1
2
3
4
5
6
7
8
9
10
11
12
13
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
SEPA
United States
Environmental Protection Agency
EPA Document #740R18008
February 21,2020
Office of Chemical Safety and
Pollution Prevention
Risk Evaluation for
T richloroethylene
CASRN: 79-01-6
CI. H
CI CI
Page 1 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
14 TABLE OF CONTENTS
15 ACKNOWLEDGEMENTS 23
16 ABBREVIATIONS 24
17 EXECUTIVE SUMMARY 28
18 1 INTRODUCTION 39
19 1.1 Physical and Chemical Properties 41
20 1.2 Uses and Production Volume 42
21 1.2.1 Data and Information Sources 42
22 1.2,2 Domestic Manufacture of Trichloroethylene 42
23 1.3 Regulatory and Assessment History 44
24 1.4 Scope of the Evaluation 46
25 1.4.1 Conditions of Use Included in the Risk Evaluation 46
26 1.4.2 Conceptual Models 56
27 1.5 Systematic Review 60
28 1.5.1 Data and Information Collection 60
29 1.5.2 Data Evaluation 66
30 1.5.3 Data Integration 67
31 2 EXPOSURES 68
32 2.1 Fate and Transport 68
33 2.1.1 Fate and Transport Approach and Methodology 69
34 2.1.2 Summary of Fate and Transport 69
35 2.1,3 Assumptions and Key Sources of Uncertainty for Fate and Transport 71
36 2.2 Environmental Exposures 71
37 2.2.1 Environmental Exposures Overview 71
38 2.2.2 Environmental Releases to Water 72
39 2.2.2.1 Results for Daily Release Estimate 72
40 2.2,2,2 Approach and Methodology 74
41 2,2.2.2.1 Water Release Estimates 74
42 2.2,2,2.2 Estimates of Number of Facilities 74
43 2,2.2.2,3 Estimates of Release Days 76
44 2,2.2.3 Assumptions and Key Sources of Uncertainty for Environmental Releases 77
45 2.2.2.3.1 Summary of Overall Confidence in Release Estimates 78
46 2.2.3 Aquatic Exposure Modeling Approach 84
47 2.2.3.1 Exposure and Fate Assessment Screening (E-FAST) Tool 2014 Inputs 85
48 2.2.3.2 E-FAST 2014 Equations 86
49 2.2.3,3 E-FAST 2014 Outputs 87
50 2.2.4 Surface Water Monitoring Data Gathering Approach 88
51 2.2.4.1 Method for Systematic Review of Surface Water Monitoring Data 88
52 2.2.4,2 Method for Obtaining Surface Water Monitoring Data from WQX/WQP 88
53 2.2,5 Geospatial Analysis Approach 89
54 2.2.6 Environmental Exposure Results 90
55 2.2.6.1 Terrestrial Environmental Exposures 90
56 2.2.6,2 Aquatic Environmental Exposures 91
Page 2 of 748
-------
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
. 91
. 93
. 97
. 97
. 98
100
100
101
108
108
109
109
111
115
119
121
125
125
125
126
128
128
134
135
136
137
137
137
138
138
141
143
151
151
152
175
177
177
179
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.2.6.2.1 Predicted Surface Water Concentrations: E-FAST 2014 Modeling
2.2.6.2.2 Monitored Surface Water Concentrations
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
2.3.1.2
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3.1.3
2.3
2.3
2.3
2.3
2.3
Results for Occupational Assessment
Approach and Methodology
1.2.1 General
1.2.2 Inhalation Exposure Monitoring Data
1.2.3 Inhalation Exposure Modeling
1.2.4 Acute and Chronic Inhalation Exposure Estimates
2.5 Dermal Exposure Modeling
2.6 Consideration of Engineering Controls and Personal Protective Equipment
2.7 Number of Workers and Occupational Non-Users Exposed
Assumptions and Key Sources of Uncertainty for Occupational Exposures
3.1
3.2
Number of Workers
Analysis of Exposure Monitoring Data
.3,3 Near-Field/Far-Field Model Framework
,3.4 Modeled Dermal Exposures
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 Potentially Exposed or Susceptible Subpopulations
2.3.2.4 Consumer Exposures Approach and Methodology
2.3.2.4.1 Modeling Approach
2.3.2.5 Consumer Exposure Scenarios and Modeling Inputs
2.3.2.5.1 Consumer Exposure Model Inputs
2.3.2.6 Consumer Exposure Results
2.3.2.6.1 Characterization of Exposure Results
2.3.2.6.2 Consumer Exposure Estimates
2.3.2.6.3 Summary of Consumer Exposure Assessment
2.3.2.7 Assumptions and Key Sources of Uncertainty for Consumer Exposures .
2.3.2.7.1 Modeling Approach Uncertainties
2.3.2.7.2 Data Uncertainties
Page 3 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
97 2.3,2,8 Confidence in Consumer Exposure Scenarios 180
98 2.3,3 Potentially Exposed or Susceptible Subpopulations 185
99 3 HAZARDS 189
100 3.1 Environmental Hazards 189
101 3,1.1 Approach and Methodology 189
102 3.1,2 Hazard Identification 189
103 3.1,3 Species Sensitivity Distributions (SSDs) 194
104 3.1.4 Weight of the Scientific Evidence 197
105 3.1,5 Concentrations of Concern 198
106 3.1.6 Summary of Environmental Hazard 199
107 3,1.7 Assumptions and Key Uncertainities for Environmental Hazard Data 200
108 3.2 Human Health Hazards 201
109 3.2.1 Approach and Methodology 201
110 3.2,2 Toxicokinetics 203
111 3,2.2,1 Physiologically-Based Pharmacokinetic (PBPK) Modeling Approach 206
112 3.2.3 Hazard Identification 210
113 3,2.3.1 Non-Cancer Hazards 210
114 3,2.3.1.1 Liver toxicity 210
115 3,2,3.1,2 Kidney toxicity 211
116 3,2.3,1,3 Neurotoxicity 211
117 3.2.3.1.4 Immunotoxicity (including sensitization) 212
118 3.2,3.1.5 Reproductive toxicity 214
119 3.2,3,1.6 Developmental Toxicity 215
120 3,2.3,1.7 Overt Toxicity Following Acute/Short Term Exposure 217
121 3.2.3.2 Genotoxicity and Cancer Hazards 218
122 3,2,3.2.1 Kidney cancer 218
123 3,2,3,2,2 Liver cancer 218
124 3,2.3,2.3 Cancer of the immune system 219
125 3.2,3.2.4 Other cancers 219
126 3.2,4 Weight of Scientific Evidence 219
127 3,2.4.1 Non-Cancer Hazards 219
128 3.2.4.1.1 Liver toxicity 219
129 3,2,4,1,2 Kidney toxicity 220
130 3,2.4,1,3 Neurotoxicity 220
131 3.2.4.1.4 Immunotoxicity 220
132 3,2,4.1.5 Reproductive toxicity 220
133 3.2.4,1,6 Developmental Toxicity 221
134 3.2.4.1,7 Overt Toxicity Following Acute/Short Term Exposure 225
135 3,2,4,2 Cancer Hazards 225
136 3.2.4.2.1 Meta-Analysis Results 226
Page 4 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
137 3,2,4,2.2 Mode of Action 227
138 3,2.5 Dose-Response Assessment 229
139 3,2.5.1 Selection of Studies for Dose-Response Assessment 229
140 3,2.5.1.1 Liver toxicity 230
141 3.2.5.1.2 Kidney toxicity 230
142 3.2.5.1.3 Neurotoxicity 230
143 3.2.5.1.4 Immunotoxicity 230
144 3.2.5.1.5 Reproductive toxicity 231
145 3.2.5.1.6 Developmental toxicity 231
146 3.2.5.1.7 Cancer 233
147 3,2.5.2 Potentially Exposed and Susceptible Subpopulations (PESS) 233
148 3.2.5,3 Derivation of Points of Departure (PODs) 234
149 3.2.5.3.1 Non-Cancer PODs for Acute Exposure 235
150 3,2,5,3,2 Non-Cancer PODs for Chronic Exposures 239
151 3.2.5.3.3 Cancer POD for Lifetime Exposures 249
152 3,2,5.4 Selected PODs for Human Health Hazard Domains 251
153 3,2,6 Assumptions and Key Sources of Uncertainty for Human Health Hazard 254
154 3.2.6.1 Confidence in Hazard Identification and Weight of Evidence 254
155 3.2,6.2 Derivation of PODs, UFs, and PBPK Results 254
156 3.2.6.3 Cancer Dose Response 256
157 3.2.6.4 Confidence in Human Health Hazard Data Integration and Representative Endpoints256
158 4 RISK CHARACTERIZATION 259
159 4.1 Environmental Risk 259
160 4.1.1 Risk Estimation Approach 259
161 4.1.2 Risk Estimation for Aquatic 260
162 4.1.3 Risk Estimation for Sediment 275
163 4.1.4 Risk Estimation for Terrestrial 275
164 4,2 Human Health Risk 277
165 4.2.1 Risk Estimation Approach 277
166 4.2.1.1 Representative Points of Departure for Use in Risk Estimation 280
167 4.2,2 Risk Estimation for Occupational Exposures by Exposure Scenario 281
168 4.2.3 Risk Estimation for Consumer Exposures by Exposure Scenario 322
169 4.3 Assumptions and Key Sources of Uncertainty for Risk Characterization 347
170 4.3.1 Environmental Risk Characterization 347
171 4.3,2 Human Health Risk Characterization 347
172 4.3.2.1 Occupational Exposure Considerations 347
173 4.3.2.2 Consumer/Bystander Exposure Considerations 348
174 4.3.2.3 Dermal Absorption Considerations 349
175 4.3.2.4 Confidence in Risk Estimates 349
176 4.4 Other Risk Related Considerations 352
177 4.4.1 Potentially Exposed or Susceptible Populations 352
178 4.4.2 Aggregate and Sentinel Exposures 352
179 4.5 Risk Conclusions 354
Page 5 of 748
-------
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
4.5.1 Environmental Risk Conclusions 354
4.5.2 Human Health Risk Conclusions 356
4.5.2.1 Summary of Risk Estimates for Workers and ONUs 356
4.5.2.2 Summary of Risk Estimates for Consumers and Bystanders 370
RISK DETERMINATION 374
5.1 Unreasonable Risk 374
5.1.1 Overview 374
5.1.2 Risks to Human Health 375
5.1.2.1 Determining Non-Cancer Risks 375
5.1.2.2 Determining Cancer Risks 376
5.1.3 Determining Environmental Risk 376
5.2 Risk Determinations for TCE 377
5.3 Detailed Risk Determinations by Condition of Use 383
5.3.1 Manufacture - Domestic manufacture 383
5.3.2 Manufacture - Import (includes repackaging and loading/unloading) 384
5.3.3 Processing - Processing as a reactant/intermediate in industrial gas manufacturing (e.g.,
manufacture of fluorinated gases used as refrigerants, foam blowing agents and solvents)385
5.3.4 Processing - Incorporation into formulation, mixture or reaction product - Solvents (for
cleaning or degreasing); adhesives and sealant chemicals; solvents (which become part of
product formulation or mixture) (e.g., lubricants and greases, paints and coatings, other
uses) 387
5.3.5 Processing - Incorporation into articles - Solvents (becomes an integral components of
articles) 388
5.3.6 Processing - Repackaging - Solvents (for cleaning or degreasing) 389
5.3.7 Processing - Recycling 390
5.3.8 Distribution in Commerce 391
5.3.9 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor degreaser
(open-top) 391
5.3.10 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor degreaser
(closed-loop) 393
5.3.11 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor degreaser
(convey orized) 394
5.3.12 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor degreaser
(web cleaner) 396
5.3.13 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Cold cleaner 397
5.3.14 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner; mold release 398
5.3.15 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant additives - Tap
and die fluid 400
5.3.16 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant additives -
Penetrating lubricant 401
5.3.17 Industrial/Commercial Use - Adhesives and sealants - Solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant 402
5.3.18 Industrial/Commercial Use - Functional fluids (closed systems) - Heat exchange fluid... 404
5.3.19 Industrial/Commercial Use - Paints and coatings - Diluent in solvent-based paints and
coatings 405
Page 6 of 748
-------
226
227
228
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
265
266
267
268
269
270
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5.3.20 Industrial/Commercial Use - Cleaning and furniture care products - Carpet cleaner; wipe
cleaning 407
5.3.21 Industrial/Commercial Use - Laundry and dishwashing products - Spot remover 408
5.3.22 Industrial/Commercial Use - Arts, crafts and hobby materials - Fixatives and finishing
spray coatings 409
5.3.23 Industrial/Commercial Use - Corrosion inhibitors and anti-scaling agents - Corrosion
inhibitors and anti-scaling agents 411
5.3.24 Industrial/Commercial Use - Processing aids - Process solvent used in battery manufacture;
process solvent used in polymer fiber spinning, fluoroelastomer manufacture, and Alcantara
manufacture; extraction solvent used in caprolactam manufacture; precipitant used in beta-
cyclodextrin manufacture 412
5.3.25 Industrial/Commercial Use - Ink, toner, and colorant products - Toner aid 414
5.3.26 Industrial/Commercial Use - Automotive care products - Brake and parts cleaners 415
5.3.27 Industrial/Commercial Use - Apparel and footwear care products - Shoe polish 416
5.3.28 Industrial/Commercial Use - Hoof polishes; gun scrubber; pepper spray; other
miscellaneous industrial and commercial uses 418
5.3.29 Disposal 419
5.3.30 Consumer Use - Solvents (for cleaning or degreasing) - Brake and parts cleaner 420
5.3.31 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol electronic
degreaser/cleaner 421
5.3.32 Consumer Use - Solvents (for cleaning or degreasing) - Liquid electronic degreaser/cleaner
422
5.3.33 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol spray degreaser/cleaner423
3.34 Consumer Use - Solvents (for cleaning or degreasing) - Liquid degreaser/cleaner 424
3.35 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol gun scrubber 425
3.36 Consumer Use - Solvents (for cleaning or degreasing) - Liquid gun scrubber 425
3.37 Consumer Use - Solvents (for cleaning or degreasing) - Mold release 426
3.38 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire cleaner 427
3.39 Consumer Use - Solvents (for cleaning or degreasing) - Liquid tire cleaner 428
3.40 Consumer Use - Lubricants and greases - Tap and die fluid 429
3.41 Consumer Use - Lubricants and greases - Penetrating lubricant 430
3.42 Consumer Use - Adhesives and sealants - Solvent-based adhesive and sealant 430
3.43 Consumer Use - Adhesives and sealants - Mirror edge sealant 431
3.44 Consumer Use - Adhesives and sealants - Tire repair cement/sealer 432
3.45 Consumer Use - Cleaning and furniture care products - Carpet cleaner 433
3.46 Consumer Use - Cleaning and furniture care products - Aerosol spot remover 434
3.47 Consumer Use - Cleaning and furniture care products - Liquid spot remover 435
3.48 Consumer Use - Arts, crafts, and hobby materials - Fixatives and finishing spray coatings
436
3.49 Consumer Use - Apparel and footwear care products - Shoe polish 436
3.50 Consumer Use - Other consumer uses - Fabric spray 437
3.51 Consumer Use - Other consumer uses - Film cleaner 438
3.52 Consumer Use - Other consumer uses - Hoof polish 439
3.53 Consumer Use - Other consumer uses - Pepper spray 440
3.54 Consumer Use - Other consumer uses - Toner aid 440
Page 7 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
271 REFERENCES 442
272 APPENDICES 463
273 Appendix A REGULATORY HISTORY 463
274 A.l Federal Laws and Regulations ...............463
275 A.2 State 'Laws and Regulations ....469
276 A.3 International Laws and Regulations..................... 470
277 Appendix B LIST OF SUPPLEMENTAL DOCUMENTS 472
278 Appendix C ENVIRONMENTAL EXPOSURES 474
279 Appendix D CONSUMER EXPOSURES 516
280 D.l Model Sensitivity .....516
281 D.l.l Continuous Variables 516
282 D.1.2 Categorical Variables 519
283 D.l Monitoring Data.... .....519
284 D.2.1 Indoor Air Monitoring 519
285 D.2.2 Personal breathing Zone Monitoring Data 521
286 Appendix E ENVIRONMENTAL HAZARDS 523
287 E.l Species Sensitivity Distribution (SSD) Methodology.... ......523
288 E,2 Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as
289 Modeled in E-FAST.... ......530
290 Appendix F BENCHMARK DOSE ANALYSIS FOR (Selgrade and Gilmour, 2010) 590
291 F.l BMDS Wizard Output Report - Mortality 590
292 F.l.l BMDS Summary of Mortality-BMR 10% 590
293 F.l.2 BMDS Summary of Mortality - BMR: 5% 593
294 F.l.3 BMDS Summary of Mortality-BMR: 1% 595
295 F.2 BMDS Wizard Output Report - Number of Mice Infected ..............598
296 F.2.1 BMDS Summary of Infected at 72 hours - BMR - 10% 598
297 Appendix G WEIGHT OF EVIDENCE FOR CONGENITAL HEART DEFECTS 600
298 G.l EPA Review of the Charles River (2019) Study .......600
299 G. 1.1 Study Methodology and Results 600
300 G.1.2 EPA Review 601
301 G. 1.2.1 Comparing Results Between Charles River and Johnson Studies 601
302 G. 1.2.2 Differences in Types of Malformations Observed 603
303 G. 1.2.3 Methodology Differences 607
304 G. 1.2.4 Adversity of Small VSDs 609
305 G.l WOE Analysis for Congenital Cardiac Defects... ........610
306 G.2.1 Methodology 610
307 G.2.2 WOE Results By Study Type 614
308 Appendix H META-ANALYSIS FOR CANCER 622
309 H. 1 Study Screening and Selection 622
310 H. 1.1 Data Quality and Inclusion/Exclusion Criteria Screening 622
311 H.1.2 Screening results 623
312 11.1.3 Pooled Cohorts 624
Page 8 of 748
-------
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
H.2 Mela-Analysis Methods and Results 625
II.2.1 Methods 625
H.2.2 Results 627
H.2.2.1 Initial Meta-Analyses 627
H.2.2.2 Sensitivity analyses 633
H.2.3 Selected RR estimates and confidence intervals by study and cancer type 641
H.2.4 Sample Stata commands for meta-analysis 647
Appendix I APPROACH FOR ESTIMATING WATER RELEASES FROM
MANUFACTURING SITES USING EFFLUENT GUIDELINES 648
Appendix J SAMPLE CALCULATIONS FOR CALCULATING ACUTE AND CHRONIC
(NON-CANCER AND CANCER) INHALATION EXPOSURE 652
J.l Example High-End AC, ADC, and LADC .652
J.2 Example Central Tendency AEG, ADC, and LADC 653
Appendix K VAPOR DEGREASING AND COLD CLEANING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODELS APPROACH AND PARAMETERS 654
K.l Model Design Equations ...655
K.2 Model Parameters ...............659
K.2.1 Far-Field Volume 664
K.2.2 Air Exchange Rate 664
K.2.3 Near-Field Indoor Air Speed 664
K.2.4 Near-Field Volume 665
K.2.5 Exposure Duration 665
K.2.6 Averaging Time 665
K.2.7 Vapor Generation Rate 665
K.2.8 Operating Hours 668
Appendix L BRAKE SERVICING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
MODEL APPROACH AND PARAMETERS 670
L.l Model Design Equations........................... ......670
L.2 Model Parameters.......... .................675
L.2.1 Far-Field Volume 678
L.2.2 Air Exchange Rate 678
L.2.3 Near-Field Indoor Air Speed 678
L.2.4 Near-Field Volume 679
L.2.5 Application Time 679
L.2.6 Averaging Time 679
L.2.7 Trichloroethylene Weight Fraction 679
L.2.8 Volume of Degreaser Used per Brake Job 680
L.2.9 Number of Applications per Brake Job 680
L.2.10 Amount of Trichloroethylene Used per Application 681
L.2.11 Operating Hours per Week 681
L.2.12 Number of Brake Jobs per Work Shift 681
Appendix M SPOT CLEANING NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
MODEL APPROACH AND PARAMETERS 682
M.l Model Design Equations....... .682
Page 9 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
357 M.2 Model Parameters. ..686
358 VI.2.1 Far-Field Volume 690
359 M.2.2 Near-Field Volume 690
360 M.2.3 Air Exchange Rate 690
361 M.2.4 Near-Field Indoor Wind Speed 690
362 M.2.5 Averaging Time 691
363 VI.2.6 Use Rate 691
364 M.2.7 Vapor Generation Rate 691
365 M.2.8 Operating Hours 691
366 M.2.9 Operating Days 692
367 M.2. lOFractional Number of Operating Days that a Worker Works 692
368 Appendix N BENCHMARK DOSE MODELING UPDATE FOR NESTED FETAL DATA
369 FROM (Johnson et al., 2003) 693
370 Appendix O CONSIDERATIONS FOR BMD MODELING AND APPLICATION OF
371 UNCERTAINTY FACTORS 695
372 O.l Selecting the BMD model to use for POD computation... ..............695
373 0.2 Uncertainty Factor Selection... ......696
374 Appendix P OCCUPATIONAL INHALATION EXPOSURE AND WATER RELEASE
375 ASSESSMENT 698
376 P.l Manufacturing................ .....698
377 P. 1.1 Exposure Assessment 698
378 P. 1.2 Water Release Assessment 698
379 P.l Processing as a Reactant ...701
380 P.2.1 Exposure Assessment 701
381 P.2.2 Water Release Assessment 702
382 P.3 Formulation of Aerosol and Non-Aerosol Products 703
383 P.3.1 Exposure Assessment 703
384 P.3.2 Water Release Assessment 703
385 P.4 Repackaging 704
386 P.4.1 Exposure Assessment 704
387 P.4.2 Water Release Assessment 704
388 P.5 Batch Open Top Vapor Degreasing ...705
389 P.5.1 Exposure Assessment 705
390 P.5.2 Water Release Assessment 708
391 P.6 Batch Closed-Loop Vapor Degreasing ...........712
392 P.6.1 Exposure Assessment 712
393 P.6.2 Water Release Assessment 712
394 P.7 Conveyorized Vapor Degreasing. .713
395 P.7.1 Exposure Assessment 713
396 P.7.2 Water Release Assessment 715
397 P.8 Web Vapor Degreasing.. 716
398 P.8.1 Exposure Assessment 716
399 P.8.2 Water Release Assessment 717
400 P.9 Cold Cleaning........... ...........718
401 P.9.1 Exposure Assessment 718
402 P.9.2 Water Release Assessment 720
Page 10 of 748
-------
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
720
720
722
.722
722
724
,724
724
726
,730
730
730
732
732
734
,735
735
736
,737
737
737
.738
738
738
739
739
739
.740
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P. 10 Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners,
Penetrating Lubricants, and Mold Releases............
P.10.1 Exposure Assessment
P. 10.2 Water Release Assessment
P. 11 Metalworking Fluids
P. 11.1 Exposure Assessment
P. 11.2 Water Release Assessment
P.12 Adhesives, Sealants, Paints, and Coatings......................................
