONU
exposures. 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 congenital heart defects
based on modeling at both exposure levels via inhalation and for congenital heart defects 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 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 separately from workers, therefore central tendency worker estimates were applied as an approximation of likely ONU
exposures. 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 inhalation routes even
when assuming the 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 central tendency worker estimates were applied as an approximation of likely
ONU exposures. 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 based on monitoring data. For modeling data, risk estimates were not above the
benchmark at either inhalation exposure level when assuming APF =10, although risk estimates were above the benchmark via the dermal
route even when assuming the highest plausible glove PF protection.
Page 335 of 803
-------
902 Table 4-26. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Industrial Setting)
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
2.8E-04
2.8E-03
1.4E-02
1.1E-02
2.5E-03
1.3E-02
2.5E-02
5.0E-02
Central Tendency
2.4E-03
2.4E-02
0.12
1.2E-02
7.5E-03
3.8E-02
7.5E-02
0.15
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.23
2.3
11.4
9.0
2.0
9.9
19.8
39.5
Central Tendency
1.9
19.4
97.1
9.6
5.9
29.7
59.3
118.6
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
1.7
17.5
87.4
69.0
13.5
67.5
135.0
270.0
Central Tendency
14.9
148.8
744.1
73.3
40.5
202.5
405.0
810.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
5.9E-02
0.59
3.0
2.3
0.65
3.2
6.5
12.9
Central Tendency
0.50
5.0
25.2
2.5
1.9
9.7
19.4
38.8
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
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
Kidney
(Maltoni et al.. 1986)
10
High End
2.8E-03
2.8E-02
0.14
0.11
1.1E-02
5.3E-02
0.11
0.21
Central Tendency
2.4E-02
0.24
1.2
0.12
3.2E-02
0.16
0.32
0.63
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.53
5.3
26.6
21.0
4.6
22.9
45.8
91.5
Central Tendency
4.5
45.3
226.7
22.3
13.7
68.6
137.3
274.5
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
5.5E-02
0.55
2.8
2.2
0.51
2.6
5.1
10.3
Central Tendency
0.47
4.7
23.6
2.3
1.5
7.7
15.4
30.8
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
4.1E-04
4.1E-03
2.1E-02
1.6E-02
3.7E-03
1.8E-02
3.7E-02
7.3E-02
Central Tendency
3.5E-03
3.5E-02
0.17
1.7E-02
1.1E-02
5.5E-02
0.11
0.22
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
3.1E-03
3.1E-02
0.15
0.12
3.4E-02
0.17
0.34
0.68
Central Tendency
2.6E-02
0.26
1.3
0.13
0.10
0.51
1.0
2.0
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.10
1.0E-02
2.0E-03
2.6E-03
3.4E-02
6.8E-03
3.4E-03
1.7E-03
Central Tendency
9.3E-03
9.3E-04
1.9E-04
1.9E-03
8.7E-03
1.7E-03
8.7E-04
4.4E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
Page 336 of 803
-------
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
MOE results for Adhesives, Sealants, Paints, and Coatings (Industrial Setting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-26. Inhalation exposures are estimated to be identical for industrial and commercial workers.
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 congenital heart defects at both dermal exposure levels
and for multiple endpoints at high-end dermal exposures 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 337 of 803
-------
929 Table 4-27. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Commercial Setting)
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
2.8E-04
2.8E-03
1.4E-02
1.1E-02
1.6E-03
8.0E-03
1.6E-02
N/A1
Central Tendency
2.4E-03
2.4E-02
0.12
1.2E-02
4.8E-03
2.4E-02
4.8E-02
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
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
Developmental -
Mortality
(Narotskv et al.. 1995)
10
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
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
5.9E-02
0.59
3.0
2.3
0.41
2.1
4.1
Central Tendency
0.50
5.0
25.2
2.5
1.2
6.2
12.3
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
1.0
10.1
50.5
39.9
3.5
17.7
35.4
N/A1
Central Tendency
8.6
86.0
429.9
42.4
10.6
53.1
106.3
Kidney
(Maltoni et al.. 1986)
10
High End
2.8E-03
2.8E-02
0.14
0.11
6.7E-03
3.4E-02
6.7E-02
Central Tendency
2.4E-02
0.24
1.2
0.12
2.0E-02
0.10
0.20
Neurotoxicity
(Arito et al.. 1994)
300
High End
0.53
5.3
26.6
21.0
2.9
14.6
29.1
Central Tendency
4.5
45.3
226.7
22.3
8.7
43.7
87.4
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
5.5E-02
0.55
2.8
2.2
0.33
1.6
3.3
Central Tendency
0.47
4.7
23.6
2.3
0.98
4.9
9.8
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
4.1E-04
4.1E-03
2.1E-02
1.6E-02
2.3E-03
1.2E-02
2.3E-02
Central Tendency
3.5E-03
3.5E-02
0.17
1.7E-02
7.0E-03
3.5E-02
7.0E-02
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
3.1E-03
3.1E-02
0.15
0.12
2.2E-02
0.11
0.22
Central Tendency
2.6E-02
0.26
1.3
0.13
6.5E-02
0.32
0.65
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
0.10
1.0E-02
2.0E-03
2.6E-03
5.3E-02
1.1E-02
5.3E-03
N/A1
Central Tendency
9.3E-03
9.3E-04
1.9E-04
1.9E-03
1.4E-02
2.7E-03
1.4E-03
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
930
Page 338 of 803
-------
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
MOE results for Adhesives, Sealants, Paints, and Coatings (Commercial Setting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-27. 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 assuming the 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 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 339 of 803
-------
956 Table 4-28. Occupational Risk Estimation - Industrial Processing Aid (12 hr)
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
5.8E-04
5.8E-03
2.9E-02
2.5E-03
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
1.7E-03
1.7E-02
8.7E-02
5.6E-03
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
0.47
4.7
23.4
2.1
1.8
8.9
17.8
35.6
Central Tendency
1.4
14.1
70.6
4.6
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
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
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.12
1.2
6.1
0.54
0.58
2.9
5.8
11.6
Central Tendency
0.37
3.7
18.3
1.2
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
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
Kidney
(Maltoni et al.. 1986)
10
High End
5.7E-03
5.7E-02
0.28
2.5E-02
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
1.7E-02
0.17
0.86
5.6E-02
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
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
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.11
1.1
5.7
0.50
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
Developmental Toxicity
(Johnson et al.. 2003)
10
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-02
Central Tendency
2.5E-03
2.5E-02
0.13
8.2E-03
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
6.3E-03
6.3E-02
0.31
2.8E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
1.9E-02
0.19
0.94
6.1E-02
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
4.9E-02
4.9E-03
9.9E-04
1.1E-02
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
1.3E-02
1.3E-03
2.5E-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 highest PPE scenarios displayed are plausible for this exposure scenario.
957
Page 340 of 803
-------
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
MOE results for Industrial Processing Aid utilized 12hr monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-28.
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 congenital heart defects at both dermal exposure levels
and for multiple endpoints at high-end dermal exposures 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 341 of 803
-------
987 Table 4-29. Occupational Risk Estimation - Commercial Printing and Copying
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
5.3E-03
5.3E-02
0.26
-
4.1E-03
2.1E-02
4.1E-02
NA2
Central Tendency
0.13
1.3
6.5
0.13
1.2E-02
6.2E-02
0.12
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
4.3
42.9
214.7
-
3.2
16.2
32.4
Central Tendency
105.9
1,058.8
5,294.1
105.9
9.7
48.6
97.1
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
32.9
329.3
1,646.4
-
22.1
110.6
221.1
Central Tendency
811.8
8,117.6
40,588.2
811.8
66.3
331.7
663.4
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
1.1
11.2
55.8
-
1.1
5.3
10.6
Central Tendency
27.5
275.3
1,376.5
27.5
3.2
15.9
31.7
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
19.0
190.2
951.0
-
9.1
45.5
91.1
NA2
Central Tendency
468.9
4,689.2
23,445.9
468.9
27.3
136.6
273.3
Kidney
(Maltoni et al.. 1986)
10
High End
5.2E-02
0.52
2.6
-
1.7E-02
8.6E-02
0.17
Central Tendency
1.3
12.9
64.4
1.3
5.2E-02
0.26
0.52
Neurotoxicity
(Arito et al.. 1994)
300
High End
10.0
100.3
501.6
-
7.5
37.5
74.9
Central Tendency
247.3
2,473.4
12,367.1
247.3
22.5
112.4
224.8
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
1.0
10.5
52.3
-
0.84
4.2
8.4
Central Tendency
25.8
257.6
1,288.2
25.8
2.5
12.6
25.2
Developmental Toxicity
(¦Johnson et al.. 2003)
10
High End
7.7E-03
7.7E-02
0.39
-
6.0E-03
3.0E-02
6.0E-02
Central Tendency
0.19
1.9
9.5
0.19
1.8E-02
9.0E-02
0.18
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
5.8E-02
0.58
2.9
-
5.5E-02
0.28
0.55
Central Tendency
1.4
14.3
71.3
1.4
0.17
0.83
1.7
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
5.4E-03
5.4E-04
1.1E-04
-
2.1E-02
4.1E-03
2.1E-03
NA2
Central Tendency
1.7E-04
1.7E-05
3.4E-06
1.7E-04
5.3E-03
1.1E-03
5.3E-04
Bold text/pink shading indicates MOE < benchmark MOE. Consistent PPE usage is not expected for this scenario and is only included as a "what-if' analysis for comparison purposes.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
2 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
988
Page 342 of 803
-------
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
MOE results for Commercial Printing and Copying utilized monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-29.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE congenital heart defects 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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for congenital heart defects 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 central
tendency worker estimates were applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for
congenital heart defects via inhalation and for multiple endpoints via dermal exposure at both 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 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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. Risk estimates remained above the benchmark at high-end inhalation exposure
but were not above the benchmark at central tendency inhalation exposure when assuming APF = 10. Risk estimates remained above the
benchmark at both dermal exposure levels even when assuming the highest plausible glove PF protection.
PPE Considerations
EPA is presenting risk estimates for respiratory protection up to APF = 50 as a what-if scenario, however EPA believes that small commercial
facilities performing commercial printing and copying are unlikely to have a respiratory protection program. Therefore, the use of respirators is
unlikely for workers in these facilities.
Page 343 of 803
-------
1020 Table 4-30. Occupational Risk Estimation - Other Industrial Uses
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
4.5E-03
4.5E-02
0.23
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
9.7E-02
0.97
4.8
9.7E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.7
36.6
183.0
-
1.8
8.9
17.8
35.6
Central Tendency
78.3
782.6
3,913.0
78.3
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
28.1
280.6
1,403.0
-
12.2
60.8
121.5
243.0
Central Tendency
600.0
6,000.0
30,000.0
600.0
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
0.95
9.5
47.6
-
0.58
2.9
5.8
11.6
Central Tendency
20.3
203.5
1,017.4
20.3
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
16.2
162.1
810.5
-
5.0
25.0
50.1
100.1
Central Tendency
346.6
3,465.9
17,329.6
346.6
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.5E-02
0.45
2.2
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.95
9.5
47.6
0.95
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.5
85.5
427.5
-
4.1
20.6
41.2
82.4
Central Tendency
182.8
1,828.2
9,140.9
182.8
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
0.89
8.9
44.5
-
0.46
2.3
4.6
9.2
Central Tendency
19.0
190.4
952.2
19.0
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.6E-03
6.6E-02
0.33
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
0.14
1.4
7.0
0.14
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
4.9E-02
0.49
2.5
-
3.0E-02
0.15
0.30
0.61
Central Tendency
1.1
10.5
52.7
1.1
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
6.7E-03
6.7E-04
1.3E-04
-
3.8E-02
7.5E-03
3.8E-03
1.9E-03
Central Tendency
7.5E-04
7.5E-05
1.5E-05
7.5E-04
9.7E-03
1.9E-03
9.7E-04
4.9E-04
Bold text/pink shading indicates MOE < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers; central tendency worker estimates were applied as an approximation of likely ONU exposures.
Page 344 of 803
-------
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
MOE results for Other Industrial Uses utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table 4-30.
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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. MOEs remained below the benchmark MOE for congenital heart defects 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 central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 dermal routes. EPA is unable to estimate ONU exposures separately from workers, therefore central tendency worker estimates
were applied as an approximation of likely ONU exposures. 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 345 of 803
-------
1045 Table 4-31. Occupational Risk Estimation - Process Solvent Recycling and Worker Handling of Wastes
Endpoint
Benchmark
MOE
Exposure Level
Inhalation (Monitoring)
Dermal (Modeling)
No PPE
Worker MOE
APF = 10
Worker MOE
APF = 50
Worker MOE
No PPE
ONU MOE1
No PPE
Worker MOE
Glove PF=5
Worker MOE
Glove PF=10
Worker MOE
Glove PF=20
Worker MOE
ACUTE NON-CANCER
Developmental -
Congenital Heart Defects
(¦Johnson et al.. 2003)
10
High End
9.7E-03
9.7E-02
0.49
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
22.4
224.3
1,121.3
22.4
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
7.9
78.9
394.7
-
1.8
8.9
17.8
35.6
Central Tendency
18,182.7
181,827.5
909,137.3
18,182.7
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
60.5
605.3
3,026.3
-
12.2
60.8
121.5
243.0
Central Tendency
139,401.1
1,394,010.5
6,970,052.6
139,401.1
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
10
High End
2.1
20.5
102.6
-
0.58
2.9
5.8
11.6
Central Tendency
4,727.5
47,275.1
236,375.7
4,727.5
1.7
8.7
17.4
34.9
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
35.0
349.6
1,748.2
-
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
Kidney
(Maltoni et al.. 1986)
10
High End
9.6E-02
0.96
4.8
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
221.2
2,212.2
11,061.2
221.2
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
18.4
184.4
922.1
-
4.1
20.6
41.2
82.4
Central Tendency
42,474.9
424,748.9
2,123,744.7
42,474.9
12.4
61.8
123.5
247.1
Reproductive Toxicity
(Cilia et al.. 1996)
30
High End
1.9
19.2
96.1
-
0.46
2.3
4.6
9.2
Central Tendency
4,424.5
44,244.7
221,223.4
4,424.5
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
1.4E-02
0.14
0.71
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
32.7
327.4
1,637.1
32.7
9.9E-03
4.9E-02
9.9E-02
0.20
Immunotoxicity -
Autoimmunity
(Keil et al.. 2009)
30
High End
0.11
1.1
5.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
244.8
2,448.2
12,241.0
244.82
9.1E-02
0.46
0.91
1.8
LIFETIME CANCER RISK
Combined Cancer Risk -
Kidney, NHL, Liver
1 x 10"4
High End
2.9E-03
2.9E-04
5.9E-05
-
3.8E-02
7.5E-03
3.8E-03
1.9E-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 < benchmark MOE. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.
Page 346 of 803
-------
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
MOE results for Process Solvent Recycling and Worker Handling of Wastes utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-31.
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 central tendency worker estimates were applied as an approximation of likely ONU exposures. MOEs were below the
benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the benchmark MOE for congenital heart
defects at high-end inhalation exposure even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for
congenital heart defects at both dermal exposure levels and for multiple endpoints at high-end dermal exposures 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. 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 central tendency worker estimates were applied as an approximation of likely ONU exposures. Risk estimates were above the
benchmark at both dermal exposure levels. Risk estimates were not above the benchmark for high-end 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.
Page 347 of 803
-------
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
4,2.3 Risk Estimation for Consumer 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 2.3.2.5.1 for
more details on the characterization of consumer exposure and [CEMModeling Results and Risk
Estimates. Docket # EPA-HQ-QPPT-2019-0500/ for MOE estimates of all modeled scenarios.
As discussed in Section 2.3.2.2, in general, the frequency of product use was considered to be too low to
create chronic risk concerns. Although high-end frequencies of consumer use for a small percentage of
consumers are up to 50 times per year, available toxicological data is based on either single or
continuous TCE exposure and it is unknown whether these use patterns are expected to be clustered
(e.g., every day for several weeks) or intermittent (e.g., one time per week). There is uncertainty
regarding the extrapolation from continuous studies in animals to the case of repeated, intermittent human
exposures. Therefore, EPA cannot fully rule out that consumers at the high-end frequency of use could
possibly be at risk for chronic hazard effects, however it is expected to be unlikely based on the above
considerations. Therefore, based on reasonably available information, EPA did not develop risk estimates
for this population.
Page 348 of 803
-------
1127 Table 4-32. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Brake and Parts
1128 Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al.. 1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
6.4E-05
5.2E-02
0.40
3.7E-02
Bystander
2.2E-04
1.8E-01
1.4
5.8E-02
Moderate-
Intensity
User
User
4.1E-04
0.33
2.5
0.11
Bystander
1.6E-03
1.3
10a
0.43
Low-
Intensity
User
User
5.2E-03
4.2
32
1.4
Bystander
2.0E-02
17
127
5.4
Dermal Exposure (permeability method)
High-
Intensity
User
Adult (>21 years)
2.2E-04
0.18
1.20
5.8E-02
Children (16-20 years)
2.4E-04
0.19
1.29
6.2E-02
Children (11-15 years)
2.2E-04
0.17
1.18
5.6E-02
Moderate-
Intensity
User
Adult (>21 years)
3.0E-03
2.3
16
0.77
Children (16-20 years)
3.2E-03
2.5
17
0.82
Children (11-15 years)
2.9E-03
2.3
16
0.75
Low-
Intensity
User
Adult (>21 years)
0.13
106
722
35
Children (16-20 years)
0.14
113
771
37
Children (11-15 years)
0.13
103
705
34
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1129
1130 MOE results for Brake and Parts Cleaner are presented in Table 4-32.
1131
1132 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1133 low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
1134 endpoints and all age groups at both moderate and high-intensity exposure levels. MOEs for bystanders
1135 were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
1136 inhalation exposure levels.
Page 349 of 803
-------
1137 Table 4-33. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol
1138 Electronic Degreaser/Cleaner
Scenario
Consumer Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredrikssonetal.. 1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
9.8E-05
8.0E-02
0.61
2.6E-02
Bystander
4.9E-04
0.40
3.0
0.13
Moderate-
Intensity
User
User
2.3E-03
1.9
15
0.61
Bystander
1.3E-02
10
78
3.3
Low-
Intensity
User
User
6.7E-02
54
414
18
Bystander
0.34
277
2123
90
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
1.6E-03
1.2
8.3
0.40
Children (16-20 years)
1.7E-03
1.3
8.9
0.43
Children (11-15 years)
1.5E-03
1.2
8.2
0.39
Moderate-
Intensity
User
Adult (>21 years)
1.8E-02
14
98
4.7
Children (16-20 years)
1.9E-02
15
105
5.0
Children (11-15 years)
1.8E-02
14
96
4.6
Low-
Intensity
User
Adult (>21 years)
0.15
119
814
39
Children (16-20 years)
0.16
127
870
42
Children (11-15 years)
0.15
117
796
38
1139
1140 MOE results for Aerosol Electronic Degreaser/Cleaner are presented in Table 4-33.
1141
1142 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1143 low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
1144 endpoints and all age groups at both moderate and high-intensity exposure levels. MOEs for bystanders
1145 were below the benchmark MOE for multiple endpoints at high and medium-intensity exposure levels.
1146
1147
1148
1149
1150
Page 350 of 803
-------
1151 Table 4-34. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
1152 Degreaser/Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
1.0E-04
8.3E-02
0.64
2.7E-02
Bystander
5.1E-04
0.41
3.2
0.13
Moderate-
Intensity
User
User
1.6E-03
1.3
9.9
0.42
Bystander
8.5E-03
6.9
53
2.2
Low-
Intensity
User
User
2.1E-02
17
132
5.6
Bystander
0.11
88
674
29
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
9.9E-04
0.78
5.3
0.26
Children (16-20 years)
1.1E-03
0.84
5.7
0.27
Children (11-15 years)
9.7E-04
0.76
5.2
0.25
Moderate-
Intensity
User
Adult (>21 years)
1.5E-02
12
80
3.8
Children (16-20 years)
1.6E-02
13
86
4.1
Children (11-15 years)
1.5E-02
11
78
3.7
Low-
Intensity
User
Adult (>21 years)
5.9E-02
47
320
15
Children (16-20 years)
6.4E-02
50
342
16
Children (11-15 years)
5.8E-02
46
313
15
1153
1154 MOE results for Liquid Electronic Degreaser/Cleaner are presented in Table 4-34.
1155
1156 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1157 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1158 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1159 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1160 levels.
1161
Page 351 of 803
-------
1162 Table 4-35. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
1163 Degreaser/Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.3E-05
1.8E-02
0.14
6.0E-02
Bystander
7.9E-05
6.4E-02
0.49
2.1E-02
Moderate-
Intensity
User
User
9.0E-05
7.3E-02
0.56
2.4E-02
Bystander
3.6E-04
0.29
2.2
9.5E-02
Low-
Intensity
User
User
6.0E-04
0.48
3.7
0.16
Bystander
2.5E-03
2.0
15
0.65
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
2.4E-04
0.19
1.3
6.1E-02
Children (16-20 years)
2.5E-04
0.20
1.4
6.6E-02
Children (11-15 years)
2.3E-04
0.18
1.3
6.0E-02
Moderate-
Intensity
User
Adult (>21 years)
1.9E-03
1.5
10a
0.49
Children (16-20 years)
2.0E-03
1.6
11
0.52
Children (11-15 years)
1.9E-03
1.5
10a
0.48
Low-
Intensity
User
Adult (>21 years)
9.5E-03
7.5
51
2.5
Children (16-20 years)
1.0E-02
8.0
55
2.6
Children (11-15 years)
9.3E-03
7.3
50
2.4
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1164
1165 MOE results for Aerosol Spray Degreaser/Cleaner are presented in Table 4-35.
1166
1167 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1168 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1169 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1170 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1171 levels.
1172
Page 352 of 803
-------
1173 Table 4-36. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid
1174 Degreaser/Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.5E-05
2.0E-02
0.16
6.6E-03
Bystander
1.0E-04
8.3E-02
0.64
2.7E-02
Moderate-
Intensity
User
User
2.4E-04
0.19
1.5
6.2E-02
Bystander
1.2E-03
1.0
7.8
0.33
Low-
Intensity
User
User
1.4E-03
1.2
8.8
0.37
Bystander
7.6E-03
6.2
47
2.0
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
2.5E-04
0.20
1.3
6.4E-02
Children (16-20 years)
2.7E-04
0.21
1.4
6.8E-02
Children (11-15 years)
2.4E-04
0.19
1.3
6.3E-02
Moderate-
Intensity
User
Adult (>21 years)
2.0E-03
1.6
11
0.51
Children (16-20 years)
2.1E-03
1.7
11
0.55
Children (11-15 years)
1.9E-03
1.5
10a
0.50
Low-
Intensity
User
Adult (>21 years)
1.5E-02
12
80
3.8
Children (16-20 years)
1.6E-02
13
86
4.1
Children (11-15 years)
1.5E-02
11
78
3.8
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1175
1176 MOE results for Liquid Degreaser/Cleaner are presented in Table 4-36.
1177
1178 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1179 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1180 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1181 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1182 levels.
1183
Page 353 of 803
-------
1184 Table 4-37. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Gun
1185 Scrubber
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
5.0E-02
40
309
13
Bystander
0.20
164
1255
53
Moderate-
Intensity
User
User
4.7E-02
38
294
12
Bystander
0.25
202
1551
66
Low-
Intensity
User
User
8.1E-02
66
506
21
Bystander
0.44
354
2715
115
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
2.5E-04
0.19
1.3
6.4E-02
Children (16-20 years)
2.6E-04
0.21
1.4
6.8E-02
Children (11-15 years)
2.4E-04
0.19
1.3
6.2E-02
Moderate-
Intensity
User
Adult (>21 years)
2.0E-03
1.6
11
0.51
Children (16-20 years)
2.1E-03
1.7
11
0.54
Children (11-15 years)
1.9E-03
1.5
10a
0.50
Low-
Intensity
User
Adult (>21 years)
2.5E-02
19
133
6.4
Children (16-20 years)
2.6E-02
21
142
6.8
Children (11-15 years)
2.4E-02
19
130
6.2
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1186
1187 MOE results for Aerosol Gun Scrubber are presented in Table 4-37.
1188
1189 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1190 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1191 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1192 benchmark MOE for congenital heart defects at high, medium, and low-intensity user inhalation
1193 exposure levels.
Page 354 of 803
-------
1194 Table 4-38. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Gun
1195 Scrubber
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
5.8E-02
47
361
15
Bystander
0.24
191
1465
62
Moderate-
Intensity
User
User
5.5E-02
45
343
14
Bystander
0.29
236
1809
77
Low-
Intensity
User
User
5.9E-02
48
370
16
Bystander
0.30
247
1893
80
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
2.7E-04
0.21
1.4
6.9E-02
Children (16-20 years)
2.8E-04
0.22
1.5
7.3E-02
Children (11-15 years)
2.6E-04
0.21
1.4
6.7E-02
Moderate-
Intensity
User
Adult (>21 years)
2.1E-03
1.7
11
0.55
Children (16-20 years)
2.3E-03
1.8
12
0.59
Children (11-15 years)
2.1E-03
1.6
11
0.54
Low-
Intensity
User
Adult (>21 years)
1.6E-02
13
86
4.1
Children (16-20 years)
1.7E-02
13
92
4.4
Children (11-15 years)
1.6E-02
12
84
4.0
1196
1197 MOE results for Liquid Gun Scrubber are presented in Table 4-38.
1198
1199 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1200 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1201 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1202 benchmark MOE for congenital heart defects at high, medium, and low-intensity user inhalation
1203 exposure levels.
1204
1205
1206
Page 355 of 803
-------
1207 Table 4-39. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Mold Release
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.3E-04
0.18
1.4
5.9E-02
Bystander
1.1E-03
0.91
7.0
0.30
Moderate-
Intensity
User
User
2.1E-03
1.7
13
0.56
Bystander
1.1E-02
9.2
71
3.0
Low-
Intensity
User
User
2.1E-02
17
130
5.5
Bystander
0.11
87
667
28
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
2.4E-03
1.9
13
6.1E-01
Children (16-20 years)
2.5E-03
2.0
14
6.5E-01
Children (11-15 years)
2.3E-03
1.8
12
6.0E-01
Moderate-
Intensity
User
Adult (>21 years)
1.8E-02
14
98
4.7
Children (16-20 years)
1.9E-02
15
104
5.0
Children (11-15 years)
1.8E-02
14
96
4.6
Low-
Intensity
User
Adult (>21 years)
0.12
94
645
31
Children (16-20 years)
0.13
101
689
33
Children (11-15 years)
0.12
92
630
30
1208
1209 MOE results for Mold Release are presented in Table 4-39.
1210
1211 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1212 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1213 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1214 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1215 levels.
1216
1217
Page 356 of 803
-------
1218 Table 4-40. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Tire
9 Cleaner
Benchmark
10
100
10
10
Scenario
Consumer
Receptor
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.4E-04
0.19
1.5
6.2E-02
Bystander
5.4E-04
0.44
3.4
1.4E-02
Moderate-
Intensity
User
User
8.9E-04
0.72
5.5
0.23
Bystander
3.6E-03
2.9
22
0.94
Low-
Intensity
User
User
6.4E-03
5.2
40
1.7
Bystander
2.6E-02
21
164
6.9
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
1.1E-03
8.5E-01
5.8
2.8E-01
Children (16-20 years)
1.2E-03
9.1E-01
6.2
3.0E-01
Children (11-15 years)
1.1E-03
8.3E-01
5.7
2.7E-01
Moderate-
Intensity
User
Adult (>21 years)
4.3E-03
3.4
23
1.1
Children (16-20 years)
4.6E-03
3.6
25
1.2
Children (11-15 years)
4.2E-03
3.3
23
1.1
Low-
Intensity
User
Adult (>21 years)
1.9E-02
15
100
4.8
Children (16-20 years)
2.0E-02
16
107
5.1
Children (11-15 years)
1.8E-02
14
97
4.7
1220
1221 MOE results for Aerosol Tire Cleaner are presented in Table 4-40.
1222
1223 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1224 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1225 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1226 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1227 levels.
1228
Page 357 of 803
-------
1229 Table 4-41. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Tire
1230 Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
7.8E-05
6.3E-02
0.48
2.0E-02
Bystander
2.4E-04
0.20
1.5
6.4E-02
Moderate-
Intensity
User
User
4.0E-04
0.32
2.5
0.10
Bystander
1.6E-03
1.3
9.9
0.42
Low-
Intensity
User
User
2.0E-03
1.6
12
0.53
Bystander
8.3E-03
6.7
51
2.2
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
4.8E-04
0.38
2.6
0.12
Children (16-20 years)
5.2E-04
0.41
2.8
0.13
Children (11-15 years)
4.7E-04
0.37
2.6
0.12
Moderate-
Intensity
User
Adult (>21 years)
1.9E-03
1.5
10a
0.50
Children (16-20 years)
2.1E-03
1.6
11
0.53
Children (11-15 years)
1.9E-03
1.5
10a
0.49
Low-
Intensity
User
Adult (>21 years)
5.8E-03
4.6
31
1.5
Children (16-20 years)
6.2E-03
4.9
33
1.6
Children (11-15 years)
5.7E-03
4.5
31
1.5
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1231
1232 MOE results for Liquid Tire Cleaner are presented in Table 4-41.
1233
1234 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1235 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1236 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1237 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1238 levels.
1239
Page 358 of 803
-------
1240 Table 4-42. Consumer Risk Estimation - Lubricants and Greases - Tap and Die Fluid
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
2.5E-04
0.20
1.6
6.6E-02
Bystander
1.3E-03
1.0
7.8
3.3E-01
Moderate-
Intensity
User
User
2.4E-03
1.9
15
0.62
Bystander
1.3E-02
10
79
3.3
Low-
Intensity
User
User
1.4E-02
11
85
3.6
Bystander
7.0E-02
57
434
18
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
2.6E-03
2.1
14
0.68
Children (16-20 years)
2.8E-03
2.2
15
0.73
Children (11-15 years)
2.6E-03
2.0
14
0.67
Moderate-
Intensity
User
Adult (>21 years)
2.0E-02
16
109
5.2
Children (16-20 years)
2.2E-02
17
116
5.6
Children (11-15 years)
2.0E-02
16
106
5.1
Low-
Intensity
User
Adult (>21 years)
7.7E-02
61
416
20
Children (16-20 years)
8.3E-02
65
445
21
Children (11-15 years)
7.6E-02
60
407
19
1241
1242 MOE results for Tap and Die Fluid are presented in Table 4-42.
1243
1244 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1245 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1246 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1247 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1248 levels.
1249
Page 359 of 803
-------
1250 Table 4-43. Consumer Risk Estimation - Lubricants and Greases - Penetrating Lubricant
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
3.2E-04
0.26
2.0
8.3E-02
Bystander
1.6E-03
1.3
9.8
4.1E-01
Moderate-
Intensity
User
User
5.4E-03
4.4
33
1.4
Bystander
2.9E-02
23
179
7.6
Low-
Intensity
User
User
0.17
139
1065
45
Bystander
0.88
712
5460
231
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
3.3E-03
2.6
18
0.86
Children (16-20 years)
3.5E-03
2.8
19
0.91
Children (11-15 years)
3.2E-03
2.6
17
0.84
Moderate-
Intensity
User
Adult (>21 years)
4.6E-02
36
248
12
Children (16-20 years)
4.9E-02
39
265
13
Children (11-15 years)
4.5E-02
36
243
12
Low-
Intensity
User
Adult (>21 years)
0.97
766
5230
250
Children (16-20 years)
1.0
818
5589
267
Children (11-15 years)
0.95
748
5111
245
125
1252
1253
1254
1255
1256
1257
1258
1259
MOE results for Penetrating Lubricant are presented in Table 4-43.
MOEs for consumer users were below the benchmark MOE for for multiple endpoints at high and
medium-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 and medium-intensity inhalation exposure levels.
Page 360 of 803
-------
1260 Table 4-44. Consumer Risk Estimation - Adhesives and Sealants - Solvent-Based Adhesive and
Sealant
Benchmark
10
100
10
10
Scenario
Consumer
Receptor
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.2E-04
1.8E-01
1.4
5.8E-02
Bystander
8.9E-04
7.3E-01
5.6
0.24
Moderate-
Intensity
User
User
6.7E-03
5.4
41
1.8
Bystander
3.6E-02
29
222
9.4
Low-
Intensity
User
User
0.56
452
3462
146
Bystander
2.8
2300
17636
746
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
6.1E-04
0.48
3.3
0.16
Children (16-20 years)
6.5E-04
0.51
3.5
0.17
Children (11-15 years)
6.0E-04
0.47
3.2
0.15
Moderate-
Intensity
User
Adult (>21 years)
5.2E-03
4.1
28
1.3
Children (16-20 years)
5.6E-03
4.4
30
1.4
Children (11-15 years)
5.1E-03
4.0
28
1.3
Low-
Intensity
User
Adult (>21 years)
0.38
300
2049
98
Children (16-20 years)
0.41
321
2189
105
Children (11-15 years)
0.37
293
2002
96
1262
1263 MOE results for Solvent-Based Adhesive and Sealant are presented in Table 4-44.
1264
1265 MOEs for consumer users were below the benchmark MOE for for multiple endpoints at high and
1266 medium-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1267 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1268 benchmark MOE for multiple endpoints at high and medium-intensity inhalation exposure levels.
1269
Page 361 of 803
-------
1270 Table 4-45. Consumer Risk Estimation - Adhesives and Sealants - Mirror Edge Sealant
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
1.1E-03
0.90
6.9
0.29
Bystander
4.7E-03
3.8
29
1.2
Moderate-
Intensity
User
User
7.4E-03
6.0
46
2.0
Bystander
4.1E-02
33
254
11
Low-
Intensity
User
User
0.17
134
1028
43
Bystander
0.91
737
5651
239
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
8.1E-03
6.4
44
2.1
Children (16-20 years)
8.7E-03
6.8
47
2.2
Children (11-15 years)
7.9E-03
6.2
43
2.0
Moderate-
Intensity
User
Adult (>21 years)
3.7E-02
29
198
9.5
Children (16-20 years)
3.9E-02
31
211
10
Children (11-15 years)
3.6E-02
28
193
9.2
Low-
Intensity
User
Adult (>21 years)
2.8E-01
221
1512
72
Children (16-20 years)
3.0E-01
237
1616
77
Children (11-15 years)
2.7E-01
216
1478
71
1271
1272
1273
1274
1275
1276
1277
1278
MOE results for Mirror Edge Sealant are presented in Table 4-45.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high and medium-
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 and medium-intensity inhalation exposure levels.
Page 362 of 803
-------
1279 Table 4-46. Consumer Risk Estimation - Adhesives and Sealants - Tire Repair Cement / Sealer
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inha
ation Exposure
High-
Intensity
User
User
3.1E-04
0.25
1.9
8.2E-02
Bystander
9.7E-04
0.79
6.1
2.6E-01
Moderate-
Intensity
User
User
5.6E-03
4.5
35
1.5
Bystander
2.3E-02
18
141
6
Low-
Intensity
User
User
6.2E-02
50
385
16
Bystander
0.23
188
1444
61
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
5.8E-04
0.46
3.1
0.15
Children (16-20 years)
6.2E-04
0.49
3.3
0.16
Children (11-15 years)
5.6E-04
0.45
3.0
0.15
Moderate-
Intensity
User
Adult (>21 years)
3.1E-03
2.5
17
0.80
Children (16-20 years)
3.3E-03
2.6
18
0.86
Children (11-15 years)
3.0E-03
2.4
16
0.78
Low-
Intensity
User
Adult (>21 years)
2.9E-02
23
158
7.5
Children (16-20 years)
3.1E-02
25
168
8.1
Children (11-15 years)
2.9E-02
23
154
7.4
1280
1281 MOE results for Tire Repair Cement Sealer are presented in Table 4-46.
1282
1283 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high and medium -
1284 intensity exposure levels via both inhalation and at all exposure levels via dermal routes. Dermal MOEs
1285 were below the benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were
1286 below the benchmark MOE for multiple endpoints at high and medium-intensity inhalation exposure
1287 levels.
1288
1289
Page 363 of 803
-------
1290 Table 4-47. Consumer Risk Estimation - Cleaning and Furniture Care Products - Carpet Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
7.0E-05
5.7E-02
0.44
1.8E-02
Bystander
3.2E-04
0.26
2.0
8.4E-02
Moderate-
Intensity
User
User
5.8E-04
0.47
3.6
0.15
Bystander
2.9E-03
2.4
18
0.77
Low-
Intensity
User
User
3.4E-03
2.7
21
0.89
Bystander
1.6E-02
13
99
4.2
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
9.1E-04
0.72
4.9
0.24
Children (16-20 years)
9.8E-04
0.77
5.3
0.25
Children (11-15 years)
8.9E-04
0.70
4.8
0.23
Moderate-
Intensity
User
Adult (>21 years)
5.5E-03
4.3
30
1.4
Children (16-20 years)
5.9E-03
4.6
32
1.5
Children (11-15 years)
5.4E-03
4.2
29
1.4
Low-
Intensity
User
Adult (>21 years)
5.5E-02
43
295
14
Children (16-20 years)
5.9E-02
46
315
15
Children (11-15 years)
5.4E-02
42
289
14
1291
1292 MOE results for Carpet Cleaner are presented in Table 4-47.
1293
1294 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1295 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1296 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1297 benchmark MOE for multiple endpoints at high, medium, and low-intensity inhalation exposure levels.
1298
Page 364 of 803
-------
1299 Table 4-48. Consumer Risk Estimation - Cleaning and Furniture Care Products - Aerosol Spot
1300 Remover
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
2.2E-04
0.17
1.3
5.7E-02
Bystander
1.1E-03
0.87
6.7
0.28
Moderate-
Intensity
User
User
1.8E-03
1.5
11
0.48
Bystander
9.8E-03
8.0
61
2.6
Low-
Intensity
User
User
1.0E-02
8.5
65
2.7
Bystander
5.3E-02
43
332
14
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
3.1E-03
2.4
17
0.80
Children (16-20 years)
3.3E-03
2.6
18
0.85
Children (11-15 years)
3.0E-03
2.4
16
0.78
Moderate-
Intensity
User
Adult (>21 years)
1.9E-02
15
100
4.8
Children (16-20 years)
2.0E-02
16
107
5.1
Children (11-15 years)
1.8E-02
14
98
4.7
Low-
Intensity
User
Adult (>21 years)
0.19
146
998
48
Children (16-20 years)
0.20
156
1066
51
Children (11-15 years)
0.18
143
975
47
1301
1302 MOE results for Aerosol Spot Remover are presented in Table 4-48.
1303
1304 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1305 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1306 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1307 benchmark MOE for multiple endpoints at high, medium, and low-intensity inhalation exposure levels.
1308
Page 365 of 803
-------
1309 Table 4-49. Consumer Risk Estimation - Cleaning and Furniture Care Products - Liquid Spot
0 Remover
Benchmark
10
100
10
10
Scenario
Consumer
Receptor
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
9.3E-05
7.5E-02
0.58
2.4E-02
Bystander
4.6E-04
0.37
2.9
0.12
Moderate-
Intensity
User
User
7.8E-04
0.63
4.9
0.21
Bystander
4.2E-03
3.4
26
1.1
Low-
Intensity
User
User
6.8E-03
5.5
42
1.8
Bystander
3.4E-02
28
214
9.1
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
1.3E-03
1.0
7.2
0.34
Children (16-20 years)
1.4E-03
1.1
7.7
0.37
Children (11-15 years)
1.3E-03
1.0
7.0
0.34
Moderate-
Intensity
User
Adult (>21 years)
8.0E-03
6.3
43
2.1
Children (16-20 years)
8.5E-03
6.7
46
2.2
Children (11-15 years)
7.8E-03
6.2
42
2.0
Low-
Intensity
User
Adult (>21 years)
0.12
94
645
31
Children (16-20 years)
0.13
101
689
33
Children (11-15 years)
0.12
92
630
30
1
1312 MOE results for Liquid Spot Remover are presented in Table 4-49.
1313
1314 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1315 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1316 benchmark MOE for multiple endpoints and all age groups at high and medium-intensity exposure
1317 levels and for multiple age groups at all exposure levels. MOEs for bystanders were below the
1318 benchmark MOE for multiple endpoints at high, medium, and low-intensity inhalation exposure levels.
1319
Page 366 of 803
-------
1320 Table 4-50. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and
1321 Finishing Spray Coatings
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
4.0E-04
0.32
2.5
0.10
Bystander
1.6E-03
1.3
10a
0.43
Moderate-
Intensity
User
User
2.5E-03
2.0
15
0.65
Bystander
1.3E-02
11
83
3.5
Low-
Intensity
User
User
1.3E-02
10
79
3.4
Bystander
6.5E-02
53
407
17
Dermal Exposure (Fraction Absorbed Method)
High-
Intensity
User
Adult (>21 years)
9.4E-03
7.4
51
2.4
Children (16-20 years)
1.0E-02
7.9
54
2.6
Children (11-15 years)
9.2E-03
7.3
50
2.4
Moderate-
Intensity
User
Adult (>21 years)
3.7E-02
29
199
9.5
Children (16-20 years)
4.0E-02
31
213
10a
Children (11-15 years)
3.6E-02
29
195
9.3
Low-
Intensity
User
Adult (>21 years)
0.33
257
1758
84
Children (16-20 years)
0.35
275
1879
90
Children (11-15 years)
0.32
252
1718
82
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1322
1323 MOE results for Fixatives and Finishing Spray Coatings are presented in Table 4-50.
1324
1325 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1326 low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
1327 endpoints and all age groups at high and medium-intensity exposure levels. MOEs for bystanders were
1328 below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation
1329 exposure levels.
1330
Page 367 of 803
-------
1331 Table 4-51. Consumer Risk Estimation - Apparel and Footwear Care Products - Shoe Polish
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
1.3E-03
1.1
8.3
0.35
Bystander
5.5E-03
4.4
34
1.4
Moderate-
Intensity
User
User
1.1E-02
8.8
67
2.9
Bystander
5.9E-02
48
366
15
Low-
Intensity
User
User
6.2E-02
50
386
16
Bystander
3.2E-01
258
1977
84
Dermal Exposure (Permeability Method)
High-
Intensity
User
Adult (>21 years)
1.4E-02
11
76
3.6
Children (16-20 years)
1.5E-02
12
81
3.9
Children (11-15 years)
1.4E-02
11
74
3.6
Moderate-
Intensity
User
Adult (>21 years)
8.5E-02
67
457
22
Children (16-20 years)
9.1E-02
71
488
23
Children (11-15 years)
8.3E-02
65
446
21
Low-
Intensity
User
Adult (>21 years)
0.85
669
4567
219
Children (16-20 years)
0.91
715
4880
234
Children (11-15 years)
0.83
654
4463
214
1332
1333
1334
1335
1336
1337
1338
1339
1340
MOE results for Shoe Polish are presented in Table 4-51.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
endpoints and all age groups at high and medium-intensity exposure levels. MOEs for bystanders were
below the benchmark MOE for multiple endpoints for high and medium-intensity inhalation exposure
levels.
Page 368 of 803
-------
1341 Table 4-52. Consumer Risk Estimation - Other Consumer Uses - Fabric Spray
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inha
ation Exposure
High-
Intensity
User
User
2.8E-04
0.23
1.7
7.3E-02
Bystander
1.1E-03
0.92
7.1
0.30
Moderate-
Intensity
User
User
1.7E-03
1.3
10a
0.44
Bystander
8.9E-03
7.2
55
2.3
Low-
Intensity
User
User
7.9E-03
6.4
49
2.1
Bystander
4.0E-02
33
251
11
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
8.1E-03
6.4
44
2.1
Children (16-20 years)
8.7E-03
6.8
47
2.2
Children (11-15 years)
7.9E-03
6.2
43
2.0
Moderate-
Intensity
User
Adult (>21 years)
1.8E-02
14
98
4.7
Children (16-20 years)
1.9E-02
15
104
5.0
Children (11-15 years)
1.8E-02
14
95
4.6
Low-
Intensity
User
Adult (>21 years)
0.10
81
554
27
Children (16-20 years)
0.11
87
592
28
Children (11-15 years)
0.10
79
541
26
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1342
1343
1344
1345
1346
1347
1348
1349
MOE results for Fabric Spray are presented in Table 4-52.
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 inhalation exposure levels.
Page 369 of 803
-------
1350 Table 4-53. Consumer Risk Estimation - Other Consumer Uses - Film Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inha
ation Exposure
High-
Intensity
User
User
5.8E-05
4.7E-02
0.36
1.5E-02
Bystander
2.4E-04
0.19
1.5
6.2E-02
Moderate-
Intensity
User
User
3.6E-04
0.29
2.2
9.4E-02
Bystander
1.9E-03
1.6
12
0.51
Low-
Intensity
User
User
1.9E-03
1.5
12
0.49
Bystander
9.5E-03
7.7
59
2.5
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
1.4E-03
1.1
7.4
0.35
Children (16-20 years)
1.5E-03
1.2
7.9
0.38
Children (11-15 years)
1.3E-03
1.1
7.2
0.34
Moderate-
Intensity
User
Adult (>21 years)
5.4E-03
4.2
29
1.4
Children (16-20 years)
5.7E-03
4.5
31
1.5
Children (11-15 years)
5.2E-03
4.1
28
1.4
Low-
Intensity
User
Adult (>21 years)
4.7E-02
37
255
12
Children (16-20 years)
5.1E-02
40
273
13
Children (11-15 years)
4.6E-02
36
249
12
1351
1352 MOE results for Film Cleaner are presented in Table 4-53.
1353
1354 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1355 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1356 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1357 benchmark MOE for multiple endpoints at high, medium, and low-intensity inhalation exposure levels.
1358
Page 370 of 803
-------
1359 Table 4-54. Consumer Risk Estimation - Other Consumer Uses - Hoof Polish
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selerade and
Gilmour. 2010)
Inha
ation Exposure
High-
Intensity
User
User
1.7E-03
1.4
10
0.44
Bystander
0.34
272
2084
88
Moderate-
Intensity
User
User
1.7E-02
14
106
4.5
Bystander
7.8
6307
48351
2045
Low-
Intensity
User
User
0.12
97
747
32
Bystander
48
38519
295309
12493
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
1.1E-02
8.8
60
2.9
Children (16-20 years)
1.2E-02
9.4
64
3.1
Children (11-15 years)
1.1E-02
8.6
59
2.8
Moderate-
Intensity
User
Adult (>21 years)
3.7E-02
29
199
9.5
Children (16-20 years)
4.0E-02
31
213
10a
Children (11-15 years)
3.6E-02
29
195
9.3
Low-
Intensity
User
Adult (>21 years)
0.33
257
1758
84
Children (16-20 years)
0.35
275
1879
90
Children (11-15 years)
0.32
252
1718
82
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1360
1361 MOE results for Hoof Polish are presented in Table 4-54.
1362
1363 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1364 low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
1365 endpoints and all age groups at high and medium-intensity exposure levels. MOEs for bystanders were
1366 below the benchmark MOE for multiple endpoints at high and medium-intensity inhalation exposure
1367 levels. MOEs for bystanders were not below the benchmark MOE for any endpoint at low-intensity
1368 inhalation exposure levels.
1369
Page 371 of 803
-------
1370 Table 4-55. Consumer Risk Estimation - Other Consumer Uses - Pepper Spray
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Immunosuppression
(Selsrade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
5.6E-02
45
346
15
Bystander
Not modeled due to simulated outdoor scenario - can be considered equal to user.
Moderate-
Intensity
User
User
0.11
90
692
29
Bystander
Not modeled due to simulated outdoor scenario - can be considered equal to user.
Low-
Intensity
User
User
0.21
169
1297
55
Bystander
Not modeled due to simulated outdoor scenario - can be considered equal to user.
Dermal Exposure (Absorption Fraction Method)
Single
Scenario
Adult (>21 years)
6.0E-02
48
325
16
Children (16-20 years)
6.4E-02
51
347
17
Children (11-15 years)
5.9E-02
46
317
15
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
MOE results for Pepper Spray are presented in Table 4-55.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high and medium-
intensity inhalation exposure levels, however MOEs were not below the benchmark for the best overall
endpoint of acute immunotoxicity. Dermal MOEs were below the benchmark MOE for multiple
endpoints and all age groups for the single scenario assessed, however MOEs were not below the
benchmark for the best overall endpoint of acute immunotoxicity. MOEs for bystanders were not
modeled because bystander exposure is considered equivalent to user exposure.
Page 372 of 803
-------
1386 Table 4-56. Consumer Risk Estimation - Other Consumer Uses - Toner Aid
Scenario
Consumer
Receptor
Benchmark
10
100
10
10
Developmental Effects -
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects -
Developmental
Neurotoxicity
(Fredriksson et al..
1993)
Developmental Effects -
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity -
Response to Infection
(Selerade and
Gilmour. 2010)
Inhalation Exposure
High-
Intensity
User
User
4.2E-04
0.34
2.6
0.11
Bystander
1.7E-03
1.4
11
0.45
Moderate-
Intensity
User
User
2.6E-03
2.1
16
0.68
Bystander
1.4E-02
11
88
3.7
Low-
Intensity
User
User
1.4E-02
11
84
3.6
Bystander
6.9E-02
56
431
18
Dermal Exposure (Absorption Fraction Method)
High-
Intensity
User
Adult (>21 years)
9.9E-03
7.8
54
2.6
Children (16-20 years)
1.1E-02
8.4
57
2.7
Children (11-15 years)
9.7E-03
7.7
52
2.5
Moderate-
Intensity
User
Adult (>21 years)
3.9E-02
31
211
10a
Children (16-20 years)
4.2E-02
33
225
11
Children (11-15 years)
3.8E-02
30
206
9.8
Low-
Intensity
User
Adult (>21 years)
0.34
272
1857
89
Children (16-20 years)
0.37
291
1984
95
Children (11-15 years)
0.34
266
1815
87
a If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1387
1388 MOE results for Toner Aid are presented in Table 4-56.
1389
1390 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1391 low-intensity inhalation exposure levels. Dermal MOEs were below the benchmark MOE for multiple
1392 endpoints and all age groups at high and medium-intensity exposure levels. MOEs for bystanders were
1393 below the benchmark MOE for multiple endpoints at high, medium, and low-intensity inhalation
1394 exposure levels.
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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-FAST, monitored data, and data from reasonably available literature to characterize
acute and chronic exposures of TCE to aquatic organisms. E-FAST estimates may underestimate
exposure to some degree, because release data used in E-FAST to estimate surface water concentrations
are based primarily on TRI and DMR reporting 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. DMR data are submitted by 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 may not be included in the DMR dataset.
E-FAST may also overestimate exposure to aquatic species, because TCE is a volatile chemical, and E-
FAST doesn't take volatilization or other post-release fate processes or downstream transport into
consideration; and, for static water bodies, E-FAST uses a dilution factor as low as one. 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-FAST 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.
Since E-FAST does not incorporate volatilization into its stream concentration estimates, volatilization
half-lives of TCE were estimated using EPISuite's Water Volatilization Program (WVOLWIN™) using
water depths, water velocities, and wind speeds representative of the two sites that showed exceedances
of the 788 and 920 |ig/L COCs (Praxair Technology Center in Tonawanda, NY and NASA Michoud in
New Orleans, LA; see Table 4-1). For the NY site, a 6-m depth, 0.9 m/s current velocity, and a 5 m/s
wind speed were applied. For the LA site, a 1.5-m depth, 3.09E-05 m/s current velocity, and 3.5 m/s
wind speed were applied; the current velocity for this site is based on the EPA/Office of Pesticides Index
Reservoir, which has a depth of 2.74 m, width of 82.2 m and flow of 25.01 nrVhr (Jones et ai. 1998).
Results predicted a half-life of about one day (26 hours) for the NY site's receiving water body and a
half-life exceeding 10 years for the LA site.
While the inability to consider fate or hydrologic transport characteristics is a limitation of the E-FAST
model, the effect of volatility on estimating instream concentrations is expected to be highly variable
and site-specific depending on stream flow and environmental conditions. For discharges to still,
shallow water bodies, E-FAST estimates are less likely to overestimate surface water concentrations, as
TCE is predicted to have a long half-life in such still water bodies. For discharges to faster-flowing,
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deeper water bodies, E-FAST estimates may inadequately reflect instream volatile losses expected
within the timeframe of one day. Therefore, the estimated concentrations provided are within the bounds
of variability and a reasonable estimation of actual instream concentrations, particularly for still or slow-
moving and shallow water bodies. Given this variation and the predicted half-life of TCE in flowing
water bodies, E-FAST surface water concentrations may best represent concentrations found at the point
of discharge. The farther from the facility, the more uncertainty, and the lower the confidence EPA has
in the concentration.
The reasonably available monitored data were 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 were collected only
in certain areas, and within a limited number of states in the U.S. There were no measurements
reasonably 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 reasonably 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 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 whether
these uncertainties overestimate or underestimate exposures. The range of air concentration estimates
from central tendency to high-end was generally not large {e.g., less than 20-fold for most exposure
scenarios). Because of this the results of risk characterization were generally not sensitive to the
individual estimates of the central tendency and high-end separately but rather were based on
considering both central tendency and high-end exposure estimates which increase the overall
confidence in the risk characterization.
Exposures for ONUs can vary substantially. EPA notes that ONUs are likely a heterogeneous population
of workers, and some could be exposed more than just occasionally to high concentrations. Most data
sources do not sufficiently describe the proximity of these employees to the 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 categorized as "occupational non-
user" have exposures similar to those in the "worker" category depending on their specific work activity
pattern. Therefore, in the absence of specific monitoring or modeling data, worker risk estimates were
applied to ONUs. In many instances, this is likely to overestimate exposures, although the central
tendency worker values may be a reasonable approximation of ONU estimates.
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Additionally, 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
following exposures during use. These sources may cause exposures to be overestimated.
Where data were not reasonably available, the modeling approaches used to estimate air concentrations
also involve uncertainties. Model parameter values did not all contain distributions known to represent
the modeled scenario. It is also uncertain whether the model equations generate results that represent
actual workplace air concentrations. It is unknown whether these uncertainties overestimate or
underestimate exposures.
Averaging Times. EPA cannot determine how accurately the assumptions of exposure frequencies
(days/yr exposed) and exposed working years may represent actual exposure frequencies and exposed
working years. For example, tenure is used to represent exposed working years, but many workers may
not be exposed during their entire tenure. It is unknown whether these uncertainties overestimate or
underestimate exposures, although the high-end values may result in overestimates when used in
combination with high-end values of other parameters.
See Section 2.3.1.3 for more details on uncertainties and assumptions underlying the occupational
exposure assessment.
Occluded Dermal Exposure
Occluded exposures were presented as a what-if scenario in Appendix H of [.Environmental Releases
and Occupational Exposure Assessment. Docket: EPA-i ¦2019-05001. Risks were not
calculated for these scenarios however because EPA does not know the likelihood or frequency of these
scenarios in the workplace. Occluded dermal exposures are likely to increase risks for workers
compared to "no-glove" scenarios as evaluated in this Risk Evaluation.
4.3,2.2 Consumer/Bystander Exposure Considerations
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
consumer products are not evaluated because as discussed in Section 2.3.2.2, in general the frequency of
product use was considered to be too low to create chronic risk concerns. Although high-end frequencies
of consumer use for a small percentage of consumers are up to 50 times per year, reasonably available
toxicological data is based on either single or continuous TCE exposure and it is unknown whether these
use patterns are expected to be clustered (e.g., every day for several weeks) or intermittent (e.g., one
time per week). There is uncertainty regarding the extrapolation from continuous studies in animals to
the case of repeated, intermittent human exposures. Therefore, EPA cannot fully rule out that consumers
at the high-end frequency of use could possibly be at risk for chronic hazard effects, however it is
expected to be unlikely based on these considerations. As discussed in Section 2.3.2.2.1, EPA also did
not assess background levels of TCE in indoor and outdoor air and may therefore be underestimating
consumer inhalation risks. However, these background exposures are likely significantly lower than the
assessed exposure estimates for each exposure scenario and would therefore be unlikely to drive risk
conclusions
The output of the consumer exposure 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, medium, and high values for highly influential
factors such as chemical mass and frequency/duration of product use, it likely captures the range of
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potential exposure levels although it does not necessarily enable characterization of the full probabilistic
distribution of all possible outcomes.
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.
EPA calculated inhalation risk estimates based on ambient air concentrations and did not derive
lifestage-specific internal doses. As stated in Section 4.4.1, EPA expects that the PBPK model and UFh
at least partially account for lifestage specific differences, however younger lifestages are likely exposed
to several fold higher internal dose of TCE compared to adults. Therefore, using air concentrations
across all lifestages may underestimate risk, especially for infant bystanders.
See Section 2.3.2.6 for more details on uncertainties and assumptions underlying the consumer exposure
assessment.
4.3.2.3 Dermal Absorption Considerations
The occupational and consumer assessment approaches utilize different models for estimating dermal
absorption. As discussed in Section 2.3.2.4.1, the occupational exposure assessment used a fractional
absorption model that accounts for evaporation of volatile chemicals such as TCE. In contrast, the
consumer assessment model varied based on whether unimpeded evaporation was expected. A
permeability/flux model was used for impeded evaporation and a fraction absorbed model was used
when evaporation was expected (Section 2.3.2.3.1). 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 likely 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 that is reasonably 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 exposure assessment since it is event based and does not require a duration input. In
contrast, for consumer applications there is reasonably available information for duration of use, hence
the CEM permeability model or the fraction absorbed model can be used for these exposure scenarios
with greater confidence. Overall, the models are considered appropriate for their respective uses based
on the reasonably available information.
4.3.2.4 Confidence in Risk Estimates
Occupational Exposure Scenarios
There is varying confidence in inhalation exposure estimates from different occupational risk scenarios,
ranging from low-to-medium to 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 2-26). 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).
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EPA acknowledges the uncertainty and lower confidence in applying worker estimates to represent
ONUs in the absence of reasonably available ONU data for certain OES. Therefore, EPA has low
confidence in risk estimates for ONUs based on this assumption. There is medium confidence in the
occupational dermal modeling approach, which was developed from a peer-reviewed publication
(Kasting and Miller. 2.006).
Consumer Exposure Scenarios
There is medium to high confidence in consumer inhalation exposure modeling (see Section 2.3.2.7),
however there is low to medium confidence in consumer dermal exposure modeling due to uncertainties
related to absorption (as discussed above) and assumptions regarding impeded vs unimpeded
evaporation for particular conditions of use.
Human Health Hazard
The human health database covers a wide range of endpoints, with most health effects supported by
animal, epidemiological, and mechanistic evidence. There is medium confidence in the integration of
human health data for acute non-cancer, medium to high confidence for cancer, and high confidence for
chronic non-cancer endpoints, although there is additional uncertainty in the dose-response analysis for
the congenital heart defects endpoint (see Section 3.2.6 for more details).
Risk Conclusions
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 high-
intensity 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 unlikely that any refinement of risk estimates would result in
variation of more than a few fold in either direction.
In considering risk estimates relative to the benchmark MOE/extra risk, 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 4.5.2.1 and 4.5.2.2). In accounting for the
totality of uncertainties, including confidence levels for each exposure scenario/COU, strength of the
human health hazard information, and range of risk estimates provided for the different aspects of the
Risk Evaluation relative to the benchmark, confidence in the risk estimates for each of the receptors and
exposure durations is as follows:
Occupational
Acute Non-Cancer Inhalation Occupational Risk (workers): Medium
Acute Non-Cancer Dermal Occupational Risk (workers): Medium
Acute Non-Cancer Inhalation Occupational Risk (ONUs): Medium (Low24 when based on central
tendency of workers without ONU-specific data)
24 EPA notes that while there is low confidence in the accuracy of the risk estimates due to low confidence in the exposure
estimates in these instances, the risk conclusions (i.e., risk estimate below or above benchmark) do not change if ONU
chronic exposure values are varied by lOx in either direction.
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Chronic Inhalation Non-Cancer Occupational Risk (workers): High
Chronic Dermal Non-Cancer Occupational Risk (workers): Medium-High
Chronic Inhalation Non-Cancer Occupational Risk (ONUs): Medium-High (Low24 when based on
central tendency of workers without ONU-specific data)
Lifetime Cancer Inhalation Occupational Risk (workers): Medium-High
Lifetime Cancer Dermal Occupational Risk (workers): Medium-High
Lifetime Cancer Inhalation Occupational Risk (ONUs): Medium-High (Low24 when based on central
tendency of workers without ONU-specific data)
Consumer
Acute Non-Cancer Inhalation Consumer Risk (users): Medium-High
Acute Non-Cancer Dermal Consumer Risk (users): Low-Medium
Acute Non-Cancer Inhalation Consumer Risk (bystanders): Medium-High
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4.4 Other Risk Related Considerations
4.4.1 Potentially Exposed or 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 [Occupational Risk Estimate
Calculator. Docket: EPA-HQ-QPPT-2019-05001 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 sexes 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 may not have been accounted for (Section 4.3.2.2). In order to
address increased internal dose among workers and ONUs compared to at-rest individuals due to
increased breathing rate, EPA used the PBPK model to derive occupational HECs for the best overall
acute and chronic non-cancer endpoints (Section 3.2.5.4.1). The use of HEC/HED99 values is expected to
account for the vast majority of physiological differences among individuals. The PBPK model does not
contain a fetal compartment (Section 3.2.2.5), therefore EPA conservatively assumed that maternal
internal dose was directly applicable to fetal exposure. While EPA did not assess risk for breast feeding
infants, evaluating developmental effects based on maternal internal dose would be protective of this
subpopulation.
EPA identified lifestage, sex, genetic polymorphisms, race/ethnicity, preexisting health status, and
lifestyle factors and nutrition status as factors affecting biological susceptibility. The use of HEC/HED99
POD values derived from relevant PBPK dose metrics accounts for the vast majority of toxicokinetic
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. Additionally, risk estimates
were provided for three developmental endpoints in order to account for the PESS group of pregnant
mothers and women of childbearing age. The (Setgrade and Gilmour. ) 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 at.. 2014; Yauck et al. 2004). While there are inconsistencies in
the data on cardiac malformations (Appendix F.3) and reduced confidence in the dose-response and
POD derivation for (Johnson et al... 2003). EPA inclusion of risk estimates for cardiac malformations
accounts for susceptible mothers (Jenkins et al.. 2007) and their offspring in addition to PESS groups
with other susceptibilities (e.g., diabetes, infection status, drug exposure, stress (Jenkins et al.. 2007).
and metabolic sensitivity due to increased enzymatic activity of cytochrome P450 2E1 (CYP2E1)
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(Cichocki et al. I *, s \ N \ 201 t^)). An individual may be a member of multiple PESS groups
(perhaps including both exposure and biological susceptibility considerations) and may exhibit multiple
concurrent susceptibilities.
EPA acknowledges that it was unable to directly account for all possible PESS considerations and
subpopulations in the risk estimates. It is unknown whether the HEC/HED99 and remaining 3x UFh for
toxicodynamic variability sufficiently accounts for the full breadth of human responses, and
subpopulations with particular disease states or genetic predispositions may fall outside of the range
covered by this UF. Additionally, EPA was unable to precisely model developmental effects due to the
lack of a fetal compartment in the model, requiring the use of default adult female parameters as a
surrogate. As previously discussed, EPA also only considered acute effects from consumer exposure.
While typical use patterns are unlikely to result in any chronic effects for the vast majority of
consumers, EPA cannot rule out that consumers at very high frequencies of use may be at risk for
chronic hazards, especially if those consumers also exhibit biological susceptibilities. EPA also cannot
rule out that certain subpopulations, whether due to very elevated exposure or biological susceptibility,
may be at risk for hazards that were not fully supported by the weight of evidence or could not be
quantified. However, in these circumstances EPA assumes that these effects are likely to occur at a
higher dose than more sensitive endpoints that were accounted for by risk estimates. In order to account
for these uncertainties, EPA's decisions for unreasonable risk are based on high-end exposure estimates
(see below in Section 4.4.2).
4.4.2 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 combined exposures to an
individual from a single chemical substance across multiple routes and across multiple pathways'' (40
CFR Section 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. Additionally, without a PBPK model containing a dermal compartment to account for
toxicokinetic processes the true internal dose for any given exposure cannot be determined. Aggregating
exposures could inappropriately overestimate total exposure, as simply adding exposures from different
routes without an available PBPK model for those routes would compound uncertainties. It is unknown
whether exposures from multiple routes would act in an additive fashion, and saturation of metabolic
processes at elevated exposures may result in a steady-state that hampers subsequent absorption relative
to excretive processes. Conversely, not aggregating exposures in any manner may potentially
underestimate total exposure for a given individual. EPA also did not consider aggregate exposure
among individuals who may be exposed both in an occupational and consumer context or incorporate
background general population exposures because there is insufficient information reasonably available
as to the likelihood of this scenario or the relative distribution of exposures from each pathway. Risk is
likely to be elevated for individuals who experience TCE exposure in multiple contexts.
EPA defines sentinel exposure as "the exposure to a single chemical substance that represents the
plausible upper bound of exposure relative to all other exposures within a broad category of similar or
related exposures'' (40 CFR Section 702.33). In 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 exposure potential, or consumers who have
higher exposure potential (e.g., those involved with do-it-yourself projects) or certain physical factors
like body weight or skin surface area exposed. In an attempt to assess "upper bound" exposures, EPA
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1750
1751
1752
1753
characterized high-end exposures in evaluating exposure using both monitoring data and modeling
approaches. As stated in [.Environmental Releases and Occupational Exposure Assessment. Docket:
EPA-HQ-OPPT-2019-050Q\ 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. 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 consumer and bystander exposures, EPA
characterized sentinel exposure through a "high-intensity use" category based on both product and user-
specific factors. In cases where sentinel exposures result in MOEs greater than the benchmark or cancer
risk lower than the benchmark {i.e., risks were not identified), EPA did no further analysis because
sentinel exposures represent the worst-case scenario. EPA's decisions for unreasonable risk are based on
high-end exposure estimates to capture individuals with sentinel exposure. In this Risk Evaluation, the
EPA considered sentinel exposure in the form of a high-end scenarios for occupational exposure
resulting from dermal and inhalation exposures, as these exposure routes are the most likely to result in
the highest exposure given the details of the manufacturing process and the potential exposure scenarios
discussed above. The calculation for dermal exposure is especially conservative given that it assumes
full contact/immersion.
Page 382 of 803
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1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
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 4-57). These facilities
had an acute RQ > 1, or a chronic RQ > 1 and 20 days or more of exceedance for the chronic COC.
Risk to the most sensitive species of algae were identified near 15 facilities with 20 days or more of
exceedances (10 of these facilities had 100 days or more of exceedances); however, as a taxonomic
group, results do not indicate risk for 95% of algae species. In other words, these facilities had RQs > 1
using the algae COC of 3 ppb but RQs < 1 using the algae HCos of 14,400 ppb. These facilities are not
included in Table 4-57 in this section, but are in Table 4-1 for reference.
EPA did not identify risks to aquatic organisms like fish and invertebrates in the ambient water where
monitored data were reasonably available. Monitored data from the Water Quality Portal and the
reasonably available literature show no exceedances of the acute COC or chronic COC in ambient water.
Monitored data from literature showed some exceedances of the algae COC of 3 ppb in ambient water;
however, the data show no exceedances of the algae COC of 14,400 ppb.
Near-facility monitoring data report levels of TCE ranging from 0.4 to 447 |ig/L (U.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
encompass the range of the modeled estimates across all OES from E-FAST.
Processing as a Reactant:
One out of 443 facilities (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 an acute RQ of 1.50 and a chronic
RQs of 3.81 with 20 days of exceedance. In other words, the surface water concentration modeled for
this facility was 1.5 times higher than the COC for acute exposures and 3.81 times higher than the COC
for chronic exposures. Therefore, EPA identified risk to aquatic organisms at this site for acute and
chronic exposures to TCE.
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 organisms. U.S. NASA Michoud Assembly Facility in New Orleans, LA had an acute RQ
> 1 (RQ = 4.97). In other words, the surface water concentration modeled for this facility was 4.97 times
higher than the acute COC of 2,000 ppb, indicating risk to aquatic organisms from acute exposures. The
facility also had a chronic RQ of 12.61 with 20 days of exceedance. This means the surface water
concentration was 12.61 higher than the COC of 788 for 20 days. Therefore, EPA identified risk to
aquatic organisms at this site for acute and chronic exposures to TCE.
Page 383 of 803
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Table 4-57. Facilities with Risk from Acute or C
ironic Exposure for Aq
uatic Organisms (RQs > 1 in bolt
)
Name. 1.nullum. and
II) ill" \cli\e Releaser
l';icilil>
Release
Media
Modeled 1 acilils or
IndusiiA Secliii" mi
i r\s r
i:i \sr
Waleibods
T\ pe
1 )a> s of
Release
Release
(ku da> i
"Oil)
S\V(
i pph)
('()(' T\pc
COC
ipphi
1 )a\ s of
1 Aceedance
(da>s \ean
Risk
Oikiiieiil
OES: Processing as a Reactant
Acute
2,000
NA
0.08
Praxair Technology
Center,
Tonawanda, NY
NPDES: NY0000281
350
0.00169
169
Chronic
788
0
0.21
Algae
3
350
56.33
Surface
NPDES NY0000281
Still body
Algae (HC05)
14,400
0
0.01
Water
Acute
2,000
NA
1.50
20
0.03
3000
Chronic
788
20
3.81
Algae
3
20
1,000.00
Algae (HC05)
14,400
0
0.21
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
Acute
2,000
NA
0.38
US Nasa Michoud
260
1.96
765.63
Chronic
788
0
0.97
Assembly Facility,
Algae (COC)
3
260
255.21
New Orleans, LA
Surface
Surrogate NPDES
Still body
Algae (HC05)
14,400
0
0.05
NPDES: LA0052256
Water
LA0003280
Acute
2,000
NA
4.97
20
25.44
9937.5
Chronic
788
20
12.61
Algae
3
20
3,312.50
Algae (HC05)
14,400
0
0.69
a. Facilities actively releasing trichloroethylene were identified via DMR, TRI, and CDR databases for 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 wastewater treatment removal rate of 81% is applied to all indirect releases, i.e., volumes characterized as being transferred off-site for
treatment at a water treatment facility prior to discharge to surface water.
c. If a valid NPDES of the direct or indirect releaser was not reasonably available in EFAST, the release was modeled using either a surrogate representative facility in
EFAST (based on location) or a representative generic industry sector. The name of the indirect releaser 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 maximum days of release per year expected. For direct releasing facilities, a minimum of 20 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 concentration was reported in place of the 7Q10 SWC.
h. To determine 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 concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero
1795
Page 384 of 803
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1796 EPA identified risks to sediment organisms near the same two facilities, one open-top vapor degreasing
1797 facility and one facility that processes TCE as a reactant. Table 4-58 shows an RQ from acute exposure
1798 near Praxair Technology Center at 1.5 and an RQ from chronic exposure at 3.26 with 20 days of
1799 exceedance for aquatic invertebrates. Table 4-58 also shows an RQ from acute exposure near US NASA
1800 Michoud Assembly Facility at 4.97 and an RQ from chronic exposure at 10.8 with 20 days of
1801 exceedance for aquatic invertebrates (Table 4-58).
1802
1803 As stated in Section 4.1.3, in ambient water, both acute and chronic exposures to TCE are less than the
1804 COC (RQs < 0). More specifically, RQs for sediment organisms are between 0.00 and 0.02 based on the
1805 highest ambient surface water concentration of 17.3 ppb from acute or chronic exposures.
Page 385 of 803
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1806 Table 4-58. Facilities with Risk from Acute or Chronic Exposure for Sediment Organisms (RQs > 1 in bold)
Name. 1 .ivauon. and ID of
\cli\e Releaser 1 acilils
Release
Media
Modeled I'acihn or
Indusm Secliii'iii
1 1 AS 1
I I \ST
Waleihods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
"HI"
SWC
(pphi
( ()( l \pe
('()('
ipph)
1 )a\ s til'
1 Aceedance
(da>s \ean
Risk
nikHieiii
OES: Processing as a Reactant
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
Surface
Water
NPDES NY0000281
Still body
350
0.00169
169
Acute (HC05)
2,000
NA
0.08
Chronic (ChV)
920
0
0.18
20
0.03
3000
Acute (HC05)
2,000
NA
1.50
Chronic (ChV)
920
20
3.26
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
US Nasa Michoud Assembly
Facility,
New Orleans, LA
NPDES: LA0052256
Surface
Water
Surrogate NPDES
LA0003280
Still body
260
1.96
765.63
Acute (HC05)
2,000
NA
0.38
Chronic (ChV)
920
0
0.83
20
25.44
9937.5
Acute (HC05)
2,000
NA
4.97
Chronic
920
20
10.8
a. Facilities actively releasing trichloroethylene were identified via DMR, TRI, and CDR databases for 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 wastewater treatment removal rate of 81% is applied to all indirect releases, i.e., volumes characterized as being transferred off-site for treatment at a water
treatment facility prior to discharge to surface water.
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 representative generic industry sector. The name of the indirect releaser 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 maximum days of release per year expected. For direct releasing facilities, a minimum of 20 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 concentration was reported in place of the 7Q10 SWC.
h.To determine 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 concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero.
1807
Page 386 of 803
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1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
4.5.2 Human Health Risk Conclusions
4.5.2.1 Summary of Risk Estimates for Workers and ONUs
Table 4-59 summarizes the representative risk estimates for inhalation and dermal 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 highlighted by bolding the
number and shading the cell in gray. When both monitoring and modeling inhalation exposures were
available, EPA presented the most reliable data source in the table. The occupational exposure
assessment and risk characterization are described in more detail in Sections 2.3.1 and 4.2.2,
respectively. Specific links to the relevant risk characterization sections are listed in Table 4-59 in the
Occupational Exposure Scenario column.
The risk summary below is based on the most robust and well-supported PODs selected from among the
most sensitive acute and chronic non-cancer endpoints, as well as cancer. EPA selected
immunosuppression (S el grade and Gilmour. ) as the best overall representative acute endpoint, and
autoimmunity from the immunotoxicity domain (Keil et al. 2009) was selected to best represent chronic
exposure based on being both robust and sensitive. While some other endpoints present lower PODs
(developmental neurotoxicity from Fredriksson et al.. 1993; congenital heart malformations from
Johnson et al.. 2003). there is lower confidence in the dose-response and extrapolation of results from
those studies (Section 3.2.6.1.1) resulting in increased uncertainty surrounding the precision of the
derived PODs for those endpoints. Therefore, EPA concluded that these were the best overall non-
cancer endpoints for use in Risk Evaluation under TSCA, based on the best available science and weight
of scientific evidence (Section 3.2.5.4.1). Occupational-adjusted PODs for these endpoints (Table 3-16)
were used in estimating occupational risks. For the majority of exposure scenarios, risks were identified
for multiple endpoints in both acute and chronic exposure scenarios, however risk estimates are only
summarized for these particular endpoints. 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 {i.e., if risks do not exceed the benchmark for APF 10
and above, the risk estimate for APF 10 is shown).
Inhalation Exposure
For acute and chronic exposures via inhalation without PPE {i.e., no respirators) there are risks for
workers relative to the benchmarks for all the OES at the high-end exposure level for non-cancer effects
from both acute and chronic exposure durations as well as for cancer. Occupational non-users (ONUs)
are expected to have lower exposure levels than workers in most instances but exposures could not
always be quantified. Therefore, when separate ONU exposure estimates were not reasonably available,
EPA provided risk estimates for ONUs based on worker values (without PPE). These instances are
indicated in Table 4-59 with "worker estimate" added to the ONU cell in the Population column. Risks
to ONUs were indicated at high-end exposure levels for all OES following chronic exposure and for
most OES following acute exposure, although central-tendency exposure levels are considered more
representative for ONUs.
When only considering central tendency inhalation exposure level, risks for any endpoint were not
identified to workers or ONUs for the following exposure scenarios:
• Formulation of Aerosol and Non-Aerosol Products
• Repackaging
• Process Solvent Recycling and Worker Handling of Wastes
Page 387 of 803
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1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
When respirators are worn (either APF 10 or 50) there are risks relative to the benchmarks for non-
cancer effects and for cancer for workers (ONUs are assumed to not consistently wear respirators) from
both acute and chronic exposure durations at high-end exposure levels for the majority of OES (risks
remain with respirator use for all exposure scenarios following chronic exposure). Risks for any
endpoint were not identified when assuming the maximum plausible APF (up to APF =50) and central
tendency exposure levels for the same exposure scenarios that did not demonstrate risk without PPE:
• Formulation of Aerosol and Non-Aerosol Products
• Repackaging
• Process Solvent Recycling and Worker Handling of Wastes
Dermal Exposure
For acute and chronic exposures via dermal contact without PPE {i.e., no gloves) there are risks to
workers for both non-cancer effects and cancer (ONUs are assumed to not have direct dermal contact
with TCE) at both high-end and central-tendency exposure levels for all OES. Risks are still identified
for all exposure scenarios (at high-end exposure levels following acute exposure and at both exposure
levels following chronic exposure) when gloves are worn even when assuming the maximum applicable
glove protection (either PF 10 or 20).
Page 388 of 803
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1872 Table 4-59. Occupational Risk Summary Ta
)le
Risk Estimates for No PPE
Risk Estimates with PPE
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Inhalation
High-
End
0.95
4.«>l-'.-02
(».3l-'.-03
47.6
(APF 50)
2.5
(API- 50)
1.31.-04
(API- 50)
Worker
Central
Tendency
:u ^
I.I
2.3I-.-04
203.5
(APF 10)
52.7
(APF 50)
4.6E-06
(APF 10)
Manufacture -
Domestic
manufacture
Domestic manufacture
Manufacturing -
Dermal
High-
End
0.5X
3.0I.-02
3.Si:-02
11.6
(PF 20)
0.61
(PI- 20)
i.'m:-o3
(PI-" 20)
Table 4-10
Central
Tendencs
\r
•). i i:-o2
'J.T-03
17.4
(PF 10)
I.S
(PI- 20)
4.«>F.-04
(PI-20)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
20.3
I.I
2.3F.-04
N/A
Inhalation
High-
End
2.1
0.1 1
2.«>l-'.-03
:u 5
i \\'\: hi)
5.3
(API 50)
5 1>I !-<>5
( \H ' 5(1)
Worker
Central
Tendency
4728
245
9.9E-07
47275
(APF 10)
2448
(APF 10)
9.9E-08
(APF 10)
Manufacture -
Import
Repackaging -
Dermal
High-
End
0.5X
3.0I-.-02
3.XI-.-02
11.6
(PF 20)
0.(»l
(PI- 20)
1.'J 1.-03
(PI-" 20)
Import
Table 4-23
Central
Tendency
\r
«>.ii-:-o2
17.4
(PF 10)
I.S
(PI" 20)
4.«)l-:-04
(i»r 20)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
4728
245
9.9E-07
N/A
Processing -
Processing as a
Intermediate in industrial
gas manufacturing (e.g.,
Processing as a
Reactant -
Table 4-11
Worker
Inhalation
High-
End
o.y?
4.«>F.-02
(..31'.-03
47.6
(APF 50)
2.5
(API- 50)
1.31.-04
(API- 50)
reactant/
intermediate
manufacture of
fluorinated gases used as
Central
Tendency
:u ^
I.I
2.3I-.-04
203.5
(APF 10)
52.7
(APF 50)
4.6E-06
(APF 10)
Page 389 of 803
-------
Risk Estimates for No PPE
Risk Estimates with PPE
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
refrigerants, foam
blowing agents and
Dermal
High-
End
0.5X
3.0l!-02
3.XI-.-02
11.6
(PF 20)
0.61
(PI" 20)
1.91-03
(PI-" 20)
solvents)
Central
Tendencs
\r
•).n:-o2
«>.T.-03
17.4
(PF 10)
I.X
(PI- 20)
4.91.-04
(PI- 20)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
:u;
i.i
2.3I-.-04
N/A
Solvents (for cleaning or
Inhalation
High-
End
2.1
O.ll
2.«>l'.-03
:u 5
i \\>\: hi)
5.3
(API- 50)
5 1>I !-<>5
( \H ' 5(1)
degreasing)
Worker
Central
Tendency
4728
245
9.9E-07
47275
(APF 10)
2448
(APF 10)
9.9E-08
(APF 10)
Processing -
Incorporation
into formulation,
mixture or
Adhesives and sealant
Formulation of
Dermal
High-
End
0.5X
3.0T.-02
3.XI-.-02
11.6
(PF 20)
0.(»l
(PI" 20)
I.9T.-03
(PI-" 20)
chemicals
Aerosol and Non-
Aerosol Products
- Table 4-22
Central
Tendencs
1.7
'XII.-112
«>.T.-03
17.4
(PF 10)
I.X
(PI- 20)
4.9|".-04
(PI" 20)
reaction product
Solvents (which become
part of product
formulation or mixture)
(e.g., lubricants and
greases, paints and
coatings, other uses)
ONU
Inhalation
High-
End
-
-
-
-
(worker
estimate)
Central
Tendency
4728
245
9.9E-07
N/A
Inhalation
High-
End
2.1
0.11
2.91 >03
20.5
(APF 10)
5.3
(API- 50)
5.9E-05
(APF 50)
Processing -
incorporated
into articles
Solvents (becomes an
integral component of
articles)
Formulation of
Aerosol and
Non-Aerosol
Products - Table
4-22
Worker
Central
Tendency
4728
245
9.9E-07
47275
(APF 10)
2448
(APF 10)
9.9E-08
(APF 10)
Dermal
High-
End
0.5X
3.0T-02
3.XI-.-02
11.6
(PF 20)
0.(»l
(PI- 20)
I.9T.-03
(PI" 20)
Central
Tendency
\r
•). i i:-o2
«>.T.-03
17.4
(PF 10)
I.X
(PI- 20)
4.9i:-04
(PI" 20)
Page 390 of 803
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
4728
245
9.9E-07
N/A
Processing -
Repackaging
Solvents (for cleaning or
degreasing)
Repackaging -
Table 4-23
Worker
Inhalation
High-
End
(..2
0.11
2.91-11 J
61.6
(APF 10)
5.3
(API- 50)
5.9E-05
(APF 50)
Central
Tendency
14182
245
9.9E-07
141825
(APF 10)
2448
(APF 10)
9.9E-08
(APF 10)
Dermal
High-
End
0.5X
3.0I-.-02
3.Sl-:-02
11.6
(PF 20)
0.(»l
(PI- 20)
I.S
(PI- 20)
1.91.-03
(PI-" 20)
4.*>i:-o4
(PI- 20)
Central
Tendency
\r
i i:-o2
«).T.-0J
17.4
(PF 10)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
14182
245
9.9E-07
N/A
Processing -
Recycling
Recycling
Process Solvent
Recycling and
Worker
Handling of
Wastes -
Table 4-31
Workers
Inhalation
High-
End
2.1
0.11
2.91.-IIJ
20.5
(APF 10)
5.3
(API- 50)
5 1>I !-(>5
( \\'\: 5(1)
Central
Tendency
4_:x
245
47275
(APF 10)
244X
( \\>\: Id)
0.(»l
(PI" 20)
I.S
(PI- 20)
k> i>i :-os
( W'i III)
I.9I.-03
(i»r 20)
4.91 :-04
(PI" 20)
Dermal
High-
End
0.5X
3.0I-.-02
3.XI-.-02
11.6
(PF 20)
Central
Tendency
\r
•). i i:-o2
9.T.-03
17.4
(PF 10)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
4728
245
9.9E-07
N/A
Page 391 of 803
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Distribution in
commerce -
Distribution
Distribution
Distribution
Distribution in commerce of TCE is the transportation associated with the moving of TCE in commerce. Exposures and
emissions are not expected.
Industrial/
commercial use ¦
Solvents (for
cleaning or
degreasing)
Batch vapor degreaser
(e.g., open-top, closed-
loop)
Batch Open-Top
Vapor
Degreasing -
Table 4-12
Workers
Inhalation
(Monitoring
Data)a
High-
End
3.0F.-02
i.j
0.20
1.5
(API- 50)
¦'.si:-o2
(API- 50)
4.01.-03
(API 50)
Central
Tendency
O.I"7
s.si:-o3
2.S1-.-02
S.5
(API- 50)
0.44
(API- 50)
5.51!-04
(API- 50)
Dermal
High-
End
0.5X
3.0I-.-02
3.S1-.-02
1 1 (.
l-'.-03
(PI" 20)
Central
Tendency
\.n
•). i i:-o2
y.T.-03
1 ~ 4
S.3I-.-02
3.T.-03
l(> 1
( \\>\: Id)
4.2
(API- 50)
- 5i :-i>5
( \H' 5(1)
Central
Tendency
5.1
0.2(»
11.-04
51 1
( \\>\: Id)
13.2
(API 50)
i> ii:-u5
i \\>\: hi)
Dermal
High-
End
0.5X
3.01-'.-02
3.XE-02
1 1 (.
-------
Risk Estimates for No PPE
Risk Estimates with PPE
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Inhalation
(Monitoring
High-
End
4.XI.-02
2.51-'.-03
0.12
2.4
(API- 50)
0.13
(API 50)
2.51.-03
(API- 50)
Workers
Data)3
Central
Tendencs
7.2E-02
3.T.-03
6.51 >02
3.6
(API- 50)
0.1')
(API- 50)
1.31.-03
(API- 50)
Conveyorized
Vapor
Degreasing -
Table 4-15
Dermal
High-
End
0.5X
3.01-'.-02
3.Xl-:-02
1 1 (.
"
( \H ' 5(1)
3.')i:-02
(API- 50)
2.3I.-04
(API- 50)
Web Vapor
Degreasing -
Table 4-17
Dermal
High-
End
0.5X
3.0I.-02
3.XI-.-02
1 1 (.
1.-03
N/A
Cold cleaner
Cold Cleaning -
Worker
Inhalation
High-
End
4.11.-02
2.11.-03
0.11
2.0
(API- 50)
0.11
(API- 50)
2.31 >03
(API- 50)
Table 4-18
Central
Tendency
O.-'O
3.61-'.-02
6.21 >03
'5 1
( \H' 5(1)
I.X
(API- 50)
1.21.-04
(API- 50)
Page 393 of 803
-------
Life Cycle
Stage/
Category
Industrial/
commercial use ¦
Solvents (for
cleaning or
degreasing)
Subcategory
Cold cleaner
Occupational
Exposure
Scenario
Cold Cleaning -
Table 4-18
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Dermal
High-
End
0.5X
3.0i:-02
3.Xi:-02
1 1 (.
.T.-03
1 ~ 4
\: 50)
0."")
(API- 50)
2.91-04
(API- 50)
Dermal
High-
End
0.3"7
i.yi:-o2
5.')I.-II2
¦7.4
(PI- 20)
0.3')
(PI- 20)
2.9I.-03
(PI" 20)
Mold release
Central
Tendency
I.I
5.XI-.-02
1.51.-02
1 1 1
"
O.X"'
2.(.i:-04
N/A
Industrial/
commercial use
- Lubricants and
greases/
lubricants and
lubricant
additives
Tap and die fluid
Metalworking
Fluids -
Table 4-25
Worker
Inhalation
(Modeling
Data)b
High-
End
'>.0
O.-T
6. (.1-114
<>uu
( \\'\: Id)
23.3
(API- 50)
i -i :-(>5
( \\>\: 5(1)
Central
Tendency
" 4
IS
1.31.-04
334.3
(APF 10)
86.6
(APF 50)
2.6E-06
(APF 50)
Dermal
High-
End
0.73
3.XI-.-02
3.01.-02
14.5
(PF 20)
o.-'o
(PI- 20)
2.3
(PI- 20)
1.51.-03
(PI" 20)
3.91.-04
(PI" 20)
Central
Tendency
2.2
0.11
7.X1-.-03
10.9
(PF5)
Page 394 of 803
-------
Risk Estimates for No PPE
Risk Estimates with PPE
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
ONU
(worker
estimate)
Inhalation
High-
End
-
-
-
-
Central
Tendency
- 4
I."7
1.31.-04
N/A
Industrial/
commercial use
- Lubricants and
greases/
lubricants and
Inhalation
High-
End
«>.sr.-»2
5.11.-03
4.«>l'.-02
4.')
(API- 50)
0.25
(API- 50)
y.T.-tM
(API 50)
Worker
Central
Tendency
0.31
i.(.i:-o2
1.41.-02
15 ^
( \\'\: 50)
0."")
(API- 50)
2.')i:-04
(API- 50)
lubricant
additives
Penetrating lubricant
Aerosol
Applications -
Table 4-19
Dermal
High-
End
0.37
i.')i:-o2
5.')I.-II2
nA
(PI- 20)
0.3')
(PI- 20)
2.«>l'.-03
(PI" 20)
Central
Tendency
I.I
5.Sl-:-02
I.5I.-02
1 1 1
(I'l' Id)
1.2
(PI- 20)
¦\(.i-:-o4
(i»r 20)
ONU
Inhalation
High-
End
2.3
0.12
2.0E-03
N/A
Central
Tendency
l(> "
O.X"'
2.(.i:-04
N/A
Inhalation
High-
End
5.91-112
3.11.-03
0.10
3.0
(API- 50)
0.15
(API- 50)
2.01 .-03
(API- 50)
Solvent-based adhesives
and sealants
Central
Tendency
0.50
2.(.i:-02
'ur-03
25 2
( \H' 5(1)
1.3
(API- 50)
l.')i;-04
( API-" 50)
Industrial/
commercial use
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-26 and
Table 4-27
Worker
Dermal
High-
End
O.(o
3.41-'.-02
3.4I.-02
12
il'l' 20)
0.(.X
(PI- 20)
I.T.-03
(PI-" 20)
- Adhesives and
sealants
(Industrial)
Central
Tendency
l.«>
0.10
S.T.-03
I'M
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Mirror edge sealant
ONU
Inhalation
High-
End
2.3
0.12
2.(.i:-03
N/A
Central
Tendency
2.5
0.13
i.')i:-o3
N/A
Industrial/
commercial use -
Functional fluids
(closed systems)
Heat exchange fluid
Other Industrial
Uses -
Table 4-30
Worker
Inhalation
High-
End
o.y?
4.«>l'.-02
(..3I-.-03
4" (>
( \H ' 5(1)
2.5
(API- 50)
1.31.-04
(API 50)
Central
Tendency
:u ^
I.I
2.3I.-04
203.5
(APF 10)
52."
(APF 50)
2.3L-OS
(APF 10)
Dermal
High-
End
0.5X
3.01.-02
3.Xi:-02
11.6
(PF 20)
0.(>l
(PI- 20)
1.8
(PI- 20)
1.') 1.-03
(i»r 20)
4.«)l-'.-04
(i»r 20)
Central
Tendencs
\r
'XII.-112
y.T-03
17.4
I.I
2.3I.-04
N/A
Industrial/
commercial use ¦
Paints and
coatings
Diluent in solvent-based
paints and coatings
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-26 and
Table 4-27
Worker
Inhalation
High-
End
5.')l.-02
3.11.-03
0.10
3.0
(API- 50)
0.15
(API 50)
2.0I.-03
(API- 50)
Central
Tendency
0.50
2.(.l'-02
«>.3l-'.-03
25 2
( \H ' 5(1)
1.3
(API- 50)
i.yi:-o4
(API- 50)
Dermal
(Industrial)
High-
End
O.(o
3.41-'.-02
3.4I.-02
12
il'l' 20)
0.(.X
(PI- 20)
I.T.-03
(PI-" 20)
Central
Tendency
l.«>
0.10
S.T.-03
I'M
il'l' Id)
2.0
(PI- 20)
4.41 >04
(PI" 20)
Dermal
(Commercial)
High-
End
0.41
2.2T.-02
5.31.-02
4.1
(PI- 10)
0.22
(PI- 10)
5.31.-03
(PI- 10)
Central
Tendency
1.2
(oi:-o2
1.41.-02
12.3
(PF 10)
o.(o
(PI- 10)
1.41.-03
(PI- 10)
Page 396 of 803
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
ONU
Inhalation
High-
End
2.3
0.12
2.61.-03
N/A
Central
Tendency
2.5
0.13
l.«>l-'.-03
N/A
Industrial/
commercial use -
Cleaning and
furniture care
products
Carpet cleaner
Spot Cleaning
and Wipe
Cleaning0 -
Table 4-21
Worker
Inhalation
(Modeling
Data)b
High-
End
0.X5
4.3I.-02
5.XI-.-03
4:5
( \\>\: 5(1)
2.1
(API- 50)'
1.21.-04
( API- 50)'
Central
Tendency
2.4
0.12
i.si:-o3
24 ^
( \\>\: Id)
(>.l
(API- 50)'
^ "i:-u5
i \pi hi)
Dermal
High-
End
0.3"7
I.T.-02
(..')i:-o2
3.7
(Pi- ior
o.r
(Pi- ior
(».«)i-:-o3
(Pi- ior
Wipe cleaning
Central
Tendency
I.I
5.(.l'-02
i.(.i:-o2
11 i
\: Id)
(>.l
(API- 50)'
' "i:-u5
i \pi hi)
Dermal
High-
End
0.3"7
I.T.-02
6.') 1.-02
3."7
(PI- 10)'
o.r
(PI- 10)'
(..«>i-:-o3
(Pi- ior
Central
Tendency
I.I
5.(> 1.-02
i.(.i:-o2
1 1 1
(I'l' Id)
0.5(»
(PI- 10)'
i.(.i:-o3
(Pi- ior
ONU
Inhalation
(Modeling
Data)b
High-
End
1.3
6.T.-02
3.(.i:-03
N/A
Central
Tendency
4.')
0.25
«>.3l-'.-04
N/A
Page 397 of 803
-------
Risk Estimates for No PPE
Risk Estimates with PPE
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Inhalation
High-
End
5.')l.-02
3.11.-03
0.10
3.0
(API- 50)
0.15
(API- 50)
2.0F.-03
(API- 50)
Central
Tendency
0.50
2.(.l'-02
•UF.-03
:5:
( \H ' 5(1)
1.3
(API- 50)
l.«)F.-04
(API 50)
Worker
Dermal
High-
End
0.
ii'i' :ui
0.(.X
(P1- 20)
I.T.-03
(i»r 20)
Industrial/
commercial use -
Fixatives and finishing
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-26 and
Table 4-27
(Industrial)
Central
Tendency
i.y
0.10
X.T.-03
I'M
ii'i' hi)
2.0
(PI- 20)
4.41 >04
(i»r 20)
Arts, crafts and
hobby materials
spray coatings
Dermal
High-
End
0.41
2.21-'.-02
5.31.-02
4.1
(PI- 10)
0.22
(PI- 10)
5.31.-03
(PI- 10)
(Commercial)
Central
Tendency
1.2
(..5I.-02
1.41.-02
i: ^
ii'i' id)
0.(»5
(PI- 10)
1.41.-03
(PI- 10)
ONU
Inhalation
High-
End
2.3
0.12
2.(.l'-03
N/A
Central
Tendency
2.5
0.13
l.«)F.-03
N/A
Inhalation
High-
End
0.12
(..3i:-03
4.')i:-02
<>.l
(API- 50)
0.31
(API- 50)
•X'H.-IU
(API- 50)
Industrial/
Worker
Central
Tendency
O.J"'
i.yi:-o2
I.3I-.-02
IS ^
( \\>\: 50)
0.<)4
(API- 50)
2.51.-04
(API- 50)
commercial use -
Corrosion
Corrosion inhibitors and
Industrial
Processing Aid -
Table 4-28
Dermal
High-
End
0.5S
3.01.-02
3.XF.-02
1 1 (.
F-03
(PI" 20)
inhibitors and
anti-scaling
agents
anti-scaling agents
Central
Tendency
1.7
«>.ir.-o2
«).T.-03
1 ~ 4
il'l' Id)
I.X
(P1- 20)
4.91-04
(Pr 20)
ONU
Inhalation
High-
End
0.54
2.XE-02
I.I 1.-02
N/A
Central
Tendency
1.2
(..n:-o2
3.')i:-03
N/A
Page 398 of 803
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Industrial/
commercial use -
Processing aids
Process solvent used in
battery manufacture
Industrial
Processing Aid -
Table 4-28
Worker
Inhalation
High-
End
0.12
(..31.-03
4.')i:-02
(>.l
(API- 50)
0.31
(API- 50)
«>.«)i-:-o4
(API- 50)
Central
Tendency
0.3"7
i.')i;-o2
1.31.-02
IS ^
( \H ' 5(1)
O.'M
(API- 50)
2.51.-04
(API- 50)
Process solvent used in
polymer fiber spinning,
fluoroelastomer
manufacture and
Alcantara manufacture
Dermal
High-
End
0.5X
3.01-'.-02
3.Sl-:-02
1 1 (.
l
(PI- 20)
l.«)|-:-03
(PI" 20)
Central
Tendency
\r
'Ui:-o2
«j.",i:-o3
1 ~ 4
il'l' Id)
I.S
(PI- 20)
4.«)|-:-04
(PI" 20)
Extraction solvent used in
caprolactam manufacture
ONU
Inhalation
High-
End
0.54
2.XI-.-02
I.I 1.-02
N/A
Precipitant used in beta-
cyclodextrin manufacture
Central
Tendency
1.2
6. II.-02
3.') 1.-03
N/A
Industrial/
commercial use -
Ink, toner and
colorant
products
Toner aid
Commercial
Printing and
Copying0 -
Table 4-29
Workers
Inhalation
High-
End
I.I
5.X r.-02
5.41 .-03
11:
i \\>\: hi)
2.9
(API- 50)'
I.I 1.-04
(API 50)*
Central
Tendency
5
1.4
1.71.-04
:~5 ^
i \\>\: hi)
"1 ^
( \H' 5(1)
i ~i:-u5
i \\>\: hi)
Dermal
High-
End
i.i
5.5i:-02
2.IT.-02
KM.
(I'l' III)
0.55
(PI- 10)'
2.11.-03
(PI- 10)'
Central
Tendencs
3.2
o.r
5.31.-03
15
(\>\: 5)
1.7
(PI- 10)'
5.31 .-04
(PI- 10)'
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
1.4
I.T.-04
N/A
Industrial/
commercial use -
Automotive care
products
Brake and parts cleaner
Aerosol
Applications -
Table 4-19
Workers
Inhalation
High-
End
«>.sr.-o2
5.11.-03
4.yi:-o2
4.')
(API- 50)
0.25
(API 50)
•J.T.-04
(API- 50)
Central
Tendency
0.31
i.(.i:-o2
I.4I-.-02
15 ^
^\\>\: 50;
o.-"j
(API 50)
2.«)|-:-04
(API 50)
Page 399 of 803
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Dermal
High-
End
0.3"7
i.yi:-o2
5.') 1.-02
7.4
(PF 20)
0.3')
(PF 20)
2.'JF.-03
(PF 20)
Central
Tendencs
I.I
5.XF-02
I.5F-02
1 1 1
\: hi)
Dermal
High-
End
0.37
I.T.-02
6.«>F.-02
3."7
(PF 10)1
0.17
(PF 10)'
6.91.-03
(PF 10)1
Central
Tendency
I.I
5.6F-02
l.(»F-02
1 1 1
-------
Life Cycle
Stage/
Category
Subcategory
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 10)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Other miscellaneous
industrial and commercial
uses
ONU
Inhalation
(Modeling
Data)b
High-
End
1.3
6.7E-02
3.61.-03
N/A
Central
Tendency
4.y
0.25
'Ui;-04
N/A
Disposal
Industrial pre-treatment
Process Solvent
Recycling and
Worker Handling
of Wastes -
Table 4-31
Workers
Inhalation
High-
End
2.1
0.11
2.«)l'.-03
:u 5
i \\>\: hi)
5.3
(API- 50)
5 1>I !-t>5
( \\'\: 5(1)
Central
Tendency
4_:s
:45
47275
(APF 10)
244X
( \\>\: Id)
0.(»l
(PI" 20)
I.S
(PI- 20)
k> i>i :-os
( W'i III)
l.«)F.-03
(PI- 20)
4.91-04
(PI" 20)
Industrial wastewater
treatment
Dermal
High-
End
0.5X
3.01'.-02
3.XI-.-02
11.6
(PF 20)
Central
Tendency
\.n
«>.ir.-o2
«).T.-03
17.4
(PF 10)
Publicly owned treatment
works (POTW)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
4728
245
9.9E-07
N/A
a Monitoring data were selected as most representative based on the EPA data hierarchy where high-quality monitoring data is preferred over modeling results or exposure limits.
b Modeling data were selected as most representative because the monitoring dataset contained a very low number of datapoints.
0 EPA believes that 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 or regularly employ dermal protection. Therefore, the use of respirators and gloves is unlikely for workers in these facilities. Consistent PPE
usage is not expected for this scenario and is only included as a "what-if' analysis for comparison purposes.
N/A = Not Applicable. ONUs are assumed to not wear respiratory protection.
1873
Page 401 of 803
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1888
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1890
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1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
4,5.2,2 Summary of Risk Estimates for Consumers and Bystanders
Table 4-60 summarizes the risk estimates for CNS effects from acute inhalation and dermal exposures
for all consumer exposure scenarios. Risk estimates that exceed the benchmark {i.e., MOEs less than the
benchmark MOE) are highlighted by bolding the number and shading the cell in gray. The consumer
exposure assessment and risk characterization are described in more detail in Sections 2.3.2 and 4.2.3,
respectively. Specific links to the relevant risk characterization sections are listed in Table 4-60 in the
Consumer Condition of Use Scenario column.
The risk summary below is based on the most robust and well-supported PODs selected from among the
most sensitive acute non-cancer endpoints. EPA selected immunosuppression (S el grade and Gilmour.
2.010) as the best overall acute endpoint based on being both robust and sensitive. While some other
endpoints present lower PODs (developmental neurotoxicity from Fredriksson et at. 1993; congenital
heart malformations from Johnson et at.. 2003). there is lower confidence in the dose-response and
extrapolation of results from those studies (Section 3.2.6.1.1) resulting in increased uncertainty
surrounding the precision of the derived PODs for those endpoints. Therefore, EPA concluded that
immunosuppression from (Selgrade and Gilmour. 2010) was the best overall endpoint for use in
evaluation of acute risks under TSCA, based on the best available science and weight of scientific
evidence (Section 3.2.5.4.1). For the majority of exposure scenarios, risks were identified for multiple
endpoints, however risk estimates are only summarized for this particular endpoint.
Inhalation
For acute inhalation exposures there are risks for non-cancer effects for consumer users relative to the
benchmarks for all COUs except Pepper Spray and for bystanders for most COUs at both medium and
high-intensity user exposure levels.
Dermal
For acute dermal exposures there are risks for non-cancer effects for consumer users (bystanders are
assumed to not have direct dermal contact with TCE) relative to the benchmarks for all COUs except for
Pepper Spray at both medium and high-intensity user exposure levels (and for most COUs at low-
intensity).
Table 4-60. Consumer Risk Summary Table
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Group1
Acute Non-Cancer
(benchmark MOE = 10)
High-Intensity
User
Moderate-
Ink'iisiis User
Low-Intensity
User
Consumer Use -
Solvents (for
cleaning or
degreasing)
Brake and Parts
Cleaner -
Table 4-32
User
Inhalation
All
J.T.-02
0.11
1.4
Dermal
21+
5.XI-.-02
0.77
35
16-20
(..2T.-02
0.X2
11
11-15
5.(.l'.-02
O."^
U
Bystander
Inhalation
All
5.XI-.-02
0.43
5.4
Aerosol electronic
degreaser/cleaner -
Table 4-33
User
Inhalation
All
2.(.i:-02
0.(.l
IS
Dermal
21+
0.40
4."7
V)
16-20
0.43
5.0
4:
Page 402 of 803
-------
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Groupa
Acute Non-Cancer
(benchmark MOE = 10)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
11-15
o.jy
4.fi
.X
Bystander
Inhalation
All
0.13
3.3
•JO
Liquid electronic
degreaser/cleaner -
Table 4-34
User
Inhalation
All
2.T.-02
0.42
5.f>
Dermal
21+
0.2(i
3.S
15
16-20
0.2"7
4.1
l(.
11-15
0.25
3."7
15
Bystander
Inhalation
All
0.13
2.2
y>
Aerosol spray
degreaser/cleaner -
Table 4-35
User
Inhalation
All
(..oi:-o2
2.4F-02
U.K.
Dermal
21+
i i:-o2
0.49
2.5
16-20
r..r.i-:-o2
0.52
2.fi
11-15
r..oi-:-o2
0.4S
2.4
Bystander
Inhalation
All
2.IF.-02
9.5 F.-02
0.f.5
Liquid
degreaser/cleaner -
Table 4-36
User
Inhalation
All
M.F-03
f».2F-02
0.3"7
Dermal
21+
f».4F-02
0.51
3.S
16-20
(i.XF-02
0.55
4.1
11-15
f».3F-02
0.50
3.S
Bystander
Inhalation
All
2.T-02
0.33
2.0
Aerosol gun
scrubber -
Table 4-37
User
Inhalation
All
1 ^
i:
:i
Dermal
21+
(».4F.-02
0.51
fi.4
16-20
fi.SF-02
0.54
f..S
11-15
f».2F-02
0.50
fi.2
Bystander
Inhalation
All
53
66
115
Liquid gun
scrubber -
Table 4-38
User
Inhalation
All
15
14
16
Dermal
21+
f».'JF-02
0.55
4.1
16-20
"'.3F-02
0.59
4.4
11-15
f.^F-02
0.54
4.0
Bystander
Inhalation
All
""
SO
Mold Release -
Table 4-39
User
Inhalation
All
5.') F-II2
0.5fi
5.5
Dermal
21+
f».l F-OI
4."7
'1
16-20
foF-OI
5.0
11-15
fi.OF-OI
4.fi
^0
Bystander
Inhalation
All
0.30
3.0
:x
Aerosol Tire Cleaner
- Table 4-40
User
Inhalation
All
f».2F-02
0.23
\r
Dermal
21+
2.SF-0I
I.I
4.S
Page 403 of 803
-------
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Groupa
Acute Non-Cancer
(benchmark MOE = 10)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
16-20
3.oi:-oi
1.2
5.1
11-15
2.T.-OI
I.I
4."7
Bystander
Inhalation
All
1.41.-02
0.94
(..9
Liquid Tire Cleaner -
Table 4-41
User
Inhalation
All
2.01'.-02
0.10
0.53
Dermal
21+
0.12
0.50
1.5
16-20
0.13
0.53
l.(>
11-15
0.12
0.4')
1.5
Bystander
Inhalation
All
(..4I-.-02
0.42
2.2
Consumer Use -
Lubricants and
greases
Tap and Die Fluid -
Table 4-42
User
Inhalation
All
(..(.i:-o2
0.(>2
3.(i
Dermal
21+
0.6X
5.2
:u
16-20
0.-\3
5.(>
:i
11-15
o.^
5.1
19
Bystander
Inhalation
All
3.31-'.-01
3.3
IS
Penetrating lubricant
- Table 4-43
User
Inhalation
All
X.3I-.-02
O.X(»
0.91
0.X4
1.4
45
Dermal
21+
i:
250
16-20
13
267
11-15
i:
245
Bystander
Inhalation
All
4.11-01
?.<.
Consumer Use -
Adhesives and
sealants
Solvent-based
adhesives and
sealants -
Table 4-44
User
Inhalation
All
5.XI-.-02
I.X
Dermal
21+
O.K.
1.3
9X
16-20
O.I"7
1.4
It >5
11-15
0.15
1.3
9(,
Bystander
Inhalation
All
0.24
'>.4
~4<>
Mirror edge sealant -
Table 4-45
User
Inhalation
All
0.2')
2.0
4^
Dermal
21+
2.1
9.5
16-20
2.2
lu
""
11-15
2.0
9.2
"i
Bystander
Inhalation
All
1.2
1 1
Tire repair cement/
sealer -
Table 4-46
User
Inhalation
All
X.2I-.-02
1.5
i<.
Dermal
21+
0.15
o.xo
7.5
16-20
O.K.
O.X(,
X.I
11-15
0.15
O.-'X
"\4
Bystander
Inhalation
All
2.(.i:-0l
13
1 V,
Carpet cleaner -
User
Inhalation
All
i.xi:-o2
0.15
0.X9
Page 404 of 803
-------
Life Cycle
Stage/
Category
Consumer use -
Cleaning and
furniture care
products
Subcategory/
Consumer
Condition of Use
Scenario
Table 4-47
Population
Exposure
Route and
Duration
Age
Groupa
Acute Non-Cancer
(benchmark MOE = 10)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Dermal
21+
0.24
1.4
14
16-20
0.25
1.5
15
11-15
0.23
1.4
14
Bystander
Inhalation
All
S.4I-.-02
0.77
4.2
Aerosol Spot
Remover -
Table 4-48
User
Inhalation
All
5.T.-02
0.4X
2.7
Dermal
21+
O.SO
4.X
4X
16-20
0.S5
5.1
51
11-15
o.-'s
4."7
4"
Bystander
Inhalation
All
0.2S
2.(i
14
Liquid Spot
Remover -
Table 4-49
User
Inhalation
All
2.4I--02
0.21
I.X
Dermal
21+
0.34
2.1
'1
16-20
0.37
2.2
33
11-15
0.34
2.0
;<)
Bystander
Inhalation
All
0.12
I.I
y.i
Consumer use -
Arts, crafts, and
hobby materials
Fixatives and
finishing spray
coatings -
Table 4-50
User
Inhalation
All
0.10
0.(i5
3.4
Dermal
21+
2.4
y.5
X4
16-20
2.(i
III
90
11-15
2.4
y.3
82
Bystander
Inhalation
All
0.43
3.5
17
Consumer use -
Apparel and
footwear care
products
Shoe polish -
Table 4-51
User
Inhalation
All
0.35
2.')
16
Dermal
21+
3.(i
22
219
16-20
3.')
23
234
11-15
3.(i
21
214
Bystander
Inhalation
All
1.4
15
X4
Consumer use -
Other consumer
uses
Fabric spray -
Table 4-52
User
Inhalation
All
"\3l'.-02
0.44
2.1
Dermal
21+
2.1
4."7
:_
16-20
2.2
5.0
:x
11-15
2.0
4.(i
:<•
Bystander
Inhalation
All
0.30
2.3
11
Film cleaner -
Table 4-53
User
Inhalation
All
1.5K-02
«UI'.-02
0.4')
Dermal
21+
0.35
1.4
i:
16-20
0.3S
1.5
i ^
11-15
0.34
1.4
i:
Bystander
Inhalation
All
(..2i:-02
0.51
2.5
Page 405 of 803
-------
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Groupa
Acute Non-Cancer
(benchmark MOE = 10)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Hoof polish -
Table 4-54
User
Inhalation
All
0.44
:<)45
12493
Dermal
21+
2.')
y.5
84
16-20
3.1
iu
90
11-15
2.S
y.j
82
Bystander
Inhalation
All
88
3653
22309
Pepper spray -
Table 4-55
User
Inhalation
All
15
29
55
Dermal
21+
16
16-20
17
11-15
15
Bystander
Not modeled - can be considered equal to user.
Toner aid -
Table 4-56
User
Inhalation
All
0.11
0.(.X
3.(i
Dermal
21+
2.(i
III
89
16-20
2.7
1 1
95
11-15
2.5
y.x
87
Bystander
Inhalation
All
0.45
3.7
18
" Inhalation exposures are based on a 2-zone model of air concentrations (Section 2.3.2.3.1) that are independent of any age-
specific exposure factors.
b If an MOE equal to the benchmark is not highlighted, the unrounded MOE is greater than the benchmark.
1906
Page 406 of 803
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5 UNREASONABLE RISK DETERMINATION
5.1 Overview
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. These
determinations do not consider costs 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 human exposure to such substance under the conditions of use (including cancer and non-
cancer 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 (PESS)); the severity of hazard (including the nature of the hazard, the irreversibility of
the hazard); and uncertainties. EPA also takes into consideration the Agency's confidence in the data
used in the risk estimate. This includes an evaluation of the strengths, limitations and uncertainties
associated with the information used to inform the risk estimate and the risk characterization. This
approach is in keeping with the Agency's final rule, Procedures for Chemical Risk Evaluation Under the
Amended Toxic Substances Control Act (82 FR 33726).25
This section describes the final unreasonable risk determinations for the conditions of use in the scope of
the Risk Evaluation. The final unreasonable risk determinations are based on the risk estimates and
consideration of other risk-related factors in the final Risk Evaluation, which may differ from the draft
Risk Evaluation due to peer review and public comments. The relevant risk-related factors for TCE are
further explained in Section 5.1.1 below and in Section 4.3 and 4.4 of the risk characterization. In
Section 5.1.1, the relevant risk-related factors are identified for each condition of use, such as the health
effects considered, the use of high-end risk estimates to address PESS and other uncertainties relevant to
each condition of use. Therefore, the final unreasonable risk determinations of some conditions of use
may differ from those in the draft Risk Evaluation.
5.1.1 Human Health
EPA's Risk Evaluation identified non-cancer adverse effects from acute (immunosuppression) and chronic
(autoimmunity) inhalation and dermal exposures to TCE, and cancer from chronic inhalation and dermal
exposures to TCE. The health risk estimates for all conditions of use are in Section 4.5 (Table 4-59 and Table
4-60).
For the TCE Risk Evaluation, EPA identified as Potentially Exposed or Susceptible Subpopulations: workers
and ONUs, including men and women of reproductive age, adolescents, and biologically susceptible
subpopulations; and consumer users (age 11 and older) and bystanders (of any age group, including infants,
toddlers, children, and elderly), including biologically susceptible subpopulations.
EPA evaluated exposures to workers, ONUs, consumer users, and bystanders using reasonably available
monitoring and modeling data for inhalation and dermal exposures, as applicable. For example, EPA assumed
that ONUs and bystanders do not have direct contact with TCE; therefore, non-cancer effects and cancer from
dermal exposures to TCE are not expected and were not evaluated. Additionally, EPA did not evaluate chronic
25 This risk determination is being issued under TSCA section 6(b) and the terms used, such as unreasonable risk, and the
considerations discussed are specific to TSCA. Other statutes have different authorities and mandates and may involve risk
considerations other than those discussed here.
Page 407 of 803
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exposures for consumer users and bystanders because EPA considered the frequency of consumer product use
to be too low to create chronic risk concerns. The description of the data used for human health exposure is in
Section 2.3. Uncertainties in the analysis are discussed in Section 4.3 and considered in the unreasonable risk
determination for each condition of use presented below in Section 5.2, including the fact that the dermal model
used does not address variability in exposure duration and frequency.
EPA did not evaluate risks to the general population, and as such the unreasonable risk determinations for
relevant conditions of use do not account for any risk to the general population. Additional details regarding the
general population are in Section 2.3.3.
5.1.1.1 Non-Cancer Risks Estimates
The risk estimates of non-cancer effects (MOEs) refer to 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. Section 3.2.5 presents the PODs for
non-cancer effects for TCE and Section 4.2 presents the MOEs for non-cancer effects.
The MOEs are compared to a benchmark MOE. The benchmark MOE 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.
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 MOE {e.g., 1000) would
indicate more uncertainty for specific endpoints and scenarios. However, these are often not the only
uncertainties in a Risk Evaluation. The benchmark MOE for acute non-cancer risks for TCE is 10, and the
benchmark MOE for chronic non-cancer risks for TCE is 30. Additional information regarding the benchmark
MOE is in Section 4.2.1.
5.1.1.2 Cancer Risks Estimates
Cancer risk estimates represent 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. 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., lxlO"6 to lxlO"4)
depending on the subpopulation exposed.26
26 As an example, when EPA's Office of Water in 2017 updated the Human Health Benchmarks for Pesticides, the
benchmark for a "theoretical upper-bound excess lifetime cancer risk" from pesticides in drinking water was identified as 1 in
1,000,000 to 1 in 10,000 over a lifetime of exposure (EPA. Human Health Benchmarks for Pesticides: Updated 2017
Technical Document (pp.5). (EPA 822-R -17 -001). Washington, DC: U.S. Environmental Protection Agency, Office of
Water. January 2017. https://www.epa.gov/sites/production/files/2015-10/documents/hh-benchmarks-techdoc.pdf). Similarly,
EPA's approach under the Clean Air Act to evaluate residual risk and to develop standards is a two-step approach that
"includes a presumptive limit on maximum individual lifetime [cancer] risk (MIR) of approximately 1 in 10 thousand" and
consideration of whether emissions standards provide 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).
Page 408 of 803
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EPA, consistent with 2017 NIOSH guidance,27 used lxlO"4 as the benchmark for the purposes of this
unreasonable risk determination for individuals in industrial and commercial work environments. The lxlO"4 is
not a bright line and EPA has discretion to make unreasonable risk determinations based on other benchmarks
as appropriate.
5.1.1.3 Determining Unreasonable Risk of Injury to Health
Calculated risk estimates (MOEs or cancer risk estimates) can provide a risk profile by presenting a range of
estimates for different health effects for different conditions of use. A calculated MOE that is less than the
benchmark MOE supports a determination of unreasonable risk of injury to health, based on non-cancer effects.
Similarly, a calculated cancer risk estimate that is greater than the cancer benchmark supports a determination
of unreasonable risk of injury to health from cancer. Whether EPA makes a determination of unreasonable risk
depends upon other risk-related factors, such as the endpoint under consideration, the reversibility of effect,
exposure-related considerations (e.g., duration, magnitude, or frequency of exposure, or population exposed),
and the confidence in the information used to inform the hazard and exposure values. A calculated MOE greater
than the benchmark MOE or a calculated cancer risk estimate less than the cancer benchmark, alone do not
support a determination of unreasonable risk, since EPA may consider other risk-based factors when making an
unreasonable risk determination.
When making an unreasonable risk determination based on injury to health of workers (who are one example of
PESS), EPA also makes assumptions regarding workplace practices and the implementation of the required
hierarchy of controls from OSHA. EPA assumes that feasible exposure controls, including engineering controls,
or use of personal protective equipment (PPE) are implemented in the workplace. EPA's decisions for
unreasonable risk to workers are based on high-end exposure estimates, in order to capture not only exposures
for PESS but also to account for the uncertainties related to whether or not workers are using PPE. However,
EPA does not assume that ONUs use PPE. For each condition of use, depending on the information available
and professional judgement, EPA assumes the use of appropriate respirators with APFs ranging from 10 to 50,
and gloves with a PF of 10 to 20. However, EPA assumes that for some conditions of use, the use of respirators
is not a standard industry practice, based on professional judgement given the burden associated with the use of
respirators, including the expense of the equipment and the necessity of fit-testing and training for proper use.
Similarly, EPA does not assume that it is a standard industry practice that workers in some small commercial
facilities (e.g., those performing spot cleaning, wipe cleaning, shoe polishing, or hoof polishing; commercial
printing and copying) have a respiratory protection program or regularly employ dermal protection. Therefore,
the use of respirators and gloves is unlikely for workers in these facilities. Section 4.2.2 explains how EPA
considers the use of PPE for each occupational exposure scenario of the Risk Evaluation, and Table 4-9
summarizes the information. Once EPA has applied the appropriate PPE assumption for a particular condition
of use in each unreasonable risk determination, in those instances when EPA assumes PPE is used, EPA also
assumes that the PPE is used in a manner that achieves the stated APF or PF.
EPA identified several acute and chronic endpoints for non-cancer effects of TCE (e.g., developmental toxicity,
reproductive toxicity, liver toxicity, kidney toxicity, neurotoxicity, and immunotoxicity). In Section 3.2.5.4.1
EPA identified the best overall non-cancer endpoints to be immunosuppression effects for acute inhalation and
dermal exposures, and autoimmunity effects for chronic inhalation and dermal exposures. EPA determined that
these were the best overall endpoints for Risk Evaluation under TSCA, based on the best available science,
weight of the scientific evidence, and confidence in the POD, and were used as the basis of risk conclusions in
27 NIOSH Current intelligence bulletin 68: NIOSH chemical carcinogen policy (Whittaker et al. 2016).
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Section 4.5.2 and risk determinations in Section 0. As described in EPA's framework rule for Risk Evaluations
[82 FR 33726], weight of the scientific evidence includes consideration of the "strengths, limitations and
relevance of the information." Neither the statute nor the framework rule requires that EPA choose the lowest
number and EPA believes that public health is best served when EPA relies upon the highest quality
information for which EPA has the greatest confidence.
Consistent with EPA guidance as indicated in the 2011 EPA TCE IRIS Assessment, in this Risk Evaluation
EPA concluded that TCE is carcinogenic to workers and ONUs by all routes of exposure. This is most strongly
supported by the data on kidney cancer. The cancer hazard analysis is described in Section 3.2.4.2. EPA
considered cancer risk estimates from chronic inhalation or dermal exposures in the unreasonable risk
determination.
When making a determination of unreasonable risk, the Agency has a higher degree of confidence where
uncertainty is low. Similarly, EPA has high confidence in the hazard and exposure characterizations when, for
example, the basis for characterizations is measured or monitoring data or a robust model and the hazards
identified for risk estimation are relevant for conditions of use. Where EPA has made assumptions in the
scientific evaluation, whether or not those assumptions are protective is also a consideration. Additionally, EPA
considers the central tendency and high-end exposure levels when determining the unreasonable risk. High-end
risk estimates (e.g., 95th percentile) are generally intended to cover individuals or sub-populations with greater
exposure (PESS) as well as to capture individuals with sentinel exposure, and central tendency risk estimates
are generally estimates of average or typical exposure.
EPA may make a determination of no unreasonable risk for conditions of use where the substance's hazard and
exposure potential, or where the risk-related factors described previously, lead the Agency to determine that the
risks are not unreasonable.
5.1.2 Environment
EPA calculated a risk quotient (RQ) to compare environmental concentrations against an effect level.
The environmental concentration is determined based on the levels of the chemical released to the
environment (e.g., surface water, sediment, soil, biota) under the conditions of use, based on the fate
properties, release potential, and reasonably available environmental monitoring data. The effect level is
calculated using concentrations of concern that represent hazard data for aquatic, sediment-dwelling, and
terrestrial organisms. Section 4.1 provides more detail regarding the risk quotients for TCE.
5,1.2.1 Determining Unreasonable Risk of Injury to the Environment
An RQ equal to 1 indicates that the exposures are the same as the concentration that causes effects. An RQ less
than 1, when the exposure is less than the effect concentration, supports a determination that there is no
unreasonable risk of injury to the environment. An RQ greater than 1, when the exposure is greater than the
effect concentration, supports a determination that there is unreasonable risk of injury to the environment.
Consistent with EPA's human health evaluations, other risk-based factors may be considered (e.g., confidence
in the hazard and exposure characterization, duration, magnitude, uncertainty) for purposes of making an
unreasonable risk determination. Due to the volatile properties of TCE, EPA also considered when it was more
likely for acute or chronic exposure durations to occur.
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162 EPA considered the effects on aquatic, sediment-dwelling, and terrestrial organisms. EPA provides estimates
163 for environmental risk in Section 4.1 and Table 4-1, while the details for determining whether there is
164 unreasonable risk to the environment are discussed in Section 5.2.2.
165 5.2 Detailed Unreasonable Risk Determination by Condition of Use
Table 5-1. Categories and Subcategories of Conditions of Use Included in the Scope of the Risk
Evaluation
Life Cycle
Stage
Category a
Subcategory b
Unreasonable
Risk
Detailed Risk
Determination
Manufacture
Domestic manufacture
Domestic manufacture
Yes
Sections 5.2.1.1, and
5.2.2
Import
Import
Yes
Sections 5.2.1.2 and
5.2.2
Processing
Processing as a
reactant/ intermediate
Processing as a
reactant/intermediate in
industrial gas
manufacturing (e.g.,
manufacture of fluorinated
gases used as refrigerants,
foam blowing agents and
solvents)
Yes
Sections 5.2.1.3 and
5.2.2
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)
Yes
Sections 5.2.1.4 and
5.2.2
Processing -
incorporation into
articles
Solvents (becomes an
integral components of
articles)
Yes
Sections 5.2.1.5 and
5.2.2
Repackaging
Solvents (for cleaning or
degreasing)
Yes
Sections 5.2.1.6 and
5.2.2
Recycling
Recycling
Yes
Sections 5.2.1.7 and
5.2.2
Distribution in
commerce
Distribution
Distribution
No
Sections 5.2.1.8 and
5.2.2
Industrial/
commercial
use
Solvent (for cleaning
or degreasing)
Batch vapor degreaser
(open-top)
Yes
Sections 5.2.1.9 and
5.2.2
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Table 5-1. Categories and Subcategories of Conditions of Use Included in the Scope of the Risk
Evaluation
Life Cycle
Stage
Category a
Subcategory b
Unreasonable
Risk
Detailed Risk
Determination
Batch vapor degreaser
(closed-loop)
Yes
Sections 5.2.1.10 and
5.2.2
In-line vapor degreaser
(convey orized)
Yes
Sections 5.2.1.11 and
5.2.2
In-line vapor degreaser
(web cleaner)
Yes
Sections 5.2.1.12 and
5.2.2
Cold cleaner
Yes
Sections 5.2.1.13 and
5.2.2
Aerosol spray
degreaser/cleaner; mold
release
Yes
Sections 5.2.1.14 and
5.2.2
Lubricants and
greases/lubricants and
Tap and die fluid
Yes
Sections 5.2.1.15 and
5.2.2
lubricant additives
Penetrating lubricant
Yes
Sections 5.2.1.16 and
5.2.2
Adhesives and
sealants
Solvent-based adhesives
and sealants; tire repair
cement/sealer; mirror edge
sealant
Yes
Sections 5.2.1.17 and
5.2.2
Functional fluids
(closed systems)
Heat exchange fluid
Yes
Sections 5.2.1.18 and
5.2.2
Paints and coatings
Diluent in solvent-based
paints and coatings
Yes
Sections 5.2.1.19 and
5.2.2
Cleaning and furniture
care products
Carpet cleaner; wipe
cleanerc
Yes
Sections 5.2.1.20 and
5.2.2.
Laundry and
dishwashing products
Spot remover d
Yes
Sections 5.2.1.21
5.2.2
Arts, crafts and hobby
materials
Fixatives and finishing
spray coatings
Yes
Sections 5.2.1.22 and
5.2.2
Corrosion inhibitors
and anti-scaling agents
Corrosion inhibitors and
anti-scaling agents
Yes
Sections 5.2.1.23 and
5.2.2
Processing aids
Process solvent used in
battery manufacture;
process solvent used in
polymer fiber spinning,
fluoroelastomer
manufacture, and
Yes
Sections 5.2.1.24 and
5.2.2
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Table 5-1. Categories and Subcategories of Conditions of Use Included in the Scope of the Risk
Evaluation
Life Cycle
Stage
Category a
Subcategory b
Unreasonable
Risk
Detailed Risk
Determination
Alcantara manufacture;
extraction solvent used in
caprolactam manufacture;
precipitant used in beta-
cyclodextrin manufacture
Ink, toner and colorant
products
Toner aid
Yes
Sections 5.2.1.25 and
5.2.2
Automotive care
products
Brake and parts cleaners
Yes
Sections 5.2.1.26 ,
and 5.2.2
Apparel and footwear
care products
Shoe polish
Yes
Sections 5.2.1.27 and
5.2.2
Other commercial uses
Hoof polishes; gun
scrubber; pepper spray;
other miscellaneous
industrial and commercial
uses
Yes
Sections 5.2.1.28 and
5.2.2
Consumer uses
Solvent (cleaning or
degreasing)
Brake and parts cleaner
Yes
Sections 5.2.1.29 and
5.2.2
Aerosol electronic
degreaser/cleaner
Yes
Sections 5.2.1.30 and
5.2.2
Liquid electronic
degreaser/cleaner
Yes
Sections 5.2.1.31 and
5.2.2
Aerosol spray
degreaser/cleaner
Yes
Sections 5.2.1.32 and
5.2.2
Liquid degreaser/cleaner
Yes
Sections 5.2.1.33 and
5.2.2
Aerosol gun scrubber
Yes
Sections 5.2.1.34 and
5.2.2
Liquid gun scrubber
Yes
Sections 5.2.1.35 and
5.2.2
Mold release
Yes
Sections 5.2.1.36 and
5.2.2
Aerosol tire cleaner
Yes
Sections 5.2.1.37 and
5.2.2
Liquid tire cleaner
Yes
Sections 5.2.1.38 and
5.2.2
Page 413 of 803
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Table 5-1. Categories and Subcategories of Conditions of Use Included in the Scope of the Risk
Evaluation
Life Cycle
Stage
Category a
Subcategory b
Unreasonable
Risk
Detailed Risk
Determination
Lubricants and greases
Tap and die fluid
Yes
Sections 5.2.1.39 and
5.2.2
Penetrating lubricant
Yes
Sections 5.2.1.40 and
5.2.2
Adhesives and
sealants
Solvent-based adhesives
and sealants
Yes
Sections 5.2.1.41 and
5.2.2
Mirror edge sealant
Yes
Sections 5.2.1.42 and
5.2.2
Tire repair cement/sealer
Yes
Sections 0 and 5.2.2
Cleaning and furniture
care products
Carpet cleaner
Yes
Sections 5.2.1.44 and
5.2.2
Aerosol spot remover
Yes
Sections 5.2.1.45 and
5.2.2
Liquid spot remover
Yes
Sections 5.2.1.46 and
5.2.2
Arts, crafts, and hobby
materials
Fixatives and finishing
spray coatings
Yes
Sections 5.2.1.47 and
5.2.2
Apparel and footwear
care products
Shoe polish
Yes
Sections 5.2.1.48 and
5.2.2
Other consumer uses
Fabric spray
Yes
Sections 5.2.1.49 and
5.2.2
Film cleaner
Yes
Sections 5.2.1.50 and
5.2.2
Hoof polish e
Yes
Sections 5.2.1.51 and
5.2.2
Pepper spray
No
Sections 5.2.1.52 and
5.2.2
Toner aid
Yes
Sections 5.2.1.53 and
5.2.2
Disposal
Disposal
Industrial pre-treatment
Yes
Sections 5.2.1.54 and
5.2.2
Industrial wastewater
treatment
Publicly owned treatment
works (POTW)
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a These categories of conditions of use appear in the Life Cycle Diagram, reflect CDR codes, and broadly
represent additional information regarding all conditions of use of TCE.
b These subcategories reflect more specific information regarding the conditions of use of TCE.
c This condition of use involves wipe cleaning. Note that the Problem Formulation described "cleaning wipes" as
a condition of use. This referred to the application of a product that is then wiped off, rather than a pre-wet
towelette.
d This includes uses assessed in the ( 4, 2014b) risk assessment.
e "Hoof polish" is in EPA's jurisdiction unless the article in question was also intended for the diagnosis, cure,
mitigation, treatment, of disease or intended to affect the structure or function of the body of animals, as described
in the FFDCA. EPA identified a single product for hoof polish containing TCE ("U.S. EPA. 2017h). and this
product is intended for only cosmetic and not medical use. Therefore, "hoof polish" was evaluated as a COU,
applicable only to products restricted to cosmetic function.
* Although EPA has identified both industrial and commercial uses here for purposes of distinguishing scenarios
in this document, the Agency interprets the authority over "any manner or method of commercial use" under
TSCA section 6(a)(5) to reach both.
5.2.1 Human Health
S 2 I 1 Manufacture - Domestic manufacture (Domestic manufacture)
Section 6(b)(4)(A) unreasonable risk determination for the domestic manufacture of TCE: Presents an
unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic inhalation exposures at the high-end and dermal
exposures at the central tendency and high-end, even when assuming use of PPE. For ONUs, EPA
found that there was unreasonable risk of non-cancer effects (autoimmunity) from chronic
inhalation exposures at the central tendency, and of cancer from chronic inhalation exposures at
the central tendency.
EPA's determination that the domestic manufacturing of TCE presents an unreasonable risk is based on the
comparison of the risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures from the condition of
use, and the uncertainties in the analysis (Section 4.3), including uncertainties related to the exposures for
ONUs:
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects and cancer from chronic inhalation at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic dermal exposures at the central tendency and
high-end support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• For ONUs, the risk estimates of non-cancer effects from acute inhalation exposures do not support an
unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
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directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed during manufacturing using monitoring data submitted by the
Halogenated Solvents Industry Alliance (HSIA) (Halogenated Solvents Industry Alliance. 2018) and
Arkema, Inc. ( ma. 2020).
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
domestic manufacturing of TCE.
5,2,1,2 Manufacture - Import (Import)
Section 6(b)(4)(A) unreasonable risk determination for the import of TCE: Presents an unreasonable
risk of injury to health (workers); does not present an unreasonable risk of injury to health (ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the import of TCE presents an unreasonable risk is based on the comparison of the
risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1,
EPA also considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3), including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed based on monitoring data using the repackaging occupational
exposure scenario.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers) from the import of
TCE.
5,2,1,3 Processing - Processing as a reactant/intermediate - Intermediate in
industrial gas manufacturing (e.gmanufacture of fluorinated gases used as
refrigerants, foam blowing agents and solvents) (Processing as a
reactant/intermediate)
Section 6(b)(4)(A) unreasonable risk determination for the processing of TCE as a reactant/intermediate:
Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic inhalation exposures at the high-end and dermal
exposures at the central tendency and high-end, even when assuming use of PPE. For ONUs, EPA
found that there was unreasonable risk of non-cancer effects (autoimmunity) from chronic
inhalation exposures at the central tendency, and of cancer from chronic inhalation exposures at
the central tendency.
EPA's determination that the processing of TCE as a reactant/intermediate presents an unreasonable risk is
based on the comparison of the risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59).
As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures from the condition of
use, and the uncertainties in the analysis (Section 4.3), including uncertainties related to the exposures for
ONUs:
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects and cancer from chronic inhalation at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic dermal exposures at the central tendency and
high-end support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• For ONUs, the risk estimates of non-cancer effects from acute inhalation exposures do not support an
unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from the manufacture of TCE as surrogate
data for the processing condition of use. EPA did not identify inhalation exposure monitoring data
related to processing TCE as a reactant. EPA believes the handling and TCE concentrations for both
conditions of use to be similar.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
processing of TCE as a reactant/intermediate.
5,2.1,4 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.glubricants
and greases, paints and coatings, other uses) (Processing into a formulation,
mixture, or reaction product)
Section 6(b)(4)(A) unreasonable risk determination for the processing of TCE into a formulation,
mixture, or reaction product: Presents an unreasonable risk of injury to health (workers); does not
present an unreasonable risk of injury to health (ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the processing of TCE into formulation, mixture, or reaction product presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to the
benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of TCE, the
exposures from the condition of use, and the uncertainties in the analysis (Section 4.3), including uncertainties
related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from repackaging as a surrogate. EPA did not
identify inhalation exposure monitoring data related to using TCE when formulating aerosol and non-
aerosol products.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers) from the processing of
TCE into formulation, mixture, or reaction product.
5,2.1,5 Processing - Incorporation into articles - Solvents (becomes an
integral component of articles) (Processing into articles)
Section 6(b)(4)(A) unreasonable risk determination for the processing of TCE into articles: Presents an
unreasonable risk of injury to health (workers); does not present an unreasonable risk of injury to
health (ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the processing of TCE into articles presents an unreasonable risk is based on the
comparison of the risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures from the condition of
use, and the uncertainties in the analysis (Section 4.3), including uncertainties related to the exposures for
ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from repackaging as a surrogate. EPA did not
identify inhalation exposure monitoring data related to using TCE when formulating aerosol and non-
aerosol products.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers) from the processing of
TCE into articles.
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5,2.1.6 Processing - Repackaging - Solvents (for cleaning or degreasing)
(Repackaging)
Section 6(b)(4)(A) unreasonable risk determination for the repackaging of TCE: Presents an
unreasonable risk of injury to health (workers); does not present an unreasonable risk of injury to
health (ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the repackaging of TCE presents an unreasonable risk is based on the comparison of
the risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section
5.1, EPA also considered the health effects of TCE, the exposures from the condition of use, and the
uncertainties in the analysis (Section 4.3), including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed based on monitoring data using the repackaging occupational
exposure scenario.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers) from the repackaging
of TCE.
*>217 Processing - Recycling - Recycling (Recycling)
Section 6(b)(4)(A) unreasonable risk determination for the recycling of TCE: Presents an
unreasonable risk of injury to health (workers); does not present an unreasonable risk of injury to
health (ONUs).
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For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the recycling of TCE presents an unreasonable risk is based on the comparison of the
risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1,
EPA also considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3), including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from repackaging as a surrogate for recycling.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers) from the recycling of
TCE.
5.2.1.8 Distribution in Commerce- Distribution (Distribution in commerce)
Section 6(b)(4)(A) unreasonable risk determination for distribution of TCE: Does not present an
unreasonable risk of injury to health (workers and ONUs).
For the purposes of the unreasonable risk determination, distribution in commerce of TCE is the
transportation associated with the moving of TCE in commerce. EPA is assuming that workers and
ONUs will not be handling TCE because the loading and unloading activities are associated with other
conditions of use and EPA assumes transportation of TCE is in compliance with existing regulations for
the transportation of hazardous materials ( ). Emissions are therefore minimal during
transportation, so there is limited exposure (with the exception of spills and leaks, which are outside the
scope of the Risk Evaluation). Based on the limited emissions and exposures from the transportation of
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chemicals, EPA determined there is no unreasonable risk of injury to health (workers and ONUs) from
the distribution in commerce of TCE.
5,2,1,9 Industrial/Commercial Use - Solvent (for cleaning or degreasing) -
Batch vapor degreaser (open-top) (Solvent for open-top batch vapor degreasing)
Section 6(b)(4)(A) unreasonable risk determination for the industrial and commercial use of TCE as a
solvent for open-top batch vapor degreasing: Presents an unreasonable risk of injury to health
(workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures and from chronic (autoimmunity) inhalation and
dermal exposures at the central tendency and high-end, even when assuming use of PPE. In
addition, for workers, EPA found that there was unreasonable risk of cancer from chronic
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures at the central tendency
and high-end, and of cancer from chronic inhalation exposures at the central tendency and high-
end.
EPA's determination that the industrial and commercial use of TCE as a solvent for open-top batch
vapor degreasing presents an unreasonable risk is based on the comparison of the risk estimates for non-
cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also
considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute and chronic inhalation exposures, and of cancer from chronic inhalation exposures at
the central tendency and high-end support an unreasonable risk determination. Similarly, when assuming
use of gloves with PF of 20, the risk estimates of non-cancer effects and cancer from chronic dermal
exposures at the central tendency and high-end support an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposure at the high-end do not support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using 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 how 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. Near-field exposure represents exposure
concentrations for workers who directly operate the vapor degreasing equipment, whereas far-field
exposure represents exposure concentrations for occupational non-users (i.e., workers in the surrounding
area who do not handle the degreasing equipment). Assumptions and key sources of uncertainty for
occupational exposures, including the near-field/ far-field framework are described in Section 2.3.1.3.
These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for open-top batch vapor degreasing.
5.2.1,10 Industrial/Commercial Use - Solvent (for cleaning or degreasing) -
Batch vapor degreaser (closed-loop) (Solvent for closed-loop batch vapor
degreasing)
Section 6(b)(4)(A) unreasonable risk determination for the industrial and commercial use of TCE as a
solvent for closed-loop batch vapor degreasing: Presents an unreasonable risk of injury to health
(workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic dermal exposures at the central tendency and high-end, even when assuming
use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from
acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the central
tendency, and of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the industrial and commercial use of TCE as a solvent for closed-loop batch
vapor degreasing presents an unreasonable risk is based on the comparison of the risk estimates for non-
cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also
considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3), including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the central tendency and high-end support an unreasonable
risk determination. Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-
cancer effects and cancer from chronic dermal exposures at the central tendency and high-end support
an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using exposure monitoring data from a Chemical Safety report
where TCE is used in closed degreasing operations. EPA assumed these reasonably available data are of
a "typical" batch closed-loop degreasing shop.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for closed-loop batch vapor degreasing.
5.2,1.11 Industrial/Commercial Use - Solvent (for cleaning or degreasing)
In-line vapor degreaser (conveyorized) (Solvent for in-line conveyorized vapor
degreasing)
Section 6(b)(4)(A) unreasonable risk determination for the industrial and commercial use of TCE as a
solvent for in-line conveyorized vapor degreasing: Presents an unreasonable risk of injury to health
(workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures and from chronic (autoimmunity) inhalation and
dermal exposures at the central tendency and high-end, even when assuming use of PPE. In
addition, for workers, EPA found that there was unreasonable risk of cancer from chronic
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures at the central tendency,
and of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the industrial and commercial use of TCE as a solvent for in-line conveyorized
vapor degreasing presents an unreasonable risk is based on the comparison of the risk estimates for non-
cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also
considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3), including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute and chronic inhalation exposures, and of cancer from chronic inhalation exposures at
the central tendency and high-end support an unreasonable risk determination. Similarly, when assuming
use of gloves with PF of 20, the risk estimates of non-cancer effects and cancer from chronic dermal
exposures at the central tendency and high-end support an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposure at the high-end do not support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures for workers were assessed using monitoring data from NIOSH investigations at
two sites using TCE in conveyorized vapor 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-Field Inhalation Exposure Model. Near-field exposure represents exposure
concentrations for workers who directly operate the vapor degreasing equipment, whereas far-field
exposure represents exposure concentrations for occupational non-users (i.e., workers in the surrounding
area who do not handle the degreasing equipment).
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for in-line conveyorized vapor degreasing.
5,2.1,12 Industrial/Commercial Use - Solvent (for cleaning or degreasing)
In-line vapor degreaser (web cleaner) (Solvent for in-line web cleaner vapor
degreasing)
Section 6(b)(4)(A) unreasonable risk determination for the industrial and commercial use of TCE as a
solvent for in-line web cleaner vapor degreasing: Presents an unreasonable risk of injury to health
(workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures at the high-end and from chronic (autoimmunity)
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. In addition, for workers, EPA found that there was unreasonable risk of cancer from
chronic inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE as a solvent for in-line web cleaner
vapor degreasing presents an unreasonable risk is based on the comparison of the risk estimates for non-
cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also
considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 20, the risk estimates of non-
cancer effects and cancer from chronic dermal exposures support an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposure at the high-end do not support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using the Web Degreasing Near-Field/Far-
Field Inhalation Exposure Model. EPA did not identify any inhalation exposure monitoring data related
to the use of TCE in web degreasing. 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. Near-field exposure represents exposure concentrations for workers who
directly operate the vapor degreasing equipment, whereas far-field exposure represents exposure
concentrations for occupational non-users {i.e., workers in the surrounding area who do not handle the
degreasing equipment). Assumptions and key sources of uncertainty for occupational exposures,
including the near-field/ far-field framework, are described in Section 2.3.1.3. These estimates were
used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for in-line web cleaner vapor degreasing.
5.2.1,13 Industrial/Commercial Use - Solvent (for cleaning or degreasing) -
Cold cleaners (Solvent for cold cleaning)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE as a
solvent for cold cleaning: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures at the high-end and from chronic (autoimmunity)
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. In addition, for workers, EPA found that there was unreasonable risk of cancer from
chronic inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE as a solvent for cold cleaning
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and
cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health
effects of TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section
4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of glove with PF of 20, the risk estimates of non-
cancer effects and cancer from chronic dermal exposures at the central tendency and high-end support
an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposure at the high-end do not support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using the Cold Cleaning Near-Field/Far-
Field Inhalation Exposure Model. EPA did not identify inhalation exposure monitoring data for the Cold
Cleaning condition of use. EPA's inhalation exposure modeling is based on a near-fi eld/far-field
approach, where a vapor generation source located inside the near-field diffuses into the surrounding
environment. Near-field exposure represents exposure concentrations for workers who directly operate
the vapor degreasing equipment, whereas far-field exposure represents exposure concentrations for
occupational non-users {i.e., workers in the surrounding area who do not handle the degreasing
equipment). Assumptions and key sources of uncertainty for occupational exposures, including the near-
field/ far-field framework are described in Section 2.3.1.3. These estimates were used for determining
worker and ONU risks.
• Dermal exposures were assessed using modeled data.
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750
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for cold cleaning.
5,2,1,14 Industrial/Commercial Use - Solvent (for cleaning or degreasing) -
Aerosol spray degreaser/cleaner; mold release (Solvent for aerosol spray
degreaser/cleaner and mold release)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE as a
solvent for aerosol spray degreaser/cleaner and mold release: Presents an unreasonable risk of injury
to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) inhalation exposures at the high-end, from chronic
(autoimmunity) inhalation exposures at the central tendency and high-end, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE as a solvent for aerosol spray
degreaser/cleaner and mold release presents an unreasonable risk is based on the comparison of the risk
estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1,
EPA also considered the health effects of TCE, the exposures from the condition of use, and the
uncertainties in the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic inhalation and dermal exposures at the central
tendency and high-end support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using the Brake Servicing Near-field/Far-
field Exposure Model. EPA did not identify inhalation exposure monitoring data related to the use of
TCE in aerosol degreasers, and used the brake servicing model as a representative scenario for this
condition of use. 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. Near-
field exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
{i.e., workers in the surrounding area who do not handle the degreasing equipment). Assumptions and
key sources of uncertainty for occupational exposures, including the near-field/ far-field framework are
described in Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE as a solvent for aerosol spray degreaser/cleaner and mold release.
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5.2,1,15 Industrial/Commercial Use - Lubricants and greases/lubricants and
lubricant additives - Tap and die fluid (Tap and die fluid)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in tap and
die fluid: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from chronic
(autoimmunity) inhalation exposures at the high-end and dermal exposures at the central
tendency and high-end, even when assuming use of PPE. In addition, for workers, EPA found that
there was unreasonable risk of cancer from chronic dermal exposures at the central tendency and
high-end, even when assuming use of PPE. For ONUs, EPA found that there was unreasonable
risk of non-cancer effects from chronic (autoimmunity) inhalation exposures at the central
tendency, and of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the industrial and commercial use of TCE in tap and die fluid presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3),
including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation at the high-end support an unreasonable risk determination. Similarly,
when assuming the use of gloves with PF of 20 the risk estimates of non-cancer effects and cancer from
chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination, and when assuming the use of respirators with APF of 50,
the risk estimates of cancer from chronic inhalation exposures at the high-end do not support an
unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from OSHA facility inspections at two sites
using TCE in metalworking fluids.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE in tap and die fluid.
5,2,1,16 Industrial/Commercial Use - Lubricants and greases/lubricants and
lubricant additives - Penetrating lubricant (Penetrating lubricant)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in
penetrating lubricant: Presents an unreasonable risk of injury to health (workers and ONUs).
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For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) inhalation exposures at the high-end, from chronic
(autoimmunity) inhalation exposures at the central tendency and high-end, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in penetrating lubricant presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic inhalation and dermal exposures at the central
tendency and high-end support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using the Brake Servicing Near-field/Far-
field Exposure Model. EPA did not identify inhalation exposure monitoring data related to this use of
TCE, and used the brake servicing model as a representative scenario for this condition of use. 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. Near-field exposure
represents exposure concentrations for workers who directly operate the vapor degreasing equipment,
whereas far-field exposure represents exposure concentrations for occupational non-users {i.e., workers
in the surrounding area who do not handle the degreasing equipment). Assumptions and key sources of
uncertainty for occupational exposures, including the near-field/ far-field framework are described in
Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (workers and ONUs) from the
industrial and commercial use of TCE in penetrating lubricant.
5,2,1,17 Industrial/Commercial Use - Adhesives and sealants - Solvent-based
adhesives and sealants; tire repair cement/sealer; mirror edge sealant (Adhesives
and sealants)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in an
adhesives and sealants: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
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855
856
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859
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861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
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886
887
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in adhesives and sealants presents
an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer
to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 10 for commercial scenarios, the
risk estimates of non-cancer effects from acute dermal exposures at the high-end, and of non-cancer
effects and cancer from chronic dermal exposures at the central tendency and high-end support an
unreasonable risk determination. When assuming the use of gloves with PF of 20 for industrial
scenarios, the risk estimates of non-cancer effects and cancer from chronic dermal exposures at the
central tendency and high-end support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using monitoring data from a NIOSH Health
Hazard Evaluation report (Chrostt 1) 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.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in adhesives and sealants.
5.2.1.18 Industrial/Commercial Use - Functional fluids (closed systems) - Heat
exchange fluid (Functional fluids)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in
functional fluids: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic inhalation exposures at the high-end and dermal
exposures at the central tendency and high-end, even when assuming use of PPE. For ONUs, EPA
found that there was unreasonable risk of non-cancer effects (autoimmunity) from chronic
inhalation exposures at the central tendency, and of cancer from chronic inhalation exposures at
the central tendency.
EPA's determination that the industrial and commercial use of TCE in functional fluids presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
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905
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911
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923
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932
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3),
including uncertainties related to the exposures for ONUs:
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects and cancer from chronic inhalation at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic dermal exposures at the central tendency and
high-end support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures do not support an
unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from loading/unloading TCE during
manufacturing as a surrogate for this condition of use. EPA did not identify inhalation exposure
monitoring data related to using TCE for other industrial uses.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in functional fluids.
5.2.1.19 Industrial/Commercial Use - Paints and coatings - Diluent in solvent-
based paints and coatings (Paints and coatings diluent)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in paints
and coatings diluent: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in paints and coatings diluent
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and
cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health
effects of TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section
4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
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941
942
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945
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949
950
951
952
953
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955
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chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 10, the risk estimates of non-
cancer effects from acute dermal exposures at the high-end, and of non-cancer effects and cancer from
chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• Inhalation exposures for workers and ONUs were assessed using 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.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in paints and coatings diluent.
5.2.1.20 Industrial/Commercial Use - Cleaning and furniture care products -
Carpet cleaner; wipe cleaning (Carpet cleaner and wipe cleaning)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in carpet
cleaner and wipe cleaning: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end, without assuming use of
respirators. In addition, for workers, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) dermal exposures, and of
cancer from chronic dermal exposures at the central tendency and high-end, without assuming use
of gloves. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from
acute (immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in carpet cleaner and wipe cleaning
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and
cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health
effects of TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section
4.3):
• Based on professional judegment regarding practices at small commercial facilities performing carpet
cleaning and wipe cleaning, EPA assumes workers are unlikely to wear respiratory protection or
regularly employ dermal protection for this condition of use.
• 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 approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
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1000
1001
1002
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{i.e., workers in the surrounding area who do not handle the degreasing equipment). Assumptions and
key sources of uncertainty for occupational exposures, including the near-field/ far-field framework are
described in Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in carpet cleaner and wipe cleaning.
5.2,1,21 Industrial/Commercial Use - Laundry and dishwashing products
Spot remover (Spot remover)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in spot
remover: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end, without assuming use of
respirators. In addition, for workers, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) dermal exposures, and of
cancer from chronic dermal exposures at the central tendency and high-end, without assuming use
of gloves. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from
acute (immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in spot remover presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• Based on professional judgement regarding practices at small commercial facilities performing spot
cleaning, EPA assumes workers are unlikely to wear respiratory protection or regularly employ dermal
protection for this condition of use.
• EPA identified minimal inhalation exposure monitoring data related to the spot cleaning use of 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. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
{i.e., workers in the surrounding area who do not handle the degreasing equipment). Assumptions and
key sources of uncertainty for occupational exposures, including the near-field/ far-field framework are
described in Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in spot remover.
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5.2.1.22 Industrial/Commercial Use - Arts, crafts and hobby materials -
Fixatives and finishing spray coatings (Fixatives and finishing spray coatings)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in fixatives
and finishing spray coatings: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures, and of
cancer from chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in fixatives and finishing spray
coatings presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer
effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the
health effects of TCE, the exposures from the condition of use, and the uncertainties in the analysis
(Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 10, the risk estimates of non-
cancer effects from acute dermal exposures at the high-end, and of non-cancer effects and cancer from
chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• Inhalation exposures for workers and ONUs were assessed using 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.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in fixatives and finishing spray coatings.
5.2.1.23 Industrial/Commercial Use - Corrosion inhibitors and anti-scaling
agents (Corrosion inhibitors and anti-scaling agents)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in
corrosion inhibitor, and anti-scaling agent: Presents an unreasonable risk of injury to health
(workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures at the high-end, and from chronic (autoimmunity)
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1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
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1107
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1109
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1112
1113
1114
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. In addition, for workers, EPA found that there was unreasonable risk of cancer from
chronic inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in corrosion inhibitors and anti-
scaling agents presents an unreasonable risk is based on the comparison of the risk estimates for non-
cancer effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also
considered the health effects of TCE, the exposures from the condition of use, and the uncertainties in
the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 20, the risk estimates of non-
cancer effects and cancer from chronic dermal exposures at the central tendency and high-end support
an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposures at the high-end do not support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using monitoring data for the use of TCE as
a processing aid from a European Commission (EC) Technical Report (EC. 2014). The data were
supplied to the EC as supporting documentation in an application for continued use of TCE under the
REACH Regulation. Because of the limited data set, EPA is unsure of the representativeness of these
data toward actual exposures to TCE for all sites covered by this condition of use.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in corrosion inhibitors and anti-scaling
agents.
5.2.1.24 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 beta-cyclodextrin manufacture
(Processing aids)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in
processing aids: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures at the high-end, and from chronic (autoimmunity)
inhalation and dermal exposures at the central tendency and high-end, even when assuming use of
PPE. In addition, for workers, EPA found that there was unreasonable risk of cancer from
chronic inhalation and dermal exposures at the central tendency and high-end, even when
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1118
1119
1120
1121
1122
1123
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1125
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assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at the
central tendency and high-end, and of cancer from chronic inhalation exposures at the central
tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in processing aids presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from acute inhalation exposures at the high-end, and of non-cancer effects and cancer from
chronic inhalation exposures at the central tendency and high-end support an unreasonable risk
determination. Similarly, when assuming the use of gloves with PF of 20, the risk estimates of non-
cancer effects and cancer from chronic dermal exposures at the central tendency and high-end support
an unreasonable risk determination.
• For workers, when assuming the use of gloves with PF of 20, the risk estimates of non-cancer effects
from acute dermal exposures at the high-end do not support an unreasonable risk determination.
• Inhalation exposures for workers and ONUs were assessed using monitoring data for the use of TCE as
a processing aid from a European Commission (EC) Technical Report (EC. 2014). The data were
supplied to the EC as supporting documentation in an application for continued use of TCE under the
REACH Regulation. Because of the limited data set, EPA is unsure of the representativeness of these
data toward actual exposures to TCE for all sites covered by this condition of use.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in processing aids.
5.2,1,25 Industrial/Commercial Use - Ink, toner, and colorant products -
Toner aid (Toner aid)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in toner
aid: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation exposures at the high-end, and from chronic (autoimmunity)
inhalation exposures at the central tendency and high-end, and of cancer from chronic inhalation
exposures at the central tendency and high-end, without assuming use of respirators. In addition,
for workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) dermal exposures, and of cancer from chronic
dermal exposures at the central tendency and high-end, without assuming use of gloves. For
ONUs, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity) and
cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the industrial and commercial use of TCE in toner aid presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
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TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3),
including uncertainties related to the exposures for ONUs:
• Based on professional judgement regarding practices at small commercial facilities using toner aid for
commercial printing and copying, EPA assumes workers are unlikely to wear respiratory protection or
regularly employ dermal protection for this condition of use.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from a NIOSH Health Hazard Evaluation
(HHE) report (Finely and Paee. 2005) using TCE in high speed printing presses.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in toner aid.