P. 12.1 Exposure Assessment
P. 12.2 Water Release Assessment
P. 13 Other Industrial Uses.....
P.13.1 Exposure Assessment
P.13.2 Water Release Assessment
P. 14 Spot Cleaning, Wipe Cleaning and. Carpet Cleaning.
P. 14.1 Exposure Assessment
P. 14.2 Water Release Assessment
P. 15 Industrial Processing Aid
P.15.1 Exposure Assessment
P.15.2 Water Release Assessment
P. 16 Commercial Printing and Copying.................
P.16.1 Exposure Assessment
P. 16.2 Water Release Assessment
P.17 Other Commercial Uses
P.17.1 Exposure Assessment
P. 17.2 Water Release Assessment
P.l 8 Process Solvent Recycling and Worker Handling of Wastes
P.18.1 Exposure Assessment
P.18.2 Water Release Assessment
P.l9 Appendix P References
Page 11 of 748
-------
433
434
435
436
437
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
470
471
472
473
474
475
476
477
478
479
480
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LIST OF TABLES
Table 1-1 Physical and Chemical Properties of TCE 41
Table 1-2 Production Volume of TCE in CDR Reporting Period (2012 to 2015) a 42
Table 1-3. Assessment History of TCE 45
Table 1-4. Categories and Subcategories of Occupational Conditions of Use and Corresponding
Occupational Exposure Scenario 47
Table 1-5. Categories and Subcategories of Consumer Conditions of Use 53
Table 2-1 Environmental Fate Characteristic of TCE 68
Table 2-2: Summary of EPA's daily water release estimates for each OES and also EPA's Overall
Confidence in these estimates 73
Table 2-3: Summary of EPA's estimates for the number of facilities for each OES 75
Table 2-4: Summary of EPA's estimates for release days expected for each OES 76
Table 2-5: Summary of Overall Confidence in Release Estimates by OES 78
Table 2-6 Industry Sector Modeled for Facilities without Site-Specific Flow Data in E-FAST 2014 86
Table 2-7. Summary of Surface Water Concentrations by OES for Maximum Days of Release Scenario
91
Table 2-8. Summary of Surface Water Concentrations by OES for 20 Days of Release Scenario 91
Table 2-9. Summary of Surface Water Concentrations by OES for 20 Days of Release Scenario for
Indirect Releases to a non-POTW WWTP 92
Table 2-10. Measured Concentrations of TCE in Surface Water Obtained from the Water Quality Portal:
2013-2017' 94
Table 2-11. Ambient Levels of TCE in U.S. Surface Water from Published Literature 95
Table 2-12: A summary for each of the 18 occupational exposure scenarios (OESs) 103
Table 2-13: Summary of inhalation exposure results for Workers based on monitoring data and
exposure modeling for each OES 104
Table 2-14: Summary of inhalation exposure results for ONUs based on monitoring data and exposure
modeling for each OES 105
Table 2-15: A summary of dermal retained dose for Workers based on exposure modeling for each OES
106
Table 2-16: Summary of the total number of workers and ONUs potentially exposed to TCE for each
OES 107
Table 2-17: Parameter Values for Calculating Inhalation Exposure Estimates 112
Table 2-18: Overview of Average Worker Tenure from U.S. Census SIPP (Age Group 50+) 114
Table 2-19: Median Year of Tenure with Current Employer by Age Group 115
Table 2-20: Glove Protection Factors for Different Dermal Protection Strategies 117
Table 2-21: EPA grouped dermal exposures associated with the various OESs into four bins 118
Table 2-22: Assigned Protection Factors for Respirators in OSHA Standard 29 CFR § 1910.134 120
Table 2-23: SOCs with Worker and ONU Designations for All Conditions of Use Except 121
Table 2-24: SOCs with Worker and ONU Designations for Dry Cleaning Facilities 122
Table 2-25: Estimated Number of Potentially Exposed Workers and ONUs under NAICS 812320.... 123
Table 2-26: Summary of overall confidence in inhalation exposure estimates by OES 128
Table 2-27. Evaluated Consumer Conditions of Use and Products for TCE 135
Table 2-28. Default Modeling Input Parameters 143
Table 2-29. Consumer Product Modeling Scenarios and Varied Input Parameters 145
Table 2-30. Consumer Product Modeling Scenarios and Additional Scenario-Specific Input Parameters
149
Table 2-31. Surface Area and Body Weight Values for Different Consumer and Bystander
Subpopulations 152
Page 12 of 748
-------
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
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
152
153
154
154
155
155
156
157
157
158
158
159
159
160
161
161
162
162
163
163
164
165
166
166
167
167
168
169
169
170
171
171
172
172
173
174
175
175
176
182
184
187
187
193
199
204
204
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 Inhalation Exposure Summary: Liquid Electronic Degreaser/Cleaner
Table 2-36. Acute Dermal Exposure Summary: Liquid Electronic Degreaser/Cleaner
Table 2-37. Acute Inhalation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Table 2-38. Acute Dermal Exposure Summary: Aerosol Spray Degreaser/Cleaner
Table 2-39. Acute Inhalation Exposure Summary: Liquid Degreaser/Cleaner
Table 2-40. Acute Dermal Exposure Summary: Liquid Degreaser/Cleaner
Table 2-41. Acute Inhalation Exposure Summary: Aerosol Gun Scrubber
Table 2-42. Acute Dermal Exposure Summary: Aerosol Gun Scrubber
Table 2-43. Acute Inhalation Exposure Summary: Liquid Gun Scrubber
Table 2-44. Acute Dermal Exposure Summary: Liquid Gun Scrubber
Table 2-45. Acute Inhalation Exposure Summary: Mold Release
Table 2-46. Acute Inhalation Exposure Summary: Aerosol Tire Cleaner
Table 2-47. Acute Dermal Exposure Summary: Aerosol Tire Cleaner
Table 2-48. Acute Inhalation Exposure Summary: Liquid Tire Cleaner
Table 2-49. Acute Dermal Exposure Summary: Liquid Tire Cleaner
Table 2-50. Acute Inhalation Exposure Summary: Tap & Die Fluid
Table 2-51. Acute Inhalation Exposure Summary: Penetrating Lubricant
Table 2-52. Acute Inhalation Exposure Summary: Solvent-based Adhesive & Sealant
Table 2-53. Acute Inhalation Exposure Summary: Mirror-Edge Sealant
Table 2-54. Acute Inhalation Exposure Summary: Tire Repair cement/Sealer
Table 2-55. Acute Inhalation Exposure Summary: Carpet Cleaner
Table 2-56. Acute Dermal Exposure Summary: Carpet Cleaner
Table 2-57. Acute Inhalation Exposure Summary: Aerosol Spot Remover
Table 2-58. Acute Dermal Exposure Summary: Aerosol Spot Remover
Table 2-59. Acute Inhalation Exposure Summary: Liquid Spot Remover
Table 2-60. Acute Dermal Exposure Summary: Liquid Spot Remover
Table 2-61. Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings
Table 2-62. Acute Inhalation Exposure Summary: Shoe Polish
Table 2-63. Acute Dermal Exposure Summary: Shoe Polish
Table 2-64. Acute Inhalation Exposure Summary: Fabric Spray
Table 2-65. Acute Inhalation Exposure Summary: Film Cleaner
Table 2-66. Acute Inhalation Exposure Summary: Hoof Polish
Table 2-67. Acute Inhalation Exposure Summary: Pepper Spray
Table 2-68. Acute Inhalation Exposure Summary: Toner Aid
Table 2-69. Evaluated Pathways for Consumer Conditions of Use
Table 2-70. Summary of Consumer Exposure Levels by Category
Table 2-71. Confidence Ratings for Acute Inhalation Consumer Exposure Modeling Scenarios .
Table 2-72. Confidence Ratings for Acute Dermal Consumer Exposure Modeling Scenarios
Table 2-73. Percentage of Employed Persons by Age, Sex, and Industry Sector
Table 2-74. Percentage of Employed Adolescent by Detailed Industry Sector
Table 3-1 Ecological Hazard Data used Quantitatively to Characterize TCE Hazard for Aquatic
Organisms
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity
Table 3-3 TCE Metabolites Identified by Pathway
Table 3-4 Common Metabolites of TCE and Related Compounds
Page 13 of 748
-------
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 3-5 List of All of the PBPK-Modeled Dose Metrics Used in the TCE IRIS Assessment 206
Table 3-6. Overall Summary Scores by Line of Evidence for Cardiac Defects from TCE 224
Table 3-7: Dose-response analysis of selected studies considered for acute exposure scenarios 238
Table 3-8: Dose-response analysis of selected studies considered for evaluation of liver toxicity 240
Table 3-9: Dose-response analysis of selected studies considered for evaluation of kidney toxicity.... 241
Table 3-10: Dose-response analysis of selected studies considered for evaluation of neurological effects
243
Table 3-11: Dose-response analysis of selected studies considered for evaluation of immune effects . 245
Table 3-12: Dose-response analysis of selected studies considered for evaluation of reproductive effects
248
Table 3-13: Dose-response analysis of selected studies considered for acute exposure scenarios 252
Table 3-14: Dose-response analysis of selected studies considered for chronic exposure scenarios 253
Table 3-15: Cancer Points of Departure for Lifetime Exposure Scenarios 254
Table 4-1. Environmental Risk Quotients for Facilities Releasing TCE to Surface Water as Modeled in
E-FAST (RQs > 1 in bold) 264
Table 4-2. RQs Calculated using Monitored Environmental Concentrations from WQX/WQP 268
Table 4-3. RQs Calculated using Monitored Environmental Concentrations from Published Literature
269
Table 4-4. Use Scenarios, Populations of Interest and Toxicological Endpoints Used for Acute and
Chronic Exposures 277
Table 4-5: Most Sensitive Endpoints from Each Health Domain for Risk Estimation 280
Table 4-6. Occupational Risk Estimation - Manufacturing 282
Table 4-7. Occupational Risk Estimation - Processing as a Reactant 284
Table 4-8. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Monitoring
Data 286
Table 4-9. Occupational Risk Estimation - Batch Open Top Vapor Degreasing - Inhalation Modeling
Data 287
Table 4-10. Occupational Risk Estimation - Batch Closed-Loop Vapor Degreasing 289
Table 4-11. Occupational Risk Estimation - Conveyorized Vapor Degreasing - Inhalation Monitoring
Data 291
Table 4-12. Occupational Risk Estimation - Conveyorized Vapor Degreasing - Inhalation Modeling
Data 292
Table 4-13. Occupational Risk Estimation - Web Vapor Degreasing 294
Table 4-14. Occupational Risk Estimation - Cold Cleaning 296
Table 4-15. Occupational Risk Estimation - Aerosol Applications 298
Table 4-16. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Monitoring Data 300
Table 4-17. Occupational Risk Estimation - Spot Cleaning and Wipe Cleaning (and Other Commercial
Uses) - Inhalation Modeling Data 301
Table 4-18. Occupational Risk Estimation - Formulation of Aerosol and Non-Aerosol Products 303
Table 4-19. Occupational Risk Estimation - Repackaging 305
Table 4-20. Occupational Risk Estimation - Metalworking Fluids - Inhalation Monitoring Data 307
Table 4-21. Occupational Risk Estimation - Metalworking Fluids - Inhalation Modeling Data 308
Table 4-22. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Industrial
Setting) 310
Table 4-23. Occupational Risk Estimation - Adhesives, Sealants, Paints, and Coatings (Commercial
Setting) 312
Table 4-24. Occupational Risk Estimation - Industrial Processing Aid (12 hr) 314
Page 14 of 748
-------
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
25. Occupational Risk Estimation
26. Occupational Risk Estimation
27. Occupational Risk Estimation
Commercial Printing and Copying 316
Other Industrial Uses 318
Process Solvent Recycling and Worker Handling of Wastes
320
28. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Brake and Parts
Cleaner 323
29. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Electronic
Degreaser/Cleaner 324
30. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
Degreaser/Cleaner 325
31. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
Degreaser/Cleaner 326
32. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid
Degreaser/Cleaner 327
33. Consumer Risk Estimati
Table 4-34. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Gun Scrubber
329
Table 4-35. Consumer Risk Estimati
Table 4-36. Consumer Risk Estimati
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
Table 4-
37. Consumer Risk Esti
38. Consumer Risk Esti
39. Consumer Risk Esti
40. Consumer Risk Esti
41. Consumer Risk Esti
42. Consumer Risk Esti
43. Consumer Risk Esti
44. Consumer Risk Esti
Table 4-45. Consumer Risk Estimati
Table 4
-48. Consumer Risk Estimation - Other Consumer Uses
-49. Consumer Risk Estimation - Other Consumer Uses
-50. Consumer Risk Estimation - Other Consumer Uses
-51. Consumer Risk Estimation - Other Consumer Uses
on - Solvents for Cleaning and Degreasing - Aerosol Gun Scrubber
328
on - Solvents for Cleaning and Degreasing - Mold Release 330
on - Solvents for Cleaning and Degreasing - Aerosol Tire Cleaner
331
mation - Solvents for Cleaning and Degreasing - Liquid Tire Cleaner332
mation - Lubricants and Greases - Tap and Die Fluid 333
mation - Lubricants and Greases - Penetrating Lubricant 334
mation - Adhesives and Sealants - Solvent-Based Adhesive and Sealant
335
mation - Adhesives and Sealants - Mirror Edge Sealant 336
mation - Adhesives and Sealants - Tire Repair Cement / Sealer 337
mation - Cleaning and Furniture Care Products - Carpet Cleaner 338
mation - Cleaning and Furniture Care Products - Aerosol Spot Remover
339
on - Cleaning and Furniture Care Products - Liquid Spot Remover
340
46. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and Finishing
Spray Coatings 341
47. Consumer Risk Estimation - Apparel and Footwear Care Products - Shoe Polish 342
Fabric Spray 343
Table 4
Table 4
Table 4
Table 4
Table 4
Table 4-52. Consumer Risk Estimation - Other Consumer Uses
Table 4-53. Facilities with Acute or Chronic Risk Identified for Aquatic Organisms (RQs > 1 in bold)
355
Table 4-54. Occupational Risk Summary Table 358
Table 4-55. Consumer Risk Summary Table 370
Table 5-1. Summary of Unreasonable Risk Determinations by Condition of Use 379
Film Cleaner 344
Hoof Polish 345
Pepper Spray 346
Toner Aid 346
Page 15 of 748
-------
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LIST OF FIGURES
Figure 1-1. TCE Life Cycle Diagram 55
Figure 1-2. TCE Conceptual Model for Industrial and Commercial Activities and Uses: Potential
Exposures and Hazards 57
Figure 1-3. TCE Conceptual Model for Consumer Activities and Uses: Potential Exposures and Hazards
58
Figure 1-4. TCE Conceptual Model for Environmental Releases and Wastes: Potential Exposures and
Hazards 59
Figure 1-5. Literature Flow Diagram for Environmental Fate and Transport 62
Figure 1-6. Literature Flow Diagram for Engineering Releases and Occupational Exposure 63
Figure 1-7. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources 64
Figure 1-8. Literature Flow Diagram for Environmental Hazard 65
Figure 1-9. Literature Flow Diagram for Human Health Hazard 66
Figure 2-1: An overview of how EPA estimated daily water releases for each OES 72
Figure 2-2. WQP Search Option. Surface water data were obtained from the WQP by querying the
Sampling Parameters search option for the characteristic (STORET data), Parameter
Code (NWIS data), and date range parameter 89
Figure 2-3. Distribution of Active Facility Releases Modeled 93
Figure 2-4. Modeled Release Characteristics (Percent Occurrence) 93
Figure 2-5: Components of an occupational assessment for each OES; please refer to Section 2.2.2.2.2
for additional details on the approach and methodology for estimating number of
facilities 100
Figure 2-6: Illustrative applications of the NF/FF model to various exposure scenarios 110
Figure 3-1. Species Sensitivity Distribution (SSD) for Algae Species Using EC50s (Etterson, 2019)... 195
Figure 3-2. Species Sensitivity Distributions (SSDs) for Acute Hazard Data Using LC50S or EC50S
(Etterson, 2019) 196
Figure 3-3. EPA Approach to Hazard Identification, Data Integration, and Dose-Response Analysis for
TCE 201
Figure 3-4 Dose-Response Analyses of Rodent Non-Cancer Effects Using the Rodent and Human PBPK
Models 208
Figure 3-5 Example of HEC99 Estimation through Interpecies, Intraspecies and Route-to- Route
Extrapolation from a Rodent Study LOAEL/NOAEL 209
Figure 4-1. Concentrations of Trichloroethylene from Releasing Facilities (Higher Release Frequency
Scenarios) and WQX Monitoring Stations: Year 2016, East US 270
Figure 4-2. Concentrations of Trichloroethylene from Releasing Facilities (Higher Release Frequency
Scenarios) and WQX Monitoring Stations: Year 2016, West US 271
Figure 4-3. Concentrations of Trichloroethylene from Releasing Facilities (20 Days of Release Scenario)
and WQX Monitoring Stations: Year 2016, East US 272
Figure 4-4. Concentrations of Trichloroethylene Releasing Facilities (20 Days of Release Scenario) and
WQX Monitoring Stations: Year 2016, West US 273
Figure 4-5. Co-location of Trichloroethylene-Releasing Facilities and WQX Monitoring Stations at the
IILC 8 Level in NC 274
Figure 4-6. Co-location of Trichloroethylene-Releasing Facilities and WQX Monitoring Stations at the
I ILC 8 Level in NM 275
Page 16 of 748
-------
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LIST OF APPENDIX TABLES
Table_Apx A-l. Federal Laws and Regulations 463
Table_Apx A-2. State Laws and Regulations 469
Table_Apx A-3. Regulatory Actions by Other Governments and Tribes 470
Table_Apx C-l. Facility-Specific Aquatic Exposure Modeling Results 474
TableApx D-l. TCE Residential Indoor Air Concentrations (|ig/m3) in the United States and Canada
520
Table Apx D-2. Personal Breathing Zone Concentrations (|ig/m3) for TCE in the United States
(General/Residential) 522
Table Apx E-l. Standard Error for all dsitributions and fitting methods using TCE's algae hazard data
(Etterson, 2019) 524
Table Apx E-2. Standard Error for all distributions and fitting methods using TCE's acute hazard data
(Etterson, 2019) 527
Table Apx E-3. Environmental RQs by Facility (with RQs > 1 in bold) 530
Table Apx F-l. Summary of BMD Modeling Results for Mortality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra
Risk 590
Table Apx F-2. Summary of BMD Modeling Results for Mortality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra
Risk 593
Table Apx F-3. Summary of BMD Modeling Results for Mortality from Introduced Infection in Mice
Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 1% Extra
Risk 595
Table Apx F-4. Summary of BMD Modeling Results for Number of Mice Infected at 72 Hours after
Infection Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR =
10% Extra Risk 598
Table Apx F-5. Plot of Incidence by Dose (ppm) with Fitted Curve for Probit Model for Number of
Mice Infected at 72 Hours after Infection Following Inhalation Exposure to TCE
(Selgrade and Gilmour 2010); BMR = 10% Extra Risk 599
Table_Apx G-l. Experimental Design 600
Table_Apx G-2. Summary of Observed Interventricular Defects 601
TableApx G-3. Incidence of total heart malformations in Johnson and Charles River studies 601
Table Apx G-4. Incidence of VSDs in Johnson and Charles River studies 602
Table Apx G-5. Heart and Cardiovascular Defects Observed in Oral TCE studies 603
Table_Apx G-6. Cardiac Defects Observed in Literature 605
Table Apx G-l. Cardiac Defects Observed After Exposure to RA or TCE 606
Table Apx G-8. Weight-of-Evidence Table for Epidemiology Studies 614
Table Apx G-9. Weight-of-Evidence Table for In Vivo Animal Toxicity Studies 616
Table_Apx G-10. Weight-of-Evidence Table for Mechanistic Studies 619
Table Apx G-ll. Overall Weight-of-Evidence Table and Summary Scores 621
Table Apx H-l. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies Identified
in EPA's Literature Search 622
Table Apx H-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based on
Inclusion/Exclusion Criteria 623
Table Apx H-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the 2011
IRIS Assessment or EPA Literature Search 624
Table_Apx H-4. Analysis of influential studies: NHL 633
Table_Apx H-5. Analysis of influential studies: Kidney cancer 633
Page 17 of 748
-------
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
754
755
756
757
758
759
760
761
762
763
764
765
766
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table_Apx H-6. Analysis of influential studies: Liver cancer 634
TableApx H-7. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
cohort studies published after U.S. EPA (2011) 641
Table Apx H-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
case-control studies 642
Table Apx H-9. Selected RR estimates for NHL associated with TCE exposure (effect in the highest
exposure group) studies 642
Table Apx H-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from cohort studies 643
Table Apx H-l 1. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from case-control studies published after U.S. EPA (2011) 644
Table Apx H-12. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from cohort studies 645
Table Apx H-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect)
from case-control studies published after U.S. EPA (2011) 646
Table Apx 1-1. Summary of OCPSF Effluent Guidelines for Trichloroethylene 648
Table Apx 1-2. Default Parameters for Estimating Water Releases of Trichloroethylene from
Manufacturing Sites 649
TableApx 1-3. Summary of Facility Trichloroethylene Production Volumes and Wastewater Flow
Rates 650
Table Apx K-l. Summary of Parameter Values and Distributions Used in the Open-Top Vapor
Degreasing Near-Field/Far-Field Inhalation Exposure Model 660
Table Apx K-2. Summary of Parameter Values and Distributions Used in the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model 661
Table Apx K-3. Summary of Parameter Values and Distributions Used in the Web Degreasing Near-
Field/Far-Field Inhalation Exposure Model 662
Table Apx K-4. Summary of Parameter Values and Distributions Used in the Cold Cleaning Near-
Field/Far-Field Inhalation Exposure Model 663
Table Apx K-5. Summary of Trichloroethylene Vapor Degreasing and Cold Cleaning Data from the
2014 NEI 665
Table Apx K-6. Distribution of Trichloroethylene Open-Top Vapor Degreasing Unit Emissions 666
Table Apx K-7. Distribution of Trichloroethylene Conveyorized Degreasing Unit Emissions 667
Table Apx K-8. Distribution of Trichloroethylene Web Degreasing Unit Emissions 668
Table Apx K-9. Distribution of Trichloroethylene Cold Cleaning Unit Emissions 668
Table Apx K-10. Distribution of Trichloroethylene Open-Top Vapor Degreasing Operating Hours... 668
Table Apx K-l 1. Distribution of Trichloroethylene Conveyorized Degreasing Operating Hours 668
Table Apx K-12. Distribution of Trichloroethylene Web Degreasing Operating Hours 669
Table Apx K-13. Distribution of Trichloroethylene Cold Cleaning Operating Hours 669
Table Apx L-l. Summary of Parameter Values and Distributions Used in the Brake Servicing Near-
Field/Far-Field Inhalation Exposure Model 676
Table Apx L-2. Summary of Trichloroethylene-Based Aerosol Degreaser Formulations 680
TableApx M-l. Summary of Parameter Values and Distributions Used in the Spot Cleaning Near-
Field/Far-Field Inhalation Exposure Model 687
Table Apx M-2. Composite Distribution of Dry Cleaning Facility Floor Areas 690
Table Apx N-l. Results for Best-Fitting Model in Comparison to Results 694
Table Apx P-l. Summary of Worker Inhalation Exposure Monitoring Data from TCE Manufacturing
698
Table Apx P-2. Summary of OCPSF Effluent Limitations for Trichloroethylene 699
Page 18 of 748
-------
767
768
769
770
771
772
773
774
775
776
111
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx P-3. Reported Water Releases of Trichloroethylene from Manufacturing Sites Reporting to
2016 TRI 700
Table Apx P-4. Estimated Water Releases of Trichloroethylene from Manufacturing Sites Not
Reporting to 2016 TRI 701
Table Apx P-5. Summary of Worker Inhalation Exposure Surrogate Monitoring Data from TCE Use as
a Reactant 702
Table Apx P-6. Water Release Estimates for Sites Using TCE as a Reactant 702
Table Apx P-7. Summary of Worker Inhalation Exposure Monitoring Data for Unloading TCE During
Formulation of Aerosol and Non-Aerosol Products 703
Table Apx P-8. Summary of Worker Inhalation Exposure Monitoring Data for Unloading/Loading TCE
from Bulk Containers 704
Table Apx P-9. Reported Water Releases of Trichloroethylene from Sites Repackaging TCE 705
Table Apx P-10. Summary of Worker Inhalation Exposure Monitoring Data for Batch Open-Top Vapor
Degreasing 706
Table Apx P-l 1. Summary of Exposure Modeling Results for TCE Degreasing in OTVDs 708
Table Apx P-12. Reported Water Releases of Trichloroethylene from Sites Using TCE in Open-Top
Vapor Degreasing 708
TableApx P-13. Summary of Worker Inhalation Exposure Monitoring Data for Batch Closed-Loop
Vapor Degreasing 712
Table Apx P-14. Summary of Worker Inhalation Exposure Monitoring Data for Conveyorized Vapor
Degreasing 713
Table Apx P-15. Summary of Exposure Modeling Results for TCE Degreasing in Conveyorized
Degreasers 715
TableApx P-16. Summary of Exposure Modeling Results for TCE Degreasing in Web Degreasers.. 717
Table Apx P-17. Summary of Exposure Modeling Results for Use of Trichloroethylene in Cold
Cleaning 719
Table Apx P-l 8. Summary of Worker and Occupational Non-User Inhalation Exposure Modeling
Results for Aerosol Degreasing 722
Table Apx P-19. Summary of Worker Inhalation Exposure Monitoring Data for TCE Use in
Metalworking Fluids 723
Table Apx P-20. ESD Exposure Estimates for Metalworking Fluids Based on Monitoring Data 723
Table Apx P-21. Summary of Exposure Results for Use of TCE in Metalworking Fluids Based on ESD
Estimates 724
Table Apx P-22. Summary of Worker Inhalation Exposure Monitoring Data for
Adhesives/Paints/Coatings 725
Table Apx P-23. Reported Water Releases of Trichloroethylene from Sites Using TCE in Adhesives,
Sealants, Paints and Coatings 726
Table Apx P-24 Summary of Occupational Exposure Surrogate Monitoring Data for Unloading TCE
During Other Industrial Uses 730
Table Apx P-25. Reported Water Releases of Trichloroethylene from Other Industrial Uses 731
Table Apx P-26. Summary of Worker Inhalation Exposure Monitoring Data for Spot Cleaning Using
TCE 732
Table Apx P-27. Summary of Exposure Modeling Results for Spot Cleaning Using TCE 734
Table Apx P-28. Reported Water Releases of Trichloroethylene from Sites Using TCE Spot Cleaning
734
Table Apx P-29. Summary of Exposure Monitoring Data for Use as a Processing Aid 735
Table Apx P-30. Reported Water Releases of Trichloroethylene from Industrial Processing Aid Sites
Using TCE 736
Page 19 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
815 TableApx P-31. Summary of Worker Inhalation Exposure Monitoring Data for High Speed Printing
816 Presses 737
817 Table Apx P-32. Reported Water Releases of Trichloroethylene from Commercial Printing and Copying
818 737
819 Table Apx P-33. Reported Water Releases of Trichloroethylene from Other Commercial Uses in the
820 2016 DMR 738
821 Table Apx P-34. Estimated Water Releases of Trichloroethylene from Disposal/Recycling of TCE .. 739
822
823
Page 20 of 748
-------
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
859
860
861
862
863
864
865
866
867
868
869
870
871
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LIST OF APPENDIX FIGURES
Figure_Apx D-l. Elasticities (> 0.05) for Parameters Applied in El 517
Figure_Apx D-2. Elasticities (> 0.05) for Parameters Applied in E3 518
FigureApx D-3. Elasticities (> 0.05) for Parameters Applied in P_DER2b 519
FigureApx E-l. SSD Toolbox interface and list of HCoss for each distribution and fitting method using
TCE's algae hazard data (Etterson, 2019) 523
Figure Apx E-2. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard data
(Etterson, 2019) 524
Figure Apx E-3. TCE algae data fit with triangular distribution fit with graphical methods (Etterson,
2019) 525
Figure Apx E-4. SSD Toolbox interface showing HCoss and P values for each distribution and fitting
method using TCE's acute hazard data (Etterson, 2019) 526
Figure Apx E-5. AICc for the four distribution options in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2019) 527
Figure Apx E-6. All distributions and fitting methods in the SSD Toolbox for TCE's acute hazard data
(Etterson, 2019) 528
Figure Apx E-7. TCE's acute hazard data fit with the normal, logistic, triangular, and Gumbel
distributions fit with maximum likelihood in the SSD Toolbox (Etterson, 2019) 529
Figure Apx F-l. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010); BMR= 10% Extra Risk 591
Figure Apx F-2. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010); BMR = 5% Extra Risk 593
Figure Apx F-3. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit Model for Mortality
from Introduced Infection in Mice Following Inhalation Exposure to TCE (Selgrade and
Gilmour 2010); BMR = 1% Extra Risk 596
FigureApx H-l. Fixed-effects model, overall association of NHL and exposure to TCE 627
Figure Apx H-2. Random-effects model, overall association of NHL and exposure to TCE 628
Figure Apx H-3. Fixed-effects model, association of NHL and high exposure to TCE 628
Figure Apx H-4. Random-effects model, association of NHL and high exposure to TCE 629
Figure Apx H-5. Fixed-effects model, overall association of kidney cancer and 630
Figure Apx H-6. Random-effects model, overall association of kidney cancer and 630
Figure Apx H-7. Fixed-effects model, overall association of liver cancer and 631
Figure Apx H-8. Random-effects model, overall association of liver cancer and 632
Figure Apx H-9. Fixed-effects model, overall association of NHL and exposure to TCE, study of
Vlaanderen et al. (2013) omitted 635
Figure Apx H-10. Fixed-effects model, association of NHL and high exposure to TCE, study of
Vlaanderen et al. (2013) omitted 635
Figure Apx H-l 1. Fixed-effects model, overall association of kidney cancer and 636
Figure Apx H-12. Fixed-effects model, overall association of liver cancer and 636
FigureApx H-13. Fixed-effects model, overall association of NHL and 637
Figure Apx H-14. Fixed-effects model, overall association of kidney cancer and 638
Figure Apx H-l 5. Fixed-effects model, overall association of liver cancer and 638
Figure_Apx H-l6. Funnel plots for publication bias 640
Figure Apx K-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 655
Page 21 of 748
-------
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx K-2. The Near-Field/Far-Field Model as Applied to the Conveyorized Degreasing Near-
Field/Far-Field Inhalation Exposure Model 656
Figure Apx K-3. The Near-Field/Far-Field Model as Applied to the Web Degreasing Near-Field/Far-
Field Inhalation Exposure Model 656
Figure Apx L-l. The Near-Field/Far-Field Model as Applied to the Brake Servicing Near-Field/Far-
Field Inhalation Exposure Model 671
Figure Apx M-l. The Near-Field/Far-Field Model as Applied to the Spot Cleaning Near-Field/Far-Field
Inhalation Exposure Model 683
Figure Apx P-l. Schematic of the Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation
Exposure Model 707
Figure Apx P-2. Belt/Strip Conveyorized Vapor Degreasing Schematic of the Conveyorized Degreasing
Near-Field/Far-Field Inhalation Exposure Model 714
FigureApx P-3. Schematic of the Web Degreasing Near-Field/Far-Field Inhalation Exposure Model716
Figure Apx P-4. Schematic of the Cold Cleaning Near-Field/Far-Field Inhalation Exposure Model ..718
Figure_Apx P-5. Schematic of the Near-Field/Far-Field Model for Aerosol Degreasing 721
Figure_Apx P-6. Schematic of the Near-Field/Far-Field Model for Spot Cleaning 733
Page 22 of 748
-------
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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).