5.2.1.26 Industrial/Commercial Use - Automotive care products - Brake and
parts cleaners (Brake and parts cleaners)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in brake
and parts cleaners: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) inhalation and dermal exposures at the high-end, and from chronic
(autoimmunity) inhalation and dermal exposures at the central tendency and high-end, even when
assuming use of PPE. In addition, for workers, EPA found that there was unreasonable risk of
cancer from chronic inhalation and dermal exposures at the central tendency and high-end, even
when assuming use of PPE. For ONUs, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) inhalation exposures at the high-end, from chronic
(autoimmunity) inhalation exposures at the central tendency and high-end, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in brake and parts cleaners presents
an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer
to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• For workers, when assuming the use of respirators with APF of 50 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end, and the risk
estimates of non-cancer effects and cancer from chronic inhalation and dermal exposures at the central
tendency and high-end support an unreasonable risk determination.
• Inhalation exposures for workers an ONUs were assessed using the Brake Servicing Near-field/Far-field
Exposure Model. EPA did not identify inhalation exposure monitoring data related to this use of TCE,
and used the brake servicing model as a representative scenario for this condition of use. 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. Near-field exposure
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represents exposure concentrations for workers who directly operate the vapor degreasing equipment,
whereas far-field exposure represents exposure concentrations for occupational non-users {i.e., workers
in the surrounding area who do not handle the degreasing equipment). Assumptions and key sources of
uncertainty for occupational exposures, including the near-field/ far-field framework are described in
Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in brake and parts cleaners.
5.2.1,27 Industrial/Commercial Use - Apparel and footwear care products -
Shoe polish (Shoe polish)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in shoe
polish: Presents an unreasonable risk of injury to health (workers and ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end, without assuming use of
respirators. In addition, for workers, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) dermal exposures, and of
cancer from chronic dermal exposures at the central tendency and high-end, without assuming use
of gloves. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from
acute (immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in shoe polish presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects and cancer to
the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures from the condition of use, and the uncertainties in the analysis (Section 4.3):
• Based on professional judgement regarding practices at small commercial facilities using shoe polish,
EPA assumes workers are unlikely to wear respiratory protection or regularly employ dermal protection
for this condition of use.
• 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 approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
{i.e., workers in the surrounding area who do not handle the degreasing equipment). Assumptions and
key sources of uncertainty for occupational exposures, including the near-field/ far-field framework are
described in Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
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In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in shoe polish.
5,2,1,28 Industrial/Commercial Use - Hoof polishes; gun scrubber; pepper
spray; other miscellaneous industrial and commercial uses (Other industrial and
commercial uses)
Section 6(b)(4)(A) unreasonable risk determination for the industrial/commercial use of TCE in other
industrial and commercial uses: Presents an unreasonable risk of injury to health (workers and
ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects from acute
(immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end, without assuming use of
respirators. In addition, for workers, EPA found that there was unreasonable risk of non-cancer
effects from acute (immunosuppression) and chronic (autoimmunity) dermal exposures, and of
cancer from chronic dermal exposures at the central tendency and high-end, without assuming use
of gloves. For ONUs, EPA found that there was unreasonable risk of non-cancer effects from
acute (immunosuppression) and chronic (autoimmunity) inhalation exposures, and of cancer from
chronic inhalation exposures at the central tendency and high-end.
EPA's determination that the industrial and commercial use of TCE in other industrial and commercial
uses presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer
effects and cancer to the benchmarks (Table 4-59). As explained in Section 5.1, EPA also considered the
health effects of TCE, the exposures from the condition of use, and the uncertainties in the analysis
(Section 4.3):
• Based on professional judgement regarding practices at small commercial facilities using miscellaneous
commercial uses, EPA assumes workers are unlikely to wear respiratory protection or regularly employ
dermal protection for this condition of use.
• 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 approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
{i.e., workers in the surrounding area who do not handle the degreasing equipment). Assumptions and
key sources of uncertainty for occupational exposures, including the near-field/ far-field framework are
described in Section 2.3.1.3. These estimates were used for determining worker and ONU risks.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers
and ONUs) from the industrial and commercial use of TCE in other industrial and commercial uses.
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5.2.1.29 Consumer Use - Solvents (for cleaning or degreasing) - Brake and
parts cleaner (Solvent in brake and parts cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in brake
and parts cleaners: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE as a solvent in brake and parts cleaner presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent in brake and parts cleaner were based on
modeled risk estimates of four aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in brake and parts cleaner.
5.2,1.30 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol
electronic degreaser/cleaner (Solvent in aerosol electronic degreaser/cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in aerosol
electronic degreaser/cleaner: Presents an unreasonable risk of injury to health (consumers and
bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the moderate and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the moderate and high intensity use.
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EPA's determination that the consumer use of TCE as a solvent in aerosol electronic degreaser/cleaner
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to
the benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent in aerosol electronic degreaser/cleaner were
based on modeled risk estimates of nine aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in aerosol electronic degreaser/cleaner.
5,2,1,31 Consumer Use - Solvents (for cleaning or degreasing) - Liquid
electronic degreaser/cleaner (Solvent in liquid electronic degreaser/cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in liquid
electronic degreaser/cleaner: Presents an unreasonable risk of injury to health (consumers and
bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE as a solvent in liquid electronic degreaser/cleaner
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to
the benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for liquid electronic degreaser/cleaner were
based on modeled risk estimates of one liquid product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
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magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in liquid electronic degreaser/cleaner.
5,2,1,32 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner (Solvent in aerosol spray degreaser/cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in aerosol
spray degreaser/cleaner: Presents an unreasonable risk of injury to health (consumers and
bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the low, moderate, and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use.
EPA's determination that the consumer use of TCE as a solvent in aerosol spray degreaser/cleaner
presents an unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to
the benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of
TCE, the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for aerosol spray degreaser/cleaner were based
on modeled risk estimates of eight aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in aerosol spray degreaser/cleaner.
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5,2.1.33 Consumer Use - Solvents (for cleaning or degreasing) - Liquid
degreaser/cleaner (Solvent in liquid degreaser/cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in liquid
degreaser/cleaner: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the low, moderate, and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use.
EPA's determination that the consumer use of TCE as a solvent in liquid degreaser/cleaner presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for liquid degreaser/cleaner were based on
modeled risk estimates of two aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in liquid degreaser/cleaner.
5.2.1.34 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol gun
scrubber (Solvent in aerosol gun scrubber)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in aerosol
gun scrubber: Presents an unreasonable risk of injury to health (consumers); does not present an
unreasonable risk of injury to health (bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute dermal exposures at the low, moderate, and high intensity use.
For bystanders, EPA found no unreasonable risk of non-cancer effects (immunosuppression) from acute
inhalation exposures at the low, moderate, and high intensity use.
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1501
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EPA's determination that the consumer use of TCE as a solvent in aerosol gun scrubber presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• For consumers, the risk estimates of non-cancer effects from acute inhalation exposures do not support
an unreasonable risk determination.
• Risk estimates for the consumer use of TCE as a solvent for aerosol gun scrubber were based on
modeled risk estimates of two aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers)
from the consumer use of TCE as a solvent in aerosol gun scrubber.
5.2,1,35 Consumer Use - Solvents (for cleaning or degreasing) - Liquid gun
scrubber (Solvent in liquid gun scrubber)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in liquid
gun scrubber: Presents an unreasonable risk of injury to health (consumers); does not present an
unreasonable risk of injury to health (bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute dermal exposures at the low, moderate, and high intensity use.
For bystanders, EPA found no unreasonable risk of non-cancer effects (immunosuppression) from acute
inhalation exposures at the low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE as a solvent in liquid gun scrubber presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• For consumers, the risk estimates of non-cancer effects from acute inhalation exposures do not support
an unreasonable risk determination.
• Risk estimates for the consumer use of TCE as a solvent for liquid gun scrubber were based on modeled
risk estimates of one liquid product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
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several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers)
from the consumer use of TCE as a solvent in liquid gun scrubber.
5.2,1,36 Consumer Use - Solvents (for cleaning or degreasing) - Mold release
(Solvent in mold release)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in mold
release: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE as a solvent in mold release presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for mold release were based on modeled risk
estimates of two aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in mold release.
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5,2.1.37 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire
cleaner (Solvent in aerosol tire cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in aerosol
tire cleaner: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the low, moderate, and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use.
EPA's determination that the consumer use of TCE as a solvent in aerosol tire cleaner presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for aerosol tire cleaner were based on modeled
risk estimates of two aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in aerosol tire cleaner.
5.2,1.38 Consumer Use - Solvents (for cleaning or degreasing) - Liquid tire
cleaner (Solvent in liquid tire cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE as a solvent in liquid
tire cleaner: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the low, moderate, and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use.
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EPA's determination that the consumer use of TCE as a solvent in liquid tire cleaner presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a solvent for liquid tire cleaner were based on modeled
risk estimates of one liquid product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE as a solvent in liquid tire cleaner.
5.2.1.39 Consumer Use - Lubricants and greases - Tap and die fluid (Tap and
die fluid)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in tap and die fluid:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate, and high intensity use.
EPA's determination that the consumer use of TCE in tap and die fluid presents an unreasonable risk is
based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a lubricant and grease in tap and die fluid were based on
modeled risk estimates of one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
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The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in tap and die fluid.
5.2,1,40 Consumer Use - Lubricants and greases - Penetrating lubricant
(Penetrating lubricant)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in a penetrating
lubricant: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the moderate and high intensity use, and
from acute dermal exposures at the high intensity use. For bystanders, EPA found unreasonable
risk of non-cancer effects (immunosuppression) from acute inhalation exposures at the moderate
and high intensity use.
EPA's determination that the consumer use of TCE in a penetrating lubricant presents an unreasonable
risk is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table
4-60). As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE as a penetrating lubricant were based on modeled risk
estimates of five aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in penetrating lubricant.
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5,2.1.41 Consumer Use - Adhesives and sealants - Solvent-based adhesives
and sealants (Solvent-based adhesives and sealants)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in solvent-based
adhesives and sealants: Presents an unreasonable risk of injury to health (consumers and
bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the moderate and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE in solvent-based adhesives and sealants presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in adhesives and sealants as solvent-based adhesive and
sealant were based on modeled risk estimates of three liquid products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in solvent-based adhesives and sealants.
5.2.1.42 Consumer Use - Adhesives and sealants - Mirror edge sealant
(Mirror edge sealant)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in mirror edge sealant:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation and dermal exposures at the moderate and high
intensity use. For bystanders, EPA found unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the high intensity use.
EPA's determination that the consumer use of TCE in mirror edge sealant presents an unreasonable risk
is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60).
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As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in adhesives and sealants as mirror edge sealant
were based on modeled risk estimates of one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure
Model Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and
bystanders depends on several factors, including the concentration of TCE in products used, use
patterns (including frequency, duration, amount of product used, room of use, and local
ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in mirror edge sealant.
5,2,1,43 Consumer Use - Adhesives and sealants - Tire repair cement/sealer
(Tire repair cement/sealer)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in tire repair
cement/sealer: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the moderate and high intensity use, and
from acute dermal exposures at the low, moderate, and high intensity use. For bystanders, EPA
found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE in tire repair cement/sealer presents an unreasonable
risk is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table
4-60). As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in adhesives and sealants as tire repair cement/sealer
were based on modeled risk estimates of five liquid products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure
Model Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and
bystanders depends on several factors, including the concentration of TCE in products used, use
patterns (including frequency, duration, amount of product used, room of use, and local
ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
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thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in tire repair cement/sealer.
5.2.1.44 Consumer Use - Cleaning and furniture care products - Carpet
cleaner (Carpet cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in carpet cleaner:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE in carpet cleaner presents an unreasonable risk is
based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in cleaning and furniture care products as carpet cleaner
were based on modeled risk estimates of one liquid formulation.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE in carpet cleaner.
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5.2,1,45 Consumer Use - Cleaning and furniture care products - Aerosol spot
remover (Aerosol spot remover)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in aerosol spot
remover: Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use and from acute dermal exposures at the moderate and high intensity use. For bystanders, EPA
found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE in aerosol spot remover presents an unreasonable
risk is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table
4-60). As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in cleaning and furniture care products as aerosol spot
remover were based on modeled risk estimates of one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE in as aerosol spot remover.
5,2,1,46 Consumer Use - Cleaning and furniture care products - Liquid spot
remover (Liquid spot remover)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in liquid spot remover:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use and from acute dermal exposures at the moderate and high intensity use. For bystanders, EPA
found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the low, moderate, and high intensity use.
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1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
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1914
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1916
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1918
1919
1920
1921
1922
EPA's determination that the consumer use of TCE in liquid spot remover presents an unreasonable risk
is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60).
As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in cleaning and furniture care products as liquid spot
remover were based on modeled risk estimates of four liquid products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of uncertainties
support EPA's determination that there is unreasonable risk of injury to health (consumers and bystanders) from
the consumer use of TCE in liquid spot remover.
5.2.1.47 Consumer Use - Arts, crafts, and hobby materials - Fixatives and
finishing spray coatings (Fixatives and finishing spray coatings)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in fixative and
finishing spray coating: Presents an unreasonable risk of injury to health (consumers and
bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the moderate and high intensity use.
EPA's determination that the consumer use of TCE in as fixative and finishing spray coating presents an
unreasonable risk is based on the comparison of the risk estimates for non-cancer effects to the
benchmarks (Table 4-60). As explained in Section 5.1, EPA also considered the health effects of TCE,
the exposures for the condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in arts, crafts, and hobby materials as fixative and
finishing spray coating were based on modeled risk estimates of one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
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1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
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1966
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in fixative and finishing spray coating.
5.2,1,48 Consumer Use - Apparel and footwear care products - Shoe polish
(Shoe polish)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in shoe polish:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the moderate and high intensity use and
from acute dermal exposures at the high intensity use. For bystanders, EPA found unreasonable
risk of non-cancer effects (immunosuppression) from acute inhalation exposures at the high
intensity use.
EPA's determination that the consumer use of TCE in shoe polish presents an unreasonable risk is based
on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in apparel and footwear care products in shoe polish were
based on modeled risk estimates of one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Permeability). Dermal exposures to
consumers result from dermal contact involving impeded evaporation while using the product. The
magnitude of dermal exposures depends on several factors, including skin surface area, concentration of
TCE in product used, permeability coefficient, and dermal exposure duration. The potential for dermal
exposures to TCE is limited by several factors including physical-chemical properties of TCE, such as
high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in shoe polish.
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1980
1981
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1988
1989
1990
1991
1992
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1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
5.2.1.49
Consumer Use - Other consumer uses - Fabric spray (Fabric spray)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in fabric spray:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects (immuno-
suppression) from acute inhalation exposures at the low, moderate, and high intensity use, and
from acute dermal exposures at the moderate and high intensity use. For bystanders, EPA found
unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation exposures at
the moderate and high intensity use.
EPA's determination that the consumer use of TCE in fabric spray presents an unreasonable risk is
based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in fabric spray were based on modeled risk estimates of one
aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in fabric spray.
5.2.1.50 Consumer Use - Other consumer uses - Film cleaner (Film cleaner)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in film cleaner:
Presents an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the low, moderate, and high intensity
use, and from acute dermal exposures at the moderate and high intensity use. For bystanders,
EPA found unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE in film cleaner presents an unreasonable risk is
based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
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2013
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2015
2016
2017
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2020
2021
2022
2023
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2025
2026
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2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
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2051
2052
2053
2054
2055
2056
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in film cleaner were based on modeled risk estimates of two
aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in film cleaner.
5.2.1.51 Consumer Use - Other consumer uses - Hoof polish (hoof polish)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in hoof polish:
Presents an unreasonable risk of injury to health (consumers); does not present an unreasonable risk
of injury to health (bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects
(immunosuppression) from acute inhalation exposures at the high intensity use, and from acute
dermal exposures at the moderate and high intensity use. For bystanders, EPA found no
unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation exposures at the
low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE in hoof polish presents an unreasonable risk is based
on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in hoof polish were based on modeled risk estimates of one
aerosol product and shoe polish and spray/coating formulations.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
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2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
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absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers)
from the consumer use of TCE in hoof polish.
2 I ^2 Consumer Use - Other consumer uses - Pepper spray (Pepper spray)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in pepper spray: Does
not present an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was no unreasonable risk of non-cancer effects (immunosuppression)
from acute inhalation and dermal exposures at the low, moderate, and high intensity use. For bystanders,
EPA found no unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation
exposures at the low, moderate, and high intensity use.
EPA's determination that the consumer use of TCE in pepper spray does not present an unreasonable
risk is based on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table
4-60). As explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the
condition of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in pepper spray were based on modeled risk estimates of
two aerosol products.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is no unreasonable risk of injury to health
(consumers and bystanders) from the consumer use of TCE in pepper spray.
5.2.1.53 Consumer Use - Other consumer uses - Toner aid (Toner aid)
Section 6(b)(4)(A) unreasonable risk determination for the consumer use of TCE in toner aid: Presents
an unreasonable risk of injury to health (consumers and bystanders).
For consumers, EPA found there was unreasonable risk of non-cancer effects (immuno-
suppression) from acute inhalation exposures at the low, moderate, and high intensity use, and
from acute dermal exposures at the moderate and high intensity use. For bystanders, EPA found
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unreasonable risk of non-cancer effects (immunosuppression) from acute inhalation exposures at
the moderate and high intensity use.
EPA's determination that the consumer use of TCE in toner aid presents an unreasonable risk is based
on the comparison of the risk estimates for non-cancer effects to the benchmarks (Table 4-60). As
explained in Section 5.1, EPA also considered the health effects of TCE, the exposures for the condition
of use, and the uncertainties in the analysis (Section 4.3):
• Risk estimates for the consumer use of TCE in toner aid were based on modeled risk estimates of
one aerosol product.
• Inhalation exposures to consumers and bystanders were evaluated with the Consumer Exposure Model
Version 2.1 (CEM 2.1). The magnitude of inhalation exposures to consumers and bystanders depends on
several factors, including the concentration of TCE in products used, use patterns (including frequency,
duration, amount of product used, room of use, and local ventilation), and application methods.
• Dermal exposures to consumers were evaluated with the CEM (Fraction Absorbed). Dermal exposures
to consumers result from dermal contact not involving impeded evaporation while using the product.
The magnitude of dermal exposures depends on several factors, including skin surface area, film
thickness, concentration of TCE in product used, dermal exposure duration, and estimated fractional
absorption. The potential for dermal exposures to TCE is limited by several factors including physical-
chemical properties of TCE, such as high vapor pressure.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (consumers
and bystanders) from the consumer use of TCE in toner aid.
5.2,1.54 Disposal - Disposal - Industrial pre-treatment; Industrial wastewater
treatment; Publicly owned treatment works (POTW) (Disposal)
Section 6(b)(4)(A) unreasonable risk determination for the disposal of TCE: Presents an unreasonable
risk of injury to health (workers); does not present an unreasonable risk of injury to health (ONUs).
For workers, EPA found that there was unreasonable risk of non-cancer effects (autoimmunity)
from chronic inhalation exposures at the high-end and dermal exposures at the central tendency
and high-end, even when assuming use of PPE. In addition, for workers, EPA found that there
was unreasonable risk of cancer from chronic dermal exposures at the central tendency and high-
end, even when assuming use of PPE. For ONUs, EPA found that there was no unreasonable risk of
non-cancer effects from acute (immunosuppression) and chronic (autoimmunity) inhalation exposures at
the central tendency, or of cancer from chronic inhalation exposures at the central tendency.
EPA's determination that the disposal of TCE presents an unreasonable risk is based on the comparison
of the risk estimates for non-cancer effects and cancer to the benchmarks (Table 4-59). As explained in
Section 5.1, EPA also considered the health effects of TCE, the exposures from the condition of use, and
the uncertainties in the analysis (Section 4.3), including uncertainties related to the exposure for ONUs:
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of non-cancer
effects from chronic inhalation exposures at the high-end support an unreasonable risk determination.
Similarly, when assuming use of gloves with PF of 20, the risk estimates of non-cancer effects and
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2166
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cancer from chronic dermal exposures at the central tendency and high-end support an unreasonable risk
determination.
• For workers, when assuming the use of respirators with APF of 50, the risk estimates of cancer from
chronic inhalation exposures at the high-end do not support an unreasonable risk determination.
• For workers, when assuming the use of respirators with APF of 10 and gloves with PF of 20, the risk
estimates of non-cancer effects from acute inhalation and dermal exposures at the high-end do not
support an unreasonable risk determination.
• Based on EPA's analysis, the data for worker and ONU inhalation exposures could not be distinguished;
however, ONU inhalation exposures are assumed to be lower than inhalation exposures for workers
directly handling the chemical substance. To account for this uncertainty, EPA considered the workers'
central tendency risk estimates from inhalation exposures when determining ONUs' unreasonable risk.
• Inhalation exposures were assessed using monitoring data from repackaging as a surrogate for disposal.
• Dermal exposures were assessed using modeled data.
In summary, the risk estimates, the health effects of TCE, the exposures, and consideration of
uncertainties support EPA's determination that there is unreasonable risk of injury to health (workers)
from disposal of TCE.
5.2,2 Environment
Section 6(b)(4)(A) unreasonable risk determination for all conditions of use of TCE: Does not present an
unreasonable risk of injury to the environment (aquatic, sediment-dwelling, and terrestrial organisms).
For all conditions of use, for aquatic organisms, the RQ values (Table 4-57 and Table 4-58) do not support an
unreasonable risk determination in water for acute and chronic exposures of TCE. To characterize the exposure
to TCE by aquatic organisms, EPA used modeled data to represent surface water concentrations near facilities
actively releasing TCE to surface water, and monitored concentrations to represent ambient water
concentrations of TCE. EPA considered the biological relevance of the species to determine the concentrations
of concern for the location of surface water concentration data to produce RQs, as well as frequency and
duration of the exposure. Some site-specific RQs that were calculated from modeled release data were greater
than or equal to one. Facilities with RQs >1 and duration of the exceedance are presented in Table 4-1.
Uncertainties related to these particular estimates are discussed in Section 4.3.1. Uncertainties in the modeled
concentrations include underestimating exposure due to limitations in data reported through TRI and DMR, and
some sites may not be included in the data analyzed. However, the modeled concentrations also overestimates
exposures because it does not take volatilization of TCE into consideration; furthermore, the model does not
indicate if the 20 days of exceedance of the chronic COC are consecutive or could occur sporadically
throughout the year. Since TCE is a volatile chemical, it is more likely that a chronic exposure duration will
occur when there are more days of exceedances. As an additional uncertainty, the model may not consider
dilution in static water bodies. The monitoring data did not reflect conditions downstream from facilities and
was limited temporally and geographically.
For sediment-dwelling invertebrates, the toxicity of TCE is similar to the toxicity to aquatic
invertebrates. TCE is expected to remain in aqueous phases and not adsorb to sediment due to its water
solubility and low partitioning to organic matter. TCE has relatively low partitioning to organic matter
and biodegrades slowly, so 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 where anaerobic condition
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prevails. Thus, the TCE detected in sediments is likely from the pore water. Therefore, for sediment-
dwelling organisms, the risk estimates, based on the highest ambient surface water concentration, do not
support an unreasonable risk determination to sediment-dwelling organisms from acute or chronic
exposures. There is uncertainty due to the lack of ecotoxicity studies specifically for sediment-dwelling
organisms and limited sediment monitoring data.
For terrestrial organisms, TCE exposure is expected to be low since physical-chemical properties do not support
an exposure pathway through water and soil pathways to these organisms.
In summary, the risk estimates, the environmental effects of TCE, the exposures, physical-chemical properties
of TCE, and consideration of uncertainties support EPA's determination that there is no unreasonable risk to the
environment from all conditions of use of TCE.
5.3 Unreasonable Risk Determination Conclusion
5.3.1 No Unreasonable Risk Determinations
TSCA section 6(b)(4) requires EPA to conduct Risk Evaluations to determine whether chemical
substances present unreasonable risk under their conditions of use. In conducting Risk Evaluations,
"EPA will determine whether the chemical substance presents an unreasonable risk of injury to health or
the environment under each condition of use within the scope of the Risk Evaluation..40 CFR
702.47. Pursuant to TSCA section 6(i)(l), a determination of "no unreasonable risk" shall be issued by
order and considered to be final agency action. Under EPA's implementing regulations, "[a]
determination made by EPA that the chemical substance, under one or more of the conditions of use
within the scope of the Risk Evaluations, does not present an unreasonable risk of injury to health or the
environment will be issued by order and considered to be a final Agency action, effective on the date of
issuance of the order." 40 CFR 702.49(d).
EPA has determined that the following conditions of use of TCE do not present an unreasonable risk of
injury to health or the environment:
• Distribution in commerce (Section 5.2.1.8, Section 5.2.2, Section 4, and Section 3)
• Consumer use in pepper spray (Section 5.2.1.52, Section 5.2.2, Section 4, and Section 3)
This subsection of the final Risk Evaluation therefore constitutes the order required under TSCA section
6(i)(l), and the "no unreasonable risk" determinations in this subsection are considered to be final
agency action effective on the date of issuance of this order. All assumptions that went into reaching the
determinations of no unreasonable risk for these conditions of use, including any considerations
excluded for these conditions of use, are incorporated into this order.
The support for each determination of "no unreasonable risk" is set forth in Section 5.2 of the final Risk
Evaluation, "Detailed Unreasonable Risk Determinations by Condition of Use." This subsection also
constitutes the statement of basis and purpose required by TSCA section 26(f).
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5.3.2 Unreasonable Risk Determinations
EPA has determined that the following conditions of use of TCE present an unreasonable risk of injury
to health:
• Manufacturing: domestic manufacture
• Manufacturing: import
• Processing: processing as a reactant/intermediate
• Processing: incorporation into a formulation, mixture or reaction product
• Processing: incorporation into articles
• Processing: repackaging
• Processing: recycling
• Industrial and commercial use as a solvent for open-top batch vapor degreasing
• Industrial and commercial use as a solvent for closed-loop batch vapor degreasing
• Industrial and commercial use as a solvent for in-line conveyorized vapor degreasing
• Industrial and commercial use as a solvent for in-line web cleaner vapor degreasing
• Industrial and commercial use as a solvent for cold cleaning
• Industrial and commercial use as a solvent for aerosol spray degreaser/cleaner and mold release
• Industrial and commercial use as a lubricant and grease in tap and die fluid
• Industrial and commercial use as a lubricant and grease in penetrating lubricant
• Industrial and commercial use as an adhesive and sealant in solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant
• Industrial and commercial use as a functional fluid in heat exchange fluid
• Industrial and commercial use in paints and coatings as a diluent in solvent-based paints and
coatings
• Industrial and commercial use in cleaning and furniture care products in carpet cleaner and wipe
cleaning
• Industrial and commercial use in laundry and dishwashing products in spot remover
• Industrial and commercial use in arts, crafts, and hobby materials in fixatives and finishing spray
coatings
• Industrial and commercial use in corrosion inhibitors and anti-scaling agents.
• Industrial and commercial use as processing aids in 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 beta-
cyclodextrin manufacture
• Industrial and commercial use as ink, toner and colorant products in toner aid
• Industrial and commercial use in automotive care products in brake parts cleaner
• Industrial and commercial use in apparel and footwear care products in shoe polish
• Industrial and commercial use in hoof polish; gun scrubber; pepper spray; other miscellaneous
industrial and commercial uses
• Consumer use as a solvent in brake and parts cleaner
• Consumer use as a solvent in aerosol electronic degreaser/cleaner
• Consumer use as a solvent in liquid electronic degreaser/cleaner
• Consumer use as a solvent in aerosol spray degreaser/cleaner
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Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
as
Consumer
use
in
Consumer
use
in
Consumer
use
in
Consumer
use
in
Consumer
use
in
Consumer
use
in
Disposal
EPA will initiate TSCA section 6(a) risk management actions on these conditions of use as required
under TSCA section 6(c)(1). Pursuant to TSCA section 6(i)(2), the "unreasonable risk"
determinations for these conditions of use are not considered final agency action.
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1 APPENDICES
2
3 Appendix A REGULATORY HISTORY
4 A.l Federal Laws and Regulations
5
Table Apx A-l. Federa
Laws and Regulations
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
(including import), processing,
distribution in commerce, use or disposal
of a chemical if EPA evaluates the risk
and concludes that the chemical presents
an unreasonable risk 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 (I I;
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 unreasonable risk 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 (
•m - 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 substances drawn 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
(Si K< -i V, December 19,
2016).
TSCA - Section 5(a)
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.
Significant New Use Rule (SNUR)
rsi FR 20535; April 8. 2016V
TCE is subject to reporting under
the SNUR for manufacture
(including import) or processing of
TCE for use in a consumer product
except for use in cleaners and
solvent degreasers, film cleaners,
hoof polishes, lubricants, mirror
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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 exposure-
related 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 importers),
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,
ChemView. 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, ChemView. Accessed
April 13, 2017).
Emergency Planning
and Community Right-
to-Know Act (EPCRA)
- Section 313
Requires annual reporting from facilities
in specific industry sectors that employ
10 or more full time equivalent
employees and that manufacture, process,
or otherwise use a Toxics Release
Inventory (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, releases and other waste
management (e.g., quantities recycled,
treated, combusted) and pollution
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
prevention activities (under section 6607
of the Pollution Prevention Act). These
data include on- and off-site data as well
as multimedia data {i.e., air, land and
water).
Federal Insecticide,
Fungicide, and
Rodenticide Act
(FIFRA) - Sections 3
and 6
FIFRA governs the sale, distribution and
use of pesticides. Section 3 of FIFRA
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
"unreasonable adverse effects on the
environment." Section 6 of FIFRA
provides EPA with the authority to
cancel pesticide registrations if either: (1)
the pesticide, labeling, or other material
does not comply with FIFRA or (2) when
used in accordance with widespread and
commonly recognized practice, the
pesticide generally causes unreasonable
adverse effects on the environment.
TCE is no longer used as an inert
ingredient in pesticide products.
Clean Air Act (CAA) -
Section 112(b)
Defines the original list of CAA
hazardous air pollutants (HAPs). Under
112(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 Administrator to modify
the list of HAPs by adding or deleting a
substance.
Lists TCE as a HAP (42 U.S.C.
7412(b)(1)).
CAA - Section 112(d)
Directs EPA to establish, by rule,
National Emission Standards for
Hazardous Air Pollutants (NESHAP) for
each category or subcategory of listed
major sources and area sources of HAPs
(listed pursuant to Section 112(c)). The
standards must require the maximum
degree of emission reduction that the
EPA determines to be achievable by each
particular source category. This is
generally referred to as maximum
achievable control technology (MACT).
For area sources, the standards must
EPA has promulgated a number of
NESHAP regulating industrial
source categories that emit
trichloroethylene and other HAPs.
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
require generally achievable control
technology (GACT) though may require
MACT.
CAA - Sections 112(d)
and 112 (f)
Risk and technology review (RTR) of
section 112(d) MACT standards. Section
112(f)(2) requires EPA to conduct risk
assessments for each source category
subject to section 112(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, taking into 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; Mav 3.
2007) and will do so, as required,
for the remaining source
categories with NESHAP.
Clean Water Act
(CWA) - Sections
301(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)(1)
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
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 (Section 301(b), 304(b),
307(b), 306) or on a case-by-case best
professional judgement basis in National
Pollutant Discharge Elimination System
(NPDES) permits, see Section
4029a)(l)(B).
Page 493 of 803
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Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
Safe Drinking Water
Act (SDWA) - Section
1412
Requires EPA to publish a non-
enforceable maximum contaminant level
goals (MCLGs) for contaminants which
1. may have an adverse effect on the
health of persons; 2. are known to occur
or there is a substantial likelihood that
the contaminant will occur in public
water systems with a frequency and at
levels of public health concern; and 3. in
the sole judgement of the Administrator,
regulation of the contaminant presents a
meaningful opportunity for health risk
reductions for persons served by public
water systems. When EPA publishes an
MCLG, EPA must also promulgate a
National Primary Drinking Water
Regulation (NPDWR) which includes
either an enforceable maximum
contaminant level (MCL), or a required
treatment technique. Public water
systems are required to comply with
NPDWRs.
TCE is subject to NPDWR under
the SDWA with a MCLG of zero
and an enforceable MCL of
0.005 mg/L (52 FR 25690, July 8,
1987).
Resource Conservation
and Recovery Act
(RCRA) - Section 3001
Directs EPA to develop and promulgate
criteria for identifying the characteristics
of hazardous waste, and for listing
hazardous waste, taking into account
toxicity, persistence, and degradability in
nature, potential for accumulation in
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
intermediates or off-specification
commercial chemical products or
manufacturing chemical
intermediates that, 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;
F001, F002; U228
Comprehensive
Environmental
Response,
Compensation and
Liability Act
(CERCLA) - Section
102(a) and 103
Authorizes EPA to promulgate
regulations designating as hazardous
substances those substances which, when
released into the environment, may
present substantial danger to the public
health or welfare or the environment.
EPA must also promulgate regulations
establishing the quantity of any
hazardous substance the release of which
must be reported under Section 103.
TCE is a hazardous substance with
a reportable quantity pursuant to
section 102(a) of CERCLA (40
CFR 302.4) and EPA is actively
overseeing cleanup of sites
contaminated with TCE pursuant
to the National Contingency Plan
(NCP) (40 CFR 751).
Page 494 of 803
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Statutes/Regulations
Description of Authority/Regulation
Description 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
Occupational Safety
and Health Act (OSH
Act)
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 (29 U.S.C. section
651 et seq.).
Under the Act, OSHA can issue
occupational safety and health standards
including such provisions as Permissible
Exposure Limits (PELs), exposure
monitoring, engineering and
administrative controls, and respiratory
protection.
In 1971, OSHA issued
occupational safety and health
standards for TCE that included a
PEL of 100 ppm as an 8-hr TWA
with an acceptable ceiling
concentration of 200 ppm. An
acceptable maximum peak above
the acceptable ceiling
concentration for an 8 hour shift is
300 ppm, based on the maximum
duration of 5 minutes in any 2
hours (29 CFR 1910.1000).
While OSHA has established a
PEL for TCE, OSHA has
recognized that many of its PELs
are outdated and inadequate for
ensuring protection of worker
health. Most of OSHA'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.
"OSHA recommends that
employers consider using the
alternative occupational exposure
limits because the Agency believes
that exposures above some of
these alternative occupational
exposure levels are in compliance
with the relevant PELS." For TCE,
the alternative occupational
exposure limits are the NIOSH
Page 495 of 803
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Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
REL of 2 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.
httos://www. osha.gov/dsg/annotat
ed-pels/
Atomic Energy Act
The Atomic Energy Act authorizes the
Department of Energy to regulate the
health and safety of its contractor
employees
10 CFR 851.23, Worker Safety
and Health Program, requires the
use of the ACGIH TLVs if they
are more protective than the
OSHA PEL. The 2012 TLV for
TCE is 10 ppm and the short-term
limit is 25 ppm CAT SDR.: ).
Federal Food, Drug,
and Cosmetic Act
(FFDCA)
Provides the FDA with authority to
oversee the safety of food, drugs and
cosmetics.
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).
Federal Hazardous
Material Transportation
Act
Section 5103 of the Act directs the
Secretary of Transportation to:
Designate material (including an
explosive, radioactive material,
infectious substance, flammable or
combustible liquid, solid or gas, toxic,
oxidizing or corrosive material and
compressed gas) as hazardous when the
Secretary determines that transporting the
material in commerce may pose an
unreasonable risk to health and safety or
property.
Issue regulations for the safe
transportation, including security, of
hazardous material in intrastate, interstate
and foreign commerce.
The Department of Transportation
(DOT) has designated TCE as a
hazardous material, and there are
special requirements for marking,
labeling and transporting it (49
CFR Part 171, 49 CFR 172, 40
CFR § 173.202 and 40 CFR §
173.242).
7
8
9
10
11
12
13
14
15
Page 496 of 803
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16
A.2 State Laws and Regulations
17
18 Table Apx A-2. State Laws and Regulations
State Actions
Description of Action
California Code of
Regulations (CCR), Title 17,
Section 94509(a)
Lists standards for VOCs for consumer products sold, supplied, offered
for sale or manufactured for use in California. As part of that
regulation, use of consumer general purpose degreaser products that
contain TCE are banned in California and safer substitutes are in use
(17 CCR, Section 94509(a)).
State Permissible Exposure
Limits (PELs)
Most states have set PELs identical to 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 consumer 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, Subchapter 8.5,
Articles 1, 2, 3 and 4), Connecticut (R.C.S.A Sections 22a-174-40,
22a-174-41, and 22a-174-44), 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 Department of Environmental Protection
Regulations), Maryland (COMAR 26.11.32.00 to 26.11.32.26),
Michigan (R 336.1660 and R 336. 1661), New Hampshire (Env-A
4100) New Jersey (Title 7, Chapter 27, Subchapter 24), New York (6
CRR-NY III A 235), Rhode Island (Air Pollution Control Regulation
No. 31) and Virginia (9VAC5 Chapter 45) all have VOC regulations or
limits for consumer products. Some of these states also require
emissions reporting.
Bans
Beginning June 1, 2022, an owner or operator of a facility required to
have an air emissions permit issued by the Pollution Control Agency
may not use TCE at its permitted facility, including in any
manufacturing, processing, or cleaning processes, except for few uses
(Minn. Stat. 116.385)
Other
TCE is on California Proposition 65 List of chemicals known to cause
cancer in 1988 or birth defects or other reproductive harm in 2014
(CCR Title 27, section 27001). TCE is on California's Safer Consumer
Products Regulations Candidate List of chemicals that exhibit a hazard
trait and are on an authoritative list (CCR Title 22, Chapter 55).
19
20
21
22
Page 497 of 803
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23
A.3 International Laws and Regulations
24
25 Table Apx A-3. Regulatory Actions by 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 under Solvent Degreasing
Regulations (SOR/2003-283) (Canada Gazette, Part II on
August 13, 2003). The purpose of the regulation is to
reduce releases of TCE into the environment from solvent
degreasing facilities using more than 1000 kilograms of
TCE per year. The regulation includes a market
intervention by establishing tradable 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 1907/2006 - REACH
(Registration, Evaluation, Authorization and Restriction
of Chemicals). Entities that would like to use TCE needed
to apply for authorization by 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 classified as a carcinogen category IB, and was
added to the EU REACH restriction of substances
classified as carcinogen category 1A or IB under the EU
Classification and 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 mixture is equal to or greater than 0.1 % w/w
(Regulation (EC) No 1907/2006 - REACH (Registration,
Evaluation, Authorization and 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).
Trichloroethylene. Accessed April, 18 2017).
Japan Chemical Substances
Control Law
TCE is regulated in Japan under the following legislation:
Page 498 of 803
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-Act on the Evaluation of Chemical Substances and Regulation
of Their Manufacture, etc. (Chemical Substances Control Law;
CSCL)
-Act on Confirmation, etc. of Release Amounts of Specific
Chemical Substances in the Environment and Promotion of
Improvements to the Management Thereof
-Industrial Safety and Health Act (ISHA)
-Air Pollution Control Law
-Water Pollution Control Law
-Soil Contamination Countermeasures Act
-Law for the Control of Household Products Containing Harmful
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).
26
27
28
Page 499 of 803
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29
30
31
32
33
M
36
37
38
39
40
41
42
43
44
45
46
47
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
Appendix B LIST OF SUPPLEMENTAL DOCUMENTS
List of supplemental documents (see Docket: I V \ Uo OW] iO I ° 0 ">00 for access to all files):
Associated Systematic Review Data Quality Evaluation and Data Extraction Documents -
Provides additional detail and information on individual study evaluations and data extractions
including criteria and scoring results:
Physical/Chemical Properties. Fate and Transport
a. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Physical-Chemical Properties Studies
b. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Environmental Fate and Transport Studies
c. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
for Environmental Fate and Transport Studies
Occupational Exposures and Releases
d. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Environmental Releases and Occupational Exposure Data
e. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Environmental Releases and Occupational Exposure Common Sources
f Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: List of Key and
Supporting Studies for Environmental Releases and Occupational Exposure
Consumer and Environmental Exposures
g. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation for Data Sources on Consumer and Environmental Exposure
h. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
Tables for Environmental Monitoring Data
i. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
for Biomonitoring Data
Environmental Hazard
j. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Environmental Hazard Studies
k. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
for Environmental Hazard Studies
Human Health Hazard
I. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Human Health Hazard Studies - Animal and Mechanistic Data
Page 500 of 803
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77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
33
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
m. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Quality
Evaluation of Human Health Hazard Studies - Epidemiological Data
n. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Updates to the
Data Quality Criteria for Epidemiological Studies
o. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
for Human Health Hazard Studies
p. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: Data Extraction
and Evaluation Tables for Genotoxicity Studies
q. Risk Evaluation for Trichloroethylene, Systematic Review Supplemental File: List of Key and
Supporting Studies for Human Health Hazard Assessment
Associated Supplemental Information Documents - Provides additional details and information
on exposure, hazard and risk assessments:
Occupational Exposures and Releases
r. Risk Evaluation for Trichloroethylene, Supplemental Information File: Environmental
Releases and Occupational Exposure Assessment
s. Risk Evaluation for Trichloroethylene, Supplemental Information File: Risk Calculator for
Occupational Exposures
t. Risk Evaluation for Trichloroethylene, Supplemental Information File: Memorandum on
Respirator Usage in Private Sector Firms
Consumer and Environmental Exposures
u. Risk Evaluation for Trichloroethylene, Supplemental Information File: Aquatic Exposure
Modeling Outputs from E-FAST
v. Risk Evaluation for Trichloroethylene, Supplemental Information File: Consumer Exposure
Assessment Model Input Parameters
w. Risk Evaluation for Trichloroethylene, Supplemental Information File: Exposure Modeling
Results and Risk Estimates for Consumer Inhalation Exposures
x. Risk Evaluation for Trichloroethylene, Supplemental Information File: Exposure Modeling
Results and Risk Estimates for Consumer Dermal Exposures
Human Health
y. Risk Evaluation for Trichloroethylene, Supplemental Information File: Data Table for
Congenital Heart Defects Weight of Evidence Analysis
z. Risk Evaluation for Trichloroethylene, Supplemental Information File: Personal
Communication to OPPT. Raw Data Values from Selgrade and Gilmour, 2010
Page 501 of 803
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125 aa. Risk Evaluation for Trichloroethylene, Supplemental Information File: PBPK Model and
126 ReadMe (zipped)
111
128 bb. Risk Evaluation for Trichloroethylene, Supplemental Information File: Internal Dose BMD
129 Modeling Results for Selgrade and Gilmour, 2010
130
131
132
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133
134
135
136
137
138
Appendix C ENVIRONMENTAL EXPOSURES
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 Table Apx C-l. These facility-specific modeling results are utilized and discussed in environmental risk
characterization presented in Section 4.1.2.
Table Apx C-l. Facility-Specific Aquatic Exposure Modeling Results
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
Mean SWC
(^g/L)
7Q10
SWC6
Og/L)
COC
Og/L)
Days of
Exceedance7
(days/yr)
OES: Manufacturing
3
0
350
1.266
0.00156
0.0051
788
0
Axiall Corporation,
Westlake, LA
NPDES: LA0007129
920
0
Surface
NPDES
Surface
14,400
0
Water
LA0007129
water
3
0
788
0
20
22.150
0.0273
0.0897
920
0
14,400
0
3
37
350
0.069
0.26
2.42
788
0
Olin Blue Cube,
Freeport, TX
NPDES: Not available
Off-site
Organic
Chemicals
Manuf.
920
0
Waste-
Surface
14,400
0
water
water
3
11
Treatment
20
1.200
4.51
42.14
788
0
920
0
14,400
0
3
17
350
0.015
0.0564
0.53
788
0
Solvents & Chemicals,
Pearland, TX
NPDES: Not available
Off-site
Organic
Chemicals
Manuf.
920
0
Waste-
Surface
14,400
0
water
water
3
5
Treatment
20
0.265
1.01
9.48
788
0
920
0
14,400
0
Page 503 of 803
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Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
40
350
0.015
0.30
2.77
788
0
Organic
Chemicals
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
12
20
0.265
5.34
49.91
788
0
920
0
14,400
0
OES: Processing as a Reactant
3
5
350
0.005
0.0188
0.18
788
0
Off-site
Organic
Chemicals
Manufacture
920
0
Waste-
Surface
14,400
0
water
water
3
2
Treatment
20
0.089
0.33
3.13
788
0
920
0
440 unknown sites8
14,400
0
NPDES: Not applicable
3
23
350
0.005
0.0989
0.92
788
0
Organic
Chemicals
Manufacture
920
0
Surface
Surface
14,400
0
Water
water
3
7
788
0
20
0.089
1.76
16.45
920
0
14,400
0
3
0
350
0.017
0.000197
0.00073
788
0
Arkema Inc.
Surface
Water
NPDES
KY0003603
Surface
water
7
920
0
Calvert City, KY
14,400
0
NPDES: KY0003603
3
0
20
0.295
0.00342
0.128
788
0
920
0
Page 504 of 803
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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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
0
350
0.0128
0.0000158
0.00005
788
0
Honeywell International -
Geismar Complex,
18
920
0
Surface
NPDES
Surface
14,400
0
Geismar, LA
Water
LA0006181
water
3
0
NPDES: LA0006181
20
0.224
0.000276
0.00090
788
0
7
920
0
14,400
0
3
350
350
0.00169
n/a
169.00
788
0
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
920
0
Surface
NPDES
Still body
14,400
0
Water
NY0000281
3
20
20
0.030
n/a
3000.00
788
20
920
20
14,400
0
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
3
0
260
0.005
0.00502
0.0188
788
0
Texas Instruments, Inc.,
Attleboro, MA
NPDES: MA0001791
920
0
Surface
NPDES
Surface
14,400
0
Water
MA0001791
water
3
0
20
0.067
0.0673
0.25
788
0
920
0
14,400
0
3
0
Accellent Inc/Collegeville
Microcoax, Collegeville, PA
NPDES: PA0042617
260
0.002
0.00711
0.0425
788
0
Surface
NPDES
Surface
920
0
Water
PA0042617
water
14,400
0
20
0.029
0.10
0.62
3
0
788
0
Page 505 of 803
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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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
260
0.001
0.0113
0.0619
788
0
Ametek Inc. U.S. Gauge Div.,
Sellersville, PA
NPDES: PA0056014
Surrogate
NPDES
PA0020460
920
0
Surface
Surface
14,400
0
Water
water
3
0
20
0.011
0.12
0.68
788
0
920
0
14,400
0
3
0
260
0.0005
0.000669
0.00311
788
0
Atk-Allegany Ballistics Lab
(Nirop),
920
0
Surface
NPDES
Surface
14,400
0
Keyser, WV
Water
WV0020371
water
3
0
NPDES: WV0020371
20
0.0061
0.00803
0.0373
788
0
920
0
14,400
0
Handy & Harman Tube
Co/East Norriton, Norristown,
PA
NPDES: PA0011436
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.
3
260
260
1.96
n/a
765.63
788
0
US Nasa Michoud Assembly
Facility,
Surrogate
NPDES
LA0003280
920
0
Surface
Still body
14,400
0
New Orleans, LA
Water
3
20
NPDES: LA0052256
20
25.44
n/a
9937.50
788
20
920
20
14,400
0
GM Components Holdings
Surface
Water
NPDES
NY0000558
Surface
water
3
117
LLC,
260
0.13
3.14
10.97
788
0
Lockport, NY
920
0
Page 506 of 803
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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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: NY0000558
14,400
0
3
20
20
1.71
41.38
144.47
788
0
920
0
14,400
0
3
27
260
0.07
1.15
4.87
788
0
Akebono Elizabethtown Plant,
Elizabethtown, KY
NPDES: KY0089672
Surrogate
NPDES
KY0022039
920
0
Surface
Surface
14,400
0
Water
water
3
16
20
0.897
14.77
62.38
788
0
920
0
14,400
0
3
0
260
0.04
0.0175
0.0752
788
0
Delphi Harrison Thermal
Systems,
920
0
Surface
NPDES
Surface
14,400
0
Dayton, OH
Water
OH0009431
water
3
0
NPDES: OH0009431
20
0.465
0.20
0.87
788
0
920
0
14,400
0
3
0
260
0.03
0.000631
0.00301
788
0
Chemours Company Fc LLC,
Washington, WV
NPDES: WV0001279
920
0
Surface
NPDES
Surface
14,400
0
Water
WV0001279
water
3
0
20
0.334
0.00703
0.0335
788
0
920
0
14,400
0
Equistar Chemicals Lp,
Surface
Water
Primary Metal
Surface
water
3
38
La Porte, TX
Forming
260
0.02
0.46
2.22
788
1
NPDES: TX0119792
Manuf.
920
1
Page 507 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
12
20
0.218
5.06
24.44
788
1
920
1
14,400
0
3
0
260
0.01
n/a
0.0425
788
0
GE Aviation,
Lynn, MA
NPDES: MA0003905
920
0
Surface
NPDES
Still water
14,400
0
Water
MA0003905
3
0
20
0.128
n/a
0.54
788
0
920
0
14,400
0
3
28
260
0.01
0.23
1.11
788
0
Certa Vandalia LLC,
Vandalia, OH
NPDES: OHO 122751
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
9
788
1
20
0.107
2.46
11.89
920
1
14,400
0
3
0
260
0.01
0.0387
0.20
788
0
GM Components Holdings
LLC Kokomo Ops,
920
0
Surface
NPDES
Surface
14,400
0
Kokomo, IN
Water
IN0001830
water
3
0
NPDES: IN0001830
20
0.086
0.33
1.73
788
0
920
0
14,400
0
Amphenol Corp-Aerospace
Surface
Water
NPDES
NY0003824
Surface
water
3
0
Operations,
260
0.01
0.00882
0.0486
788
0
Sidney, NY
920
0
Page 508 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: NY0003824
14,400
0
20
0.082
0.0723
0.40
3
0
788
0
920
0
14,400
0
Emerson Power Trans Corp,
Maysville, KY
NPDES: KY0100196
Surface
Water
Surrogate
NPDES
KY0020257
Surface
water
260
0.01
0.000076
0.0004
3
3
788
3
920
3
14,400
3
20
0.081
0.000995
0.00522
3
0
788
0
920
0
14,400
0
Olean Advanced Products,
Olean, NY
NPDES: NY0073547
Surface
Water
Surrogate
NPDES
NY0027162
Surface
water
260
0.01
0.00462
0.0188
3
0
788
0
920
0
14,400
0
20
0.068
0.0314
0.13
3
0
788
0
920
0
14,400
0
Hollingsworth Saco Lowell,
Easley, SC
NPDES: SC0046396
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.00469
0.11
0.52
3
24
788
0
920
0
14,400
0
20
0.061
1.40
6.78
3
6
788
1
920
0
14,400
0
Trelleborg YSH Incorporated
Sandusky Plant,
Sandusky, MI
Surface
Water
NPDES
MI0028142
Surface
water
260
0.00360
0.21
1.76
3
1
788
0
920
0
Page 509 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: MI0028142
14,400
0
3
4
20
0.047
2.69
23.04
788
0
920
0
14,400
0
3
2
260
0.00355
0.20
1.06
788
0
Timken Us Corp Honea Path,
Honea Path, SC
NPDES: SC0047520
Surrogate
NPDES
SC0000698
920
0
Surface
Surface
14,400
0
Water
water
3
5
20
0.0462
2.63
13.77
788
0
920
0
14,400
0
3
0
260
0.00228
0.0068
0.0548
788
0
Johnson Controls
920
0
Incorporated,
Surface
NPDES
Surface
14,400
0
Wichita, KS
Water
KS0000850
water
3
0
NPDES: KS0000850
20
0.0296
0.0898
0.72
788
0
920
0
14,400
0
3
21
National Railroad Passenger
Corporation (Amtrak)
Wilmington Maintenance
260
0.00203
0.0467
0.230
788
0
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Facility,
Water
water
3
3
Wilmington, DE
20
0.026
0.60
2.89
788
0
NPDES: DE0050962
920
0
14,400
0
Electrolux Home Products
Surface
Water
NPDES
MI0002135
Surface
water
3
0
(Formerly Frigidaire),
260
0.00201
0.00644
0.0171
788
0
Greenville, MI
920
0
Page 510 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: MI0002135
14,400
0
3
0
20
0.026
0.0834
0.22
788
0
920
0
14,400
0
3
0
260
0.00194
0.00896
0.0523
788
0
Rex Heat Treat Lansdale Inc,
Lansdale, PA
NPDES: PA0052965
Surrogate
NPDES
PA0026182
920
0
Surface
Surface
14,400
0
Water
water
3
0
20
0.025
0.12
0.67
788
0
920
0
14,400
0
3
0
260
0.00177
n/a
0.220
788
0
920
0
Carrier Corporation,
Syracuse, NY
NPDES: NY0001163
Surface
NPDES
Still water
14,400
0
Water
NY0001163
3
0
20
0.023
n/a
2.84
788
0
920
0
14,400
0
3
18
260
0.00117
0.0269
0.130
788
0
Cascade Corp (0812100207),
Springfield, OH
NPDES: OH0085715
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
3
20
0.015
0.35
1.67
788
0
920
0
14,400
0
USAF-Wurtsmith Afb,
Surface
Water
Surrogate
Surface
water
0.00075
3
3
0
Oscoda, MI
NPDES
260
0.00115
0.000320
788
0
NPDES: MI0042285
MI0028282
920
0
Page 511 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
0
20
0.015
0.00417
0.00983
788
0
920
0
14,400
0
3
0
260
0.00112
0.00413
0.00916
788
0
AAR Mobility Systems,
Cadillac, MI
NPDES: MI0002640
Surrogate
NPDES
MI0020257
920
0
Surface
Surface
14,400
0
Water
water
3
0
20
0.014
0.0517
0.11
788
0
920
0
14,400
0
3
0
260
0.00107
n/a
0.130
788
0
Eaton Mdh Company Inc,
Kearney, NE
NPDES: NE0114405
Surrogate
NPDES
NE0052647
920
0
Surface
Still water
14,400
0
Water
3
0
20
0.014
n/a
1.69
788
0
920
0
14,400
0
3
0
260
0.000500
0.00178
0.0106
788
0
Lake Region Medical,
Trappe, PA
NPDES: PA0042617
920
0
Surface
NPDES
Surface
14,400
0
Water
PA0042617
water
3
0
788
0
20
0.007
0.0249
0.15
920
0
14,400
0
Motor Components LLC,
Surface
Water
NPDES
NY0004081
Surface
water
3
0
Elmira, NY
260
0.00096
0.0143
0.0618
788
0
NPDES: NY0004081
920
0
Page 512 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
0
20
0.0125
0.19
0.83
788
0
920
0
14,400
0
3
17
260
0.000897
0.0206
0.0997
788
0
Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
2
20
0.012
0.28
1.33
788
0
920
0
14,400
0
3
0
260
0.000806
0.0378
0.0821
788
0
GE (Greenville) Gas Turbines
LLC,
920
0
Surface
NPDES
Surface
14,400
0
Greenville, SC
Water
SC0003484
water
3
0
NPDES: SC0003484
20
0.010
0.47
1.02
788
0
920
0
14,400
0
3
16
260
0.000747
0.0172
0.0830
788
0
Parker Hannifin Corporation,
Waverly, OH
NPDES: OH0104132
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
2
20
0.010
0.23
1.11
788
0
920
0
14,400
0
Mahle Engine Components
Surface
Water
NPDES
MI0004057
Surface
water
3
0
Usa Inc,
260
0.000742
0.00808
0.0336
788
0
Muskegon, MI
920
0
Page 513 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: MI0004057
14,400
0
20
0.010
0.11
0.45
3
0
788
0
920
0
14,400
0
General Electric Company -
Waynesboro,
Waynesboro, VA
NPDES: VA0002402
Surface
Water
NPDES
VA0002402
Surface
water
260
0.000733
0.00241
0.00705
3
0
788
0
920
0
14,400
0
20
0.010
0.0329
0.0962
3
0
788
0
920
0
14,400
0
Globe Engineering Co Inc,
Wichita, KS
NPDES: KS0086703
Surface
Water
Surrogate
NPDES
KS0043036
Surface
water
260
0.00173
0.00175
0.00853
3
0
788
0
920
0
14,400
0
20
0.023
0.0232
0.110
3
0
788
0
920
0
14,400
0
Gayston Corp,
Dayton, OH
NPDES: OHO 127043
Surface
Water
Surrogate
NPDES
OH0024881
Surface
water
260
0.000643
0.000281
0.00121
3
0
788
0
920
0
14,400
0
20
0.008
0.0035
0.0150
3
0
788
0
920
0
14,400
0
Styrolution America LLC,
Channahon, IL
NPDES: IL0001619
Surface
Water
NPDES
IL0001619
Surface
water
260
0.000637
0.0000845
0.00022
1
3
0
788
0
920
0
Page 514 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
0
20
0.008
0.00106
0.00278
788
0
920
0
14,400
0
3
0
260
0.000612
0.000291
0.00079
788
0
Remington Arms Co Inc,
Ilion, NY
NPDES: NY0005282
9
920
0
Surface
NPDES
Surface
14,400
0
Water
NY0005282
water
3
0
788
0
20
0.008
0.00380
0.0104
920
0
14,400
0
3
0
260
0.000480
0.0000218
0.00008
788
0
United Technologies
Corporation, Pratt And
Whitney Division,
East Hartford, CT
NPDES: CT0001376
22
920
0
Surface
NPDES
Surface
14,400
0
Water
CT0001376
water
3
0
20
0.006
0.000273
0.00103
788
0
920
0
14,400
0
3
0
260
0.000470
0.000629
0.00292
788
0
Atk-Allegany Ballistics Lab
(Nirop),
920
0
Surface
NPDES
Surface
14,400
0
Keyser, WV
Water
WV0020371
water
3
0
NPDES: WV0020371
20
0.006
0.00803
0.0373
788
0
920
0
14,400
0
Sperry & Rice Manufacturing
Surface
Water
NPDES
IN0001473
Surface
water
3
0
Co LLC,
260
0.000328
0.00117
0.00569
788
0
Brookville, IN
920
0
Page 515 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: IN0001473
14,400
0
20
0.004
0.0143
0.0694
3
0
788
0
920
0
14,400
0
Owt Industries,
Pickens, SC
NPDES: SC0026492
Surface
Water
NPDES
SC0026492
Surface
water
260
0.000314
0.000820
0.00213
3
0
788
0
920
0
14,400
0
20
0.004
0.0104
0.0272
3
0
788
0
920
0
14,400
0
Boler Company,
Hillsdale, MI
NPDES: MI0053651
Surface
Water
Surrogate
NPDES
MI0022136
Surface
water
260
0.000269
0.00461
0.0204
3
0
788
0
920
0
14,400
0
20
0.003
0.0514
0.23
3
0
788
0
920
0
14,400
0
Mccanna Inc.,
Carpentersville, IL
NPDES: IL0071340
Surface
Water
Surrogate
NPDES
IL0027944
Surface
water
260
0.000268
0.000260
0.00091
1
3
0
788
0
920
0
14,400
0
20
0.003
0.00291
0.0102
3
0
788
0
920
0
14,400
0
Cutler Hammer,
Horseheads, NY
NPDES: NY0246174
Surface
Water
Surrogate
NPDES
NY0004081
Surface
water
260
0.000238
0.00352
0.0153
3
0
788
0
920
0
Page 516 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
20
0.003
0.0443
0.19
3
0
788
0
920
0
14,400
0
US Air Force Offutt Afb Ne,
Offiitt A F B, NE
NPDES: NE0121789
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000159
0.00366
0.0177
3
5
788
0
920
0
14,400
0
20
0.002
0.0460
0.22
3
2
788
0
920
0
14,400
0
Troxel Company,
Moscow, TN
NPDES: TN0000451
Surface
Water
NPDES
TN0000451
Surface
water
260
0.000134
0.000254
0.00074
1
3
0
788
0
920
0
14,400
0
20
0.002
0.00379
0.0111
3
0
788
0
920
0
14,400
0
Austin Tube Prod,
Baldwin, MI
NPDES: MI0054224
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000114
0.00262
0.0127
3
3
788
0
920
0
14,400
0
20
0.001
0.023
0.11
3
1
788
0
920
0
14,400
0
LS Starrett Precision Tools,
Athol, MA
NPDES: MA0001350
Surface
Water
NPDES
MA0001350
Surface
water
260
0.000102
0.000339
0.00153
3
0
788
0
920
0
Page 517 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
0
20
0.001
0.00333
0.015
788
0
920
0
14,400
0
3
2
260
0.0000883
0.00203
0.00981
788
0
Avx Corp,
Raleigh, NC
NPDES: NC0089494
Primary Metal
Forming
Manuf.
920
0
Surface
Surface
14,400
0
Water
water
3
1
20
0.001
0.023
0.11
788
0
920
0
14,400
0
Indian Head Division, Naval
Surface Warfare Center,
Indian Head, MD
NPDES: MD0003158
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.
General Dynamics Ordnance
Tactical Systems,
Red Lion, PA
NPDES: PA0043672
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.
Trane Residential Solutions -
Fort Smith,
Fort Smith, AR
NPDES: AR0052477
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.
Lexmark International Inc.,
Lexington, KY
NPDES: KY0097624
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.
Alliant Techsystems
Operations LLC,
Elkton, MD
NPDES: MD0000078
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.
Daikin Applied America, Inc.
(Formally Mcquay
International),
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.
Page 518 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
Scottsboro, AL
NPDES: AL0069701
Beechcraft Corporation,
Wichita, KS
NPDES: KS0000183
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.
Federal-Mogul Corp,
Scottsville, KY
NPDES: KY0106585
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.
Cessna Aircraft Co (Pawnee
Facility),
Wichita, KS
NPDES: KS0000647
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.
N.G.I,
Parkersburg, WV
NPDES: WV0003204
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.
Hyster-Yale Group, Inc,
Sulligent, AL
NPDES: AL0069787
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.
Hitachi Electronic Devices
(Usa), Inc.,
Greenville, SC
NPDES: SC0048411
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: Spot Cleaning and Carpet Cleaning
3
0
300
0.00008
0.000205
0.00388
788
0
Boise State University,
Boise, ID
NPDES: IDG911006
Surrogate
NPDES
ID0023981
920
0
Surface
Surface
14,400
0
Water
water
3
0
788
0
20
0.001
0.00256
0.0485
920
0
14,400
0
Venetian Hotel And Casino,
Las Vegas, NV
NPDES: NV0022888
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.
Page 519 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
63,746 unknown sites
NPDES: All POTW SIC
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.
OES: Repackaging
3
194
250
1.108
5.33
27.18
788
0
Hubbard-Hall Inc,
Waterbury, CT
NPDES: Unknown
Off-site
Receiving
Facility:
920
0
Waste-
Surface
14,400
0
water
Recycle Inc.;
water
3
20
Treatment
POTW (Ind.)
20
13.85
66.45
339.11
788
1
920
1
14,400
0
3
2
250
0.003
0.32
6.52
788
0
Oiltanking Houston Inc,
Houston, TX
NPDES: TX0091855
Surrogate
NPDES
TX0065943
920
0
Surface
Surface
14,400
0
Water
water
3
4
788
0
20
0.041
4.36
89.13
920
0
14,400
0
3
0
250
0.00550
0.00000677
0.00002
788
0
St. Gabriel Terminal,
Saint Gabriel, LA
NPDES: LA0005487
23
920
0
Surface
NPDES
Surface
14,400
0
Water
LA0005487
water
3
0
20
0.069
0.0000850
0.00027
788
0
9
920
0
14,400
0
Vopak Terminal Westwego
Inc,
Westwego, LA
NPDES: LAO 124583
3
0
Surface
Water
Surrogate
Surface
water
250
0.00468
0.00000576
0.00001
788
0
NPDES
89
920
0
LA0042064
14,400
0
20
0.058
0.0000714
3
0
Page 520 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
0.00023
5
788
0
920
0
14,400
0
Research Solutions Group Inc,
Pelham, AL
NPDES: AL0074276
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.
Carlisle Engineered Products
Inc, Middlefield, OH
NPDES: OH0052370
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: Process Solvent Recycling and Worker Handling of Wastes
3
250
250
0.004
n/a
11.76
788
0
Clean Water Of New York
Surrogate
NPDES
NJ0000019
920
0
Inc,
Surface
Still body
14,400
0
Staten Island, NY
Water
3
20
NPDES: NY0200484
20
0.047
n/a
138.24
788
0
920
0
14,400
0
Reserve Environmental
Services,
Ashtabula, OH
NPDES: OH0098540
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.
3
0
Receiving
Facility:
Middlesex
250
24.1
n/a
2.85
788
0
Veolia Es Technical Solutions
Off-site
920
0
LLC,
Waste-
Still body
14,400
0
Middlesex, NJ
water
Cnty UA;
3
20
NPDES: NJ0020141
Treatment
NPDES
20
301.78
n/a
35.72
788
0
NJ0020141
920
0
14,400
0
Clean Harbors Deer Park
Off-site
3
110
LLC,
Waste-
POTW (Ind.)
Surface
250
0.35
1.68
8.57
788
0
La Porte, TX
water
water
920
0
NPDES: TX0005941
Treatment
14,400
0
Page 521 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
3
19
20
4.36
20.92
106.75
788
0
920
0
14,400
0
3
6
250
0.04
0.19
0.98
788
0
Clean Harbors El Dorado
Off-site
920
0
LLC,
Waste-
POTW (Ind.)
Surface
14,400
0
El Dorado, AR
water
water
3
11
NPDES: AR0037800
Treatment
20
0.455
2.21
11.26
788
0
920
0
14,400
0
OES: Adhesives, Sealants, Paints, and Coatings
3
8
Able Electropolishing Co Inc,
Chicago, IL
NPDES: Not available
POTW
Adhesives and
Sealants
Manuf.
Surface
250
0.298
0.86
7.28
788
0
water
920
0
14,400
0
3
0
250
0.00033
0.00252
0.00716
788
0
920
0
Garlock Sealing Technologies,
Palmyra, NY
NPDES: NY0000078
Surface
NPDES
Surface
14,400
0
Water
NY0000078
water
3
0
20
0.00407
0.0312
0.0889
788
0
920
0
14,400
0
Ls Starrett Co,
Athol, MA
NPDES: MAR05B615
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 assumptions.
Aerojet Rocketdyne8, Inc.,
Surface
Surface
250
0.013
0.20
1.67
3
0
East Camden, AR
Water
water
788
0
Page 522 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: AR0051071,
ARR00A521, ARR00A520
Adhesives and
Sealants
Manuf.
920
0
14,400
0
20
0.160
2.42
20.57
3
3
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
920
0
14,400
0
Best One Tire & Service8,
Nashville, TN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
920
0
14,400
0
20
0.160
2.42
20.57
3
3
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
920
0
14,400
0
Bridgestone Aircraft Tire
(Usa), Inc. 8,
Mayodan, NC
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
920
0
14,400
0
20
0.160
2.42
20.57
3
3
788
0
Page 523 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Clayton Homes Inc8,
Oxford, NC
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Cmh Manufacturing, Inc.
Surface
14,400
0
Dba Schult Homes - Plant
9588,
Richfield, NC
NPDES: Not available
Water
Adhesives and
Sealants
Manuf.
Surface
3
3
water
20
0.160
2.42
20.57
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
Page 524 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
2
250
0.013
0.31
1.10
788
0
920
0
Surface
NPDES
14,400
0
Water
NY0000558
3
11
Delphi Thermal Systems8,
Surface
water
20
0.160
3.87
13.50
788
0
Lockport, NY
NPDES: NY0000558
920
0
14,400
0
No info on
3
0
receiving
facility;
Adhesives and
788
0
POTW
250
0.013
0.0374
0.32
920
0
Sealants
Manuf.
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Green Bay Packaging Inc -
Coon Rapids8,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Coon Rapids, MN
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
Mastercraft Boat Company8,
250
0.013
0.20
1.67
3
0
Page 525 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
Vonore, TN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
788
0
920
0
14,400
0
20
0.160
2.42
20.57
3
3
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
920
0
14,400
0
Michelin Aircraft Tire
Company8,
Norwood, NC
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
920
0
14,400
0
20
0.160
2.42
20.57
3
3
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
920
0
14,400
0
M-Tek, Inc8,
Manchester, TN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
920
0
14,400
0
20
0.160
2.42
20.57
3
3
Page 526 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.08
0.18
788
0
920
0
Surface
NPDES
14,400
0
Water
IL0000230
3
7
Olin Corp8,
Surface
water
20
0.160
1.03
2.26
788
0
East Alton, IL
NPDES: IL0000230
920
0
14,400
0
No info on
3
0
receiving
facility;
Adhesives and
788
0
POTW
250
0.013
0.0374
0.32
920
0
Sealants
Manuf.
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Parker Hannifin Corp -
Paraflex Division8,
Surface
Adhesives and
Sealants
Manuf.
Surface
14,400
0
Manitowoc, WI
NPDES: Not available
Water
water
3
3
788
0
20
0.160
2.42
20.57
920
0
14,400
0
Page 527 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Parrish Tire Company8,
Yadkinville, NC
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Republic Doors And Frames8,
Mckenzie, TN
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
Page 528 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Ro-Lab Rubber
Company Inc. 8,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Tracy, CA
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Roy ale Comfort Seating, Inc.
8 - Plant No. 1,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Taylorsville, NC
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
Snider Tire, Inc. 8,
Statesville, NC
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
250
0.013
0.20
1.67
788
0
Water
water
920
0
14,400
0
Page 529 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
3
20
0.160
2.42
20.57
788
0
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Snyder Paper Corporations,
Hickory, NC
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Stellana Us8,
Lake Geneva, WI
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
14,400
0
Water
water
3
3
20
0.160
2.42
20.57
788
0
920
0
14,400
0
Page 530 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Thomas Built Buses -
Courtesy Road8,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
High Point, NC
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Unicel Corp8,
Escondido, CA
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
Page 531 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Acme Finishing Co Llc8,
Elk Grove Village, IL
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.000295
0.00081
788
0
8
920
0
Surface
NPDES
14,400
0
Water
CA0004111
3
0
Aerojet Rocketdyne, Inc. 8,
Surface
water
20
0.160
0.00363
0.0101
788
0
Rancho Cordova, CA
NPDES: CA0004111
920
0
14,400
0
No info on
3
0
receiving
facility;
Adhesives and
0.32000
0
788
0
POTW
250
0.013
0.0374000
920
0
Sealants
Manuf.
14,400
0
Allegheny Cnty Airport Auth/
Surface
Water
Adhesives and
Surface
water
3
0
Pgh Intl Airport8, Coroapolis
Pittsburgh, PA
NPDES: Not available
Sealants
250
0.013
0.20
1.67
788
0
Manuf.
920
0
Page 532 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
14,400
0
3
3
20
0.160
2.42
20.57
788
0
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.0115
0.0631
788
0
920
0
Surface
NPDES
14,400
0
Amphenol Corp -
Aerospace Operations8,
Sidney, NY
Water
NY0003824
3
0
Surface
water
20
0.160
0.14
0.78
788
0
920
0
NPDES: NY0003824
14,400
0
No info on
3
0
POTW
receiving
facility;
Adhesives and
Sealants
250
0.013
0.03740
0.3200
788
0
920
0
Manuf.
14,400
0
3
0
250
0.013
0.20
1.67
788
0
Aprotech Powertrain8,
Asheville, NC
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
920
0
Water
water
14,400
0
20
0.160
2.42
20.57
3
3
788
0
Page 533 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Coating & Converting Tech
Water
3
3
Corp /
Adhesive Coatings8,
Philadelphia, PA
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Corpus Christi Army Depot8,
Corpus Christi, TX
NPDES: Not available
Water
Adhesives and
Sealants
Manuf.
Surface
3
3
water
20
0.160
2.42
20.57
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
Page 534 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Electronic Data Systems
Camp Pendleton8, Camp
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Pendleton, CA
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Florida Production
Engineering, Inc. 8,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Ormond Beach, FL
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
Goodrich Corporations,
Surface
Surface
250
0.013
0.20
1.67
3
0
Jacksonville, FL
Water
water
788
0
Page 535 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
NPDES: Not available
920
0
14,400
0
3
3
20
0.160
2.42
20.57
788
0
Adhesives and
Sealants
Manuf.
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Kasai North America Inc8,
Madison Plant, Madison, MS
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
Kirtland Air Force Base8,
Albuquerque, NM
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
920
0
Water
water
14,400
0
20
0.160
2.42
20.57
3
3
788
0
Page 536 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Marvin Windows & Doors8,
Adhesives and
Surface
788
0
Warroad, MN
NPDES: Not available
Sealants
Manuf.
20
0.160
2.42
20.57
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Mcneilus Truck &
Manufacturing Inc8,
Surface
14,400
0
Water
Adhesives and
Sealants
Manuf.
Surface
3
3
Dodge Center, MN
NPDES: Not available
water
20
0.160
2.42
20.57
788
0
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
Page 537 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Metal Finishing Co. 8 -
Wichita (S Mclean Blvd),
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Wichita, KS
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Murakami Manufacturing Usa
Inc8, Campbellsville, KY
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
Peterbilt Motors Denton
Surface
Surface
250
0.013
0.20
1.67
3
0
Facility8,
Water
water
788
0
Page 538 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
Denton, TX
920
0
NPDES: Not available
14,400
0
3
3
20
0.160
2.42
20.57
788
0
Adhesives and
Sealants
Manuf.
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Portsmouth Naval Shipyard8,
Kittery, ME
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
R.D. Henry & Co. 8,
Wichita, KS
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
920
0
Water
water
14,400
0
20
0.160
2.42
20.57
3
3
788
0
Page 539 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
250
250
0.013
n/a
10.83
788
0
920
0
Surface
NPDES
14,400
0
Water
RI0000281
3
20
Raytheon Company8,
20
0.160
n/a
133.33
788
0
Portsmouth, RI
NPDES: RI0000281
Still body
920
0
14,400
0
No info on
3
0
receiving
facility;
Adhesives and
788
0
POTW
250
0.013
0.03740
0.32
920
0
Sealants
Manuf.
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Rehau Inc8,
Cullman, AL
Surface
Adhesives and
Surface
water
14,400
0
Water
Sealants
3
3
NPDES: Not available
Manuf.
788
0
20
0.160
2.42
20.57
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
Page 540 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Rotochopper Inc8,
Saint Martin, MN
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Rubber Applications8,
Mulberry, FL
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
250
0.013
0.20
1.67
3
0
Page 541 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
Surface
Water
14,400
0
3
3
Sapa Precision Tubing
Adhesives and
Surface
water
20
0.160
2.42
20.57
788
0
Rockledge, Llc8,
Rockledge, FL
NPDES: Not available
Sealants
920
0
Manuf.
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Thomas & Betts8,
Albuquerque, NM
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
Thomas Built Buses - Fairfield
Surface
Water
Adhesives and
Surface
water
250
0.013
0.20
1.67
788
0
Road8,
High Point, NC
NPDES: Not available
Sealants
920
0
Manuf.
14,400
0
20
0.160
2.42
20.57
3
3
Page 542 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Timco,
Water
3
3
Dba Haeco Americas
Adhesives and
Surface
788
0
Airframe Services8,
Greensboro, NC
NPDES: Not available
Sealants
Manuf.
20
0.160
2.42
20.57
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
Trelleborg Coated Systems
Us, Inc8 -
Grace Advanced Materials,
920
0
Surface
Adhesives and
Surface
water
14,400
0
Water
Sealants
3
3
Rutherfordton, NC
Manuf.
788
0
NPDES: Not available
20
0.160
2.42
20.57
920
0
14,400
0
POTW
250
0.013
0.0374
0.32
3
0
Page 543 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
U.S. Coast Guard Yard -
Curtis Bay8,
Water
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Curtis Bay, MD
NPDES: Not available
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
3
0
250
0.013
0.20
1.67
788
0
920
0
Surface
14,400
0
Water
3
3
Viracon Inc8,
Owatonna, MN
NPDES: Not available
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
water
920
0
14,400
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
920
0
14,400
0
OES: Industrial Processing Aid
Page 544 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
300
0.019
n/a
0.14
788
0
Occidental Chemical Corp
Niagara Plant,
920
0
Surface
NPDES
Still body
14,400
0
Niagara Falls, NY
Water
NY0003336
3
0
NPDES: NY0003336
20
0.292
n/a
2.200
788
0
920
0
14,400
0
3
0
300
0.001
0.00016
0.00041
788
0
Stepan Co Millsdale Road,
Elwood, IL
NPDES: IL0002453
9
920
0
Surface
NPDES
Surface
14,400
0
Water
IL0002453
water
3
0
20
0.008
0.00128
0.00335
788
0
920
0
14,400
0
3
140
300
0.38
1.82
9.30
788
0
Entek International LLC,
Lebanon, OR
NPDES: N/A
Off-site
No info on
920
0
Waste-
receiving
Surface
14,400
0
water
facility;
water
3
20
Treatment
POTW (Ind.)
20
5.65
27.11
138.34
788
0
920
0
14,400
0
3
0
300
0.008
0.0336
0.15
788
0
National Electrical Carbon
Products
Dba Morgan Adv Materials,
Fostoria, OH
NPDES: OH0052744
Off-site
Receiving
Facility: City
of Fostoria;
NPDES
OH0052744
920
0
Waste-
Surface
14,400
0
water
water
3
1
Treatment
20
0.115
0.50
2.32
788
0
920
0
14,400
0
Page 545 of 803
-------
Modeled
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
3
0
300
0.005
0.00478
0.0141
788
0
PPG Industries Inc Barberton,
Barberton, OH
Off-site
Receiving
Facility: City
of Barberton;
920
0
Waste-
Surface
14,400
0
water
water
0
NPDES: OH0024007
NPDES
OH0024007
3
Treatment
20
0.070
0.067
0.20
788
0
920
0
14,400
0
3
0
300
0.008
0.00572
0.0206
788
0
Daramic LLC,
Corydon, IN
NPDES: IN0020893
920
0
Surface
NPDES
Surface
14,400
0
Water
IN0020893
water
3
0
788
0
20
0.114
0.0816
0.29
920
0
14,400
0
OES: Commercial Printing and Copying
3
0
250
0.00020
0.000662
0.00292
788
0
920
0
Printing And Pub Sys Div,
Weatherford, OK
NPDES: OK0041785
Surface
Printing
Surface
14,400
0
Water
water
3
0
20
0.00250
0.00827
0.0365
788
0
920
0
14,400
0
OES: Other Industrial Uses
Eli Lilly And Company-
Lilly Tech Ctr,
3
35
Surface
NPDES
Surface
250
1.553
1.63
9.03
788
0
920
0
Indianapolis, IN
NPDES: IN0003310
Water
IN0003310
water
14,400
0
20
19.410
20.47
113.09
3
17
Page 546 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
Oxy Vinyls LP - Deer Park
Pvc,
Deer Park, TX
NPDES: TX0007412
Surface
Water
NPDES
TX0007412
Surface
water
250
0.148
0.13
0.49
3
1
788
0
920
0
14,400
0
20
1.854
1.58
5.98
3
9
788
0
920
0
14,400
0
Washington Penn Plastics,
Frankfort, KY
NPDES: KY0097497
Surface
Water
Surrogate
NPDES
KY0028410
Surface
water
250
0.032
1.25
7.53
3
22
788
0
920
0
14,400
0
20
0.399
15.62
94.12
3
13
788
0
920
0
14,400
0
Natrium Plant,
New Martinsville, WV
NPDES: WV0004359
Surface
Water
NPDES
WV0004359
Surface
water
250
0.022
0.000566
0.00262
3
0
788
0
920
0
14,400
0
20
0.274
0.00695
0.0322
3
0
788
0
920
0
14,400
0
Leroy Quarry,
Leroy, NY
NPDES: NY0247189
Surface
Water
Surrogate
NPDES
NY0030546
Surface
water
250
0.019
0.16
0.71
3
0
788
0
920
0
14,400
0
20
0.242
2.05
8.91
3
3
Page 547 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
250
0.010
0.0738
0.20
788
0
George C Marshall Space
Flight Center,
Surrogate
NPDES
AL0025585
920
0
Surface
Surface
14,400
0
Huntsville, AL
Water
water
3
8
NPDES: AL0000221
20
0.128
0.96
2.63
788
0
920
0
14,400
0
3
30
250
0.009
0.67
2.92
788
0
Whelan Energy Center Power
Plant,
920
0
Surface
NPDES
Surface
14,400
0
Hastings, NE
Water
NE0113506
water
3
13
NPDES: NEO113506
20
0.118
8.95
38.96
788
0
920
0
14,400
0
3
0
250
0.0002
0.0000266
0.00010
788
0
Army Cold Regions Research
& Engineering Lab,
Surrogate
NPDES
NHO100099
3
920
0
Surface
Surface
14,400
0
Hanover, NH
Water
water
3
0
NPDES: NH0001619
20
0.0029
0.000398
0.00154
788
0
920
0
14,400
0
3
0
Corning - Canton Plant,
Surface
Water
Surrogate
Surface
water
250
0.0002
0.000101
0.00034
788
0
Canton, NY
NPDES
0
920
0
NPDES: NY0085006
NY0034762
14,400
0
20
0.0028
0.00152
0.00510
3
0
Page 548 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
Ames Rubber Corp Plant #1,
Hamburg Boro, NJ
NPDES: NJ0000141
Surface
Water
Surrogate
NPDES
NJ0000141i
Surface
water
250
0.00011
0.00258
0.0149
3
53i
788
50i
920
50i
14,400
50i
20
0.00133
0.0304
0.18
3
6
788
4
920
4
14,400
4
Gorham,
Providence, RI
NPDES: RIG85E004
Surface
Water
POTW (Ind.)
Surface
water
250
0.0001
0.00253
0.0129
3
0
788
0
920
0
14,400
0
20
0.0012
0.0253
0.13
3
0
788
0
920
0
14,400
0
Solvay - Houston Plant,
Houston, TX
NPDES: TX0007072
Surface
Water
NPDES
TX0007072
Surface
water
350
0.024
0.22
4.44
3
3
788
0
920
0
14,400
0
20
0.414
3.72
75.93
3
5
788
0
920
0
14,400
0
Akzo Nobel Surface
Chemistry LLC,
Morris, IL
NPDES: IL0026069
Surface
Water
NPDES
IL0026069
Surface
water
350
0.000329
0.000300
0.00068
8
3
0
788
0
920
0
14,400
0
20
0.006
0.00546
0.0125
3
0
Page 549 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
920
0
14,400
0
3
0
350
0.000318
0.0000214
0.00009
788
0
Solutia Nitro Site,
Nitro, WV
NPDES: WV0116181
Surrogate
NPDES
WV0023229
41
920
0
Surface
Surface
14,400
0
Water
water
3
0
20
0.006
0.000401
0.00176
788
0
920
0
14,400
0
3
0
350
0.000202
0.00395
0.037
788
0
Amphenol Corporation -
Columbia,
Organic
Chemicals
Manufacture
920
0
Surface
Surface
14,400
0
Columbia, SC
Water
water
3
1
NPDES: SC0046264
20
0.004
0.0791
0.74
788
0
920
0
14,400
0
3
350
350
0.000095
n/a
9.50
788
0
Keeshan and Bost Chemical
920
0
Co., Inc.,
Surface
NPDES
Still body
14,400
0
Manvel, TX
Water
TX0072168
3
20
NPDES: TX0072168
20
0.002
n/a
200.00
788
0
920
0
14,400
0
Chemtura North and South
Plants,
Morgantown, WV
NPDES: WV0004740
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.
Indorama Ventures Olefins,
LLC,
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.
Page 550 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
Sulphur, LA
NPDES: LA0069850
Emerson Power Transmission,
Ithaca, NY
NPDES: NY0002933
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.
William E. Warne Power
Plant,
Los Angeles County, CA
NPDES: CA0059188
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.
Raytheon Aircraft 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.
OES: Other Commercial Uses
Corning Hospital,
Corning, NY
NPDES: NY0246701
Surface
Water
Surrogate
NPDES
NY0025721
Surface
water
250
0.013
0.00597
0.0271
3
0
788
0
920
0
14,400
0
20
0.159
0.0735
0.33
3
0
788
0
920
0
14,400
0
Water Street Commercial
Bldg,
Dayton, OH
NPDES: OHO 141496
Surface
Water
Surrogate
NPDES
OH0009521
Surface
water
250
0.003
0.00131
0.00564
3
0
788
0
920
0
14,400
0
20
0.035
0.0153
0.0658
3
0
788
0
920
0
14,400
0
Union Station North Wing
Office Building, Denver, CO
NPDES: COG315293
Surface
Water
Surrogate
NPDES
C000200959
Surface
water
250
0.00040
0.0196
0.0881
3
2139
788
2139
920
2139
14,400
2139
20
0.00499
0.24
1.10
3
18
Page 551 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
COC
(lig/L)
Days of
Exceedance7
(days/yr)
788
17
920
17
14,400
17
3
21310
250
0.00028
0.0137
0.0617
788
21310
Confluence Park Apartments,
Denver, CO
NPDES: COG315339
Surrogate
NPDES
C0002009510
920
21310
Surface
Surface
14,400
21310
Water
water
3
17
20
0.00354
0.17
0.77
788
17
920
17
14,400
17
3
250
250
0.00027
n/a
9.00
788
0
Park Place Mixed Use
Surrogate
NPDES
MD0052868
920
0
Development,
Surface
Still body
14,400
0
Annapolis, MD
Water
n/a
3
20
NPDES: MD0068861
20
0.00334
110.00
788
0
920
0
14,400
0
Tree Top Inc Wenatchee
Plant,
Wenatchee, WA
NPDES: WA0051527
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.
Wynkoop Denver LLCP St,
Denver, CO
NPDES: COG603115
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.
Greer Family Lie,
South Burlington, VT
NPDES: VT0001376
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 III Site,
Mclean, VA
NPDES: VA0090093
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: N/A (WWTP)
New Rochelle STP,
Still body
365
0.043
n/a
0.70
3
0
Page 552 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
New Rochelle, NY
NPDES: NY0026697
Surface
Water
NPDES
NY0026697
788
0
920
0
14,400
0
20
0.786
n/a
12.79
3
20
788
0
920
0
14,400
0
Everett Water Pollution
Control Facility,
Everett, WA
NPDES: WA0024490
Surface
Water
NPDES
WA0024490
Surface
water
365
0.016
0.13
0.17
3
0
788
0
920
0
14,400
0
20
0.299
2.37
3.11
3
7
788
0
920
0
14,400
0
Sullivan WWTP,
Sullivan, MO
NPDES: MOO 104736
Surface
Water
NPDES
MOO 104736
Surface
water
365
0.010
0.16
0.61
3
2
788
0
920
0
14,400
0
20
0.176
2.81
10.97
3
7
788
0
920
0
14,400
0
Sunnyside STP,
Sunnyside, WA
NPDES: WA0020991
Surface
Water
NPDES
WA0020991
Surface
water
365
0.005
0.00146
0.00673
3
0
788
0
920
0
14,400
0
20
0.083
0.0242
0.110
3
0
788
0
920
0
14,400
0
POTW (Ind.)
365
0.002
0.0505
0.26
3
0
Page 553 of 803
-------
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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
788
0
Port Of Sunnyside Industrial
WWTF,
Sunnyside, WA
NPDES: WA0052426
920
0
Surface
Water
Surface
water
14,400
0
3
5
20
0.035
0.88
4.51
788
0
920
0
14,400
0
3
0
365
0.002
0.0505
0.26
788
0
U.S. Air Force Shaw AFB SC,
Shaw AFB, SC
NPDES: SC0024970
920
0
Surface
POTW (Ind.)
Surface
14,400
0
Water
water
3
4
20
0.032
0.81
4.12
788
0
920
0
14,400
0
3
0
365
0.0004
0.000304
0.00194
788
0
Gnf-A Wilmington-Castle
Hayne WWTP,
920
0
Surface
NPDES
Surface
14,400
0
Wilmington, NC
Water
NC0001228
water
3
0
NPDES: NC0001228
20
0.0067
0.00533
0.0340
788
0
920
0
14,400
0
3
0
365
0.0003
0.00758
0.0387
788
0
Cameron Trading Post
WWTP,
920
0
Surface
POTW (Ind.)
Surface
14,400
0
Cameron, AZ
Water
water
3
0
NPDES: NN0021610
20
0.0047
0.13
0.64
788
0
920
0
14,400
0
Coal Grove WWTP,
365
0.0002
0.00000250
3
0
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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
Mean SWC
(lig/L)
7Q10
SWC6
(lig/L)
coc
(lig/L)
Days of
Exceedance7
(days/yr)
Coal Grove, OH
NPDES: OHO 104558
0.00001
27
788
0
920
Surface
Water
NPDES
OH0029432
Surface
water
14,400
20
0.0031
0.0000375
0.00019
788
920
0
14,400
0
139
140
141
142
1 Release media are either direct (release from facility directly to surface water) or indirect (transfer of wastewater from active facility to a receiving POTW or
non-POTW WWTP facility). A wastewater treatment removal rate of 81% is applied to all indirect releases, i.e., volumes characterized as being transferred
off-site for treatment at a water treatment facility prior to discharge to surface water.
2 If a valid NPDES of facility was not available in EFAST, the release was modeled using either a surrogate representative facility in EFAST (based on
location discharging into the same water body) or a representative generic industry sector.
3 EFAST uses ether the "surface water" model, for rivers and streams, or the "still watef' model, for lakes, bays, and oceans.
4 Modeling was conducted with the maximum days of release per year expected. For direct releasing facilities, a minimum of 20 days was also modeled.
5 The daily release amount was calculated from the reported annual release amount divided by the number of release days per year.
6 For releases discharging to lakes, bays, estuaries, and oceans, the acute scenario mixing zone water concentration was reported in place of the 7Q10 SWC.
7 To determine the PDM days of exceedance for still bodies of water, the release days provided by the EPA Engineers is equal to the days of exceedance only
if the predicted surface water concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero.
8 Predicted water releases for the indicated sites changed slightly between modeling and publication of the Risk Evaluation. For the 440 unknown sites in the
Processing as a Reactant OES changed from 1.75 kg/yr to 2.2 kg/yr. For the sites listed under the Adhesives, Sealants, Paints, and Coatings OES, annual
release predictions changed from 3.25 kg/yr to 4.4 kg/yr. These slight differences (i.e., between 0.5 to 1.2 kg/yr) are unlikely to impact risk characterization.
9 The predicted days of exceedance are presented although the estimated 7Q10 never approaches the lowest COC due to the fact that the EFAST database has
minimum stream flow of 0 MLD and a mean stream flow of 2.69 MLD for this site. Therefore, these days of exceedances were not considered in
environmental risk characterization.
10 The predicted days of exceedance are presented although the estimated 7Q10 never approaches the lowest COC due to the fact that the EFAST database has
minimum stream flow of 0 MLD and a mean stream flow of 0 MLD for this site. Therefore, these days of exceedances were not considered in environmental
risk characterization.
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Appendix D CONSUMER EXPOSURES
For additional consumer modeling support files, please see the following supplemental documents: 24.
Final Risk Evaluation for Trichloroethylene Supplemental Information File Consumer Exposure
Assessment Model Input Parameters.xlsx; 25. Final Risk Evaluation for Trichloroethylene Supplemental
Information File Exposure Modeling Results and Risk Estimates for Consumer Inhalation
Exposures.xlsx; 26. Final Risk Evaluation for Trichloroethylene Supplemental Information File
Exposure Modeling Results and Risk Estimates for Consumer Dermal Exposures.xlsx.
D.l Consumer Inhalation Exposure
CEM predicts indoor air concentrations from consumer product use by implementing a deterministic,
mass-balance calculation utilizing an emission profile determined by implementing appropriate emission
scenarios. The model uses a two-zone representation of 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 simulated period. In some instances of 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 lm3, and far-field, which reflects the remainder
of Zone 1. Each zone is considered well-mixed. Product users are exposed to airborne concentrations
estimated within the near-field during the time of use and otherwise follow their prescribed activity
pattern. Bystanders follow their prescribed activity pattern and are exposed to far-field concentrations
when they are in Zone 1. Background concentrations can be set to a non-zero concentration if desired.
For acute exposure scenarios, emissions from each incidence of product usage are estimated over a
period of 72 hours using the following approach that 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.
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 30-
second 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, 3-
and 24-hour TWAs were quantified for use in Risk Evaluation based on alignment relevant acute human
health hazard endpoints.
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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. El was applied for all liquid formulations in the
modeling of TCE consumer 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 (U.S. EPA. 2017b). The
remainder of chemical is assumed to contact the target surface and volatilize at a rate that depends 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 El 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 (U.S. EPA. ). 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. 2.019b).
D.2 Consumer Dermal Exposure
Two models were used to evaluate consumer dermal exposures, the Fraction Absorbed model (P_DE2a
within CEM) and the Permeability model (P_DER2b within CEM). A brief comparison of these two
dermal models through the calculation of acute dose rates (ADRs) is provided below. They have been
applied to distinct exposure conditions, with the permeability model applied to scenarios likely to
involve occluded dermal contact where evaporation may be inhibited and the fraction absorbed model
applied to scenarios less likely to involve occluded dermal contact.
The dermal models described below were run for all consumer conditions of use to provide a
comparison between the two results while recognizing each model is unique in its approach to
estimating dermal exposure and may not be directly comparable. Keeping these limitations in mind, the
full suite of exposure results from both models is shown for all conditions of use in 26. Final Risk
Evaluation for Trichloroethylene Supplemental Information File Exposure Modeling Results and Risk
Estimates for Consumer Dermal Exposures.xlsx.
Because neither model considers the mass of chemical as an input in the absorbed dose equations, both
have the potential to overestimate the dermal absorption by modeling a mass which is larger than the
mass used in a scenario. Therefore, when utilizing either of the CEM models for dermal exposure
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estimations, a mass check is necessary outside of the CEM model to make sure the mass absorbed does
not exceed the typical mass used for a given scenario.
CEM Absorption Fraction Model (P_DER2a)
The fraction absorbed model estimates the mass of a chemical absorbed through the applicational of a
fractional absorption factor to the mass of chemical present on or in the skin following a use event. The
initial dose or amount retained on the skin is determined using a film thickness approach. A fractional
absorption factor is then applied the initial dose to estimate absorbed dose. The fraction absorbed is
essentially the measure of two competing processes, evaporation of the chemical from the skin surface
and penetration deeper into the skin. It can be estimated using an empirical relationship based on Frasch
and Bunge (2015). Due to the model's consideration of evaporative processes, it was considered to be
more representative of dermal exposure under unimpeded exposure conditions. For additional details on
this model, please see Appendix D and the CEM User Guide Section 3: Detailed Descriptions of Models
within CEM ( i).
ADR =¦
SA
AR X Fabs x x FQac x Dil xWFx EDac x CF1
ATac
Where:
ADR = Acute daily dose rate (mg/kg-day)
AR = Amount retained in the skin (g/cm2, film thickness [cm] multiplied by product density)
Fabs = Absorption fraction (see below)
Dac = Duration of use (min/event)
SA/B W = Surface area to body weight ratio (cm2/kg)
FQac = Frequency of use (events/day, 1 for acute exposure scenarios)
Dil = Product dilution fraction (unitless, 1 [no dilution] for all TCE scenarios)
WF = Weight fraction of chemical in product (unitless)
EDac = Exposure duration (1 day for acute exposure scenarios)
CF1 = Conversion factor (1,000 mg/g)
ATcr = Averaging time (1 day for acute exposure scenarios)
The fraction absorbed (Fabs) term is estimated using the ratio of evaporation from the stratum corneum to
the dermal absorption rate through the stratum corneum, as informed by gas phase mass transfer
coefficient, vapor pressure, molecular weight, water solubility, real gas constant, and permeability
coefficient.
3 +1
FRabs =
1
3(1+*)
Where:
X = Ratio of the evaporation rate from the stratum corneum (SC) to the dermal absorption rate
a = Constant (2.906)
Dac = Duration of use (min/event)
tiag = Lag time for chemical transport through SC (hr)
CFi = Conversion factor (60 min/hr)
CEM Permeability Model (P_DER2b)
The permeability model estimates the mass of a chemical absorbed and dermal flux based on a
permeability coefficient (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 throughout the
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exposure duration. KP is a measure of the rate of chemical flux through the skin. The parameter can
either be specified by the user (if measured data are reasonably available) or be estimated within CEM
using a chemical's molecular weight and octanol-water partition coefficient (Kow). The permeability
model does not inherently account for evaporative losses (unless the available flux or KP values are
based on non-occluded, evaporative conditions), which can be considerable for volatile chemicals in
scenarios where evaporation is not impeded. While the permeability model does not explicitly represent
exposures involving such impeded evaporation, the model assumptions make it the preferred model for
an such a scenario. For TCE, a measured dermal permeability coefficient (KP 0.0023 cm/hr) is used,
based on measured dermal flux from a human dermal absorption test with neat TCE (Kezic et al. 2001).
For additional details on this model, please see Appendix D and the CEM User Guide Section 3:
Detailed Descriptions of Models within CEM ( 019a).
The acute form of the dermal permeability model is given below:
SA
KpxDacx px-swx FQac x Dil xWF x EDac x CF1
ADR = —
Where:
ATac * CF2
ADR = Potential acute dose rate (mg/kg-day)
Kp = Permeability coefficient (cm/hr)
Dac = Duration of use (min/event)
p = Density of formulation (g/cm3)
SA/B W = Surface area to body weight ratio (cm2/kg)
FQac = Frequency of use (events/day, 1 for acute exposure scenarios)
Dil = Product dilution fraction (unitless, 1 [no dilution] for all TCE scenarios)
WF = Weight fraction of chemical in product (unitless)
EDac = Exposure duration (1 day for acute exposure scenarios)
CF1 = Conversion factor (1,000 mg/g)
CF2 = Conversion factor (60 min/hr)
ATac = Averaging time (1 day for acute exposure scenarios)
D.3 Model Sensitivity
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).
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 of TCE.
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.
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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 summarized below for the inhalation and dermal models used to evaluate consumer
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. 2017b).
D.3 .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 FigureApx D-l). The
next most sensitive parameters demonstrate negative elasticity and include: VolBuilding (building
volume); AER_Zone2 (air exchange rate in Zone 2); AERZonel (air exchange rate in Zone 1);
VolZonel (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.
El Elasticity for ADR and CADD
WF
VP*
VP
Vol_Zonel
Vol_Building
Q_zl2
MW*
MW
M_Chronic
M_Acute
AER_Zone2
AER Zonel
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Elasticity (% change in dose/% change in variable)
¦ ADR Negative ~ ADR Positive H CADD Negative ~ CADD Positive
1.2
Figure Apx D-l. Elasticities (> 0.05) for Parameters Applied in El
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
exposures from 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.
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E3 Elasticity for ADR and CADD
_ WF
— VP*
^^^^5 VolZonel
VolBuilding
_ Q_zl2
— MW*
MW
^ M_Chronic
MAcute
h DurationChronic
^ DurationAcute
CSATA
= Aerosol_Fraction
AER_Zone2
¦ AERZoriel
-0.7 -0.4 -0.1 0.2 0.5 0.8 1.1
Elasticity (% change in dose/% change in variable)
¦ ADR Negative ~ ADR Positive ~ CADD Negative ~ CADD Positive
354
355 FigureApx D-2. Elasticities (> 0.05) for Parameters Applied in E3
356
357 For acute exposures generated from the dermal permeability model, the chemical properties that inform
358 absorption rate, or absorption rate estimates, have the greatest elasticities (see Figure Apx D-3). For
359 TCE, dermal exposures from consumer product formulations were modeled using a measured Kp
360 (permeability coefficient). Therefore, LogKow (octanol/water partition coefficient) and MW (molecular
361 weight) were not used to estimate skin penetration.
362
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387
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392
MW*
LogKow*
-2 0 -1.0 0.0 1.0 2.0 3.0
Elasticity (% change in dose/% change in variable)
¦ ADR Negative ~ ADR Positive ~ CADD Negative ~ CADD Positive
FigureApx D-3. Elasticities (> 0.05) for Parameters Applied in P_DER2b
D.3 .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.
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.4 Monitoring Data
D.4.1 Indoor Air Monitoring
Systematic review identified indoor air monitoring studies reporting levels of TCE in residential indoor
air samples. 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 [Data Extraction
Tables for Environmental Monitoring Data. Docket: EPA-HO-OPPT-2019-0500\.
Concentrations of TCE in residential indoor air in the United States and Canada collected from nine
studies identified during Systematic Review are summarized in Table Apx D-l. 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 non-
detect (detection limits varied) to 42 |ig/m3. 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 |ig/m3, with a couple central tendency measurements above 3 |ig/m3.
Data extracted for residential indoor air samples from studies conducted outside of North America, as
well as studies conducted in schools and commercial establishments in the US and other countries, are
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393 provided in [Data Extraction Tables for Environmental Monitoring Data. Docket: EPA-HQ-OPPT-
394 2019-0500],
395
396 TableApx D-l. TCE Residential Indoor Air Concentrations (jig/m3) in the United States and
397 Canada
Study Info
Site Description
LOQ
Min.
Mean
Median
Max.
Variance
Data Eval.
Score
(Chin et at 2014)
Detroit, MI area; Homes
0.09
ND
0.07
0.04
1.48
0.14 (SD)
High
US, 2009-2010 (n=126; DF =
(n=126) with children
0.06)
with asthma
(Dodson et al. 2008)a
Boston, MA; Interior
0.04
ND
0.6
0.2
2.2 (95th)
1.7 (SD)
High
US, 2004-2005 (n=83; DF =
room of residences
0.93)
(Dodson et al. 2008)a
Boston, MA; Basement
0.04
ND
0.4
0.1
1.4 (95th)
1.1 (SD)
High
US, 2004-2005 (n=52; DF =
of residences
0.75)
(Dodson et al. 2008)a
US, 2004-2005 (n=10; DF =
Boston, MA; Apartment
hallway of residences
0.04
ND
3.7
0.3
23 (95th)
7.3 (SD)
High
0.9)
(Dodson et al, 2008)a
Boston, MA; Garage of
0.04
ND
3.3
0.1
42 (95th)
10 (SD)
High
US, 2004-2005 (n=16; DF =
residences
0.63)
(Jia et al. 2008a)
Ann Arbor, Ypsilanti,
0.008
ND
0.06
0.03
2.01
--
Medium
US, 2004-2005
and Dearborn MI;
(n=252; DF = 0.56)
Residences (n=159) in
industrial, urban, and
suburban cities over two
seasons
(Adeate et al. 2004)
US, 2000 (n=113; DF = 0.828)
Minneapolis, MN;
Inside home, during the
winter. Sampling from
room where child spent
the most time.
ND
(10th
0.1)
0.3
Medium
(Adgate et al, 2004)
US, 2000 (n=113; DF = 0.737)
Minneapolis, MN;
Inside home, during the
spring. Sampling from
room where child spent
the most time.
ND
(10th
0.1)
0.2
Medium
US, 2000 (n=32;)DF = 0.47)
Los Angeles, CA;
Homes (n=35) in inner-
city neighborhood,
sampled in the fall
0.13
ND
0.2
0.1
0.8
0.2 (SD)
High
(Sax et al.. 2004)
US, 2000 (n=40; DF = 0.68)
Los Angeles, CA;
Homes (n=40) in inner-
city neighborhood,
sampled in the winter
0.13
ND
0.2
0.2
1.2
0.3 (SD)
High
(Sax et al. 2004)
New York, NY; Homes
0.13
ND
1.1
0.4
19
3.2 (SD)
High
US, 1999 (n=36; DF = 0.92)
(n=38) in inner-city
neighborhood, sampled
in the winter
(Sax et al. 2004)
New York, NY; Homes
0.13
ND
0.3
0.1
2.6
0.5 (SD)
High
US, 1999 (n=30; DF = 0.44)
(n=41) in inner-city
neighborhood, sampled
in the summer
Page 563 of 803
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Study Info
Site Description
LOQ
Min.
Mean
Median
Max.
Variance
Data Eval.
Score
(Su et al.. 2013)b
US, 1999-2001 (n=539; DF =
NR)
Elizabeth, NJ; Houston,
TX; and Los Angeles,
CA; Non-smoking
households (n=310)
0.99
0.22
1.74 (95th)
7.29 (SD)
Medium
CClavton et al.. 1999)°
US, 1995-1997 (n=402; DF =
0.361)
IL, IN, OH, MI, MN,
WI (Great Lakes
Region); Non-
institutionalized persons
residing in households
in six states
ND
3.84
0.56
2.28 (90th)
High
(Lindstrom et al., 1995)
US, 1994 (n=9; DF = 0.56)
Denver, CO; Homes,
occupied (n=9)
0.12
ND
0.64
0.61
0.66 (SD)
Medium
("Chan et al.. 1990)
Homes (n=6), main
floor
ND
1.6
5
Medium
CA, 1987 (n=6; DF = 0.83)
(Chan et al.. 1990)
CA, 1986 (n=12;DF = 0.42)
Homes (n=12), main
floor
ND
0.5
2
Medium
Study Info: The information provided includes the citation; country 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 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).
a 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.
0 Samples from this study were collected as part of the NHEXAS Phase 1 field study.
398
399 D.4.2 Personal breathing Zone Monitoring Data
400 Concentrations of TCE (TCE) in the personal breathing zones of residents in the United States collected
401 from seven studies identified during Systematic Review are summarized in Table Apx D-2. Overall, the
402 measured concentration dataset contains approximately 2,750 samples that were collected between 1981
403 and 2001, and represents time spent in various microenvironments {i.e., home, school, work, transit)
404 during the monitoring period. Only the 3-hr samples from Heavner et al. (1995) represent time inside the
405 home only. Concentrations ranged from non-detect (limits varied) to 327.3 |ig/m3. The highest
406 concentration was observed in samples collected in 2000 as part of the NHANES 1999-2000 study (Jia
407 et al.. 2008b). The study states that the top ten highest concentrations exceeded 300 (,ig/nr\ which they
408 suggest may indicate exposure from immediate contact with solvents. The 95th percentile concentration
409 in this study is 7.4 [j,g/m3. All other studies showed maximum concentrations less than 10 |ig/m3.
410 Median concentrations ranged from ND to 1.05 (J,g/m3; and average concentrations ranged from 0.66 to
411 13 |ig/m3.
412
413 Data extracted for residential/general personal breathing zones studies conducted outside of North
414 America, as well as studies conducted in schools and commercial establishments in the US and other
415 countries, is provided in [Data Extraction Tables for Environmental Monitoring Data. Docket: EPA-
416 HQ-OPPT-2019-0500],
417
Page 564 of 803
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418 TableApx D-2. Personal Breathing Zone Concentrations (jig/m3) for TCE in the United States
419 (General/Residential)
Data
Study Info
Type
Site Description
LOD
Min.
Mean
Median
Max
Variance
Eval.
Score
CSu et al. 2013V
48-hr
Elizabeth, NJ; Houston,
US, 1999-2001
(n=544; DF = 0.23)
TX; and Los Angeles, CA;
Adults (n=309) and
ND
1.44
0.22
2.37
(95th)
10.74
(SD)
Medium
children (n=118) from 310
non-smoking households.
Ola et al. 2008bV
48-to
Nation-wide; Adults (ages
0.4
(GM)
327.3
3.4
(GSD)
US, 1999-2000
72-hr
20-59 years) in NHANES
0.44
ND
ND
(7.4 -
High
(n=665; DF = 0.229)
study
95th)
(Sexton et al. 2007)
US, 1999
(n=333;DF = 0.925)
48-hr
Minneapolis -St. Paul, MN;
Adults, non-smoking
(n=70) living in three
neighborhoods: (inner-city,
blue-collar/near
manufacturing plants, and
affluent)
--
ND
1
0.2
1.8
(90th)
--
High
(Clayton et al. 1999V
6-day
IL, IN, OH, MI, MN, WI
US, 1995-1997
(n=386; DF = 0.394)
(Great Lakes Region);
Non-institutionalized
persons
--
ND
5.27
0.63
5.98
(90th)
--
High
(Heavner et al. 1995)
3-hrs
Columbus, OH; Non-
US, 1991
(n=24; DF = NR)
(in
home
only)
smoking women (n=24)
with non-smoking
husbands
--
ND
1.84
1.05
9.08
2.39
Medium
(Heavner et al, 1995)
3-hrs
Columbus, OH; Non-
US, 1991
(n=25; DF = NR)
(in
home
only)
smoking (n=25) women
with smoking husbands
--
ND
0.66
ND
3.41
1.04
Medium
(Wallace. 1987V
US, 1981-1984
12-hrs
Elizabeth and Bayonne, NJ,
Los Angeles, CA, and
(n=772; DF = 0-0.97)
Contra Costa, CA; Adults
3.8 to
13
in industrial/ chemical
--
--
--
--
--
High
manufacturing and /or
petroleum refining regions
of the US.
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 limit. 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).
a 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
(ig/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.
d Samples from this study were collected as part of the TEAMS study.
420
Page 565 of 803
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421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
Appendix E ENVIRONMENTAL HAZARDS
E.l 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. 2020). It runs on Matlab 2018b (9.5) for Windows 64
bit. For this TCE Risk Evaluation, EPA created two SSDs with the SSD Toolbox, one using only algae
hazard data and the other using acute hazard data for all other aquatic species. This appendix outlines the
methodology used to create each.
For the acute SSD, acute hazard data for fish, amphibians, and invertebrates were curated to prioritize
study quality and to assure comparability between toxicity values. For example, the dataset included
only LCsos for fish and amphibians, and EC50s or LCsos that measured immobilization and mortality for
aquatic invertebrates. The dataset included both saltwater and freshwater species, 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. Table Apx E-l shows the data that was used in the algae
SSD, as well as data that was not included in the SSD and why.
With this dataset, the Toolbox was used to apply a variety of algorithms to fit and visualize SSDs with
different distributions. Figure Apx E-l shows the Toolbox interface after each distribution and fitting
method was fit to the data. An FtCos is calculated for each.
Page 566 of 803
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442 Table Apx E-l. Acute Toxicity Data for Aquatic Organisms used in SSD
Species
LCso (mg/L)*
Source (quality rating)
Used in SSD
Amphibians
African clawed frogs
(Xenopus laevis)
434.0
(Fort et al.. 1993) (hiah)
Yes
African clawed frogs
(Xenopus laevis)
434 (geometric mean)
(Fort et al.. 1991) (medium)
Yes, used a geometric mean of two values for
LC50S in the study
African clawed frogs
(Xenopus laevis)
441 (geometric mean)
(Fort et al.. 2001) (medium)
Yes, used a geometric mean of three values for
LC50S in the study
Fish
Fathead minnow (Pimephales
promelas)
44.1
(Geiaer et al.. 1985) (hiah)
Yes
Fathead minnow (Pimephales
promelas)
40.7
(Alexander et al.. 1978) (hiah)
Yes
Fathead minnow (Pimephales
promelas)
66.8
(Alexander et al.. 1978) (medium)
No, because this value from a static study was
rated medium for quality and a high-quality
flow through value from the same study was
available
American flagfish
(Jordanella floridae)
28.28
(Smith et al.. 1991) (hiah)
Yes
American flagfish (Jordanella
floridae)
31.00
(Smith et al.. 1991) (medium)
No, because this value from a static study was
rated medium for quality and a high-quality
flow through value from the same study was
available
Fathead minnow (Pimephales
promelas)
46.7 (geometric mean)
(Broderius et al.. 2005) (hiah)
Yes, used a geometric mean of three values for
LC50S in the study
Bluegill (Lepomis
macrochirus)
45
(Buccafusco et al.. 1981) (medium)
Yes
Sheepshead minnows
(Cyprinodon variegatus)
52
(Ward et al.. 1986) (medium)
Yes
Page 567 of 803
-------
Species
LCso (mg/L)*
Source (quality rating)
Used in SSD
Sheepshead minnows
(Cyprinodon variegatus)
99
(Ward et al.. 1986) (medium)
No, because this LC50 measured initial TCE
concentrations and the average concentrations
were available in the same study
Invertebrates
Daphnia magna
18
(LeBlanc. 1980) (hiah)
Yes
Daphnia magna
22
(LeBlanc, 1980) (high)
No, because a 48-hour value was available in
the same paper
Daphnia magna
7.75
(Abernethv et al.. 1986) (medium)
Yes
Daphnia magna
33.85
(Dobaradaran et al.. 2012)
(medium)
Yes
Daphnia magna
43.14
(Dobaradaran et al.. 2012)
(medium)
No, because a 48-hour value was available in
the same paper
Daphnia magna
28.39
(Dobaradaran et al.. 2012)
(medium)
No, because a 48-hour value was available in
the same paper
Daphnia magna
26.55
(Dobaradaran et al.. 2012)
(medium)
No, because a 48-hour value was available in
the same paper
Mysidopsis bahia (Mysid
shrimp)
14
(Ward et al.. 1986) (medium)
Yes
Ceriodaphnia dubia
17.08
(Niederlehner et al.. 1998) (hiah)
Yes
443 *ECsos measuring immobilization were also used for invertebrates, because it is difficult to distinguish between death and immobilization for
444 aquatic invertebrates.
Page 568 of 803
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445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
FigureApx E-l. SSD Toolbox interface showing HCoss and P values for each distribution and
itting method using TCE's acute hazard data (Etterson, 2020)
.-#! SSD Toolbox
File Plot
~
C:\Users\KKoehrn\Documents\RAD\TCE\SSD_TCE_algae_files\AII_species.xlsx
Fit Distribution
Distribution:
burr
Fitting method
metropolis-hastings
Iterations
50000
Goodness of Fit:
Iterations: 1 qqO
Scaling parameters
~ Scale to Body Weight
Scaling factor:
Target weight:
Toolbox
Status:
Ready
Distribution Method
HC05
normal
normal
normal
normal
logistic
logistic
logistic
logistic
triangular
triangular
triangular
triangular
gumbel
gumbel
gumbel
gumbel
weibull
weibull
weibull
burr
burr
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
GR
MH
ML
MH
7.1130
6.3275
5.2472
3.8935
6.9555
6.4792
4.5570
3.7129
7.2234
6.1216
5.6917
3.0649
11.9649
9.1953
7.2366
8.5581
1.6667
3.5984
2.7397
11.9599
12.1952
0.2597
0.1778
0.0919
0.3752
0.5874
0.3187
0.1379
0.3930
0.1419
0.1469
0.0909
0.4562
0.4975
0.7303
0.3067
0.3462
0.0500
0.0310
0.5208
0.4196
0.7406
The SSD Toolbox's output contained several methods for choosing an appropriate distribution and
fitting method, including goodness-of-fit, standard error, and sample-size corrected Akaike Information
Criterion (AICc, fBurnham and Anderson, 20021). However, choosing the distribution with the best fit
was challenging with a small dataset (e.g., hazard data for 8 algae species). Most P values for goodness-
of-fit were above 0.05, showing no evidence for lack of fit. However for the Weibull distribution, the
maximum likelihood and graphical methods fitting methods had P values for goodness-of-fit below 0.05
showing lack of fit, so they were eliminated. For all other distributions P values for goodness-of-fit were
> 0.05 (Figure Apx E-l). Standard error was mixed across fitting methods for some distributions but
generally the lowest for the burr distribution (Table_Apx E-2) shows that the Gumbel distribution has
the lowest AICc, indicating it may be the best distribution for this data though the relative AIC support
compared to other distributions is weak. Because the ability for these measures to distinguish between
distributions was limited, visual inspection of the distributions was also used; however, no distributions
could be eliminated through this method either (Figure Apx E-3).
Page 569 of 803
-------
463
464
TableApx E-2. Standard Error for all distributions and fitting methods using TCE's acute
Normal
Distribution
Logistic
Distribution
Triangular
Distribution
Gumbel
Distribution
Weibull
Distribution
Bun-
Distribution
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
MH
ML
MH
Standard
Error for
HC,;,5 (mg/L)
5.5
5.4
4.6
3.7
5.3
5.5
4.0
3.5
6.7
5.0
4.1
4.0
4.2
4.7
4.2
4.0
2.8
5.5
0.4
465
466
467
Figure Apx E-2. AICc for the five distribution options in the SSD Toolbox for TCE's acute hazard
data (Etterson, 2020)
ModelSelection
I Qanitle Averaging
Percentile of interest:
Model-averaqed HCp:
Model-averaqed SE of HCd:
CV of HCd:
X
9.9687
0.54512
5.4341
0 Calculate variance from Bootstrap
AICc Table
Distribution
AICc
delta AICc
Wt
HCp
SE HCp
1
gumbel
84.9297
0
0.5574
11.9649
4.2045
2
logistic
87.3190
2.3892
0.1688
6.9555
5.3025
3
triangular
87.9162
2.9865
0.1252
7.2234
6.6589
4
normal
88.0905
3.1608
0.1148
7.1130
5.4969
5
burr
90.5325
5.6028
0.0338
11.9599
5.4910
468
Page 570 of 803
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469 FigureApx E-3. All distributions and fitting methods in the SSD Toolbox for TCE's acute hazard
470 data (Etterson, 2020)
0.5 1 1.5 2 2.5
Toxicity Value (Log 10[EC50]) mg/L
Daphnia magna
Mysidopsis bahia
Lepomis macrochirus (blue gill) ¦
Pimephales promelas (fathead minnow)!
dan ell a floridae (flagfish)
tlaphnia dubia
• normal distribution
¦ logistic distribution
triangular distribution
¦ gumbel-ML
weibull distribution
burr distribution
Xenopus laevi^fAf
Cyprinodon variegatus (sheepshead) 1
471
All
473 Because there was no obvious distribution that was the best fit using goodness-of-fit, standard error, and
474 sample-size corrected AICc„ EPA used five distributions to calculate an HCos. including normal, logistic,
475 triangular, Gumbel, and Burr distributions using the maximum likelihood fitting method. EPA did not
476 use the Weibull distribution was not used, because the maximum likelihood fitting method for Weibull
477 was eliminated because its P value for goodness-of-fit was < 5. The model-averaged HCos from all five
478 distributions was 10 mg/L or 10,000 ug/L, and the SSDs showed aquatic invertebrates were the most
479 sensitive species (Figure Apx E-4).
Page 571 of 803
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480
481
482
483
484
485
486
487
488
489
490
491
492
FigureApx E-4. TCE's acute hazard data fit with the normal, logistic, triangular, Guinbel, and
Burr distributions fit with maximum likelihood in the SSD Toolbox (Etterson, 2020)
1
0.9
0.8
0.7
>,
io.e
.Q
o
o 0.5
>
| 0.4
d
O
0.3
0.2
0.1
0
normal distribution
logistic distribution
triangular distributior
gumbel distribution
bun distribution
1
Xenopus laevis
(Africa^^fed frog) •
~ HC05
Cyprinodon variegatus (sheepshead) • //
Lepomis
macrochints (bluegill) • / /
Pimephales promelas (fathead minnov/)mf
{prdarteila fbridae (Hagfish)
* ifriodaphnia dubia
/ f Daphnia magna
/ • Mvsidoosis bahia
/
¦
0.5
1 1.5 2 2.5
Toxicity Value (Log 10[EC50]) mg/L
3.5
For the algae SSD, algae hazard data were curated to prioritize study quality and to assure comparability
between toxicity values (e.g., comparing ECsos to 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. Table_Apx E-3 shows the data that was used in the algae SSD, as well as data that
was not included in the SSD and why.
With this dataset, the Toolbox was used to apply a variety of algorithms to fit and visualize SSDs with
different distributions. Figure_Apx E-5 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.
Page 572 of 803
-------
493 Table Apx E-3. Algae Toxicity Data used in SSD
Species
ECso for growth (mg/L)
Source (quality rating)
Used in SSD
Saltwater
Skeletonema costatum
95
(Ward et al.. 1986) (medium)
Yes
Freshwater
Chlamydomonas reinhartdtii
36.5
(Brack and Rottler. 1994) (high)
Yes
Chlamydomonas reinhartdtii
520
(Lukavskv et al.. 2011) (medium)
Yes
Chlorella kessleri
321, geometric mean of two
population growth rate values
(Lukavskv et al.. 2011) (medium)
Yes
Desmodesmus quadricauda
447, geometric mean of two
population growth rate values
(Lukavskv et al.. 2011) (medium)
Yes
Desmodesmus subspicatus
536, geometric mean of two
population growth rate values
(Lukavskv et al.. 2011) (medium)
Yes
Mycrocystis aeruginosa
130
(Lukavskv et al.. 2011) (medium)
Yes
Raphidocelis subcapitata
26.24
(Tsai and Chen. 2007) (hiah)
Yes
Raphidocelis subcapitata
315, geometric mean of two
population growth rate values
(Lukavskv et al.. 2011) (medium)
Yes
Synechococcus elongatus
800
(Lukavskv et al.. 2011) (medium)
Yes
Synechococcus leopoliensis
424, geometric mean of two
population growth rate values
(Lukavskv et al.. 2011) (medium)
Yes
Chlamydomonas reinhardtii
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Chlamydomonas reinhardtii
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
Page 573 of 803
-------
Species
ECso for growth (mg/L)
Source (quality rating)
Used in SSD
more biologically relevant effect than
photosynthesis.
Chlorella kessleri
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Chlorella kessleri
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Raphidocelis subcapitata
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Raphidocelis subcapitata
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Desmodesmus quadricauda
500
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Desmodesmus quadricauda
600
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
Page 574 of 803
-------
Species
ECso for growth (mg/L)
Source (quality rating)
Used in SSD
more biologically relevant effect than
photosynthesis.
Desmodesmus subspicatus
400
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Desmodesmus subspicatus
400
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Synechococcus elongatus
600
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Synechococcus elongatus
700
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Synechococcus leopoliensis
480
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Synechococcus leopoliensis
450
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
Page 575 of 803
-------
Species
ECso for growth (mg/L)
Source (quality rating)
Used in SSD
more biologically relevant effect than
photosynthesis.
Microcystis aeruginosa
100
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
Microcystis aeruginosa
250
(Lukavskv et al.. 2011) (medium)
No, because an ECso measuring population
growth rate was available in the same study
for this species and that was considered a
more biologically relevant effect than
photosynthesis.
494
Page 576 of 803
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495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
FigureApx E-5. SSD Toolbox interface and list of HCoss for each distribution and fitting method
using TCE's algae hazard data (Etterson, 2020)
3] SSD Toolbox
File Plot
C:\Users\KKoehrn\Documents\RAD\TCE\SSD_TCE_algae_files\Algae_revised.xlsx
Fit Distribution
Distribution:
burr
Fitting method
metro polis-hastings
Iterations
50000
Goodness of Fit:
Iterations: 1000
Scaling parameters
~ Scale to Body Weight
Scaling factor:
Target weight:
Ma
Toolbox
Status:
Ready
Distribution
Method
HC05
p
1
normal
ML
70.7294
0.1019
2
normal
MO
65.4897
0.1349
3
normal
GR
51.6901
0.3187
4
normal
MH
48.4139
0.0980
5
logistic
ML
61.3796
0.0549
6
logistic
MO
66.6519
0.0849
7
logistic
GR
47.1567
0.2408
8
logistic
MH
41.6877
0.1102
9
triangular
ML
86.6386
0.1399
10
triangular
MO
63.8997
0.1738
11
triangular
GR
54.9999
0.2318
12
triangular
MH
49.6005
0.1212
13
gumbel
ML
81.3972!
0.0729!
14
gumbel
MO
86.4467
0.0390
15
gumbel
GR
71.5082
0.1828
16
gumbel
MH
63.6185
0.1856
17
weibull
ML
49.8877
0.1878
18
weibull
GR
35.4743
0.3467
19
weibull
MH
50.5267
0.2890
20
burr
ML
81.3743
0.0619
21
burr
MH
79.6576
0.2792
The SSD Toolbox's output contained several methods for choosing an appropriate distribution and
fitting method, including goodness-of-fit, standard error, and sample-size corrected Akaike Information
Criterion (AICc, l"Burnham and Anderson. 20021). However, choosing the distribution with the best fit
was challenging with a small dataset (e.g., hazard data for 9 algae species). Most P values for goodness-
of-fit were above 0.05, showing no evidence for lack of fit. However for the Gumbel distribution, the
moment estimator fitting method had a P value for goodness-of-fit below 0.05 showing lack of fit, so it
was eliminated. For all other distributions P values for goodness-of-fit were > 0.05, providing no help in
discriminating among distributions (Figure_Apx E-5). Standard error was lowest across fitting methods
for the Gumbel and Burr distributions (Table Apx E-4). And the AICc Table (Figure Apx E-6) showed
that triangular, normal, and Weibull distributions may be the best fit. Because the ability for these
measures to distinguish between distributions was limited, visual inspection of the distributions was
used; however, no distributions could be eliminated through this method either (Figure Apx E-7).
Page 577 of 803
-------
513
514
TableApx E-4. Standard Error for all distributions and fitting methods using TCE's algae
Normal
Distribution
Logistic
Distribution
Triangular
Distribution
Gumbel
Distribution
Weibull
Distribution
Bun-
Distribution
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
GR
MH
ML
MH
Standard
Error for
HC,;,5 (mg/L)
34
32
26
27
34
37
28
28
32
29
30
29
25
27
27
24
40
33
32
30
1.3
^igure Apx E-6. AICc Table for algae hazard data (Etterson, 2020)
311 VI o del Selection
X
Qanitle Averaging
Percentile of interest:
Model-averaqed HCp:
Model-averaaed SE of HCp:
CV of HCp:
72.3439
0.49254
0 Calculate variance from Bootstrap
AICc Table
Distribution
AICc
delta AICc
Wt
HCp
SE HCp
1
triangular
125.6893
0
0.2918
86.6386
32.2857
2
normal
126.3679
0.6786
0.2078
70.7294
34.2956
3
weibull
126.5614
0.8720
0.1887
49.8877
40.2157
4
gumbel
126.7124
1.0231
0.1749
81.3972
25.0179
5
logistic
127.4514
1.7621
0.1209
61.3796
34.2615
6
burr
131.5138
5.8244
0.0159
81.3743
30.2109
517
Page 578 of 803
-------
518 FigureApx E-7. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard
519 data (Etterson, 2020)
Synechococcus elongatt
Desmodesmus subspicati
Synechococcus leopoSefiSis •
• Ctydnydomonas reinhartdtii
faphidocelis subcapitata
-Q
-Q
o
0.6
Desmodesmus quadrtau
• Chlorella kessleri
fcrocystis aeruginosa
• Skeleton ema costatum
logistic distribution
triangular distribution
gumbel distribution
weibull distribution
normal distribution
burr distribution
Toxicity Value (Log 10[EC50]) mg/L
521
522 Because there was no obvious distribution that was the best fit using goodness-of-fit, standard error, and
523 sample-size corrected AICc, EPA used a all six distributions to calculate an HCos. Using the normal,
524 logistic, triangular, Gumbel, Weibull, and Burr distributions, EPA calculated a modeled average HCos of
525 72 mg/L or 72,000 jig/L.
Page 579 of 803
-------
526 FigureApx E-8. TCE algae data fit with all distributions using the maximum likelihood fitting
527 method (Etterson, 2020)
• Cfmmydomonas reinhartdtii
'kaletonema costatum
• Raphidocelis subcapitata
2 2.5 3 3.5
Toxicity Value (Log 10[EC50]) mg/L
Desmodesmus quadrica
Chlorella kessleri
crocystis aeruginosa
normal distribution
logistic distribution
triangular distribution
gumbel distribution
weibull distribution
burr distribution
HC05
Synechococcus elongatus
Desmodesmus subspicatus
4.5
530
Page 580 of 803
-------
531 E.2 Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as
532 Modeled in E-FAST
533
534 Table Apx E-5. Environmental RQs by Facility (with RQs > 1 in bold)
Name. I.ocalion. and
II) dI" \cli\ e Releaser
l-';icilil\
Release
Media
Modeled
1 acilil> or
IlldllsliA
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s i>|"
Release
Release
(ku da>)
~nio
SWC
(pph)
\aile
Risk
Ouoiieiils
(llslliu
COC ol
2.(100
pph)
Chrome
Risk
Ouoiieiils
(llslliu
iii\ eriehrale
( ()( ol'^:o
pph)
Chrome
Risk
OiiDlienls
(llslliu
lish (()(
ol' "SS
pph)
\luae
(.)iii>lieiils
(IISIIIU
( <)( ol ^
pph)
\luae
(.)iii>lieiils
(iisinu
( ()( ol
14.400
pph)
OES: Adhesives, Sealants, Paints, and Coatings
Able Electropolishing
Co Inc,
Chicago, IL
NPDES: Not available
POTW
Adhesives
and Sealants
Manuf.
Surface
water
250
0.298
7.28
0.00
0.01
0.01
2.43
0.00
Garlock Sealing
Technologies,
Palmyra, NY, NPDES:
NY0000078
Surface
Water
NPDES
NY0000078
Surface
water
250
0.00033
0.00716
0.00
0.00
0.00
0.00
0.00
20
0.00407
0.0889
0.00
0.00
0.00
0.03
0.00
Ls Starrett Co,
Athol, MA
NPDES: MAR05B615
Surface
Water
Not assessed (below the min risk level).
Aerojet Rocketdyne,
Inc.,
East Camden, AR
NPDES: AR0051071,
ARR00A521,
ARR00A520
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Best One Tire &
Service,
Nashville, TN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
Page 581 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)C ol'
2.00(1
pphi
Chrome
Risk
Ouolieiils
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
Oik»lieiils
(llslliu
C()( ol
14.400
pphi
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Bridgestone Aircraft
Tire (Usa), Inc.,
Mayodan, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Clayton Homes Inc,
Oxford, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Cmh Manufacturing,
Inc.
Dba Schult Homes -
Plant 958,
Richfield, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Delphi Thermal
Systems,
Lockport, NY
NPDES: NY0000558
Surface
Water
NPDES
NY0000558
Surface
water
250
0.013
1.1
0.00
0.00
0.00
0.37
0.00
20
0.16
13.5
0.01
0.01
0.02
4.50
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
Page 582 of 803
-------
Name. I.ocalkm. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)C ol'
2.00(1
pphi
Chrome
Risk
Ouolieiils
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
Oik»lieiils
(llslliu
C()( ol
14.400
pphi
Green Bay Packaging
Inc - Coon Rapids,
Coon Rapids, MN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Mastercraft Boat
Company,
Vonore, TN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Michelin Aircraft Tire
Company,
Norwood, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
M-Tek, Inc,
Manchester, TN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Olin Corp,
East Alton, IL
NPDES: IL0000230
Surface
Water
NPDES
IL0000230
Surface
water
250
0.013
0.18
0.00
0.00
0.00
0.06
0.00
20
0.16
2.26
0.00
0.00
0.00
0.75
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 583 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)C ol'
2.00(1
pphi
Chrome
Risk
Ouolieiils
(iisinu
in\ eriehrale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
Oik»lienls
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
Parker Hannifin Corp
Paraflex Division,
Manitowoc, WI
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Parrish Tire Company,
Yadkinville, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Republic Doors And
Frames,
Mckenzie, TN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Ro-Lab Rubber
Company Inc.,
Tracy, CA
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 584 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
Oik»lieiils
(llslliu
C()( ol
14.400
pphi
Royale Comfort
Seating, Inc. - Plant
No. 1,
Taylorsville, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Snider Tire, Inc.,
Statesville, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Snyder Paper
Corporation,
Hickory, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 585 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
liidiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s of
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
of "SS
pphi
\luae
Ouoiieiils
(llslliu
( ()( of ^
pphi
\luae
(.)iiolienls
(llslliu
C()( of
14.400
pphi
Stellana Us,
Lake Geneva, WI
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Thomas Built Buses -
Courtesy Road,
High Point, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Unicel Corp,
Escondido, CA
NPDES: Not
available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 586 of 803
-------
Name. I.oealion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 aeihi> or
IndiMrs
Seclor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\eule
Risk
Ouolieiils
(llslliu
( ()C of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)ik>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
Acme Finishing Co
Lie,
Elk Grove Village, IL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Aerojet Rocketdyne,
Inc.,
Rancho Cordova, CA
NPDES: CA0004111
Surface
Water
NPDES
CA0004111
Surface
water
250
0.013
0.000818
0.00
0.00
0.00
0.00
0.00
20
0.16
0.0101
0.00
0.00
0.00
0.00
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Allegheny Cnty
Airport Auth/
Pgh Intl Airport,
Coroapolis
Pittsburgh, PA
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
Page 587 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Amphenol Corp -
Aerospace Operations,
Sidney, NY
NPDES: NY0003824
Surface
Water
NPDES
NY0003824
Surface
water
250
0.013
0.0631
0.00
0.00
0.00
0.02
0.00
20
0.16
0.78
0.00
0.00
0.00
0.26
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Aprotech Powertrain,
Asheville, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Coating & Converting
Tech Corp/
Adhesive Coatings,
Philadelphia, PA
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
Page 588 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
liidiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Oik»1iciiIs
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)uoiieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Corpus CMsti Army
Depot,
Corpus CMsti, TX
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Electronic Data
Systems
Camp Pendleton,
Camp Pendleton, CA
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 589 of 803
-------
Name. I.ocalioii. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s of
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
of "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( of ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( of
14.400
pphi
Florida Production
Engineering, Inc.,
Ormond Beach, FL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Goodrich Corporation,
Jacksonville, FL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Kasai North America
Inc,
Madison Plant,
Madison, MS
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
Page 590 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)C ol'
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
(.)uoiieiils
(llslliu
( ()( ol ^
pphi
\luae
Ouolieiils
(llslliu
C()( ol
14.400
pphi
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Kirtland Air Force
Base,
Albuquerque, NM
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Marvin Windows &
Doors,
Warroad, MN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Mcneilus Truck &
Manufacturing Inc,
Dodge Center, MN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
Page 591 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
liidiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C<)( ol
14.400
pphi
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Metal Finishing Co. -
Wichita (S Mclean
Blvd),
Wichita, KS
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Murakami
Manufacturing Usa
Inc, Campbellsville,
KY
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Peterbilt Motors
Denton Facility,
Denton, TX
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
Page 592 of 803
-------
Name. I.oealioii. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 aeihi> or
liidiMrs
Seelor mi
I I AST
i:i \sr
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\eule
Risk
Oui»lieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
in\ eilehiale
( ()C nl'^:o
pphi
Chrome
Risk
Oiiiilieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
Oiiiilieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiii|ieiils
(llslliu
('()( ol
14.400
pphi
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Portsmouth Naval
Shipyard,
Kittery, ME
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
R.D. Henry & Co.,
Wichita, KS
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Raytheon Company,
Portsmouth, RI
NPDES: RI0000281
Surface
Water
NPDES
RI0000281
Still
body
250
0.013
10.83
0.01
0.01
0.01
3.61
0.00
Page 593 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s of
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eriehrale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
of "SS
pphi
\luae
Ouoiieiils
(llslliu
("OC Ol" '
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.16
133.33
0.07
0.14
0.17
44.44
0.01
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Rehau Inc,
Cullman, AL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Rotochopper Inc,
Saint Martin, MN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Rubber Applications,
Mulberry, FL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
Page 594 of 803
-------
Name. I.oealion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
1 > lv
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swe
ipphi
\eule
Risk
Oik»1iciiIs
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouoiieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Sapa Precision Tubing
Rockledge, Lie,
Rockledge, FL
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Thomas & Betts,
Albuquerque, NM
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Thomas Built Buses -
Fairfield Road,
High Point, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
Page 595 of 803
-------
Name. I.ocalion. and
II) of \cli\e Releaser
l';icilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Oik»1iciiIs
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouoiieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Timco,
Dba Haeco Americas
Airframe Services,
Greensboro, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Trelleborg Coated
Systems Us, Inc -
Grace Advanced
Materials,
Rutherfordton, NC
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Page 596 of 803
-------
Name. I.oealion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seelor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\eule
Risk
Oui»lieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)uolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
U.S. Coast Guard
Yard - Curtis Bay,
Curtis Bay, MD
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
Viracon Inc,
Owatonna, MN
NPDES: Not available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.02
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.00
0.11
0.00
OES: Commercial Printing and Copying
Printing And Pub Sys
Div,
Weatherford, OK
NPDES: OK0041785
Surface
Water
Printing
Surface
water
250
0.0002
0.00292
0.00
0.00
0.00
0.00
0.00
20
0.0025
0.0365
0.00
0.00
0.00
0.01
0.00
OES: Industrial Processing Aid
Page 597 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
('()( ol
14.400
pphi
Occidental Chemical
Corp Niagara Plant,
Niagara Falls, NY
NPDES: NY0003336
Surface
Water
NPDES
NY0003336
Still
body
300
0.019
0.14
0.00
0.00
0.00
0.05
0.00
20
0.292
2.2
0.00
0.00
0.00
0.73
0.00
Stepan Co Millsdale
Road,
Elwood, IL
NPDES: IL0002453
Surface
Water
NPDES
IL0002453
Surface
water
300
0.001
0.000419
0.00
0.00
0.00
0.00
0.00
20
0.008
0.00335
0.00
0.00
0.00
0.00
0.00
Entek International
LLC,
Lebanon, OR
NPDES: N/A
Off-site
Waste-
water
Treatment
No info on
receiving
facility;
POTW (Ind.)
Surface
water
300
0.38
9.3
0.00
0.01
0.01
3.10
0.00
20
5.65
138.34
0.07
0.15
0.18
46.11
0.01
National Electrical
Carbon Products
Dba Morgan Adv
Materials,
Fostoria, OH
NPDES: OH0052744
Off-site
Waste-
water
Treatment
Receiving
Facility: City
of Fostoria;
NPDES
OH0052744
Surface
water
300
0.008
0.15
0.00
0.00
0.00
0.05
0.00
20
0.115
2.32
0.00
0.00
0.00
0.77
0.00
Page 598 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
('()( ol
14.400
pphi
PPG Industries Inc
Barberton,
Barberton, OH
NPDES: OH0024007
Off-site
Waste-
water
Treatment
Receiving
Facility: City
of Barberton;
NPDES
OH0024007
Surface
water
300
0.005
0.0141
0.00
0.00
0.00
0.00
0.00
20
0.07
0.2
0.00
0.00
0.00
0.07
0.00
Daramic LLC,
Corydon, IN
NPDES: IN0020893
Surface
Water
NPDES
IN0020893
Surface
water
300
0.008
0.0206
0.00
0.00
0.00
0.01
0.00
20
0.114
0.29
0.00
0.00
0.00
0.10
0.00
OES: Manufacturing
Axiall Corporation,
Westlake, LA
NPDES: LA0007129
Surface
Water
NPDES
LA0007129
Surface
water
350
1.266
0.0051
0.00
0.00
0.00
0.00
0.00
20
22.15
0.0897
0.00
0.00
0.00
0.03
0.00
Olin Blue Cube,
Freeport, TX
NPDES: Not available
Off-site
Waste-
water
Treatment
Organic
Chemicals
Manuf.
Surface
water
350
0.069
2.42
0.00
0.00
0.00
0.81
0.00
20
1.2
42.14
0.02
0.05
0.05
14.05
0.00
Solvents & Chemicals,
Pearland, TX
NPDES: Not available
Off-site
Waste-
water
Treatment
Organic
Chemicals
Manuf.
Surface
water
350
0.015
0.53
0.00
0.00
0.00
0.18
0.00
Page 599 of 803
-------
Name. I.ocalion. and
II) of \cli\e Releaser
l';icilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C<)( ol
14.400
pphi
20
0.265
9.48
0.00
0.01
0.01
3.16
0.00
Surface
Water
Organic
Chemicals
Manuf.
Surface
water
350
0.015
2.77
0.00
0.00
0.00
0.92
0.00
20
0.265
49.91
0.02
0.05
0.06
16.64
0.00
OES: Waste Water Treatment Plant (WWTP)
New Rochelle STP,
New Rochelle, NY
NPDES: NY0026697
Surface
Water
NPDES
NY0026697
Still
body
365
0.043
0.7
0.00
0.00
0.00
0.23
0.00
20
0.786
12.79
0.01
0.01
0.02
4.26
0.00
Everett Water
Pollution Control
Facility,
Everett, WA
NPDES: WA0024490
Surface
Water
NPDES
WA0024490
Surface
water
365
0.016
0.17
0.00
0.00
0.00
0.06
0.00
20
0.299
3.11
0.00
0.00
0.00
1.04
0.00
Sullivan WWTP,
Sullivan, MO
NPDES: MOO 104736
Surface
Water
NPDES
MOO 104736
Surface
water
365
0.01
0.61
0.00
0.00
0.00
0.20
0.00
20
0.176
10.97
0.01
0.01
0.01
3.66
0.00
Page 600 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
1 > lv
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
nuoiieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
nuoiieiils
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
nuoiieiils
i iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)uolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C<)( ol
14.400
pphi
Sunnyside STP,
Sunnyside, WA
NPDES: WA0020991
Surface
Water
NPDES
WA0020991
Surface
water
365
0.005
0.00673
0.00
0.00
0.00
0.00
0.00
20
0.083
0.11
0.00
0.00
0.00
0.04
0.00
Port Of Sunnyside
Industrial WWTF,
Sunnyside, WA
NPDES: WA0052426
Surface
Water
POTW (Ind.)
Surface
water
365
0.002
0.26
0.00
0.00
0.00
0.09
0.00
20
0.035
4.51
0.00
0.00
0.01
1.50
0.00
U.S. Air Force Shaw
AFB SC,
Shaw AFB, SC
NPDES: SC0024970
Surface
Water
POTW (Ind.)
Surface
water
365
0.002
0.26
0.00
0.00
0.00
0.09
0.00
20
0.032
4.12
0.00
0.00
0.01
1.37
0.00
Gnf-A Wilmington-
Castle Hayne WWTP,
Wilmington, NC
NPDES: NC0001228
Surface
Water
NPDES
NC0001228
Surface
water
365
0.0004
0.00194
0.00
0.00
0.00
0.00
0.00
20
0.0067
0.034
0.00
0.00
0.00
0.01
0.00
Page 601 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s of
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
of "SS
pphi
\luae
(.)ik>lieiils
(llslliu
( ()( of ^
pphi
\luae
(.)iii>lieiils
(llslliu
('()( of
14.400
pphi
Cameron Trading Post
WWTP,
Cameron, AZ
NPDES: NN0021610
Surface
Water
POTW (Ind.)
Surface
water
365
0.0003
0.0387
0.00
0.00
0.00
0.01
0.00
20
0.0047
0.64
0.00
0.00
0.00
0.21
0.00
Coal Grove WWTP,
Coal Grove, OH
NPDES: OHO 104558
Surface
Water
NPDES
OH0029432
Surface
water
365
0.0002
0.0000127
0.00
0.00
0.00
0.00
0.00
20
0.0031
0.00019
0.00
0.00
0.00
0.00
0.00
OES: Other Commercial Uses
Corning Hospital,
Corning, NY
NPDES: NY0246701
Surface
Water
Surrogate
NPDES
NY0025721
Surface
water
250
0.013
0.0271
0.00
0.00
0.00
0.01
0.00
20
0.159
0.33
0.00
0.00
0.00
0.11
0.00
Water Street
Commercial Bldg,
Dayton, OH
NPDES: OHO 141496
Surface
Water
Surrogate
NPDES
OH0009521
Surface
water
250
0.003
0.00564
0.00
0.00
0.00
0.00
0.00
20
0.035
0.0658
0.00
0.00
0.00
0.02
0.00
Page 602 of 803
-------
Name. I.ocalion. and
II) of \cli\e Releaser
1 acilil>
Release
Media
Modeled
1 acilils or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
1 > lv
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\aile
Risk
OllOlieills
(IISIIIU
( ( )( ol
2.00(1
pphi
Chrome
Risk
(.)|ll>IICIIIs
(llsillU
in\ eilebiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
Union Station North
Wing Office Building,
Denver, CO
NPDES: COG315293
Surface
Water
Surrogate
NPDES
C00020095
Surface
water
250
0.0004
0.0881
0.00
0.00
0.00
0.03
0.00
20
0.00499
1.1
0.00
0.00
0.00
0.37
0.00
Confluence Park
Apartments,
Denver, CO
NPDES: COG315339
Surface
Water
Surrogate
NPDES
C00020095
Surface
water
250
0.00028
0.0617
0.00
0.00
0.00
0.02
0.00
20
0.00354
0.77
0.00
0.00
0.00
0.26
0.00
Park Place Mixed Use
Development,
Annapolis, MD
NPDES: MD0068861
Surface
Water
Surrogate
NPDES
MD0052868
Still
body
250
0.00027
9
0.00
0.01
0.01
3.00
0.00
20
0.00334
110
0.06
0.12
0.14
36.67
0.01
Tree Top Inc
Wenatchee Plant,
Wenatchee, WA
NPDES: WA0051527
Surface
Water
Not assessed (below the min risk level).
Wynkoop Denver
LLCP St,
Denver, CO
NPDES: COG603115
Surface
Water
Not assessed (below the min risk level).
Page 603 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swe
ipphi
\cule
Risk
OllOlieills
(IISIIIU
( ( )( ol
2.00(1
pphi
Chrome
Risk
(.)|ll>IICIIIs
(llsillU
in\ eilebiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
Greer Family Lie,
South Burlington, VT
NPDES: VT0001376
Surface
Water
Not assessed (below the min risk level).
John Marshall III Site,
Mclean, VA
NPDES: VA0090093
Surface
Water
Not assessed (below the min risk level).
OES: Other Industrial Uses
Eli Lilly And
Company-
Lilly Tech Ctr,
Indianapolis, IN
NPDES: IN0003310
Surface
Water
NPDES
IN0003310
Surface
water
250
1.553
9.03
0.00
0.01
0.01
3.01
0.00
20
19.41
113.09
0.06
0.12
0.14
37.70
0.01
Oxy Vinyls LP - Deer
Park Pvc,
Deer Park, TX
NPDES: TX0007412
Surface
Water
NPDES
TX0007412
Surface
water
250
0.148
0.49
0.00
0.00
0.00
0.16
0.00
20
1.854
5.98
0.00
0.01
0.01
1.99
0.00
Washington Penn
Plastics,
Frankfort, KY
NPDES: KY0097497
Surface
Water
Surrogate
NPDES
KY0028410
Surface
water
250
0.032
7.53
0.00
0.01
0.01
2.51
0.00
20
0.399
94.12
0.05
0.10
0.12
31.37
0.01
Page 604 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
liidusir>
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\cule
Risk
nuoiieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
nuoiieiils
(iisinu
in\ eriehrale
( < )C ol'^:o
pphi
Chrome
Risk
nuoiieiils
i iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)uolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
Natrium Plant,
New Martinsville, WV
NPDES: WV0004359
Surface
Water
NPDES
WV0004359
Surface
water
250
0.022
0.00262
0.00
0.00
0.00
0.00
0.00
20
0.274
0.0322
0.00
0.00
0.00
0.01
0.00
Leroy Quarry,
Leroy, NY
NPDES: NY0247189
Surface
Water
Surrogate
NPDES
NY0030546
Surface
water
250
0.019
0.71
0.00
0.00
0.00
0.24
0.00
20
0.242
8.91
0.00
0.01
0.01
2.97
0.00
George C Marshall
Space Flight Center,
Huntsville, AL
NPDES: AL0000221
Surface
Water
Surrogate
NPDES
AL0025585
Surface
water
250
0.01
0.2
0.00
0.00
0.00
0.07
0.00
20
0.128
2.63
0.00
0.00
0.00
0.88
0.00
Whelan Energy Center
Power Plant,
Hastings, NE
NPDES: NEO113506
Surface
Water
NPDES
NE0113506
Surface
water
250
0.009
2.92
0.00
0.00
0.00
0.97
0.00
20
0.118
38.96
0.02
0.04
0.05
12.99
0.00
Page 605 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I T AST
I T \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()C of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)ik>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iK>lienls
(llslliu
C()( ol
14.400
pphi
Army Cold Regions
Research &
Engineering Lab,
Hanover, NH
NPDES: NH0001619
Surface
Water
Surrogate
NPDES
NHO100099
Surface
water
250
0.0002
0.000103
0.00
0.00
0.00
0.00
0.00
20
0.0029
0.00154
0.00
0.00
0.00
0.00
0.00
Corning - Canton
Plant,
Canton, NY
NPDES: NY0085006
Surface
Water
Surrogate
NPDES
NY0034762
Surface
water
250
0.0002
0.00034
0.00
0.00
0.00
0.00
0.00
20
0.0028
0.0051
0.00
0.00
0.00
0.00
0.00
Ames Rubber Corp
Plant #1,
Hamburg Boro, NJ
NPDES: NJG000141
Surface
Water
Surrogate
NPDES
NJ0000141
Surface
water
250
0.00011
0.0149
0.00
0.00
0.00
0.00
0.00
20
0.00133
0.18
0.00
0.00
0.00
0.06
0.00
Gorham,
Providence, RI
NPDES: RIG85E004
Surface
Water
POTW (Ind.)
Surface
water
250
0.0001
0.0129
0.00
0.00
0.00
0.00
0.00
20
0.0012
0.13
0.00
0.00
0.00
0.04
0.00
Emerson Power
Transmission,
Ithaca, NY
Surface
Water
Not assessed (below the min risk level).
Page 606 of 803
-------
Name. I.ocalkm. and
II) of \cli\e Releaser
1 acilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\aile
Risk
Ouolieiils
(llslliu
( ()C ol'
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' nl'^:o
pphi
Chrome
Risk
Oiiiilieiils
(iisinu
I'ish (()(
nl" "SS
pphi
\luae
(.)iiii|ieiils
(llslliu
('()(' Hi' '
pphi
\luae
Oiiiilieiils
(llslliu
('()( ol
14.400
pphi
NPDES: NY0002933
William E. Warne
Power Plant,
Surface
Water
Not assessed (below the min risk level).
Los Angeles County,
CA
NPDES: CA0059188
Raytheon Aircraft
Co(Was Beech
Aircraft), Boulder, CO
Surface
Water
Not assessed (below the min risk level).
NPDES: COG315176
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
Texas Instruments,
Inc.,
Attleboro, MA
NPDES: MA0001791
Surface
Water
NPDES
MA0001791
Surface
water
260
0.005
0.0188
0.00
0.00
0.00
0.01
0.00
20
0.067
0.25
0.00
0.00
0.00
0.08
0.00
Accellent
Inc/Collegeville
Microcoax,
Collegeville, PA
NPDES: PA0042617
Surface
Water
NPDES
PA0042617
Surface
water
260
0.002
0.0425
0.00
0.00
0.00
0.01
0.00
20
0.029
0.62
0.00
0.00
0.00
0.21
0.00
Ametek Inc. U.S.
Gauge Div.,
Sellersville, PA
NPDES: PA0056014
Surface
Water
Surrogate
NPDES
PA0020460
Surface
water
260
0.001
0.0619
0.00
0.00
0.00
0.02
0.00
20
0.011
0.68
0.00
0.00
0.00
0.23
0.00
Page 607 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
1 > lv
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
nuoiieiiis
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)uoiieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C<)( ol
14.400
pphi
Atk-Allegany
Ballistics Lab (Nirop),
Keyser, WV
NPDES: WV0020371
Surface
Water
NPDES
WV0020371
Surface
water
260
0.0005
0.00311
0.00
0.00
0.00
0.00
0.00
20
0.0061
0.0373
0.00
0.00
0.00
0.01
0.00
Handy & Harman
Tube Co/East
Norriton, Norristown,
PA
NPDES: PA0011436
Surface
Water
Not assessed (below the min risk level).