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.
Page 23 of 748
-------
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
ABBREVIATIONS
°c
80
ACGM
AEGL
ADD
AF
AQS
ATCM
atm
ATSDR
BAF
BCF
BIOWIN
BW34
CAA
CARB
CASRN
CBI
CCR
CDC
CDR
CEHD
CEM
CEPA
CERCLA
CFC
CFR
CH
CHIRP
ChV
cm3
CNS
coc
cou
CPCat
CSCL
CWA
CYP
DCA
DCVC
DCVG
DEVL
DIY
DMR
ECso
ECCC
ECHA
Degrees Celsius
Vacuum Permittivity
American Conference of Governmental Industrial Hygienists
Acute Exposure Guideline Level
Average Daily Dose
Assessment Factor
Air Quality System
Airborne Toxic Control Measure
Atmosphere(s)
Agency for Toxic Substances and Disease Registries
Bioaccumulation Factor
Bioconcentration Factor
The EPI Suite™ module that predicts biodegradation rates
body weight314
Clean Air Act
California Air Resources Board
Chemical Abstracts Service Registry Number
Confidential Business Information
California Code of Regulations
Centers for Disease Control and Prevention
Chemical Data Reporting
Chemical Exposure Health Data
Consumer Exposure Model
Canadian Environmental Protection Act
Comprehensive Environmental Response, Compensation, and Liability Act
Chi orofluorocarb on
Code of Federal Regulations
Chloral Hydrate
Chemical Risk Information Platform
Chronic Value
Cubic Centimeter(s)
Central Nervous System
Concentration of Concern
Conditions of Use
Chemical and Product Categories
Chemical Substances Control Law
Clean Water Act
Cytochrome P450
Dichloroacetic acid
S-dichlorovinyl-L-cysteine
S -di chl orovinyl -glutathi one
Dermal Exposure to Volatile Liquids
Do-It-Yourself
Discharge Monitoring Report
Effect concentration at which 50% of test organisms exhibit an effect
Environment and Climate Change Canada
European Chemicals Agency
Page 24 of 748
-------
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
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
HC05
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
3
m
Cubic Meter(s)
MACT
Maximum Achievable Control Technology
MATC
Maximum Acceptable Toxicant Concentration
MCCEM
Multi-Chamber Concentration and Exposure Model
MCL
Maximum Contaminant Level
Page 25 of 748
-------
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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
National Toxicology 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
OSHA
Occupational Safety and Health Administration
OSF
Oral Slope Factor
OST
Office of Science and Technology
OTVD
Open-Top Vapor Degreaser
OW
Office of Water
PBPK
Physiologically-Based Pharmacokinetic
PBZ
Personal Breathing Zone
PCE
Tetrachloroethylene
PECO
Population, Exposure, Comparator, and Outcome
PEL
Permissible Exposure Limit
PESS
Potentially Exposed or Susceptible Subpopulations
POD
Point of Departure
Page 26 of 748
-------
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Trichl oroethyl 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)
Page 27 of 748
-------
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
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EXECUTIVE SUMMARY
This draft risk evaluation for trichloroethylene was performed in accordance with the Frank R.
Lautenberg Chemical Safety for the 21st Century Act and is being disseminated for public comment
and peer review. The Frank R. Lautenberg Chemical Safety for the 21st Century Act amended the
Toxic Substances Control Act (TSCA), the Nation's primary chemicals management law, in June
2016. As per EPA's final rule, Procedures for Chemical Risk Evaluation Under the Amended Toxic
Substances Control Act (82 FR 33726). EPA is taking comment on this draft, and will also obtain peer
review on this draft risk evaluation for trichloroethylene. All conclusions, findings, and determinations
in this document are preliminary and subject to comment. The final risk evaluation may change in
response to public comments received on the draft risk evaluation and/or in response to peer review,
which itself may be informed by public comments. The preliminary conclusions, findings, and
determinations in this draft risk evaluation are for the purpose of identifying whether the chemical
substance presents unreasonable risk or no unreasonable risk under the conditions of use, in
accordance with TSCA section 6, and are not intended to represent any findings under TSCA
section 7.
TSCA § 26(h) and (i) require EPA to use scientific 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. To meet these TSCA § 26 science standards,
EPA used the TSCA systematic review process described in the Application of Systematic Review in
TSCA Risk Evaluations document ( 0- 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.
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 conditions of use (COUs), including the following categories of use: solvent for
cleaning and degreasing, lubricants and greases, adhesives and sealants, functional fluids in a closed
system, paints and coatings, laundry and dishwashing products, arts, crafts and hobby materials, and
process solvent recycling and worker handling of wastes. 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 ( ).
Page 28 of 748
-------
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Approach
EPA used reasonably available information (defined in 40 Code of Federal Regulations (CFR) 702.33
as "information that EPA possesses, or can reasonably obtain and synthesize for use in risk
evaluations, considering the deadlines for completing the evaluation "), in a fit-for-purpose approach,
to develop a risk evaluation that relies on the best available science and is based on the weight of the
scientific evidence. EPA used previous analyses as a starting point for identifying key and supporting
studies to inform the exposure, fate, and hazard assessments. EPA also evaluated other studies
published since the publication of previous analyses. EPA reviewed the information and evaluated the
quality of the methods and reporting of results of the individual studies using the evaluation strategies
described in Application of Systematic Review in TSCA Risk Evaluations ( 18b).
In the scope document and problem formulation, EPA identified the conditions of use and presented
three conceptual models and an analysis plan for this draft risk evaluation. These have been carried into
the draft risk evaluation where EPA has evaluated the risk to the environment and human health, using
both monitoring data and modeling approaches, for the conditions of use (identified in Section 1.4.1 of
this draft risk evaluation). EPA quantitatively evaluated the risk to aquatic species from exposure to
surface water. EPA evaluated the risk to workers, from inhalation and dermal exposures, and
occupational non-users (ONUs)1, from inhalation exposures, by comparing the estimated exposures to
acute and chronic human health hazards. EPA also evaluated the risk to consumers, from inhalation and
dermal exposures, and bystanders, from inhalation exposures, by comparing the estimated exposures to
acute human health hazards.
EPA used environmental fate parameters, physical-chemical properties, modeling, and monitoring data
to assess ambient water exposure to aquatic organisms. While trichloroethylene is present in various
environmental media, such as groundwater, surface water, and air, EPA determined during problem
formulation that no further analysis beyond what was presented in the problem formulation document
(Section 2.5.3.3 in (U .S. EPA. 2018dV) would be done for environmental exposure pathways for land
application of biosolids and sediment, and water or soil pathways for terrestrial organisms, in this draft
risk evaluation because TCE is not anticipated to partition to biosolids during wastewater treatment. It
is expected to primarily volatilize. However, exposures to aquatic organisms from ambient surface
water, are assessed and presented in this draft risk evaluation. These analyses are described in Sections
2.1 and 2.2.
EPA reviewed the environmental hazard data using the data quality review evaluation metrics and the
rating criteria described in the Application of Systematic Review in TSCA Risk Evaluations (
2018b). 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 (resulting in a geometric mean of 16
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. 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. In occupational settings, EPA
evaluated acute and chronic inhalation exposures to workers and ONUs, and acute and chronic dermal
1 ONUs are workers who do not directly handle trichloroethylene but perform work in an area where trichloroethylene is
present.
Page 29 of 748
-------
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exposures to workers. EPA used inhalation monitoring data from literature sources, where reasonably
available and that met data evaluation criteria, as well as, modeling approaches, where reasonably
available, to estimate potential inhalation exposures. Dermal doses for workers were estimated in these
scenarios since dermal monitoring data was not reasonably available. In consumer settings, EPA
evaluated acute inhalation exposures to both consumers and bystanders, and acute dermal exposures to
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 draft risk evaluation.
EPA evaluated reasonably available information for human health hazards and identified hazard
endpoints including acute and chronic toxicity for non-cancer effects and cancer, as described in Section
3.2. EPA used the Framework for Human Health Risk Assessment to Inform Decision Making (U.S.
E 14a) 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 1'ix'liloroethylene: Decreasing. Spot Cleaning am! \&
Cuff; 1 ^ (1 c. ! 1* \ JO I lb), < < sicological Review o! < i i.-liloroethyten^ 0 v >1 P \ . and other
national and international assessments listed in Table 1-3], (however all data sources from prior
assessments were independently reviewed for this risk evaluation). EPA also screened and evaluated
studies that were published since these reviews (i.e., from 2010 - 2017, in addition to select studies
published after completion of the literature search).
EPA developed a hazard and dose-response analysis using endpoints observed in inhalation and oral
hazard studies, evaluated the weight of the scientific evidence considering EPA and National Research
Council (NRC) risk assessment guidance, and selected the points of departure (POD) for acute, chronic
and non-cancer endpoints, and inhalation unit risk (IUR) and cancer slope factors (CSF) 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.
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 201 1 EPA TCE IRIS Assessment ( ) and included sensitivity
analyses, as needed, to partition the results based on both heterogeneity and study quality. See Appendix
H for full details and results. The 2019 meta-analysis of all relevant studies examining kidney cancer,
liver cancer, or NHL (Appendix H) concluded that there is a statistical significant association between
TCE exposure and increased incidence of all three cancers. 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 ( ) and cited in the 2014 TSCA Work Plan
Chemical Risk Assessment ( j). A linear non-threshold assumption was applied to the
TCE cancer dose-response analysis because there is sufficient evidence that TCE-induced kidney cancer
operates primarily through a mutagenic mode of action while it cannot be ruled out for the other two
cancer types.
Page 30 of 748
-------
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 organisms. EPA
included a qualitive assessment describing trichloroethylene exposure from sediments for aquatic
organisms, and land-applied biosolids, water, and soil for terrestrial organisms. Trichloroethylene is not
expected to accumulate in sediments, and is expected to be mobile in soil, and migrate to water or
volatilize to air. The results of the risk characterization are in Section 4.1, including a table (Table 4-1).
that summarizes 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 under the conditions of use in
the scope of the risk evaluation. Estimated releases from specific facilities result in modeled surface
water concentrations that exceed the aquatic benchmark (RQ > 1) for either chronic, acute, and/ or
algae concentrations of concern for the following conditions of use in various locations (see Table
4-1): processing as a reactant; open top vapor degreasing; repackaging; adhesives; sealants; paints and
coatings; industrial processing aid; other industrial uses; other commercial uses; process solvent
recycling and worker handling of wastes; and waste water treatment plants. Details of these estimates
are in Section 4.1.2.
Qualitative consideration of the physical-chemical and fate characteristics, as well as consideration of
the conditions of use for TCE indicated limited presence in terrestrial environments and aquatic
sediments (Section 4.1.3 and 4.1.4). Therefore EPA did not find risks for sediment or terrestrial
organisms.
Human Health Risks: Risks were estimated following both acute and chronic exposure for
representative endpoints from every hazard domain.
For workers and ONUs, EPA estimated potential cancer risk from chronic exposures to
trichloroethylene using inhalation unit risk or dermal cancer slope factor values multiplied by the
chronic exposure for each COU. For workers and ONUs, EPA also estimated potential non-cancer
risks resulting from acute and chronic inhalation and dermal exposures using a Margin of Exposure
(MOE) approach. For workers, EPA estimated risks using several occupational exposure scenarios,
with scenario-specific assumptions regarding the expected use of personal protective equipment (PPE)
for respiratory and dermal exposures for workers directly handling trichloroethylene. More
information on respiratory and dermal protection, including EPA's approach regarding the
occupational exposure scenarios for trichloroethylene, is in Section 2.3.1.
For 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 well-supported PODs selected from
among the most sensitive acute and chronic endpoints, acute and chronic non-cancer and cancer risks
were indicated for all exposure scenarios and occupational conditions of use under high-end2 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 APF = 50. When
2 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.
Page 31 of 748
-------
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
only considering the central tendency3 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 (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 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 they are 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 4-54 in Section 4.5.1.0.
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.
For consumers, risks were identified for multiple acute endpoints acute risks were indicated for all
consumer conditions of use except Pepper Spray at both medium and high-intensity acute inhalation
and dermal consumer exposure scenarios. 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 4-55 in Section 4.5.2.2.
Uncertainties: Key assumptions and uncertainties in the environmental risk estimation include
uncertainties regarding the hazard data for aquatic species and surface water concentrations.
Additionally the reasonably available environmental monitoring data was limited temporally and
3 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.
Page 32 of 748
-------
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
geographically.
For the human health risk estimation, key assumptions and uncertainties are related to data on
exposures, exposure model input parameters, and the estimates for ONU inhalation exposures for COUs
in which monitoring data or probabilistic modeling data was not reasonably available. Additional
sources of uncertainty related to human health hazard include selection of the appropriate
Physiologically-Based Pharmacokinetic (PBPK) dose-metric for each endpoint, the dose-response for
the congenital heart defect endpoint, and the adjustment of the cancer PODs to account for cancer at
multiple sites. Assumptions and key sources of uncertainty in the risk characterization are detailed in
Section 4.3.
Potentially Exposed or Susceptible Subpopulations (PESS): TSCA § 6(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 § 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 using
trichloroethylene and ONUs in the vicinity of trichloroethylene use than the exposures experienced by
the general population. Risk estimates were also provided separately for ONUs when sufficient data
were reasonably available. EPA was unable to provide separate risk 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. 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.
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 § 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
Page 33 of 748
-------
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
pathways at this time within a condition of use because of the uncertainties present in the current
exposure estimation procedures, which may may lead to an underestimate or overestimate of the actual
total exposure.
The EPA defines sentinel exposure as "the 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 § 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. EPA
considered sentinel exposures by considering risks to populations who may have upper bound (e.g.,
high-end, high intensities of use) exposures.
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); 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 5.1.
Environmental Risks: EPA identified risks from acute and chronic exposures for aquatic organisms (e.g.,
aquatic invertebrates and fish) near two facilities releasing TCE to surface water. One facility had an
acute RQ greater than 1 (RQ = 3.11), exceeding the acute COC of 3,200 ppb and indicating risk to
aquatic organisms from acute exposures. This facility is one of 59 facilities modeled by EPA that use
TCE for open-top vapor degreasing (see Section 4.5.1). This facility and one other facility (one of 11
facilities that process TCE as a reactant) had chronic RQs greater than 1, exceeding the chronic COC of
788 ppb for 20 days (see Section 4.5.1). Monitored data from the Water Quality Portal and grey
literature show no exceedances of the acute COC and the chronic COC in ambient water. 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 52,000 ppb. Therefore, EPA did not identify risks for
acute or chronic exposure durations in ambient water for areas where monitored data were reasonably
available. Given the uncertainties in the modeling data and exceedance of the acute RQ for only one data
point and of the chronic RQ for only two data points out of 70 facilities modeled, EPA does not consider
these risks unreasonable (see Section 5.1).
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. As described below, risks to general population were not relevant for these
conditions of 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. Risks from acute
exposures include developmental toxicity and pulmonary immunotoxicity. For chronic exposures, EPA
identified risks of non-cancer effects (liver toxicity, kidney toxicity, neurotoxicity, immunotoxicity,
Page 34 of 748
-------
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
reproductive toxicity, and developmental toxicity) as well as cancers of liver, kidney, and non-Hodgkin
Lymphoma.
Risk to the General Population: General population exposures to TCE may occur from industrial and/ or
commercial uses; industrial releases to air, water or land; and other conditions of use. As part of the
problem formulation for TCE, EPA found those exposure pathways are covered under the jurisdiction of
other environmental statutes, administered by EPA, which adequately assess and effectively manage
those exposures, i.e., CAA, SDWA, CWA, and RCRA. EPA believes this TSCA risk evaluation should
focus on those exposure pathways associated with TSCA conditions of use that are not subject to the
regulatory regimes discussed above because those pathways are likely to represent the greatest areas of
concern to EPA. Therefore, EPA did not evaluate hazards or exposures to the general population in this
risk evaluation, and there is no risk determination for the general population ( ).
Risk to Workers: EPA evaluated workers' acute and chronic inhalation and dermal occupational
exposures for cancer and non-cancer risks and determined whether any risks are unreasonable. The
drivers for EPA's determination of unreasonable risk for workers are immunosuppression resulting from
acute inhalation and dermal exposures, autoimmunity resulting from chronic inhalation and dermal
exposures, and cancer resulting from chronic inhalation and dermal exposures. For workers, EPA
determined that all applicable conditions of use for TCE presented unreasonable risks. The
determinations reflect the severity of the effects associated with the occupational exposures to TCE and
incorporate consideration of expected PPE (frequently estimated to be a respirator of APF 10 or 50 and
gloves with PF 5 - 20). A full description of EPA's determination for each condition of use is in Section
5.3.
Risk to Occupational Non-Users (ONUs): EPA evaluated ONU acute and chronic inhalation
occupational exposures for cancer and non-cancer risks and determined whether any risks are
unreasonable. The drivers for EPA's determination of unreasonable risks to ONUs are
immunosuppression resulting from acute inhalation exposures, autoimmunity resulting from chronic
inhalation exposures, and cancer resulting from chronic inhalation exposures. The determinations reflect
the severity of the effects associated with the occupational exposures to TCE and the expected absence
of PPE for ONUs. For dermal exposures, because ONUs are not expected to be dermally exposed to
TCE, dermal risks to ONUs generally were not evaluated. For inhalation exposures, EPA, where
possible, used monitoring or modeling information to estimate ONU exposures and to describe the risks
separately from directly exposed workers. For some conditions of use, EPA did not separately calculate
risk estimates for ONUs and workers. For these conditions of use, there is uncertainty in the ONU risk
estimates since the data or modeling did not distinguish between worker and ONU inhalation exposure
estimates. ONU inhalation exposures are expected to be lower than inhalation exposures for workers
directly handling the chemical substance; however, the relative exposure of ONUs to workers in these
cases cannot be quantified. To account for this uncertainty, EPA considered the central tendency risk
estimate when determining ONU risk for those conditions of use for which ONU exposures were not
separately estimated, and determined that most of applicable conditions of use present unreasonable
risks. Estimated numbers of occupational non-users are in Section 2.3.1.2.7.
Risk to Consumers: EPA evaluated consumer acute inhalation and dermal exposures for non-cancer
risks and determined whether any risks are unreasonable. The driver for EPA's determination of
unreasonable risk is immunosuppression from acute inhalation and dermal exposures. Generally, risks
for consumers were indicated by acute inhalation and dermal exposure at low, medium, and high
intensity use. For consumers, EPA determined that consumer conditions of use present unreasonable
Page 35 of 748
-------
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
risks, except for pepper spray. A full description of EPA's determination for each condition of use is in
Section 5.1.
Risk to Bystanders (from consumer uses): EPA evaluated bystander acute inhalation exposures for non-
cancer risks and determined whether any risks are unreasonable. The driver for EPA's determination of
unreasonable risk is immunosuppression from acute inhalation exposures. Generally, risks for
bystanders were indicated by acute inhalation exposure scenarios at low, medium, and high intensity
use. Because bystanders are not expected to be dermally exposed to TCE, dermal non-cancer risks to
bystanders were not identified. For bystanders, EPA determined that consumer conditions of use present
unreasonable risks, except for pepper spray. A full description of EPA's determination for each
condition of use is in Section 5.1.
Summary of risk determinations:
EPA's preliminary determination regarding environmental risks are summarized above and presented in
more detail in Section 5.1.
EPA has preliminarily determined that the following condition of use of TCE does not present an
unreasonable risk of injury under any scenarios. The details of this determination are presented in Table
5-1 in Section 5.2.
Conditions of Use that Do Not Present an Unreasonable Risk
• Pepper Spray (consumers and bystanders)
EPA has preliminarily determined that the following conditions of use of TCE present an unreasonable
risk of injury to health to workers (including, in some cases, occupational non-users) or to consumers
(including, in some cases, bystanders). The details of these determinations are presented in Table 5-1 in
Section 5.2.
Manufacturing that Presents an Unreasonable Risk
• Domestic manufacture
• Import (including repackaging and loading/unloading)
Processing that Presents an Unreasonable Risk
• Processing as a reactant/intermediate
• Incorporation into a formulation, mixture or reaction product (solvents for cleaning or
degreasing)
• Incorporation into a formulation, mixture or reaction product (adhesives and sealant chemicals)
• Incorporation into a formulation, mixture or reaction product (solvents which become part of
product formulation or mixture)
• Incorporation into articles
• Repackaging
Page 36 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Recycling
1502
Distribution that Presents an Unreasonable Risk
• Distribution
1503
Industrial/Commercial Uses that Present an Unreasonable Risk
• As a solvent for batch vapor degreasing (open-top)
• As a solvent for batch vapor degreasing (closed-loop)
• As a solvent for in-line vapor degreasing (conveyorized)
• As a solvent for in-line vapor degreasing (web-cleaner)
• As a solvent for cold cleaning
• As a solvent for aerosol spray degreaser/cleaner
• As a solvent for mold release
• As a lubricant and grease in tap and die fluid
• As a lubricant and grease in penetrating lubricant
• As an adhesive and sealant in solvent-based adhesives and sealants
• As an adhesive and sealant in solvent in tire repair cement/sealer
• As an adhesive and sealant in solvent in mirror edge sealant
• As a functional fluid in heat exchange fluid
• In paints and coatings as a diluent in solvent-based paints and coatings
• In cleaning and furniture care products as carpet cleaner
• In cleaning and furniture care products as wipe cleaning
• In laundry and dishwashing products as spot remover
• In arts, crafts, and hobby materials as fixatives and finishing spray coatings
• As corrosion inhibitors and anti-scaling agents
• As processing aids in process solvent use in battery manufacture
• As processing aids in process solvent used in polymer fiber spinning, fluoroelastomer
manufacture and Alcantara manufacture
• As processing aids in extraction solvent used in caprolactam manufacture
• As processing aids in precipitant used in beta-cyclodextrin manufacture
• As ink, toner and colorant products in toner aid
• In automotive care products as brake parts cleaner
• In apparel and footwear care products as shoe polish
• As hoof polish
• As gun scrubber
• As pepper spray
• Other miscellaneous industrial and commercial uses
1504
Disposal that Presents an Unreasonable Risk
Page 37 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Disposal
1505
Consumer I ses Unit Present ;i 11 I nrensonnhle Kisk
• As a solvent in brake and parts cleaner
• As a solvent in aerosol electronic degreaser/cleaner
• As a solvent in liquid electronic degreaser/cleaner
• As a solvent in aerosol spray degreaser/cleaner
• As a solvent in liquid degreaser/cleaner
• As a solvent in aerosol gun scrubber
• As a solvent in liquid gun scrubber
• As a solvent in mold release
• As a solvent in aerosol tire cleaner
• As a solvent in liquid tire cleaner
• As a lubricant and grease (tap and die fluid)
• As a lubricant and grease (penetrating lubricant)
• As an adhesive and sealant (solvent-based adhesive and sealant)
• As an adhesive and sealant (mirror edge sealant)
• As an adhesive and sealant (tire repair cement/sealer)
• As a cleaning and furniture care product (carpet cleaner)
• As a cleaning and furniture care product (aerosol spot remover)
• As a cleaning and furniture care product (liquid spot remover)
• In arts, crafts, and hobby materials as fixative and finishing spray coating
• In apparel and footwear products as shoe polish
• As fabric spray
• As film cleaner
• As hoof polish
• As toner aid
1506
1507
Page 38 of 748
-------
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1 INTRODUCTION
This document presents the draft 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 EPA published the scope of the risk evaluation for TCE (U.S. EPA. 20171) in June 2017, and the
problem formulation in May, 2018 ( d), 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. The problem formulation (U.S. EPA.