US Nasa Michoud
Assembly Facility,
New Orleans, LA
NPDES: LA0052256
Surface
Water
Surrogate
NPDES
LA0003280
Still
body
260
1.96
765.63
0.38
0.83
0.97
255.21
0.05
20
25.44
9937.5
4.97
10.80
12.61
3312.50
0.69
GM Components
Holdings LLC,
Lockport, NY
NPDES: NY0000558
Surface
Water
NPDES
NY0000558
Surface
water
260
0.13
10.97
0.01
0.01
0.01
3.66
0.00
20
1.71
144.47
0.07
0.16
0.18
48.16
0.01
Akebono
Elizabethtown Plant,
Elizabethtown, KY
NPDES: KY0089672
Surface
Water
Surrogate
NPDES
KY0022039
Surface
water
260
0.07
4.87
0.00
0.01
0.01
1.62
0.00
Page 608 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
1 > lv
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Oik»1iciiIs
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
nuoiieiiis
(iisinu
in\ eriebrale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.897
62.38
0.03
0.07
0.08
20.79
0.00
Delphi Harrison
Thermal Systems,
Dayton, OH
NPDES: OH0009431
Surface
Water
NPDES
OH0009431
Surface
water
260
0.04
0.0752
0.00
0.00
0.00
0.03
0.00
20
0.465
0.87
0.00
0.00
0.00
0.29
0.00
Chemours Company
Fc LLC,
Washington, WV
NPDES: WV0001279
Surface
Water
NPDES
WV0001279
Surface
water
260
0.03
0.00301
0.00
0.00
0.00
0.00
0.00
20
0.334
0.0335
0.00
0.00
0.00
0.01
0.00
Equistar Chemicals
LP,
La Porte, TX
NPDES: TX0119792
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.02
2.22
0.00
0.00
0.00
0.74
0.00
20
0.218
24.44
0.01
0.03
0.03
8.15
0.00
GE Aviation,
Lynn, MA
NPDES: MA0003905
Surface
Water
NPDES
MA0003905
Still
water
260
0.01
0.0425
0.00
0.00
0.00
0.01
0.00
20
0.128
0.54
0.00
0.00
0.00
0.18
0.00
Page 609 of 803
-------
\;ime. l.oc;ilioii. ;md
II) of \cli\e Releaser
l;:icilil\
Release
\ledi;i
Modeled
l';icilil> or
IndiMrs
Seelor mi
I I AST
I T \ST
\\ ;ileihod>
1 > lv
1 );i> s tif
Release
Release
(ku d;i\)
~ni<)
SWC
ipphi
\ciile
Risk
Oik»1iciiIs
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehi';ile
( ()(' ol'^:o
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
I'ish (<)(
ol' "SS
pphi
Muiie
(^uolieiils
( IISMIil
( ()( ol ^
pphi
Mu;ie
(^iiolienls
(llslllil
C<)( ol
14.400
pphi
Certa Vandalia LLC,
Vandalia, OH
NPDES: OHO 122751
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.01
1.11
0.00
0.00
0.00
0.37
0.00
20
0.107
11.89
0.01
0.01
0.02
3.96
0.00
GM Components
Holdings LLC
Kokomo Ops,
Kokomo, IN
NPDES: IN0001830
Surface
Water
NPDES
IN0001830
Surface
water
260
0.01
0.2
0.00
0.00
0.00
0.07
0.00
20
0.086
1.73
0.00
0.00
0.00
0.58
0.00
Amphenol Corp-
Aerospace Operations,
Sidney, NY
NPDES: NY0003824
Surface
Water
NPDES
NY0003824
Surface
water
260
0.01
0.0486
0.00
0.00
0.00
0.02
0.00
20
0.082
0.4
0.00
0.00
0.00
0.13
0.00
Emerson Power Trans
Corp,
Maysville, KY
NPDES: KY0100196
Surface
Water
Surrogate
NPDES
KY0020257
Surface
water
260
0.01
0.0004
0.00
0.00
0.00
0.00
0.00
20
0.081
0.00522
0.00
0.00
0.00
0.00
0.00
Page 610 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
i:i \sr
Walerhods
1 > lv
1 )a> s of
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( of
2.00(1
pphi
Chrome
Risk
nuoiieiiis
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
of "SS
pphi
\luae
(.)uoiieiils
(llslliu
( ()( of ^
pphi
\luae
(.)iiolienls
(llslliu
C()( of
14.400
pphi
Olean Advanced
Products,
Olean, NY
NPDES: NY0073547
Surface
Water
Surrogate
NPDES
NY0027162
Surface
water
260
0.01
0.0188
0.00
0.00
0.00
0.01
0.00
20
0.068
0.13
0.00
0.00
0.00
0.04
0.00
Hollingsworth Saco
Lowell,
Easley, SC
NPDES: SC0046396
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.00469
0.52
0.00
0.00
0.00
0.17
0.00
20
0.061
6.78
0.00
0.01
0.01
2.26
0.00
Trelleborg YSH
Incorporated
Sandusky Plant,
Sandusky, MI
NPDES: MI0028142
Surface
Water
NPDES
MI0028142
Surface
water
260
0.0036
1.76
0.00
0.00
0.00
0.59
0.00
20
0.047
23.04
0.01
0.03
0.03
7.68
0.00
Timken Us Corp
Honea Path,
Honea Path, SC
NPDES: SC0047520
Surface
Water
Surrogate
NPDES
SC0000698
Surface
water
260
0.00355
1.06
0.00
0.00
0.00
0.35
0.00
20
0.0462
13.77
0.01
0.01
0.02
4.59
0.00
Page 611 of 803
-------
Name. I.ocalioii. and
II) of \cli\e Releaser
l';icilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
1 > lv
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\aile
Risk
Ouolieiils
(llslliu
( <)C of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C<)( ol
14.400
pphi
Johnson Controls
Incorporated,
Wichita, KS
NPDES: KS0000850
Surface
Water
NPDES
KS0000850
Surface
water
260
0.00228
0.0548
0.00
0.00
0.00
0.02
0.00
20
0.0296
0.72
0.00
0.00
0.00
0.24
0.00
National Railroad
Passenger Corporation
(Amtrak) Wilmington
Maintenance Facility,
Wilmington, DE
NPDES: DE0050962
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.00203
0.23
0.00
0.00
0.00
0.08
0.00
20
0.026
2.89
0.00
0.00
0.00
0.96
0.00
Electrolux Home
Products (Formerly
Frigidaire),
Greenville, MI
NPDES: MI0002135
Surface
Water
NPDES
MI0002135
Surface
water
260
0.00201
0.0171
0.00
0.00
0.00
0.01
0.00
20
0.026
0.22
0.00
0.00
0.00
0.07
0.00
Rex Heat Treat
Lansdale Inc,
Lansdale, PA
NPDES: PA0052965
Surface
Water
Surrogate
NPDES
PA0026182
Surface
water
260
0.00194
0.0523
0.00
0.00
0.00
0.02
0.00
20
0.025
0.67
0.00
0.00
0.00
0.22
0.00
Carrier Corporation,
Syracuse, NY
NPDES: NY0001163
Surface
Water
NPDES
NY0001163
Still
water
260
0.00177
0.22
0.00
0.00
0.00
0.07
0.00
Page 612 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
i:i \sr
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
20
0.023
2.84
0.00
0.00
0.00
0.95
0.00
Cascade Corp
(0812100207),
Springfield, OH
NPDES: OH0085715
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.00117
0.13
0.00
0.00
0.00
0.04
0.00
20
0.015
1.67
0.00
0.00
0.00
0.56
0.00
USAF-Wurtsmith Afb,
Oscoda, MI
NPDES: MI0042285
Surface
Water
Surrogate
NPDES
MI0028282
Surface
water
260
0.00115
0.000753
0.00
0.00
0.00
0.00
0.00
20
0.015
0.00983
0.00
0.00
0.00
0.00
0.00
AAR Mobility
Systems,
Cadillac, MI
NPDES: MI0002640
Surface
Water
Surrogate
NPDES
MI0020257
Surface
water
260
0.00112
0.00916
0.00
0.00
0.00
0.00
0.00
20
0.014
0.11
0.00
0.00
0.00
0.04
0.00
Eaton Mdh Company
Inc,
Kearney, NE
NPDES: NE0114405
Surface
Water
Surrogate
NPDES
NE0052647
Still
water
260
0.00107
0.13
0.00
0.00
0.00
0.04
0.00
Page 613 of 803
-------
Name. I.ocalion. and
II) of \cli\e Releaser
l';icilil>
Release
Media
Modeled
1 acilils or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
swc
ipphi
\aile
Risk
Ouolieiils
(llslliu
( <)C of
2.00(1
pphi
Chrome
Risk
nuoiieiiis
(iisinu
in\ eilehiale
( < )C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
C()( ol
14.400
pphi
20
0.014
1.69
0.00
0.00
0.00
0.56
0.00
Lake Region Medical,
Trappe, PA
NPDES: PA0042617
Surface
Water
NPDES
PA0042617
Surface
water
260
0.0005
0.0106
0.00
0.00
0.00
0.00
0.00
20
0.007
0.15
0.00
0.00
0.00
0.05
0.00
Motor Components L
LC,
Elmira, NY
NPDES: NY0004081
Surface
Water
NPDES
NY0004081
Surface
water
260
0.00096
0.0618
0.00
0.00
0.00
0.02
0.00
20
0.0125
0.83
0.00
0.00
0.00
0.28
0.00
Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000897
0.0997
0.00
0.00
0.00
0.03
0.00
20
0.012
1.33
0.00
0.00
0.00
0.44
0.00
GE (Greenville) Gas
Turbines LLC,
Greenville, SC
NPDES: SC0003484
Surface
Water
NPDES
SC0003484
Surface
water
260
0.000806
0.0821
0.00
0.00
0.00
0.03
0.00
20
0.01
1.02
0.00
0.00
0.00
0.34
0.00
Page 614 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eriehrale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
Oik»lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C<)( ol
14.400
pphi
Parker Hannifin
Corporation,
Waverly, OH
NPDES: OH0104132
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000747
0.083
0.00
0.00
0.00
0.03
0.00
20
0.01
1.11
0.00
0.00
0.00
0.37
0.00
Mahle Engine
Components Usa Inc,
Muskegon, MI
NPDES: MI0004057
Surface
Water
NPDES
MI0004057
Surface
water
260
0.000742
0.0336
0.00
0.00
0.00
0.01
0.00
20
0.01
0.45
0.00
0.00
0.00
0.15
0.00
General Electric
Company -
Waynesboro,
Waynesboro, VA
NPDES: VA0002402
Surface
Water
NPDES
VA0002402
Surface
water
260
0.000733
0.00705
0.00
0.00
0.00
0.00
0.00
20
0.01
0.0962
0.00
0.00
0.00
0.03
0.00
Globe Engineering Co
Inc,
Wichita, KS
NPDES: KS0086703
Surface
Water
Surrogate
NPDES
KS0043036
Surface
water
260
0.00173
0.00853
0.00
0.00
0.00
0.00
0.00
20
0.023
0.11
0.00
0.00
0.00
0.04
0.00
Page 615 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I T AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iK>lienls
(llslliu
C()( ol
14.400
pphi
Gayston Corp,
Dayton, OH
NPDES: OHO 127043
Surface
Water
Surrogate
NPDES
OH0024881
Surface
water
260
0.000643
0.00121
0.00
0.00
0.00
0.00
0.00
20
0.008
0.015
0.00
0.00
0.00
0.01
0.00
Styrolution America
LLC,
Channahon, IL
NPDES: IL0001619
Surface
Water
NPDES
IL0001619
Surface
water
260
0.000637
0.000221
0.00
0.00
0.00
0.00
0.00
20
0.008
0.00278
0.00
0.00
0.00
0.00
0.00
Remington Arms Co
Inc,
Ilion, NY
NPDES: NY0005282
Surface
Water
NPDES
NY0005282
Surface
water
260
0.000612
0.000799
0.00
0.00
0.00
0.00
0.00
20
0.008
0.0104
0.00
0.00
0.00
0.00
0.00
United Technologies
Corporation, Pratt And
Whitney Division,
East Hartford, CT
NPDES: CT0001376
Surface
Water
NPDES
CT0001376
Surface
water
260
0.00048
0.0000822
0.00
0.00
0.00
0.00
0.00
20
0.006
0.00103
0.00
0.00
0.00
0.00
0.00
Page 616 of 803
-------
Name. I.ocaliou. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
liidiisirs
Seclor mi
I I AST
i:i \sr
Walerhods
1 > lv
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
nuoiieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
nuoiieiils
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
nuoiieiils
i iisinu
I'ish ((K
ol' "SS
pphi
\luae
nuoiieiils
(llsllIU
( ()( ol ^
pphi
\luae
(.)iiolieuls
(llslllU
C()( ol
14.400
pphi
Atk-Allegany
Ballistics Lab (Nirop),
Keyser, WV
NPDES: WV0020371
Surface
Water
NPDES
WV0020371
Surface
water
260
0.00047
0.00292
0.00
0.00
0.00
0.00
0.00
20
0.006
0.0373
0.00
0.00
0.00
0.01
0.00
Sperry & Rice
Manufacturing Co
LLC,
Brookville, IN
NPDES: IN0001473
Surface
Water
NPDES
IN0001473
Surface
water
260
0.000328
0.00569
0.00
0.00
0.00
0.00
0.00
20
0.004
0.0694
0.00
0.00
0.00
0.02
0.00
Owt Industries,
Pickens, SC
NPDES: SC0026492
Surface
Water
NPDES
SC0026492
Surface
water
260
0.000314
0.00213
0.00
0.00
0.00
0.00
0.00
20
0.004
0.0272
0.00
0.00
0.00
0.01
0.00
Boler Company,
Hillsdale, MI
NPDES: MI0053651
Surface
Water
Surrogate
NPDES
MI0022136
Surface
water
260
0.000269
0.0204
0.00
0.00
0.00
0.01
0.00
20
0.003
0.23
0.00
0.00
0.00
0.08
0.00
Mccanna Inc.,
Carpentersville, IL
NPDES: IL0071340
Surface
Water
Surrogate
NPDES
IL0027944
Surface
water
260
0.000268
0.000911
0.00
0.00
0.00
0.00
0.00
Page 617 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
nuoiieiiis
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
(.)uolieiils
(llslliu
( ()( ol ^
pphi
\luae
Ouolieiils
(llslliu
C()( ol
14.400
pphi
20
0.003
0.0102
0.00
0.00
0.00
0.00
0.00
Cutler Hammer,
Horseheads, NY
NPDES: NY0246174
Surface
Water
Surrogate
NPDES
NY0004081
Surface
water
260
0.000238
0.0153
0.00
0.00
0.00
0.01
0.00
20
0.003
0.19
0.00
0.00
0.00
0.06
0.00
US Air Force Offutt
Afb Ne,
Offutt A F B, NE
NPDES: NE0121789
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000159
0.0177
0.00
0.00
0.00
0.01
0.00
20
0.002
0.22
0.00
0.00
0.00
0.07
0.00
Troxel Company,
Moscow, TN
NPDES: TN0000451
Surface
Water
NPDES
TN0000451
Surface
water
260
0.000134
0.000741
0.00
0.00
0.00
0.00
0.00
20
0.002
0.0111
0.00
0.00
0.00
0.00
0.00
Austin Tube Prod,
Baldwin, MI
NPDES: MI0054224
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000114
0.0127
0.00
0.00
0.00
0.00
0.00
20
0.001
0.11
0.00
0.00
0.00
0.04
0.00
Page 618 of 803
-------
Name. Local ion. and
II) of \cli\e Releaser
lacilil>
Release
Media
Modeled
1 acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\aile
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
(.)|ll>IICIIIs
(llsillU
in\ eilebiale
( ()C nl''ij:o
ppbi
( lii'oine
Risk
(.)|lli|ICIIIs
(iisinu
I'ish (()(
ol' "SS
ppbi
\luae
(.)|Ki|ICIIIs
(llslliu
('()(' Hi' '
pphi
\luae
(.)|Ki|ICIIls
(llslliu
('()( ol
14.400
ppbi
LS Starrett Precision
Tools,
Athol, MA
NPDES: MA0001350
Surface
Water
NPDES
MA0001350
Surface
water
260
0.000102
0.00153
0.00
0.00
0.00
0.00
0.00
20
0.001
0.015
0.00
0.00
0.00
0.01
0.00
Avx Corp,
Raleigh, NC
NPDES: NC0089494
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.0000883
0.00981
0.00
0.00
0.00
0.00
0.00
20
0.001
0.11
0.00
0.00
0.00
0.04
0.00
Indian Head Division,
Naval Surface Warfare
Center,
Indian Head, MD
NPDES: MD0003158
Surface
Water
Not assessed (below the min risk level).
General Dynamics
Ordnance Tactical
Systems,
Red Lion, PA
NPDES: PA0043672
Surface
Water
Not assessed (below the min risk level).
Trane Residential
Solutions - Fort Smith,
Fort Smith, AR
NPDES: AR0052477
Surface
Water
Not assessed (below the min risk level).
Lexmark International
Inc.,
Lexington, KY
NPDES: KY0097624
Surface
Water
Not assessed (below the min risk level).
Page 619 of 803
-------
Name. Local ion. and
II) of \cli\e Releaser
1 acilil>
Release
Media
Modeled
1 acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~nio
SWC
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol'
2.00(1
pphi
('limine
Risk
(.)|ll>IICIIIs
(llsillU
in\ eilehiale
( ()( ' ol'^:o
pphi
Chrome
Risk
(.)ik>iiciiIs
(iisinu
I'ish (()(
nl" "SS
pphi
\luae
(.)iki|iciiIs
(llslliu
('()(' Hi' '
pphi
\luae
(.)iiii|ieiils
(llslliu
('()( ol
14.400
pphi
Alliant Techsystems
Operations LLC,
Elkton, MD
NPDES: MD0000078
Surface
Water
Not assessed (below the min risk level).
Daikin Applied
America, Inc.
(Formally Mcquay
International),
Scottsboro, AL
NPDES: AL0069701
Surface
Water
Not assessed (below the min risk level).
Beechcraft
Corporation,
Wichita, KS
NPDES: KS0000183
Surface
Water
Not assessed (below the min risk level).
Federal-Mogul Corp,
Scottsville, KY
NPDES: KY0106585
Surface
Water
Not assessed (below the min risk level).
Cessna Aircraft Co
(Pawnee Facility),
Wichita, KS
NPDES: KS0000647
Surface
Water
Not assessed (below the min risk level).
N.G.I,
Parkersburg, WV
NPDES: WV0003204
Surface
Water
Not assessed (below the min risk level).
Hyster-Yale Group,
Inc,
Sulligent, AL
NPDES: AL0069787
Surface
Water
Not assessed (below the min risk level).
Hitachi Electronic
Devices (Usa), Inc.,
Greenville, SC
NPDES: SC0048411
Surface
Water
Not assessed (below the min risk level).
Page 620 of 803
-------
Name. I.ocalion. and
II) of \cli\e Releaser
1 acilil>
Release
Media
Modeled
l acilils or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\aile
Risk
Ouolieiils
(llslliu
( ()C ol'
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C<)( ol
14.400
pphi
OES: Process Solvent Recycling and Worker Handling of Wastes
Clean Water Of New
York Inc,
Staten Island, NY
NPDES: NY0200484
Surface
Water
Surrogate
NPDES
NJ0000019
Still
body
250
0.004
11.76
0.01
0.01
0.01
3.92
0.00
20
0.047
138.24
0.07
0.15
0.18
46.08
0.01
Reserve
Environmental
Services,
Ashtabula, OH
NPDES: OH0098540
Surface
Water
0.00
0.00
0.00
0.00
0.00
Veolia Es Technical
Solutions LLC,
Middlesex, NJ
NPDES: NJ0020141
Off-site
Waste-
water
Treatment
Receiving
Facility:
Middlesex
Cnty UA;
NPDES
NJ0020141
Still
body
250
24.1
2.85
0.00
0.00
0.00
0.95
0.00
20
301.78
35.72
0.02
0.04
0.05
11.91
0.00
Clean Harbors Deer
Park LLC,
La Porte, TX
NPDES: TX0005941
Off-site
Waste-
water
Treatment
POTW (Ind.)
Surface
water
250
0.35
8.57
0.00
0.01
0.01
2.86
0.00
20
4.36
106.75
0.05
0.12
0.14
35.58
0.01
Page 621 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( <)(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iiolienls
(llslliu
('<)( ol
14.400
pphi
Clean Harbors El
Dorado LLC,
El Dorado, AR
NPDES: AR0037800
Off-site
Waste-
water
Treatment
POTW (Ind.)
Surface
water
250
0.04
0.98
0.00
0.00
0.00
0.33
0.00
20
0.455
11.26
0.01
0.01
0.01
3.75
0.00
OES: Processing as a Reactant
440 unknown sites
NPDES: Not
applicable
Off-site
Waste-
water
Treatment
Organic
Chemicals
Manufacture
Surface
water
350
0.005
0.18
0.00
0.00
0.00
0.06
0.00
20
0.089
3.13
0.00
0.00
0.00
1.04
0.00
Surface
Water
Organic
Chemicals
Manufacture
Surface
water
350
0.005
0.92
0.00
0.00
0.00
0.31
0.00
20
0.089
16.45
0.01
0.02
0.02
5.48
0.00
Arkema Inc.
Calvert City, KY
NPDES: KY0003603
Surface
Water
NPDES
KY0003603
Surface
water
350
0.017
0.000737
0.00
0.00
0.00
0.00
0.00
20
0.295
0.128
0.00
0.00
0.00
0.04
0.00
Page 622 of 803
-------
Name. I.ocalkm. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I T AST
IT \ST
Walerhods
T\ pe
1 )a> s (.if
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( ()( ol
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilebiale
( ()(' nl'^:o
pphi
Chrome
Risk
Oiiiilieiils
(iisinu
I'ish (()(
ol' "SS
pphi
\luae
(.)iiii|ieiils
(llslliu
('()(' Hi' '
pphi
\luae
Oiiiilieiils
(llslliu
('()( ol
14.400
pphi
US DOE Paducah
Site,
Kevil, KY
NPDES: KYO102083
Surface
Water
Not assessed (below the min risk level).
GNF-A Wilmington-
Castle Hayne,
Wilmington NC
NPDES: NC0001228
Surface
Water
Not assessed (below the min risk level).
Solvay - Houston
Plant,
Houston, TX
NPDES: TX0007072
Surface
Water
NPDES
TX0007072
Surface
water
350
0.024
4.44
0.00
0.00
0.01
1.48
0.00
20
0.414
75.93
0.04
0.08
0.10
25.31
0.01
Honeywell
International -
Geismar Complex,
Geismar, LA
NPDES: LA0006181
Surface
Water
NPDES
LA0006181
Surface
water
350
0.0128
0.0000518
0.00
0.00
0.00
0.00
0.00
20
0.224
0.000907
0.00
0.00
0.00
0.00
0.00
Praxair Technology
Center,
Tonawanda, NY
NPDES: NY0000281
Surface
Water
NPDES
NY0000281
Still
body
350
0.00169
169
0.08
0.18
0.21
56.33
0.01
20
0.03
3000
1.50
3.26
3.81
1000.00
0.21
Page 623 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
laeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
I T \ST
Walerhods
T\ pe
1 )a> s tif
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)( of
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()(' ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
l ish (()(
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
('<)( ol
14.400
pphi
US DOE Paducah
Site,
Kevil, KY
NPDES: KYO102083
Surface
Water
Not assessed (below the min risk level).
GNF-A Wilmington-
Castle Hayne,
Wilmington NC
NPDES: NC0001228
Surface
Water
Not assessed (below the min risk level).
Akzo Nobel Surface
Chemistry LLC,
Morris, IL
NPDES: IL0026069
Surface
Water
NPDES
IL0026069
Surface
water
350
0.000329
0.000688
0.00
0.00
0.00
0.00
0.00
20
0.006
0.0125
0.00
0.00
0.00
0.00
0.00
Solutia Nitro Site,
Nitro, WV
NPDES: WV0116181
Surface
Water
Surrogate
NPDES
WV0023229
Surface
water
350
0.000318
0.0000941
0.00
0.00
0.00
0.00
0.00
20
0.006
0.00176
0.00
0.00
0.00
0.00
0.00
Amphenol
Corporation -
Columbia,
Columbia, SC
NPDES: SC0046264
Surface
Water
Organic
Chemicals
Manufacture
Surface
water
350
0.000202
0.037
0.00
0.00
0.00
0.01
0.00
20
0.004
0.74
0.00
0.00
0.00
0.25
0.00
Keeshan and Bost
Chemical Co., Inc.,
Surface
Water
NPDES
TX0072168
Still
body
350
0.000095
9.5
0.00
0.01
0.01
3.17
0.00
Page 624 of 803
-------
Name. I.ocalion. and
II) of \eli\e Releaser
1 aeilil>
Release
Media
Modeled
1 acihi> or
IndiMrs
Seclor mi
I I AST
i:i \sr
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
swc
ipphi
\cule
Risk
Ouolieiils
(llslliu
( <)C ol'
2.00(1
pphi
Chrome
Risk
Oik»iiciiIs
(iisinu
in\ eilehiale
( ()C ol'^:o
pphi
Chrome
Risk
Oiii»lieiils
(iisinu
I'ish ((K
ol' "SS
pphi
\luae
(.)iii>lieiils
(llslliu
( ()( ol ^
pphi
\luae
(.)iii>lieiils
(llslliu
C()( ol
14.400
pphi
Manvel, TX
NPDES: TX0072168
20
0.002
200
0.10
0.22
0.25
66.67
0.01
Chemtura North and
South Plants,
Morgantown, WV
NPDES: WV0004740
Surface
Water
Not assessed (below the min risk level).
Indorama Ventures
Olefins, LLC,
Sulphur, LA
NPDES: LA0069850
Surface
Water
Not assessed (below the min risk level).
OES: Repackaging
Hubbard-Hall Inc,
Waterbury, CT
NPDES: Unknown
Off-site
Waste-
water
Treatment
Receiving
Facility:
Recycle Inc.;
POTW (Ind.)
Surface
water
250
1.108
27.18
0.01
0.03
0.03
9.06
0.00
20
13.85
339.11
0.17
0.37
0.43
113.04
0.02
Oiltanking Houston
Inc,
Houston, TX
NPDES: TX0091855
Surface
Water
Surrogate
NPDES
TX0065943
Surface
water
250
0.003
6.52
0.00
0.01
0.01
2.17
0.00
20
0.041
89.13
0.04
0.10
0.11
29.71
0.01
St. Gabriel Terminal,
Saint Gabriel, LA
NPDES: LA0005487
Surface
Water
NPDES
LA0005487
Surface
water
250
0.0055
0.0000223
0.00
0.00
0.00
0.00
0.00
20
0.069
0.000279
0.00
0.00
0.00
0.00
0.00
Vopak Terminal
Westwego Inc,
Westwego, LA
Surface
Water
Surrogate
NPDES
LA0042064
Surface
water
250
0.00468
0.0000189
0.00
0.00
0.00
0.00
0.00
Page 625 of 803
-------
Name. I.ocalkm. and
II) of \cli\e Releaser
l';icilil>
Release
Media
Modeled
1 acilils or
IndiMrs
Seclor mi
I I AST
I I \ST
Walerhods
T\ pe
1 )a> s til'
Release
Release
(ku da>)
~ni<)
SWC
ipphi
\aile
Risk
Ouolieiils
(llslliu
( <)C of
2.00(1
pphi
Chrome
Risk
Ouolieiils
(iisinu
in\ eriehrale
( <)(' ol'^20
pphi
Chrome
Risk
Ouolieiils
(iisinu
fish (()(
ol' "SS
pphi
\luae
Ouolieiils
(llslliu
( ()( ol ^
pphi
\luae
Ouolieiils
(llslliu
C<)C of
14.400
pphi
NPDES: LAO 124583
20
0.058
0.000235
0.00
0.00
0.00
0.00
0.00
Research Solutions
Group Inc,
Pelham, AL
NPDES: AL0074276
Surface
Water
Not assessed (below the min risk level).
Carlisle Engineered
Products Inc,
Middlefield, OH
NPDES: OH0052370
Surface
Water
Not assessed (below the min risk level).
OES: Spot Cleaning and Carpet Cleaning
Boise State
University,
Boise, ID
NPDES: IDG911006
Surface
Water
Surrogate
NPDES
ID0023981
Surface
water
300
0.00008
0.00388
0.00
0.00
0.00
0.00
0.00
20
0.001
0.0485
0.00
0.00
0.00
0.02
0.00
Venetian Hotel And
Casino,
Las Vegas, NV
NPDES: NV0022888
Surface
Water
Not assessed (below the min risk level).
63,746 unknown sites
NPDES: All POTW
SIC
Surface
Water or
POTW
Not assessed (below the min risk level).
a. Facilities actively releasing trichloroethylene were identified via DMR, TRI, and CDR databases for 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 wastewater treatment removal rate of 81% is applied to all indirect releases, i.e., volumes characterized as being transferred off-site
for treatment at a water treatment facility prior to discharge to surface water.
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 representative generic industry sector. The name of the indirect releaser 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 maximum days of release per year expected. For direct releasing facilities, a minimum of 20 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 concentration was reported in place of the 7Q10 SWC.
Page 626 of 803
-------
Name. Location. and
Release
Modeled
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the predicted surface water concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero.
535
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561
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578
579
Appendix F WEIGHT OF SCIENTIFIC EVIDENCE FOR
CONGENITAL HEART DEFECTS
F.l Background
F.l.l (Johnson et; all., 2003) and (Dawson et ai, 1993)
The congenital heart defects endpoint for TCE has been widely discussed since the release of the 2011
IRIS Assessment ( ). The primary basis for this endpoint was a developmental drinking
water study in rats, (Johnson et. ai. 2003). that has been the source of extensive controversy. The study
administered 0 ppb, 2.5 ppb, 250 ppb, 1.5 ppm, and 1100 ppm to pregnant Sprague-Dawley rats via
drinking water for the entire duration of pregnancy. On the last day of pregnancy, dams were
euthanized, and the heart and great vessels of fetuses were examined for abnormalities. The study
reported statistically significant increases in variety of cardiac defects at multiple dose levels in the
incidence of a broad array of cardiac defects. EPA considered the constellation of observed effects in
totality, as opposed to any particular individual defects.
The authors reported in followup errata (Johnson et. ai, 2005) that the study data were derived from a 6-
year academic research program and consolidated data from several cohorts. Control data were
combined from 6 independent cohort experiments; the data from the highest two TCE doses had been
previously published by the laboratory (Dawson et. ai. 1993). Although study methods were generally
consistent throughout the research program, there are potential concerns of genetic drift due to the TCE
dose groups being administered up to 6 years apart, and the control vehicle used in the Dawson et al.,
1993 study was filtered tap water while distilled water was used in all subsequent study cohorts. Both
(Dawson et. al. 1993) and (Johnson et ai.. 2003) were deficient in adequate reporting of methods and
raw scoring data; however, many of those concerns have been alleviated by subsequent communications
to EPA (Johnson. 2 >08). The positive findings reported in (Dawson et al. 1993) and (Johnson et.
al. 2003) have not been confirmed by another laboratory, so controversy over the results remains.
F.l.2 Updates to the original publications
Much of the controversy surrounding the reliability of 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 comprising the highest two doses of the (Johnson et al... 2003)
study in which data were not pooled and only a single vehicle was used. Unfortunately, EPA was
unable to use a nested benchmark dose (BMD) model because individual pup 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 Evaluation of Human Health Hazard Studies. Docket:
EPA- 0500]. which incorporated all available information on the two studies,
including subsequent errata and communications to EPA (Johnson et al.. I'01/1; Johnson. 2014. 2.008;
Johnson et al.. 2005). While the original publications had extensive data and methodology reporting
issues, many of the data quality concerns from the original study were mitigated by the information
provided in these updates. These updates provided the following information which was lacking in the
initial publications:
1) Individual fetal cardiac malformation data for each litter
2) Individual maternal terminal body weight data
3) Detailed description of fetal evaluation procedures including:
- methods used to blind fetal examiners to treatment group
Page 628 of 803
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581
582
583
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586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
- protocol for unanimous confirmation of any observed cardiac defects by the three
principle investigators
3) Additional information on animal husbandry and randomized group assignment of dams to
study group
4) Transparency regarding experimental variables across the dates of the experiments
The (Johnson et. ai. 2003) / (Dawson et. ai. 1993) publications had several important limitations,
however these updates also highlighted several strengths of the research. These are presented in
TableApx F-l.
Tsihlc_A|)x I'"-1. Strengths and Limitations of ( )
Strengths
Limitations
Positive findings required unanimous agreement
among experts
Tap water was used for earlier testing; distilled
water was used later
Methods, supplier, and investigators remained
consistent across time
Study took place over 6 years with a few years in-
between examinining the highest and lowest two
dose groups
Details of dissection, preservation, and
examination methods were provided
Individual fetus data could not be tied to a
particular dam
Dams were randomly assigned to control or
treatment groups
Control animals were pooled from multiple studies
that did not all occur at the same time as the
treated animal studies
Fully blinded examination
Details for the dates of individual animal
measurements are not available, precluding more
granular analysis
The results of (Johnson et. ai.. 2003) have not been confirmed in any other publications. Subsequent rat
studies administering TCE via oral gavage (Fisher et at.. 2001) or inhalation (Carney et at... 2006) did
not find any statistically significant increase in congenital heart defects. Therefore, (Charles River
Laboratories. 2.019) attempted to replicate the (Johnson et. ai.. 2003) utilizing the same administration
route and study design.
F.2 EPA Review of the Charles River ( ) Study
F.2.1 Study Methodology and Results
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 of Trichloroethylene (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 according to principles of Good Laboratory Practice
with the stated purpose of replicating the findings of (Dawson etai. 1993) and (Johnson et ai. 2003).
which observed increased cardiac malformations in the fetuses of pregnant female Sprague Dawley rats
administered TCE in drinking water.
Page 629 of 803
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609
610
611
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613
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615
616
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618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
The study utilized 6 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 TableApx F-2, which is adapted from Text Table 4 in the study.
Table Apx F-2. Experimental Design of (Charles Mygr Laboratories, 2019)
(Iroup
Treatment
Target
Concentration
Route of
Administration
Nil in her of
I'emales (Dams)
1
Vehicle (water)
0 ppm
Drinking Water
25
2
Retinoic Acid
3 mg/ml
Gavage
25
3
TCE
0.25 ppm
Drinking Water
25
4
TCE
1.5 ppm
Drinking Water
25
5
TCE
500 ppm
Drinking Water
25
6
TCE
1000 ppm
Drinking Water
25
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
malformations 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 F-3 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. EPA also identified that the
RA-treated group actually had 41.2% affected, as opposed to 42.2% as was presented in Text Table 14
of the study.
Table Apx F-3. Summary of Observed Interventricular Defects
Dosage:
0 ppm
(Vehicle)
15 mg/kg-day
RA
0.25 ppm
TCE
1.5 ppm
TCE
500 ppm
TCE
1000 ppm
TCE
# Affected
Fetuses (Litters)
7(5)
112 (23)
4(4)
5(3)
13(8)
12(6)
Mean %
Affected / Litter
per Group
2.4%
41.2%
(p < 0.01)
1.4%
1.5%
3.8%
3.7%
Page 630 of 803
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636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
Dosage:
0 ppm
(Vehicle)
15 mg/kg-day
RA
0.25 ppm
TCE
1.5 ppm
TCE
500 ppm
TCE
1000 ppm
TCE
Size of Opening
(Number of
Fetuses)
2mm (1)
-------
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
(soft tissue), p. 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 of VSDs only is presented
in TableApx F-5, with the incidence of membranous VSDs displayed in parentheses.
Table Apx F-5. Incidence of VSDs in Johnson and Charles River studies.
Johnson 2003
Charles River 2019
% fetuses
%
affected
fetuses
Dose
(mem. only)
Source
affected
Source/Notes
(Charles River
0 ppm
0.66%
(0.33%)
(Johnson et al. 2003).
Table 2
2.5%
Table 15
(soft tissue), p. 86
2.5 ppb
0%
(Johnson et al. 2003).
Table 2
N/A
N/A
(Charles River
0.25 ppm
0%
(Johnson et al. 2003).
Table 2
1.4%
Laboratories, 2019),
Table 15
(soft tissue), p. 86
(Charles River
1.5 ppm
2.21%
(1.66%)
(Johnson et al. 2003).
Table 2
1.5%
Tab|c !.
(soft tissue), p. 86
(Charles River
500 ppm
N/A
N/A
3.9%
Laboratories. 2019).
Table 15
(soft tissue), p. 86
(Charles River
1000 (Charles River) or
3.81%
(Johnson et al. 2003).
3.5%
Laboratories. 2019).
1100 (Johnson) ppm
(2.86%)
Table 2
Table 15
(soft tissue), p. 86
F.2.2.2 Differences in Types of Malformations Observed
The majority of cardiac malformations observed in the Johnson study were not VSDs (see Table 2 in
(Johnson et at.. 20031 while the Charles River study only identified VSDs in controls and TCE-treated
offspring. Of note, two major categories of heart malformations identified in the Johnson study that are
absent from even the positive control group of the Charles River study are atrial septal defects and valve
defects. The Charles River study methodology appeared to be focused primarily on identification of VSDs
over other heart defects, which may explain the observed positive bias toward detection of VSDs in
vehicle control and low-dose fetuses as compared to both the Johnson study and historical control data.
Table Apx F-6 compares the heart defects observed across all in vivo oral studies. Fisher at al. (2001). a
Page 632 of 803
-------
674 gavage study that also did not find a statistically significant association of TCE exposure with congenital
675 cardiac defects, is also included for comparison. Of note, the (Fisher et al.. 2001) study utilized the same
676 dissection and evaluation methodology as the (Johnson et al.. 2003) studies. There is substantial overlap in
677 the many type of defects identified in the three studies, while only membranous VSDs were observed in
678 TCE-treated animals in (Charles River Laboratories. 2019) (great blood vessel variation was identified in a
679 few TCE-treated pups but was considered incidental by the study authors). When comparing the results
680 from (Fisher et al.. 2001) and (Charles River Laboratories. 2019). EPA acknowledges that differences in
681 dosing method, vehicle volume, and other variables may also contribute to any observed differences.
682
683 Table Apx F-6. Heart and Cardiovascular Defects Observed in Select Oral TCE studies
Trichloroethylene (TCE)
Retinoic Acid (RA)
Johnson et al. (2003)a
Charles River (2019)
Fisher et al. (2001)
Charles River (2019)
Fisher et al. (2001)
Septal defects
Ventricular septal defect
(VSD) (perimembranous,
subaortic, muscular)
Ventricular septal
defect (VSD)
(membranous)
Ventricular septal
defect (VSD)
(membranous,
subaortic, muscular)
Ventricular septal defect
(VSD) (membranous,
aortic, muscular)
Atrial septal defect (ASD)
Atrial septal defect (ASD)
Atrial septal defect (ASD)
Valve defects
Mitral valve defect
Mitral valve defect
Mitral valve defect
Tricuspid valve defect
Tricuspid valve defect
Tricuspid valve defect
Pulmonary valve defect
Pulmonary valve defect
Aortic valve defects
(multiple)
Aortic stenosis
Aortic stenosis
Atrium, ventricle, and miscellaneous structural abnormalities
Atrioventricular septal
defect (endocardial
cushion defects)
Endocardial cushion
defects
Right ventricle enlarged
Right ventricle enlarged
Left ventricle aneurysm
dissecting
Heart ventricle, small
Left atrial hypertrophy
Cleft, apex of heart
Great vessel structural abnormalities
Transposition of the
great vessels
Transposition of the
great vessels
Aortic arch effects
Aortic arch effects
Major blood vessel
variation
Major blood vessel
variation
Pulmonary artery
hypoplasia
Pulmonary artery
hypoplasia
Aortic hypoplasia
Innominate artery short
Innominate artery effect
Page 633 of 803
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Trichloroethylene (TCE)
Retinoic Acid (RA)
Coronary artery/sinus
Stenotic carotid
Truncus dilated
Positional abnormalities of the heart and great vessels
Situs inversus
Situs inversus
Dextrocardia
Abnormal looping
Overriding aorta
a 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.
684
685 EPA's conclusion that the Charles River study insufficiently sensitive to non-VSD defects was supported
686 by the limited variety of malformations observed in the RA positive control based on a compiled literature
687 search:
688 1. EPA searched HERO and PubMed for studies investigating heart defects and malformations that
689 occur during prenatal exposure to all-trans retinoic acid (RA). Of the 37 studies reviewed, 12
690 studies were excluded from analysis because they were abstracts, book chapters, reviews, or
691 studies that did not expose animals to all-trans RA. Thus, EPA reviewed 25 studies and
692 compared the results of these studies to those reported by the Charles River and Johnson studies.
693 2. In all species examined, a total of 35 heart defects were associated with prenatal exposure to RA
694 in the identified literature.
695 3. The Charles River study reported 10 types of heart defects in animals exposed to RA.
696 4. Heart defects associated with TCE exposure partially overlap defects associated with RA
697 exposure. The Johnson study identified 10 types of cardiac defects in TCE-exposed fetuses.
698 Charles River only identified one defect (membranous VSDs) associated with TCE exposure
699 (major blood vessel variation was observed in 1-2 TCE-treated fetuses, but this effect was not
700 considered treatment-related).
701 5. All 35 defects associated with RA exposure were observed in rodents in the literature review. If
702 we limit the analysis to studies examining only rats, 31 of the total 35 defects were observed.
703 Only 6 of the 35 defects were noted in chickens, and 2 of the 35 were noted in zebrafish.
704 Therefore, the differences between defects captured in the Charles River study and the general
705 literature cannot be explained simply by inclusion of additional experimental species in the
706 general literature.
707
708 EPA therefore concludes that Charles River did not capture the entirety of cardiac defects that were
709 expected upon exposure to RA.
710
711 EPA searched HERO using the following keywords:
712 • Retinoic Acid
713 • Retinoic Acid + Cardiac
714 EPA also searched PubMed using the following keywords:
715 • retinoic acid (RA)-induced cardiac defects
716 • retinoic acid AND (cardiac defects OR cardiac malformations OR heart defects OR heart
717 malformations OR cardiac teratogenesis OR aorta OR ventricle OR endocardial cushion OR
718 pulmonary valve OR mitral valve OR aortic valve OR ventricular septum OR atrial septum OR
719 tricuspid valve OR aneurysm).
720
Page 634 of 803
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721
722
723
724
725
726
727
728
729
730
731
732
733
TableApx F-7 presents all of the cardiac defects found in the literature search and TableApx F-8
provides the full list of identified studies and observed defects. Table Apx F-9 compares the types of
defects observed across the Johnson and Charles River studies with those identified in the literature
search. Several defects associated with TCE exposure as well as several RA-induced defects in the
Charles River study were not associated with 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 associated with TCE exposure. Of note, atrial septal defects, which were the most common type
of malformation identified in the Johnson study, were identified in 5 other RA studies but not in the
Charles River study, including a human study (Siu et al.. 2002).
Tab
e Apx F-7. Cardiac Defects Observed in Literature
Number of
Cardiac Defect *
Studies
VSD
12
ASD
5
Tetralogy Fallot
1
Hypoplastic Left Heart Syndrome
1
Tricuspid Atresia
1
Aortic Valve Stenosis
1
Pulmonary Trunk Stenosis
3
Right Ventricular Hypertrophy
2
Left Ventricular Hypertrophy
1
Right Atrial Hypertrophy
2
Left Atrial Hypertrophy
1
CAVC
1
Situs Inversus
2
Dextrocardia
5
d Transposition
12
I Transposition
1
Cleft Apex
1
CoA
1
ARSA
2
IAA
1
Left Circumflex Aorta
1
Right aortic arch defect (RAA)
4
Double Aortic Arch
1
Cervical Aortic Arch
1
Hypoplastic Aortic Arch
1
Truncus Arteriosus
7
PDA
1
Innominate Artery Absent
1
Innominate Artery Short
1
Right Carotid Off Aorta
1
Right Subclavian Artery Absent
1
DORV
10
Endocardial Cushion Defect
3
Abnormal Heart Looping
7
Other
14
* Abbreviations defined in Table Apx F-9
Table Apx F-8. List of RA Studies Identified in the Literature Search and Observed Defects in Each
Page 635 of 803
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Study
Inclusion?
Species
Strain (if
applicable/reported
Defects observed
(Siu et al.. 2002)
Yes
human
N/A
ASD, right ventricular hypertrophy, right atrial
hypertrophy, PDA
(Broekhuizen M et
al.. 1998)
Yes
chicken
White Leghorn
DORV, other (abnormal brandling)
(Broekhuizen et
al.. 1995)
Yes
chicken
White Leghorn
VSD, d-transposition
(Rataiska et al..
2009)
Yes
mouse
Balb/c inbred and F1
cross of B57BL/
6xCBA
d-transposition DORV, other (abnormal conal vein, right
ventricle hypoplasia, aortic hypoplasia, other non-specified)
(Yasui et al.. 1999)
Yes
mouse
Jcl:ICR
VSD, dextrocardia, DORV, other (hypoplasia/dysplasia)
(Kim et al.. 1995)
Yes
mouse
DDY
VSD, dextrocardia, d-transposition, IAA, left circumflex
aorta, RAA, DORV, other (right subclavian artery)
(Kolodziriska et
al.. 2013)
Yes
mouse
F1 cross of C57BL/6
and CBA mouse
inbred strains
VSD, tetralogy of Fallot, d-transposition, truncus arteriosus,
DORV, other (noncompaction)
(Kraft et al.. 1994)
Yes
rat
Sprague -Dawley
described as Wistar
derived
No defects observed; fetuses/conceptuses exposed ex vivo
only
(Laborde et al..
1995)
Yes
mouse
CD-I
No cardiac defects observed
(Narematsu et al..
2015)
Yes
chicken
Not reported
d-transposition
(Rataiska et al..
2005)
Yes
mouse
CFW/LL andMIZZ
VSD, ASD, hypoplastic left heart syndrome, d-
transposition, RAA, hypoplastic aortic arch, truncus
arteriosus, DORV, other (dicuspid aortic valve,
hypomorphic semiluminar valve, great vessel spiraling)
(Tavlor et al..
1980)
Yes
hamster
golden Syrian
VSD, ASD, tricuspid atresia, pulmonary trunk stenosis, d-
transposition, RAA, truncus arteriosus, DORV, abnormal
hear looping, other (overriding aorta complex, mitral-aortic
continity, aortic hypoplasia, left ventricular hypoplasia,
univentricular heart, atrioventricularis)
(Fisher et al..
2001)
Yes
rat
Sprague-Dawley
Crl:CDR (SD) BR
VSD, ASD, aortic valve stenosis, right ventricular
hypertrophy, right atrial hypertrophy, left atrial
hypertrophy, situs invertus, dextrocardia, d-transposition,
cleft apex, ARSA, RAA, truncus arteriosus, innominate
artery absent, immomina artery short, right carotid off aorta,
right subclavian artery absent, other (pulmonary artery
hypoplasia, right subclavian artery defect)
(Brus et al.. 1995)
Yes
rat
Wistar
VSD, pulmonary trunk stenosis
(Dickman and
Smith. 1996)
Yes
chicken
Not reported
Situs inversus, abnormal heart looping, other (cardia bifia,
clustered heart tissue)
(Yuetal.. 2003)
Yes
rat
Sprague Dawley
VSD, ASD, ARSA, CoA, double aortic arch, cervical aortic
arch, truncus arteriosus, DORV
(Davis and Sadler.
1981)
Yes
mouse
ICR
VSD, d-transposition, truncus arteriosus, DORV,
endocardial cushion defect
Page 636 of 803
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Study
Inclusion?
Species
Strain (if
applicable/reported
Defects observed
(Bouman et al..
1998)
Yes
chicken
Not reported
Abnormal heart looping
(Bouman et al..
1995)
Yes
chicken
Not reported
VSD, abnormal heart looping
(Xavier-Neto et
al.. 1999)
Yes
Chicken;
zebrafish
Transgenic mice on
FVB background
Other (hearts with marked atrial dominance)
(Nakaiima et al..
1996)
Yes
mouse
ICR
Endocardial cushion defect, other (hypoplasticity of the
proximal parietal and septal ridges in the outflow tract -
develop from encocardial cushion)
(Lee et al.. 1998)
Yes
rat
Wistar
Left ventricle hypertrophy, dextrocardia, d-transposition, 1-
transposition, abnormal heart looping
(Kim et al.. 1999)
Yes
rat
Wistar
CAVC, dextrocardia, endocardial cushion defects, abnormal
heart looping
(Ostadalova et al..
1995)
Yes
rat
Wistar
VSD, pulmonary trunk stenosis, DORV
(Hasa et al.. 2008)
Yes
zebrafish
Danio rerio
Abnormal heart looping, other (pericardial edema)
(Baraka et al..
2009)
No. RA not used
to induce defects.
N/A
N/A
N/A
(Turton et al..
1992)
No. No effects at
lower dosage and
no fetuses
available at
higher doses.
N/A
N/A
N/A
(Miura et al..
1990)
No. Not RA,
study on 13-cis-
ra"
N/A
N/A
N/A
(Pan and Baker.
2007)
No. Review only.
N/A
N/A
N/A
(Roberts et al..
2006)
No. No RA
exposure.
N/A
N/A
N/A
(Sinning. 1998)
No. Review only.
N/A
N/A
N/A
(Smith and
Dickman. 1997)
No. Review only.
N/A
N/A
N/A
(Stefanovic and
Zaffran. 2017)
No. Review only.
N/A
N/A
N/A
(Pexieder et al..
1990)
No. Abstract
only.
N/A
N/A
N/A
(Van Maldereem
et al.. 1992)
No. Exposure is
to isotretinoin.
N/A
N/A
N/A
(Oku et al.. 1995)
No. Abstract
only.
N/A
N/A
N/A
(Iwase et al.. 1998)
No. Abstract
only.
N/A
N/A
N/A
734
Page 637 of 803
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735
736 Table Apx F-9. Cardiac Defects Observed After Exposure to RA or TCE
Chemical:
TCE
TCE
RA
RA
RA
Malformation Class
Malformation Name
Charles
River
2019
Johnson
2003
Charles
River
2019
Other
Literature
(No.
Studies)
Other
Literature
Species1
Atrium, Ventricle and Valve
Defects
Ventricular Septal Defect
(VSD)2
V
V
V
V (12)
C. H. M.
R
Atrium, Ventricle and Valve
Defects
Atrial Septal Defect (ASD)
V
V (5)
Hu. H. R
Atrium, Ventricle and Valve
Defects
Double outlet ventricle
(DORV)
a/(10)
C. H. M.
R
Atrium, Ventricle and Valve
Defects
Tetralogy of Fallot
Vd)
M
Atrium, Ventricle and Valve
Defects
Hypoplastic Left Heart
Syndrome
Vd)
R
Atrium, Ventricle and Valve
Defects
Tricuspid defects
V
Vd)
H
Atrium, Ventricle and Valve
Defects
Aortic valve defects
V3
Vd)
R
Atrium, Ventricle and Valve
Defects
Mitral valve defects
V
Atrium, Ventricle and Valve
Defects
Right ventricular hypertrophy
V(2)
R
Atrium, Ventricle and Valve
Defects
Left ventriclular hypertrophy
Vd)
R
Atrium, Ventricle and Valve
Defects
Right atrial hypertrophy
V (2)
R
Atrium, Ventricle and Valve
Defects
Left atrial hypertrophy
Vd)
R
Atrium, Ventricle and Valve
Defects
Small ventricle
V
Atrium, Ventricle and Valve
Defects
Complete Atrioventricular
Canal defect (CAVC)
V
V(1)
R
Symmetry
Situs Inversus
V
V( 2)
C.R
Symmetry
Dextrocardia
V( 5)
M.R
Symmetry
d-Transposition of the great
arteries
V
V (12)
C. H. M.
R
Symmetry
1-Transposition of the Great
Arteries
V(1)
R
Symmetry
Cleft, apex of heart
V(1)
R
Aortic Arch Defects
Coarctation of the Aorta
(CoA)
V
V(1)
R
Aortic Arch Defects
Left aortic arch with aberrant
right subclavian artery
(ARSA)
a/4
V (2)
R
Aortic Arch Defects
left circumflex aorta
V(1)
M
Aortic Arch Defects
Right aortic arch defects
(RAA)
V
V (4)
H. M.R
Aortic Arch Defects
Double aortic arch
V(1)
R
Aortic Arch Defects
Cervical aortic arch
V(1)
R
Aortic Arch Defects
Interruption of the aortic arch
(IAA)
V
V(1)
M
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Chemical:
TCE
TCE
RA
RA
RA
Malformation Class
Malformation Name
Charles
River
2019
Johnson
2003
Charles
River
2019
Other
Literature
(No.
Studies)
Other
Literature
Species1
Aortic Arch Defects
Hypoplastic aortic arch
V(1)
R
Aortic Arch Defects
Stenotic aortic arch
a/
Other vessel defects
Pulmonary trunk stenosis
V(3)
H.R
Other vessel defects
Truncus Arteriosus (dilated
truncus)
V(7)
H. M.R
Other vessel defects: incomplete
postnatal development
Patent Ductus Arteriosus
(PDA)
V(1)
R
Other vessel defects
Innominate artery absent
V(1)
R
Other vessel defects
Innominate artery short
V(1)
R
Other vessel defects
Right carotid off aorta
V(1)
R
Other vessel defects
Stenotic carotid
a/
Other vessel defects
Right subclavian artery absent
V(1)
R
Other vessel defects
Pulmonary artery hypoplasia
a/
Other vessel defects
Coronary artery/sinus defects
a/
Other early developmental
defect
Endocardial cushion defects
V(3)
M.R
Other early developmental
defect
Abnormal heart looping
V
V(7)
C. H. R. Z
Other5
a/7
V (14)
C. H. M.
R.Z
1 Human (Hu), Chicken (C), Hamster, (H), Mouse (M), Rat (R), Zebrafish (Z).
2 Most studies reviewed did not specify among perimembranous, muscular or subarterial VSDs, so these were included all as "VSDs" for the
literature review comparison.
3 Aortic valve defects included aortic valve defect with fenestrated leaflets and aortic valve stenosis described as aortic valve defect with fused
leaflets creating aortic valvular stenosis.
4Chicken (C), Hamster, (H), Mouse (M), Rat (R), Zebrafish (Z).
4Retroesophageal aortic arch described in Charles River study was tagged as ARSA defect.
5 Major blood vessel variation (right carotid and subclavian arteries arose independently from the aortic arch [no brachiocephalic trunk] or right
subclavian artery coursed retroesophageal and joined the aortic arch adjacent to ductus arteriosus [no brachiocephalic trunk]) tagged to RAA
defects.
5 If EPA was unsure of the general malformation class, the defect was categorized as "other".
0 "Other" defect in HSIA study (RA exposure groups) was a major blood vessel variation (an elongated brachiocephalic trunk or a missing
brachiocephalic trunk due to right carotid and right subclavian arising independently from the aortic arch, or due to a retroesophageal right
subclavian; or (right carotid and subclavian arteries arose independently from the aortic arch [no brachiocephalic trunk] or right subclavian
artery coursed retroesophageal and joined the aortic arch adjacent to ductus arteriosus [no brachiocephalic trunk]).
737 F.2.2.3 Methodology Differences
738 There are likely several contributing factors explaining why the Charles River study did not identify atrial
739 or valve defects. In the Johnson study, the materials and methods section described examination of the
740 internal structure of the heart for all fetuses. The dissection methodology allows detailed examination of
741 the atrial septum. In contrast, the Charles River study states that the fetal evaluation methods were
742 conducted according to Stuckhardt and Poppe (1984), which does not include examination of atrial
743 septal defects. Therefore, the methodology used by the Charles River study was likely to miss this
744 important category of cardiac malformations. As shown in Table Apx F-9, five other studies were
745 identified in the literature that observed atrial septal defects following RA exposure, while none were
746 observed in the Charles River study.
747
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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:
1. 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 1 in one column, Group 2 in the next column, etc. The Dawson and
Johnson methods of randomization did not include consideration of, or stratification by, body
weight.
4. Husbandry differences, the Charles River study individually housed the pregnant females,
whereas the Dawson and Johnson studies group-housed the females, so several dams were
consuming treated drinking water from the same bottle. Thus, there would be greater precision in
the Charles River dose calculations.
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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 at... 1993) and (Johnson et at..
2003) were Harlan Sprague-Dawley rats purchased from Harlan Laboratories Inc., Indianapolis,
IN. The Charles River rats were Crl:CD(SD) Sprague-Dawley rats from Charles River
Laboratories in Raleigh, NC. It is unknown what influence the source or strain differences might
have had 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 incidences of cardiac malformations.
6. Technical confirmation of diagnosis. The Charles River report did not specify whether cardiac
abnormalities were confirmed by 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 examination of 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 (Mateds et at... 2016)). Therefore,
there is high confidence in the determination of observed defects in the Dawson and Johnson
studies. Of note, neither study was designed to confirm diagnoses of normal fetal morphology.
F.2.2.4 Adversity of Small VSDs
In addition to the lack of a statistically significant increase in cardiac defects, the Charles River study
claims that the
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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 would have
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.
F.2.2.5 Conclusions
In short, the methodology and positive control 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 (1984) 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 of VSDs as (Johnson et at..
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 1.5ppm. 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 indication of the potentially narrowed sensitivity 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.. 2001). 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 the results do not entirely contradict the
conclusions of that study. Based on these considerations along with some data reporting errors, (Charles
River Laboratories. 2019) received a Medium in data quality evaluation, the same as (Dawson, et al..
1993) and (Johnson, et al.. 2003).
While (Charles River Laboratories. 2019) was not considered a close enough replication to (Johnson et
al.. 2003) to sway the weight of evidence for the endpoint on it's own, EPA did consider (Charles River
Laboratories. 2019) to be an overall well-conducted study, and it was incorporated into the WOE
analysis for the cardiac defects endpoint along with all other relevant studies identified in the literature.
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F.3 WOE Analysis for Congenital Cardiac Defects
F.3.1 Methodology
1) EPA identified, collected and reviewed a sampling of recent literature on systematic approaches
to performing weight-of-evidence evaluation. 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. 20051
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 (Gough. 2.007).
c. Rhomberg et al. 2013. A survey of frameworks for best practices in weight-of-evidence
analyses. Crit Rev 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
(Rooney et al.. 2014).
e. NTP. 2015. Handbook for Conducting a Literature-Based Health Assessment Using
OHAT Approach for Systematic Review and Evidence Integration (NTP. ).
f. EPA. 2016. Weight of Evidence in Ecological Assessment. Risk Assessment Forum.
EPA/100/R16/001 ( ).
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-Evidence Concepts: Introduction
and Application to Sediment Management (Engineers. 2018).
i. 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 (1-69) (EFSA.: ).
k. Linkov 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 Pharm 91: S46-S60 (Bridges et al.. 2017).
n. Dekant and Bridges. 2016. Assessment of reproductive and developmental effects of
DINP, DnHP and DCHP using quantitative weight of evidence. Reg Tox Pharm 81: 397-
406 (Dekant and Bridges. ^ ).
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 (Bridges 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
(Gangwal et al.. 2012).
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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 (Reif et al...
2013 Y
r. Klimisch et al. 1997. A Systematic Approach for Evaluating the Quality of Experimental
Toxicological and Ecotoxicological Data. Reg Tox Pharm 25: 1-5 (Klimisch et al.. 1997).
2) Upon review of the various weight-of-evidence approaches that have been proposed, EPA chose
to adopt the method presented by EPA Risk Assessment Forum ( 016i). 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 (U.S. EPA. 20160 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 0 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
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 ( ) weight-of-evidence method, EPA developed an
Excel spreadsheet /EPA, 2019. Data Table for Congenital Heart Defects Weight of Evidence
Analysis. Docket: EPA-HQ-OPPT-2019-050QL 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. 20160 as inherent properties that make
evidence convincing. For our implementation, because each piece of evidence is
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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.
ii. Outcome/strength is defined in ( 0160 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.
iii. Relevance is defined in (U.S. EPA. 20160 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
such as testing of TCE metabolites rather 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 (horizontally) 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. 20160. In
other words, if any one of the three grading areas was low, then even if other
aspects of the study 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 -) at a 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-quantitative representation of all overall study grades
within that line of evidence, with considerations given to both the amplitude of the
overall study grades along with the consistency of the strength direction across studies.
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When results were mixed, 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-ambiguous results (not a strength score of 0) were considered more informative than
ambiguous studies. Additionally, consistent overall study grades of lower amplitude (e.g.,
all +) may have resulted in a summary score of a higher amplitude (++). In this way,
WOE determination 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 summarize the 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 summary scores were determined by
integrating the overall grades for each study, in the manner as described in Section c.
Because of these different methodologies and 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: This 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 Congenital Heart Defects Weight of Evidence Analysis. Docket: EPA-HQ-QPPT-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 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.
This analysis included all relevant primary literature cited in (Makris et al.. 2016). the 2014 TCE Work
Plan Chemical Risk Assessment (U.S. EPA. 2014b). and any additional on-topic studies identified in the
systematic review literature search (U.S. EPA. 20171). 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.. 2.018) and the recently completed in vivo drinking water study (Charles River
Laboratories. 2019). comprising 45 studies in total (42 scoring Acceptable). Several studies cited in
previous reviews were screened out as off-topic because the study reports did not indicate direct
assessment of cardiac defects, cardiovascular effects, or any related outcomes. These studies were:
(Beliles et al.. 1980; Bross et al.. 1983; Cosby and Dukelow. 1992; Dorfmueller et al. 1979; Elovaara et
al.. 1979; Narotskv andKavlock. 1995; Narotskv et al.. 1995). Additional studies were initially included
but were determined to be not rated (NR) after thorough evaluation through the WOE criteria (Ruckart
et al.. 2013; Palbvkin et al.. 2011. see below). These two studies are indicated by blue text in the
supplemental data table, however they are not included in the tables below.
F.3.2 WOE Results By Study Type
Data evaluated to assess the weight-of-evidence for congenital heart defects from exposure to TCE
include studies from three lines of evidence: epidemiology studies, in vivo animal toxicity studies, and
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1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
mechanistic studies. For this analysis, the three lines of evidence will be considered both individually
and collectively.
Table Apx F-10 shows the weight-of-evidence for 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 of (++) relevance scores because
they examined associations of TCE exposure in humans, however 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 from a 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,
(Goldberg et al.. 1990) was rated Unacceptable in data quality evaluation and therefore did not
contribute to the WOE. The Bove reports (1996; 1995) (considered here as a single study because the
two papers contain the same data on cardiac defects) reported elevated but nonsignificant increases in
odds ratios. Yauck et al. (2004) reported a positive association between congenital heart defects 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 Apx F-10. Weight-of-Evidence Table for Epidemiology Studies
Evidence Area
Reliability
Strength
Relevance
Overall Grade
TCE
(Laeakos et al.. 1986)
+
0/-
++
0/-
(Bove. 1996; Bove et al..
1995)
+
0
++
0
(Yauck et al.. 2004)
+
0/+
++
0/+
(Forand et al.. 2012)
+
++
++
+
(Gilboa et al.. 2012)
+/++
-
++
-
(Brender et al.. 2014)
+
+
++
+
(Goldbers et al.. 1990)
0
+
++
0
METABOLITES (TCA, DCA)
(Wrieht et al.. 2017)
++
+
+
+
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1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
Evidence Area
Reliability
Strength
Relevance
Overall Grade
Integrated Area Scores
(all epidemiology)
+
0/+
++
Summary Score (all epidemiology)
+
Possible scores for reliability and relevance were 0, +, ++, or +++ for unusable, low, medium and high.
Possible scores for strength and overall weight were —, —, -, 0, +, ++, or +++, with ranges inbetween, for results ranging from
strongly negative to ambiguous to strongly positive.
Red text identifies studies that scored Unacceptable in data quality evaluation and a 0 for reliability. The WOE scores are
provided for reference but were not incorporated into the overall score for the line of evidence.
Table Apx F-l 1 shows the weight-of-evidence for the various in vivo animal studies that were
considered in this review. The four TCE oral studies were considered of (++) relevance because they
used a natural route of exposure (drinking water or gavage) in a mammallian study. 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 incidence of cardiac defects were observed in pups from the (Dawson et al.. 1993) and
(Johnson et al.. 2003) 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 controls
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 appeared to be of
reduced sensitivity in its ability to detect all types of cardiac defects (see Appendix F.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 the administered dose. These studies were considered
mostly reliable with ratings of (+++) (Smith et al.. 1989) and (++) (Fisher et al.. 2001; Epstein et al..
1992). Only (Johnson et al.. 1998) received a lower reliability 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 positive strength scores (+) in the (Johnson et al.. 1998) and
(Epstein 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 only a single dose level was used. The overall summary score for the oral metabolite
studies was (+).
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1175
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 0 in reliability for limited reporting of study details
(Hardin et al.. 1981) and use of a nonstandard exposure duration with insufficient details on exposure
method (Healy 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 (Carney et al.. 2006) data but reduced by the
weaknesses of the other studies and the limited number of acceptable studies with non-ambiguous
results.
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 of TCE exposure with developmental cardiac defects.
Table Apx F-ll. Weight-of-Evidence r
"able for In Vivo Animal Toxicity Studies
Evidence Area
Reliability
Strength
Relevance
Overall Grade
ORAL
TCE
(Dawson et al.. 1993)
++
+
++
+
(Johnson et al.. 2003)
+
++
++
+
(Fisher et al.. 2001)
+
0/-
++
0/-
(Charles River
Laboratories. 2019)
++
0/-
++
0/-
Integrated Area Scores
+/++
0/+
++
Summary Score (TCE)
0/+
METABOLITES (TCA, DCA)
(Smith etal.. 1989)
+++
++
+
+
(Smith etal.. 1992)
+++
++
+
+
(Johnson et al.. 1998)
0/+
+
+
0/+
(Fisher et al.. 2001)
++
-
+
-
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1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
Evidence Area
Reliability
Strength
Relevance
Overall Grade
(Epstein et al.. 1992)
++
+
+
Integrated Area Scores
++
+
+
Summary Score (Metabolites)
Integrated Area Scores
(all oral studies)
++
+
++
Summary Score (all oral studies)
+
INHALATION
TCE
(Schwetz et al.. 1975)
+
0/-
+
0/-
(Dorfmueller et al..
1979)
0/+
0/-
+
0/-
(Carnev et al.. 2006)
+++
—
++
—
(Hardin et al.. 1981)
0
-
++
0
(Healv et al.. 1982)
0
-
++
0
Integrated Area Scores
(all inhalation studies)
+/++
-
+/++
Summary Score (all inhalation studies)
-
OTHER ROUTES (Uterine Infusion)
(Dawson et al.. 1990)
0/+
+
+
0/+
Integrated Area Scores
(in vivo - all routes)
+/++
0/+
+/++
Summary Score (in vivo - all routes)
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 negative to ambiguous to strongly positive.
Red text identifies studies that scored Unacceptable in data quality evaluation. The WOE scores are provided for reference but
were not incorporated into the overall score for the line of evidence.
Mechanistic studies that inform the weight-of-evidence for developmental heart 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 contribute to the observed valvulo-septal defects
(e.g., altered calcium flux, inhibition of stem cell differentiation and endothelial cell proliferation) as
well as altered expression of oxidative metabolism enzymes. A mechanistic study from Palbykin et al.
(2011) was graded as not relevant and was dropped from the analysis because it merely examined
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.
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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; Selmin et al.. 2.008; 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 (Jiang 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 aU )). 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). The 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.. 2010; 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
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
zebrafish 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 DC A) to effect cardiac development in zebrafish. The study by Wirbisky et al. (2016) was
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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 summary score for zebrafish embryo studies was (+). The overall summary score for
mechanistic studies across all species and study designs was (+/++), with the overall score increased 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.
Table Apx F-12. Weight-of-Evidence r
"able for Mechanistic Studies
Evidence Area
Reliability
Strength
Relevance
Overall Grade
MAMMALIAN CELLS/TISSUE
TCE
(Saillenfait et al.. 1995)
++
+/++
(Collier etal.,2003)
++
+
+
+
(Selmin et al.. 2008)
++
+
+
+
(Caldwell et al.. 2008)
++
+
+
+
(Ou etal.,2003)
+++
++
+
+
(Jians et al.. 2015)
+++
++
+
+
(Dow and Green. 2000)
0/+
+
+/++
0/+
(Green et al.. 2004)
++
+
+/++
+
METABOLITES (TCA, DCA, Trichloroethanol, Chloral)
(Saillenfait et al.. 1995)
++
+/++
(Collier etal.,2003)
++
+
+/++
+
(Hunter et al.. 1996)
++
++
+/++
+
(Selmin et al.. 2008)
++
+
+
+
(Dow and Green. 2000)
++
+
+
+
Integrated Area Scores
++
+
+
Summary Score (all mammalian tissue studies)
+
CHICK EMBRYO
TCE
(Loeberet al.. 1988)
+/++
++
+
+
(Bover et al.. 2000)
++
+
+
+
(Mishima et al.. 2006)
++
+
+
+
(Drake et al.. 2006a)
++
+
+
+
(Drake et al.. 2006b)
++
0
+
0
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1264
Evidence Area
Reliability
Strength
Relevance
Overall Grade
(Rufer et al.. 2010)
++
++
+
+
(Makwana et al.. 2010)
++
+
+
+
(Makwana et al.. 2013)
++
+
+
+
METABOLITES (TCA)
(Harris et al.. 2018)
++
+
+
+
(Drake et al.. 2006a)
++
+
+
+
(Drake et al.. 2006b)
++
0
+
0
Integrated Area Scores
++
+
+
Summary Score (all chick studies)
+/++
ZEBRAFISH EMBRYO
TCE
(Wirbiskv et al.. 2016)
+++
+++
+
+
METABOLITES (DCA)
(Hassoun et al.. 2005)
+++
++
+
+
(Williams et al.. 2006)
++
++
+
+
Integrated Area Scores
+++
++/+++
+
Summary Score (all zebrafish studies)
+
Integrated Area Scores
(all mechanistic studies)
+++
+/++
+
Summary Score (all mechanistic studies)
+/++
Possible scores for reliability and relevance were 0, +,
Possible scores for strength and overall weight were -
strongly negative to ambiguous to strongly positive.
++, or +++, with ranges inbetween, for unusable, low, medium and high.
-, —, -, 0, +, ++, or +++, with ranges inbetween, for results ranging from
In summary, the database contains a large and diverse set of studies pertinent to assessing congenital
heart defects from TCE exposure (overall relevance was rated as ++). Well-designed, conducted and
reported studies were located for all categories, although the epidemiology studies were limited to
ecological or case-control study designs with potential for misclassification of exposure and 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 epidemiology studies as a group provide suggestive evidence for an
effect of TCE on cardiac defects in humans (summary score of +). Even though there are some
uncertainties associated with the relevant epidemiological literature, the observation of a positive
association between TCE exposure and CHDs in multiple exposed human populations increases the
plausibility of the positive results from other evidence areas. 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 (which may be most relevant to the majority of human exposure scenarios)
contributed negative evidence (-). Mechanistic studies provided solid, consistent supporting information
for effects of TCE and metabolites on cardiac development and precursor effects (summary score of
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1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1211
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
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1296
1297
1298
1299
+/++) despite lack of support for any particular adverse outcome pathway (AOP). Overall, the database
is both reliable and relevant and provides positive overall evidence that TCE may produce cardiac
defects in humans (based on positive evidence from epidemiology studies, ambiguous evidence from
animal toxicity studies, and stronger positive evidence from mechanistic studies).
Table Apx F-13. Overall Weight-of-Evidence Table and Summary Scores
Evidence Area
Reliability
Strength
Relevance
Summary
Score
Epidemiology studies
+
+
++
+
In vivo animal toxicity studies
+/++
0/+
+/++
0
Mechanistic studies
+++
+/++
+
+/++
Integrated Area Scores
++
+
++
+
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 negative to ambiguous to strongly positive.
F.3.3 Mode of Action Discussion
A number of studies have been conducted to elucidate the mode of action for TCE-related cardiac
teratogenicity. During early cardiac morphogenesis, outflow tract and atrioventricular endothelial cells
differentiate into mesenchymal cells. These mesenchymal cells have characteristics of smooth muscle-
like 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 endothelial cushion 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).
Other studies have 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). TCE exposure in both in rats (Dow and
Green. 2000) and humans (Green et al.. 2004) is also associated with folate deficiency, a known
susceptibility factor for CHDs (Mao et al.. 2017).
Early stages of cardiac development are quite similar across various species (Makris et al.. 2016). and
these mechanistic data provide support to the plausibility of TCE-related cardiac effects in humans
(U.S. EPA. 201 le). Teratogens may function through a multitude of pathways, often resulting in a
constellation of effects. Therefore, evidence of a single dominant MOA is not required in order for the
data to support a plausible mechanism of TCE-induced congenital heart defects.
Several in vitro studies have observed non-monotonic dose responses in gene activation and other
molecular changes following TCE exposure at varying concentrations (Palbykin et al.. 2011; Makwana
et al.. 2010). Specifically, TCE exposure induced expression of oxidative stress genes (Makwana et al..
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2010) and increased DNA hypermethylation of a calcium-ATP pump promoter in developing cardiac
tissue (Patbykin et at.. 2011) only at lower and not higher doses, resulting in multimodal calcium
responses (Caldwell et ai. 2008). TCE also increased significantly increased gene expression of the
oxidative metabolism enzyme CYP2H1 specifically in cardiac tissue only at the lower dose ((Makwana
et at.. 2.013)). In (Harris et at.. 2018). expression of genes involved in cardiac development and
metabolism were either reduced (low dose) or increased (high dose), depending on the administered
concentration. These results may explain the non-monotonic polynomial dose-response observed in
(Johnson et at.. 2003). whereby toxicological outcomes present at different doses equating to either
inhibition or activation of particular gene expression (Harris et at.. 2018). This differential gene
expression would in turn lead to dose-specific downstream metabolic and phenotypic effects.
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Appendix G CONSIDERATIONS FOR BMD MODELING AND
APPLICATION OF UNCERTAINTY FACTORS
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 Benchmark Dose Technical Guidance Document ( 2.012a). 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 represented 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 were used to estimate the BMD and BMDL, and to facilitate a consistent basis of
comparison across endpoints, studies, and assessments. According to ( ), smaller BMRs
can be used to account for more sever (or "frank") effects, and standard EPA practice applies a BMR of
1-5%) for developmental and mortality endpoints. 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.
E ) and Appendix I (for (Setgrade and Gilmour. 2010)). A comparison of results from updated
BMDL modeling runs with results from (U.S. EPA. ) for (Johnson et al.. 2003) are provided in
Appendix I. Where dose-response modeling was not feasible, NOAELs or LOAELs were also identified
and are summarized.
G.l Selecting the BMD 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).
1) Assess goodness-of-fit, using a value of a = 0.1 to determine a critical value (or a = 0.05 or a = 0.01)
if there is reason to use a specific model(s) rather than fitting a suite of models.
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 determining the BMDL. The remaining
criteria for selecting the BMDL are necessarily somewhat 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
Akaike's Information Criteria (AIC)28 may be used to calculate the BMDL for 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 same as "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
90
Akaike's Information Criteria—a measure of information loss from a dose-response model that can be used to
compare a set of models. Among a specified set of models, the model with the lowest AIC is considered the best. If two or
more models share the lowest AIC, an average of the BMDLs could be used, but averaging was not used in this assessment
because for the one occasion in which models shared the lowest AIC, a selection was made based on visual fit.
Page 656 of 803
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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).
5) If the BMDL estimates from the remaining models are not sufficiently close, some model dependence
of the estimate can be assumed. Expert statistical judgment 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.
6) In some cases, modeling attempts may not yield useful results. When this occurs and the most
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.
G.2 Uncertainty Factor Selection
After the PODs were determined for each study/endpoint, uncertainty factors (UFs) were used to derive
acceptable benchmark margins of mxposure (MOEs). UFs are used to address differences between study
conditions and conditions of human environmental exposure. These include:
(a) Extrapolating from laboratory animals to humans (UFa):
If a POD is derived from experimental animal data, it is divided by an UF to reflect pharmacokinetic and
pharmacodynamic 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 of 3 for pharmacokinetic 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 performed, in accordance with U.S. EPA inhalation
dosimetry guidance (U.S. EPA. 1994b). in which case, residual pharmacokinetic differences are
considered to be negligible. Therefore, 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-population studies are divided by an UF to address
sensitive humans. Similarly, the animals used in most laboratory animal studies are considered to be
typical or average responders, and the human (intraspecies) variability UF is also applied to PODs from
such studies to address sensitive subgroups. The standard value for the human variability UF is 10,
which breaks down (approximately) to a factor of 3 for pharmacokinetic variability (which is removed if
the PBPK model is used) and a factor of 3 for pharmacodynamic variability. This standard value was
used for all of the PODs in this assessment.
(c) Uncertainty in extrapolating from subchronic to chronic exposures (UFs):29
29
Chronic exposure covers > 10% of expected lifetime. Rodent studies exceeding 90 days of exposure are considered
chronic, and rodent studies covering from 4 weeks to 90 days of exposure are considered subchronic. For human studies,
chronic exposure exceeds 7-8years, on average (U.S. EPA. 1994b').
Page 657 of 803
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Chronic risk estimates apply to long-term exposure over decades, but sometimes the best (or only)
reasonably available data come from less-than-lifetime studies. Lifetime exposure can induce effects
that may not be apparent or as large in magnitude in a shorter study; consequently, a dose that elicits a
specific level of response from a lifetime exposure may be less than the dose eliciting the same level of
response from a shorter exposure period. Thus, PODs based on subchronic exposure data are generally
divided by a subchronic-to-chronic UF, which has a standard value of 10. If there is evidence suggesting
that exposure for longer time periods does not increase the magnitude of an effect, a lower value of 3 or
one might be used. For some reproductive and developmental effects, chronic exposure is that which
covers 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.
(d) Uncertainty in extrapolating from LOAELs to NOAELs (UFl):
PODs are intended to be estimates of exposure levels without appreciable risk under the study
conditions so that, after the application of appropriate UFs for interspecies extrapolation, human
variability, and/or duration extrapolation, the absence of appreciable risk is conveyed. Under the
NOAEL/LOAEL approach to determining a POD, however, adverse effects are sometimes observed at
all study doses. If the POD is a LOAEL, then it is divided by an UF to better estimate a NOAEL. The
standard value for the LOAEL-to-NOAEL UF is 10, although a value of 3 is sometimes used if the
effect is considered minimally adverse at the response level observed at the LOAEL or is an early
marker for an adverse effect. For NOAEL or BMDL values, the UFl is 1.
Page 658 of 803
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Appendix H BENCHMARK DOSE ANALYSIS FOR (S« ^
JL JL ^
)
H.l Applied Dose/Concentration
H.l.l BMDS Wizard Output Report - Mortality
The benchmark dose (BMD) modeling of dichotomous data were conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version 1.11). All reasonably 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 10% extra risk were used in the
BMD modeling, per technical direction. Adequacy of model fit was judged based on the x2 goodness-
of-fit p-value (p > 0.1), magnitude of scaled residuals, and visual inspection of the model fit.
All models except for the Probit and Logistic provided adequate overall fit to the data, based on the %2
goodness-of-fit p-value (p > 0.1). Among the remaining models, the Quantal Linear, Multistage,
Weibull, Gamma and Log-Logistic models all showed poor fit at the 25 ppm data point, based on scaled
residuals ranging from > | 1.5 | to > | 2 | . 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 =
P/o, however, the BMDLs are no longer within 3-fold; the results at this BMR show model-dependence.
This reflects the lack of information reasonably 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 Log-
Probit, was selected.
H.l.1.1 BMDS Summary of Mortality - BMR 10%
Table Apx H-l. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade
and Gilmour 201(
); BMR = 10% Extra Risk
Model3
Goodness of fit
BMDioPct
(ppm)
BMDLioPct
(ppm)
Basis for model selection
/j-valuc
AIC
Gamma
0.292
342.35
43.5
31.2
All models provided adequate
overall fit to the data except for
the Probit and Logistic models
Dichotomous-Hill
0.563
340.91
44.7
36.2
Logistic
0.0074
351.35
66.2
57.6
(based on the yl goodness-of-fit
p-value). Although the Quantal
Linear model provided adequate
overall fit, the scaled residual
nearest the BMD was > | 2 |,
indicating poor fit in that part of
the curve. With or without the
Quantal Linear, the BMDLs are
sufficiently close (< 3 fold), so the
model with the lowest AIC was
selected (Log-Probit).
LogLogistic
0.370
341.62
43.3
31.6
Probit
0.0211
348.55
61.1
53.3
LogProbit
0.582
338.72
46.6
39.6
Weibull
0.259
342.81
42.5
30.3
Multistage 2ob
0.177
344.14
39.9
27.9
Multistage 3oc
Multistage 4od
0.177
344.14
39.9
27.9
Page 659 of 803
-------
Multistage 5°e
Multistage 6of
Quantal-Linear
0.230
343.25
33.0
26.6
a Selected model in bold; scaled residuals for selected model for doses 0, 5, 10,25, 50, 100, and 200 ppm were 0.38, -0.08, -
0.18, -1.16, 1.08, 0.22, -1.02, respectively.
b The Multistage 2° model may appear equivalent to the Multistage 3° model, however differences exist in digits not displayed
in the table. This also applies to the Multistage 4° model. This also applies to the Multistage 5° model. This also applies to the
Multistage 6° model.
c The Multistage 3° model may appear equivalent to the Multistage 2° model, however differences exist in digits not displayed
in the table.
d For the Multistage 4° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 3° model.
e For the Multistage 5° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 4° model.
f For the Multistage 6° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 5° model.
LogProbit Model, with BMR of 10% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the BMDL
LogProbit
0.5
0.4
0
0
50
100
150
200
FigureApx H-l. Plot of Incidence by Applied 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 + (1-Background) *
CumNorm(Intercept+Slope*Log(Dose)),where CumNorm(.) is the cumulative normal distribution
function
Slope parameter is restricted as slope >= 1
Benchmark Dose Computation.
BMR = 10% Extra risk
BMD = 46.6299
BMDL at the 95% confidence level = 39.5537
Parameter Estimates
Page 660 of 803
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Variable
Estimate
Default Initial
Parameter Values
background
0.0281182
0.0338983
intercept
-5.1238E+00
-5.2930E+00
slope
1
1
Analysis of Deviance Table
Model
Log(likelihood)
# Param's
Deviance
Test d.f.
p-value
Full model
-165.36
7
Fitted model
-167.36
2
4.00401
5
0.55
Reduced model
-208.64
1
86.5627
6
<0001
AIC: =338.719
Goodness of Fit Table
Dose
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
ChiA2 = 3.78 d.f = 5 P-value = 0.5818
Page 661 of 803
-------
H.l.1.2 BMDS Summary of Mortality - BMR: 5%
Table Apx H-2. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade
and Gilmour 201(
); BMR = 5% Extra Risk
Model"
Goodness of fit
BMDspct
(ppm)
BMDLspct
(ppm)
Basis for model selection
/j-valuc
AIC
Gamma
0.292
342.35
26.2
15.7
All models provided adequate
overall fit to the data except for
the Probit and Logistic models
Dichotomous-Hill
0.563
340.91
33.9
22.5
Logistic
0.0074
351.35
40.3
34.4
(based on the yl goodness-of-fit
p-value). However, The Quantal
Linear, Multistage, Weibull,
Gamma and Log-Logistic models
all showed poor fit at the 25 ppm
data point, based on scaled
residuals ranging from > | 1.5 | to
> | 2 |. This was the data point
closest to the BMD for all of these
models except the Quantal Linear.
With or without these models, the
BMDLs are sufficiently close (< 3
fold), so the model with the
lowest AIC was selected (Log-
Probit).
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
27.5
Weibull
0.259
342.81
24.5
14.9
Multistage 2°
Multistage 3ob
Multistage 4°°
Multistage 5od
Multistage 6oe
0.177
344.14
20.6
13.6
Quantal-Linear
0.230
343.25
16.0
12.9
a Selected model in bold; scaled residuals for selected model for doses 0, 5, 10,25, 50, 100, and 200 ppm were 0.38, -0.08, -
0.18, -1.16, 1.08, 0.22, -1.02, respectively.
b For the Multistage 3° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 2° model.
c For the Multistage 4° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 3° model.
d For the Multistage 5° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 4° model.
e For the Multistage 6° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 5° model.
LogProbit Model, with BMR of 5% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the BMDL
LogProbit
0.0
0.5
0.4
0.2
0.1
0
0
50
100
150
200
dose
FigureApx H-2. Plot of Incidence by Applied 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 662 of 803
-------
The form of the probability function is: P[response] = Background + (1-Background) *
CumNorm(Intercept+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
Default Initial
Parameter Values
background
0.0281182
0.0338983
intercept
-5.1238E+00
-5.2930E+00
slope
1
1
Analysis of Deviance Table
Model
Log(likelihood
)
# Param's
Deviance
Test d.f.
p-value
Full model
-165.36
7
Fitted model
-167.36
2
4.00401
5
0.55
Reduced model
-208.64
1
86.5627
6
<0001
AIC: =338.719
Goodness of Fit Table
Dose
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
ChiA2 = 3.78 d.f = 5 P-value = 0.5818
Page 663 of 803
-------
H.l.1.3 BMDS Summary of Mortality - BMR: 1%
Table Apx H-3. Summary of BMD Modeling Results for Mortality from Applied Dose in Selgrade
and Gilmour 201(
); BMR =1% Extra Risk
Model"
Goodness of fit
BMDiPct
(ppm)
BMDLiPct
(ppm)
Basis for model selection
/j-valuc
AIC
Gamma
0.292
342.35
8.52
3.22
All models provided adequate
overall fit to the data except for
the Probit and Logistic models
Dichotomous-Hill
0.563
340.91
19.1
7.62
Logistic
0.0074
351.35
10.2
8.35
(based on the yl goodness-of-fit
p-value). However, The Quantal
Linear, Multistage, Weibull,
Gamma and Log-Logistic models
all showed poor fit at the 25 ppm
data point, based on scaled
residuals ranging from > | 1.5 | 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 of BMDLs is indicative of
model dependence 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 Log-
Probit, was selected.
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 3°°
Multistage 4od
Multistage 5°e
Multistage 6of
0.177
344.14
4.27
2.66
Quantal-Linear
0.230
343.25
3.14
2.53
a Selected model in bold; scaled residuals for selected model for doses 0, 5, 10,25, 50, 100, and 200 ppm were 0.38, -0.08, -
0.18, -1.16, 1.08, 0.22, -1.02, respectively.
b The Multistage 2° model may appear equivalent to the Multistage 3° model, however differences exist in digits not displayed
in the table. This also applies to the Multistage 4° model. This also applies to the Multistage 5° model. This also applies to the
Multistage 6° model.
c The Multistage 3° model may appear equivalent to the Multistage 2° model, however differences exist in digits not displayed
in the table.
d For the Multistage 4° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 3° model.
e For the Multistage 5° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 4° model.
f For the Multistage 6° model, the beta coefficient estimates were 0 (boundary of parameters space). The models in this row
reduced to the Multistage 5° model.
Page 664 of 803
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LogProbit Model, with BMR of 1% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the BMDL
Log Pro bit
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
50
100
150
200
FigureApx H-3. Plot of Incidence by Applied 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 + (1-Background) *
CumNorm(Intercept+Slope*Log(Dose)),where CumNorm(.) is the cumulative normal distribution
function
Slope parameter is restricted as slope >= 1
Benchmark Dose Computation.
BMR =1% Extra risk
BMD = 16.4027
BMDL at the 95% confidence level = 13.9135
Parameter Estimates
Variable
Estimate
Default Initial
Parameter Values
background
0.0281182
0.0338983
intercept
-5.1238E+00
-5.2930E+00
slope
1
1
Analysis of Deviance Table
Model
Log(likelihood
)
# Param's
Deviance
Test d.f.
p-value
Full model
-165.36
7
Fitted model
-167.36
2
4.00401
5
0.55
Reduced model
-208.64
1
86.5627
6
<0001
AIC: =338.719
Page 665 of 803
-------
Goodness of Fit Table
Dose
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
ChiA2 = 3.78 d.f = 5 P-value = 0.5818
Page 666 of 803
-------
H.1.2 BMDS Wizard Output Report - Number of Mice Infected
The benchmark dose (BMD) modeling of dichotomous data were conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version 1.11). All reasonably 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 10% extra risk were used in the
BMD modeling, per technical direction. Adequacy of model fit was judged based on the x2 goodness-
of-fit p-value (p > 0.1), magnitude of scaled residuals, and visual inspection of the model fit.
All models except for the Probit and Logistic provided adequate overall fit to the data, based on the %2
goodness-of-fit p-value (p > 0.1). Among the remaining models, the Quantal Linear, Multistage,
Weibull, Gamma and Log-Logistic models all showed poor fit at the 25 ppm data point, based on scaled
residuals ranging from > | 1.5 | to > | 2 | . 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 =
P/o, however, the BMDLs are no longer within 3-fold; the results at this BMR show model-dependence.
This reflects the lack of information reasonably 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 Log-
Probit, was selected.
H.l.2.1 BMDS Summary of Infected at 72 hours - BMR - 10%
Table Apx H-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
Model"
Goodness of fit
BMDioPct
(ppm)
BMDLioPct
(ppm)
Basis for model selection
/j-valuc
AIC
Gamma
0.190
23.637
4t34
All models provided adequate fit
to the data (based on the yl
goodness-of-fit p-value), although
a BMDL could not be calculated
for the Dichotomous-Hill model.
The BMDS Wizard recommended
the Probit model because it had
the lowest AIC. BMDs and
BMDLs from all models are well
below the lowest data point and
cannot be considered reliable.
Dichotomous-Hill
0.164
23.965
error**
Logistic
0.428
21.584
g 3g
LogLogistic
0.164
23.965
Probit
0.448
21.445
LogProbit
0.383
21.877
g gg
Weibull
0.189
23.606
44t3-
Multistage 2°
0.202
23.480
US
433
Multistage 3°
0.228
23.267
12 g
4t43-
Quantal-Linear
0.425
21.639
g 55
4t34
a Selected model in bold; scaled residuals for selected model for doses 0, 50,100, and 200 ppm were -0.23, 0.86, -0.82, 0.38,
respectively.
b BMD or BMDL computation failed for this model.
Page 667 of 803
-------
Probit Model, with BMR of 10% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the BMDL
1
0.8
0.6
0.4
0.2
0
0
50
100
150
200
Figure_Apx H-4. 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
H.2 Internal Dose (TotOxMetabBW34)
Benchmark dose (BMD) modeling was conducted with the newest version of EPA's BMD
software (BMDS version 3.1.2) using the internal dose metric median TotMetabBW34 (see [Internal
Dose BMD Modeling Results for Selgrade and Gilmour, 2010. Docket: EPA-HQ-QPPT-2019-05001 for
full results including data for the AUCBld dose metric). All available dichotomous models
(Dichotomous Hill, Gamma, Logistic, Log-Logistic, Probit, Log-Probit, Weibull, and Multistage) were
fit to the incidence data for mortality due to introduced infection in mice following inhalation exposure
to TCE (Selgrade and Gilmour 2010). BMRs of 1%, 5%, and 10% extra risk were used in the BMD
modeling, per technical direction. All models were run using the default parameter restrictions
implemented in BMDS v3.1.2, i.e., Weibull, Gamma - a (power) > 1; Log Logistic, Dichotomous Hill -
slope > 1; Multistage - P > 0; Logistic, Probit, Log-Probit - unrestricted. Adequacy of model fit was
judged based on the x2 goodness-of-fit /rvalue (p > 0.1), magnitude of scaled residuals, and visual
inspection of the model fit.
All models except for the 1-degree Multistage and Logistic models provided adequate overall fit
to the data, based on the %2 goodness-of-fit p-value (p > 0.1). The models with adequate overall fit also
showed adequate fit near the predicted BMD, based on scaled residuals (< | 2 |). BMDLs for the
adequately fit models at BMR = 10%, 5%, and 1% were sufficiently close (within 3-fold), so the model
with the lowest AIC, the Log-Probit, was selected. Using the Log-Probit model, BMD/BMDLs at BMR
= 10%, 5%, and l%were 15.19/12.13, 11.22/8.19, and 6.35/3.84 for median TotMetabBW34,
respectively.
H.2.1 BMDS Wizard Output Summary - Mortality
BMD modeling was performed based on the incidence data from (Selgrade and Gilmour. 2010) after
translating the applied dose/concentration into the internal dose metric of TotMetabBW34 as described
in Appendix J.
Page 668 of 803
-------
Table Apx H-5. Study incidence data based on median internal dose metric
Applied ilusc (ppm)
Tol\k.-l;ibU\V34
\
IncklciKV
0
0
118
4
5
2.127
38
1
10
4.143
39
1
25
9.536
78
2
50
16.839
116
20
100
28.842
78
26
200
47.241
38
19
H.2.1.1 BMDS Summary of Mortality - BMR 10%
Table Apx H-6. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade
and Gilmour 2010; BMR = 10% Extra Risk
Model"
Restriction
BMD
BMDL
Goodness of fit
BMDS
Recommendation
BMDS
Recommendation Notes
/j-valuc
AIC
Dichotomous Hill
Restricted
15.4
12.7
0.6
340.8
Viable - Alternate
Gamma
Restricted
15.2
11.8
0.4
341.2
Viable - Alternate
Log-Logistic
Restricted
15.1
11.8
0.5
340.9
Viable - Alternate
Multistage Degree 6
Restricted
15.6
11.4
0.3
342.6
Viable - Alternate
Multistage Degree 5
Restricted
15.6
11.4
0.3
342.6
Viable - Alternate
Multistage Degree 4
Restricted
15.6
11.4
0.3
342.6
Viable - Alternate
Multistage Degree 3
Restricted
15.6
11.4
0.3
342.6
Viable - Alternate
Multistage Degree 2
Restricted
15.6
11.4
0.3
342.6
Viable - Alternate
Multistage Degree 1
Restricted
9.8
7.9
0.1
348.1
Questionable
Goodness of fit p-value
<0.1
Residual for Dose Group
Near BMD > 2
Weibull
Restricted
14.9
11.4
0.3
341.9
Viable - Alternate
Logistic
Unrestricted
17.6
15.6
0.1
344.8
Questionable
Goodness of fit p-value
<0.1
Log-Probit
Unrestricted
15.2
12.1
0.6
339.8
Viable -
Recommended
Lowest AIC
Probit
Unrestricted
16.4
14.5
0.2
342.9
Viable - Alternate
11 Selected model in bold: scaled residuals for selected model for the dose group near BMD and control dose group were 0.77
ad 0.46, respectively.
Page 669 of 803
-------
LogProbit Model with BMR of 10% Extra Risk for the BMD and 0.95 Lower
Confidence Limit for the BMDL
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 10 20 30 40
Dose
FigureApx H-5. Plot of Incidence by Internal Dose with Fitted Curve for Log-
Probit Model for Mortality from Selgrade and Gilmour 2010; BMR = 10% Extra
Risk
H.2.1.2 BMDS Summary of Mortality - BMR 5%
Table Apx H-7. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade
and Gilmour 2010; BMR = 5% Extra Risk
Model3
Restriction
BMD
BMDL
Goodness of fit
BMDS
Recommendation
BMDS
Recommendation
Notes
/j-valuc
AIC
Dichotomous Hill
Restricted
12.3
8.8
0.6
340.8
Viable - Alternate
Gamma
Restricted
10.5
7.2
0.4
341.2
Viable - Alternate
Log-Logistic
Restricted
10.5
7.3
0.5
340.9
Viable - Alternate
Multistage Degree 6
Restricted
10.4
6.2
0.3
342.6
Viable - Alternate
Multistage Degree 5
Restricted
10.4
6.2
0.3
342.6
Viable - Alternate
Multistage Degree 4
Restricted
10.4
6.2
0.3
342.6
Viable - Alternate
Multistage Degree 3
Restricted
10.4
6.2
0.3
342.6
Viable - Alternate
Multistage Degree 2
Restricted
10.4
6.2
0.3
342.6
Viable - Alternate
Multistage Degree 1
Restricted
4.8
3.9
0.1
348.1
Questionable
Goodness of fit p-value
<0.1
Residual for Dose
Group Near BMD > 2
Weibull
Restricted
9.8
6.6
0.3
341.9
Viable - Alternate
Logistic
Unrestricted
11.2
9.6
0.1
344.8
Questionable
Goodness of fit p-value
<0.1
Log-Probit
Unrestricted
11.2
8.2
0.6
339.8
Viable -
Recommended
Lowest AIC
Probit
Unrestricted
10.3
8.8
0.2
342.9
Viable - Alternate
a Selected model in bold: scaled residuals for selected model for the dose group near BMD and control dose group were -1.25
ad 0.46, respectively.
Page 670 of 803
-------
LogProbit Model with BMR of 5% Extra Risk for the BMD and
0.95 Lower Confidence Limit for the BMDL
0 5 10 15 20 25 30 35 40 45
Dose
FigureApx H-6. Plot of Incidence by Internal Dose with Fitted Curve for Log-
Probit Model for Mortality from Selgrade and Gilmour 2010; BMR = 5% Extra
Risk
H.2.1.3 BMDS Summary of Mortality - BMR 1%
Table Apx H-8. Summary of BMD Modeling Results for Mortality from Internal Dose in Selgrade
and Gilmour 2010; BMR =1% Extra Risk
Model"
Restriction
BMD
BMDL
Goodness of fit
BMDS
Recommendation
BMDS
Recommendation
Notes
/j-valuc
AIC
Dichotomous Hill
Restricted
7.8
3.6
0.6
340.8
Viable - Alternate
Gamma
Restricted
4.8
2.3
0.4
341.2
Viable - Alternate
Log-Logistic
Restricted
4.7
2.5
0.5
340.9
Viable - Alternate
Multistage Degree 6
Restricted
3.8
1.3
0.3
342.6
Viable - Alternate
Multistage Degree 5
Restricted
3.8
1.3
0.3
342.6
Viable - Alternate
Multistage Degree 4
Restricted
3.8
1.3
0.3
342.6
Viable - Alternate
Multistage Degree 3
Restricted
3.8
1.3
0.3
342.6
Viable - Alternate
Multistage Degree 2
Restricted
3.8
1.3
0.3
342.6
Viable - Alternate
Multistage Degree 1
Restricted
0.9
0.8
0.1
348.1
Questionable
Goodness of fit p-value
<0.1
Residual for Dose
Group Near BMD > 2
Weibull
Restricted
3.8
1.9
0.3
341.9
Viable - Alternate
Logistic
Unrestricted
3.0
2.5
0.1
344.8
Questionable
Goodness of fit p-value
<0.1
Log-Probit
Unrestricted
6.4
3.8
0.6
339.8
Viable -
Recommended
Lowest AIC
Probit
Unrestricted
2.8
2.2
0.2
342.9
Viable - Alternate
Page 671 of 803
-------
a Selected model in bold: scaled residuals for selected model for the dose group near BMD and control dose group were -0.13
and 0.46, respectively.
LogProbit Model with BMR of 1% Extra Risk for the BMD and 0.95
Lower Confidence Limit for the BMDL
Dose
FigureApx H-7. Plot of Incidence by Internal Dose with Fitted Curve for Log-
Probit Model for Mortality from Selgrade and Gilmour 2010; BMR =1% Extra
Risk
Page 672 of 803
-------
Appendix I BENCHMARK DOSE MODELING UPDATE FOR
NESTED FETAL DATA FROM (*
BMD modeling of the nested fetal data for cardiac defects from (Johnson et at.. 2003) was done to verify
the BMD modeling results reported in Appendix F.4.2.1 of the EPA 2011 IRIS Toxicological Review
for TCE Appendices (U.S. EPA.! ).
1) BMD modeling was performed using the nested logistic model in BMDS (v3.1.1) with and
without a litter specific covariate to account for intra-litter similarity (litter effects) based on pre-
treatment condition and with and without modeling of intra-litter correlation to account for intra-
litter similarity based on effects during treatment. IRIS also used the nested logistic model with
and without litter specific covariate and intra-litter correlation. Previous modeling from (
EPA... 201 lb) 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 (!_ ^ j LAW was
performed using applied dose and two alternative internal dose metrics based on PBPK modeling
(avg amount of TCE metabolized by oxidation/kg3/4-day and AUC for TCE in blood). The 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 reasonably 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 covariate for 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 the
2011 IRIS Assessment Appendices (U.S. EPA. ) for the nested models are incorrect,
apparently due to a problem with the software used at that time. These results suggested that the
models did not have adequate fit to the data (p = 0.0128). The re-analysis exercise reported in
Appendix F.4.2.1.2 of (U.S. EPA. 201 lb) was performed to show that the p-values were much
higher than indicated in the raw modeling results and that model fit was acceptable. This
approach still relied on the subgrouping of individual litter results but regrouped the litter data
100 times and reported the percentage of times the estimated p-value indicated appropriate
model fit. Calculation of p-values for the nested models in the current version of BMDS follows
a bootstrap methodology similar to that described in Appendix F.4.2.1.2. of the IRIS
appendices. Because the original p-values in presented in (U.S. EPA. 2.01 lb) were incorrect,
comparisons of 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-specific covariate but with intra-litter correlation. This was true for the
current modeling as well.
5) Results from the models without litter-specific covariate, including the best-fitting model,
closely matched the results from the IRIS assessment (see Table Apx 1-1).
6) Results for the models that included the litter-specific covariate differed from the IRIS results,
because a different covariate was used (litter size rather than TWtGn, due to missing data).
Page 673 of 803
-------
7) Model fits (AICs) and BMD/BMDL values are identical (within rounding error) between the
updated modeling results and those reported in (U.S. EPA. ).
Table Apx 1-1. Results for Best-Fitting Model in Comparison to Results
Reported in IRIS ( , Highlighted)
Model
Covariate
Intra-litter
Correlation
Dose Metric
BMR
AIC
p-valued
BMD
BMDL
Nested
Logistic
Not Used
Modeled
Applied Dose3
0.10
243.815
0.665
0.71114
0.227675
243.815
NR
0.711 14
0.227675
0.05
243.815
0.641
0.336856
0.107846
243.815
NR
0.336856
0.107846
0.01
243.815
0.661
0.064649
0.020698
243.815
NR
0.064649
0.020698
T otOxMetabBW 3 4b
0.10
243.816
0.642
0.489388
0.156646
243.815
NR
0.489442
0.156698
0.05
243.816
0.642
0.231816
0.074201
ND
NR
ND
ND
0.01
243.816
0.636
0.04449
0.014241
243.815
NR
0.0444948
0.0142453
AUCCBldc
0.10
243.816
0.656
0.022279
0.00713
243.816
NR
0.0222789
0.00712997
0.05
243.816
0.656
0.010553
0.003377
ND
NR
ND
ND
0.01
243.816
0.656
0.002025
0.000648
243 S 1 h
\R
5
MS 1 7")
a0, 0.00045, 0.048, 0.218 mg/kg-day
bTotal oxidative metabolism scaled by body weight to the 3/4-power: 0, 0.00031, 0.033, 0.15
°AUC of TCE in blood: 0, 0.0000141, 0.00150254, 0.00682727
d p-values from the 2011 IRIS Assessment are not reported because the original values were incorrect.
ND = no data
NR = not relevant; original p-values as calculated by BMDS software in 2011 were incorrect (e.g., p = 0.0129 for 1% BMR
without litter-specific covariate and with intra-litter correlation).
The resulting BMDLs and AICs (a measure of model fit, see Appendix I) agreed with results in the 2011
IRIS Assessment 0, ^ l'P \ 20 l I h). However, the p-value of = 0.661 from the updated BMDS nested
model run is significantly improved on the improperly calculated p values from (U.S. EPA. 201 lb),
confirming strong model fit.
Page 674 of 803
-------
Appendix J PBPK MODELING UPDATES FOR
REPRESENTATIVE ACUTE AND CHRONIC
ENDPOINTS
J.l Derivation of Internal Dose Metric Results for (Selgrade and
J.l.l Methods
MCSim (v5.6.6) was used to sample from the joint posterior distributions for the PBPK model [PBPK
ModelandReadMe (zipped). Docket: EPA~H()~()PPT~2019~0S00~\ parameters and Python (v3.6.5) was
used for all post processing and analysis of MCSim output. For each exposure simulation, desired
percentiles were reported for each internal dose metric: TotMetabBW34 and AUCCBld.
The PBPK model translated the external applied concentration (ppm) from (Selgrade and Gilmour.
2010) to a corresponding internal dose metric (TotMetabBW34 and AUCCBld). These two metrics were
selected as the primary and alternative dose metrics for this endpoint under the assumption that the
metabolic contribution to this endpoint matches that for other immune endpoints (see (U.S. EPA. 2 )
and Table 3-11). Internal dose metric values were output as predicted 1st, 5th, 10th, 50th, 90th, 95th, and
99th percentiles for mouse. The median (50th percentil values) were then subject to BMD modeling
(Appendix H.2 and [.Internal Dose BMD Modeling Results for Selgrade and Gilmour, 2010. Docket:
EPA -HO-QPPT-20194)5001).
Exposure parameters:
Inhalation exposure
Dose concentrations (ppm): [5, 10, 25, 500, 200]
Inhalation duration: 3 hours
Sex: Female
Species: Mouse
Body weight: 0.025 kg (average Female CD1 mouse at 5-6 weeks)
Internal dose metrics: TotMetabBW34 and AUCCBld
J.1.2 Results
The modeling results for the analysis of cumulative mortality following exposure to TCE and S.
zooepidemicus infection in (Selgrade and Gilmour. 20 i 0) are described in this section below.
• Predictions of mouse internal dose metrics utilized the female mouse-specific joint posterior
parameter distributions from the TCE PBPK model.
In (Selgrade and Gilmour. ), individual mice were exposed to increasing concentrations of TCE
through inhalation and subsequently infected with S. zooepidemicus. Selgrade and Gilmour observed a
dose-dependent effect on cumulative mortality following exposure to TCE. Therefore, EPA utilized
study-matched exposure variables and the mouse-specific parameters of the TCE PBPK model to predict
the corresponding internal dose metrics for each exposure reported in the study.
Page 675 of 803
-------
Table Apx J-l. Selected percentiles for TotMetabBW34 and AUCCBld for female mouse simulations
Internal Dose
Metric
Route
Dose
(ppm)
mean
SD
1.00%
25.00%
50.00%
75.00%
99.00%
TotMetabB W34 1.1
Inhalation
5
2.29423 1
1.032454
0.655835
1.528783
2.126685
2.865015
5.503253
TotMetabBW34 2.1
Inhalation
10
4.437913
2.033409
1.22793
2.93177
4.143145
5.56502
10.89413
TotMetabBW34 3.1
Inhalation
25
10.24195
4.90276
2.641508
6.67256
9.535745
12.81168
25.25738
TotMetabBW34 4.1
Inhalation
50
19.99376
9.430442
5.518223
11.73308
18.2659
23.6544
49.59246
TotMetabBW34 5.1
Inhalation
100
32.563
17.17391
7.451471
19.9501
28.8424
41.81023
85.19594
TotMetabBW34 6.1
Inhalation
200
54.27246
29.99192
12.51255
32.71683
47.2414
68.7213
148.7853
AUCCBld 1.1
Inhalation
5
0.310672
0.108683
0.13889
0.234156
0.288099
0.367049
0.63204
AUCCBld 2.1
Inhalation
10
0.636832
0.22911
0.278085
0.474244
0.589897
0.757263
1.31563
AUCCBld 3.1
Inhalation
25
1.681136
0.63107
0.700461
1.221415
1.55746
2.01261
3.574621
AUCCBld 4.1
Inhalation
50
4.118071
1.633029
1.667898
2.56827
3.79901
4.284455
9.310272
AUCCBld 5.1
Inhalation
100
7.710392
3.010024
2.946904
5.549918
7.21414
9.32249
16.86953
AUCCBld 6.1
Inhalation
200
17.05727
6.84398
6.371642
12.23283
15.8771
20.6827
38.34951
Median (50th percentile) values were used for BMD modeling; SD = Standard Deviation
J.2 Derivation of Human Equivalent Concentrations/Doses for Best
Overall Acute and Chronic Non-Cancer Endpoints
EPA utilized the PBPK model to obtain Human Equivalent Concentrations (HECs) and Human
Equivalent Doses (HEDs) for (Selgrade a nour. 2010) in the same manner as they were derived for
other endpoints in (U.S. EPA.: ). Additionally, EPA utilized the PBPK model to derive PODs
specific to occupational scenarios for the best overall acute and chronic non-cancer endpoints from
(Selgrade and Gitmour. 2010) and (Kelt et at.. 2009). respectively (Section 3.2.5.4.1).
J.2.1 Methods
BMD modeling results for the mouse (Appendix H.2 and [.Internal Dose BMD Modeling Results for
Selgrade and Gilmour, 2010. Docket: EPA-HQ-QPPT-2019-0500J) were used to predict human
equivalent concentrations (HEC) and human equivalent doses (HED) based on the internal dose point-
of-departure (PoD) derived during the BMD modeling step. HEC/HED calculations occurred for
multiple exposure scenarios and idPODs as outlined below:
Acute (single dose) and chronic (repeat dosing for 100 weeks)
idPOD for (Selgrade and Gilmour. 2010) endpoints (TotMetabBW34 and AUCCBld)
idPOD for (Kelt et at. 2009) endpoints (TotMetabBW34)
Respiratory rates (QM) assuming default and occupational (1.25 m3/hr) respiration
Setpoint simulations of the human-specific joint posterior parameter distributions were run spanning a
large range of possible inhalation concentrations and doses. For the Selgrade idPOD's, we assumed an
acute exposure and calculated the HEC/HEDs following a 24-hour simulation. The average daily
HEC/HED for the Keil idPOD was determined using a 100-week (700 day) simulation. Using
interpolation, the HEC and HED were determined from the simulated 99th and 50th percentile for each
internal dose metric.
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Determination of Occupational Respiration Rate
EPA assumes a respiration rate of 1.25 m3/hr for occupational scenarios based on light activity levels
from Table 6-43 in (U.S. EPA. 2011c). The TCE PBPK model assumes a respiratory dead volume of
30%. In order to translate respiration rate (QM) to alveolar ventilation rate (QP) the following equation
was used:
QP = QM* 0.7
Using this transformation, the 'QPmeas' input to the model for occupational alveolar ventilation was
0.875 m3/hr or 875 L/hr. FigureApx J-l illustrates the difference between the default respiration rate
probability distribution (median value of 0.64 m3/hr) vs. the single value (1.25 m3/hr) for occupational
respiratory rate. The absence of variability in the respiration rate for the occupational scenario reduced
the overall uncertainty in the HEC/HED calculations. At higher HEC percentiles, the default respiratory
rate approaches the occupational rate, resulting in reduced differences among HEC values.
16
14
L2
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.5 L0 1.5 2.0
Respiratory Rate (m2/hr)
Figure Apx J-l. Distribution of default (resting) respiration rates compared to occupational
respiratory rate.
J.2.2 Results
Using the internal dose point-of-departure (idPOD) for (Selgrade and Gilmour. 2010). EPA first
calculated the HECs and HEDs for the 99th and 50th percentile outputs for each dose metric's idPOD at
default parameters of resting respiration rate and continuous exposure. EPA also calculated the
corresponding HECs and HEDs for occupational scenarios using the occupational respiration rate for
and 8hr/day exposure duration. For the (Keil et al.. 2009) chronic endpoint, EPA compared the HEC50/99
and HED50/99 results across default and occupational input parameters conditions following both 8 hours
and 24 hours of exposure. Below is a summary of the idPODs used in this section of the analysis:
(Selgrade and Gilmour. 2010) TotMetabBW34: 3.84 mg TCE metabolized/d/kg3/4
(Selgrade and Gilmour. 2010) AUCCBld: 0.3853 mg TCE-hr/L
(Keil et al.. 2009) TotMetabBW34: 0.139 mg TCE metabolized/d/kg3/4
Table Apx J-2 presents the tabulated HEDs and HECs for each endpoint.
default
occupational
Page 677 of 803
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TableApx J-2. Human equivalent concentrations and human equivalent doses for the Selgrade and Keil endpoints under both
default and occupational respiratory conditions.
Studv
Selgrade and Gilmour, 2010
Keil ct al., 2009
Exposure
scenario
Acute
Chronic
Dose metric
used
TolMelabBW34
AUCBld
TolMclabBW34
idPOD
Exposure
duration
3.840
0.3853
0.139
8h single-day
24h single-day
8h single-day
24h single-day
8h repealed
24h repealed
Respiration
Default
Occupational
Default
Occupational
Default
Occupational
Default
Occupational
Default
Occupational
Default1
Occupational
HEC99 (ppm)
2.959
2.343
0.973
0.792
1.735
1.663
0.617
0.585
0.100
0.083
0.033
0.027
HEC50 (ppm)
8.242
4.458
2.841
1.535
2.936
2.648
1.032
0.926
0.276
0.153
0.092
0.051
HED99
(mg/kg/d)
1.331
1.335
1.336
1.338
1.145
1.282
1.236
1.357
0.048
0.048
0.048
0.048
HED50
(mg/kg/d)
1.355
1.380
1.362
1.385
9.066
9.024
12.134
11.794
0.048
0.049
0.048
0.049
'Values presented in (U.S. EPA. 20.1. lei and Section 3.2.5.3.2. They are presented here for comparison to occupational values.
Page 678 of 803
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13
14
15
Appendix K META-ANALYSIS FOR CANCER
K.1 Study Screening and Selection
All epidemiologic studies included in the U.S. EPA 2011 IRIS assessment of TCE (Appendix C, (U.S.
EPA. 201 lb) were considered to be informative and carried forward for meta-analysis. Informative
epidemiologic 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 systematic literature search.
Studies examining only other cancer types were excluded from consideration.
K.1.1 Data Quality and Inclusion/Exclusion Criteria Screening
Relevant studies were evaluated for data quality and were additionally screened through
inclusion/exclusion criteria developed based on the criteria established in the 2011 IRIS assessment
(Appendix C, (U.S. EPA.: )), as described in TableApx K-l. Results of this criteria screening are
presented in Table Apx K-2.
Table Apx K-l. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies
Identified in EPA's Literature Search
Inclusion C rilcriit
Kxclusion ( ritcriii
Study Design
Cohort and case control studies.
Geographic-based, ecological, or proportionate mortality ratio
(PMR) study design.
Participant Selection
Adequate selection in cohort studies of exposure and
control groups and of cases and controls in case-
control studies.
Inadequate selection in cohort studies (exposed and control
groups were not similar, and differences were not controlled for
in the statistical analysis). Controls were drawn from a very
dissimilar population 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
biomarkers, 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, individual biomarkers, water distribution models, or
industrial hygiene data indicating a high probability of TCE use
(cohort studies).
Reports as least 2 levels of exposure (e.g.,
exposed/unexposed).
The range and distribution of exposure are not adequate to
determine an exposure-response relationship. No description is
provided on the levels or range of exposure.
Outcome Assessment
Evaluation of incidence or mortality from kidney
cancer, liver cancer, or NHL. RR estimates and
corresponding CIs (or information to allow
calculation).
Data for non-cancer health outcomes or incidence or mortality
reported for cancers other than kidney, liver, or NHL. All
hemato- and lymphopoietic cancer reported as broad category.
Statistical Power (sensitivity)
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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16
17 TableApx K-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based
18 on Inclusion/Exclusion Criteria
Studios recommended lor inclusion in (inanlitaliM' mela-analYsis:
Studies
Primary reason(s)
(Bove et al. 2014a)
(Bove et al., 20Mb)
(Buhagen et al.. 2016)
(Christensen et al. 2013)
(Cocco et al. 2013)
(Hansen et al, 2013)
(LiDworth et al. 2011)
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-control studies; 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 distribution models, or obtained from
subjects using questionnaire (case-control studies); RR
estimates for kidney cancer, liver cancer, or NHL with
confidence intervals
(Purdue et al. 2016)
(Silver et al. 2014)
(Vlaanderen et al. 2013)
Studios NO T recommended lor inclusion in uuanlilalne nio(:i-iin:ilvsis:
Studies
Primary reason(s)
(Alanee et al. 2015)
Weakness with respect to analytical study design (i.e.,
geographic-based, ecological orPMR design).
(Alanee et al. 2015)
TCE exposure potential not assigned to individual subjects
using JEM, individual biomarkers, water distribution models,
or industrial hygiene data from other process indicating a high
probability of TCE use (cohort studies).
(Bassie et al.. 2016)
(Ruckart et al.. 2013)
Examined noncancer health outcomes or cancer incidence or
mortality for cancers other than kidney, liver, or NHL. All
hemato- and lymphopoietic cancer reported as broad category.
(Bahr et al. 2011)
EPA reviewer scored the study as Unacceptable (Rationale:
Repeated examples of poor quality, study design and execution
and ignorance of potential biases that went unmentioned even
in the discussion indicate inexperience and poor quality
control).
19 K.1.2 Screening results
20 Data quality and inclusion/exclusion criteria screening identified ten studies suitable for use in meta-
21 analysis. Of these, there were nine new studies with suitable informative data on the association of
22 exposure to TCE and NHL (Bove et at... 2014a; Bove et at. 1014b I hristensen et at.. 20), Cucco et at..
23 2013; Hansen et at... 2013; Lipworth et ol JO 1 I i'urdue -n ot l • Silver et at.. JO I !, \ Uianderen et
24 at.. 2013). eight new studies with informative data for kidney cancer (Bove et at.. 2014a; Buhagen et at..
25 2016; Christensen et at.. 2013; Hansen et at.. 201
-------
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
with results from previous studies identified in the 2011 IRIS assessment ( lie). 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 TableApx K-3). This resulted in a smaller set of data included in the meta-analysis as compared to
the total list of studies.
K.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 earlier case-control studies of NHL including (Cocco et al... 2010).
(Miligi et al.. 2006). and (Purdue et al.. 2011). 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 (Boice et al.. 1999) and (Ch.risten.sen. et al. 2013) updated an earlier
population-based case-control study (Siemiatvcki. 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
bionionitored workers (Hansen et al.. 20 i 3). However, both studies were retained in the analysis because
any overlap would have been 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 K-3.
Table Apx K-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the
2011 IRIS Assessment or EPA Literature Search
Study renewed
Oilier assessed studies with p;irlicip;inls from (lie siinie cohort
2011 IRIS Assessment
(Anttila et al. 1995") (Finland only)
Included in (Hansen et al.. 2013)
(Axelson et al.. 1994) (Sweden onlv)
Included in (Hansen et al.. 2013)
(Boice et al.. 1999)
Updated in (LiDworth et al.. 2011)
(Boice et al.. 2006)
(Zhao et al.. 2005) (partial)
(B riming et al., 2003)
None
(Cliarbotel et al.. 2006)
None
(Cocco et al.. 2010)
Included in (Cocco et al.. 2013)
(Dosemeci et al.. 1999)
None
(Greenland et al.. 1994)
None
(Hansen et al.. 2001) (Denmark onlv)
(Raasctiou-Nielsen et al.. 2003) (partial): Included in (Hansen et al.. 2013)
(Hardell et al.. 1994)
None
(Miligi et al., 2006)
Included in (Cocco et al.. 2013)
(Moore et al.. 2010)
None
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65
66
67
68
69
70
71
72
73
74
75
76
Study renewed
Oilier assessed studies with participants from the same cohort
(Morgan et aL 1998)
None
(Nordstrom et aL, 1998)
None
(Pens son and Fredrikson, 1999)
None
(Pescli et aL 2000)
None
(Purdue et aL 2011)
Included in (Cocco et aL. 2013)
(Raaschou-Nielsen et aL. 2003)
Partial overlap with (Hansen et aL. 2001)
(Radican et aL, 2008)
None
(Siemiatvcki, 1991)
Undated in (Christensen et aL. 2013)
(Wane et aL, 2009)
None
(Zhao et aL, 2005)
(Boice et aL. 2006) (partial)
New Studies Identified in EPA Literature Search
(Bove et al., 2014a)
None
(Bove et aL, 2014b)
None
(Buhagen et aL, 2016)
None
(Cocco et aL, 2013)
(Cocco et aL. 2010); (Miligi et aL. 2006); (Purdue et aL. 2011)
(Christensen et aL. 2013)
(Siemiatvcki. 1991)
(Hansen et aL. 2013)
(Hansen et aL. 2001); (Anttila et aL. 1995); (Raaschou-Nielsen et aL. 2003)
(partial)
(LiDwortli et aL. 2011)
(Boice et aL. 1999)
(Purdue et aL. 2016)
None
(Silver et aL. 2014)
None
(Vlaanderen et aL, 2013)
None
K.2 Meta-Analysis Methods and Results
K.2.1 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 reasonably 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 standardized mortality 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 exposed to trichloroethylene or 2) comparisons of
groups with the highest and lowest level of exposure to trichloroethylene, in that order. For NHL,
estimates of association for the most highly exposed group were also abstracted, when they were
reasonably available. For each comparison, the most fully adjusted risk estimate was selected.
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95
96
97
98
99
100
101
102
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104
105
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107
108
109
110
111
112
113
114
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116
117
118
119
120
121
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 IRIS assessment (s H' \ r< u I e) 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 K.2.3.
If the standard error (SE) of RR was reported in the publication, the standard error of ln(RR) was taken
as ln(SE). If SE was not reported and the CI was reasonably symmetric around the point estimate (< 5%
difference between upper and lower half CI), it was approximated as (ln(upper bound CI)-ln(lower
bound CI))/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)1/2, where O is the observed number of cases
(Greenland & O'Rourke, 2008). If RR was 1 or >1, SE was estimated from the upper half CI, as
(ln(upper bound CI) - ln(RR))/l .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.
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% CIs. Heterogeneity was assessed using the I2 statistic
(Higgins et al. 2.003) 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 Greenlar 9). 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 CIs displayed on forest plots were
estimated by the software and may differ slightly from those reported in the original publications.
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 turn 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 K.2.4.
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142
143
144
145
146
147
148
K.2.2 Results
K.2.2.1 Initial Meta-Analyses
Non-Hodgkin lymphoma
In the fixed-effects model for NHL (Figure Apx K-l), the meta-RR for overall exposure to TCE was
1.02 (95% CI 0.97-1.08) with moderate heterogeneity between studies (I2 38.4%, p 0.05). The large
study by Vlaanderen et al. (2013) was heavily weighted in the fixed-effects model. Fitting a random-
effects model (Figure Apx K-2) to the same set of studies reduced the weight of the (Vlaanderen et al..
2013) study and gave a meta-estimate of 1.14 (95% CI 1.00-1.30).
In the 2011 TCE meta-analysis of NHL, there was some indication of heterogeneity (I2-value was 26%,
suggesting low-to-moderate heterogeneity). Little to no heterogeneity was found for kidney or renal
cancers. Additional analyses focused on the studies with the highest exposure, because if TCE exposure
increases the risk of NHL, the effects should be more apparent in the highest exposure groups. Analysis
showed that the summary effect estimate of the highest exposed groups 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 identified for 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-effects models comparing the highest to
lowest exposure groups in each study also weighted the (Vlaanderen 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
K-3 and Figure Apx K-4). Extracted RR estimates and confidence intervals from each NHL study are
presented in TableApx K-7, TableApx K-8, and TableApx K-9.
Figure Apx K-l. Fixed-effects model, overall association of NHL and exposure to TCE.
Study
ID
Bove 2014a
Bove 2014b
Hansen 2013
Lipworth 2011
Silver 2014
Vlaan-deren 2013
Christensen 2013
Cocco 2013
Greenland 1994
Morgan 1998
Raaschou-Nielsen 2003
Radican 2008
Zhao 2005
Hardell 1994
Nordstrom 1998
Persson 1999
Purdue_2011
Wang_2009
Overall {l-squared = 38.5%, p = 0.049}
%
RR (95% CI)
Weight
1.15(0,56, 2.38)
0.63
0.33 (0.14t 0.80)
0.43
1.21 (0.85, 1.72)
2.66
1.02(0.54,1.91)
0.84
0.87(0.56, 1.34)
1.78
0.97(0.91, 1 04)
74.48
1.20(0.37, 3.89)
0.24
1.40(0,97, 2.04)
2.38
0.76 (0.24, 2.42)
0.25
1.01 (0,53, 1.94)
0.78
1.24 (1.01, 1.52)
7.96
1.36 (0.77, 2.40)
1.03
1.44(0.90, 2.30)
1.51
— 7.17(1.26,40.79)
0.11
1,50 (0.69, 3.26)
0.55
1.20 (0.55, 2.63)
0.54
1.40(0.81,2.42)
1.10
1.20(0.85, 1.70)
2.75
1.02(0.97, 1.09)
100.00
~1~
10
Page 684 of 803
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149 FigureApx K-2. Random-effects model, overall association of NHL and exposure to TCE.
150
151
152
Study
ID
Bove 2014a
Bov© 2014b
Hansen 2013
Lipworth 2011
Silver 2014
Vlaanderen 2013
Christensen 2013
Cocco 2013
Greenland 1994
Morgan 1998
Raaschou-NieJsen 2003
Radican 2008
Zhao 2005
Hardell 1994
Nordstrom 1998
Persson 1999
Purdue_2011
Wang_2009
Overall (l-squared = 38.5%, p = 0.049)
NOTE: Weights are from random effects analysis
RR (95% CI)
%
Weight
~T
1.15(0.56,
2.38)
2.84
0.33 (0.14,
0.80)
2.01
1.21 (0.85,
1.72)
8.40
1.02 (0.54.
1.91)
3.64
0.87 (0.56,
1.34)
6.46
0.97 (0.91,
1.04)
20.23
1.20 (0.37.
3.89)
1.18
1.40 (0.97,
2.04)
7.86
0.76 (0.24,
2.42)
1.22
1.01 (0.53,
1.94)
3.40
1.24 (1.01,
1.52)
14.09
1.36 (0.77,
2.40)
4.29
1.44 (0.90,
2.30)
5.74
- 7.17(1.26,
40.79)
0.56
1.50 (0.69,
3.26)
2.52
1.20 (0.55,
2.63)
2.48
1.40 (0.81,
2.42)
4.51
1.20 (0.85,
1.70)
8.57
1.14(1.00,
1.30)
100.00
Figure Apx K-3. Fixed-effects model, association of NHL and high exposure to TCE.
Study
ID
Hansen 2013
Vlaander«n 2013
Chiislenaen 2013
Cocco 2013
Morgan 1998 —
naaschou-Nielseri 2003
Radican 2008
Zhao 2005
Purdue 2011
Wang_2009
Owall
3.30(1.09.10.01)
0-90
2.20 (
1.0® (
0.90. 5,39)
0.93,1.15)
1.38
100,00
153
154
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158
159
160
161
162
163
164
165
166
167
FigureApx K-4. Random-effects model, association of NHL and high exposure to TCE.
Study
10
Weight
Hansen 2013
Vlaanderen 2013
Chrtstenseo 2013
COCCO2013
Mwgan 1998 -
Raaschou-N»elsen 2003
Radican 2008
Zhao 200S
Purdue 2011
Wang_2009
Overall (l-squared = 50.7%. p = 0.032)
NOTE: Weights are irom random effects analysis
1
o
0.66 <0.25,1.75)
7.12
0.95 <0.85,1.07)
26.85
1.00 <0.2B, 3.44)
4.95
2.20 <0 71,6 87)
566
0.81 <0.10, 6.47)
1.97
1.60 <1.12, 2.29)
20.08
1.40 <0.71,2.76)
11.48
1.30 <0.52, 3.24)
7.89
3.30 <1.09, 10.01)
5.88
2.20 <0.90, 5.39)
8.11
1.33 <0.98, 1.80)
100.00
Kidney Cancer
For kidney cancer, the fixed effects model (Figure Apx K-5) gave a meta-RR of 1.06 (95% CI 1.00-
1.11) for overall exposure, with moderate, statistically-significant heterogeneity (I2 41.1%, p 0.04). As
for NHL, the study of (Vlaanderen et al.. 2013) was heavily weighted. In the random-effects model
(Figure Apx K-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 TableApx K-10 and TableApx K-l 1.
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169
FigureApx K-5. Fixed-effects model, overall association of kidney cancer and
exposure to TCE.
170
171
172
173
174
Study
ID
Bove 2014a
Buhagen 2016
Hansen 2013
Llpworth 2011
Silver 2014
Vlaanderen 2013
Chrislensen 2013
Purdue 2016
Greenland 1994
Morgan 1998
Radican 2008
Zhao 2005
Bruning 2003
Charbotel 2006
Dosemeci 1999
Moore 2010
Pesch 2000
Raaschou-Nielsen 2003
Overall (l-squared = 41.1%, p = 0,036)
%
RR {95% CI)
Weight
1.52 (0.64, 3.60)
0.35
1.70 (0.94, 3.06)
0.76
1.04 (0.73, 1.48)
2.11
0.85 (0.33, 2.18)
0.30
1.24(0.87, 1.76)
2.11
1.00 (0.94, 1.06)
75,79
0.90 (0.36, 2.21)
0.32
0.80 (0.41, 1.56)
0.59
0.99 (0.30, 3.29)
0.18
1.14(0.51,2.58)
0.40
1.18(0.47, 2.95)
0.31
1.72 (0.38, 7.85)
0.11
2.47 (1.36, 4.49)
0.73
1.88 (0.89, 3.97)
0.47
1.30(0.89, 1.89)
1.87
2.05(1.13, 3.73)
0.73
1.24(1.03, 1.49)
7.72
1.20 (0.96,1.50)
5.16
1.06 (1.00,1.11)
100.00
Figure Apx K-6. Random-effects model, overall association of kidney cancer and
exposure to TCE.
175
176
177
Study
ID
Bove 2014a
Buhagen 2016
Hansen 2013
Lipworth 2011
Silver 2014
Vlaanderen 2013
Chrislensen 2013
Purdue 2016
Greenland 1994
Morgan 1998 —
Radican 2008 —
Zhao 2005
BrQnlng 2003
Charboiel 2006
Dosemeci 1999
Mooce 2010
Pesch 2000
Raaschou-Nielsen 203
Overall {l-squared = 41.1 %, p = 0.036)
NOTE: Weights are from random effects analysis
~r
o
%
RR (95% CI)
Weight
1,52 (0.64, 3,60)
1,97
1.70 (0.94, 3.06)
3.83
1.04(0.73,1.48)
8.09
0.85 (0.33, 2.18)
1.68
1.24(0,87,1.76)
8.09
1,00(0,94,1.06)
20,59
0.90(0,36, 2.21)
1.82
0,80(0.41, 1,56)
3.11
0,99 (0,30, 3.29)
1.06
1.14(0.51,2,58)
2.19
1.18(0.47, 2.95)
1.76
1.72 (0.38, 7.85)
0.68
2.47 (1.36, 4.49)
3.73
1.88 (0,89, 3.97)
2.54
1,30(0,89, 1.89)
7.50
2.05 (1.13, 3.73)
3.73
1,24(1,03, 1.49)
14.82
1.20(0.96,1.50)
12.83
1.22 (1.07,1.38)
100.00
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180
181
182
183
184
185
186
187
188
189
Liver cancer
Fixed- and random-effects models 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 K-7 and Figure_Apx
K-8). Heterogeneity was moderate and not statistically significant (I2 36.5%, p 0.107). Extracted RR
estimates and confidence intervals from each liver cancer study are presented in TableApx K-12 and
TableApx K-13.
Figure Apx K-7. Fixed-effects model, overall association of liver cancer and
exposure to TCE.
Weight
Bove 2014a
Hansen 2013
Llpworth 2011
Sliver 2014
Vlaandefen 2013
Chrlslensen 2013
Bo*ce 2006
Greenland 1994 —
Morgan 1998
Radican 2008
Raaschou-Nialsen 2003
Overall (l-squared = 36.5%, p = 0.107)
o
0.86 (0.37, 2.00)
1.83 (1,29, 2,69)
0.83(0,36,1.92)
0.99(0.50,1.97)
1.00(0.90,1.11)
1.10(0.13, 9.50)
1.2B (0.48, 3.41)
0.54 (0.11, 2.64)
1.48(0.56, 3.91)
1.12(0.57, 2.19)
1.35(1.04,1.75)
1.08 (0.99,1.18)
1,15
6.79
1.15
1.74
72.94
0.18
0.85
0.32
0.87
1.81
12.21
100.00
Page 688 of 803
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190
191
FigureApx K-8. Random-effects model, overall association of liver cancer and
exposure to TCE.
Bove 20i4a
Hansen 2013
Llpworth 2011
Sliver 2014
Vlaanderen 2013
Chnslensen 2013
Bo»cc 2006
Greenland 1994
Morgan 1998
Radiean 200B
Raaschou-Nielsan 2003
Overall (l-squarad = 36.5%, p = 0.107)
NOTE; Weights are from random effects analysis
$
RR {95% CI)
%
Weight
0.66 (0-37, 2.00)
4.49
1.83 (1,29, 2.59)
16.23
0.83(0.36, 1.92)
4.49
0.99(0.50,1.97)
6.35
1.00(0.90,1.11)
31.50
1.10(0.13, 9,50)
0.77
1.28(0.46, 3.41)
3.43
0.54(0.11.2.64)
1.39
1.48(0.56. 3.91)
3.50
1.12(0.57, 2.19)
6.57
1.35 (1.04,1.75)
21.28
1.18(0.96. 1.43)
100.00
192
193
Page 689 of 803
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194 K.2.2.2 Sensitivity analyses
195 Removal of Vlaanderen et al. (2013)
196 In analyses of influential observations, the study of (Vlaanderen et al.. 2013) strongly influenced the
197 meta-RRs for all three cancers (TableApx K-4, TableApx K-5, and TableApx K-6). No other single
198 study had an appreciable impact on the overall association. Further meta-analyses were conducted to
199 characterize the sensitivity of the results to the influence of that study.
200
Table Apx K-4. Analysis 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
1.09
Hansen et al. 2013
1.02
0.96
1.08
Lipworth et al. 2011
1.02
0.97
1.09
Silver etal. 2014
1.03
0.97
1.09
Vlaanderen et al. 2013
1.20
1.07
1.34
Christensen et al. 2013
1.02
0.97
1.08
Cocco et al. 2013
1.02
0.96
1.08
Greenland et al. 1994
1.02
0.97
1.09
Morgan etal. 1998
1.02
0.97
1.09
Raaschou-Nielsen 2003
1.01
0.95
1.07
Radican et al. 2008
1.02
0.96
1.08
Zhao et al. 2005
1.02
0.96
1.08
Hardell et al. 1994
1.02
0.96
1.08
Nordstrom et al. 1998
1.02
0.96
1.08
Persson and Fredrikson 1999
1.02
0.97
1.08
Purdue etal. 2011
1.02
0.96
1.08
Wang et al. 2009
1.02
0.96
1.08
Table Apx K-5. Analysis of influential studies: Kidney cancer
Study omitted
Estimate
95% CI
Bove et al. 2014a
1.06
1.00
1.11
Buhagen et al. 2016
1.05
1.00
1.11
Hansen et al. 2013
1.06
1.00
1.11
Lipworth et al. 2011
1.06
1.01
1.11
Silver etal. 2014
1.05
1.00
1.11
Vlaanderen et al. 2013
1.26
1.14
1.40
Christensen et al. 2013
1.06
1.01
1.11
Purdue et al. 2016
1.06
1.01
1.12
Greenland et al. 1994
1.06
1.00
1.11
Morgan etal. 1998
1.06
1.00
1.11
Radican et al. 2008
1.06
1.00
1.11
Zhao et al. 2005
1.06
1.00
1.11
B riming et al. 2003
1.05
1.00
1.11
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204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
Table Apx K-5. Analysis of influential studies: Kidney cancer
Study omitted
Estimate
95% CI
Charbotel et al. 2006
1.05
1.00
1.11
Dosemeci et al. 1999
1.05
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
1.05
1.00
1.11
Table Apx K-6. Analysis of influential studies: Liver cancer
Study omitted
Estimate
95% CI
Bove et al. 2014a
1.09
0.99
1.19
Hansen et al. 2013
1.04
0.95
1.14
Lipworth et al. 2011
1.09
0.99
1.19
Silver etal. 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
Greenland et al. 1994
1.08
0.99
1.19
Morgan etal. 1998
1.08
0.99
1.18
Radican et al. 2008
1.08
0.99
1.19
Raaschou-Nielsen et al.
2003
1.05
0.95
1.16
Meta-RRs for each cancer were re-estimated by omitting that study from the fixed-effects model. For
NHL, omitting the study of (Vlaanderen et at.. 2.013) from the analysis of overall exposure to TCE
(Figure_Apx K-9) substantially reduced between-study heterogeneity (I2 9.7%, 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
(FigureApx K-10), no heterogeneity remained when the (Vlaanderen et at.. 2013) study was omitted (I2
0.0%), p 0.56); the meta-RR for high exposure was 1.53 (95% CI 1.19-1.97). Omitting the study of
(Vlaanderen et at.. 2013) from the model for kidney cancer (Figure Apx K-l 1), gave a meta-RR of 1.26
(95%) CI 1.14-1.40) with no indication of heterogeneity (I2 0.0%, p 0.57). Dropping that study from the
analysis of liver cancer (
Figure_Apx K-12) similarly eliminated the heterogeneity among studies (I2 0.0%, p 0.56) and gave a
meta-RR of 1.34 (95% CI 1.13-1.59). Meta-RR values for all three tissues increased without the
(Vlaanderen et at.. 2013) study and achieved statistical significance.
Page 691 of 803
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221
222
223
224
225
226
227
228
229
FigureApx K-9. Fixed-effects model, overall association of NHL and exposure to TCE, study of
Vlaanderen et al. (2013) omitted.
Study
ID
Bove 2014a
Bove 2014b
Hansen 2013
Lipworth 2011
Silver 2014
Christensen 2013
Cocco 2013
Green land 1994
Morgan 1996
Raaschou-Nielsen 2003
Radlcan 2008
Zhao 2005
Hardell 1994
Nordstrom 1998
Persson 1999
Purdue_2011
Wang_2009
Overall {l-squared ¦ 9.7%, p = 0.340)
%
RR (95% CI)
Weight
1.15 {0.56, 2.38)
2.46
0.33 {0.14, 0.80)
1.67
1,21 {0.85,1.72)
10.41
1.02 {0.54,1.91)
3.29
0.87 {0.56, 1.34)
6.97
1.00 {0.31, 3.24)
0.94
1.40 {0.97, 2.04)
9.35
0,76 {0.24, 2.42)
0.97
1.01 {0.53,1.94)
3.04
1.24 {1.01, 1.52)
31.19
1,36 {0.77,2.40)
4.04
1.44 {0.90, 2.30)
5.91
- 7.17 {1.26, 40.79}
0.43
1.50 {0.69, 3.26)
2,15
1.20 {0.55, 2.63)
2.11
1,40 {0.81,2.42)
4.30
1.20 {0.85,1.70)
10.77
1.20 {1.07,1.34)
100.00
~~r~
10
Figure Apx K-10. Fixed-effects model, association of NHL and high exposure to TCE, study of
Vlaanderen et al. (2013) omitted.
Hansen 2013
Cnrisl
Cocco 2013
Morgan 1898
Raaachou-Welaan 2003
Radican 2006
Zhao 2005
Purdue 2011
Wang 2009
Overall (l-squarsd = 0,0%. p = 0,5!
0.68 (0.25,1.76)
1.00 {0.29. 3.44)
2,30 {0.71.6.67)
0.61 {0.10. 6,47)
1,80 {1.12, 2.29)
1.40 {0.71, 2.78)
1.30(0.52,3.24)
3.30(1.09,10-01)
2,20 {0,90. S.»)
1.SS{1.19. 1,97)
Page 692 of 803
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230
231
232
233
234
235
236
237
238
239
FigureApx K-ll. Fixed-effects model, overall association of kidney cancer and
exposure to TCE, study of Vlaanderen et al. (2013) omitted.
Study
ID
Bove 2014a
Buhagen 2016
Hansen 2013
Lfpworth 2011
Silver 2014
Christensen 2013
Purdue 2016
Greenland 1994
Morgan 1998
Radican 2008
Zhao 2005
Briining 2003
Charbotef 2006
Dosemed 1999
Moore 2010
Pesch 2000
Raaschou-Nielsen 2003
Overall (I-squared = 0.0%, p = 0,566)
O
%
RR (95% Cf)
Weight
1.52 (0.64, 3.60)
1.46
1.70 (0.94, 3.06)
3.13
1.04 (0.73, 1.48)
8.69
0.85 (0.33, 2.18)
1.22
1.24 (0.87, 1.76)
8.69
0,90 (0.36, 2.21)
1.33
0.80 (0.41,1,56)
2.44
0.99 (0.30,3.29)
0.75
1.14(0.51,2.58)
1.64
1.18 (0.47,2.95)
1.29
1.72(0.38, 7.85)
0.47
2.47(1.36, 4.49)
3.03
1.88 (0.89, 3.97)
1.93
1.30 (0.89, 1.89)
7.72
2.05 (1.13, 3.73)
3.03
1.24(1.03, 1.49)
31.88
1.20 (0.96, 1.50)
21.30
1.26(1.14,1.40)
100,00
Figure Apx K-12. Fixed-effects model, overall association of liver cancer and
exposure to TCE, study of Vlaanderen et al. (2013) omitted.
Bova 2014a
Hanson 2013
Lipworth 2011
SHver 2014
Christensen 2013
Bonce 2006
Greenland 1994
Morgan 1996
Radican 2008
Raaschou-Nwlswi 2003
Overall |l*squar«f» 0,0%, p = 0.557)
o
RR <96% CO
%
Weight
0.06 (0.37, 2.00)
4.25
1.03 (1.29, 2.59)
25.09
0.83 (0.36,1.92)
4.25
0.99 (0.50, 1.97)
6.42
- 1.10(0.13,9.50)
0-65
1.28 (0.48, 3.41)
3.14
0.54 (0.11.2.64)
1.20
1.48(0.56, 3.91)
3.21
1.12(0.57, 2.19)
6.68
1.35 (1.04, 1.75)
45.11
1.34 (1.13,1.59)
100.00
Page 693 of 803
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242
243
244
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246
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249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
Stratification by Data Quality
Fixed-effects meta-analyses for each cancer were also stratified by the study quality score assigned in
EPA's review to assess whether the strength of association varied between highest- and lower-quality
studies. In this manner, the meta-RR was compared among studies scoring High in data quality to those
scoring Medium or Low. For NHL (Figure Apx K-13), there was no heterogeneity among studies
scored as high quality (I2 0.0%, p 0.78) and the meta-RR was 1.29 (95% CI 1.04-1.59), while among
studies scored medium or low the meta-RR was 1.01 (95% CI 0.95-1.07) with moderate heterogeneity
(I2 40.0%), p 0.06). Studies of kidney cancer (Figure_Apx K-14) that scored high for data quality gave a
meta-RR of 1.14 (95% CI 0.85-1.53) with no indicated heterogeneity (I2 0.0% p 0.45), whereas lower-
ranked studies gave a meta-RR of 1.06 (95% CI 1.00-1.11) with significant heterogeneity (I2 50.0% p
0.02). In contrast, moderate, non-significant heterogeneity (I2 36.0% p 0.21), remained among the three
studies of liver cancer (Figure Apx K-15) scored high for data quality; the meta-RR among those
studies was 1.59 (95% CI 1.17-2.16). Lower scoring studies showed no heterogeneity (I2 0.0% p 0.56)
and a meta-RR of 1.04 (95% CI 0.95-1.15). Fitting a random-effects model reduced the meta-RR for
highly scored studies to 1.42 (95% CI 0.88-2.30) but did not change the estimate for lower-scored
studies. For all three tissues, the meta-RR was greater among the high quality studies compared to
medium or low quality studies. Statistical significance was not always achieved due to the low number
of studies scored High, however this stratification demonstrates stronger associations of cancer with
TCE exposure among higher-quality data.
Figure Apx K-13. Fixed-effects model, overall association of NHL and
exposure to TCE stratified by study quality score.
$5udv
ID
Medium*1! ow
Bove 2014a
Bove 2014b
Silver 2014
Vfaantferen 2013
Christiansen 2013
Greenland 19B4
Morgan 1998
Raaschou-Nielsen 2003
Radican 2008
Hardelt 1994
Norostrom 1998
Persson 1993
IVdue 2011
Wang 2009
Subtotal {t-squared 40=0%, p ^ 0 06!)
High
Hansen 2013
Lipwnrth 2011
Cocco 2013
Zhao 2005
Subtotal (l-squared -- 0.00,
RR (95% Cf)
%
Weight
p - 0,784)
Heterogeneity between groups: p = 0.02?
Overall (l-squared = 38,5%, p - 0.0495
1.15 {0.56,
0.33 {0.14,
0.8? {0.56,
0.97 (0.91,
1.20(0.37,
0.76(0.24,
1,01 {0.53,
1,24 {1.01,
1.36 {0.77,
7.17(1.26,
1.50(0.69,
1.20 {0.55,
1,40 (0.61,
1,20 {0.85,
1.01 {0.95,
2.38)
0.80)
1.34)
1.04)
3.89)
2.42)
1.94)
1.52)
2.40)
40.79)
3.26)
2.63)
2.42)
1.70)
1,07)
.21 {0.85, 1,72)
1.02(0.54, 1.91)
1,40 (0.97, 2,04)
1.44 (0.90, 2.30)
1.29(1.04, 1.59)
0.63
0.43
1.78
74.48
0.24
0.25
0.78
7.96
1.03
0.11
0.55
0.54
1.10
2.75
92,61
2.66
0.84
2.38
1.51
7.39
,02 {0.97, 1.09) 100.00
Page 694 of 803
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268
269
FigureApx K-14. Fixed-effects model, overall association of kidney cancer and
exposure to TCE stratified by study quality score.
270
271
272
273
Study
ID
Medium/Low
Bove 2014a
Bit hag en 2016
Silver 2014
Vlaanderen 2013
Christensen 2013 —
Purdue 2016
Greenland 1994
Morgan 1998
Radican 2008
Bruning 2003
Dosemeci 1999
Moore 2010
Pesch 2000
Raaschou-Nielsen 2003
Subtotal (l-squared = 50.0%, p = 0.017)
High
Hansen 2013
Lipworth2011 —
Zhao 2005
Charbotei 2006
Subtotal (l-squared = 0.0%, p = 0.453)
%
RR (95% CI) Weight
Heterogeneity between groups: p = 0.614
Overall (l-squared = 41.1%, p = 0.036}
<Ł>
1,52 (0.64,
1.70 (0.94,
1.24 (0.87,
1.00 (0.94,
0.90 (0.36,
0.80 (0.41,
0.99 (0.30,
1.14(0.51,
1.18 (0.47,
2.47 (1.36,
1.30 (0.89,
2.05(1.13,
1.24 (1.03,
1.20 (0.96,
1.06 (1.00,
3.60)
3.06)
1.76)
1.06)
2.21
1.56)
3.29)
2.58)
2.95)
4.49)
1.89)
3.73)
1.49)
1.50)
1.11)
0.35
0.76
2.11
75.79
0.32
0.59
0.18
0.40
0.31
0.73
1.87
0.73
7.72
5.16
97.02
1.04 (0.73, 1.48) 2.11
0.85(0.33,2.18) 0.30
1.72(0.38,7.85) 0.11
1.88(0.89,3.97) 0.47
1.14(0.85,1.53) 2.98
1.06(1.00,1.11) 100.00
I
10
Figure Apx K-15. Fixed-effects model, overall association of liver cancer and
exposure to TCE stratified by study quality score.
Study
ID
RR (95% CI)
%
Weight
274
275
276
277
Medium/Low
Bove 2014a
Silver 2014
Vlaanderen 2013
Christensen 2013
Greenland 1994
Morgan 1998
Radican 2008
Raaschou-Nielsen 2003
Subtotal (l-squared = 0.0%. p = 0.557)
High
Hansen 2013
Lipworth 2011
Boice 2006
Subtotal (l-squared = 36.0%r p = 0.209)
Heterogeneity between groups: p = 0.009
Overall (l-squared = 36.5%, p = 0.107)
O
0.86 {0.37, 2.00)
0.99(0.50,1.97)
1.00 (0.90,1.11)
1.10(0.13, 9 50)
0.54(0.11.2.64)
1.48(0.56, 3.91)
1.12(0.57, 2.19)
1.35(1.04. 1.75)
1.04 (0.95, 1.15)
1.83(1.29, 2.59)
0.83 (0.36. 1.92)
1.28 (0.48, 3.41)
1.59(1.17, 2.16)
1.08(0.99, 1.18)
~1
10
1.15
1.74
72.94
0.18
0.32
0.87
1.81
12.21
91.21
6.79
1.15
0.85
8.79
Page 695 of 803
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289
290
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292
Assessment of Publication Bias
Funnel plots can be used to assess publication bias, a systematic error that occurs if statistically
significant studies are more likely to be submitted and published than nonsignificant studies. One feature
of publication bias is that smaller studies tend to have larger effect sizes than larger studies, since
smaller studies need larger effect sizes in order to be statistically significant. To measure this, funnel
plots plot standard error (SE) vs natural log of the RR (LnEst) to compare study size and effect size. If
there is no relationship, the studies should be symmetrically distributed around the summary RR
estimate (the vertical line), while publication bias is indicated by the points veering towards higher RR
estimates with increasing SEs {i.e., toward the lower right).
Funnel plots including all studies (Figure Apx K-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 at.. 2.013) study omitted, however, the plots became more symmetrical,
consistent with an absence of publication bias among the remaining studies (Figure Apx K-16, d-f).
Page 696 of 803
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293
294
295
296
297
298
299
a.
Figure Apx K-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.
b.
Funnel plot with pseudo 95% confidence limits
Funnel plot with pseudo 95% confidence limits
/ \
/ *
/ • \
/ \
/ « V N
-1 0 1
LnEst
Funnel plot with pseudo 95% confidence limits
Funnel plot with pseudo 95% confidence limits
300
301
302
303
e.
Funnel plot with pseudo 95% confidence limits
/ \
/ \
' i N
/ • \
/ \
/ • \
/ ' \
/ / •
0
LnEst
Funnel plot with pseudo 95% confidence limits
Page 697 of 803
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304
K.2.3 Selected RR estimates and confidence intervals by study and cancer type
305 Table Apx K-7. Selected RR estimates for NHL associated with TCE exposure (overall effect) from cohort studies published after
306 U.S. EPA (2011)
Study
RR
95%
LCL
95%
UCL
RR
type
In
RR
SE
(In
RR)
Alternate RR
estimates (95% CI)
Comments
Bove et al.
(2014a)
(2799547)
1.15
0.56
2.34
HR
0.140
0.37
None
Adjusted hazard ratio for males and females;
cumulative exposure for high exposure in enlisted
personnel; reference group had no exposure to TCE;
10-year lag time; specific ICD codes were not
reported.
Bove et al.
(2014b)
(2800329)
0.32
0.05
2.10
HR
-1.1
0.45
None
Adjusted hazard ratio for males and females, Camp
Lejeune cohort; cumulative exposure to TCE,
>median vs 5 yr exposure in workers, routine and
intermittent exposure; referent category was
nonexposed factory workers
Silver et al.
(2014)
(2799800)
0.87
0.57
1.35
HR
-0.14
0.22
None
Hazard ratio at 5 modified exposure years for males
and females; cumulative exposure; adjusted for sex
and paycode; 10-year lag time; specific ICD codes
not reported.
Page 698 of 803
-------
Study
RR
95%
LCL
95%
UCL
RR
type
In
RR
SE
(In
RR)
Alternate RR
estimates (95% CI)
Comments
Vlaanderen
etal. (2013)
2128436
0.97
0.91
1.04
HR
-0.030
0.034
0.95 (0.84-1.06) HR for
men and women;
cumulative exposure for
high exposure groups
only (n=353 cases)
ICD-7 200 + 202; hazard ratio for men and
women; third tertile of cumulative exposure
(n=1211 cases); occupationally unexposed
individuals were used as the reference group;
unlagged exposure (up to 20 years of lag time had
a negligible impact on HR)
307
308 TableApx K-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from case-control studies
309 published after U.S. EPA (2011) ^
Study
RR
95%
LCL
95%
UCL
In RR
SE
(In RR)
Alternate RR
estimates (95% CI)
Comments
Christensen
etal. (2013)
(2127914)
1.2
0.5
2.9
0.18
0.45
1.0(0.3-3.5) OR for
substantial exposure
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
weighting proportionately
Cocco et al.
(2013)
(2129584)
1.4
0.9
2.1
0.34
0.22
1.0 (0.8-1.2); any vs no
exposure in all subjects
Specific ICD codes not reported; odds ratio for
males and females; all study subjects with high
probability of exposure ; adjusted by age, sex, and
contributing study (50 cases, 38 controls).
310
311
312 Table Apx K-9. Selected RR estimates for NHL associated with TCE exposure (effect in the highest exposure group) studies
313 published after U.S. EPA (2011)
Study
RR
95%
LCL
95%
UCL
log RR
SE
(log RR)
Alternate RR
estimates (95% CI)
Comments
Cohort Studies
Page 699 of 803
-------
Hansen et al.
(2013)
(2128005)
0.66
0.21
2.03
HRR
-0.42
0.50
None
Vlaanderen et al.
(2013)2128436
Nested Case-
control
0.95
0.84
1.06
HR
-0.051
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
exposure
Case-Control Studies
Christensen et
al. (2013)
(2127914)
1.0
0.3
3.5
0.00
0.63
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
weighting proportionately.
Cocco et al.
(2013)
(2129584)
2.2
0.7
6.7
0.79
0.58
1.4 (1.0-2.1) OR for
>150 ppm intensity
level among all
subjects.
Specific ICD codes were not reported; odds ratio for males and
females; >75 ppm intensity level for study subjects with high
probability of exposure (9 cases, 5 controls); adjusted by age, sex,
and study.
314
315 TableApx K-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from cohort studies
316 published after U.S. EPA (2011) ^
Study
RR
95%
LCL
95%
UCL
RR
type
In RR
SE (In
RR)
Alternate RR
estimates (95% CI)
Comments
Bove et al.
(2014a)
(2799547)
1.52
0.64
3.61
HR
0.419
0.44
None
Adjusted hazard ratio for males and females; cumulative
exposure for high exposure in enlisted personnel;
reference group had no exposure to TCE; 10-year lag
time
Buhagen et al.
(2016)3502047
1.7
1.0
3.0
SIR
0.53
0.30
None
14 cases had confirmed occupational exposure to TCE.
Hansen et al.
(2013)
(2128005)
1.04
0.71
1.50
SIR
0.039
0.18
1.11 (0.67-1.73)
SIR for 20-year lag
time; 1.01 (0.70-
1.42) SIR for no
lag
Standard incidence ratio for males and females in three
populations (Denmark, Sweden, and Finland); 10-year
lag time; study also reports hazard rate ratios for kidney
cancer based on urinary TCE metabolite
Page 700 of 803
-------
95%
95%
RR
SE (In
Alternate RR
Study
RR
LCL
UCL
type
In RR
RR)
estimates (95% CI)
Comments
Lipworth et
0.85
0.33
2.19
RR
-0.16
0.48
0.42 (0.13-1.42)
Relative risk; sex and race combined; >5 yr exposure in
al. (2011)
RR for 1-4 yr
workers, routine and intermittent exposure; referent
(1235276)
exposure; 0.52
(0.21-1.30) RR for
<1 yr exposure;
0.66 (0.38-1.07)
SMR for routine
and intermittent
exposure for at
least 1 yr
(compared with
general population)
category was nonexposed factory workers
Silver et al.
1.24
0.87
1.77
HR
0.215
0.18
None
Hazard ratio at 5 modified exposure years for males and
(2014)
females; cumulative exposure; adjusted for sex and
(2799800)
pay code; 10-year lag time
Vlaanderen et
1.00
0.95
1.07
HR
0.00
0.030
0.86 (0.75-0.98) HR for
Hazard ratio for males and females; third tertile of
al. (2013)
(2128436)
men and women;
cumulative exposure for
high exposure groups
only (n=251 cases)
cumulative exposure (n=1372 cases); occupationally
unexposed individuals were used as the reference
group; unlagged exposure (up to 20 years of lag time
had a negligible impact on HR)
317
318 TableApx K-ll. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from case-control studies
319 published after U.S. EPA (2011)
Study
RR
95%
LCL
95%
UCL
In
RR
SE (In
RR)
Alternate RR estimate
(95% CI)
Comments
Christensen
et al. (2013)
(2127914)
0.9
0.4
2.4
-0.11
0.46
0.6(0.1-2.8) OR for
substantial exposure
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 weighting proportionately
Page 701 of 803
-------
Purdue et
0.8
0.4
1.5
-0.22
0.34
OR 0.9 (0.5- 1.9) for third
Odds ratio for kidney cancer in group with highest
al. (2016)
tertile of cumulative hours
probability of exposure (>90%; 32 cases, 32 controls);
(3482059)
exposed, any exposure
adjusted for age, sex, race, study center, education level,
intensity (23 cases, 19
smoking status, BMI and
controls).
history of hypertension
320
321 TableApx K-12. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from cohort studies
322 published after U.S. EPA (2011)
Study
RR
95%
LCL
95%
UCL
RR
type
In RR
SE (In
RR)
Alternate RR
estimates (95% CI)
Comments
Bove et al.
(2014a)
(2799547)
0.86
0.37
1.97
HR
-0.15
0.43
None
Adjusted hazard ratio for males and females;
cumulative exposure for high exposure in
enlisted personnel; reference group had no
exposure to TCE; 10-year lag time
Hansen et
al. (2013)
(2128005)
1.83
1.24
2.56
SIR
0.604
0.177
2.09 (1.34-3.11) SIR for
20-year lag time; 1.77
(1.24-2.45) SIR for no
lag
Liver and biliary passages; standard incidence
ratio for males and females in three populations
(Denmark, Sweden, and Finland); 10-year lag
time; study also reports hazard rate ratios for
liver and biliary passages cancer based on
urinary TCE metabolite
Lipworth et
al. (2011)
(1235276)
0.83
0.36
1.91
RR
-0.19
0.43
0.69 (0.28-1.71) RR for
1-4 yr exposure; 0.67
(0.32-1.42) RR for <1 yr
exposure
0.89 (0.57-1.33) SMR
for routine and
intermittent exposure for
at least 1 yr (compared
with general population)
Liver and biliary passages; relative risk; sex and
race combined; >5 yr exposure in workers,
routine and intermittent exposure; referent
category was nonexposed factory workers
Silver et al.
(2014)
(2799800)
0.99
0.50
1.95
HR
-0.010
0.35
None
Liver, biliary passages, and gallbladder; hazard
ratio at 5 modified exposure years for males and
females; cumulative exposure; adjusted for sex
and paycode; 10-year lag time
Page 702 of 803
-------
Study
RR
95%
LCL
95%
UCL
RR
type
In RR
SE (In
RR)
Alternate RR
estimates (95% CI)
Comments
Vlaanderen
et al.
(2013)
2128436
1.00
0.90
1.11
HR
0.00
0.054
1.02 (0.82-1.25) HR for
men and women;
cumulative exposure for
high exposure groups
only (n=106 cases)
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 of lag time had a
negligible impact on HR)
323
324
325
TableApx K-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from case-control studies
Study
RR
95%
LCL
95%
UCL
In
RR
SE (In
RR)
Alternate
RR estimate
(95% CI)
Comments
Christensen et
al. (2013)
(2127914)
1.1
0.1
8.5
0.095
1.1
2.1 (0.2-18) OR
for substantial
exposure
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
beer, wine, and spirit intake using population and cancer controls
weighting proportionately
326
Page 703 of 803
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327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
K.2.4 Sample Stata commands 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(0.1, 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(0.1,
0.2, 0.5, 1.0,2.0,5.0,10)
Fixed-effects model stratifying by quality score (HiQ):
metan LnEst SE, eform 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
Page 704 of 803
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353
354
355
356
357
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359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
Appendix L APPROACH FOR ESTIMATING WATER
RELEASES FROM MANUFACTURING SITES
USING EFFLUENT GUIDELINES
This appendix presents a methodology for estimating water releases of TCE from manufacturing
sites using effluent guidelines (EGs). This 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). EGs 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:
• 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 EG 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 L-l.