2018d) presented three conceptual models and an analysis plan. Based on EPA's analysis of the
conditions of use, physical-chemical and fate properties, environmental releases, and exposure
pathways, the problem formulation preliminarily concluded that further analysis was necessary for
exposure pathways to ecological receptors exposed via surface water along with human workers and
consumers. The conclusions of the problem formulation were that no further analysis was necessary in
the risk evaluation for sediment, soil and land-applied biosolid pathways leading to exposure to
terrestrial and aquatic organisms and for water pathways leading to exposure to terrestrial organisms.
Further analysis was not conducted for biosolid, soil and sediment pathways, and for water pathways of
exposure to terrestrial organisms, based on a qualitative assessment of the physical-chemical properties
and fate of trichloroethylene in the environment and a quantitative comparison of hazards and exposures
for aquatic and terrestrial organisms. The qualitative assessment for trichloroethylene is presented in
Appendix E. EPA also excluded from risk evaluation ambient air, drinking water, land disposal, ambient
water, and waste incineration pathways leading to exposures to the general population and terrestrial
organisms since those pathways are regulated under other environmental statutes administered by EPA
which adequately assess and effectively manage exposures. EPA received comments on the published
problem formulation for trichloroethylene and has considered the comments specific to
trichloroethylene, as well as more general comments regarding EPA's chemical risk evaluation approach
for developing the draft risk evaluations for the first 10 chemicals EPA is evaluating.
The EPA indicated in the analysis plan of the problem formulation that it would review the full study
reports obtained for physical and chemical properties, environmental fate properties, environmental
hazard and human health hazard studies. For human exposure pathways, the EPA further analyzed
inhalation exposures to vapors and mists for workers, occupational non-users consumers, and
bystanders. Dermal exposures were analyzed for skin contact with liquids for workers and consumers.
For environmental release pathways, the EPA further analyzed surface water exposure to aquatic
vertebrates, invertebrates, and plants.
In this draft risk evaluation, Section 1.1 presents the basic physical-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 problem formulation. This
section also includes a discussion of the systematic review process utilized in this draft risk evaluation.
Section 1 provides a discussion and analysis of the exposures, both health and environmental, that can
be expected based on the conditions of use for trichloroethylene. Section 3 discusses environmental and
health hazards 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 proposed
Page 39 of 748
-------
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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)).
As per EPA's final rule, Procedures for Chemical Risk Evaluation Under the Amend {c
Substances Control Act (82 FR 33726 (July 20, 2017)), this draft risk evaluation will be subject to both
public comment and peer review, which are distinct but related processes. EPA is providing 60 days for
public comment on any and all aspects of this draft 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 will be conducted in accordance with EPA's regulatory procedures for chemical risk
evaluations, including using the EPA Peer Review Handbook and other methods consistent with 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. Peer review will therefore address aspects of the underlying science as outlined in the
charge to the peer review panel such as hazard assessment, assessment of dose-response, exposure
assessment, and risk characterization.
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 believes peer
reviewers will be most effective in this role if they receive the benefit of public comments on draft risk
evaluations prior to peer review. For this reason, and consistent with standard Agency practice, the
public comment period will precede peer review on this draft risk evaluation. The final risk evaluation
may change in response to public comments received on the draft risk evaluation and/or in response to
peer review, which itself may be informed by public comments. EPA will respond to public and peer
review comments received on the draft risk evaluation and will explain changes made to the draft risk
evaluation for trichloroethylene in response to those comments in the final risk evaluation.
EPA 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
trichloroethylene. Thus, in addition to any new comments on the draft risk evaluation, the public should
re-submit or clearly identify at this point any previously filed comments, modified as appropriate, that
are relevant to this risk evaluation and that the submitter feels have not been addressed. EPA does not
intend to further respond to comments submitted prior to the publication of this draft risk evaluation
unless they are clearly identified in comments on this draft risk evaluation.
EPA continues to review the recent court decision in Safer Chemicals Healthy Families v. EPA, Nos.
17-72260 et al. (9th Cir. 2019). This draft risk evaluation does not reflect any changes that may occur as
a result of that decision. EPA is still seeking public comment on and peer review of this version,
however. EPA will communicate the Agency's plans, including the possibility of supplemental versions,
in response to the court decision as appropriate.
Page 40 of 748
-------
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
Table 1-1. Physical and Chemical
'roperties of TCE
Property
Value a
References
Molecular Formula
C2HCI3
Molecular Weight
131.39 g/mole
Physical Form
Colorless, liquid, sweet,
pleasant odor, resembles
chloroform
CO'Neil et al. 2006)
Melting Point
-84.7°C
aide. 2007)
Boiling Point
87.2°C
aide. 2007)
Density
1.46 g/cm3 at 20°C
( 1000)
Vapor Pressure
73.72 mmHg at 25°Cb
( ibert and Danner.
Vapor Density
4.53
( Jeil et al. 2006)
Water Solubility
1,280 mg/L at 25°C
(Horvath et al.. 1999)
Octanol/W ater Partition
Coefficient (Log K0W)
2.42
( leiiee et al. 1980)
Henry's Law Constant
9.85E-03 atmm3/mole
aeighton and Calo.
1)
Flash Point
90°C (closed cup)
ffiCB. 2000)
Auto Flammability
410°C (Estimated)
(wt ;5)
Viscosity
0.545 mPas at 25°C
aide. 2007)
Refractive Index
1.4775 at 20°C
CO'Neil et al. 2001)
Dielectric Constant
3.4 80 at 16°C
(Weast and Selbv.
r\ 5)
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
niniHu. 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.
Page 41 of 748
-------
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (EP A-740-R1 -7014) and
any additional information that was learned since the publication of that document. The previous
research was based on reasonably available information, including the Use and Market Profile for
Trichloroethylene, (EPA-HQ-QPPT-2016-073 7-0056). public meetings, and meetings with companies,
industry groups, chemical users and other stakeholders to aid in identifying conditions of use and
verifying conditions of use identified by the EPA. The information and input received from the public,
stakeholder meetings and the additional contacts was incorporated into this section to the extent
appropriate. Thus, EPA believes the manufacture, processing, distribution, use and disposal activities
constitute the conditions of use within the scope of the risk evaluation for trichloroethylene, based on
reasonably available information.
1.2.2 Domestic Manufacture of Trichloroethylene
A life cycle diagram is provided (Figure 1-1) depicting the conditions of use that are within the scope of
the risk evaluation during various life cycle stages including manufacturing, processing, use (industrial,
commercial, consumer; when distinguishable), distribution and disposal. The information is grouped
according to Chemical Data Reporting (CDR) processing codes and use categories (including functional
use codes for industrial uses and product categories for industrial, commercial and consumer uses), in
combination with other data sources (e.g., published literature and consultation with stakeholders), to
provide an overview of conditions of use. The EPA notes that some subcategories of use may be
grouped under multiple CDR categories.
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
Table 1-2 for TCE from the EPA's CDR database (U.S. EPA. 2016d). For the 2016 CDR reporting
period, non-confidential data indicate a total of 13 manufacturers and importers of TCE in the United
States.
Table 1-2 Production Volume of TCE in CDR Reporting Period (2012 to 2015) a
Reporting Year
2012
2013
2014
2015
Total Aggregate
Production Volume (lbs)
220,536,812
198,987,532
191,996,578
171,929,400
Page 42 of 748
-------
1658
1659
1660
1661
1662
1663
1664
1665
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
1693
1694
1695
1696
1697
1698
1699
1700
1701
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Reporting Year
2012
2013
2014
2015
aThe CDR data for the 2016 reporting period is available via ChemView (httos://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.
As reported in the Use Document [EPA-IK * < I .01 0 0003 (\ > H \ < i )], as well as in
the 2014 TCE risk assessment ( »), 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. Also reflected in the life cycle
diagram is the fact that TCE, as a widely used solvent, has numerous applications across industrial,
commercial and consumer settings.
Descriptions of the industrial, commercial and consumer use categories identified from the 2016 CDR
and included in the life cycle diagram (Figure 1-1) are summarized below ( 1). The
descriptions provide a brief overview of the use category; the [.Environmental Releases and
Occupational Exposure Assessment. Docket: EPA-HQ-OPPT-2019-0500)J 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 ( b).
The following describes several industrial/commercial CDR use categories where TCE has been used;
the [.Environmental Releases and Occupational Exposure Assessment. Docket: EPA-HQ-OPPT-2019-
0500)] 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
Page 43 of 748
-------
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
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
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
The EPA has identified assessments conducted by other agency programs and organizations (see Table
1-3). Depending on the source, these assessments may include information on conditions of use,
hazards, exposures, and potentially exposed or susceptible subpopulations (PESS)—information useful
to the EPA in preparing this risk evaluation. Table 1-3 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-QPPT-2016-0737) using the literature search strategy (see Strategy for Conducting Literature
Searches for Trichloroethylene: Supplemental File for the TSCA Scope Document, EP A-HQ-OPPT -
2( ] to ensure that the EPA is considering information that has been made available since these
assessments were conducted.
In its previous TCE Risk Assessment ( >), 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 (-1 t ^ I
Page 44 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1750 December 12, 2016; 82 FR 7432; January 19, 2017) to address risks from use of TCE. Along with other
1751 reasonably available information, the EPA used the existing TSCA risk assessments to inform its
1752 development of the TCE risk evaluation.
1753
1754 Table 1-3. Assessment History of TCE
Authoring Organization
Assessment
EPA Assessments
Office of Chemical Safety
and Pollution Prevention
(OCSPP)/ Office of
Pollution Prevention and
Toxics (OPPT)
TSCA Work Plan Chemical Risk Assessment Trichloroethvlene: Decreasing. Spot Cleaning
and Arts & Crafts Use (U.S. EPA. 2014b)
OCSPP/OPPT
Supplemental Occupational Exposure and Risk Reduction Technical Report in Support of
Risk Management Options for Trichloroethvlene (TCE) Use in Aerosol Degreasing (U.S.
EPA. 2016f)
OCSPP/OPPT
Supplemental Exposure and Risk Reduction Technical Report in Support of Risk
Management Options for Trichloroethvlene (TCE) Use 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.
2016g)
OCSPP/OPPT
Supplemental Occupational Exposure and Risk Reduction Technical Report in Support of
Risk Management Options for Trichloroethvlene (TCE) Use in Vapor Degreasing 1RIN
2070-AK111 (U.S. EPA. 2016h)
Integrated Risk Information
System (IRIS)
Toxicological Review of Trichloroethvlene (U.S. EPA. 201 le)
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) 79-01-6
(U.S. EPA. 2015b)
Other U.S.-Based Organizations
Agency for Toxic
Substances and Disease
Registries (ATSDR)
Final Toxicological Profile for Trichloroethvlene
(ATSDR 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
Page 45 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Authoring Organization
Assessment
Institute i'or Health uiid
Consumer Protection,
European Chemicals
Bureau
Australia National
Industrial Chemicals
Notification and
Assessment Scheme
(NICNAS)
Trichloroethvlene: Priority Existing Chemical Assessment Report No. 8 (NICNAS. 2000)
1755 1,4 Scope of the Evaluation
1756 1.4.1 Conditions of Use Included in the Risk Evaluation
1757 TSCA § 3(4) defines the conditions of use (COUs) as "the circumstances, as determined by the
1758 Administrator, under which a chemical substance is intended, known, or reasonably foreseen to be
1759 manufactured, processed, distributed in commerce, used, or disposed of." The conditions of use are
1760 described below in Table 1-4 and Table 1-5. No additional information was received by the EPA
1761 following the publication of the problem formulation (U.S. EPA. 2018d) that would update or otherwise
1762 require changes to the life cycle diagram (Figure 1-1) as presented in the problem formulation (U.S.
1763 EPA. 2018d). Nonetheless, EPA decided to reorganize the conditions of use for this risk evaluation. In
1764 this risk evaluation, the COUs as described in (I v H ) were evaluated for occupational
1765 scenarios based on corresponding occupational exposure scenarios (OES) (Table 1-4). The occupational
1766 COUs are also applicable to environmental receptors based on water releases from these activities.
1767
1768 "Lace wig and hair extension glues" have been eliminated as a COU since the publication of the
1769 problem formulation ( 18d). EPA, after consultation with the FDA, has determined that this
1770 use, previously identified in the problem formulation as a conditions of use, is not a condition of use
1771 because it falls outside the scope of EPA's jurisdiction. TSCA sec. 3(2) excludes from the definition of
1772 "chemical substance" cosmetics as they are defined in the Federal Food, Drug and Cosmetic Act
1773 (FFDCA) when manufactured, processed, or distributed in commerce for use as a cosmetic. Because the
1774 glue for lace wigs and hair extensions is a cosmetic within section 201(i) of the FFDCA, any TCE used
1775 for these purposes is exempted from TSCA.
1776
1777 Consumer scenarios were evaluated separately from occupational scenarios, and EPA re-categorized
1778 certain COUs based on product function. None of these changes resulted in any difference in how these
1779 products are or would have been assessed, they simply reflect a recategorization in order to improve
1780 clarity. Additionally, subcategories were added based on availability of differing forms of a product
1781 (e.g., aerosol vs liquid). The updated consumer conditions of use and explanations for the changes are
1782 presented in Table 1-5.
Page 46 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 1-4. Categories and Subcategories of Occu
pational 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
20
16d)
Import
Import
Repackaging
20
16d)
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
20
16d); EPA-
OOl \l p \ (t'M M'PT-
.oi o ; ooi< r \
HO-OPPT-2016-0737-
0026; EPA-HO-OPPT-
Processing -
Incorporation into
formulation, mixture
or reaction product
Solvents (for cleaning or
degreasing)
Formulation of Aerosol
and Non-Aerosol
Products
2016d)
Adhesives and sealant
chemicals
2016d)
Solvents (which become part of
product formulation or mixture)
(e.g., lubricants and greases,
paints and coatings, other uses)
2016d); EPA-
HO-OPPT-2016-073 7-
0003; EPA-HO-OPPT-
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 47 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Distribution in commerce
Distribution
Distribution
Not assessed as a
separate operation;
exposures/releases from
distribution are
considered within each
condition of use.
EP A-HG-GPPT-2016-
Industrial/commercial use
Solvents (for cleaning
or degreasing)
Batch vapor degreaser (e.g.,
open-top, closed-loop)0
Batch Open-Top Vapor
Degreasing;
Batch Closed-Loop
Vapor Degreasing
FP
20
20
20
A-HO-OPPT-2016-
37-00
i * < r \
16ill { P \ U'O-OPPT-
In-line vapor degreaser (e.g.,
conveyorized, web cleaner)c
Conveyorized Vapor
Degreasing;
Web Vapor Degreasing
EP
20
20
20
A-HO-OPPT-2016-
37-00
1 H.), * < r \
16ill i P \ U'O-OPPT-
37-0056
Cold cleaner
Cold Cleaning
EP A-HG-GPPT-2016-
o ; ooo;, ^ * i r \
H < r \ < MT i
Aerosol spray degreaser/
cleanerc
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases
EP
20
20
20
20
A-HO-OPPT-2016-
37-00
1 H>1 * < r \
160, * < r \
l Hfl' \ U'O-OPPT-
Mold release
EP A-HO-OPPT-2016-
o ; ooo;, < r \ .[(^
5
Page 48 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Lubricants and
greases/lubricants and
lubricant additives
Tap and die fluid
Metalworking Fluids
2016d); EPA-
HO-OPPT-2016-0737-
0003; EPA-HO-OPPT-
.01 0 ; 00.8, EPA-
HO-OPPT-2016-073 7-
0056
Penetrating lubricant
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases;
Metalworking Fluids
2016d). EPA-
HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-
-0i o ; ooo;, \ r \
HO-OPPT-2016-0737-
0028
Adhesives and sealants
Solvent-based adhesives and
sealants
Adhesives, Sealants,
Paints, and Coatings
2016d). EPA-
HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-
Tire repair cement/sealer
2016d). EPA-
HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-
Mirror edge sealant
EP A-HO-OPPT-2016-
o ; ooo^ * i r \
im, < r \ *
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Paints and coatings
Diluent in solvent-based paints
and coatings
Adhesives, Sealants,
Paints, and Coatings
(TJ.S. EPA. 2016d\ EPA-
HO-OPPT-2016-073 7-
0056; EPA-HO-OPPT-
2016-0737-0003; EPA-
HO-OPPT-2016-073 7-
0010; EPA-HO-OPPT-
2016-0737-0015; EPA-
HO-OPPT-2016-073 7-
0027;
Cleaning and furniture
care products
Carpet cleaner
Spot Cleaning, Wipe
Cleaning and Carpet
Cleaning
EP A-HO-OPPT-2016-
0737-0056; EPA-HO-
OPPT-2016-073 7-0003
Wipe cleaning d
EP A-HO-OPPT-2016-
0737-0056; EPA-HO-
OPPT-2016-073 7-0003
Laundry and
dishwashing products
Spot removerc
EP A-HO-OPPT-2016-
0737-0003. (U.S. EPA.
2014b\ (U.S. EPA.
2016a). EPA-HO-OPPT-
2016-0737-0056
Arts, crafts and hobby
materials
Fixatives and finishing spray
coatings 0
Adhesives, Sealants,
Paints, and Coatings
(U.S. EPA. 2014b)
Corrosion inhibitors
and anti-scaling agents
Corrosion inhibitors and anti-
scaling agents
Industrial Processing Aid
(U.S. EPA. 2016d)
Processing aids
Process solvent used in battery
manufacture
(U.S. EPA. 2017h)
Process solvent used in polymer
fiber spinning, fluoroelastomer
manufacture and Alcantara
manufacture
(U.S. EPA. 2017h)
Page 50 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Extraction solvent used in
caprolactam manufacture
* n \ :0i h)
Precipitant used in beta-
cyclodextrin manufacture
* n \ :0i h)
Ink, toner and colorant
products
Toner aid
Commercial Printing and
Copying
EP A-HG-GPPT-2016-
o ; oo¦> ,ir\ ^
3
Automotive care
products
Brake and parts cleaner
Aerosol Applications:
Spray
Degreasing/Cleaning,
Automotive Brake and
Parts Cleaners,
Penetrating Lubricants,
and Mold Releases
EP A-HG-GPPT-2016-
0 ; 00 ¦> , i V \ iKt
3
Apparel and footwear
care products
Shoe polish
Other Commercial Uses
* n \ :0i h)
Other uses
Hoof polishes 6
EP A-HO-OPPT-2016-
0 ; 00 ¦> , i V \ iKt
3
Pepper spray
EP A-HO-OPPT-2016-
0 ; 00 ¦> , i V \ iKt
3
Gun scrubber
EP A-HO-OPPT-2016-
0 ; 00¦> ,H'\ ^[Q;
3
Other miscellaneous industrial
and commercial uses
2017h)
Disposal
Disposal
Industrial pre-treatment
2017f)
Page 51 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category a
Subcategory b
Occupational Exposure
Scenario (OES)
References
Industrial wastewater treatment
Process Solvent
Recycling and Worker
Handling of Wastes
Publicly owned treatment works
(POTW)
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. 20Mb) 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. 2017h). and this product is intended for onlv cosmetic and not medical use. Therefore, "hoof polish" was evaluated as a COU. applicable onlv to products
restricted to cosmetic function.
1784
1785
Page 52 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 1-5. 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 53 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 2017f) and
Preliminary Information on Manufacturing. Processing, Distribution. Use, and Disposal: TCE (U.S. EPA.
20.1.7c) 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 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).
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 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 2.3.2.6.2 as leading to dermal contact with impeded evaporation.
1787
Page 54 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MFG/ IMPORT
PROCESSING
INDUSTRIAL, COMMERCIAL, CONSUMER USES
WASTE DISPOSAL
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 Degreasing
(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, Incl. 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)
1788
1789
1790
1791
1792
1793
1794
~ 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.
Figure 1-1. TCE Life Cycle Diagram
The life cycle diagram depicts the conditions of use that are within the scope of the risk eval uation during various life cycle stages including
manufacturing, processing, use (industrial, commercial, consumer), distribution and disposal. The production volumes shown are for
reporting year 2015 from the 2016 CDR reporting period ( J.S. EPA. 2016d). Activities related to distribution (e.g., loading and unloading)
will be considered throughout the TCE life cycle, rather than using a single distribution scenario.
Page 55 of 748
-------
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1.4.2 Conceptual Models
The conceptual models for this draft risk evaluation are shown in Figure 1-2,
Figure 1-3, and
Figure 1-4. 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
(I ). 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 1-2). Consumer exposure was assessed for various
pathways for users age 11 and older along with bystanders of all ages (
Figure 1-3).
The potential pathways that were determined to be included in the risk evaluation but not to warrant
further analysis in this draft risk evaluation were: exposure to both humans and ecological organisms
due to land application of biosolids following wastewater treatment, exposure to organisms through the
sediment compartment, 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 potential 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.
• 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 ( 2018d) and as a result, the EPA confirms further
analysis of the pathways outlined in the conceptual models. The conceptual models from the problem
formulation are shown below in Figure 1-2,
Figure 1-3, and
Figure 1-4.
Page 56 of 748
-------
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
INDUSTRIAL AND COMMERCIAL
ACTIVITIES / USES EXPOSURE PATHWAY EXPOSURE ROUTE RECEPTORSc HAZARDS
Figure 1-2. 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 1-4.
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.
0 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.
Page 57 of 748
-------
1846
mi
1849
1850
1851
1852
1853
1854
1855
1856
1857
CONSUMER
ACTIVITIES / USES EXPOSURE PATHWAY EXPOSURE ROUTE RECEPTORS c HAZARDS
Figure 1-3. TCE Conceptual Model for Consumer Activities and Uses: Potential Exposures and Hazards
The conceptual model presents the exposure pathways, exposure routes and hazards to human receptors from consumer activities and uses of
TCE.
a Some products are used in both commercial and consumer applications. Additional uses of TCE are included in Table 1-4.
b Exposure may occur through mists that deposit in the upper respiratory tract however, based on physical chemical properties, mists of TCE
will likely be rapidly absorbed in the respiratory tract or evaporate and not result in an oral exposure. Although less likely given the physical-
chemical properties, oral exposure may also occur from incidental ingestion of residue on hand/body.
0 Receptors include Potentially Exposed or Susceptible Subpopulations (PESS).
Page 58 of 748
-------
RELEASES AND WASTES FROM EXPOSURE PATHWAY
INDUSTRIAL / COMMERCIAL / CONSUMER USES
RECEPTORS
HAZARDS
KEY:
Grey Text
Pathways and receptors that were not
further analyzed
f
Pathways that were further analyzed.
~
Pathways that were notfurther analyzed.
1860 Figure 1-4. TCE Conceptual Model for Environmental Releases and Wastes: Potential Exposures and Hazards
1861 The conceptual model presents the exposure pathways, exposure routes and hazards to human and environmental receptors from
1862 environmental releases and wastes of TCE.
1863 a Industrial wastewater or liquid wastes may be treated on-site and then released to surface water (direct discharge), or pre-treated and released
1864 to POTW (indirect discharge).
1865
Page 59 of 748
-------
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
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; ( 7i)].
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.4 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)
4 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.
Page 60 of 748
-------
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
Although EPA conducted a comprehensive search and screening process as described above, EPA made
the decision to leverage the literature published in previous assessments5 when identifying relevant key
and supporting data6 and information for developing the TCE risk evaluation. This is discussed in the
Strategy for Conducting Literature Searches for Trichloroethylene: Supplemental Document to the
TSCA Scope Document ( ). 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 that 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 (\ v « « \ JO I ^). 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.
5 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. 2017e).
6 Key and supporting data and information are those that support key analyses, arguments, and/or conclusions in the risk
evaluation.
Page 61 of 748
-------
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
Figures 1-5 to 1-9 below depict the literature flow diagrams illustrating the results of this process for
each scientific discipline-specific evidence supporting the draft risk evaluation. Each diagram provides
the total number of references at the start of each systematic review stage (i.e., data search, data
screening, data evaluation, data extraction/data integration) and those excluded based on criteria guiding
the screening and data quality evaluation decisions.
EPA made the decision to bypass the data screening step for data sources that were highly relevant to the
draft risk evaluation as described above. These data sources are depicted as "key/supporting data
sources" in the literature flow diagrams. Note that the number of "key/supporting data sources" were
excluded from the total count during the data screening stage and added, for the most part, to the data
evaluation stage depending on the discipline-specific evidence. The exception was the engineering
environmental releases and occupational exposure data sources that were subject to a combined data
extraction and evaluation step (Figure 1-6).
*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 1-5. 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 enviromnental 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-HO-OPPT-2019-0500] and the extracted data are presented in
Table 1-1.
Page 62 of 748
-------
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Figure 1-6. Literature Flow Diagram for Engineering Releases and Occupational Exposure
Note: Literature search results for environmental release and occupational exposure yielded 10,132 data sources. Of these data
sources, 159 were determined to be relevant for the risk evaluation through the data screening process. These relevant data
sources were entered into the data extraction/evaluation phase. After data extraction/evaluation, EPA identified several data
gaps and performed a supplemental, targeted search to fill these gaps (e.g., to locate information needed for exposure modeling).