Table Apx L-l. Summary of OCPSF Effluent Guidelines for Trichloroethylene
OCPSF Subpart
Maximum
for Any
One Day
(Jig/L)
Maximum
for Any
Monthly
Average
(^g/L)
Basis
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
Subpart K - Indirect Discharge Point
Sources
69
26
Pretreatment Standards
for Existing Sources
(PSES) and
Pretreatment Standards
for New Sources
(PSNS)
Page 705 of 803
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379
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
BAT = Best Available Technology Economically Achievable; 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 1-1 to estimate daily releases and
Equation D-2 to estimate annual releases using the parameters in TableApx L-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-pipe biological 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 |ig/L) from Subparts J and K, a high-end
daily release using the limit for the maximum for any one day (69 |ig/L) from Subparts J and K,
and an annual release using the maximum monthly average from Subparts J and K.
Equation L-l
DLxPWx PV
DR ~ 1,000,000,000 x OD
Equation L-2
DLxPW x PV
AD =
1,000,000,000
Table Apx L-2. Default Parameters for Estimating Water Releases of Trichloroethylene
from Manufacturing Sites
Parameter
Parameter Description
Default Value
1 nit
DR
Daily release rate
Calculated from
equation
kg/site-day
DL
Discharge limita
Max Daily: 69
Average Daily: 26
Annual: 26
^g/L
PW
Produced waterb
10
L/kg
PV
Annual TCE production volume
Site-specific
kg/site-yr
OD
Operating Daysc
350
days/yr
AR
Annual release rate
Calculated from
equation
kg/site-yr
a Discharge limits are based on the maximum discharge limits allowed in the OCPSF EG, which correspond to the
discharge limits for direct discharge point sources with no biological end-of-pipe treatment (Subpart J) and indirect
discharge points sources (Subpart K) (citation for 40 C.F.R. 414). There is no "average" daily discharge limit set by
the EGs; therefore, EPA assumed that the average daily discharge concentration would be equal to the maximum
monthly average discharge limit.
b The amount of produced water per kilogram of TCE produced is based on the SpERC developed by the European
Solvent Industry Group for the manufacture of a substance, which estimates 10 m3 of wastewater generated per
metric ton of substance produced and converted to 10 L/kg (European Solvents Indi_ ~oup (ESIG).
2012).
0 Due to large throughput, manufacturing sites are assumed to operate seven days per week and 50 weeks per year
with two weeks per year for shutdown activities.
Page 706 of 803
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411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
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 10 m3 of wastewater generated per
metric ton of substance produced (equivalent to 10 L water/kg of substance produced) (European
Solvents Industry Group (ESIG1 , ) In lieu of TCE-specific information, EPA estimated
wastewater flow using the SpERC specified wastewater production volume and the annual TCE
production rates for each facility. TableApx L-3 provides estimated daily production volume
and wastewater flow for each facility that EPA used the EG to assess water releases.
Table Apx L-3. Summary of Facility Trichloroethylene Production Volumes and
Wastewater Flow Rates
Nile
Annual Production
Volume
(kg/site-vr)
Annual
Operating Days
(davs/yr)
Daily
Production
Volume
(kg/site-day)
Daily
Wastewater
Mow
(1./site-day)
Solvents &
Chemicals,
Pearland, TXa
20,382,094
350
58,234
582,345
" The 2015 annual production volumes in the 2016 CDR for this site was either claimed as CBI or withheld. EPA
estimated the production volume by subtracting known site production volumes from the national production
volume and averaging the result over all the sites with CBI or withheld production volumes and converting from
pounds to kilograms.
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-pipe biological 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 water release calculations for TCE at
manufacturing sites based on the estimated production volume for Solvents & Chemicals
(44,934,862 lbs/yr or 20,382,094 kg/yr):30
Max DR =
6910 X 20,382,094^
L kg yr
1,000,000,000^ x 350^^
kg yr
= 0.04
kg
day
30 This estimated production volume is equal to the estimated production volume assessed for all manufacturing sites.
Page 707 of 803
-------
26^x10-^x 20,382,094^ hn
441 Average DR = ^= 0.015
1,000,000,000^x 350^^ day
Kg yr
442
26^x10-^x 20,382,094^ hn
443 AR=—± ^ aa yT = 5.3 —
i,ooo,ooo,ooo^| yr
444
Page 708 of 803
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445
446
447
448
449
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451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
SAMPLE CALCULATIONS FOR
CALCULATING ACUTE AND CHRONIC (NON-
CANCER AND CANCER) INHALATION
EXPOSURE
Sample calculations for high-end and central tendency acute and chronic exposure
concentrations for one setting, Manufacturing, are demonstrated below. The explanation of the
equations and parameters used is provided in [EnvironmentalReleases and Occupational
Exposure Assessment. Docket: EPA-H()-OPPT-2019-050Q\. The final values will have two
significant figures since they are based on values from modeling.
Appendix M
M.l Example High-End AC, ADC, and LADC
Calculate AChe:
Calculate ADChe:
CHE x ED
AC„E =
AT,
acute
2.6 ppm x 8 hr/day
AC»°= 24 hr/day =°*>PPm
CuE x ED xEFx EWY
ADC HE = — —
Hb Aj
2.6 ppm x 8^- x 250^^x40 years
ADC in = - j— r r = 0. 59 ppm
0 ^ _ days „.hours\
(40 years x 365 ^ x 24
Calculate LADChe:
CHE x ED xEFx EWY
LADChe = —
AT
LADC
2.6 ppm x 25°x 40 years
LADChe = 7 J— r r = 0. 30 ppm
0 ^ _ days „.hours\
(78 years x 365 ^ x 24
Page 709 of 803
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475
476
All
478
479
480
481
482
483
484
485
486
487
488
489
490
M.2 Example Central Tendency AEC, ADC, and LADC
Calculate ACct:
Cct x ED
ACct =
AT,
acute
0.03 ppm x 8 hr/day
AC"= 24 hr I day = ^PP
-------
491
492
493
494
495
496
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
527
528
529
530
531
532
533
534
Appendix N VAPOR DECREASING AND COLD CLEANING
NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
MODELS APPROACH AND PARAMETERS
This appendix presents the modeling approach and model equations used in the following models:
• 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 assumed 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 far-
field:
• 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 reasonably 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 95th 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 50th 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.
Page 711 of 803
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550
N.l Model Design Equations
Figure_Apx N-l through Figure_Apx N-3 illustrate the near-field/far-field modeling approach as it was
applied by EPA to each vapor degreasing and cold cleaning model. As the figures show, volatile TCE
vapors evaporate into the near-field, resulting in worker exposures at a TCE concentration Cnf. The
concentration is directly proportional to the evaporation rate of TCE, (denoted by "G" in Figure 2-7),
into the near-field, whose volume is denoted by Vnf. The ventilation rate for the near-field zone (Qnf)
determines how quickly TCE dissipates into the far-field, resulting in occupational non-user exposures
to TCE at a concentration Cff. Vff 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.
Far-Field
Near-Field
FigureApx N-l. 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
Page 712 of 803
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555
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557
558
559
560
561
Near-Field
FigureApx N-2. The Near-Field/Far-Field Model as Applied to the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model
Far-Field
CL
Q W
NF ^
Near-Field
\lSj
1
1
•¦¦Hi
_W Q
N
Figure Apx N-3. The Near-Field/Far-Field Model as Applied to the Web Degreasing Near-
Field/Far-Field Inhalation Exposure Model
The model design equations are presented below in Equation K-l through Equation K-18. Note the
design equations are the same for each of the models discussed in this appendix.
Page 713 of 803
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599
600
Near-Field Mass Balance
Equation K-l
Far-Field Mass Balance
V,
dC,
NF
NF
dt
— CffQnf CnfQnf + ^
Equation K-2
Where:
V,
dC
FF
FF '
dt
CnfQnf CffQnf CffQff
Vnf =
near-field volume;
Vff =
far-field volume;
Qnf =
near-field ventilation rate;
Qff =
far-field ventilation rate;
Cnf =
average near-field concentration;
Cff =
average far-field concentration;
G
average vapor generation rate; and
t
elapsed time.
Both of the previous equations can be solved for the time-varying concentrations in the near-field and
far-field as follows (Nicas. 2.009):
Equation K-3
Equation K-4
Where:
Equation K-5
CNF = G{k1 + k2eXlt — k3eX2t)
C,
FF
= g(tt~
\Qff
+ k4eXlt — kseX2t
fci =
Equation K-6
Equation K-7
Equation K-8
Equation K-9
kn =
ko =
(qnp + Qfp) Q"
QnfQff + ^-2^nf(.Qnf + Qff)
QnfQffVnf(.^1 ~ ^2)
QnfQff + A.1Vnf(.Qnf + Qff)
QnfQffVnf(.^i ~ ^2)
(A1VNF + Qnf\
^ _ (^2Vnf + Qnf\ ^
5 v qnf ) 3
Page 714 of 803
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619
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622
623
624
625
626
627
628
629
630
631
632
633
Equation K-10
= 0.5
(QnfVff + Vnf(Qnf + Qff)\ /QnfVff + Vnf(Qnf + Qff)\ _ . (QnfQff\
\ KvfKff ) J\ Vnf^ff J \VNFVFF)
Equation K-ll
^ _ q 5 _ / Qnf^ff + Vnf(Qnf + Qff)
Vnf^ff
/Qnf^ff + Vnf(Qnf + Qff)\ _ . /QnfQff\
\ ^NF^FF ) \VNFVFF)
EPA calculated the hourly TWA concentrations in 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 P.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 denominator must 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 denominator reflect eight hours
regardless of the values selected for ti and t2.
Equation K-12
r —
lNF,TWA —
Jt2 CNFdt Jt2 G(/cx + k2eXlt — k3eX2t)dt
C, dt
Vavg
(/Cl
17 +
k2eAlt2
A,
:) - G (k
+
k2eAltl k3e'
1,
Vavg
Equation K-13
r — —1
lFF,TWA — rt
Ł CFFdt Ł c(^ + k4e^ - kse^)
dt
C"
-------
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
Equation K-14
FSA = 2(LNFH NF) + 2(WNFHNF) + (LnfWnf)
Where: Lnf, Wnf, and Hnf are the length, width, and height of the near-field, respectively. The near-
field 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
1
Qnf — — vnfFSA
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-25:
Equation K-16
Qff = VffAER
Using the model inputs described in Appendix E.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 for each model.
N.2 Model Parameters
TableApx N-l through TableApx N-4 summarize the model parameters and their values for each of
the models discussed in this Appendix. Each parameter is discussed in detail in the following
subsections.
Page 716 of 803
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TableApx N-l. Summary of Parameter Values and Distributions Used in the Open-Top Vapor Degreasing Near-Field/Far-Field
Inhalation Exposure Model
Input
Parameter
Deterministic Values
Uncertainty Analysis Distribution Parameters
Symbol
Unit
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distribution
Type
Comments
Far-field
volume
Vff
ft3
10,594
Midpoint
10,594
70,629
17,657
Triangular
See Section N.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section N.2.2
Near-field
indoor wind
speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section N.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
See Section N.2.4
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
ti
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
8
—
2
8
—
—
See Section N.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section N.2.6
Vapor
generation
rate
G
mg/hr
2.34E+07
Average
4.54E+02
4.67E+07
—
Discrete
See Section N.2.7
lb/hr
51.50
Average
0.001
103.00
—
Discrete
Operating
hours per
day
OH
hr/day
8
—
—
Discrete
See Section E.2.8
Page 717 of 803
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TableApx N-2. Summary of Parameter Values and Distributions Used in the Conveyorized Degreasing Near-Field/Far-Field
Inhalation Exposure Model
Input
Parameter
Symbol
Unit
Deterministic Values
Uncertainty Analysis Distribution Parameters
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distribution
Type
Far-field
volume
Vff
ft3
10,594
Midpoint
10,594
70,629
17,657
Triangular
See Section N.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section N.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section N.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section N.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
ti
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
24
—
24
8
—
Constant
Value
See Section N.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section N.2.6
Vapor
generation
rate
G
mg/hr
1.6E+07
Average
3.63E+05
3.29E+07
—
Discrete
See Section N.2.7
lb/hr
36.6
Average
0.80
72.5
—
Discrete
Operating
hours per
day
OH
hr/day
24
—
—
—
—
Constant
See Section E.2.8
Page 718 of 803
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TableApx N-3. Summary of Parameter Values and Distributions Used in the Web Degreasing Near-Field/Far-Field Inhalation
Exposure Model
Input
Parameter
Symbol
Unit
Deterministic Values
Uncertainty Analysis Distribution Parameters
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distribution
Type
Far-field
volume
Vff
ft3
10,594
Midpoint
10,594
70,629
17,657
Triangular
See Section N.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section N.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section N.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section N.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
ti
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
8
—
8
8
—
Constant
Value
See Section N.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section N.2.6
Vapor
generation
rate
G
mg/hr
—
—
1.12E+05
1.12E+05
—
Discrete
See Section N.2.7; Single Data
Point
Operating
hours per
day
OH
hr/day
24
—
—
—
—
Constant
See Section P.2.8
Page 719 of 803
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TableApx N-4. Summary of Parameter Values and Distributions Used in the Cold Cleaning Near-Field/Far-Field Inhalation
Exposure Model
Input
Parameter
Symbol
Unit
Deterministic Values
Uncertainty Analysis Distribution Parameters
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distribution
Type
Far-field
volume
Vff
ft3
10,594
Midpoint
10,594
70,629
17,657
Triangular
See Section N.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section N.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section N.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section N.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
ti
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
—
—
3
8
—
Discrete
See Section N.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section N.2.6
Vapor
generation
rate
G
mg/hr
5.14E+05
Average
6.28E+02
1.02E+06
—
Discrete
See Section N.2.7
lb/hr
1.13
Average
0.001
2.26
—
Discrete
Operating
hours per
day
OH
hr/day
—
—
—
—
—
—
See Section P.2.8
Page 720 of 803
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674
675
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678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
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699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
N.2.1 Far-Field Volume
EPA used the same far-field volume distribution for each of the models discussed. The far-field volume
is based on information obtained from (Yon Grote et at. 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
(Von Grote et at.. 2003). These are the highest and lowest values EPA identified in the literature;
therefore, EPA assumes a triangular distribution bound from 300 m3 (10,594 ft3) to 2,000 m3 (70,629 ft3)
with a mode of 500 m3 (the midpoint of 400 and 600 m3) (17,657 ft3).
N.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 (Hettweg et ai. 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). (Hell wee et al.. 2.009) 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 ( ), in agreement with the low end reported by (Hellwee 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 hr"1), with a minimum of 2 hr"1,
per the risk assessment peer reviewer (U.S. EPA. 2013a) and a maximum of 20 hr"1 per (Hellweg et al...
2009).
N.2.3 Near-Field Indoor Air Speed
(Baldwin andMavnard. 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 Mavna '8a) 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 industrial
distribution for facilities performing vapor degreasing and/or cold 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 andMavnard. 1998a) (1998).
EPA fit the air speed surveys representative of industrial facilities to a lognormal distribution with the
following parameter values: mean of 22.414 cm/s and standard deviation of 19.958 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 andMavnard. 1998a) (1998)) to prevent the model from sampling
values that approach infinity or are otherwise unrealistically large.
(Baldwin andMavnard. 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 721 of 803
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718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
N.2.4 Near-Field Volume
EPA assumed a near-field of constant dimensions of 10 ft x 10 ft x 6 ft resulting in a total volume of 600
ft3.
N.2.5 Exposure Duration
EPA assumed the maximum exposure 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 cleaning machine operating time (see Appendix
E.2.8 for discussion of operating hours).
N.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 used for each of the models.
N.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 2011a) data were 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 N-5 summarizes the data available in the 2014 NEI.
TableApx N-5. Summary of Trichloroethylene Vapor Degreasing and Cold Cleaning Data from
the 2014 NEI
Units with Zero
Emissions
Units without
Units Used
Unit Type
Total Units
Accompanying
Operating Hours
in
Analysis3
Open-Top Vapor Degreasers
149
29
62
76
Convey orized Degreasers
8
0
5
3
Web Degreasersb
1
0
0
1
Cold Cleaning Machines
17
1
6
10
a - Some units with zero emissions also did not include accompanying operating hours; therefore, subtracting the units 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 - No web degreasers reported in the 2014 NEI. One web degreaser reported in the (U.S. EPA. 201 la) was used in this
analysis.
Source: (U.S. EPA 2018a): (U.S. EPA 2011a)
Table Apx N-6 through Table Apx N-9 summarize the distribution of hourly unit emissions for each
machine type calculated from the annual emission in the 2014 NEI.
Page 722 of 803
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754 TableApx N-6. Distribution of Trich
oroethylene Open-Top Vapor Degreasing Unit Emissions
Count
of
Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
1
103.00
0.0132
1
63.95
0.0132
1
19.04
0.0132
1
13.20
0.0132
1
12.18
0.0132
1
9.47
0.0132
1
9.21
0.0132
1
8.14
0.0132
1
7.30
0.0132
1
6.93
0.0132
1
6.64
0.0132
1
6.61
0.0132
1
6.44
0.0132
1
6.40
0.0132
1
6.32
0.0132
1
5.10
0.0132
1
5.06
0.0132
1
4.89
0.0132
1
4.85
0.0132
1
4.14
0.0132
1
3.96
0.0132
1
3.82
0.0132
1
3.77
0.0132
1
3.68
0.0132
3.66
0.0263
1
3.64
0.0132
1
3.43
0.0132
1
3.40
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
0.0132
1
2.20
0.0132
1
1.97
0.0132
1
1.96
0.0132
1
1.73
0.0132
1
1.62
0.0132
1
1.59
0.0132
Page 723 of 803
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Count
of
Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
1
1.44
0.0132
1
1.33
0.0132
1
1.22
0.0132
1
1.09
0.0132
0.93
0.0263
1
0.90
0.0132
0.84
0.0263
1
0.83
0.0132
1
0.79
0.0132
0.79
0.0395
1
0.70
0.0132
1
0.62
0.0132
1
0.60
0.0132
1
0.43
0.0132
1
0.42
0.0132
1
0.39
0.0132
1
0.38
0.0132
1
0.38
0.0132
1
0.35
0.0132
1
0.23
0.0132
1
0.18
0.0132
1
0.15
0.0132
1
0.15
0.0132
1
0.14
0.0132
1
0.11
0.0132
1
0.10
0.0132
0.10
0.0263
1
0.07
0.0132
1
0.03
0.0132
1
0.001
0.0132
755
756
TableApx N-7. Distribution of Trichloroethylene Conveyorized Degreasing Unit Emissions
Unit
Count
Emissions
Fractional
of Units
(lb/unit-hr)
Probability
1
72.48
0.3333
1
1.51
0.3333
1
0.80
0.3333
757
Page 724 of 803
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764
765
766
767
768
769
770
111
772
TableApx N-8. Distribution of Trichloroethylene Web Degreasing Unit Emissions
Count
of Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
—
0.247
1.00
TableApx N-9. Distribution of Trichloroethylene Cold Cleaning Unit Emissions
Count
of Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
1.00
2.26
0.1000
1.00
0.83
0.1000
1.00
0.83
0.1000
1.00
0.83
0.1000
1.00
0.83
0.1000
1.00
0.05
0.1000
1.00
0.01
0.1000
1.00
0.01
0.1000
1.00
0.01
0.1000
1.00
0.00
0.1000
N.2.8 Operating Hours
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 N-10 through Table Apx
N-13 summarize the distribution of operating hours per day for each machine type.
Table Apx N-10. Distribution of Trichloroethylene Open-Top Vapor Degreasing Operating Hours
Operating
Count of
Hours
Fractional
Occurrences
(hr/day)
Probability
—
24
0.4048
—
16
0.0952
—
8
0.2381
—
6
0.0476
—
4
0.0714
—
2
0.1429
Table Apx N-ll. Distribution of Trichloroethylene Conveyorized Degreasing Operating Hours
Count of
Occurrences
Operating
Hours
(hr/day)
Fractional
Probability
—
24
1.0000
Page 725 of 803
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773 TableApx N-12. Distribution of Trichloroethylene Web Degreasing Operating Hours
Count of
Occurrences
Operating
Hours
(hr/day)
Fractional
Probability
—
24
1.0000
774
775
Table Apx N-13. Distribution of Trichloroethylene Cold Cleaning Operating Hours
Operating
Count of
Hours
Fractional
Occurrences
(hr/day)
Probability
—
24
0.4000
—
8
0.5000
—
3
0.1000
776
111
Page 726 of 803
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Appendix O BRAKE SERVICING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODEL APPROACH AND
PARAMETERS
This appendix presents the modeling approach and model equations used in the Brake Servicing Near-
Field/Far-Field Inhalation Exposure Model. The model was developed through review of the literature
and consideration of existing EPA exposure models. This model uses a near-field/far-field approach
(Nicas. 2009). where an aerosol application located inside the near-field generates a mist of droplets, and
indoor air movements lead to the convection of the droplets between the near-field and far-field.
Workers are assumed to be exposed to TCE droplet 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 far-
field:
• Far-field size;
• Near-field size;
• Air exchange rate;
• Indoor air speed;
• Concentration of TCE in the aerosol formulation;
• Amount of degreaser used per brake job;
• Number of degreaser applications per brake job;
• Time duration of brake j ob;
• 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 distributions based on reasonably 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 95th 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 50th percentile value was selected to represent central tendency exposure
level. The following subsections detail the model design equations and parameters for the brake
servicing model.
O.l Model Design Equations
In brake servicing, the vehicle is raised on an automobile lift to a comfortable working height to allow
the worker (mechanic) to remove the wheel and access the brake system. Brake servicing can include
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853
inspections, adjustments, brake pad replacements, and rotor resurfacing. These service types often
involve disassembly, replacement or repair, and reassembly of 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, carburetor cleaners, and general purpose degreasers
interchangeably (CARB, 2000). Automotive brake cleaners can come in aerosol or liquid form (CARB.
2000): this model estimates exposures from aerosol brake cleaners (degreasers).
FigureApx O-l illustrates the near-field/far-field modeling approach as it was applied by EPA to brake
servicing using an aerosol degreaser. The application of the aerosol degreaser immediately generates a
mist of droplets in the near-field, resulting in worker exposures at a TCE concentration Cnf. The
concentration is directly proportional to the amount of aerosol degreaser 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 near-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. Vff 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.
Figure Apx O-l. The Near-Field/Far-Field Model as Applied to the Brake Servicing Near-
Field/Far-Field Inhalation Exposure Model
In brake servicing using an aerosol degreaser, aerosol degreaser droplets enter the near-field in non-
steady "bursts," where each burst results in a sudden rise in the near-field concentration. The near-field
and far-field concentrations then decay with time until the next burst causes a new rise in near-field
concentration. Based on site data from automotive maintenance and repair shops obtained by CARB
(CARB. 2000) for brake cleaning activities and as explained in Sections 0.2.5 and 0.2.9 below, the
model assumes a worker will perform an average of 11 applications of the degreaser product per brake
job with five minutes between each application and that a worker may perform one to four brake jobs
per day each taking one hour to complete. EPA modeled two scenarios: one where the brake jobs
occurred back-to-back and one where brake jobs occurred one hour apart. In both scenarios, EPA
V,
FF
NF C
Non-
volatile Source \ <5-
c,
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893
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897
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899
900
assumed the worker does not perform a brake job, and does not use the aerosol degreaser, during the
first hour of the day.
EPA denoted the top of each five-minute period 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,o to to,n (e.g., 8 am to 9 am). Then, in both scenarios, the worker begins the
first brake job during the second hour, ti,o (e.g., 9 am to 10 am). The worker applies the aerosol
degreaser at the top of the second 5-minute period and each subsequent 5-minute period 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 performed
back-to-back, if performing more than one brake job on the given day. Therefore, the second brake job
begins at the top of the third hour (e.g., 10 am), and the worker applies the aerosol degreaser at the top
of the second 5-minute period and each subsequent 5-minute period (e.g., 10:05 am, 10:10 am,... 10:55
am). In the second scenario, the brake jobs are performed every other hour, if performing more than one
brake job on the given day. Therefore, the second brake job begins at the top of the fourth hour (e.g., 11
am), and the worker applies the aerosol degreaser at the top of the second 5-minute period and each
subsequent 5-minute period (e.g., 11:05 am, 11:10 am,... 11:55 am).
In the first scenario, after the worker performs the last brake job, the workers and occupational non-users
(ONUs) continue to be exposed as the airborne concentrations decay 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 exposed as the airborne concentrations decay 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 exposed once they leave work.
Based on data from CARB (GARB. 2000). EPA assumes each brake job requires one 14.4-oz can of
aerosol brake cleaner as described in further detail below. The model determines the 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).
The model design equations are presented below.
Near-Field Mass Balance
Equation L-l
Far-Field Mass Balance
Equation L-2
Where:
Vnf
Vff
Qnf
Qff
Cnf
Cff
t
near-field volume;
far-field volume;
near-field ventilation rate;
far-field ventilation rate;
average near-field concentration;
average far-field concentration; and
elapsed time.
Page 729 of 803
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903
904
905
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910
911
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913
914
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919
920
921
922
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924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
Solving the above equations in terms of the time-varying concentrations in the near-field and far-field
yields Equation L-3 and Equation L-4, which EPA applied to each of the 12 five-minute increments
during each hour of the day. For each five-minute increment, EPA calculated the initial near-field
concentration at the top of the period (tm,n), accounting for both the burst of TCE from the degreaser
application (if the five-minute increment is during a brake job) and the residual near-field concentration
remaining after the previous five-minute increment (tm,n-i; except during the first hour and tm,o of the first
brake job, in which case there would be no residual TCE from a previous application). The initial far-
field concentration is equal to the residual far-field concentration remaining after the previous five-
minute increment. EPA then calculated the decayed concentration in the near-field and far-field at the
end of the five-minute period, just before the degreaser application at the top of the next period (tm,n+i).
EPA then calculated a 5-minute TWA exposure for the near-field and far-field, representative of the
worker's and ONUs' exposures to the airborne concentrations during each five-minute increment using
Equation L-13 and Equation L-14. The k coefficients (Equation L-5 through Equation L-8) are a
function of the initial near-field and far-field concentrations, and therefore are re-calculated at the top of
each five-minute period. In the equations below, 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+1" is used, if the value of
n+1 is greater than 11, the value at "m+1, 0" is used.
Equation L-3
C,
NF.tr,
= (k
1 fir
,Ai t
+ k
2, tr>
Equation L-4
CpF t ^=(^3t eXlt-kAt eX2t)
rr>Lm,n+1 V 3>Lm,n ^>lm,n J
Where:
Equation L-5
Equation L-6
Ki
to f
Ł,lr
QnF (CFF,o(tm,n) CWF0(tmn)^ A2VNFCNF,o(tm,n)
VnfVi ~ ^2)
QnF (j-'NF.O (j*m,n) ~ ^FF, 0 (*771,71)) + ^l^NF^NF,o(^m,n)
VnfVi ~ ^2)
Equation L-7
(.QnF + ^iVnf)(.QnF (CFF,o(tm,n) CWF0(tmn)^ N
3,tm,n
Qnf^nf (^1 ^2)
Equation L-8
(.QnF + ^2^Nf)(.QnF (j-'NF.O (j'm.n) ^FF.oiSm.n}) + ^l^NF^NF.oiSm.n})
l4,t
m,n
QnfVnf O^i — ^2)
Page 730 of 803
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941
942
943
944
945
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947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
Equation L-9
Aj_ = 0.5
( Qnf^ff + Vnf(Qnf + Qff)
VNFVFF
+
/Qnf^ff + Vnf(Qnf + Qff)\ _ . /QnfQff\
\ ^NF^FF I ^ ^NF^FF '
Equation L-10
X2 = 0.5
_ /QnF^FF + ^NF (Qnf + C?ff)\
V ^NF^FF J
fQnf^ff + Vnf(.Qnf + Qff)\ _ . /QnfQff\
VnfVFf J \VNFVFF)
Equation L-ll
0, m = 0
CNF,o{pm,n) j —f1,000——^ + CWF(tmn_1) , n> 0 for all m where brake job
\ Vnf \ .Q /
occurs
Equation L-12
r 0, m = 0
FF,o\tm,n) — [CFF(tmn_x) , /or all n where m > 0
Equation L-13
C,
'kit , k2t
^m,n-1 | ACm.n-! ^t-,
, ^1 ^2
'kit , k2t
Um,n-1 j Acm,n-l
, ^1 ^2
NF, 5-min TWA, tn
^2 ^1
Equation L-14
kz.tm.n-! j ŁAiti | ^4tm,n-1
Ai A2 / \ Ai A2
CfF, 5-min TWA, tmjTl t — t
After calculating all near-field/far-field 5-minute TWA exposures (/.
-------
967
968
969
970
971
972
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
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
Equation L-17
r _ Im=o[C/vF,5-min TWA,tm,n x 0-0833 hr\
WVF, 1-lir TWA ~
Equation L-18
r _ Hn=o [^ff,5-niin TWA,tmjl x 0.0833 hr\
CFF,1-hr TWA ~
EPA calculated rolling 1-hour TWA's throughout the workday and the model reports the maximum
calculated 1-hour TWA.
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 FigureApx O-l). 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 hemisphere's curved surface and
half of the hemisphere's circular surface per Equation L-19, below:
Equation L-19
FSA = x AtcR^p^ + x TcRftF^j
Where: Rnf is 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
1
Qnf — 2 vnfFSA
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:
Equation L-21
Qff = Vff^ER
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.
0.2 Model Parameters
Table Apx O-l summarizes the model parameters and their values for the Brake Servicing Near-Field/
Far-Field Inhalation Exposure Model. Each parameter is discussed in detail in the following subsections.
Page 732 of 803
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TableApx O-l. Summary of Parameter Values and Distributions Used in the Brake Servicing Near-Field/Far-Field Inhalation
Exposure Model
Input
Parameter
Symbol
Unit
Constant Model
Parameter Values
Variable Model Parameter Values
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
Far-field volume
Vff
m3
—
—
206
70,679
3,769
Triangular
Distribution based on data
collected bv CARB (CARD.
2000).
Air exchange
rate
AER
lir1
—
—
1
20
3.5
Triangular
(Demon et al.. 2009) identifies
typical AERs of 1 lir1 and 3 to 20
lir1 for occupational settings
without and with mechanical
ventilation systems, respectively.
(Hellwee et al.. 2009) identifies
average AERs for occupational
settings utilizing mechanical
ventilation systems to be between
3 and 20 lir1. (Golsteiin et al..
2014) indicates a characteristic
AER of 4 lir1. Peer reviewers of
EPA's 2013 TCE draft risk
assessment commented that
values around 2 to 5 lir1 may be
more likelv (U.S. EPA. 2013a). in
agreement with (Golsteiin et al..
2014). A trianeular distribution 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 lir1).
Near-field indoor
wind speed
Vkf
Mir
—
—
0
23,882
—
Lognonnal
Lognonnal distribution fit to
commercial-type workplace data
from (Baldwin and Mavnard.
1998a).
cm/s
—
—
0
202.2
—
Lognonnal
Near-field radius
Rnf
m
1.5
—
—
—
—
Constant
Value
Constant.
Starting time for
each application
period
tl
lir
0
—
—
—
—
Constant
Value
Constant.
Page 733 of 803
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Input
Parameter
Symbol
Unit
Constant Model
Parameter Values
Variable Model Parameter Values
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
End time for
each application
period
t2
hr
0.0833
—
—
—
—
Constant
Value
Assumes aerosol degreaser is
applied in 5-minute increments
during brake job.
Averaging Time
tavg
hr
8
—
—
—
—
Constant
Value
Constant.
TCE weight
fraction
wtfrac
wt frac
—
—
0.40
1.00
—
Discrete
Discrete distribution of TCE-
based aerosol product
formulations based on products
identified in EPA's Preliminary
Information on Manufacturing,
Processing, Distribution, Use, and
Disposal for TCE (U.S. EPA.
2017c). Where the weieht fraction
of TCE in the formulation was
given as a range, EPA assumed a
uniform distribution within the
reported range for the TCE
concentration in the product.
Degreaser Used
per Brake Job
wd
oz/job
14.4
—
—
—
—
Constant
Value
Based on data from CARB
(CARB. 2000).
Number of
Applications per
Job
na
Applications/
job
11
—
—
—
—
Constant
Value
Calculated from the average of
the number of applications per
brake and number of brakes per
job.
Amount Used
per Application
Amt
g TCE/
application
—
—
14.8
37.1
—
Calculated
Calculated from wtfrac, Wd, and
Na.
Operating hours
per week
OHpW
hr/week
—
—
40
122.5
—
Lognonnal
Lognonnal distribution fit to the
operating hours per week
observed in CARB (CARB.
2000) site visits.
Number of
Brake Jobs per
Work Shift
Nj
jobs/site-shift
—
—
1
4
—
—
Calculated from the average
number of brake jobs per site per
year, OHpW, and assuming 52
operating weeks per year and 8
hours per work shift.
1013
Page 734 of 803
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1015
1016
1017
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1019
1020
1021
1022
1023
1024
1025
1026
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1028
1029
1030
1031
1032
1033
1034
1035
1036
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1038
1039
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1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
0.2.1 Far-Field Volume
The far-field volume is based on information obtained from (CAKES. 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 m3 with an average shop volume of 3,769 m3. 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 ( 00)).
CARB measured the physical dimensions of the portion of the facility 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.
0.2.2 Air Exchange Rate
The air exchange rate (AER) is based on data from (Demou et at.. 2009). (Hettweg et al. 2009).
(Golsteiin et at.. 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,
(Hettweg et al.. 2009) identifies average AERs for occupational settings using mechanical ventilation
systems to vary from 3 to 20 hr"1. (Golsteiin 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 (Golsteiin et al.. 2014) and the low end reported by (Demou et al.. 2009) and
(Hettweg 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 hr"1), with a minimum of 1 hr"1, per (Demou et al.. 2009) and a maximum of 20 hr"1 per (Demon et
al.. 2.009) and (Hettweg et al.. 2009)).
0.2.3 Near-Field Indoor Air Speed
(Baldwin andMavnard. 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 Mavna 8a) 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 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 Mavn; _ )8a).
EPA fit the air speed surveys representative of commercial facilities to a lognormal distribution with the
following parameter values: mean of 10.853 cm/s and standard deviation of 7.883 cm/s. In the model,
the lognormal distribution is truncated at a maximum allowed value of 202.2 cm/s (largest surveyed
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mean air speed observed in (Baldwin andMavnard. 1998a) to prevent the model from sampling values
that approach infinity or are otherwise unrealistically large.
(Baldwin andMavnard. 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.
0.2.4 Near-Field Volume
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 FigureApx O-l). The near-field volume is
calculated per Equation L-22. EPA defined a near-field radius (Rnf) of 1.5 meters, approximately 4.9
feet, as an estimate of the working height of the wheel, as measured from the floor to the center of the
wheel.
Equation L-22
1 4
Vnf = 2 x 2n^F
0.2.5 Application Time
EPA assumed an average of 11 brake cleaner applications per brake job (see Section F.2.9). CARB
observed, from their site visits, that the visited facilities did not perform more than one brake job in any
given hour (CARB. 2000). Therefore, EPA assumed a brake job takes one hour to perform. Using an
assumed average of 11 brake cleaner applications per brake job and one hour to perform a brake job,
EPA calculates an average brake cleaner application frequency of once every five minutes (0.0833 hr).
EPA models an average brake job of having no brake cleaner application during its first five minutes
and then one brake cleaner application per each subsequent 5-minute period during the one-hour brake
job.
0.2.6 Averaging Time
EPA was interested in estimating 8-hr TWAs for use in risk calculations; therefore, a constant averaging
time of eight hours was used.
0.2.7 Trichloroethylene Weight Fraction
EPA reviewed the Preliminary Information on Manufacturing, Processing, Distribution, Use, and
Disposal: Trichloroethylene report (I '• J j* \ \} \ (c) for aerosol degreasers that 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/electrical parts cleaners, and a mold cleaner. EPA includes all of these
aerosol degreasers in the estimation of TCE content as: 1) automotive maintenance and repair facilities
may use different degreaser products interchangeably as observed by (CARB. 2000); and 2) EPA uses
this brake servicing model as an exposure scenario representative of all commercial-type aerosol
degreaser applications.
EPA used a discrete distribution to model the TCE weight fraction based on the number of occurrences
of each product type. In some instances, the concentration of TCE was reported as a range. For these
product types, EPA used a uniform distribution to model the TCE weight fraction within the product
type. Table Apx 0-2 provides a summary of the reported TCE content reported in the safety data sheets
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1120
1121
1122
identified in (U.S. EPA. 2017c). the number of occurrences of each product type, and the fractional
probability of each product type.
Table Apx Q-2. Summary of Trichloroethylene-Based Aerosol Degreaser Formulations
Name of Aerosol
Degreaser Product
Identified in fL S. EPA,
2017c)
Trichloroethylene
Weight Percent
Number of
Occurrences
Fractional
Probability
C-60 Solvent Degreaser
90-100%
1
0.063
Fusing Machine Cleaner
40-60%
1
0.063
Solvent Degreaser
> 90%
1
0.063
Electro Blast
90-100%
1
0.063
Electro Solv
90-100%
1
0.063
Pro Tools NF Solvent
Degreaser
60-100%
1
0.063
Aerosolve II
>90%
1
0.063
Power Solv II
90-100%
1
0.063
Zep 45
40-50%
1
0.063
Super Solv
90-100%
1
0.063
Parts Cleaner
45-55%
1
0.063
Electronic Contact Cleaner &
Protectant - Aerosol
97%
1
0.063
Flash Free Electrical Degreaser
98%
1
0.063
Chlorinated Brake & Parts
Cleaner - Aerosol
98%
1
0.063
MR 351 - Mold Cleaner
69%
1
0.063
C-60 Solvent [TCE Cleaner]
Degreaser
90-100%
1
0.063
Total
16
1.000
0.2.8 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
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).
0.2.9 Number of Applications per 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 times per 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/brake x 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.
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0.2.10 Amount of Trichloroethylene 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 14.8 to 37.1 grams.
Equation L-23
Where:
Amt
Wd
Wtfrac
Na
Amt =
Wd x wtfrac x 28.3495^-
oz
Na
Amount of TCE used per application (g/application);
Weight of degreaser used per brake job (oz/job);
Weight fraction of TCE in aerosol degreaser (unitless); and
Number of degreaser applications per brake job (applications/job).
0.2.11 Operating Hours per Week
(GARB. 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).
0.2.12 Number of Brake Jobs per Work Shift
(GARB. 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.
Equation L-24
Where:
Nj
OHpW
N,=
936^- x 8^
site-year shif t
r„wee/cs „.. ...
52 x OHpW
yr r
Number of brake jobs per work shift (j ob s/site-shift); and
Operating hours per week (hr/week).
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1199
1200
Appendix P
SPOT CLEANING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODEL APPROACH AND
PARAMETERS
This appendix presents the modeling approach and model equations used in the Spot Cleaning Near-
Field/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 (AIHA. 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.
The model uses the following parameters to estimate exposure concentrations in the near-field and far-
field:
• Far-field size;
• Near-field size;
• Air exchange rate;
• Indoor air speed;
• Spot cleaner use rate;
• Vapor generation rate;
• Weight fraction of TCE 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 reasonably 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 95th and 50th percentile values. The
statistics were calculated directly in @Risk. The 95th percentile value was selected to represent a high-
end 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.
P.l Model Design Equations
Figure Apx P-l 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 near-
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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.
Vff 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 outdoor air.
Near-Field
Volatile Source
Figure Apx P-l. The Near-Field/Far-Field Model as Applied to the Spot Cleaning Near-Field/Far-
Field Inhalation Exposure Model
The model design equations are presented below in Equation M-l through Equation M-16.
Near-Field Mass Balance
Equation M-l
KvF ^ = CffQnf ~ CnfQnF + G
Far-Field Mass Balance
Equation M-2
dCFF
^FF~dt~ = ^NF®NF ~ ^FF^NF ~ Cff Qff
Where:
Vnf =
near-field volume;
Vff =
far-field volume;
Qnf =
near-field ventilation rate;
Qff =
far-field ventilation rate;
Cnf =
average near-field concentration;
Cff =
average far-field concentration;
G
average vapor generation rate; and
t
elapsed time.
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Both of the previous equations can be solved for the time-varying concentrations in the near-field and
far-field as follows (AIHA. 2QHQ'
Equation M-3
Equation M-4
Where:
Equation M-5
Equation M-6
Equation M-7
Equation M-8
Equation M-9
Equation M-10
Xx = 0.5
CNF = G(k1 + k2ex^ - k3ex^)
C
FF
= g(tt"
\Qff
+ kAeXlt — kc,eX2t
fci =
(<3kf f(2 J Q"
^ _ QnfQff + ^-2^nf(.Qnf + Qff)
QnfQffVnf(.^1 ~ ^2)
ko =
QnfQff + A.1Vnf(.Qnf + Qff)
QnfQffVnf(.^i ~ ^2)
/A1VNF + Qnf\
_ /A2Vnf + Qnf\ ,
5 v qnf ) 3
( QnfQff + Vnf(Qnf + Qff)
V vnfvff /
+
/QnfQff + ^nf(Qnf + Qff)\ _ . /Q/vfQff\
\ ^/VF^FF / ^ ^/VF^FF /
Equation M-ll
Az = 0.5
^Qnf^ff + Vnf(Qnf + QffT
VNFVFF
/ QnfQff + ^nf(.Qnf + Qff)\
\ ^/VF^FF /
^ /QnfQff\
V K/vf ^ff '
EPA calculated the hourly TWA concentrations in the near-field and far-field using the following
equations. Note that the numerator and denominator of Equation M-12 and Equation M-1313, use two
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different sets of time parameters. The numerator is based on the operating hours for the scenario while
the denominator is fixed to an averaging time span, t_avg, of 8 hours (since EPA is interested in
calculating 8-hr TWA exposures). Mathematically, the numerator 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 inherently includes an integral over the balance of the
eight hours equal to zero that is summed to the integral from ti to t2.
Equation M-12
Jft2 CNFdt J^ G(:' •< - j dt
C,,JWA = = ^ =
n ( t2 , k4eXlt2 kreX2t2\ n ( U , k4eXltl kseX2tl\
GWr + — —)-G\Q7r + — —)
tavg
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-14, below:
Equation M-14
FSA = 2{LnfHnf) + 2 (WnfHnf) + (LnfWnf)
Where: Lnf, Wnf, and Hnf are the length, width, and height of the near-field, respectively. The near-
field 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
1
Qnf — 2 vnfFSA
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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Equation M-16
Qff — VppAER
Using the model inputs in Table H-l, EPA estimated TCE inhalation exposures for workers in the near-
field and for occupational non-user 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.
P.2 Model Parameters
Table Apx P-l summarizes the model parameters and their values for the Spot Cleaning Near-Field/Far-
Field Exposure Model. Each parameter is discussed in detail in the following subsections.
Page 743 of 803
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TableApx P-l. Summary of Parameter Values and Distributions Used in the Spot Cleaning
i Near-Field/Far-Field Inhalation Exposure Model
Constant
Input
Parameter
Symbol
Unit
Model
Parameter
Values
Variable Model Parameter Values
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
Floor Area
A
ft2
—
—
500
20,000
—
Beta
Facility floor area is based on data
from the (C ARB, 2006) and King
Countv (Whittaker and Johanson,
2011) studv. ERG fit a beta function
to this distribution with parameters: ai
= 6.655, oi2 = 108.22, min = 500 ft2,
max = 20,000 ft2.
Far-field
volume
Vff
ft3
—
—
6,000
240,000
—
—
Floor area multiplied by height.
Facility height is 12 ft (median value
per (CARB, 2006) studv).
Near-field
length
Lnf
ft
10
—
—
—
—
—
Near-field
width
Wnf
ft
10
—
—
—
—
—
EPA assumed a constant near-field
volume.
Near-field
height
Hnf
ft
6
—
—
—
—
—
Air exchange
rate
AER
hr1
—
—
1
19
3.5
Triangular
Values based on (von Grote et al„
2006). and (U.S. EPA, 2013a). The
mode represents the midpoint of the
ranee reported in (U.S. EPA, 2013a).
Near-field
cm/s
—
—
0
202.2
—
Lognormal
Lognormal distribution fit to the data
indoor wind
speed
Vnf
ft/hr
—
—
0
23,882
—
Lognormal
presented in (Baldwin and Mavnard,
1998a).
Starting time
tl
hr
0
—
—
—
—
—
Constant value.
Exposure
Duration
t2
hr
—
—
2
5
—
Uniform
Equal to operating hours per day.
Averaging time
tavg
hr
8
—
—
—
—
—
Constant value.
Page 744 of 803
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Constant
Input
Parameter
Symbol
Unit
Model
Parameter
Values
Variable Model Parameter Values
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
Use rate
UR
gal/yr
8.4
—
—
—
—
—
(IRT A, 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.
mg/hr
—
—
2.97E+03
9.32E+04
—
Calculated
G is calculated based on UR and
Vapor
generation rate
G
g/min
—
—
0.05
1.55
—
Calculated
assumes 100% volatilization and
accounts for the weight fraction of
TCE.
TCE weight
fraction
wtfirac
wt firac
—
—
0.1
1
—
Uniform
(IRTA, 2007) observed TCE-based
spotting agents contain 10% to 100%
TCE.
Operating
hours per day
OH
hr/day
—
—
2
5
—
Uniform
Determined from a California survey
performed bv (Morris and Wolf,
2005) and an analvsis of two model
plants constructed by the researchers
Operating days
per year
OD
days/yr
—
—
249
313
300
Triangular
Operating days/yr distribution assumed
as triangular distribution with min of
250, max of 312, and mode of 300.
Page 745 of 803
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Input
Parameter
Symbol
Unit
Constant
Model
Parameter
Values
Variable Model Parameter Values
Comments
Value
Basis
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
Fractional
number of
operating days
that a worker
works
/
Dimensionles
s
1
—
0.8
1.0
—
Uniform
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).
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-time workers,
EPA defines/as a uniform distribution
ranging from 0.8 to 1.0. The 0.8 value
was derived from the observation that
the weighted average of 200 day/yr
worked (from BLS/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.
1315
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P.2.1 Far-Field Volume
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 California Dry Cleaning Industry Technical Assessment Report (CARB, 2006) and the
Local Hazardous Waste Management Program in King County A Profile of 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 height from the CARB (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 TableApx P-2
TableApx P-2. Composite Distribution of Dry Cleaning Facility Floor Areas
Percentile
Floor Area
(as
Value (ft2)
fraction)
Source
20,000
1
King County
3,000
0.96
King County
2,000
0.84
King County
1,600
0.5
CARB 2006
1,100
0.1
CARB 2006
500
0
CARB 2006
EPA fit a beta function to this distribution with parameters: ai = 6.655, <12 = 108.22, min = 500 ft2, max
= 20,000 ft2.
P.2.2 Near-Field Volume
EPA assumed a near-field of constant dimensions of 10 ft wide by 10 ft long by 6 ft high resulting in a
total volume of 600 ft3.
P.2.3 Air Exchange Rate
(von Grote et al.. 2006) indicated typical air exchange rates (AERs) of 5 to 19 hr"1 for dry cleaning
facilities in Germany. (Klein and Kurz. 1994a) indicated AERs of 1 to 19 hr"1, with a mean of 8 hr"1 for
dry cleaning facilities in Germany. During the 2013 peer review of EPA's 2013 draft risk assessment of
TCE, a peer reviewer indicated that air exchange rate values around 2 to 5 hr"1 are likely (U.S. EPA
2013a). in agreement with the low end of the ranges reported by von Grote et al. and (Klein and Kurz.
1994a). A triangular distribution 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).
P.2.4 Near-Field Indoor Wind Speed
(Baldwin and Mavnard. 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 Mavnard. 1998a) and categorizing the air speed
surveys into settings representative of industrial facilities and representative of commercial facilities.
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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 Mayni '8a).
The air speed surveys representative of commercial facilities were fit to a lognormal distribution with
the following parameter values: mean of 10.853 cm/s and standard deviation of 7.883 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. andMavnard. 1998a) to prevent the model from
sampling values that approach infinity or are otherwise unrealistically large.
(Baldwin andMavnard. 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.
P.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.
P.2.6 Use Rate
EPA used a top-down approach to estimate use rate based on the volume of TCE-based spotting agent
sold in California and the number of textile cleaning facilities in California.
(IRTA. 2.007) estimated 42,000 gal of TCE-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.
P.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 ( 8d). To calculate an hourly 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.
P.2.8 Operating Hours
(Morris and Wolf. 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 100,000 lb of clothes annually. The authors modeled the small dry
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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.
P.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 year.31 The low-end operating days per year is based on the assumption 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.
P.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 uniform 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 of 300 day/yr).
The low end of the range, 0.8, was derived from the observation that the weighted average of 200 day/yr
worked (from BLS/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).
31 For modeling purposes, the minimum value was set to 249 days per year and the maximum to 313 days per year; however,
these values have a probability of zero; therefore, the true range is from 250 to 312 days per year.
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Appendix Q OCCUPATIONAL INHALATION EXPOSURE AND
WATER RELEASE ASSESSMENT
Q.l Manufacturing
Q.l.l Exposure Assessment
EPA assessed inhalation exposures during manufacturing using identified inhalation exposure
monitoring data. TableApx Q-l summarizes 8-hr TWA samples obtained from data submitted by
Arkema, Inc., a TCE manufacturer (Arkema.. 2020). and by the Halogenated Solvents Industry Alliance
(HSIA) (Halogenated Solvents Industry Alliance. 2018) via public comment for one company listed as
"Company B". HSIA also provided "General 12-hr" full-shift exposure data from "Company A".
However, "Company A" data points were listed as "Not detected <0.062 ppm. Two additional studies
with monitoring data for manufacturing were identified; however, the data from these studies were not
used as the data were from China and almost 30 years old and are unlikely to be representative of
current conditions at U.S. manufacturing sites. No data were found to estimate ONU exposures during
TCE manufacturing. EPA estimates that ONU exposures are lower than worker exposures, since ONUs
do not typically directly handle the chemical.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 50 data points from 2 sources, and the
data quality ratings from systematic review for these data were high. The primary limitations of these
data 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. 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 high.
Table Apx Q-l. Summary of Worker Inhalation Exposure Monitoring Data from TCE
Manufacturing
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC"
(ppm)
Nil m her
of l);i(;i
Points
Con riclence
of Air
Concenlriilion
lliyli-Lnd
:.4o
us:
U.Vj
ujy
50
High
Central
Tendency
0.12
3.8E"2
2.6E"2
l.OE2
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
Q.l.2 Water Release Assessment
In general, potential sources of water releases in the chemical industry may include the following:
equipment cleaning operations, aqueous wastes from scrubbers/decanters, reaction water, process water
from washing intermediate products, and trace water settled in storage tanks (OECD. 2019). Based on
the process for manufacturing TCE, EPA expects the sources of water releases to be from aqueous
wastes from decanters used to separate catalyst fines, caustic neutralizer column, and caustic scrubbers;
and water removed from the TCE product in drying columns (Most. 1989). Additional water releases
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may occur if a site uses water to clean process equipment; however, EPA does not expect this to be a
primary source of water releases from manufacturing sites as equipment cleaning is not expected to
occur daily and manufacturers would likely use an organic solvent to clean process equipment.
Of the five manufacturing sites assessed, three reported in the 2016 TRI (one of these three sites
reported zero water releases to TRI). Additionally, one of these sites also reported to 2016 DMR. For the
sites that reported water releases, EPA assessed water releases as reported in the 2016 TRI and 2016
DMR. For the remaining two sites, EPA assessed water releases at the maximum daily and maximum
average monthly concentrations allowed under the Organic Chemicals, Plastics and Synthetic Fibers
(OCPSF) Effluent Guidelines (EG) and Standards (40 C.F.R. Part 414) ( M9g). The OCPSF
EG applies to facilities classified under the following SIC codes:
• 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 apply to these sites. Subparts I, J, and K of the OCPSF EG set limits for the
concentration of TCE in wastewater effluents 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 40 C.F.R. Part 414 (U.S.
). Direct dischargers are facilities that discharge effluents directly to surface waters and
indirect dischargers are facilities that discharge effluents to publicly-owned treatment works (POTW).
The OCPSF limits for TCE are provided in Table Apx Q-2.
Table Apx Q-2. Summary of OCPSF Effluent Limitations for Trichloroethylene
OCPSF Subpart
.Maximum
for Any One
Day
(iili/I)
.Maximum for
Any Monthly
Average
(MS/')
Basis
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
Subpart K - Indirect Discharge
Point Sources
69
26
Pretreatment Standards for
Existing Sources (PSES) and
Pretreatment Standards for New
Sources (PSNS)
BAT = Best Available Technology Economically Achievable; NSPS = New Source Performance Standards; PSES =
Pretreatment Standards for Existing Sources; PSNS = Pretreatment Standards for New Sources.
Source: (U.S. EPA. 2019g)
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
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for the manufacture of a substance estimates 10 m3 of wastewater generated per metric ton of substance
produced (ESIG. 2012). In lieu of TCE-specific information, EPA estimated water releases using the
SpERC specified wastewater production volume and the annual TCE production rates from each facility.
EPA estimated both a maximum daily release and an average daily release using the OCPSF EG
limitations for TCE for maximum on any one day, and maximum for any monthly average, respectively.
Prevalence of end-of-pipe biological treatment at TCE manufacturing sites is unknown; therefore, EPA
used limitations for direct discharges with no end-of-pipe biological treatment and indirect dischargers
to address the uncertainty at these sites. EPA estimated annual releases from the average daily release
and assuming 350 days/yr of operation.32
TableApx Q-3 summarizes water releases from the manufacturing process for sites reporting to TRI
and Table Apx Q-4 summarizes water releases from sites not reporting to TRI. The estimated total
annual release across all sites is 79.2 - 472.3 kg/yr discharged to surface water or POTWs.
Table Apx Q-3. Reported Water Releases of Trichloroethylene from Manufacturing Sites
Reporting to 2016 TRI
Nile
Annual
Release"
(kg/sile-vr)
Annual
Release
Days
(davs/vr)
Average
Daily
Release"
(kg/sile-dav)
NPDKSC ode
Release
Media
Olin Blue Cube, Freeport,
TX
24
350
0.07
TX0059447
non-POTW
WWT
Geon Oxy Vinyl Laporte
Plant,
Laporte, TX
0
N/A
0
TX0070416
N/A
Occidental Chemical Corp.
Wichita, KS
0
N/A
0
Not available
N/A
Axiall Corporation dba
Eagle US 2 LLC,
Westlake, LAb
49.9-443c
350
0.14-1.27
LA0000761d
Surface
Water
POTW = Publicly-Owned Treatment Works; WWT = Wastewater Treatment; N/A = Not applicable
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 300 days of operation per year.
b Axiall was purchased by Westlake Chemical in 2016. The site at 1300 PPG Drive Westlake, LA dba Eagle US 2 LLC.
°First value based on 2016 TRI, second value based on 2016 DMR data (U.S. EPA. 2016a').
dBased on Eagle US 2 LLC NPDES Permit provided in DMR Data (U.S. EPA. 2016a').
32 Due to large throughput, manufacturing sites are assumed to operate seven days per week and 50 weeks per year with two
weeks per year for shutdown activities.
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TableApx Q-4. Estimated Water Releases of Trichloroethylene from Manufacturing Sites Not
Reporting to 2016 TRI
Site
Annual
Operalin
g Days
(davs/vr)
Daily
Prod ucl io
n Volume"
(kg/she-
da v)
Daily
Was lew ale
r I-low1'
(l./sile-day)
Maxiinu
in Daily
Release'
(kg/sile-
day)
Averag
e Daily
Release
(i
(kg/sile-
day)
Averag
e
Annual
Release
V
(kg/sile-
vr)
npdi:
SCode
Releas
e
Media
Solvents
&
Chemicals
5
Pearland,
TX
35
0
58,234
582,345
0.04
0.02
5.3
Not available
Surface
Water
or
POTW
POTW = Publicly-Owned Treatment Works
a Daily production volume calculated using the annual production volume and dividing by the annual operating days per year
(300 days/yr).
b The estimated wastewater flow rate is calculated assuming 10 m3 of wastewater is produced per metric ton of TCE
produced (equivalent to 10 L wastewater/kg of TCE) based on the SpERC for the manufacture of a substance (ESIG. 20.1.21.
0 The maximum daily release is calculated using the maximum daily concentration from the OCPSF EG, 26 |ig/L. and
multiplying by the daily wastewater flow.
d The average daily release is calculated using the maximum monthly average concentration from the OCPSF EG, 69 |ig/L.
and multiplying by the daily wastewater flow.
e The average annual release is calculated as the maximum monthly average concentration multiplied by the daily wastewater
production, and 350 operating days/year.
Q.2 Processing as a Reactant
Q.2.1 Exposure Assessment
EPA did not identify inhalation exposure monitoring data related processing TCE as a reactant.
Therefore, EPA used monitoring data from the manufacture of TCE as surrogate. EPA believes the
handling and TCE concentrations for both conditions of use to be similar. However, EPA is unsure of
the representativeness of these surrogate data toward actual exposures to TCE at all sites covered by this
condition of use.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 50 data points from 2 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 representativeness of 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.
The surrogate data were obtained from (HSIA) via public comment fllalogemated Solvents Industry
Alliance. 2.018) and from the TCE manufacturer Arkema ( :ma, 2020). presented in Table Apx Q-5
below. No data were found to estimate ONU exposures during use of TCE as a reactant. EPA estimates
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that ONU exposures are lower than worker exposures, since ONUs do not typically directly handle the
chemical.
TableApx Q-5. Summary of Worker Inhalation Exposure Surrogate Monitoring Data from TCE
Use as a Reactant
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Number of
Diilii Points
Confidence Killing ol' Associated
Air Concentriition D;il;i
High-End
2.46
0.82
0.56
0.29
50
Medium
Central
Tendency
0.12
3.8E"2
2.6E"2
1.0E"2
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
Q.2.2 Water Release Assessment
In general, potential sources of water releases in the chemical industry may include the following:
equipment cleaning operations, aqueous wastes from scrubbers/decanters, reaction water, process water
from washing intermediate products, and trace water settled in storage tanks (OECD. 2019). Based on
the use as a reactant, EPA expects minimal sources of TCE release to water.
Two of the three sites reporting to TRI did not report any water releases of TCE; the other TRI site
reported 13 lb/yr (5.9 kg/yr) released to water. For the two sites found through DMR data, total water
releases were calculated to be approximately 11 lb/yr (5 kg/yr). Based on the information for these 5
sites, an average annual release of approximately 2.2 kg/site-yr was calculated. Using this estimate, and
assuming 440 sites as a high-end estimate, the total TCE water discharge from these 440 sites equal
approximately 968 kg/yr. Table Apx Q-6 summarizes the low and high end water release estimates.
Table Apx Q-6. Water Release Estimates for Sites Using TCE as a
leactant
Number of Silos
An niiiil
Kele.ise
(Uii/sik'-\ r)
Anniiiil
Kele.ise Dsijs
(da\s/\ n
l)ail\
Kele.ise
(kii/sile-il;i>)
Ni»i)i:s
Code
Kele.ise Mediii
Low End Number of Sites
Arkema Inc., Calvert City, KY
5.9
350
0.02
KY0003603
Surface Water
Honeywell International -
Geismar Complex, Geismar,
LA
4.5
350
0.01
LA0006181
Surface Water
Praxair Technology Center,
Tonawanda, NY
0.6
350
1.7E-03
NY0000281
Surface Water
High End Number of Sites
440 unknown sites
2.2a
350
6.3E-03
N/A
Surface Water
orPOTW
a Calculated from the total yearly water releases of TCE from DMR and TRI data, and diving by the number of reporting sites
(5 sites). Mexichem Fluor Inc. and Halocarbon Products Corp reported no water releases to TRI.
Q.3 Formulation of Aerosol and Non-Aerosol Products
Q.3.1 Exposure Assessment
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, as
EPA believes the handling and TCE concentrations for both conditions of use to be similar. However,
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EPA is unsure of the representativeness of these surrogate data toward actual exposures to TCE at all
sites covered by this condition of use.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 limitations of these data
include the uncertainty of the representativeness of 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.
TableApx Q-7 summarizes the 8-hr TWA from monitoring data from unloading/loading TCE from
bulk containers. The data were obtained from a Chemical Safety Report (DOW Deutschland. 2014b).
No data were found to estimate ONU exposures during formulation of aerosol and non-aerosol products.
EPA estimates that ONU exposures are lower than worker exposures, since ONUs do not typically
directly handle the chemical.
Table Apx Q-7. Summary of Worker Inhalation Exposure Monitoring Data for Unloading TCE
During Formulation of Aerosol and Non-Aerosol Products
Sceiiiirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Number of
Points
Confidence
killing of Air
CoiHTnlnilion
D:il;i
High-End
1.1
0.4
0.3
0.1
33
Medium
Central
Tendency
4.9E-4
1.6E-4
1.1E-4
4.5E-5
AC= Acute Exposure and ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Q.3.2 Water Release Assessment
In general, potential sources of water releases in the chemical industry may include the following:
equipment cleaning operations, aqueous wastes from scrubbers/decanters, reaction water, process water
from washing intermediate products, and trace water settled in storage tanks ("OECD. 2019). Based on
the use in formulations and the amount of TCE used for this condition of use, EPA expects minimal
sources of TCE release to water.
None of the sites reporting to TRI reported any water releases of TCE. All releases were to off-site land,
incineration or recycling. Based on this information, EPA does not have enough information to estimate
water releases of TCE for this condition of use.
Q.4 Repackaging
Q.4.1 Exposure Assessment
EPA identified inhalation exposure monitoring data related unloading/loading TCE into/from bulk
transport containers. Table Apx Q-8 summarizes the 8-hr TWA from monitoring data from
unloading/loading TCE from bulk containers. The data were obtained from a Chemical Safety Report
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(DOW Deutschlamd. 2014b). It should be noted that this study indicates that the filling system uses a
"largely automated process" (DOW Deutschlamd. 2014b). Therefore, EPA is unsure of the
representativeness of these data toward actual exposures to TCE for all sites covered by this condition of
use.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 33 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 representativeness of 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 high.
No data were found to estimate ONU exposures during formulation of aerosol and non-aerosol products.
EPA estimates that ONU exposures are lower than worker exposures, since ONUs do not typically
directly handle the chemical.
TableApx Q-8. Summary of Worker Inhalation Exposure Monitoring Data for
Unloading/Loading TCE from Bulk Containers
Sceiiiirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Number of
Points
Confidence
killing of Air
CoiHTnlnilion
D:il;i
High-End
1.1
0.4
0.26
0.1
33
Medium to High
Central
Tendency
4.9E-4
1.6E-4
1.1E-4
4.5E-5
AC= Acute Exposure and ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Q.4.2 Water Release Assessment
EPA expects the primary source of water releases from repackaging activities to be from the use of
water or steam to clean bulk containers used to transport TCE or products containing TCE. EPA expects
the use of water/steam for cleaning containers to be limited at repackaging sites as TCE is an organic
substance and classified as a hazardous waste under RCRA. EPA expects the majority of sites to use
organic cleaning solvents which would be disposed of as hazardous waste (incineration or landfill) over
water or steam.
Water releases during repackaging were assessed using data reported in the 2016 DMR and 2016 TRI.
One of the 20 sites reporting to TRI reported water releases of TCE to off-site wastewater treatment. All
other sites reporting to TRI reported releases to off-site land or incineration. EPA assessed annual
releases as reported in the 2016 DMR and assessed daily releases by assuming 250 days of operation per
year. A summary of the water releases reported to the 2016 DMR and TRI can be found in Table Apx
Q-9.
Page 756 of 803
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1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
Table Apx Q-9. Reported Water Releases of Trichloroethylene from Sites Repackaging TCE
Silo l(kiili(>
Aniuiiil
Release
(kg/sile-
\ r);i
Anmiiil Release
l)n\s (d;i\s/\n
l):iil\ Rele;ise
(kii/sik'-(l;i\ )•'
SIMMS
Code
Release
Media
Hubbard-Hall Inc, Waterbury,
CT
277
250
1.1
Not
available
Non-POTW
WWT
St. Gabriel Terminal, Saint
Gabriel, LA
1.4
250
5.5E-03
LA0052353
Surface
Water
Vopak Terminal Westwego
Inc, Westwego, LA
1.2
250
4.7E-03
LAO 124583
Surface
Water
Oiltanking Houston Inc,
Houston, TX
0.8
250
3.3E-03
TX0091855
Surface
Water
Research Solutions Group Inc,
Pelham, AL
0.01
250
3.3E-05
AL0074276
Surface
Water
Carlisle Engineered Products
Inc, Middlefield, OH
1.7E-3
250
6.8E-06
OH0052370
Surface
Water
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 250 days of operation per year.
Sources: (U.S. EPA. 2016a1 and (TJ.S. EPA. 2017c)
Q.5 Batch Open Top Vapor Degreasing
Q.5.1 Exposure Assessment
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 how 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. The following subsections detail the results of EPA's
occupational exposure assessment for batch open-top vapor degreasing based on inhalation exposure
monitoring data and modeling.
Table Apx Q-10 summarizes the 8-hr TWA monitoring data for the use of TCE in OTVDs. The data
were obtained from NIOSH Health Hazard Evaluation reports (HHEs). NIOSH HHEs are conducted at
the request of employees, employers, or union officials, and provide information on existing and
potential hazards present in the workplaces evaluated (Daniels et at.. 1988). (Ruhe et at.. 1981). (Barsan.
1991). (Ruhe. 1982.). (Rosensteel and Lucas. 1975). (Seitz and Driscoll. 1989). (Gorman etal. 1984).
(Gilles et al.. 1977). (Vandervort and Potakoff 1' >" »). and (Lewis. 1980).
Data from these sources cover exposures at several industries including metal tube production, valve
manufacturing, jet and rocket engine manufacture, air conditioning prep and assembly, and AC motor
parts (Ruhe et al... 1981). (Barsan. 1991). (Rosensteel and Lucas. 1975). (Gorman et al.. 1984).
(Vandervort and Polakoff. 1973). and (Lewis. 1980). Except for one site, sample times ranged from
approximately five to eight hours (Ruhe et al.. 1981). (Barsan. 1991). (Rosensteel and Lucas. 1975).
(Gorman et al.. 1984). and (Lewis. 1980). The majority of samples taken at the other site were taken for
2 hours or less (Vandervort and Polakoff. 1973). Where sample times were less than eight hours, EPA
converted to an 8-hr TWA assuming exposure outside the sample time was zero. For sample times
greater than eight hours, EPA left the measured concentration as is. It should be noted that additional
sources for degreasing were identified but were not used in EPA's analysis as they either: 1) did not
specify the machine type in use; or 2) only provided a statistical summary of worker exposure
monitoring.
Page 757 of 803
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1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
TableApx Q-10. Summary of Worker Inhalation Exposure Monitoring Data for Batch Open-Top
Vapor Degreasing
Scenario
8-hr
TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Nilmher of
Dili:t Points
Confidence U;ilin<>
of Air
Concentriilion
Workers
High-End
77.8
25.9
17.8
9.1
113
Medium
Central Tendency
13.8
4.6
3.2
1.3
Occupational non-users
High-End
9.1
3.0
2.1
1.1
10
Medium
Central Tendency
1.1
0.4
0.3
0.1
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 limitations of
these data 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. 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.
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. 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 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 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-hr TWA data in this scenario is medium to low.
Figure Apx Q-l illustrates the near-field/far-field model that can be applied to open-top vapor
degreasing (AIRA. 2009). As the figure shows, volatile TCE vapors evaporate into the near-field,
resulting in worker exposures at a concentration Cnf. The concentration is directly proportional to the
evaporation rate of TCE, G, into the near-field, whose volume is denoted by Vnf. The ventilation rate
for the near-field zone (Qnf) determines how quickly TCE dissipates into the far-field, resulting in
occupational non-user exposures to TCE at a concentration Cff. Vff 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.
Page 758 of 803
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1728
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1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
Far-Field
FigureApx Q-l. Schematic of the Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation
Exposure Model
To estimate the TCE vapor generation rate, the model developed a distribution from the reported annual
emission rates and annual operating times reported in the 2014 NEI. NEI records where the annual
operating time was not reported were excluded from the distribution.
Batch degreasers are assumed to operate between two and 24 hours per day, based on NEI data on the
reported operating hours for OTVD using TCE. EPA performed a Monte Carlo simulation with 100,000
iterations and the Latin Hypercube sampling method in @Risk to calculate 8-hour TWA near-field and
far-field exposure concentrations. Near-field exposure represents exposure concentrations for workers
who directly operate the vapor degreasing equipment, whereas far-field exposure represents exposure
concentrations for occupational non-users (i.e., workers in the surrounding area who do not handle the
degreasing equipment).
Table Apx Q-l 1 presents a statistical summary of the exposure modeling results. These exposure
estimates represent modeled exposures for the workers and occupational non-users. For workers, the
50th percentile exposure is 34.8 ppm 8-hr TWA, with a 95th percentile of 388 ppm 8-hr TWA.
Both of these values are an order of magnitude higher than identified in the monitoring data. This may
be due to the limited number of sites from which the monitoring data were taken whereas the model is
meant to capture a broader range of scenarios. It is also uncertain of the underlying methodologies used
to estimate emissions in the 2014 NEI data.
Page 759 of 803
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1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
Table Apx Q-ll. Summary of Exposure Modeling Results for TCE Degreasing in OTVDs
8-hr TWA
AC '
ADC
LAIK
Confidence killing of Air
Percentile
(ppm)
(ppm)
(ppm)
(ppm)
ConceiKriilion D;it;i
Workers (Near-field)
High-End
388
129.3
88.5
35.3
Central
Tendency
34.8
79.0
8.0
3.0
N/A - Modeled Data
Occupational non-users (Far-Field)
High-End
237
79.0
54.0
21.1
Central
Tendency
18.1
6.0
4.1
1.5
N/A - Modeled Data
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
a Acute exposures calculated as a 24-hr TWA.
Q.5.2 Water Release Assessment
The primary source of water releases from OTVDs is wastewater from the water separator. Water in the
OTVD may come from two sources: 1) Moisture in the atmosphere that condenses into the solvent when
exposed to the condensation coils on the OTVD; and/or 2) steam used to regenerate carbon adsorbers
used to control solvent emissions on OTVDs with enclosures (Durkee. 2014; Kamegsbere and
Kameesbere. 2.011; NIOSH. 2002a. b, c, d). The water is removed in a gravity separator and sent for
disposal (NIOSH. 2002a. b, c, d). The current disposal practices of the wastewater are unknown;
however, a 1982 EPA (Gilbert et al. 1982) report estimated 20% of water releases from metal cleaning
(including batch systems, conveyorized systems, and vapor and cold systems) were direct discharges to
surface water and 80% of water releases were discharged indirectly to a POTW.
Water releases for OTVDs were assessed using data reported in the 2016 TRI and 2016 DMR. Due to
limited information in these reporting programs, these sites may in fact not operate OTVDs, but may
operate other solvent cleaning machines or perform metalworking activities. They are included in the
OTVD assessment as EPA expects OTVDs to be the most likely condition of use. EPA assessed annual
releases as reported in the 2016 TRI or 2016 DMR and assessed daily releases by assuming 260 days of
operation per year, as recommended in the 2017 ESD on Use of Vapor Degreasers, and averaging the
annual releases over the operating days. A summary of the water releases reported to the 2016 TRI and
DMR can be found in TableApx Q-12.
TableApx Q-12. Reported Water Releases of Trichloroethylene from Sites Using TCE in Open-
Top Vapor Degreasing
Sile Itlonlil>
Amiiiiil
Release
(k;i/si(c-\r)
Amiiiiil
Release
l);i\ s
(d;i\s/\r)
l);iil\
Release
(kii/silo-
d;i>)
SIMMS
Code
Rolen so Mediii
I S \asa \ 1 iclioud \ssemhl\
Facility, New Orleans, LA
5()'J
2<>u
1
Surface Walcr
GM Components Holdings LLC,
Lockport, NY
34.2
260
0.13
NY0000558
Surface Water
Akebono Elizabethtown Plant,
Elizabethtown, KY
17.9
260
0.07
KY0089672
Surface Water
Delphi Harrison Thermal
Systems, Dayton, OH
9.3
260
0.04
OH0009431
Surface Water
Page 760 of 803
-------
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
(l;i\)
M»i)i:s
Cock'
Rolen so Media
Chemours Company Fc LLC,
Washington, WV
6.7
260
0.03
WV0001279
Surface Water
Equistar Chemicals LP, La
Porte, TX
4.4
260
0.02
TX0119792
Surface Water
GE Aviation, Lynn, MA
2.6
260
0.01
MA0003905
Surface Water
Certa Vandalia LLC, Vandalia,
OH
2.1
260
0.01
OHO 122751
Surface Water
GM Components Holdings LLC
Kokomo Ops, Kokomo, IN
1.7
260
0.01
IN0001830
Surface Water
Amphenol Corp-Aerospace
Operations, Sidney, NY
1.6
260
0.01
NY0003824
Surface Water
Emerson Power Trans Corp,
Maysville, KY
1.6
260
0.01
KY0100196
Surface Water
Olean Advanced Products,
Olean, NY
1.4
260
0.01
NY0073547
Surface Water
Texas Instruments, Inc.,
Attleboro, MA
1.3
260
5.18E-03
MA0001791
Surface Water
Hollingsworth Saco Lowell,
Easley, SC
1.2
260
4.69E-03
SC0046396
Surface Water
Trelleborg YSH Incorporated
Sandusky Plant, Sandusky, MI
0.9
260
3.60E-03
MI0028142
Surface Water
Timken Us Corp Honea Path,
Honea Path, SC
0.9
260
3.55E-03
SC0047520
Surface Water
Johnson Controls Incorporated,
Wichita, KS
0.6
260
2.28E-03
KS0000850
Surface Water
Accellent Inc/Collegeville
Microcoax, Collegeville, PA
0.6
260
2.22E-03
PA0042617
Surface Water
National Railroad Passenger
Corporation (Amtrak)
Wilmington Maintenance
Facility, Wilmington, DE
0.5
260
2.03E-03
DE0050962
Surface Water
Electrolux Home Products
(Formerly Frigidaire),
Greenville, MI
0.5
260
2.01E-03
MI0002135
Surface Water
Rex Heat Treat Lansdale Inc,
Lansdale, PA
0.5
260
1.94E-03
PA0052965
Surface Water
Carrier Corporation, Syracuse,
NY
0.5
260
1.77E-03
NY0001163
Surface Water
Globe Engineering Co Inc,
Wichita, KS
0.5
260
1.74E-03
KS0086703
Surface Water
Cascade Corp (0812100207),
Springfield, OH
0.3
260
1.17E-03
OH0085715
Surface Water
USAF-Wurtsmith AFB, Oscoda,
MI
0.3
260
1.15E-03
MI0042285
Surface Water
AAR Mobility Systems,
Cadillac, MI
0.3
260
1.12E-03
MI0002640
Surface Water
Eaton Mdh Company Inc,
Kearney, NE
0.3
260
1.07E-03
NE0114405
Surface Water
Motor Components L C, Elmira,
NY
0.3
260
9.64E-04
NY0004081
Surface Water
Salem Tube Mfg, Greenville, PA
0.233
260
8.97E-04
PA0221244
Surface Water
Ametek Inc. U.S. Gauge Div.,
Sellersville, PA
0.227
260
8.72E-04
PA0056014
Surface Water
Page 761 of 803
-------
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
(l;i\)
M»i)i:s
Cock'
Rolen so Media
GE (Greenville) Gas Turbines
LLC, Greenville, SC
0.210
260
8.06E-04
SC0003484
Surface Water
Parker Hannifin Corporation,
Waverly, OH
0.194
260
7.47E-04
OH0104132
Surface Water
Mahle Enginecomponents USA
Inc, Muskegon, MI
0.193
260
7.42E-04
MI0004057
Surface Water
General Electric Company -
Waynesboro, Waynesboro, VA
0.191
260
7.33E-04
VA0002402
Surface Water
Gayston Corp, Dayton, OH
0.167
260
6.43E-04
OH0127043
Surface Water
Styrolution America LLC,
Channahon, IL
0.166
260
6.37E-04
IL0001619
Surface Water
Remington Arms Co Inc, Ilion,
NY
0.159
260
6.12E-04
NY0005282
Surface Water
Lake Region Medical, Trappe,
PA
0.1
260
5.06E-04
Not available
Surface Water
United Technologies
Corporation, Pratt And Whitney
Division, East Hartford, CT
0.1
260
4.80E-04
CT0001376
Surface Water
Atk-Allegany Ballistics Lab
(Nirop), Keyser, WV
0.1
260
4.70E-04
WV0020371
Surface Water
Techalloy Co Inc, Union, IL
0.1
260
4.27E-04
IL0070408
Surface Water
Owt Industries, Pickens, SC
0.1
260
3.14E-04
SC0026492
Surface Water
Boler Company, Hillsdale, MI
0.1
260
2.69E-04
MI0053651
Surface Water
Mccanna Inc., Carpentersville,
IL
0.1
260
2.68E-04
IL0071340
Surface Water
Cutler Hammer, Horseheads,
NY
0.1
260
2.38E-04
NY0246174
Surface Water
Sperry & Rice Manufacturing
Co LLC, Brookville, IN
8.54E-02
260
3.28E-04
IN0001473
Surface Water
US Air Force Offutt Afb Ne,
Offiitt A F B, NE
4.14E-02
260
1.59E-04
NE0121789
Surface Water
Troxel Company, Moscow, TN
3.49E-02
260
1.34E-04
TN0000451
Surface Water
Austin Tube Prod, Baldwin, MI
2.96E-02
260
1.14E-04
MI0054224
Surface Water
LS Starrett Precision Tools,
Athol, MA
2.65E-02
260
1.02E-04
MA0001350
Surface Water
Avx Corp, Raleigh, NC
2.30E-02
260
8.83E-05
NC0089494
Surface Water
Handy & Harman Tube Co/East
Norriton, Norristown, PA
1.61E-02
260
6.17E-05
PA0011436
Surface Water
Indian Head Division, Naval
Surface Warfare Center, Indian
Head, MD
1.08E-02
260
4.16E-05
MD0003158
Surface Water
General Dynamics Ordnance
Tactical Systems, Red Lion, PA
6.34E-03
260
2.44E-05
PA0043672
Surface Water
Trane Residential Solutions -
Fort Smith, Fort Smith, AR
3.46E-03
260
1.33E-05
AR0052477
Surface Water
Lexmark International Inc.,
Lexington, KY
3.23E-03
260
1.24E-05
KY0097624
Surface Water
Alliant Techsystems Operations
LLC, Elkton, MD
3.02E-03
260
1.16E-05
MD0000078
Surface Water
Daikin Applied America, Inc.