The supplemental search yielded 8 relevant data sources that bypassed the data screening step [List of Key and Supporting
Studies for Environmental Releases and Occupational Exposure. Docket: EPA-HQ-OPPT-2019-0500)] and were evaluated
and extracted in accordance with Appendix D: Data Quality Criteria for Occupational Exposure and Release Data of the
Application of Systematic Review for TSCA Risk Evaluations document (U.S. EPA, 2018b'). Of the 152 sources from which
data were extracted and evaluated, 43 sources only contained data that were rated as unacceptable based on serious flaws
detected during the evaluation. Of the 124 sources forwarded for data integration, data from 36 sources were integrated, and
73 sources contained data that were not integrated (e.g., lower quality data that were not needed due to the existence of higher
quality data, data for release media that were removed from scope after data collection).
*The quality of data in these sources (n=73) were acceptable for risk assessment purposes, but they were ultimately excluded
from further consideration based on EPA's integration approach for environmental release and occupational exposure
data/information. EPA's approach uses a hierarchy of preferences that guide decisions about what types of data/information
are included for further analysis, synthesis and integration into the environmental release and occupational exposure
assessments. EPA prefers using data with the highest rated quality among those in the higher level of the hierarchy of
preferences (i.e., data > modeling > 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.
Page 63 of 748
-------
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
'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-extracts ble 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 1-7. Literature Flow Diagram for Consumer and Environmental Exposure Data Sources
Note: 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 lias 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.
Page 64 of 748
-------
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
Figure 1-8. Literature Flow Diagram for Environmental Hazard
Note: 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 (Enviromnent 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 has since been determined to be out of scope.
The literature search process for enviromnental 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 (Environment 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.
Page 65 of 748
-------
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
Data Extraction/Data Integration (n = 170)
Figure 1-9. Literature Flow Diagram for Human Health Hazard
Note: 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 assessments. Of the 5,954 new studies screened for relevance, 5,869 were
excluded as off topic. The remaining 85 new studies together with the 95 key and 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,
20.1.761,
The "Key/Supporting Studies" box represents data sources cited in an existing assessment (U.S. EPA. 201.1.el 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-HQ-OPPT-2019-0500],
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 ( !b). 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; ( )], and gave all
studies an overall high, medium, low or unacceptable confidence rating during data evaluation.
The results of the data quality evaluations for key studies are summarized in Section 2.1 (Fate and
Transport), Section 2.2.2 (Releases to the Environment), Section 2.2.6 (Environmental Exposures),
Section 2.3 (Human Exposures), Section 3.1 (Environmental Hazards) and Section 3.2 (Human Health
Hazards). Supplemental files7 also provide details of the data evaluations including individual metric
scores and the overall study score for each data source (Docket: EPA-HQ-OPPT-2019-0500).
7 See Appendix B for the list of all supplemental files.
Page 66 of 748
-------
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
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 (U.S. EPA.
2018e). EPA defines "reasonably available information" to mean information that EPA possesses, or can
reasonably obtain and synthesize for use in risk evaluations, considering the deadlines for completing
the evaluation (Procedures for Chemical Risk Evaluation Under the Amended Toxic Substances Control
Act (82 FR 33726).
EPA used previous assessments (see Table 1-3) to identify key and supporting information and then
analyzed and synthesized available evidence regarding TCE's chemical properties, environmental fate
and transport properties and its potential for exposure and hazard. EPA's analysis also considered recent
data sources that were not considered in the previous assessments (Section 1.5.1) as well as reasonably
available information on potentially exposed or susceptible subpopulations.
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.
Page 67 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1 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 form the systematic review process.
17
18 Table 2-1 Environmental Fate Characteristic of TCE
Property or
Endpoint
Value a
References
Data Quality
Rating
Indirect
photodegradation
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)
(Billing et al.„ 1975)
High
Biodegradation
38.9% after 28 days (aerobic OECD 302B
Inherent biodegradability test)
(Tobaias et al„ 2016)
High
100% degradation after 60 days (anaerobic
serum bottle test)
(Long et al„ 1993)
High
100%) degradation after 40 days (anaerobic
groundwater microcosms with added
hydrogen/acetate)
(Schmidt and Tiehm.
2008)
High
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
Page 68 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Property or
Endpoint
Value a
References
Data Quality
Rating
99.98% degradation after 2 or 4 days (anaerobic
continuous flow)
100% degradation after 20 days (aerobic with
Methane culture, aerobic with phenol culture)
(Vogel and McCartv.
1985)
(Long et al.„ 1993)
High
High
Bi oconcentrati on
factor (BCF)
17 (Bluegill)
(Barrows et al.„ 1980)
High
Bioaccumulation
factor (BAF)
24 (estimated)
( 2012b)
High
Organic
carbon: water
partition coefficient
(Log Koc)
1.8 (estimated)
( 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 ( b). 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 (U .S. EPA. 2012b). a predictive tool for physical/chemical and environmental fate properties
29 (https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface). EPI Suite™ was
30 reviewed by the EPA Science Advisory Board
31 (http://YOsemite.epa.eov/sab/sabprodiict.nsf/02ad90bl36fc21eS5256eba00436459/CCF982BA.9816
32 F9CFCFA8525735200739805/$File/si pdf) and the individual models have been peer
33 reviewed in numerous articles published in technical journals. Citations for such articles are available in
34 the EPI Suite™ help files. Table 2-1 provides environmental fate data that EPA considered while
3 5 assessing the fate of TCE.
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.
43
Page 69 of 748
-------
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
If TCE is released to wastewater treatment, the removal percentage of TCE is estimted 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. Therefore, TCE is
not anticipated to partition to biosolids during wastewater treatment. Any TCE present in the water
portion of biosolids following wastewater treatment and land application would be expected to rapidly
volatilize into air. To further support this analysis, TCE was not detected in EPA's Targeted National
Sewage Sludge Survey (TNSSS) nor was it reported in biosolids during EPA's Biennial Reviews for
Biosolids, a robust biennial literature review conducted by EPA's Office of Water ( ).
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 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 was also cited in the 2004 Ell TCE Risk Assessment (ECB. 2004).
During the systematic review process, a high-quality aerobic serum bottle biodegradation study, in
which 100%) degradation occurred in 20 days was reported in methane and phenol cultures. The result
indicates that the aerobic degradation rate with either methane or phenol culture is "fast", is different
from the BIOWIN 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 ( !c). The added anaerobic biodegradation data confirms that TCE anaerobic
biodegradation rate is "fast".
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 (U.S.
Page 70 of 748
-------
93
94
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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
). These log Koc values (1.8-2.17) 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 EP1 Suite™ estimation. Therefore,
TCE is not expected to accumulate in aquatic organisms due to low BCF and BAF.
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,
interlaboratory variability and variability due to factors such as the specific microbial populations used,
water, soil and sediment chemistry, 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 is fast.
That range of Log Koc values (1.8-2.17) 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 (Kow).
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 2.2.2). The surface water modeling was conducted with EPA's Exposure and Fate Assessment
Screening Tool, version 2014 (E-FAST 20141 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 concentration of concern
(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
Page 71 of 748
-------
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 the conditions of use (COUs) listed in Table 1-4 into 18 Occupational Exposure
Scenarios (OES). For each OES, a daily water release was estimated based on annual releases, release
days, and the number of facilities (Figure 2-1). In this section, EPA describes its approach and
methodology for estimating daily water releases, and for each OES, provides a summary of release days,
number of facilities, and daily water releases. For detailed facility level results, see Appendix P of this
document and the "Water Release Assessment" section for each OES in [.Environmental Releases and
Occupational Exposure Assessment. Docket: EPA-HO-OPPT-2019-0500)\.
Figure 2-1: An overview of how EPA estimated daily water releases for each OES.8
2.2.2.1 Results for Daily Release Estimate
EPA combined its estimates for annual releases, release days, and number of facilities to estimate a
range for daily water releases for each OES. A summary of these ranges across facilities is presented in
Table 2-2. See Table 2-5 for more details 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.
8 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 72 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
169 Table 2-2: Summary of EPA's daily water release estimates for each OES and also EPA's Overall
170 Confidence in these estimates.
Occupational Exposure
Scenario (OES)
Estimat
Release
Acros
(kg/sil
ed Daily
; 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
Conveyorized Vapor
Degreasing
2.53E-07
1.96
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
171 a Water releases from OTVD were repeated for other degreasing operations and for MWF because the releases were
172 estimated using TRI and DMR data. Due to the limited information in these reporting programs, these sites may in fact not
173 operate OTVDs, but may operate other solvent cleaning machines or perform metalworking activities (e.g., closed-loop
174 degreasing, conveyorized degreasing, web cleaning, or cold cleaning) or use of TCE as a metalworking fluid. They are
175 included in the OTVD assessment as EPA expects OTVDs to be the most likely condition of use. EPA assessed annual
176 releases as reported in the 2016 TRI or 2016 DMR and assessed daily releases by assuming 260 days of operation per year, as
177 recommended in the 2017 ESD on Use of Vapor Degreasers, and averaging the annual releases over the operating days.
Page 73 of 748
-------
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.2,2.2 Approach and Methodology
2.2.2.2.1 Water Release Estimates
Where available, EPA used 2016 TR1 ( ) and 2016 DMR ( i) data to
provide a basis for estimating releases. Facilities are only required to report to TRI if the facility has 10
or more full-time employees, is included in an applicable NAICS code, and manufactures, processes, or
uses the chemical in quantities greater than a certain threshold (25,000 pounds for manufacturers and
processors of TCE and 10,000 pounds for users of TCE). Due to these limitations, some sites that
manufacture, process, or use TCE may not report to TRI and are therefore not included in these datasets.
For the 2016 DMR ( ), EPA used the Water Pollutant Loading Tool within EPA's
Enforcement and Compliance History Online (ECHO) to query all TCE point source water discharges in
2016. DMR data are submitted by National Pollutant Discharge Elimination System (NPDES) permit
holders to states or directly to the EPA according to the monitoring requirements of the facility's permit.
States are only required to load major discharger data into DMR and may or may not load minor
discharger data. The definition of major vs. minor discharger is set by each state and could be based on
discharge volume or facility size. Due to these limitations, some sites that discharge TCE may not be
included in the DMR dataset.
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 I.
2.2.2.2.2 Estimates of Number of Facilities
Where available, EPA used 2016 CDR ( ), 2016 TRI ( ), 2016
Discharge Monitoring Report (DMR) (I v
-------
225
226
227
228
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• 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
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 North American Industry Classification System (NAICS) codes for the industry
sectors associated with these uses.
• Estimate total number of sites using the U.S. Census' Statistics of US Businesses (SUSB) (U.S.
Census Bureau. 2015) data on total establishments by 6-digit NAICS.
• Use market penetration data to estimate the percentage of establishments likely to be using TCE
instead of other chemicals.
• Combine the 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 PES.
Occupational Exposure
Scenario (OES)
Number of
Facilities
Notes
Manufacturing
5
Based on CDR reporting
Processing as a Reactant
5 to 440
Based on TRI and DMR reporting, and Census data for
NAICS 325120 (Industrial Gas Manufacturing)
Formulation of Aerosol and
Non-Aerosol Products
19
Based on TRI reporting
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
Page 75 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
N il in her of
l-'acilities
Notes
Cold Cleaning
13
Based on NEI reporting
Aerosol Applications: Spray
Degreasing/Cleaning,
Automotive Brake and Parts
Cleaners, Penetrating
Lubricants, and Mold Releases
4,366
Based on Census data and market penetration estimates
based on California Air Resources Board (CARB) survey
of automotive maintenance and repair facilities
Metalworking Fluids
-
No information identified to estimate number of facilities
Adhesives, Sealants, Paints, and
Coatings
70
Based on NEI, TRI, and DMR reporting
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
Copying
-
No information identified to estimate number of facilities
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
257
258
259
260
261
262
2,2.2,2,3 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.
Occupational Exposure
Scenario (OES)
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
Page 76 of 748
-------
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Release
Days
Notes
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.
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.
Page 77 of 748
-------
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
Table 2-5: Summary of Overall Confidence in Release Estimates by OES.
Occupational Exposure
Scenario (OES)
Overall Confidence in Release Estimates
Manufacturing
Wastewater discharges are assessed using reported discharges from the 2016
TRI for three 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 three sites reporting to TRI also reported to DMR. This information
was also assessed. The same uncertainties discussed above for TRI releases
also apply to the DMR data. Based on this 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 two sites were 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 sites operate at the limits set by the EG; actual releases
may be lower for sites operating below the limits or higher for sites not in
compliance with the OCPSF EG. Based on this information EPA has a
medium confidence in the wastewater discharge estimates for these two sites.
Page 78 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Release Estimates
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,
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
Page 79 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Omipsilioiiiil Kxposurc
Sronsirio (OKS)
Ovcrsill ( onfklciKT in Uclcsisc Kslimsilcs
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,
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.
Page 80 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Release Estimates
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
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.
Page 81 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Release Estimates
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.
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 rcoortcd 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.
Page 82 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Release 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
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 was 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
Page 83 of 748
-------
318
319
320
321
322
323
324
325
326
327
328
329
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Kxposure
Scenario (OKS)
Overall Confidence in Release Ksliniales
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
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
(I £007). 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/.
Page 84 of 748
-------
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.2.3.1 Exposure and Fate Assessment Screening (E-FAST) Tool 2014 Inputs
The required modeling inputs are discussed below.
Chemical release to wastewater (WWR)
Annual wastewater loading estimates (kg/site/year or lb/site/year) were predicted in Section 2.2.2 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. Below is an example calculation:
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 2.2.2 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.
Removalfrom 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.
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.
Page 85 of 748
-------
378
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 without Site-Specific Flow Data in E-FAST 2014
Condition of Use
Industry Sector in E-FAST 2014 for
Stream Flow Data1
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
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 return the number of days per year the selected COCs are exceeded. The COCs used in the
PDM module of E-FAST 2014 for TCE were 3, 788, and 52,000 ppb.
2,2.3.2 E-FAST 2014 Equations
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).
Page 86 of 748
-------
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
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
For free-flowing water body assessments, E-FAST 2014 can calculate surface water concentrations for
four streamflow conditions (7Q10, harmonic mean, 30Q5, and 1Q10 flows) using the following equation:
where:
SWC
WWR
WWT
SF
CF1
CF2
WWR xCFl X
SWC = ^ i22-
SFXCF2
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)
(Eq. 1)
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)
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 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).
2.2.3.3 E-FAST 2014 Outputs
E-FAST 2014 provides esitmates 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
Page 87 of 748
-------
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
2,2.4 Surface Water Monitoring Data Gathering Approach
2.2.4.1 Method for 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. No concentration data from the US were identified prior to 2000.
2.2.4.2 Method for Obtaining Surface Water Monitoring Data from
WQX/WQP
For this aquatic exposure assessment, the primary source for the 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 1960's to compile water quality monitoring data. NWIS and STORET now use
common web services, allowing data to be published through the WQP tool. The WQP tool and User
Guide is accessed from the following website: (http://www.waterqiialitYdata.iis/portal.isp).
Data Retrieval from WQP
Surface water data for TCE were downloaded from the WQP on October 3, 2018. The WQP can be
searched through three different search options: Location Parameters, Site Parameters, and Sampling
Parameters. Three queries were performed using the Sampling Parameters search, as shown in Figure
2-2. One query obtained STORET data using the Characteristics parameter (selected "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.iises.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.
Page 88 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
SAMPLING PARAMETERS
Sample Media;
Characteristic All
Group:
Characteristics:
« Trichtoroethylene
Project ID: All
Parameter Code:
WWISONLYJ
Minimum results
per site:
Date range- 01-01-2013
from:
Biological sampling parameters: ?
Assemblage: All
Taxonomic Name: All
SAMPLING PARAMETERS
Sample Media: All
Characteristic All
Group:
Characteristics: All
Project ID: All
Parameter Code: 34485
4NWIS ONLY)
Minimum results
per site:
Date range - 01-01-2013
from:
Biological sampling parameters: ?
Assemblage; All
Taxonomic Name: All
to: 12-31-2017
SAMPLING PARAMETERS
Sample Media: All
Characteristic All
Group:
Characteristics: All
Project ID: All
Parameter Code: 39
[NW1S ONLY)
Minimum results
per site:
Date range ¦
from:
01-01-2013
Biological sampling parameters: ?
Assemblage: All
Taxonomic Name: All
Figure 2-2. WQP Search Option. Surface water data were obtained from the WQP by querying
the Sampling Parameters search option for the characteristic (STORET data), Parameter Code
(NWIS data), and date range parameter
Data Filtering and Cleansing
The "Site data only" and "Sample results (physical/chemical metadata)" files were linked together using
the common field "Monitoring Location Identifier" and then filtered and cleansed. Specifically,
cleansing focused on obtaining samples were only for the media of interest (i.e., surface water), were not
quality control samples (i.e., field blanks), were of high analytical quality (i.e., no quality control issues,
sample contamination, or estimated values), and were not associated with contaminated sites (i.e.,
Superfund).
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 %
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 to conduct a watershed analysis at the
Hydrologic Unit Code (HUC) 8 and HUC 12 level. The purpose of the analysis is to identify if any the
observed surface water concentrations could be associated with the modeled facility releases. In
addition, the analysis included a search for Superfund sites within 1 to 5 miles of the surface water
monitoring stations to possible exclude these monitoring sites from the analysis. A U.S. map was
developed to provide a spatial representation of the measured and predicted concentrations. HUCs with
co-located monitoring stations and facility releases were identified and examined further. Maps were
developed on a U.S. scale to provide a spatial display of the concentrations, as well as at the HUC scale
to focus on co4ocated monitoring stations and facility releases.
Page 89 of 748
-------
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Geographic Coordinates
The location of the monitoring stations was determined from the geographic coordinates (latitude and
longitude) provided in WQP. Releases from facilities were located based on the geographic coordinates
for the NPDES, TRI, and/or FRS of the mapped facility, as provided by FRS. For indirect dischargers,
the location of the receiving facility was mapped if known. If not known, the location of the indirect
discharger was mapped. Superfund sites in 2016 were identified and mapped using geographic
coordinates of the "front door," as reported in the Superfund Enterprise Management System (SEMS)
database in Envirofacts.
Surface Water Concentrations
The surface water concentrations associated with the monitoring stations and facility releases are
denoted on the maps using COCs to determine the concentration thresholds:
>52,000 |i/L (exceeds all COC for algae, aquatic invertebrate, and fish
orange
788-51,999 |i/L (exceeds the COC for algae and aquatic invertebrate, but not for fish)
green
3-787 |i/L (exceeds the COC for algae, but not for aquatic invertebrate or fish)
blue
Detected, but less than 3 |i/L (less than all COC)
purple Not Detected (applies only to measured concentrations; detection limits vary)
For the predicted concentrations, the concentrations represent conditions under low flow conditions (i.e.,
7Q10 flows). The harmonic mean concentrations were not mapped, but are presented in the detailed
summary tables.
Symbols and Layering
Due to the scale of the maps, some symbols may overlap each other if the monitoring stations and
facilities are near each other or there are multiple releases modeled for the same facility (i.e., one facility
is both a direct discharger and a receiving facility). As such, the maps are layered to make sure that the
most important information is always be visible. The following rules were applied:
monitoring stations (small circles) are always on top of indirect discharge releases (medium triangles),
which are always on top of direct discharge releases (large squares), and
within each symbol type (monitoring station, direct release, and indirect release), a higher concentration
level is always on top of a lower concentration level (i.e., from top to bottom: >52,000 |i/L (red), 788-
51,999 |i/L (orange), 3-787 |i/L (green), <3|i/L (blue), and not detected (purple).
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.
Page 90 of 748
-------
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
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 category in Table 2-7 through Table
2-9. A break-out of facility-specific modeling results organized per OES, with predicted surface water
concentrations and associated days of COC exceedance, are included in Appendix C. These 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 ppb (Table 2-7). For the 20
days of release/year scenario for direct dischargers, predicted surface water concentrations under 7Q10
flow conditions ranged from 0.00019 to 9,937.5 ppb (Table 2-8). For comparison purposes, indirect
releases to non-POTW WWTPs were also modeled for the 20 days of release/year scenario, resulting in
surface water concentrations of 0.2 to 339.11 ppb (Table 2-9).
Table 2-7. Summary of Surface Water Concentrations by OES for Maximum Days of Release
Scenario
OES
No. of Releases
Modeled
Surface Water Concentration
(7Q10) (ppb)
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
Table 2-8. Summary of Surface Water Concentrations by OES for 20 Days of Release Scenario
For Direct Releases
Page 91 of 748
-------
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
OES
No. of Releases
Modeled
Surface Water Concentration
(7Q10) (ppb)
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
Table 2-9. Summary of 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) (ppb)
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 predicted surface water concentrations did not exceed the highest COC
(52,000 ppb) for any facility and only exceeded the COC of 788 ppb 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 2.2.2, releases of TCE were estimated 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.
Page 92 of 748
-------
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NC
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
As shown in Figure 2-4, direct and indirect dischargers accounted for 70% and 30% of the total releases
modeled, respectively. Site-specific waterbody flow/dilution data (identified viaNPDES) were available
in E-FAST 2014 for the majority of the releases (58%); surrogate waterbody flow/dilution data were
used in only 15% of the cases, with the remaining cases (26%) run using a representative industry sector
SIC code. For releases modeled with a NPDES (including a surrogate NPDES), surface water
concentrations were calculated for free-flowing water bodies in 86% of the cases, and still water bodies
for the remaining cases (14%).
Direct Releaser
70%
Indirect Releaser
Industry Sector
Site-Specific NPDES
Surrogate NPDES
Free-Flowing
86% 1
Still Water
14%
Figure 2-4. Modeled Release Characteristics (Percent Occurrence)
2,2.6.2.2 Monitored Surface Water Concentrations
Measured Concentrations ofTCE from WQX/WQP
A summary of the WQX data obtained from the WQP is provided in Table 2-10 below for years 2013-
2017. Per year, the cleansed datasets evaluated contained between 46 and 793 surface water samples
collected from 89 to 193 unique monitoring stations. Detection frequencies were low, ranging from 0 to
Page 93 of 748
-------
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
8.7%. Concentrations ranged from not detected (ND; <0.022-5) to 0.11 |ig/L in 2013, ND (<0.022-5) to
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-
5) to 2.0 |ig/L in 2017. Peaks were observed in 2014 and 2017; however, caution should be used in
interpreting trends with these data due to the small number of samples and the lack of samples collected
from the same sites over multiple years. The quantitative environmental assessment used the 2016 data
set only. For the 2016 data, concentrations in all samples were non-detect. No samples in the 2013-2017
dataset had concentrations exceeding the lowest COC of 3 |ig/L.
Table 2-10. Measured Concentrations of TCE in Surface Water Obtained from the Water Quality
Portal: 2013-20171
Year
Detection
Frequency
Concentration (|ig/L) in all samples
Concentrations (|ig/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^
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 Quality Portal (www.waterqualitvdata.us') on 10/3/2018. STORET surface water data
was obtained by selecting "TCE (STORET)" for the Characteristic. NWIS surface water data were obtained by selecting
"34485; 39180" for the Parameter Codes. Samples were filtered for surface water media and locations only. Results were
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 '/? 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
Hg/L).
Characterization of WOX Data
The original dataset downloaded contained 31,456 samples for years 2013 through 2017. Following the
filtering and cleansing procedure, only 7% of the samples remained (2,273 samples). The majority of the
samples were excluded because they were an off-topic media (i.e., groundwater, artificial, bulk
deposition, leachate, municipal waste, or stormwater) or location type (i.e., landfill, spring, or well). A
smaller number of samples were excluded because they were quality control samples, estimated values,
or had other quality control issues. Samples associated with one Superfund site (Palermo Wellfield
Superfund Site) were also excluded.
For the 2016 cleansed dataset (473 samples), observations were made in 10 states/territories (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
Page 94 of 748
-------
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Systematic review of published literature yielded only a minimal amount of surface water monitoring
data for TCE; a summary of the individual studies is provided in Table 2-11. In six U.S. studies
encompassing 1,177 surface water samples collected from river and oceans throughout the nation
between 1979 and 2001, reported concentrations of TCE ranged from below the detection limit (0.0001
to 0.08) to 17.3 |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 (an urban area)
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 (USGS. 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 (\ v << \ l ) reported much higher
concentrations of TCE in surface water, up to 447 |ig/L. These samples were collected in 1976/1977
from the vicinity of facilities producing and/or using methylchloroform, thus the concentrations reflect
historical levels of TCE and are not considered to be representative of current conditions. Not enough
information is 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: EPA-HQ-OPPT-2019-0500]).
Table 2-11. Ambient Levels of TCE in U.S. Surface Water from Published Literature
Concentration (jig/L)
Location
Type
Site Information
Dates
Sampled
N
(Det. Freq.)
Range
Central
Tendency
(Standard
Deviation)
Source
Data Quality
Score
Anchorage, AK;
Chester Creek (6 urban
sampling sites)
1998-2001
11(0)
All samples ND
(<0.08)
(USGS,
2006)
Medium
Nation-wide; Surface
water for drinking water
sources (rivers and
reservoirs)
1999-2000
375 (0.008)
ND
(<0.2) -
2.0
NR
(USGS,
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
Ambient
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
unpolluted and
anthropogenic
influences)
1980
11 (0.27)
ND -
0.05
NR
(Saner.
1981)
Medium
Page 95 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Concentration (jig/L)
Location
Type
Site Information
Dates
Sampled
N
(Det. Freq.)
Range
Central
Tendency
(Standard
Deviation)
Source
Data Quality
Score
Two Bridges, NJ;
Passaic River
1996-1998
10 (0.4)
NR
Median: 0.1
(Robinson
et al. 2004)
Medium
Eastern Pacific Ocean
(California, US to
Valparaiso, Chile)
1979-1981
30 (0.9)
ND
(0.0001)
- 0.0007
Mean: 0.3
(0.002);
Median:
0.0002
(Singh et
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 receiving stream at
the plant outfall and
upstream and
downstream of the
outfall.
1976
6(1.0)
0.9-126
NR
(U.S. EPA.
1977)
High
Near
Facility
(methyl-
chloroform
producer
or user)
Geismar, LA (Vulcan
Materials Plant); 3
surface water samples
collected from the
receiving stream at the
plant outfall and
upstream and
downstream of the
outfall.
1976
3 (1.0)
5-74
NR
(U.S. EPA.
1977)
High
Lake Charles, LA (PPG
Industries); Stream
samples (bottom and
surface) collected from
the receiving stream at
the plant outfall and
upstream and
downstream of the
outfall.
1976
5 (1.0)
29 - 447
Mean: 282
(156);
Median:
353
(U.S. EPA.
1977)
High
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.
1977)
High
NR = Not reported
ND = Not detected; detection limit reported in parethesis if reasonably available
Page 96 of 748
-------
682
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
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.2.6.2.3 Geospatial Analysis Comparing Predicted and Measured Surface Water Concentrations
A geospatial analysis at the watershed level (HUC-8 and HUC-12) was conducted to compare the
measured and predicted surface water concentrations in 2016 and investigate if the facility releases may
be associated with the observed concentrations in surface water. Overall, there are 39 US
states/territories with either a measured concentration or a predicted concentration; at the watershed
level, there are 155 HUC-8 areas and 241 HUC-12 areas with either measured or predicted
concentrations.
The monitoring stations co-located with facilities in the same HUC in the 2016 set were assessed for
proximity to Superfund sites to determine if the Superfund sites could be contributing to TCE releases,
and thus would not fall under the scope of this evaluation. No Superfund sites were identified within 5
miles of these sites.
Co-location of releasing facilities and monitoring sampling locations was examined for presence in the
same watershed (HUC-8 and HUC-12). Co-location does not mean there is an upstream/downstream
connection between release and sampling sites.
2.2.6.3 Assumptions and Key Sources of Uncertainty for Environmental
Exposures
E-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. 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
Page 97 of 748
-------
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 trichl or ethylene in
ambient surface water samples, they cannot be interpreted as reflecting concentrations downstream of
direct release sites, which could be higher than reported measured levels.
The WQP Tool contains data from USGS-NWIS and STORET databases, and is one of the largest
environmental monitoring databases in the US; however, comprehensive information needed for data
interpretation is not always 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.
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 trichl or ethylene 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
Page 98 of 748
-------
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-
OPPT-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 99 of 748
-------
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.3 Human Exposures
2.3,1 Occupational Exposures
EPA categorized the conditions of use (COUs) listed in Table 1-4 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 2-5).
ONUs include employees that work at the site where TCE is manufactured, processed, used, recycled, or
disposed of,9 but these employees do not directly handle the chemical and are therefore expected to have
lower inhalation exposures and are not expected to have dermal exposures. For detailed occupational
exposure results, see Appendix P 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-HO-OPPT-2019-0500\. 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.
Figure 2-5: Components of an occupational assessment for each OES;10 please refer to Section
2.2.2.2.2 for additional details on the approach and methodology for estimating number of facilities.
9 Occupational exposures from distribution are considered within each condition of use.
111TRI = 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 100 of 748
-------
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
859
860
861
862
863
864
865
866
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 this monitoring data was assessed and EPA established an overall confidence for the data
when integrated into the occupational exposure assessment.
Where monitoring data was reasonably available, EPA used this data to characterize central tendency
and high end inhalation exposures. Where no inhalation monitoring data was identified, but inhalation
exposure models were 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 occupational exposure scenarios (OESs) by
indicating whether monitoring data was reasonably available, how many data points were identified, the
quality of the data, EPA's overall confidence in the data, whether the data was used to estimate
inhalation exposures for workers and ONUs, and also whether EPA used modeling to estimate
inhalation and dermal exposures for workers and ONUs.
In many cases, EPA did not have monitoring data to estimate inhalation exposure for ONUs. In some
cases, this was addressed with the use of exposure models. However, approximately 50% of OESs do
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 percent11 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.
11 The absorbed fraction is a function of indoor air speed, which differs for industrial and commercial settings.
Page 101 of 748
-------
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
904
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 North American Industrial
Classification (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.
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 102 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
905
906
907
908
Table 2-12: A summary for each of the 18 occupational exposure scenarios (OESs).
Note: 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
Inhalation Exposure
Dermal Exposure
Modeling0
Occupational Exposure .
Scenario TOES) 1
Monitoring
Modeling
Ovei
Confit
'all 1
ence J
Monitoring
Data
# Data
Points
Data Quality
Rating
Worker
ONU
Worker
ONU
Worker
ONU j
Worker
ONU
Manufacturing j
16
H
3C
3c
3C
M to H
L j
-
Processing as a Reactant |
S
16
M
3C
3c
3C
L to M
L |
S
-
Formulation of Aerosol and Non- '
Aerosol Products ¦
S
33
H
3C
3c
3C
M
L |
S
-
Repackaging |
S
33
H
3C
3C
3C
M to H
L |
S
-
Batch Open-Top Vapor Degreasing J
S
123
M
M
M j
S
-
Batch Closed-Loop Vapor |
Degreasing I
S
19
H
3C
3C
3C
M to H
L j
S
-
Conveyorized Vapor Degreasing '
S
18
M
3C
L to M
Lto M j
S
-
Web Vapor Degreasing |
3c
-
-
3C
3C
L to M
Lto M|
S
-
Cold Cleaning j
3C
-
-
3C
3C
L to M
Lto Mj
S
-
Aerosol Applications3 |
3C
-
-
3C
3C
M
M |
S
-
Metalworking Fluids '
s
3
H
3C
3C
L to M
L J
S
-
Adhesives, Sealants, Paints, and |
Coatings 1
s
24
M to H; Mb
3C
3C
L to M
Lto m!
S
-
Other Industrial Uses j
s
16
M
3C
3C
3C
L to M
L j
S
-
Spot Cleaning and Wipe Cleaning |
s
8
M
3C
M
M |
S
-
Industrial Processing Aid j
s
34
H
3C
3C
M to H
Lto Mj
S
-
Commercial Printing and Copying |
s
20
M
3C
3C
3C
L to M
L 1
S
-
Other Commercial Uses j
s
8
M
3C
M
M |
S
-
Process Solvent Recycling and I
Worker Handling of Wastes 1
s
33
H
3C
3C
3C
M to H
L |
S
-
909
910
911
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and
b. For Workers, data quality is M to H; For ONUs, data quality is is M.
c. EPA lias a medium level of confidence in its dermal exposure estimates which are based on high-end/central tendency
Mold Releases
parameters and commercial/industrial settings.
Page 103 of 748
-------
912
913
914
915
916
917
918
919
920
921
922
923
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
i
1
1
Inhalation Monitoring (Worker, ppm) j
Inhalation Modeling (Worker, ppm)
Scenario (OES)
| TWA
AC
ADC
LADC i
TWA
AC
ADC
LADC
1 HE
CT
HE
CT
HE
CT
HE
CT 1
HE
CT
HE
CT
HE
CT
HE
CT
Manufacturing
j 2.6
0.38
0.86
0.13
0.59
8.6E-02
0.30
3.4E-02' -
-
-
-
-
-
-
-
Processing as a Reactant
| 2.6
0.38
0.86
0.13
0.59
8.6E-02
0.30
3.4E-02| -
-
-
-
-
-
-
-
Formulation of Aerosol and Non-
Aerosol Products
11.14
1
4.9E-04
0.38
1.6E-04
0.26
1.1E-04
0.13
4.5E-05I -
1
-
-
-
-
-
-
-
Repackaging
j 1.14
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
11.45
0.46
0.48
0.15
0.33
0.10
0.17
4.2E-02I
-
-
-
-
-
-
-
-
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 ( -
-
-
-
-
-
-
- j 14.1
5.9
4.7
2.0
3.2
1.4
1.3
0.51
Cold Cleaning | -
-
-
-
-
-
-
- | 57.2
3.3
19.1
1.1
13.1
0.76
5.2
0.28
Aerosol Applications3
-
-
-
-
-
-
1
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
1
4.6
13.2
1.5
9.0
1.1
4.6
0.42 | -
1
-
-
-
-
-
-
-
Other Industrial Uses
1 2.6
0.38
0.86
0.13
0.59
0.09
0.30
3.4E-02I -
-
-
-
-
-
-
-
Spot Cleaning and Wipe Cleaning
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
0.08
Industrial Processing Aidb
112.8
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-02J
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
i11
4.9E-04
0.38
1.6E-04
0.26
1.1E-04
0.13
4.5E-05j -
-
-
-
-
-
-
-
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 104 of 748
-------
924
925
926
927
928
929
930
931
932
933
934
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-14: Summary of inhalation exposure results for ONUs based on monitoring data and exposure modeling for each OES.
[Note: 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
1
1
1
Inhalation Monitoring (ONU, ppm) 1
Inhalation Modeling (ONU, ppm)
Scenario (OES)
! TWA
AC
ADC
LADC !
TWA
AC
A]
DC
LADC
HE
CT
HE
CT
HE
CT
HE
CT !
HE
CT
HE
CT
HE
CT
HE
CT
Manufacturing 1 -
-
-
-
-
-
-
1
-
-
-
-
-
-
-
¦
Processing as a Reactant ¦ -
-
-
-
-
-
-
1
-
-
-
-
-
-
-
Formulation of Aerosol and Non- J -
Aerosol Products i
-
-
-
-
-
-
1
1
1
-
-
-
-
-
-
-
Repackaging
| -
-
-
-
-
-
-
1
-
-
-
-
-
-
-
-
Batch Open-Top Vapor Degreasing
19.1
1.1
3.0
0.37
2.1
0.25
1.06
0.10 1
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
-
-
-
-
-
-
- j 1878.
1 o
23.3
626.0
7.8
428.8
5.3
168.
3
3.6
Web Vapor Degreasing I -
-
-
-
-
-
-
- I 9.6
3.1
3.2
1.0
2.2
0.71
0.87
0.27
Cold Cleaning [ -
-
-
-
-
-
-
- 1 34.7
1.8
11.6
0.61
7.9
0.42
3.1
0.15
Aerosol Applicationsa ! -
-
-
-
-
-
-
- ! 1.0
0.14
0.35
4.7E-02
0.24
3.2E-02
0.09
1.2E-02
Metalworking Fluids
¦
-
-
-
-
-
-
1
-
-
-
-
-
-
-
Adhesives, Sealants, Paints, and
Coatings
| 1.0
1
0.94
0.33
0.31
0.23
0.21
0.12
8.5E-02| -
-
-
-
-
-
-
-
Other Industrial Uses ! -
-
-
-
-
-
-
-
-
-
-
-
-
-
Spot Cleaning and Wipe Cleaning
1
¦
-
-
-
-
-
-
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 -
-
-
-
-
-
-
- 1 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
¦
-
-
-
-
-
-
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 105 of 748
-------
935
936
937
938
939
940
941
942
943
944
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-15: A summary of dermal retained dose for Workers based on exposure modeling for each OES
[Note: 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
¦
1
1
I
¦
1
1
¦
Max TCE
¦ ¦
| Non-Occluded Worker Dermal Retained Dose |
j (mg/day) \
Occluded Worker
Dermal Retained
Occupational Exposure j
Bin 1
1
Weight
j No
Protective
Protective
Protective ¦
Dnsp
Scenario (OES)
1
Fraction
j Gloves
Gloves
Gloves
Gloves j
(mg/day)
1
1
(Max Yderm)
1 (PF
= D
(PF
= 5)
(PF =
10)
(PF
= 20) |
1
"
1
«
! HE
CT
HE
CT
HE
CT
HE
CT !
HE 1
«
CT
Manufacturing
1
1 |
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 I - I -
Processing as a Reactant 1
1 1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 1 - 1 -
Formulation of Aerosol and Non- '
1 ¦
1.0
' 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 '
-
Aerosol Products
l
¦
I
¦
¦
I
Repackaging
I
1
i ;
1.0
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
i
-
Batch Open-Top Vapor Degreasing |
2 I
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
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
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 j
2,247 j
749
Cold Cleaning
1
2 I
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 |
2,247 |
749
Aerosol Applications3
1
3 1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
-
1
1
-
Metalworking Fluids
1
4 !
0.8
j 147.49
49.16
29.50
9.83
14.75
4.92
-
1,798 !
599
Adhesives, Sealants,
Industrial ¦
3 !
0.9
165.92
55.31
33.18
11.06
16.59
5.53
-
I I
Paints, and Coatings
Commercial |
3 1
0.9
1260.50
86.83
52.10
17.37
26.05
8.68
-
Other Industrial Uses
1
1 |
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 |
|
-
Spot Cleaning and Wipe Cleaning '
4 1
1.0
1289.44
96.48
57.89
19.30
28.94
9.65
-
1
2,247 1
749
Industrial Processing Aid !
i !
1.0
j 184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 '
1
¦
-
Commercial Printing and Copying ¦
4 !
0.35
j 101.30
33.77
20.26
6.75
10.13
3.38
-
1
786 j
262
Other Commercial Uses |
4 I
1.0
|289.44
96.48
57.89
19.30
28.94
9.65
-
2,247 |
749
Process Solvent Recycling and Worker 1
1 1
1.0
1184.36
61.45
36.87
12.29
18.44
6.15
9.22
3.07 1
1
-
Handling of Wastes
1
1
1
1
1
a. Aerosol Applications: Spray Degreasing/Cleaning, Automotive Brake and Parts Cleaners, Penetrating Lubricants, and Mold Releases
Page 106 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
945
946
947
Table 2-16: Summary of the total number of workers and ONUs potentially exposed to TCE for each OES
[Note: EPA's approach and methodology for estimating the number of facilities using TCE and the number of workers and ONUs potentially
Occupational Exposure j
Scenario (OES) 1
Total
Exposed
Workers
Total
Exposed
ONUs
Total
Exposed
Number of
Facilitiesb
Notes
Manufacturing 1
350
170
530
5
Processing as a Reactant J
120 to 6,100
55 to 2,900
180 to 9,000
5 to 440
Formulation of Aerosol and Non- ¦
306
99
405
19
Aerosol Products 1
Repackaging I
36
12
48
22
Batch Open-Top Vapor Degreasing 1
4,922
2,889
7,810
194
Batch Closed-Loop Vapor Degreasing '
50
18
68
4
Conveyorized Vapor Degreasing ¦
92
32
130
8
Web Vapor Degreasing |
-
-
-
1
EPA does not have data to estimate the total
workers and ONUs exposed to TCE.
Cold Cleaning 1
660
400
1,100
13
Aerosol Applications3 !
14,200
1,690
15,900
4,366
Metalworking Fluids ¦
Based on ESD on the Use of Metalworking
Fluids, EPA estimates 46 Workers and 2 ONUs
per site; the number of sites that use TCE-based
metalworking fluids is unknown to EPA.
Adhesives, Sealants, Paints, and J
3,000
1,400
4,400
70
Coatings ¦
Other Industrial Uses |
2,300
1,000
3,300
49
Spot Cleaning and Wipe Cleaning 1
244,000
25,300
269,000
63,748
Based on assumption of 100% market
penetration.
Industrial Processing Aid J
310
140
450
18
Commercial Printing and Copying ¦
Based on NIOSH HHE, EPA estimates 44
Workers and 74 ONUs per site; EPA does not
have data to estimate total number of sites
Other Commercial Uses 1
-
-
-
-
EPA does not have data to estimate the total
workers and ONUs exposed to TCE
Process Solvent Recycling and ¦
380
140
520
30
Worker Handling of Wastes i
948
949
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.
Page 107 of 748
-------
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( 2).
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
Page 108 of 748
-------
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2. Modeling approaches:
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 ( 94a)
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) (Micas. 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 ( ).
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 ai.
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 ai. 2018).
Page 109 of 748
-------
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 2-6.
Open-Top Vapor Degreasing and Cold Cleaning
Far-Field
- Near-Field -
I®"
Ts) c,
R
W>
Conveyorized Degreasing
Q„, ~
Spot Cleaning
¦ Far-Field ¦
Brake Servicing
\
cff
i" /-
VcNk
* Volatile Source \<^ Qff
1
^m /*Qh'
MM
Web Degreasing
Far-Field
Figure 2-6: 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 Cne- The concentration is directly proportional to the evaporation rate of TCE,
(denoted by G in Figure 2-6), into the near-field, whose volume is denoted by Vnp. 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 (Q\i) determines how quickly TCE dissipates into the far-
Page 110 of 748
-------
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Vnf ^ = 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 K; Appendix L;
and Appendix M.
2.3.1.2.4 Acute and Chronic Inhalation Exposure Estimates
This report assesses TCE exposures to workers in 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):
AC =
C x ED
AT,
acute
Where:
AC
C
ED
AT acute
= acute exposure concentration
= contaminant concentration in air (TWA)
= exposure duration (hr/day)
= 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
Page 111 of 748
-------
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
1127
1128
1129
1130
1131
1132
1133
1134
1135
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
day hr
AT = WYx 365 —x 24—
yr day
day hr
ATC = LTx 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 J for example
calculations).
Table 2-17: Parameter Values for Calculating Inhalation Exposure Estimates
Parameter Name
Sy m hoi
Value
1 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)51
350,400 (high-end)b
hr
Averaging Time, cancer
ATC
683,280
hr
a 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
Page 112 of 748
-------
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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-digit NAICS level (where 3-digit
NAICS are less granular and 6-digit NAICS are the most granular). Dividing the total, annual hours
worked by the number of employees yields the average number of hours worked per employee per year
for each NAICS.
EPA has identified 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.
Page 113 of 748
-------
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EPA uses these values for central tendency and high-end ADC and LADC calculations, respectively.
The BLS ( i. 2014) provides information on employee tenure with current employer obtained
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' ( nsus Bureau. 2019) Survey of Income and Program Participation (S1PP)
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 Qj.S.
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.12 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
Bure* ). EPA calculated the average tenure for the following age groups: 1) workers age 50 and
older; 2) workers age 60 and older; and 3) workers of all ages employed at time of survey. EPA used
tenure data for age group "50 and older" to determine the high-end lifetime working years, because the
sample size in this age group is often substantially higher than the sample size for age group "60 and
older". For some industries, the number of workers surveyed, or the sample size, was too small to
provide a reliable representation of the worker tenure in that industry. Therefore, EPA excluded data
where the sample size is less than five from our analysis.
Table 2-18 summarizes the average tenure for workers age 50 and older from SIPP data. Although the
tenure may differ for any given industry sector, there is no significant variability between the 50th and
95th percentile values of average tenure across manufacturing and non-manufacturing sectors.
Table 2-18: Overview of Average Worker Tenure from U.S. Census SIPP (Age Group 50+)
Industry Sectors
Average
\Yorkii
50,h Percentile
lg Years
95"' Percentile
Maxim ii in
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.
12 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).
Page 114 of 748
-------
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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 wii
th 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: (U.S. BLS. 2014).
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 was 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-OPPT-2019-0500)\ 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
Page 115 of 748
-------
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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 trichl or ethylene 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 ( ).
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 ( ).
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 ( ).
EPA did not find any SDSs with applicable use towards 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
Page 116 of 748
-------
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
PF values, which vary depending on the type of glove used and the presence of employee training
program, are shown in Table 2-20:
( Qu Xfabs)
n C v uusj y j-,j,
uexp pp ^ 1derm ^ 1 1
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.
• 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 ((
2013a").
• 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)
• 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. 2006). 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 Protect ion Characteristics
Setting
Protection
l-'actor. 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.. 20.1.7')
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
scenario, open or closed system use of TCE, and large or small-scale use. Four different groups or
"bins" were created based on this analysis (Table 2-21).
Page 117 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1335 Table 2-21: EPA grouped dermal exposures associated with the various OESs into four bins.
Bin #
Description
1
Bin 1 covers industrial uses that generally occur in closed systems. For these uses, dermal exposure is
likely limited to chemical loading/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
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
Page 118 of 748
-------
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 and NIOSH recommend that employers utilize the hierarchy of controls to address hazardous
exposures in the workplace. The hierarchy of controls strategy outlines, in descending order of priority,
the use of elimination, substitution, engineering controls, administrative controls, and lastly personal
protective equipment (PPE). The hierarchy of controls prioritizes the most effective measures first which
is to eliminate or substitute the harmful chemical (e.g., use a different process, substitute with a less
hazardous material), thereby preventing or reducing exposure potential. Following elimination and
substitution, the hierarchy recommends engineering controls to isolate employees from the hazard,
followed by administrative controls, or changes in work practices to reduce exposure potential (e.g.,
source enclosure, local exhaust ventilation systems). Administrative controls are policies and procedures
instituted and overseen by the employer to protect worker exposures. As the last means of control, the
use of personal protective equipment (e.g., respirators, gloves) is recommended, when the other control
measures cannot reduce workplace exposure to an acceptable level. 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.13 Respirator
selection provisions are provided in § 1910.134(d) and require that appropriate respirators are selected
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
13 OSHA does not require controls to be used unless a hazard assessment determines that the hazard is significant enough to
require mitigation.
Page 119 of 748
-------
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( 3R. 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 § 1910.132. The knowledge of the range of respirator APFs is intended to assist employers in
selecting the appropriate type of respirator that could provide a level of protection needed for a specific
exposure scenario. Table 2-22 lists the range of APFs for respirators. The complexity and burden of
wearing respirators increases with increasing APF. The APFs are not to be assumed to be
interchangeable for any conditions of use, any workplace, or any worker or ONU.
Table 2-22: Assigned Protection Factors for I
tespirators in OSHA Standard 29 CFR § 1910
Type of Uespiralor
Quarter
Mask
Half
Mask
I-Mil
Facepiece
1 lelmel/
Mood
Loose-
filling
l-'acepiece
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)
Page 120 of 748
-------
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.3.1.2.7 Number of Workers and Occupational Non-Users Exposed
This section summarizes the methods that EPA used to estimate the number of workers who are
potentially exposed to TCE in each of its conditions of use. The method consists of the following steps:
1. Identify the North American Industry Classification System (NAICS) codes for the industry
sectors associated with each condition of use.
2. Estimate total employment by industry/occupation combination using the Bureau of Labor
Statistics' Occupational Employment Statistics data (U.S. BLS. 2016).
3. Refine the estimates based on BLS Occupational Employment Statistics data where they are not
sufficiently granular by using the U.S. Census' (U.S. Census Bureau. 2015) Statistics of U.S.
Businesses (SUSB) data on total employment by 6-digit NAICS.
4. Estimate the percentage of employees likely to be using TCE instead of other chemicals (i.e., the
market penetration 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.
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
Page 121 of 748
-------
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
19-2031
Chemists
O
19-4000
Life, Physical, and Social Science Technicians
O
47-1000
Supervisors of Construction and Extraction Workers
O
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.
Table 2-24: SOCs with Worker and ONU Designations for Dry Cleaning Facilities
SOC
Occii pillion
IH'siuiiiilinn
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
Page 122 of 748
-------
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
After identifying relevant NAICS and SOC codes, EPA used BLS data to determine total employment
by industry and by occupation based on the NAICS and SOC combinations. For example, there are
110,640 employees associated with 4-digit NAICS 8123 (Drycleaning and Laundry Services) and SOC
51-6010 (Laundry and Dry-Cleaning Workers).
Using a combination 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 Drycleaners;
• 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.
Table 2-25: Estimated Number of Potentia
ly Exposed Workers and O
NTJs 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
Page 123 of 748
-------
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
O
1,660
46.0%
763
8123
51-6040
Shoe and Leather Workers
O
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. 20.1.5'): (U.S. BLS. 20.1.6')
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
EPA then estimated the total number of establishments by obtaining the number of establishments
reported in the U.S. Census Bureau's SUSB (1; S Census 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:
Page 124 of 748
-------
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
a. Obtaining the number of establishments from SUSB 0 ; S Census Bureau. 2015) at the 6-
digit NAICS level (Step 5) for each NAICS code in the condition of use and summing these
values; or
b. Obtaining the number of establishments from the Toxics Release Inventory (TRI), Discharge
Monitoring Report (DMR) data, National Emissions Inventory (NEI), or literature for the
condition of use.
2. Estimating the number of establishments that use TCE by taking the total number of
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. However, this approach assumes that the distribution of
occupation types (SOC codes) in each 6-digit NAICS is equal to the distribution of occupation types at
the parent 5-digit NAICS level. If the distribution of workers in occupations with TCE exposure differs
from the overall distribution of workers in each NAICS, then this approach will result in inaccuracy.
Second, EPA's judgments about which industries (represented by NAICS codes) and occupations
(represented by SOC codes) are associated with the uses assessed in this report are based on EPA's
understanding of how TCE is used in each 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
Page 125 of 748
-------
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 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. 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 was not reasonably available, EPA used
reported statistics (i.e., median, mean, 90th percentile, etc.). Since EPA could not verify these values,
there is an added level of uncertainty.
EPA calculated ADC and LADC values assuming workers and ONUs are regularly exposed during their
entire working lifetime, which likely results in an overestimate. 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.
Page 126 of 748
-------
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• 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.
• 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.
• 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 ( XX)) 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 (1 c. « ^ \ rV I . ) were
provided as ranges. For each Monte Carlo iteration the model selects a TCE concentration within
Page 127 of 748
-------
1663
1664
1665
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
1693
1694
1695
1696
1697
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (IRTA. 2007). 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 (GARB. 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: EPA-HQ-OPPT-2019-0500)\
for the development and underlying research of this model.
2.3.1.3.5 Summary of Overall Confidence in Inhalation Exposure Estimates
Table 2-26 provides a summary of EPA's overall confidence in its inhalation exposure estimates for
each of the Occupational Exposure Scenarios assessed.