(Formally Mcquay
International), Scottsboro, AL
2.15E-03
260
8.26E-06
AL0069701
Surface Water
Page 762 of 803
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1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
(l;i\)
M»i)i:s
Cock'
Rolen so Media
Beechcraft Corporation,
Wichita, KS
2.04E-03
260
7.86E-06
KS0000183
Surface Water
Federal-Mogul Corp, Scottsville,
KY
1.50E-03
260
5.78E-06
KY0106585
Surface Water
Cessna Aircraft Co (Pawnee
Facility), Wichita, KS
1.36E-03
260
5.24E-06
KS0000647
Surface Water
N.G.I, Parkersburg, WV
3.43E-04
260
1.32E-06
WV0003204
Surface Water
Hyster-Yale Group, Inc,
Sulligent, AL
2.35E-04
260
9.03E-07
AL0069787
Surface Water
Hitachi Electronic Devices
(USA), Inc., Greenville, SC
6.58E-05
260
2.53E-07
SC0048411
Surface Water
WWT = Wastewater Treatment
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 260 days of operation per year.
Sources: 2016 TRI (US. EPA. 2017c): 2016 DMR (U.S. EPA. 2016a)
Data from TRI and DMR may not represent the entirety of sites using TCE in OTVDs. EPA did not
identify other data sources to estimate water releases from sites not reporting to TRI or DMR. However,
sites operating degreasers are regulated by the following national ELGs:
• Electroplating Point Source Category Subparts A, B, D, E, F, G, and H (U.S. EPA... 2019d);33
• Iron and Steel Manufacturing Point Source Category Subpart J ( 2);
• Metal Finishing Point Source Category Subpart A ( 2019D;34
• Coil Coating Point Source Category Subpart D ( );
• Aluminum Forming Point Source Category Subparts A, B, C, D, E, and F ( 319a);
and
• Electrical and Electronic Components Point Source Category Subparts A and B (U.S. EPA.
2019c).
All above ELGs set discharges limits based on the total toxic organics (TTO) concentration in the
wastewater stream and not a specific TCE limit. TTO is the summation of the concentrations for a
specified list of pollutants which may be different for each promulgated ELG and includes TCE for the
above referenced ELGs. Therefore, the concentration of TCE in the effluent is expected to be less than
the TTO limit.
The operation of the water separator via gravity separation is such that the maximum concentration of
TCE leaving the OTVD is equal to the solubility of TCE in water, 1,280 mg/L (Durkee. 2014). In cases
where this concentration exceeds the limit set by the applicable ELGs, EPA expects sites will perform
some form of wastewater treatment for the effluent stream leaving the OTVD to ensure compliance with
the ELG prior to discharge. EPA did not identify information on the amount of wastewater generated
from OTVDs to estimate releases from sites not reporting to TRI or DMR.
33 The Electroplating ELG applies only to sites that discharge to POTW (indirect discharge) that were in operation before
July 15, 1983. Processes that began operating after July 15, 1983 and direct dischargers are subject to the Metal Finishing
ELG (40 C.F.R Part 433).
34 The Metal Finishing ELG do not apply when wastewater discharges from metal finishing operations are already regulated
by the Iron and Steel, Coil Coating, Aluminum Forming, or Electrical and Electronic Components ELGs.
Page 763 of 803
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1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
Q.6 Batch Closed-Loop Vapor Degreasing
Q.6.1 Exposure Assessment
EPA identified inhalation exposure monitoring data from a European Chemical Safety report using TCE
in closed degreasing operations. However, it is unclear how representative these data are of a "typical"
batch closed-loop degreasing shop. TableApx Q-13 summarizes the 8-hr TWA monitoring data for the
use of TCE in vapor degreasers. The data were obtained from a Chemical Safety Report (DOW
Deutschland. 2014a).
Data from these sources cover exposures at several industries where industrial parts cleaning occurred
using vapor degreasing in closed systems. It should be noted that additional sources for degreasing were
identified but were not used in EPA's analysis as they either: 1) did not specify the machine type in use;
or 2) only provided a statistical summary of worker exposure monitoring.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 representativeness of 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 high.
Table Apx Q-13. Summary of Worker Inhalation Exposure Monitoring Data for Batch Closed-
Loop Vapor Degreasing
Scenario
8-hr
TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Number of l);K;i
Points
Confidence R;tlin» of
Air Concentmtion
Diitii
High-End
1.4
0.5
0.3
0.2
Central
Tendency
19
0.5
0.2
0.1
0.04
High
AC = Acute Concentration, ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Q.6.2 Water Release Assessment
Similar to OTVDs, the primary source of water releases from closed-loop systems is wastewater from
the water separator. However, unlike OTVDs, no water is expected to enter the system through
condensation (Durkee. 2014). The reason for this is that enclosed systems flush the work chamber with
water-free vapor (typically nitrogen gas) after the parts to be cleaned are added to the chamber and the
chamber is sealed but before the solvent enters (Durkee. 2014). Multiple flushes can be performed to
reduce the concentration of water to acceptable levels prior to solvent cleaning (Durkee. 2014).
Therefore, the primary source of water in closed-loop systems is from steam used to regenerate carbon
adsorbers (Durkee. 201 I; Kaneesbere and Kameesbere. -01 J; ^ U »SH. 2002a. b, c, d). Similar to
OTVDs, the water is removed in a gravity separator and sent for disposal (NIOSH. 2002a. b, c, d). As
indicated in the OTVD assessment, current disposal practices of the wastewater are unknown with the
latest available data from a 1982 EPA (Gilbert et al.. 1982) report estimating 20% of water releases were
direct discharges to surface water and 80% of water releases were discharged indirectly to a POTW.
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EPA assumes the TRI and DMR data cover all water discharges of TCE from closed-loop vapor
degreasing. However, EPA cannot distinguish between degreaser types in TRI and DMR data; therefore,
a single set of water release for all degreasing operations is used for OTVDs.
Q.7 Conveyorized Vapor Degreasing
Q.7.1 Exposure Assessment
EPA identified inhalation exposure monitoring data from NIOSH investigations at 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-Field
Inhalation Exposure Model. The following subsections detail the results of EPA's occupational
exposure assessment for batch open-top vapor degreasing based on inhalation exposure monitoring data
and modeling.
TableApx Q-14 summarizes the 8-hr TWA monitoring data for the use of TCE in conveyorized
degreasing. The data were obtained from two NIOSH Health Hazard Evaluation reports (HHEs)
(Crandall and Albrecht. 1989). (Kinn.es. 1998).
Table Apx Q-14. Summary of Worker Inhalation Exposure Monitoring Data for Conveyorized
Vapor Degreasing
Sccnsirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Vim her ol'
Dili:t Points
C'onluk'iHT killing of
Air C'oiHTiitriition D;it:i
High-End
48.3
16.1
11.0
5.6
18
Medium
Central Tendency
32.4
10.8
7.4
2.9
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 limitations of
these data 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. 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. 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 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 emissions data available in the 2014 NEI were only found for three total units, and the
underlying methodologies used 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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FigureApx Q-2 illustrates the near-field/far-field model that can be applied to conveyorized vapor
degreasing. 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 evaporation rate of TCE, G, into the near-field, whose volume is denoted by Vnf.
The ventilation rate for the near-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. Vff 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 outdoor air.
Far-Field i
Figure Apx Q-2. Belt/Strip Conveyorized Vapor Degreasing Schematic of the Conveyorized
Degreasing Near-Field/Far-Field Inhalation Exposure Model
To estimate the TCE vapor generation rate, the model uses the annual emission rate and annual
operating time from the single conveyorized degreasing unit reported in the 2014 NEI. Because the
vapor generation rate is based a limited data set, it is unknown how representative the model is of a
"typical" conveyorized degreasing site.
EPA performed a Monte Carlo simulation with 100,000 iterations and the Latin Hypercube sampling
method in @Risk to calculate 8-hour TWA near-field and far-field exposure concentrations. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
{i.e., workers in the surrounding area who do not handle the degreasing equipment).
Table Apx Q-15 presents a statistical summary of the exposure modeling results. These exposure
estimates represent modeled exposures for the workers and occupational non-users. For workers, the
50th percentile exposure is 40.8 ppm 8-hr TWA, with a 95th percentile of 3,043 ppm 8-hr TWA.
The high-end value is two orders of magnitude higher than identified in the monitoring data, but the
central tendency is comparable to the monitoring data. This may be due to the limited number of sites
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from which the monitoring data were taken or that limited data for conveyorized degreaser were
reported to the 2014 NEI data (data were only found for three total units). It is also uncertain of the
underlying methodologies used to estimate emissions in the 2014 NEI data.
TableApx Q-15. Summary of Exposure Modeling Results for TCE Degreasing in Conveyorized
Degreasers
Seen si rio
8-hr TWA
(ppm)
AC '
(ppm)
ADC
(ppm)
I.AIK
(ppm)
D;it:i Qiiiililv Kilting
of Assoeiiiled Air
C oneenlriilion D:it;i
Workers (Near-field)
High-End
3,043
1,014.4
694.8
275.2
N/A - Modeled Data
Central
Tendency
40.8
13.6
9.3
5.3
Occupational non-users (Far-Field)
High-End
1,878
626
428.8
168.3
N/A - Modeled Data
Central
Tendency
23.3
7.8
5.3
3.6
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
a Acute exposures calculated as a 24-hr TWA.
Q.7.2 Water Release Assessment
Similar to OTVDs, the primary source of water releases from conveyorized systems is expected to be
from wastewater from the water separator with the primary sources of water being: 1) Moisture in the
atmosphere that condenses into the solvent when exposed to the condensation coils on the system;
and/or 2) steam used to regenerate carbon adsorbers used to control solvent emissions (Durkee. 2014;
Kanegsberg and Kanegsberg. 2011; NIOSH. 2002a. b, c, d). The current disposal practices of the
wastewater are unknown; however, a 1982 EPA (Gilbert et al. 1982) report estimated 20% of water
releases from metal cleaning (including batch systems, conveyorized systems, and vapor and cold
systems) were direct discharges to surface water and 80% of water releases were discharged indirectly to
a POTW.
EPA assumes the TRI and DMR data cover all water discharges of TCE from conveyorized degreasing.
However, EPA cannot distinguish between degreaser types in TRI and DMR data; therefore, a single set
of water release for all degreasing operations is presented in Section Q.5.2 for OTVDs.
Q.8 Web Vapor Degreasing
Q.8.1 Exposure Assessment
EPA did not identify inhalation exposure monitoring data related to the use of TCE in web degreasing.
Therefore, EPA used the Near-Field/Far-Field Model to estimate exposures to workers and ONUs. The
following details the results of EPA's occupational exposure assessment for use in web degreasers based
on inhalation exposure modeling.
Figure Apx Q-3 illustrates the near-field/far-field model that can be applied to web degreasing. 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
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evaporation rate of TCE, G, into the near-field, whose volume is denoted by Vnf. The ventilation rate
for the near-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. Vff 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 outdoor air.
Far-Field
Q
v* r
Qnp H
Near-Field
-> Qn
FigureApx Q-3. Schematic of the Web Degreasing Near-Field/Far-Field Inhalation Exposure
Model
To estimate the TCE vapor generation rate, the model uses the annual emission rate and annual
operating time from the single web degreasing unit reported in the (U.S. EPA. 2011). Because the vapor
generation rate is based a limited data set, it is unknown how representative the model is of a "typical"
web degreasing site.
EPA performed a Monte Carlo simulation with 100,000 iterations and the Latin Hypercube sampling
method in @Risk to calculate 8-hour TWA near-field and far-field exposure concentrations. Near-field
exposure represents exposure concentrations for workers who directly operate the vapor degreasing
equipment, whereas far-field exposure represents exposure concentrations for occupational non-users
(i.e., workers in the surrounding area who do not handle the degreasing equipment).
Table Apx Q-16 presents a statistical summary of the exposure modeling results. These exposure
estimates represent modeled exposures for the workers and occupational non-users. For workers, the
50th percentile exposure is 5.9 ppm 8-hr TWA, with a 95th percentile of 14.1 ppm 8-hr TWA.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 generation rates were derived from TCE unit emissions and operating hours
reported in the 2014 National Emissions Inventory. The primary limitations of the air concentration
outputs from the model include the uncertainty of the representativeness of these data toward the true
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distribution of inhalation concentrations for the industries and sites covered by this scenario. Added
uncertainties include that emissions data available in the 2011 NEI were only found for one unit, and the
underlying methodologies used 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
medium to low.
TableApx Q-16. Summary of Exposure Modeling Results for TCE Degreasing in Web
Degreasers
Scciiiirio
8-hr TWA
(ppm)
AC '
(ppm)
ADC
(ppm)
I.AIK
(ppm)
COnfiiloiKT killing
of Air
Concent nilion
l);il:i
Workers (Near-field)
High-End
14.1
4.7
3.2
1.4
Central
Tendency
5.9
2.0
1.4
0.5
N/A - Modeled Data
Occupational non-users (Far-Field)
High-End
9.6
3.2
2.2
0.9
Central
Tendency
3.1
1.0
0.7
0.3
N/A - Modeled Data
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
a Acute exposures calculated as a 24-hr TWA.
Q.8.2 Water Release Assessment
Similar to OTVDs, the primary source of water releases from web systems is expected to be from
wastewater from the water separator with the primary sources of water being: 1) Moisture in the
atmosphere that condenses into the solvent when exposed to the condensation coils on the system;
and/or 2) steam used to regenerate carbon adsorbers used to control solvent emissions (Durkee. 2.014;
Kanegsberg and Kanegsberg. 2011; NIOSH. 2002a. b, c, d). The current disposal practices of the
wastewater are unknown; however, a 1982 EPA (Gilbert et al. 1982) report estimated 20% of water
releases from metal cleaning (including batch systems, conveyorized systems, and vapor and cold
systems) were direct discharges to surface water and 80% of water releases were discharged indirectly to
a POTW.
EPA assumes the TRI and DMR data cover all water discharges of TCE from web vapor degreasing.
However, EPA cannot distinguish between degreaser types in TRI and DMR data; therefore, a single set
of water release for all degreasing operations is used for OTVDs.
Q.9 Cold Cleaning
Q.9.1 Exposure Assessment
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. The following details the results of EPA's occupational exposure
assessment for cold cleaning based on modeling.
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FigureApx Q-4 illustrates the near-field/far-field model that can be applied to cold cleaning. 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
evaporation rate of TCE, G, into the near-field, whose volume is denoted by Vnf. The ventilation rate
for the near-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. Vff 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 outdoor air.
Far-Field
NF
Near-Field
NF
vo ati e Source
> Qh
Figure Apx Q-4. Schematic of the Cold Cleaning Near-Field/Far-Field Inhalation Exposure
Model
To estimate the TCE vapor generation rate, the model developed a distribution from the reported annual
emission rates and annual operating times reported in the 2014 NEI (U.S. EPA 2018a). NEI records
where the annual operating time was not reported were excluded from the distribution. Because the
vapor generation rate is based a limited data set (ten total units), it is unknown how representative the
model is of a "typical" cold cleaning site.
Cold cleaners are assumed to operate between 3 to 24 hours per day, based on NEI data on the reported
operating hours for cold cleaners using TCE. EPA performed a Monte Carlo simulation with 100,000
iterations and the Latin Hypercube sampling method in @Risk to calculate 8-hour TWA near-field and
far-field exposure concentrations. Near-field exposure represents exposure concentrations for workers
who directly operate the vapor degreasing equipment, whereas far-field exposure represents exposure
concentrations for occupational non-users (i.e., workers in the surrounding area who do not handle the
cold cleaning equipment).
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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
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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 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 emissions data available in the 2014 NEI were only found for ten total units,
and the underlying methodologies used 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.
TableApx Q-17 presents a statistical summary of the exposure modeling results. Estimates of AC,
ADC, and LADC for use in assessing risk were made using the approach and equations described in
Appendix B. These exposure estimates represent modeled exposures for the workers and occupational
non-users. For workers, the 50th percentile exposure is 3.33 ppm 8-hr TWA, with a 95th percentile of
57.2 ppm 8-hr TWA.
Table Apx Q-17. Summary of Exposure Modeling Results for Use of Trichloroethylene in Cold
Cleaning
Scciiiirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.ADC
(ppm)
ConCiili'iuT killing of Air
CoiHTiilriilion D:il;i
Workers (Near-field)
High-End
57.2
19.1
13.1
5.2
N/A - Modeled Data
Central
Tendency
3.33
1.11
0.8
0.3
Occupational non-users (Far-Field)
High-End
34.7
11.6
7.9
3.1
N/A - Modeled Data
Central
Tendency
1.8
0.6
0.4
0.2
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
Q.9.2 Water Release Assessment
Similar to OTVDs, the primary source of water releases from cold cleaners is expected to be from
wastewater from the water separator with the primary source of water expected to be from moisture in
the atmosphere that condenses into the solvent. Water may also enter vapor degreasers via steam used to
regenerate carbon adsorbers; however, it is unclear if carbon adsorbers would be used in conjunction
with cold cleaning equipment. The current disposal practices of the wastewater are unknown; however, a
1982 EPA (Gilbert et at.. 1982) report estimated 20% of water releases from metal cleaning (including
batch systems, conveyorized systems, and vapor and cold systems) were direct discharges to surface
water and 80% of water releases were discharged indirectly to a POTW.
EPA assesses water release using TRI and DMR data. However, EPA cannot distinguish between
degreasers and cold cleaners in TRI and DMR data; therefore, a single set of water release for all
degreasing and cold cleaning operations is used for OTVDs.
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Q.10 Aerosol Applications: Spray Degreasing/Cleaning, Automotive
Brake and Parts Cleaners, Penetrating Lubricants, and Mold
Releases
Q.10.1 Exposure Assessment
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 used the brake servicing model as a representative scenario for this
condition of use as there was ample data describing the brake servicing use and it is a significant use of
TCE-based aerosol products. The following details the results of EPA's occupational exposure
assessment for aerosol degreasing and aerosol lubricants based on modeling.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 condition of use. 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. Based on these strengths and limitations of the air concentrations, the overall confidence for
these 8-hr TWA data in this scenario is medium.
Figure Apx Q-5 illustrates the near-field/far-field for the aerosol degreasing scenario. As the figure
shows, TCE in aerosolized droplets immediately volatilizes into the near-field, resulting in worker
exposures at a concentration Cnf. The concentration is directly proportional to the amount of aerosol
degreaser 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 near-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. Vff 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.
In this scenario, TCE mists enter the near-field in non-steady "bursts," where each burst results in a
sudden rise in the near-field concentration, followed by a more gradual rise in the far-field
concentration. The near-field and far-field concentrations then decay with time until the next burst
causes a new rise in near-field concentration.
Based on site data from maintenance and auto repair shops obtained by CARB ( 2000) for brake
cleaning activities, the model assumes a worker will perform 11 applications of the degreaser product
per brake job with five minutes between each application and that a worker may perform one to four
brake jobs per day each taking one hour to complete. EPA modeled two scenarios, one where the brake
cleaning jobs occurred back-to-back and one where braking cleaning jobs occurred one hour apart.
Based on data from CARB (CARB. 2000). EPA assumes each brake job requires 14.4 oz of aerosol
brake cleaner. The model determines the application rate of TCE using the weight fraction of TCE in the
aerosol product. EPA uses uniform distribution of weight fractions for TCE based on facility data for the
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aerosol products in use (CARB. 2000). It is uncertain whether the use rate and weight fractions for brake
cleaning are representative of other aerosol degreasing and lubricant applications.
nf c
Non-
volatile Source \ <2-
FigureApx Q-5. Schematic of the Near-Field/Far-Field Model for Aerosol Degreasing
EPA performed a Monte Carlo simulation with 1,000,000 iterations and the Latin hypercube sampling
method to model near-field and far-field exposure concentrations in the aerosol degreasing scenario. The
model calculates both 8-hr TWA exposure concentrations and acute 24-hr TWA exposure
concentrations. TableApx Q-18 presents a statistical summary of the exposure modeling results.
For workers, the exposures are 7.63 ppm 8-hr TWA at the 50th percentile and 23.98 ppm 8-hr TWA at
the 95th percentile. For occupational non-users, the model exposures are 0.14 ppm 8-hr TWA at the 50th
percentile and 1.04 ppm 8-hr TWA at the 95th percentile.
TableApx Q-18. Summary of Worker and Occupational Non-User Inhalation Exposure
Modeling Results for Aerosol Degreasing
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Confidence Rating of Air
Concentration Data
Workers (Near-field)
High-End
24.0
8.0
5.5
2.2
N/A - Modeled Data
Central Tendency
7.6
2.5
1.7
0.6
Occupational non-users (Far-Field)
High-End
1.0
0.4
0.2
0.1
N/A - Modeled Data
Central Tendency
0.1
0.05
0.03
0.01
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Q.10.2 Water Release Assessment
EPA does not expect releases of TCE to water from the use of aerosol products. Due to the volatility of
TCE the majority of releases from the use of aerosol products will likely be to air as TCE evaporates
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2194
2195
from the aerosolized mist and the substrate surface. There is a potential that TCE that deposits on shop
floors during the application process could possibly end up in a floor drain (if the shop has one) or could
runoff outdoors if garage doors are open. However, EPA expects the potential release to water from this
to be minimal as there would be time for TCE to evaporate before entering one of these pathways. This
is consistent with estimates from the International Association for Soaps, Detergents and Maintenance
Products (AISE) SpERC for Wide Dispersive Use of Cleaning and Maintenance Products, which
estimates 100% of volatiles are released to air (Products. 2012). EPA expects residuals in the aerosol
containers to be disposed of with shop trash that is either picked up by local waste management or by a
waste handler that disposes shop wastes as hazardous waste.
Q.ll Metalworking Fluids
Q.ll.l Exposure Assessment
EPA identified inhalation exposure monitoring data from OSHA facility inspections (OSHA. 2017) at
two sites using TCE in metalworking fluids. Due to small sample sizes, it is unclear how representative
these data are of "typical" MWF use. Therefore, EPA supplemented the identified monitoring data with
an assessment of inhalation exposures using the ESD on the Use of Metalworking Fluids (OECD.
2X ). The following subsections detail the results of EPA's occupational exposure assessment for
TCE use in MWFs based on inhalation exposure monitoring data and modeling.
TableApx Q-19 summarizes the 8-hr TWA monitoring data for the use of TCE in MWFs. No data were
found to estimate ONU exposures from use in metalworking fluids. Data from this source covers
exposures at a facility that produces various electrical resistors (Gilles and Philbin. 1976). The data were
provided as full-shift TWAs.
Table Apx Q-19. Summary of Worker Inhalation Exposure Monitoring Data for TCE Use in
Metalworking Fluids
Scenario
8-hr
TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Nil m her
of D;it:i
Points
Confidence Kilting
ol Air
Concenlriition D;il:i
High-End
75.4
25.1
17.2
OO
00
Central
Tendency
69.7
23.2
15.9
6.3
3
High
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 limitations of these data include
limited dataset (3 data points from 1 site), and the uncertainty of the representativeness of 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 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
Page 774 of 803
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2199
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2206
2207
2208
2209
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2211
2212
2213
2214
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2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
estimate inhalation exposures. The primary limitations of the exposure outputs from this model include
the uncertainty of the representativeness of 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 from one 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.
The ESD estimates typical and high-end exposures for different types of metalworking fluids. These
estimates are provided in TableApx Q-20 and are based on aNIOSH study of 79 small metalworking
facilities (OI ). The concentrations for these estimates are for the solvent-extractable portion
and do not include water contributions (OECD. ^ ). The "typical" mist concentration is the
geometric mean of the data and the "high-end" is the 90th percentile of the data (OECD. ).
Table Apx Q-20. ESD Exposure Estimates for Metalworking Fluids Based on Monitoring Data
Type of Mcl:il\\orkin» l luiil
Typic.il Mist (oncenlralion
(m»/m();i
Mi^h-Kiul Misl ( oncenlrnlion
(m»/iir,)h
Conventional Soluble
0.19
0.87
Semi-Synthetic
0.20
0.88
Synthetic
0.24
1.10
Straight Oil
0.39
1.42
a The typical mist concentration is the geometric mean of the data (OECD. 2011b)
b The high-end mist concentration is the 90th percentile of the data (OECD. 2011b")
Source: (OECD. 20.1.1b")
The recommended use of the TCE-based metalworking fluid is an oil-based cutting and tapping fluid;
therefore, EPA assesses exposure to the TCE-based metalworking fluids using the straight oil mist
concentrations and the max concentration of TCE in the metalworking fluid. Straight oils are not diluted;
therefore, the concentration of TCE specified in the SDS (98%) (U.S. EPA. 2017b) is equal to the
concentration of TCE in the mist. Table Apx Q-21 presents the exposure estimates for the use of TCE-
based metalworking fluids. The ESD estimates an exposure duration of eight hours per day; therefore,
results are presented as 8-hr TWA exposure values. It should be noted that these estimates may
underestimate exposures to TCE during use of metalworking fluids as they do not account for exposure
to TCE that evaporates from the mist droplets into the air. This exposure is difficult to estimate and is
not considered in this assessment.
Table Apx Q-21. Summary of Exposure Results for Use of TCE in Metalworking Fluids Based on
ESD Estimates
Scenario
8-hr TWA
(ppm)'1
ADC
(ppm)
I.AIK
(ppm)
Da I a Quality
Rating of
Associated Air
C'oncentriition Dala
High-End
0.3
0.1
0.03
N/A - Modeled Data
Central Tendency
0.1
0.02
6.0E-3
ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
11 The TCE exposure concentrations are calculated by multiplying the straight oil mist concentrations in Table Apx Q-20 by
98% (the concentration of TCE in the metalworking fluid) and converting to ppm.
Page 775 of 803
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2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
The monitoring data obtained is two orders of magnitude higher than the modeling data. It is uncertain if
the limited monitoring data set (three sample points), or the age of the monitoring data (1976) is
representative of exposures to TCE for all sites covered by this condition of use.
Q.11.2 Water Release Assessment
The ESD states that water releases from use of straight oil metalworking fluids may come from disposal
of container residue and dragout losses from cleaning the part after shaping (C ). Facilities
typically treat wastewater onsite due to stringent discharge limits to POTWs (OE 1 lb). Control
technologies used in onsite wastewater treatment in the MP&M industry include ultrafiltration, oil/water
separation, and chemical precipitation (OECD. 2 ). Facilities that do not treat wastewater onsite
contract waste haulers to collect wastewater for off-site treatment (OE 1 lb).
EPA assesses water release using TRI and DMR data. However, EPA cannot distinguish between sites
using metalworking fluids and sites using TCE in degreasers in TRI and DMR data; therefore, a single
set of water release for degreasing and metalworking fluid operations is used for OTVDs.
Q.12 Adhesives, Sealants, Paints, and Coatings
Q. 12.1 Exposure Assessment
EPA identified inhalation exposure monitoring data from a NIOSH a Health Hazard Evaluation report
(HHE) (Chrostek. 1981) using TCE in coating applications and from OSHA facility inspections (OSHA.
2017) at three sites using TCE in adhesives and coatings. The following details the results of EPA's
occupational exposure assessment for coating applications based on inhalation exposure monitoring
data.
TableApx Q-22 summarizes the 8-hr TWA monitoring data for the use of TCE in coatings. The data
were obtained from a HHE (Chrostek. 1981) and from OSHA data (OSHA. 2017). EPA assumed this
data is applicable to ONU exposure. However, due to the limited data set and the various types of
application methods that may be employed, EPA is unsure of the representativeness of these data toward
actual exposures to TCE for all sites covered by this condition of use.
Table Apx Q-22. Summary of Worker Inhalation Exposure Monitoring Data for
Adhesives/Paints/Coatings
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Nilmher of
Diilii Points
Confidence k;ilin<>
ol Air
Coiicentriition l);il:i
Workers
High-End
39.5
13.2
9.0
4.6
22
Medium
Central
Tendency
4.6
1.6
1.1
0.4
Occupational non-users
High-End
1.0
0.3
0.2
0.1
2
Medium
Central
Tendency
0.9
0.3
0.2
0.1
AC = Acute Concentration, ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Page 776 of 803
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2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 representativeness of 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.
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 1 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-hr TWA data in
this scenario is medium to low.
EPA did not find data to provide inhalation exposure estimates for commercial adhesive, sealant, paint
and coating applications. Therefore, EPA uses the industrial data discussed above as surrogate for
commercial coatings, as EPA believes the activities and exposures will be similar between industrial and
commercial sites covered by this condition of use.
Q.12.2 Water Release Assessment
In general, potential sources of water releases from adhesive, sealants, and paints/coatings use may
include the following: equipment cleaning operations, and container cleaning wastes (OECD. 201 la).
Water releases for adhesives, sealants, paints and coating sites were assessed using data reported from
three sites in the 2016 TRI and 2016 DMR. For the sites in the 2014 NEI (where release information is
not provided), an average release per site was calculated from the total releases of the three
aforementioned sites reporting water releases to DMR and TRI, and dividing the total release by the
total number of sites in TRI and DMR (17 sites). This average release per site was used to estimate
releases from the sites provided in the 2014 NEI. EPA assessed daily releases by assuming 250 days of
operation per year, as recommended in the 2011 ESD on the Application of Radiation Curable Coatings,
Inks, and Adhesives via Spray, Vacuum, Roll and Curtain Coating, and averaging the annual releases
over the operating days (OECD. 201 la). A summary of the water releases can be found in TableApx
Q-23.
Table Apx Q-23. Reported Water Releases of Trichloroethylene from Sites Using TCE in
Adhesives, Sealants, Paints and Coatings
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
d;i\)
M»i)i:s
Cock'
Rolen so Media
Able Electropolishing Co Inc,
Chicago, IL
74.4
250
0.30
Not available
POTW
Garlock Sealing Technologies,
Palmyra, NY
0.08
250
3.3E-04
NY0000078
Surface Water
Ls Starrett Co, Athol, MA
9.1E-04
250
3.6E-06
MAR05B615
Surface Water
Page 777 of 803
-------
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
d;i\)
M»i)i:s
Cock'
Release Media
Aerojet Rocketdyne, Inc., East
Camden, AR
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Best One Tire & Service,
Nashville, TN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Bridgestone Aircraft Tire
(USA), Inc., Mayodan, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Clayton Homes Inc, Oxford, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Cmh Manufacturing, Inc. Dba
Schult Homes - Plant 958,
Richfield, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Delphi Thermal Systems,
Lockport, NY
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Green Bay Packaging Inc - Coon
Rapids, Coon Rapids, MN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Mastercraft Boat Company,
Vonore, TN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Michelin Aircraft Tire
Company, Norwood, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
M-Tek, Inc, Manchester, TN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Olin Corp, East Alton, IL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Parker Hannifin Corp - Paraflex
Division, Manitowoc, WI
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Parrish Tire Company,
Yadkinville, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Republic Doors And Frames,
Mckenzie, TN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Ro-Lab Rubber Company Inc.,
Tracy, CA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Roy ale Comfort Seating, Inc. -
Plant No. 1, Taylorsville, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Snider Tire, Inc., Statesville, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Snyder Paper Corporation,
Hickory, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Stellana Us, Lake Geneva, WI
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Thomas Built Buses - Courtesy
Road, High Point, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Page 778 of 803
-------
Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
d;i\)
M»i)i:s
Cock'
Release Media
Unicel Corp, Escondido, CA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Acme Finishing Co Lie, Elk
Grove Village, IL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Aerojet Rocketdyne, Inc.,
Rancho Cordova, CA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Allegheny Cnty Airport
Auth/Pgh Intl Airport,
Pittsburgh, PA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Amphenol Corp - Aerospace
Operations, Sidney, NY
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Aprotech Powertrain, Asheville,
NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Clayton Homes Inc, Oxford, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Coating & Converting Tech
Corp/Adhesive Coatings,
Philadelphia, PA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Corpus Christi Army Depot,
Corpus Christi, TX
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Electronic Data Systems Camp
Pendleton, Camp Pendleton, CA
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Florida Production Engineering,
Inc., Ormond Beach, FL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Goodrich Corporation,
Jacksonville, FL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Kasai North America Inc,
Madison Plant, Madison, MS
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Kirtland Air Force Base,
Albuquerque, NM
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Marvin Windows & Doors,
Warroad, MN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Mcneilus Truck &
Manufacturing Inc, Dodge
Center, MN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Metal Finishing Co. - Wichita (S
Mclean Blvd), Wichita, KS
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Page 779 of 803
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Silo l(k'ii(i(\
Amiiiiil
Kck'sisi*
Anniiiil
Release
l);i\ s
(d;i\s/\r)
l);iil>
Release
(kg/sile-
d;i\)
M»i)i:s
Cock'
Release Media
Michelin Aircraft Tire
Company, Norwood, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Murakami Manufacturing Usa
Inc, Campbellsville, KY
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Peterbilt Motors Denton Facility,
Denton, TX
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Portsmouth Naval Shipyard,
Kittery, ME
4.4
250
1.8E-02
Not available
Surface Water or
POTW
R.D. Henry & Co., Wichita, KS
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Raytheon Company,
Portsmouth, RI
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Rehau Inc, Cullman, AL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Rotochopper Inc, Saint Martin,
MN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Rubber Applications, Mulberry,
FL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Sapa Precision Tubing
Rockledge, Lie, Rockledge, FL
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Thomas & Betts, Albuquerque,
NM
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Thomas Built Buses - Fairfield
Road, High Point, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Timco, Dba Haeco Americas
Airframe Services, Greensboro,
NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Trelleborg Coated Systems Us,
Inc - Grace Advanced Materials,
Rutherfordton, NC
4.4
250
1.8E-02
Not available
Surface Water or
POTW
U.S. Coast Guard Yard - Curtis
Bay, Curtis Bay, MD
4.4
250
1.8E-02
Not available
Surface Water or
POTW
Viracon Inc, Owatonna, MN
4.4
250
1.8E-02
Not available
Surface Water or
POTW
2304 POTW = Publicly Owned Treatment Works
2305 Releases of 4.4 kg/site-yr for NEI sites estimated from total releases from TRI and DMR sites and divided by the 3 sites
2306 reporting water releases and the 14 sites reporting zero water releases in TRI).
2307 a Daily releases are back-calculated from the annual release rate and assuming 250 days of operation per year.
2308 Sources: (U.S. EPA. 2018a. 2017c. 2016a)
Page 780 of 803
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2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
Q.13 Other Industrial Uses
Q.13.1 Exposure Assessment
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 as a surrogate.
See section Q.l.l for additional information on the data used. EPA assumes the exposure sources,
routes, and exposure levels are similar to those during loading at a TCE manufacturing facility.
However, EPA is unsure of the representativeness of these surrogate data toward actual exposures to
TCE at all sites covered by this condition of use.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 50 data points from 2 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 representativeness of 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.
TableApx Q-24 summarizes the 8-hr TWA from monitoring data from TCE manufacturing. The data
were obtained from obtained from data submitted by Ark em a. Inc. (Arkema. 2020) and the Halogenated
Solvents Industry Alliance (HSIA) (Halogenated Solvents Industry Alliance. 2018) via public comment.
No data were found to estimate ONU exposures during other industrial uses of TCE. EPA estimates that
ONU exposures are lower than worker exposures, since ONUs do not typically directly handle the
chemical.
Table Apx Q-24. Summary of Occupational Exposure Surrogate Monitoring Data for Unloading
TCE During Other Industrial Uses
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Nilmher of
Dsitii Points
Confidence killing of
Air ConceiKriilion
D:i(ii
High-End
2.46
0.82
0.56
0.29
50
Medium
Central
Tendency
0.12
3.8E"2
2.6E"2
1.0E"2
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
Q.13.2 Water Release Assessment
Specifics of the processes and potential sources of release for other industrial uses are unknown.
However, general potential sources of water releases in the chemical industry may include the
following: equipment cleaning operations, aqueous wastes from scrubbers/decanters, reaction water,
process water from washing intermediate products, and trace water settled in storage tanks (OECD.
2019).
EPA assessed water releases using the annual discharge values reported to the 2016 TRI and the 2016
DMR by the 49 sites using TCE in other industrial uses. In the 2016 TRI, all 28 reported zero discharge
to water. In the 2016 DMR, twenty-one sites reported a direct discharge to surface water (indirect
discharges not reported in DMR data).
Page 781 of 803
-------
2349
2350 To estimate the daily release, EPA assumed a default of 250 days/yr of operation and averaged the
2351 annual release over the operating days. Table Apx Q-25 summarizes the water releases from the 2016
2352 TRI and DMR for sites with non-zero discharges.
2353
Table Apx Q-25. Reported Water Releases of Trichloroethy
ene from Ot
ier Industrial Uses
Silo l(kii(il\
Anniiiil
Koloiiso
(kii/sile-\ n
Anniiiil
Ki'k'sisi*
l);i\ s
(d;i\s/\ r)-1
l);iil\
Kck'sisi*
(kfi/siU1-
d;i\)'
SIMMS
Code
Kclcsisc
Medisi
Eli Lilly And Company-Lilly Tech Ctr,
Indianapolis, IN
388
250
1.6
IN0003310
Surface
Water
Oxy Vinyls LP - Deer Park Pvc, Deer Park,
TX
37
250
0.15
TX0007412
Surface
Water
Solvay - Houston Plant, Houston, TX
8.3
250
0.03
TX0007072
Surface
Water
Washington Penn Plastics, Frankfort, KY
8.0
250
0.03
KY0097497
Surface
Water
Natrium Plant, New Martinsville, WV
5.5
250
2.2E-02
WV0004359
Surface
Water
Leroy Quarry, Leroy, NY
4.8
250
1.9E-02
NY0247189
Surface
Water
George C Marshall Space Flight Center,
Huntsville, AL
2.6
250
1.0E-02
AL0000221
Surface
Water
Whelan Energy Center Power Plant, Hastings,
NE
2.4
250
9.4E-03
NE0113506
Surface
Water
Akzo Nobel Surface Chemistry LLC, Morris,
IL
0.1
250
4.6E-04
IL0026069
Surface
Water
Solutia Nitro Site, Nitro, WV
0.1
250
4.4E-04
WV0116181
Surface
Water
Amphenol Corporation - Columbia,
Columbia, SC
0.1
250
2.8E-04
SC0046264
Surface
Water
Army Cold Regions Research & Engineering
Lab, Hanover, NH
0.1
250
2.3E-04
NH0001619
Surface
Water
Corning - Canton Plant, Canton, NY
0.1
250
2.2E-04
NY0085006
Surface
Water
Keeshan And Bost Chemical Co., Inc.,
Manvel, TX
0.03
250
1.3E-04
TX0072168
Surface
Water
Ames Rubber Corp Plant #1, Hamburg Boro,
NJ
0.03
250
1. 1E-04
NJG000141
Surface
Water
Gorham, Providence, RI
0.02
250
9.2E-05
RIG85E004
Surface
Water
Emerson Power Transmission, Ithaca, NY
0.02
250
6.9E-05
NY0002933
Surface
Water
Chemtura North and South Plants,
Morgantown, WV
8.3E-03
250
3.3E-05
WV0004740
Surface
Water
Indorama Ventures Olefins, LLC, Sulphur,
LA
5.1E-03
250
2.0E-05
LA0069850
Surface
Water
William E. Warne Power Plant, Los Angeles
County, CA
3.1E-03
250
1.2E-05
CA0059188
Surface
Water
Raytheon Aircraft Co (Was Beech Aircraft),
Boulder, CO
2.3E-03
250
9.2E-06
COG315176
Surface
Water
2355 a Annual release amounts are based on the site reported values. Therefore, daily releases are calculated from the annual
23 56 release rate and assuming 250 days of operation per year.
2357 Sources: (U.S. EPA. 2017c. 2016a)
2358
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2398
Q.14 Spot Cleaning, Wipe Cleaning and Carpet Cleaning
Q.14.1 Exposure Assessment
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. The following subsections detail the results of EPA's occupational exposure assessment for spot
cleaning based on inhalation exposure monitoring data and modeling.
TableApx Q-26 summarizes the 8-hr TWA monitoring data and acute TWAs from the monitoring data
for the use of TCE in spot cleaning. No data were found to estimate ONU exposures during spot
cleaning. The data were obtained from NIOSH a Health Hazard Evaluation report (HHE) (Burton and
Monesterskc 5), as well as a NIOSH Report on Control of Health and Safety Hazards on
Commercial Dry cleaners document (NIOSH. 1997). NIOSH HHEs are conducted at the request of
employees, employers, or union officials, and provide information on existing and potential hazards
present in the workplaces evaluated. NIOSH Health and Safety documents represents NIOSH research
in collaboration with industry, labor and other government organizations to protect the health of workers
in industry.
For full shift values, sample times ranged from approximately seven to nine hours (Burton and
Monesterskt 5). Where sample times were less than eight hours, EPA converted to an 8-hr TWA
assuming exposure outside the sample time was zero. For sample times greater than eight hours, EPA
left the measured concentration as is. Because of the limited data set, EPA is unsure of the
representativeness of these data toward actual exposures to TCE for all sites covered by this condition of
use.
Table Apx Q-26. Summary of Worker Inhalation Exposure Monitoring Data for Spot Cleaning
Using TCE
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LAIK
(ppm)
Number of 8-
lir TWA D;il:i
Points
Confidence
killing of Air
Concent r;i(ion
Dntii
High-End
2.8
l.U
0.7
0.3
8
Medium
Central
Tendency
0.4
0.1
0.1
0.04
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 from 2 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 representativeness of 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
Page 783 of 803
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2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
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 concentrations, the overall confidence for these 8-hr TWA data in this
scenario is medium to low.
Despite these limitation, the modeling and monitoring results match each other very closely. Therefore,
the overall confidence is medium.
Wolf and Morris (TRTA. 2007) estimated 42,000 gal of TCE-based spotting agents are sold in California
annually. Review of SDS's identified TCE-based spotting agents contain 10% to 100% TCE. The study
also estimated approximately 5,000 textile cleaning facilities in California. Results in average of 8.4
gal/site-yr of TCE-based spotting agents used.
FigureApx Q-6 illustrates the near-field/far-field modeling approach that EPA applied to spot cleaning
facilities. As the figure shows, chemical 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 near-
field zone (Qnf) determines how quickly the chemical of interest dissipates into the far-field {i.e., the
facility space surrounding the near-field), resulting in occupational non-user exposures at a
concentration Cff. Vff denotes the volume of the far-field space into which the chemical of interest
dissipates out of the near-field. The ventilation rate for the surroundings, denoted by Qff, determines
how quickly the chemical dissipates out of the surrounding space and into the outdoor air.
Far-Field
Volatile Source
Figure Apx Q-6. Schematic of the Near-Field/Far-Field Model for Spot Cleaning
EPA performed Monte Carlo simulations, applying one hundred thousand iterations and the Latin
hypercube sampling method. Table Apx Q-27 presents a statistical summary of the exposure modeling
results. The 50th and 95th percentile near-field exposures are 0.96 ppm and 2.77 ppm 8-hr TWA,
Page 784 of 803
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2447
2448
2449
2450
2451
2452
2453
2454
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2456
2457
2458
2459
2460
respectively. These results are comparable to the monitoring data. For occupational non-users (far-field),
model 50th and 95th percentile exposure levels are 0.48 ppm and 1.75 ppm 8-hr TWA, respectively. EPA
assumes no engineering controls are used at dry cleaning shops, which are typically small, family owned
businesses.
The modeling results are comparable to the monitoring data. However, EPA is unsure of the
representativeness of these data toward actual exposures to TCE for all sites covered by this condition of
use.
Table Apx Q-27. Summary of Exposure Modeling Results for Spot Cleaning Using TCE
Sccnsirio
8-hr TWA
(ppm)
AC (24-hr)
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Diitii Qusilily killing of
Associated Air C'onccnlrsition
Dsilsi
Workers (Near-field)
High-End
2.8
0.9
0.6
0.3
N/A - Modeled Data
Central Tendency
1.0
0.3
0.2
0.1
Occupational non-users (Far-Field)
High-End
1.8
0.6
0.4
0.2
N/A - Modeled Data
Central Tendency
0.5
0.2
0.1
0.04
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Q.14.2 Water Release Assessment
TCE releases to water from spot cleaning will depend upon whether the stained surface is washed with
water after spotting. For example, TCE-based cleaners used to pre-spot garments prior to cleaning in
water or hydrocarbon-based machines would be a source of TCE in wastewater.
Water releases for spot cleaning were assessed using data reported in the 2016 DMR. No sites
discharging TCE from spot cleaning activities were found in the 2016 TRI. EPA assessed annual
releases as reported in the 2016 DMR and assessed daily releases by assuming 300 days of operation per
year. A summary of the water releases reported to the 2016 DMR can be found in TableApx Q-28. The
annual release for each of the unknown sites is calculated by taking the average annual release of the
two sites reporting to DMR.
Table Apx Q-28. Reported Water Releases of Trichloroethylene from Sites Using TCE Spot
Cleaning
Silo
Anniiiil
Kclcsisc'
(kg/silc-vcsir)
Anniisil
Kclcsisc Diivs
(dsivs/vr)
Dsiilv Kclcsisc
(kg/silc-dsiv)'1
Mcdisi of Kclcsisc
Boise State University, Boise, ID
0.02
300
8.0E-05
Surface Water
Venetian Hotel And Casino, Las
Vegas, NV
8.8E-3
300
2.9E-05
Surface Water
63,746 Unknown Sites
0.02
300
5.4E-05
Surface Water or POTW
POTW = Publicly Owned Treatment Works
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 300 days of operation per year.
Sources: 2016 DMR (U.S. EPA. 2016a)
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2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
Q.15 Industrial Processing Aid
Q.15.1 Exposure Assessment
EPA did identify inhalation exposure monitoring data related using TCE when used as an industrial
processing aid from one site. The following details the results of EPA's occupational exposure
assessment for use of TCE as an industrial processing aid based on inhalation exposure monitoring data.
Table Apx Q-29 summarizes the 12-hr TWA monitoring data and acute TWAs from the monitoring
data for the use of TCE as a processing aid for both workers and for ONUs. The data were obtained
from a European Commission (EC) Technical Report (EC. 2.014). The data were supplied to the EC as
supporting documentation in an application for continued use of TCE under the REACH Regulation.
The data indicate a full shift is 12 hours. Therefore, all exposures were calculated using a 12-hr shift.
Because of the limited data set, EPA is unsure of the representativeness of these data toward actual
exposures to TCE for all sites covered by this condition of use.
Table Apx Q-29. Summary of Exposure Monitoring Data for Use as a Processing Aid
Scenario
12-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Nilmher ol'
12-hr l):i(:i
Points
Confidence killing
ol' Air Concenlmlion
Diilii
Workers
High-End
12.8
6.4
4.4
2.2
30
Medium to High
Central Tendency
4.2
2.1
1.5
0.6
Occupational non-users
High-End
2.9
1.4
1.0
0.5
4
Medium
Central Tendency
1.3
0.7
0.4
0.2
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 12-hr 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 30 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 representativeness of 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 12-hr TWA data
in this scenario is medium to high.
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 4 data points from 1 source, and the data quality ratings from systematic review for the data
point was high. The primary limitations of this single data point 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. Based on these strengths and limitations of the inhalation
air concentration data, the overall confidence for these 12-hr TWA data in this scenario is medium to
low.
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2499
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2501
2502
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2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
Q.15.2 Water Release Assessment
In general, potential sources of water releases in the chemical industry may include the following:
equipment cleaning operations, aqueous wastes from scrubbers/decanters, reaction water, process water
from washing intermediate products, and trace water settled in storage tanks (OECD. 2019). Based on
the use as a processing aid and the amount of TCE used for this condition of use, EPA expects minimal
sources of TCE release to water.
Water releases during use as a processing aid were assessed using data reported in the 2016 TRI as well
as 2016 DMR. Four of the 16 sites reporting to TRI provided water releases. The remaining 12 sites
reported all releases were to off-site land, incineration or recycling. EPA assessed annual releases as
reported in the 2016 TRI and assessed daily releases by assuming 300 days of operation per year. A
summary of the water releases reported to the 2016 DMR and 2016 TRI can be found in TableApx
Q-30.
Table Apx Q-30. Reported Water Releases of Trichloroethylene from Industrial Processing Aid
Sites Using TCE
Silo 1 don 1 i l>
Amiiiiil
Kck'iiso
(kii/sile-.M-)-1
An iiii;il
Uok'iiso
l);i\ s
(d;i\s/\ r)
Diiih Kokiiso
(k«i/si(e-d;i.\ )•'
NI'DIS
Cock'
Koloiiso
Modiii
Entek International LLC, Lebanon, OR
113
300
0.4
Not
available
POTW
Occidental Chemical Corp Niagara
Plant, Niagara Falls, NY
5.8
300
0.02
NY0003336
Surface
Water
National Electrical Carbon Products Dba
Morgan Adv Materials, Fostoria, OH
2.3
300
7. 6E-03
Not
available
POTW
Daramic LLC, Corydon, IN
2.3
300
0.01
Not
available
Surface
Water
PPG Industries Inc Barberton,
Barberton, OH
1.4
300
4.5E-3
OH0123897
POTW
Stepan Co Millsdale Road, Elwood, IL
0.2
300
5.5E-04
IL0002453
Surface
Water
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 300 days of operation per year.
POTW = Publicly Owned Treatment Works
Sources: (US. EPA. 2017c. 20.1.631
Q.16 Commercial Printing and Copying
Q.16.1 Exposure Assessment
EPA identified inhalation exposure monitoring data from a NIOSH a Health Hazard Evaluation report
(HHE) (Finely and Paee. 2005) using TCE in high speed printing presses. The following details the
results of EPA's occupational exposure assessment for printing applications based on inhalation
exposure monitoring data. Table Apx Q-31 summarizes the 8-hr TWA monitoring data for the use of
TCE in printing. The data were obtained from a HHE (Finely and Page. 2.005).
EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
to determine a level of confidence for the 8-hr 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 20 data points from 1 source, and the
data quality ratings from systematic review for these data were medium. The primary limitations of
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2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
these data include a limited dataset, and the uncertainty of the representativeness of 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.
TableApx Q-31. Summary of Worker Inhalation Exposure Monitoring Data for High Speed
Printing Presses
SiTiiiirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Number of
D:ilii Points
Confidi'iUT k;i(in<> of
Air CoiHTiitriilion
D;il:i
High-End
2.1
0.7
0.5
0.2
20
Medium
Central
Tendency
0.1
0.03
0.02
8.0E-3
AC = Acute Concentration, ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
No monitoring data were available to estimate ONU exposures. EPA estimates that ONU exposures are
lower than worker exposures, since ONUs do not typically directly handle the chemical.
Q.16.2 Water Release Assessment
A potential source of water releases from Printing/copying use would come from clean-out of printing
equipment if the ink is water-based (OECD.: ). Based on the use in printing/copying and the amount
of TCE used for this condition of use, EPA expects minimal sources of TCE release to water.
Water releases during use in printing and copying were assessed using data reported in the 2016 DMR.
One site provided water releases. EPA assessed annual releases as reported in the 2016 DMR and
assessed daily releases by assuming 250 days of operation per year. A summary of the water releases
reported to the 2016 DMR can be found in Table Apx Q-32.
Table Apx Q-32. Reported Water Releases of Trichloroethylene from Commercial Printing and
CoPying
Sin* 1 don I i (>
Aniiiiiil
Uokiiso
(Uii/sik'-M-)1
An iiii;il
Kck'iiso
l);i\ s
( Kok'iiso
(kii/sik'-il;i\ )•'
Ni»i)i:s
Cock'
Koloiiso
Mcdiii
Printing and Pub Sys Div, Weatherford,
OK
0.05
250
2.0E-4
OK0041785
Surface
Water
11 Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 250 days of operation per year.
As only one site was identified with water releases for this condition of use, EPA acknowledges this site
does not represent the entirety of commercial printing and copying sites using TCE. However, data is
not reasonably available to estimate water releases from additional sites. Based on reasonably available
EPA models releases from containers may be up to: 1) 0.3% to 0.6% for small containers (<20 gal) or
drums that are emptied via pouring; or 2) 2.5% to 3% for drums emptied via pumping; however, not all
sites are expected to dispose of container residues to water. Additional water release sources of TCE at
these sites may exist and will vary depending on the use rate of the TCE-based products.
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2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
Q.17 Other Commercial Uses
Q.17.1 Exposure Assessment
EPA did not identify any inhalation exposure monitoring data related to TCE use in other commercial
uses, including use as a laboratory chemical for research, development, and testing services. See Section
Q. 14.1 for the assessment of worker exposure during spot cleaning activities. EPA assumes that some of
the other commercial uses may have analogous exposure sources, routes, and exposure levels similar to
those for spot cleaners.
Q.17.2 Water Release Assessment
Specifics of the processes and potential sources of release for these uses are unknown. Based on the
volatility of TCE, EPA expects the majority of TCE used for these applications to evaporate and be
released to air. EPA expects residuals in containers to be disposed of with general site trash that is either
picked up by local waste management or by a waste handler that disposes wastes as hazardous waste.
TableApx Q-33 summarizes non-zero water releases from sites using TCE in other commercial uses
reported in the 2016 DMR. To estimate the daily release for the sites in Table Apx Q-33, EPA assumed
a default of 250 days/yr of operation and averaged the annual release over the operating days. These data
are not expected to capture the entirety of water releases from these uses; however, EPA does not have
information to estimate water releases from sites not reporting to DMR.
Table Apx Q-33. Reported Water Releases of Trichloroethylene from Other Commercial Uses in
the 2016 DMR
Silo l(kiili(\
Amiiiiil
Release
(kg/si le-
>")
Aiiiiii ;il
Release
l);i\ s
(d;i\s/\ r)
l);iil\
Release
(kii/sik'-
d;i\)
SIMMS
Cock'
Release
Media
Corning Hospital, Corning, NY
3.2
250
0.013
NY0246701
Surface
Water
Water Street Commercial Bldg, Dayton, OH
0.7
250
2.8E-03
OH0141496
Surface
Water
Union Station North Wing Office Building, Denver, CO
1.0E-01
250
4.0E-04
COG315293
Surface
Water
Confluence Park Apartments, Denver, CO
7.1E-02
250
2.8E-04
COG315339
Surface
Water
Park Place Mixed Use Development, Annapolis, MD
6.7E-02
250
2.7E-04
MD0068861
Surface
Water
Tree Top Inc Wenatchee Plant, Wenatchee, WA
9.0E-03
250
3.6E-05
WA0051527
Surface
Water
Wynkoop Denver LLCP St, Denver, CO
7.8E-03
250
3.1E-05
COG603115
Surface
Water
Greer Family LLC, South Burlington, VT
1.3E-03
250
5.0E-06
VT0001376
Surface
Water
John Marshall III Site, Mclean, VA
4.7E-04
250
1.9E-06
VA0090093
Surface
Water
11 Annual release amounts are based on the site reported values. Therefore, daily releases are calculated from the annual
release rate and assuming 250 days of operation per year.
Sources: (U.S. EPA. 2016a)
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2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
Q.18 Process Solvent Recycling and Worker Handling of Wastes
Q.18.1 Exposure Assessment
EPA did not identify any inhalation exposure monitoring data related to waste handling/recycling. See
Section Q.4.1 for the assessment of worker exposure from chemical unloading activities. EPA assumes
the exposure sources, routes, and exposure levels are similar to those at a repackaging facility.
Q.18.2 Water Release Assessment
Potential sources of water releases at disposal/recycling sites may include the following: aqueous wastes
from scrubbers/decanter, trace water settled in storage tanks, and process water generated during the
disposal/recycling process.
EPA assessed water releases using the values reported to the 2016 TRI and DMR by the 30
disposal/recycling sites. In the 2016 TRI, three of sites reported non-zero indirect discharges to off-site
wastewater treatment; one site reported discharges to both off-site wastewater treatment as well as
discharge to a POTW. All sites in TRI for this condition of use reported zero direct discharges to surface
water.
To estimate the daily release, EPA used a default assumption of 250 days/yr of operation as and
averaged the annual release over the operating days. Table Apx Q-34 summarizes the water releases
from the 2016 DMR and 2016 TRI for sites with non-zero discharges.
Table Apx Q-34. Estimated Water Releases of Trichloroethylene from Disposal/Recycling of TCE
Silo Ilk-milt
Amiiiiil
Kok'.iso
(kii/sik'-
\ r);i
Anniiiil Kcloiiso
D.ijs (d;i\s/\ n
l);iil\ Kcloiiso
(k?i/si(i'-(l;i> )•'
NI'DIS
Cock'
Ki'lciisc Modiii
\ colia Ls Technical
Solutions LLC,
Middlesex, NJ
6035
250
24.1
Not
available
POTW W W 1 (ii nv,u)
and Non-POTW WWT
(99.98%)
Clean Harbors Deer Park
LLC, La Porte, TX
87.1
250
0.3
TX0005941
Non-POTW WWT
Clean Harbors El Dorado
LLC, El Dorado, AR
9.1
250
0.04
AR0037800
Non-POTW WWT
Clean Water Of New
York Inc, Staten Island,
NY
0.9
250
3.8E-03
NY0200484
Surface Water
Reserve Environmental
Services, Ashtabula, OH
3.9E-04
250
1.6E-06
OH0098540
Surface Water
POTW = Publicly-Owned Treatment Works; WWT = Wastewater Treatment
a Annual release amounts are based on the site reported values. Therefore, daily releases are back-calculated from the annual
release rate and assuming 250 days of operation per year.
Sources: (US. EPA. 2017c) and (TJ.S. EPA. 20.1.631
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U.S. EPA. (U.S. Environmental Protection Agency). (2001a). Guide to industrial assessments for
pollution prevention and energy efficiency [EPA Report], (EPA/625/R-99/003). Cincinnati, OH:
Office of Research and Development, National Risk Management Research Laboratory, Center
for Environmental Research Information.
U.S. EPA. (U.S. Environmental Protection Agency). (2001b). Risk assessment guidance for superfund:
Volume III - Part A, Process for conducting probabilistic risk assessment [EPA Report], (EPA
540-R-02-002). Washington, DC: U.S. Environmental Protection Agency, Office of Emergency
and Remedial Response.
U.S. EPA. (U.S. Environmental Protection Agency). (2006). Risk assessment for the halogenated solvent
cleaning source category [EPA Report], (EPA Contract No. 68-D-01-052). Research Triangle
Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards.
https://www.regulations.gov/document Ts _ IPA-HQ J _\_k -2002-0009-0022
U.S. EPA. (U.S. Environmental Protection Agency). (201 1). The 201 1 National Emissions Inventory.
Retrieved from https://www.epa.gov/air-emissions-inventories/2011 -n ati on al -em i s si on s-
inventory-nei-data
(U S Environmental Protection Agency). (2013). ChemSTEER user guide - Chemical
screening tool for exposures and environmental releases. Washington, D.C.
https://www.epa.eov/sites/production/files/2015-05/docum.ents/user guide.pdf
(U.S. Environmental Protection Agency). (2014a). Degreasing with TCE in commercial
facilities: Protecting workers [EPA Report], Washington, DC: U.S. Environmental Protection
Agency, Office of Pollution Prevention and Toxics.
(U.S. Environmental Protection Agency). (2014b). TSCA work plan chemical risk
assessment. Trichloroethylene: Degreasing, spot cleaning and arts & crafts uses. (740-R1-4002).
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Washington, DC: Environmental Protection Agency, Office of Chemical Safety and Pollution
Prevention, http://www2.epa.gov/sites/production/files/2015-
09/documents/tce opptworkplanchemra final 0
(U.S. Environmental Protection Agency). (2016a). EPA Discharge Monitoring Report Data.
Retrieved from https://cfpub.epa. gov/dmr/
U.S. EPA. (U.S. Environmental Protection Agency). (2016b). Instructions for reporting 2016 TSCA.
chemical data reporting. (EPA/600/R-09/052F). Washington, DC: U.S. Environmental Protection
Agency, Office of Pollution Prevention and Toxics, https://www.epa.gov/chemical-data-
reporting/instructions-reporting-2016-tsca-chemical-data-reporting
(U.S. Environmental Protection Agency). (2017a). Chemical data reporting under the Toxic
Substances Control Act. Available online at https://www.epa.gov/chemical-data-reporting
(accessed August 29, 2017).
(U.S. Environmental Protection Agency). (2017b). Preliminary information on manufacturing,
processing, distribution, use, and disposal: Trichloroethylene [Comment], (EPA-HQ-OPPT-
2016-0737-003). Washington, DC: Office of Chemical Safety and Pollution Prevention.
https://www\ regulations.gov/doeument?D=EPA~H.Q"QPPT"2.016-0737-0003
(U.S. Environmental Protection Agency). (2017c). Toxics Release Inventory (TRI), reporting
year 2016. Retrieved from https://www.epa.gov/toxics-release-inventorv-tri-program/tri-data-
and-tools
(U.S. Environmental Protection Agency). (2017d). Trichloroethylene market and use report.
Washington, DC: U.S. Environmental Protection Agency, Office of Chemical Safety and
Pollution Prevention, Chemistry, Economics, and Sustainable Strategies Division.
05/documents/instructions for reporting 2016 tsca cdi i ^>iav2'M •
(U.S. Environmental Protection Agency). (2018a). 2014 National Emissions Inventory
Report, https://www.epa.gov/air-emissions-inventories/ ati on al-em is si on s-in ventory-n ei -
data
(U.S. Environmental Protection Agency). (2018b). Application of systematic review in TSCA
Risk Evaluations. (740-P1-8001). Washington, DC: U.S. Environmental Protection Agency,
Office of Chemical Safety and Pollution Prevention.
06/documents/final application of sr in tsc if
(U.S. Environmental Protection Agency). (2018c). Problem Formulation of the Risk
Evaluation for trichloroethylene. (EPA-740-R1-7014). Washington, DC: Office of Chemical
Safety and Pollution Prevention, United States Environmental Protection Agency.
https://www.epa.gov/sites/proditction/files/2018-06/docum ents/tce problem formulation 03 -3 ]:
* [
U.S. EPA. (U.S. Environmental Protection Agency). (2018d). TRI Reporting Forms and Instructions
(RFI) Guidance Document.
https://ofmpub.epa.gov/apex/guideme ext/f?p=guideme ext:41:0::NQ:::
(U.S. Environmental Protection Agency). (2019a). Aluminum forming point source category.
(40 CFR Part 467). Washington, D C. https://www.ecfr.gov/cgi-bin/text-
idx?^ if 178f42f8141 c0887e5f4&mc=tme&node=pt40.32.467&rgn=div5
(U.S. Environmental Protection Agency). (2019b). Coil coating point source category. (40
CFR Part 465). Washington, D C. https://www.ecfr.gov/cgi-bin/text-
idx?^ if 178f42f8141 c0887e5f4&mc=tme&node=pt40.32.465&rgn=div5
U.S. EPA. (U.S. Environmental Protection Agency). (2019c). Electrical and electronic components point
source category. (40 CFR Part 469). Washington, D C. https://www.ecfr.gov/ ;/text-
idx?< >f 178f42.fi; 141 c0887e5f4&mc=tme&node=pt40.32.469&rgn=div5
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U.S. EPA. (U.S. Environmental Protection Agency). (2019d). Electroplating Point Source Category. (40
CFR Part 413). Washington, D.C. https://www.ecfr.gov/cei-bin/text-
(U.S. Environmental Protection Agency). (2019e). Iron and steel manufacturing point source
category. (40 CFR Part 420). Washington, D.C. https://www.ecfr.gov/cgi-bin/text-
idx?S H} /1 5al9d4dd729dfale53fb9cj I 'c 1 i'706&mc=true&node=pt40.31.420&rgn=div5
U.S. EPA. (U.S. Environmental Protection Agency). (2019f). Metal finishing point source company. (40
CFR Part 433). Washington, D.C. https://www.ecfr.gov/cgi-bin/text-
idx?SID=l 17c2452100fl78f42fB141c0887e5f4&mc=true&node=pt40.32.433&rgn=div5
U.S. EPA. (U.S. Environmental Protection Agency). (2019g). Organic chemicals, plastics, and synthetic
fibers. (40 CFR Part 414). Washington, D.C. https://www.ecfr.gov/cgi-bin/text-
idx?SID=5c5al 9d4dd72.9db 1 e53ft)9c J ic=true&node=pt4^' } l IMA rgn=div5
U.S. EPA. (U.S. Environmental Protection Agency). (2019h). Risk Evaluation for trichloroethylene.
Washington, D.C.
Vandervort. R; Polakoff. PL. (1973). Health hazard evaluation report no. HHE 72-84-31, Dunham-Bush,
Incroprated, West Hartford, Connecticut, Part 2. (HHE 72-84-31). Cincinnati, OH: National
Institute for Occupational Safety and Health.
von Grot irlimann. C: Scheringer. M; Hungerbuhler. K. (2006). Assessing occupational exposure
to perchloroethylene in dry cleaning. J Occup Environ Hyg 3: 606-619.
http://dx.doi.ors 30/15459620600912173
Von. Grote. J; Hurlimann. JC: Scheringer. M; Hungerbuhler. K. (2003). Reduction of Occupational
Exposure to Perchloroethylene and Trichloroethylene in Metal Degreasing over the Last 30
years: Influence of Technology Innovation and Legislation. J Expo Anal Environ Epidemiol 13:
325-340. http://dx.doi.org/ 3/si.iea.750Q288
Whittaker. SG; Johanson. CA. (201 1). A profile of the dry cleaning industry in King County,
Washington: Final report. (LHWMP 0048). Seattle, WA: Local Hazardous Waste Management
Program in King County.
http://www.hazwastehelp.org/publications/publications detail.aspx?DoeID=Oh73%2fQitg9Q%3
d~
Young. ML. (2012). Pre-spotting step toward better cleaning. Available online at
https://americandrvcleaner.com/articles/pre-spotting-step-toward-better-cleaning
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Appendix R MASS BALANCE
EPA developed a mass balance to account for the amount of TCE entering and leaving all facilities in
the United States. EPA quantified the amount of trichloroethylene associated with each of its life cycle
stages from introduction into commerce in the U.S. (from both domestic manufacture and import),
processing, use, release, and disposal using 2016 CDR, 2017 TRI, 2017 NEI and readily available
market data. Due to limitations in the available data (e.g., reporting thresholds, CBI claims, data from
different years), the mass balance may not account for all of the TCE in commerce in the U.S. or could
potentially allocate portions of the production volume inaccurately. The following subsections described
EPA's approach to developing the mass balance and the result of the mass balance.
R.l Approach for Developing the Mass Balance
EPA used the reported aggregated production volume of 171,929,400 lbs from the 2016 CDR data as the
amount of TCE manufactured and imported to the U.S. (U.S. EPA. 2016c).Starting with this volume,
EPA estimated the portion of the volume used domestically versus or exported. EPA used the reported
aggregated production volume of 171,929,400 lbs from the 2016 CDR data as the amount of TCE
manufactured and imported to the U.S. (U.S. EPA. 2016d). Starting with this volume, EPA estimated
the portion of the volume used domestically versus or exported. The export volume was estimated to be
10,531,608 lbs in 2015; however, this does not account for export volumes claimed as CBI in the 2016
CDR (U.S. EPA. 2016d). The domestic use volume was assumed to be anything not reported as
exported in the 2016 CDR plus any volume reported as transferred for off-site recycling in the 2017
TRI. EPA only considered the off-site recycling volume as EPA assumes any volume reported for on-
site recycling is reused at the site with consumption, disposal, and treatment of the recycled volume
accounted for in the facility's other reported TRI values and thus already accounted for in the mass
balance. EPA assumed the volume reported for off-site recycling is reintroduced into commerce similar
to virgin (i.e., unused directly from manufacturer or importer) TCE. This resulted in a total of
161,666,878 lbs, or 94% of the total production volume, being used domestically.
Use volumes were determined based on the 2014 TCE risk assessment, which estimated 83.6% of the
domestic use volume is used as an intermediate, 14.7% is used as a degreasing solvent, and 1.7% is for
miscellaneous uses (U.S. EPA. 2.014b). Accounting for exports and the off-site recycled volume, this
resulted in 135,153,510 lbs for intermediate uses, 23,765,031 lbs for degreasing uses, and 2,748,337 lbs
for miscellaneous uses.
During manufacture, processing, and use, a portion of volume of TCE at a given site may be released to
the environment on-site or end up in waste streams that are ultimately sent off-site for treatment,
disposal, energy recovery, or recycling. EPA used data from the 2017 TRI (U.S. EPA. 2020b) and 2017
NEI ( 20a) to quantify volumes associated with each end-of-life activities. 2017 TRI data
was grouped into the following categories of end-of-life activities: wastewater discharges, air emissions,
land disposal, off-site recycling, energy recovery, and waste treatment.
In addition to surface water discharges, the volume estimated for wastewater discharges includes the
total volume reported by facilities as transferred to off-site wastewater treatment (non-POTW) and off-
site POTW treatment. It does not account for subsequent removal from wastewater streams into air or
sludge that may occur at such treatment sites. The amount calculated for land disposal includes the
releases from all on-site and off-site underground injection, surface impoundment, land application,
landfills, and any other land disposal reported in the 2017 TRI.
For recycling, TRI includes volumes for both on- and off-site recycling. As stated above, EPA assumed
that any volume reported as recycled on-site is reused at the site with consumption, disposal, and
treatment of the recycled volume accounted for in the facility's other reported TRI values and not further
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3077
considered for the mass balance. EPA assumed the volume reported for off-site recycling is reintroduced
into commerce similar to virgin {i.e., unused directly from manufacturer or importer) TCE.
The calculated amount of TCE released as air emissions include data from both 2017 TRI (
2020b) and 2017 NEI (l__S ! P \ 2020a). The air emissions include the total reported fugitive air
emissions and stack air emissions from 2017 TRI reporters as well as all nonpoint source emission totals
from NEI. NEI also collects data from point sources which may include sites that also report to TRI. To
avoid double counting any volume reported in both TRI and NEI, EPA excluded a point emission source
if the facility also reported TCE to TRI. Such sites were identified by cross-walking TRIFDs reported in
TRI to those in NEI. EPA also excluded emissions from any point source in NEI reported as being from
landfills, POTW, or wastewater treatment facilities. EPA assumed that emissions from these sources are
already accounted for in the "wastewater treatment" and "land disposal" volumes from TRI. Finally,
EPA excluded air emissions from any point source reported as being from remediation activities. These
volumes are assumed to be from historical uses of TCE such that any volume associated with those
activities are not assumed to be related to the current year's production volume.
Any unused, spent, or waste TCE not accounted for above is expected to be sent for further waste
management. These methods can be reported to TRI specifically as energy recovery or generally as
waste treatment. However, volumes reported as sent for off-site energy recovery or treatment can be
double counted if the site receiving the waste TCE is also required to report to TRI for TCE. This double
count was addressed by comparing the RCRA IDS of reported downstream waste processors with the
RCRA IDs of reporting facilities. For the purpose of the mass balance, the treatment and energy
recovery volumes also assume 100% destruction/removal efficiencies which is likely unrealistic.
Therefore, some portion of these values may also be counted in releases.
The end-of-life stage also accounts for TCE that is consumed in a reaction from intermediate uses. To
estimate the amount that is consumed in reaction, EPA identified in the sites in TRI that report TCE uses
as a reactant and subtracted out the volume reported as released, disposed of, or otherwise managed as
waste at each site from the intermediate use volume and assumed the remainder was consumed. EPA
acknowledges that some portion of the intermediate use volume may remain as unintended impurities in
products from the reaction; however, this volume cannot be quantified.
R.2 Results and Uncertainties in the Mass Balance
Figure Apx R-l shows the result of the mass balance. The overall percentage of TCE accounted for at
the end-of-life is 101.5% of the 2016 CDR production volume. The 1.5% of the volume that is
overcounted is potentially due to incomplete reporting data and comparison of data from different years.
Other sources of uncertainty include limitations in reporting requirements (e.g., reporting thresholds),
CBI claims on exported volumes, and unknown volumes of unreacted TCE remaining in products.
Page 800 of 803
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3078
3079
Production
Uses
Manufacture
and Import
Volume (lbs):
171,929,400
Total Manufacture, Import,
and Off-Site Recycle
Volume (lbs): 161,666,878
Intermediate Use (83.6% of domestic use volume)
Includes:
Processing as a Reactant
Industrial Processing Aid
% of Total PV:
78.5%
Volume (lbs):
135,153,510
Degreasing Solvent (14.7% of domestic use volume)
Includes:
Aerosol Degreasing
Formulation of Aerosol and Non-Aerosol Products
Batch Closed-Loop Vapor Degreasing Systems
Batch Open Top Vapor Degreasing
Cold Cleaning
Conveyorized Vapor Degreasing
Web Vapor Degreasing
% of Total PV:
13.8%
Volume (lbs):
23,765,031
Miscellaneous (1.7% of domestic use volume)
Includes
Metal working Fluids
Commercial Printing and Copying
Adhesives, Sealants, Paints, and Coatings
Spot Cleaning, Wipe Cleaning and Carpet Cleaning
Other Industrial/Commercial Uses
% of Total PV:
1.6%
Volume (lbs):
2,748,337
3080
3081
Figure Apx R-l. Mass Balance for Trichloroethylene
End-of-Life
Exported
% of Total PV:
6.12%
Volume (lbs):
10,531,608
Consumed in Reaction
% of Total PV:
Volume (lbs):
82.76%
142,520,044
Energy Recovery and Treatment
% of Total PV:
Volume (lbs):
436%
7,496,284
Wastewater Discharges
% of Total PV:
Volume (lbs):
0.006360%
10,934
Air Emissions: Fugitive and Stack
% of Total PV:
Volume (lbs):
6.89%
11,850,311
Land Disposal
% of Total PV: 0.07%
Volume (lbs): 119,434
Off-site Recycled
% of Total PV: 0.16%
Volume (lbs): 269,086
Unreacted TCE Remaining
in Products
% of Total PV: Unknown
Volume (lbs): Unknown
Page 801 of 803
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Appendix S LEVEL TIT FUGACITY RESULTS
EPA ran the level III fugacity model in EPISuite™ using emissions from a mass balance developed to
account for the amount of TCE entering and leaving all facilities in the United States. For the mass
balance EPA attempted to quantify the amount of trichloroethylene associated with each of its life cycle
stages from introduction into commerce in the U.S. (from both domestic manufacture and import),
processing, use, release, and disposal. The mass balance development and uncertainties are detailed in
Appendix R. Physical chemical and environmental fate properties used as input to the model were taken
from Table 1-1 and Table 2-1 in the Risk Evaluation, respectively. The model was run holding the
environmental release steady at 1000 kg/hour but varying the receiving medium. Releases range from
1000 kg/hour simultaneously for air, soil and water to 1000 kg/hour for two of the three media and
finally, lOOOkg/hour released to a single medium. A total of seven iterations were executed. The model
was run using annual emissions to air and water from the mass balance converted to kilograms per hour.
Land disposal, energy recovery and treatment, and off-site recycling were not considered as
environmental releases. Results are shown below.
Level III Fugacity Model (Full-Output): EQC Default
Chem Name : TRICHLOROETHENE
Molecular Wt: 131.39
Henry's LC : 0.00985 atm-m3/mole (user-entered)
Vapor Press : 69 mm Hg (user-entered)
Log Kow : 2.42 (user-entered)
Soil Koc : 108 (EQC Model Default)
Mass Amount
Half-Life
Emissions
(percent)
(hr)
(kg/hr)
Air
99.2
240
614
Water
0.696
10000
0.567
Soil
0.132
10000
0
Sediment
0.00553
10000
0
Fugacity
Reaction
Advection
Reaction
Advection
(atm)
(kg/hr)
(kg/hr)
(percent)
(percent)
Air
8.86e-011
138
477
22.4
77.6
Water
1.25e-010
2.32e-008
0.334
3.77e-009
0.0544
Soil
8.92e-011
4.41e-009
0
7.17e-010
0
Sediment
1.39e-010
1.84e-010
5.31e-005
3e-011
8.65e-006
Persistence Time: 78.2 hr
Reaction Time: 349 hr
Advection Time: 101 hr
Percent Reacted: 22.4
Percent Advected: 77.6
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3149
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3152
3153
Water Compartment Percents:
Mass Amount
Half-Life
Emissions
(percent)
(hr)
(kg/hr)
Air
99.2
240
614
Water
0.696
10000
0.567
water
(0.696)
biota
(9.15e-006)
suspended
sediment
(0.000113)
Soil
0.132
10000
0
Sediment
0.00553
10000
0
Half-Lives (hr), (based upon user-entry):
Air: 240
Water: 10000
Soil: 10000
Sediment: 10000
Advection Times (hr):
Air: 100
Water: 1000
Sediment: 50000
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