Table 2-26: Summary of overall confidence in inhalation exposure estimates by PES.
Occupational Kxposure
Scenario (OKS)
Overall Confidence in Inhalation Kxposurc Kstimalcs
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 16
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
Page 128 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Inhalation Exposure Estimates
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 16
data points from 1 source, and the data quality ratings from systematic review
for these data were medium. The primary limitations of these data include the
uncertainty of the 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
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 medium. The primary limitations of these data
Page 129 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Inhalation Exposure Estimates
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
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
Page 130 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Omipsilioiiiil Kxposuro
Sronsirio (OKS)
Ovcrsill ( onfklciKT in 1 nliiihilion Kxposurc Kstiinnlcs
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
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
Page 131 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Inhalation Exposure Estimates
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
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
Page 132 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Omipsilioiiiil Kxposuro
Sronsirio (OKS)
Ovcrsill ( onfklciKT in 1 nliiihilion Kxposurc Kstiinnlcs
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 16
data points from 1 source, and the data quality ratings from systematic review
for these data were medium. The primary limitations of these data include the
uncertainty of the 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 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. 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 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
Page 133 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Occupational Exposure
Scenario (OES)
Overall Confidence in Inhalation Exposure Estimates
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 medium. 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.
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.
1698
1699 2.3.2 Consumer Exposures
1700 TCE can be found in consumer and commercial products that are available for purchase at common
1701 retailers and can therefore result in exposures to household consumers (i.e., receptors who use a product
1702 directly) and bystanders (i.e., receptors who are a non-product users that are incidentally exposed to the
1703 product or article) (U.S. EPA. 2017c. h).
Page 134 of 748
-------
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2,3.2,1 Consumer Conditions of Use Evaluated
Conditions of use associated with consumer exposure were described in the Problem Formulation (U.S.
18d). 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 (1 c. « ^ \ JO I . Additional online research was
undertaken following Problem Formulation to confirm TCE concentrations and compile a
comprehensive list of products that may be available to consumers for household use. 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 ( |). 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 T<
CE
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
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
Page 135 of 748
-------
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life
Cycle
Stage
Category
Product Subcategory
Form1
No. of
Products
Utilized in
Modeling1
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-HQ-OPPT-2019-0500] 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. 20.1.7c) and fit within the broader Solvents for
Cleaning and Degreasing category.
6 Based on additional research into the specific product(s) associated with the broader lubricants and greases
category, the subcategory name was updated from penetrating lubricant to lubricant.
7 The spot remover subcategory was listed in Table 2-3 of the Problem Formulation as being associated with the
laundry and dishwashing products category; however, the same spot remover conditions of use are now associated
with the cleaning and furniture care products category. This change does not impact evaluated conditions of use, as
the evaluated product scenarios are based on the spot remover product(s) and not a broader category of use.
8 Note that this subcategory is referred to as "clear protective coating spray" in U.S. EPA (20.1.4b) and as "spray
fixative" in the TCE Significant New Use Rule (80 FR 47441).
9 Fabric spray (specifically an anti-fray spray) was added following Problem Formulation based on identification in
the final 2014 TCE Work Plan Chemical Risk Assessment (U.S. EPA. 2014b).
10 The toner aid subcategory was listed in Table 2-3 of the Problem Formulation as being associated with the Ink,
toner, and colorant products category; however, the toner aid use is not like use of a toner or pigment; therefore, the
same toner aid condition of use is now associated with the other consumer use category. This change does not
impact evaluated conditions of use, as the evaluated product scenarios are based on the toner aid product(s) and not
a broader category of use.
11 Note that the problem 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 2.3.2.6.2 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 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 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.
Page 136 of 748
-------
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
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. Instantaneous exposures to skin are
expected to evaporate before significant dermal absorption occurs based on TCE's physical chemical
properties which include the vapor pressure, water solubility and log Kow. The log Kow estimate is 0.8%
absorption and 99.2% volatilization and is derived from IHSkinPerm, a mathematical tool for estimating
dermal absorption. Exposure that occurs as a deposition over time or a repeated exposure that maintains
a thin layer of liquid TCE had greater absorption based on the estimate from IHSkinPerm for an 8-hr
exposure is 1.6% absorption and 98.4% volatilization. Dermal exposures to liquid TCE are expected to
be concurrent with inhalation exposures, which are anticipated to reflect the preponderance of overall
exposure from a use or activity for most consumer exposure scenarios. This agrees with the NIOSH skin
notation profile for TCE, which estimates a low hazard potential by dermal absorption for systemic
effects when inhalation and dermal exposures are concurrent (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. An example of this is a user holding a rag soaked with TCE
against their palm during a cleaning activity. Therefore, dermal exposures are quantified and presented
for consumer use scenarios that may involve dermal contact with impeded evaporation.
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 Potentially Exposed or Susceptible Subpopulations
As part of the Problem Formulation ( ), EPA identified consumers and bystanders
associated with use of TCE-containing consumer products as a potentially exposed and susceptible
subpopulation due to greater exposure. Additionally, higher-intensity users (i.e., those using consumer
products for longer durations and in greater amounts) were considered and evaluated. Exposures and
risks for these subpopulations are considered and evaluated herein. Consumers are considered to include
children and adults age 11 and up, but bystanders in the home exposed via inhalation are considered to
include any age group, from infant to adult, including pregnant women. Highly exposed (high-intensity
users) and potentially exposed or susceptible subpopulations (PESS) within this overall schema as
receptor categories overlap, as individuals may belong to multiple receptor groups.
Page 137 of 748
-------
1776
1777
1778
1779
1780
1781
1782
1783
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
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2,3.2,4 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 ( ), 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.2.
2.3.2.4.1 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.l .
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
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;
Page 138 of 748
-------
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
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• 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.
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
(2 ) and are said to reflect reasonable worst-case overspray potential ( ). 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 ( ). 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 ( >).
Modeling Dermal Exposure
CEM also contains a dermal modeling component that estimates absorbed dermal doses resulting from
dermal contact with chemicals found in consumer products. Based on the described dermal exposure
conditions (i.e., dermal contact with impeded evaporation) and the chemical- and scenario-specific input
parameters available for use in modeling (e.g., scenario-specific use duration, measured dermal
permeability coefficient), "P_DER2b: Dermal Dose from Product Applied to Skin, Permeability Model"
was selected as the most appropriate model to estimate dermal exposures from consumer products
containing TCE. P_DER2b estimates 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
Page 139 of 748
-------
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
of chemical directly in contact with the skin throughout the exposure duration. The acute form of the
model is given below:
SA
K x Dac x p x w x FQac x Dil xWFx EDac x CF1
ADR = — -
ATac X CF2
Where:
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/BW
= 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 (days)
CF1
= Conversion factor (1,000 mg/g)
CF2
= Conversion factor (60 min/hr)
ATac
= Averaging time (days, 1 for acute exposure scenarios)
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). Note 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 (e.g., a scenario wherein dermal contact involved impeded evaporation, or where there is
potential for dermal immersion). Furthermore, it incorporates scenario-specific product use durations
and distinct surface area to body weight ratios for various user populations. For additional details on
P_DER2b, please see the CEM User Guide Section 3: Detailed Descriptions of Models within CEM
(I M-
For TCE, a measured dermal permeability coefficient (Kp 0.019 cm/hr) is applied, based on findings
from Poet (2000). as summarized and presented in the 2017 NIOSH Skin Notation Profile for TCE
(Hudson andDotson. 2017). The permeability coefficient selected was based on a human water-patch
test and was within range of the estimated Kp values presented in the 2017 NIOSH Skin Notation Profile
(0.01197 cm/hr) (Hudson and Dotson. 2017) and within the CEM model (0.028 cm/hr), both predicted
using chemical properties.
Dermal exposure estimates are only quantified and presented for consumer exposure scenarios that
could involve such dermal contact with impeded evaporation (e.g., application or cleaning with a rag
pressed against user's hand), per the focus described in Section 2.3.2.2.2.
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
Page 140 of 748
-------
1923
1924
1925
1926
1927
1928
1929
1930
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
1962
1963
1964
1965
1966
1967
1968
1969
1970
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.l 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-HQ-OPPT-2019-0500].
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 (U.S. EPA. 1987). referred to as the "Westat survey" or "Westat" herein, and
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 model P_DER2b.
In the model sensitivity analysis, summarized in Appendix D. 1 and shown in the user guide appendices
( '019b). 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.
2.3.2.5 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);
Page 141 of 748
-------
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• 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 based on the identified conditions of use were built based on
identified TCE products that may be available to consumers, including solvents for cleaning and
degreasing, lubricants and greases, adhesives and sealants, and other uses. The subcategories of use (i.e.,
consumer product types) cited in Table 2-27 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. Additional online research was undertaken following Problem Formulation to
confirm TCE concentrations and compile a comprehensive list of products that may be available to
consumers for household use. Specific product characteristics obtained from manufacturer websites
and/or Safety Data Sheets (SDSs) such as form/formulation type, weight fraction and density, were used
to select the most appropriate product-specific inputs (e.g., weight fraction and formulation type)
associated with each consumer condition of use. Please see Supplemental File {Consumer Exposure
Assessment Model Input Parameters. Docket: EPA-HQ-OPPT-2019-0500]fov 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
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 and was used to
derive certain default parameters in EPA's CEM 2.1. Westat ( ) 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 2.3.2.6.2.
Page 142 of 748
-------
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.3.2.5.1 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 (0.019 cm/hr), 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 (1987) is the basis for duration of use and mass of
product used and product SDSs are the basis for weight fraction and formulation type.
Table 2-30 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 were utilized, along
with the general chemical-specific characteristics and other model defaults. Please see Supplemental
File [Consumer Exposure Assessment Model Input Parameters. Docket: EPA-HQ-OPPT-2019-0500] 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 be directly using 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. 2011c)
Air Exchange Rate
(hr1)
0.452
Central Tendency
(Median)
(U.S. EPA. 2011c)
Interzonal
Ventilation Rate
(m3/hr)3
Garage: 109
NA
Default (U.S. 1 1.9a, b)
All other rooms
modeled: 107
Emission
Characteristics
Background Air
Concentration
(mg/m3)
0
Minimum
Page 143 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Gas Phase Mass
Transfer
Coefficient (m/hr)
Emission Factor
(ug/m2/hr)
Based on chemical properties and estimated
within CEM
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. 1 1.9a, b)
Use Start Time
9 AM5
NA
NA
Frequency of Use
1 event per day
NA
Default (U.S. 1 1.9a, b)
Acute Averaging
Time
1 day
NA
Surface Area to
Body Weight Ratio
Inside of One Hand
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 patterns (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.
2054
2055
Page 144 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-29 Consumer Product Modeling Scenarios and Varied Input Parameters
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight Fractions
Selected for
Selected
Duration of Use
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of
Product Used
(g, [oz])
Consumer
Category
Weight
Fraction
Modeling
(% TCE)
Westat
Survey
(min)
(%
TCE)2
Min2
Mid
Max
Scenario
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Solvents
Brake &
Aerosol
0 - 100
20
60
100
Brake
1
15
120
1.23-
47.9
191.6
766.5
for
Cleaning
Parts
Cleaner
(4)
Quieters /
Cleaners
1.62
[1]
[4]
[16]
and
Electronic
Aerosol
30 - 100
30
65
100
Specialized
0.17
2
30
1.25-
1.8
22.5
337.1
Degreasing
Degreaser/
Cleaner
(9)
Electronics
Cleaners
(for TV,
VCR,
Razor, etc.)
1.52
[0.04]
[0.5]
[7.5]
Electronic
Liquid
100
100
Specialized
0.17
2
30
1.46
1.7
21.6
323.8
Degreaser/
Cleaner
(1)
Electronics
Cleaners
(for TV,
VCR,
Razor, etc.)
[0.04]
[0.5]
[7.5]
Spray
Aerosol
60 - 100
60
100
Engine
5
15
120
1.46-
130.8
521.4
2157.4
Degreaser/
Cleaner
(8)
Degreasing5
1.52
[2.91]
[11.6]
[48]
Liquid
Liquid
90 - 100
100
Solvent-
2
15
120
1.456
24.1
139.9
1377.7
Degreaser/
Cleaner
(2)
Type
Cleaning
Fluids or
Degreasers
[0.56]
[3.25]
[32]
Gun
Aerosol
60 - 1006
60
100
Solvent-
2
15
120
1.36-
NA
0.7
NA
Scrubber
(2)
Type
Cleaning
Fluids or
Degreasers7
1.465
[0.45
mL]8
Gun
Liquid
1008
100
Solvent-
2
15
120
1.36
NA
0.6
NA
Scrubber
(1)
Type
Cleaning
Fluids or
Degreasers7
[0.45
mL]8
Page 145 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight Fractions
Selected for
Selected
Duration of Use
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of
Product Used
(g, [oz])
Consumer
Category
Weight
Fraction
Modeling
(% TCE)
Westat
Survey
(min)
(%
TCE)2
Min2
Mid
Max
Scenario
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Mold
Aerosol
40 -68.9
40
68.9
Other
0.08
2
30
0.77-
4.3
23.4
212.9
Release
(2)
Lubricants
(Excluding
Automotive)
1.44
[0.1]
[0.55]
[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
Tap & Die
Aerosol
98
98
Other
0.08
2
30
0.9
2.7
14.8
134.5
and
Fluid
(1)
Lubricants
[0.1]
[0.55]
[5]
Greases
(Excluding
Automotive)
Penetrating
Aerosol
5-50
5
27.5
50
Other
0.08
2
30
0.636-
4.2
23.1
209.9
Lubricant
(5)
Lubricants
(Excluding
Automotive)
1.42
[0.1]
[0.55]
[5]
Adhesives
Solvent-
Liquid
5 ->90
5
47.5
90
Contact
0.33
4.25
60
1.33-
1.3
10.7
185.2
and
Sealants
based
Adhesive &
Sealant
(3)
Cement,
Super
Glues, and
Spray
Adhesives
1.45
[0.03]
[0.25]
[4.32]
Mirror-edge
Aerosol
20-40
40
Contact
0.33
4.25
60
0.614
0.5
4.5
78.4
Sealant
(1)
Cement,
Super
Glues, and
Spray
Adhesives
[0.03]
[0.25]
[4.32]
Tire Repair
Liquid
65-95
65
80
95
Contact
0.33
4.25
60
1.45
1.3
10.7
185.2
Cement/
Sealer
(5)
Cement,
Super
Glues, and
Spray
Adhesives
[0.03]
[0.25]
[4.32]
Page 146 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Product
Sub-
Categories
Form
(No. of
Pdts)1
Range
of
Weight Fractions
Selected for
Selected
Duration of Use
Range
of
Product
Density
(g/cm3)4
Mass [Volume] of
Product Used
(g, [oz])
Consumer
Category
Weight
Fraction
Modeling
(% TCE)
Westat
Survey
(min)
(%
TCE)2
Min2
Mid
Max
Scenario
10th
%ile3
50th
%ile
95th
%ile
10th
%ile
50th
%ile
95th
%ile
Cleaning
and
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]
Furniture
Spot
Aerosol
20-30
30
Spot
0.25
5
30
1.562
11.5
61.4
514.1
Care
Products
Remover
(1)
Removers
[0.25]
[1.33]
[11.13]
Spot
Liquid
<50-
50
75
Spot
0.25
5
30
1.25-
10.7
57.0
477.2
Remover
(4)
>75
Removers
1.45
[0.25]
[1.33]
[11.13]
Arts,
Fixatives &
Aerosol
20-30
30
Aerosol
0.25
5
60
0.704
9.4
45.2
306.0
Crafts, and
Hobby
Materials
Finishing
Spray
Coatings
(1)
Rust
Removers9
[0.45]
[2.17]
[14.7]
Apparel
Shoe Polish
Aerosol
10-20
20
Spray Shoe
0.5
5
30
0.512
2.9
15.4
151.4
and
(1)
Polish
[0.19]
[1.02]
[10]
Footwear
Care
Products
Other
Fabric Spray
Aerosol
20-40
40
Water
1.4
10
60
0.614
11.4
49.9
326.8
Consumer
Uses
(1)
Repellents /
Protectors
(for Suede,
Leather, and
Cloth)
[0.63]
[2.75]
[18]
Film
Aerosol
80 - 100
100
Aerosol
0.25
5
60
1.45-
19.4
93.4
632.9
Cleaner
(2)
Rust
Removers9
1.456
[0.45]
[2.17]
[14.7]
Hoof Polish
Aerosol
(1)
3010
30
Spray Shoe
Polish11
0.5
5
30
0.512-
0.704
4.0
[0.19]
21.2
[1.02]
208.2
[10]
Pepper
Aerosol
91.5
91.5
NA12
NA
0.0812
NA
1.25
NA
4.0
NA
Spray
(2)
[0.108
l12
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]
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-HQ-QPPT-2019-0500] for the full product list utilized.
Page 147 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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-HQ-OPPT-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 on EPA/EPAB research and the Eezox Premium Gun Care testing results (ASTM B117-5 Salt Spray Fog Test), 0.42-0.45 mL 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.
111 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.
12Based on EPA/EPAB research that found one spray from the most common civilian canister is estimated to be approximately 0.0216-0.108 ounces (based on
a pepper spray manufacturer's website). Spraying occurred between 3 and 5 seconds (converted to minutes for use in modeling) before obtaining desired effect
(Bertilsson et al.. 2017).
2057
2058
2059
2060
2061
Page 148 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Applied4
Dermal
Surface Area
Exposed5
Solvents for Cleaning
and Degreasing
Brake & Parts
Cleaner
Aerosol (4)
Garage
(90)
E3
0.45
109
P_DER2b
10% of hands
Electronic Degreaser/
Cleaner
Aerosol (9)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Electronic
Degreaser/Cleaner
Liquid (1)
Utility
(20) '
El
0.45
107
P_DER2b
Inside of one
hand
Spray
Degreaser/Cleaner
Aerosol (8)
Garage
(90)
E3
0.45
109
P_DER2b
10% of hands
Liquid
Degreaser/Cleaner
Liquid (2)
Utility
(20) '
El
0.45
107
P_DER2b
Inside of one
hand
Gun Scrubber
Aerosol (2)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Gun Scrubber
Liquid (1)
Utility
(20) '
El
0.45
107
P_DER2b
Inside of one
hand
Mold Release
Aerosol (2)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Tire Cleaner
Aerosol (2)
Garage
(90)
E3
0.45
109
P_DER2b
10% of hands
Tire Cleaner
Liquid (1)
Garage
(90)
El
0.45
109
P_DER2b
Inside of one
hand
Lubricants and
Greases
Tap & Die Fluid
Aerosol (1)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Penetrating Lubricant
Aerosol (5)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Adhesives and
Sealants
Solvent-based
Adhesive & Sealant
Liquid (3)
Utility
(20) '
El
0.45
107
NA
Inside of one
hand
Mirror-edge Sealant
Aerosol (1)
Bathroom
(15)
E3
0.45
107
NA
10% of hands
Tire Repair Cement/
Sealer
Liquid (5)
Garage
(90)
El
0.45
109
NA
Inside of one
hand
Carpet Cleaner
Liquid (1)
Bedroom
(36)
El
0.45
107
P_DER2b
Inside of one
hand
Page 149 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Applied4
Dermal
Surface Area
Exposed5
Cleaning and
Furniture Care
Products
Spot Remover
Aerosol (1)
Utility
(20) '
E3
0.45
107
P_DER2b
10% of hands
Spot Remover
Liquid (4)
Utility
(20) '
El
0.45
107
P_DER2b
Inside of one
hand
Arts, Crafts, and
Hobby Materials
Fixatives & Finishing
Spray Coatings
Aerosol (1)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Apparel and
Footwear care
products
Shoe Polish
Aerosol (1)
Utility
(20) '
E3
0.45
107
NA
Inside of one
hand
Other Consumer Uses
Fabric Spray
Aerosol (1)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Film Cleaner
Aerosol (2)
Utility
(20) '
E3
0.45
107
NA
10% of hands
Hoof Polish
Aerosol (1)
Barn6
E3
46
109
NA
10% of hands
Pepper Spray
Aerosol (2)
Outside7
E3
1007
0
NA
10% of hands
Toner Aid
Aerosol (1)
Utility
(20) '
E3
0.45
107
NA
10% of hands
'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. h). as well as the 2014 TSCA Work Plan Chemical Risk Assessment for TCE (U.S. EPA 2014b). It is possible
that specific products and/or 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 enviromnent (room of use) was generally based on the Westat (1987) survey of consumer behavior patterns, w hich rcoortcd 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.
4A11 scenarios utilized the P_DER2b model for dermal exposure - Dermal Dose from Product Applied to Skin, Permeability Model
5Surface area exposed only applied in dermal scenarios. The indicated surface areas are combined with mean receptor body weights to get surface area to body weight
ratios (SA:BW) that are used in estimating dermal dose.
6For 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 exchange rate from 0.45 to 4 hr1. which more closelv aliens with recommended ventilation levels in a horse barn (Pennsylvania State University. 2016)
7The outdoor enviromnent 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).
2063
Page 150 of 748
-------
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
14b). The inputs included in the 2014 assessment differed from those used in this assessment for
similar conditions of use, either due to updated parameter data (e.g., Zone 2 volume), or professional
judgment. The most notable difference between the 2014 scenarios related to the single mass used
parameter selected. In the 2014 assessment, aerosol degreaser was modeled assuming 24 g (0.85 oz) and
clear protecting coating spray was modeled assuming 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 2019 risk evaluation.
2.3.2.6 Consumer Exposure Results
Acute inhalation and dermal exposure results are presented below for each consumer condition of use.
Dermal exposure results are only presented for those scenarios deemed to have the potential for dermal
contact with impeded evaporation per the scope presented in the May 2018 Problem Formulation (U.S.
18d). These conditions of use are organized by product subcategories and are also referred to
herein as consumer modeling scenarios. Inhalation estimates are presented in terms of acute indoor air
concentrations (ppm) resulting from a single consumer use event within a one-day exposure period; they
are provided for users and bystanders. Acute dermal exposure estimates are presented as an acute dose
(mg/kg/day); they are provided for users only.
2.3.2.6.1 Characterization of Exposure Results
As described in Section 2.3.2.4.1, the consumer exposure modeling approach was deterministic, but a
range of exposure results were estimated based on varying three parameters: weight fraction, mass of
product 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, 3- and 24-hr TWA air concentrations are provided for consumers and bystanders. These are
based on the relevant human health hazard metrics. The 3-hr TWA air concentrations are higher than the
24-hr air concentrations in all scenarios due to the shorter averaging time surrounding the use event.
Likewise, the air concentrations associated with the user are higher than those associated with the
bystander in all scenarios due to the higher concentration of chemical expected in the room of use (Zone
1) coupled with the greater amount of time a consumer is assumed to be in the room of use (during and
after use event) compared with the bystander. While it is assumed that a bystander of any age, including
pregnant women and children, could be exposed to the reported concentrations, the concentrations
themselves are not unique for specific subpopulations. The concentrations reported reflect the
concentration a consumer or bystander would be exposed to.
Page 151 of 748
-------
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 differences in surface area to
body weight ratios in these groups. Results are not presented specifically for pregnant women or women
of reproductive age; however, the range of results presented for adults and children age groups are
expected to cover dermal exposures for pregnant women as well, with the children (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 (I v «« \
Table 2-31. Surface Area and Body Weight Values for Different Consumer and Bystander
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
2.3.2,6,2 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: EPA-HQ-OPPT-2019-0500\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-32. Acute Inhalation Exposure Summary: Brake & Parts Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
3.97E+02
5.76E+01
(120)
(100)
(766.5)
Bystander
1.00E+02
1.67E+01
Page 152 of 748
-------
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
6.60E+01
9.06
(15)
(60)
(191.6)
Bystander
1.48E+01
2.26
Low-Intensity User
10th %ile
Min
10th %ile
User
5.16
7.09E-01
(1)
(20)
(47.9)
Bystander
1.19
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.
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-33. Acute Derma
Exposure Summary: Brake &
'arts 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)
7.63E+01
Children (16-20 years)
7.14E+01
Children (11-15 years)
7.80E+01
Central
Tendency
50th %ile
(15)
Mid
(60)
Adult (>21 years)
5.72
Children (16-20 years)
5.35
Children (11-15 years)
5.85
Low-Intensity
User
10th %ile
(1)
Min
(20)
Adult (>21 years)
1.27E-01
Children (16-20 years)
1.19E-01
Children (11-15 years)
1.30E-01
1 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-HO-OPPT-2019-0500\ for the full range of results based
on all iterations of this modeling scenario.
Page 153 of 748
-------
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-34. Acute Inhalation Exposure Summary: Aerosol E ectronic Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
2.81E+02
3.76E+01
(30)
(100)
(337.1)
Bystander
5.03E+01
7.56
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
1.19E+01
1.58
(2)
(65)
(22.5)
Bystander
1.96
2.95E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
4.15E-01
5.55E-02
(0.5)2
(30)
(1.8)
Bystander
7.21E-02
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.
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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: EPA-HQ-OPPT-2019-0500\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-35. Acute Inhalation Exposure Summary: Liquid Electronic Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(30)
(100)
95th %ile
(337.1)
User
2.70E+02
3.61E+01
Bystander
4.83E+01
7.26
Moderate-Intensity User
50th %ile
(2)
(100)
50th %ile
(22.5)
User
1.75E+01
2.33
Bystander
2.90
4.36E-01
Low-Intensity User
10th %ile
(0.5)2
(100)
10th %ile
(1.8)
User
1.30
1.74E-01
Bystander
2.27E-01
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.
Page 154 of 748
-------
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-36. 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)
4.30E+01
Children (16-20 years)
4.03E+01
Children (11-15 years)
4.39E+01
Moderate-
Intensity User
50th %ile
(2)
(100)
Adult (>21 years)
2.88
Children (16-20 years)
2.68
Children (11-15 years)
2.92
Low-Intensity
User
10th %ile
(0-5)2
(100)
Adult (>21 years)
7.15E-01
Children (16-20 years)
6.70E-01
Children (11-15 years)
7.31E-01
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: EPA-HO-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-37. Acute Inhalation Exposure Summary: Aerosol Spray Degreaser/Cleaner
Duration of
Weight
Mass Used
(g)
Product
3-hr Max
24-hr Max
Scenario Description
Use
Fraction1
User or
TWA
TWA
(min)
(%)
Bystander
(ppm)
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.12E+03
1.62E+02
(120)
(100)
(2157.4)
Bystander
2.82E+02
4.71E+01
Moderate-Intensity User
50th %ile
Max
50th %ile
User
2.99E+02
4.11E+01
(15)
(100)
(521.4)
Bystander
6.70E+01
1.02E+01
Low-Intensity User
10th %ile
Min
10th %ile
User
4.54E+01
6.20
(5)
(60)
(130.8)
Bystander
9.83
1.50
Actual product weight fractions were: 60-100% and 90-100%.
Page 155 of 748
-------
2227
2228
2229
2230
2231
2232
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
2014 Acute Inhalation 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
'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.
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 are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-38. Acute Dermal Exposure Summary: Aerosol Spray Degreaser/Cleaner
Scenario
Description
Duration
of Use
(min)
Weight
Fraction1
(%)
Receptor
Acute ADR
(mg/kg/day)
95th %ile
(120)
Max
(100)
Adult (>21 years)
7.16E+01
High-Intensity
User
Children (16-20 years)
6.70E+01
Children (11-15 years)
7.32E+01
Moderate-
Intensity User
50th %ile
Max
Adult (>21 years)
8.94
(15)
(100)
Children (16-20 years)
8.37
Children (11-15 years)
9.15
Low-Intensity
User
10th %ile
(5)
Min
(60)
Adult (>21 years)
1.79
Children (16-20 years)
1.67
Children (11-15 years)
1.83
1 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.
Page 156 of 748
-------
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-HO-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-39. Acute Inhalation Exposure Summary: Liquid Degreaser/Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(120)
(100)
95th %ile
(1337.7)
User
1.05E+03
1.46E+02
Bystander
2.28E+02
3.61E+01
Moderate-Intensity User
50th %ile
(15)
(100)
50th %ile
(139.9)
User
1.17E+02
1.56E+01
Bystander
1.97E+01
2.96
Low-Intensity User
10th %ile
(2)
(100)
10th %ile
(24.1)
User
1.95E+01
2.60
Bystander
3.24
4.86E-01
Actual product weight fractions were: 90-100% and 100%.
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-40. 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)
1.71E+02
Children (16-20 years)
1.60E+02
Children (11-15 years)
1.75E+02
Moderate-
Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.14E+01
Children (16-20 years)
2.01E+01
Children (11-15 years)
2.19E+01
Low-Intensity
User
10th %ile
(2)
(100)
Adult (>21 years)
2.85
Children (16-20 years)
2.68
Children (11-15 years)
2.92
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%).
Page 157 of 748
-------
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 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-HO-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-41. Acute Inhalation Exposure Summary: Aerosol Gun Scrubber
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
(0.7)
User
5.35E-01
7.44E-02
(120)
(100)
Bystander
1.16E-01
1.83E-02
Moderate-Intensity User
50th %ile
Max
(0.7)
User
5.87E-01
7.83E-02
(15)
(100)
Bystander
9.87E-02
1.48E-02
Low-Intensity User
10th %ile
Min
(0.7)
User
3.41E-01
4.55E-02
(2)
(60)
Bystander
5.64E-02
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%).
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-42. Acute Dermal Exposure Summary: Aerosol Gun Scrubber
Scenario
Description
Duration
of Use
(min)
Weight
Fraction
(%)
Receptor
Acute ADR
(mg/kg/day)
95th %ile
(120)
Max
(100)
Adult (>21 years)
6.90E+01
High-Intensity
User
Children (16-20 years)
6.45E+01
Children (11-15 years)
7.06E+01
Moderate-
Intensity User
50th %ile
(15)
Max
Adult (>21 years)
8.62
(100)
Children (16-20 years)
8.07
Children (11-15 years)
8.82
Low-Intensity
User
10th %ile
(2)
Min
(60)
Adult (>21 years)
6.90E-01
Children (16-20 years)
6.48E-01
Children (11-15 years)
7.08E-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%).
Page 158 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 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-HO-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-43. Acute Inhalation Exposure Summary: Liquid Gun Scrubber
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(120)
(100)
(0.7)
User
4.58E-01
6.37E-02
Bystander
9.94E-02
1.57E-02
Moderate-Intensity User
50th %ile
(15)
(100)
(0.7)
User
5.03E-01
6.71E-02
Bystander
8.46E-02
1.27E-02
Low-Intensity User
10th %ile
(2)
(100)
(0.7)
User
4.65E-01
6.22E-02
Bystander
8.09E-02
1.22E-02
Modeling was based on the upper-end of the narrow range of liquid degreasing formulation weight fractions (90-100%).
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-44. Acute Dermal Exposure Summary: Liquid 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)
1.60E+02
Children (16-20 years)
1.50E+02
Children (11-15 years)
1.63E+02
Moderate-
Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.00E+01
Children (16-20 years)
1.87E+01
Children (11-15 years)
2.04E+01
Low-Intensity
User
10th %ile
(2)
(100)
Adult (>21 years)
2.68
Children (16-20 years)
2.50
Children (11-15 years)
2.72
Page 159 of 748
-------
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-45. Acute Inhalation Exposure Summary: Mold Release
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.22E+02
1.64E+01
(30)
(68.9)
(212.9)
Bystander
2.19E+01
3.29
Moderate-Intensity User
50th %ile
Max
50th %ile
User
1.31E+01
1.75
(2)
(68.9)
(23.4)
Bystander
2.16
3.25E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
1.32
1.77E-01
(0.5)2
(40)
(4.3)
Bystander
2.30E-01
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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 160 of 748
-------
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Dermal Exposures. Docket: EPA-HQ-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-46. Acute Inhalation Exposure Summary: Aerosol Tire Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
1.04E+02
1.57E+01
(60)
(100)
(317)
Bystander
4.39E+01
6.84
Moderate-Intensity User
50th %ile
Max
50th %ile
User
3.04E+01
4.17
(15)
(100)
(52.9)
Bystander
6.80
1.04
Low-Intensity User
10th %ile
Min
10th %ile
User
4.25
5.81E-01
(5)
(70)
(10.5)
Bystander
9.21E-01
1.40E-01
Actual product weight fractions were: 70-90% and 80-100%.
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-47. 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)
1.58E+01
Children (16-20 years)
1.48E+01
Children (11-15 years)
1.61E+01
Moderate-
Intensity User
50th %ile
(15)
Max
(100)
Adult (>21 years)
3.94
Children (16-20 years)
3.69
Children (11-15 years)
4.03
Low-Intensity
User
10th %ile
(5)
Min
(70)
Adult (>21 years)
9.17E-01
Children (16-20 years)
8.61E-01
Children (11-15 years)
9.38E-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.
Page 161 of 748
-------
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-OPPT-2019-0500\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-48. Acute Inhalation Exposure Summary: Liquid Tire Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(100)
95th %ile
(706.4)
User
3.33E+02
4.76E+01
Bystander
9.79E+01
1.52E+01
Moderate-Intensity User
50th %ile
(15)
(100)
50th %ile
(117.9)
User
6.77E+01
9.28
Bystander
1.52E+01
2.32
Low-Intensity User
10th %ile
(5)
(100)
10th %ile
(23.4)
User
1.35E+01
1.85
Bystander
2.93
4.47E-01
1 Single weight fraction of 80-100% available.
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-49. 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)
8.78E+01
Children (16-20 years)
8.23E+01
Children (11-15 years)
8.99E+01
Moderate-
Intensity User
50th %ile
(15)
(100)
Adult (>21 years)
2.20E+01
Children (16-20 years)
2.06E+01
Children (11-15 years)
2.24E+01
Low-Intensity
User
10th %ile
(5)
(100)
Adult (>21 years)
7.33
Children (16-20 years)
6.85
Children (11-15 years)
7.49
1 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
Page 162 of 748
-------
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-50. Acute Inhalation Exposure Summary: Tap & Die Fluid
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(30)
(98)
95th %ile
(134.5)
User
1.10E+02
1.47E+01
Bystander
1.97E+01
2.95
Moderate-Intensity User
50th %ile
(2)
(98)
50th %ile
(14.8)
User
1.18E+01
1.57
Bystander
1.95
2.93E-01
Low-Intensity User
10th %ile
(0.5)2
(98)
10th %ile
(2.7)
User
2.03
2.78E-01
Bystander
4.96E-01
8.53E-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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-51. Acute Inhalation Exposure Summary: Penetrating Lubricant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
8.74E+01
1.17E+01
(30)
(50)
(209.9)
Bystander
1.56E+01
2.35
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
5.16
6.88E-01
(2)
(27.5)
(23.1)
Bystander
8.53E-01
1.28E-01
Page 163 of 748
-------
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
Low-Intensity User
10th %ile
Min
10th %ile
User
1.62E-01
2.16E-02
(0.5)2
(5)
(4.2)
Bystander
2.80E-02
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-52. Acute Inhalation Exposure Summary: Solvent-based Adhesive & Sealant
Duration of
Weight
Mass Used
(g)
Product
3-hr Max
24-hr Max
Scenario Description
Use
Fraction1
User or
TWA
TWA
(min)
(%)
Bystander
(ppm)
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
2.46E+02
3.22E+01
(60)
(90)
(185.2)
Bystander
2.68E+01
4.06
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
7.76
1.00
(4.25)
(47.5)
(10.7)
Bystander
6.86E-01
1.03E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
6.72E-02
8.83E-03
(0.5)2
(5)
(1.3)
Bystander
8.68E-03
1.30E-03
1 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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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.
Page 164 of 748
-------
2501
2502
2503
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
2530
2531
2532
2533
2534
2535
2536
2537
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-53. Acute Inhalation Exposure Summary: Mirror-Edge Sealant
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(40)
95th %ile
(78.4)
User
2.45E+01
3.33
Bystander
5.21
7.84E-01
Moderate-Intensity User
50th %ile
(4.25)
(40)
50th %ile
(4.5)
User
8.31
1.11
Bystander
1.34
2.01E-01
Low-Intensity User
10th %ile
(0.5)2
(40)
10th %ile
(0.5)
User
1.68E-01
2.24E-02
Bystander
2.71E-02
4.07E-03
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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.
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-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Page 165 of 748
-------
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
2563
2564
2565
2566
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-54. Acute Inhalation Exposure Summary: Tire Repair cement/Sealer
Duration of
Weight
Mass Used
(g)
Product
3-hr Max
24-hr Max
Scenario Description
Use
Fraction1
User or
TWA
TWA
(min)
(%)
Bystander
(ppm)
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
8.30E+01
1.18E+01
(60)
(95)
(185.2)
Bystander
2.44E+01
3.80
Moderate-Intensity User
50th %ile
Mid
50th %ile
User
4.85
6.64E-01
(4.25)
(80)
(10.7)
Bystander
1.07
1.63E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
4.32E-01
5.97E-02
(0.5)2
(65)
(1.3)
Bystander
1.05E-01
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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
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: EPA-HQ-OPPT-2019-0500\ for the full range of results based on all
iterations of this modeling scenario.
Table 2-55. Acute Inhalation Exposure Summary: Carpet Cleaner
Duration of
Weight
Mass Used
(g)
Product
3-hr Max
24-hr Max
Scenario Description
Use
Fraction1
User or
TWA
TWA
(min)
(%)
Bystander
(ppm)
(ppm)
High-Intensity User
95th %ile
(99)
95th %ile
User
3.90E+02
5.26E+01
(30)
(526.6)
Bystander
7.65E+01
1.15E+01
Moderate-Intensity User
50th %ile
(99)
50th %ile
User
4.75E+01
6.36
(5)
(62.9)
Bystander
8.39
1.26
Low-Intensity User
10th %ile
(99)
10th %ile
User
8.14
1.10
(0.5)2
(11.8)
Bystander
1.55
2.33E-01
1 Single weight fraction of >99% available.
Page 166 of 748
-------
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-56. 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)
4.65E+01
Children (16-20 years)
4.36E+01
Children (11-15 years)
4.77E+01
Central-
Tendency
50th %ile
(5)
(99)
Adult (>21 years)
7.77
Children (16-20 years)
7.28
Children (11-15 years)
7.93
Low-Intensity
User
10th %ile
(0.5)2
(99)
Adult (>21 years)
3.89E-01
Children (16-20 years)
3.64E-01
Children (11-15 years)
3.98E-01
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.
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-HQ-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-57. Acute Inhalation Exposure Summary: Aerosol Spot Remover
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(30)
(30)
95th %ile
(514.1)
User
2.50E+02
3.24E+01
Bystander
2.28E+01
3.43
Page 167 of 748
-------
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(61.4)
User
2.93E+01
3.78
Bystander
2.49
3.75E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
10th %ile
(11.15)
User
4.34
5.65E-01
Bystander
4.59E-01
6.90E-02
1 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 are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-58. Acute Derma
Exposure Summary: Aerosol S
pot 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)
5.52
Children (16-20 years)
5.16
Children (11-15 years)
5.64
Moderate-
Intensity User
50th %ile
(5)
(30)
Adult (>21 years)
9.18E-01
Children (16-20 years)
8.61E-01
Children (11-15 years)
9.42E-01
Low-Intensity
User
10th %ile
(0.5)2
(30)
Adult (>21 years)
9.18E-02
Children (16-20 years)
8.61E-02
Children (11-15 years)
9.42E-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.
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-HQ-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Page 168 of 748
-------
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-59. Acute Inhalation Exposure Summary: Liquid Spot Remover
Duration of
Weight
Mass Used
(g)
Product
3-hr Max
24-hr Max
Scenario Description
Use
Fraction1
User or
TWA
TWA
(min)
(%)
Bystander
(ppm)
(ppm)
High-Intensity User
95th %ile
Max
95th %ile
User
2.98E+02
3.99E+01
(30)
(75)
(477.2)
Bystander
5.34E+01
8.02
Moderate-Intensity User
50th %ile
Max
50th %ile
User
3.55E+01
4.73
(5)
(75)
(57)
Bystander
5.80
8.72E-01
Low-Intensity User
10th %ile
Min
10th %ile
User
4.09
5.47E-01
(0.5)2
(50)
(10.7)
Bystander
7.14E-01
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 are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-60. 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)
3.21E+01
Children (16-20 years)
3.00E+01
Children (11-15 years)
3.28E+01
Moderate-
Intensity User
50th %ile
(5)
Max
(75)
Adult (>21 years)
5.33
Children (16-20 years)
4.99
Children (11-15 years)
5.45
Low-Intensity
User
10th %ile
(0.5)2
Min
(50)
Adult (>21 years)
3.55E-01
Children (16-20 years)
3.33E-01
Children (11-15 years)
3.63E-01
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
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.
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
Page 169 of 748
-------
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-61. Acute Inhalation Exposure Summary: Fixatives & Finishing Spray Coatings
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(30)
95th %ile
(306)
User
6.83E+01
9.31
Bystander
1.51E+01
2.28
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(45.2)
User
1.13E+01
1.50
Bystander
1.84
2.77E-01
Low-Intensity User
10th %ile
(0.5)2
(30)
10th %ile
(9.4)
User
2.17
2.90E-01
Bystander
3.76E-01
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.
E 14b). 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 bystandersare included below.
2014 Acute Inhalation Exposure Summary: Fixatives & Finis
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
ling Spray Coatings
'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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Apparel and 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
Page 170 of 748
-------
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
category; 97.7% reported use of aerosol formulations. The room of use (Zone 1) was set to the utility
room (20 m3).
Inhalation exposures for users and bystanders are 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-OPPT-2019-0500] for the full range of results based on all
iterations of this modeling scenario.
Table 2-62. Acute Inhalation Exposure Summary: Shoe Polish
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(30)
(20)
95th %ile
(151.4)
User
2.52E+01
3.38
Bystander
4.52
6.79E-01
Moderate-Intensity User
50th %ile
(5)
(20)
50th %ile
(15.4)
User
2.56
3.41E-01
Bystander
4.18E-01
6.28E-02
Low-Intensity User
10th %ile
(0.5)
(20)
10th %ile
(2.9)
User
4.46E-01
5.96E-02
Bystander
7.74E-02
1.16E-02
Single weight fraction of 10-20% available.
Dermal exposures are also presented for this scenario, as it is assumed that the product could be applied
in a manner leading to dermal contact with impeded evaporation, thereby increasing the likelihood
and/or duration of dermal absorption. There is uncertainty surrounding the duration of dermal contact
with impeded evaporation. The exposure durations modeled could exceed the duration of such dermal
contact; therefore, the higher-end durations may result in an overestimation of dermal exposure.
Table 2-63. Acute E
~ermal 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)
3.02
Children (16-20 years)
2.82
Children (11-15 years)
3.08
Moderate-
Intensity User
50th %ile
(5)
(20)
Adult (>21 years)
5.00E-01
Children (16-20 years)
4.70E-01
Children (11-15 years)
5.14E-01
Low-Intensity
User
10th %ile
(0.5)
(20)
Adult (>21 years)
5.00E-02
Children (16-20 years)
4.70E-02
Children (11-15 years)
5.14E-02
Single weight fraction of 10-20% available.
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 (U.S. EPA. 2014b).
Page 171 of 748
-------
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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; 72.1% 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-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-64. Acute Inhalation Exposure Summary: Fabric Spray
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(40)
95th %ile
(326.8)
User
1.93E+02
2.53E+01
Bystander
2.10E+01
3.18
Moderate-Intensity User
50th %ile
(10)
(40)
50th %ile
(49.9)
User
3.24E+01
4.18
Bystander
2.75
4.13E-01
Low-Intensity User
10th %ile
(1.4)
(40)
10th %ile
(11.4)
User
5.64
7.35E-01
Bystander
6.09E-01
9.15E-02
1 Single product weight fraction of 20-40% available.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Film Cleaner
Exposure to TCE in film cleaner products was evaluated based on two aerosol products with weight
fractions ranging 80-100% TCE.
Westat Survey data on aerosol rust removers were used as the basis for duration of use and mass of
product used. This Westat category was selected as a surrogate, as there were no well-aligned product
categories for this use. However, survey responses for the selected surrogate category reported 98.3%
use of aerosol formulations, which is supportive of its application to the modeled product scenario.
Duration of use and mass of product data were also reviewed for reasonableness and were considered
more reasonable (i.e., lower) than the higher use patterns associated with most of the solvent degreasing
or cleaning categories. The room of use (Zone 1) was set to the utility room (20 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-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-65. Acute Inhalation Exposure Summary: Film Cleaner
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(100)
95th %ile
(632.9)
User
4.71E+02
6.42E+01
Bystander
1.04E+02
1.57E+01
Moderate-Intensity User
50th %ile
(100)
50th %ile
User
7.77E+01
1.03E+01
Page 172 of 748
-------
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product
User or
Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
(5)
(93.4)
Bystander
1.27E+01
1.91
Low-Intensity User
10th %ile
(0.5)2
(100)
10th %ile
(19.4)
User
1.49E+01
1.99
Bystander
2.59
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
Hoof Polish
Exposure to TCE in hoof polish products was evaluated based on one aerosol product with an
unreported weight fraction. Modeling was based on the upper-end of the narrow range of shoe polish
and spray fixative/coating formulation weight fractions (20-30%).
Westat Survey data on spray shoe polish were used as the basis for duration of use and mass of product
used. This Westat category was selected as a surrogate, as there were no well-aligned product categories
for this use. Survey data indicate that 11.7% of respondents used products in this category; 97.7%
reported use of aerosol formulations. The room of use (Zone 1) was set to approximate a barn
environment. This was done by using a garage (90 m3) but increasing the default air exchange rate of a
residential room from 0.45 to 4 air exchanged per hour, which was based on recommended ventilation
rates for a horse stable (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
Consumer Inhalation Exposures. Docket: EPA-HQ-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-66. Acute Inhalation Exposure Summary: Hoof Polish
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(30)
(30)
95th %ile
(208.2)
User
1.76E+01
2.21
Bystander
8.83E-02
1.10E-02
Moderate-Intensity User
50th %ile
(5)
(30)
50th %ile
(21.2)
User
1.73
2.16E-01
Bystander
3.81E-03
4.76E-04
Low-Intensity User
10th %ile
(0.5)
(30)
10th %ile
(4)
User
2.46E-01
3.08E-02
Bystander
6.23E-04
7.79E-05
1 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%).
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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.
Page 173 of 748
-------
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Internal 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. Spraying occurred between 3 and 5 seconds (0.05-0.08 min)
before obtaining desired effect (Bertilsson et ai. ). 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
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-OPPT-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Table 2-67. Acute Inhalation Exposure Summary: Pepper Spray
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
Single Scenario
(0.5)2
(91.5)
(4)
User
1.42E-01
1.77E-02
Bystander
1.42E-01
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
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).
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-2019-0500] for the full range of results based
on all iterations of this modeling scenario.
Page 174 of 748
-------
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 2-68. Acute Inhalation Exposure Summary: Toner Ait
Scenario Description
Duration of
Use
(min)
Weight
Fraction1
(%)
Mass Used
(g)
Product User
or Bystander
3-hr Max
TWA
(ppm)
24-hr Max
TWA
(ppm)
High-Intensity User
95th %ile
(60)
(20)
95th %ile
(434.7)
User
6.47E+01
8.82
Bystander
1.43E+01
2.16
Moderate-Intensity User
50th %ile
(5)
(20)
50th %ile
(64.2)
User
1.07E+01
1.42
Bystander
1.74
2.62E-01
Low-Intensity User
10th %ile
(0.5)2
(20)
10th %ile
(13.3)
User
2.05
2.73E-01
Bystander
3.55E-01
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.
There are no dermal exposures quantified for this scenario, as this use pattern is not expected to involve
dermal contact with impeded evaporation.
2.3.2.6.3 Summary of Consumer Exposure Assessment
Table 2-69 displays the consumer conditions of use evaluated for acute inhalation and/or dermal
exposures.
Table 2-69. Evaluated Pathways for Consumer Conditions of Use
Life
Cycle
Stage
Categories
Product Subcategories
Form
Acute
Inhalation
Exposure
Acute
Dermal
Exposure
Use
Solvents for
Cleaning and
Degreasing
Brake & Parts Cleaner
Aerosol
Electronic Degreaser/Cleaner
Aerosol
Electronic Degreaser/Cleaner
Liquid
Aerosol Spray Degreaser/Cleaner
Aerosol
Liquid Degreaser/Cleaner
Liquid
Gun Scrubber
Aerosol
Gun Scrubber
Liquid
Mold Release
Aerosol
Tire Cleaner
Aerosol
Tire Cleaner
Liquid
Lubricants and
Greases
Tap & Die Fluid
Aerosol
Penetrating Lubricant
Aerosol
Adhesives and
Sealants
Solvent-based Adhesive & Sealant
Liquid
Mirror-edge Sealant
Aerosol
Tire Repair Cement/Sealer
Liquid
Cleaning and
Furniture Care
Products
Carpet Cleaner
Liquid
Spot Remover
Aerosol
Spot Remover
Liquid
Arts, Crafts, and
Hobby Materials
Fixatives & Finishing Spray Coatings
Aerosol
Apparel and
Footwear Care
Products
Shoe Polish
Aerosol
Page 175 of 748
-------
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life
Cycle
Stage
Categories
Product Subcategories
Form
Acute
Inhalation
Exposure
Acute
Dermal
Exposure
Other Consumer
Uses
Fabric Spray
Aerosol
V
Film Cleaner
Aerosol
V
Hoof Polish
Aerosol
V
Pepper Spray
Aerosol
V
Toner Aid
Aerosol
V
A range in acute inhalation and acute dermal exposures is provided in Table 2-70, 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-HQ-OPPT-2019-0500\.
Table 2-70. 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
1.19E-01 - 1.75E+02
Bystander
8.47E-03 - 4.71E+01
Lubricants and
Greases
User
2.16E-02 - 1.47E+01
NA
Bystander
4.21E-03 - 2.95
Adhesives and
Sealants
User
8.83E-03 - 3.22E+01
NA
Bystander
1.30E-03 - 4.06
Cleaning and
Furniture Care
Products
User
5.47E-01 - 5.26E+01
8.61E-02 - 4.77E+01
Bystander
6.90E-02 - 1.15E+01
Arts, Crafts, and
Hobby Materials
User
2.90E-01 - 9.31
NA
Bystander
5.66E-02 - 2.28
Apparel and Footwear
Care Products
User
5.96E-02 - 3.38
4.70E-02 - 3.08
Bystander
1.16E-02 - 6.79E-01
Other Consumer Uses
User
1.77E-02 - 6.42E+01
NA
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).
Page 176 of 748
-------
2848
2849
2850
2851
2852
2853
2854
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
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2.3.2.7 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.
2.3.2.7.1 Modeling Approach Uncertainties
Deterministic vs. Stochastic
With deterministic approaches like the one applied in this evaluation of consumer exposure, the output
of the model is fully determined by the choices of parameter values and initial conditions. Stochastic
approaches feature inherent randomness, such that a given set of parameter values and initial conditions
can lead to an ensemble of different model outputs. The overall approach to the CEM modeling is
intended to capture a range 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
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.2 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.
Page 177 of 748
-------
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Dermal exposures are quantified and presented for scenarios that may involve dermal contact with
impeded evaporation based on professional considerations of the formulation type and likely use pattern.
However, there is uncertainty surrounding the assumption that such dermal contact with impeded
evaporation would occur for those scenarios. For example, for aerosol formulations, it is possible that
aerosol degreasing or cleaning products may be sprayed and left to drip or dry from the target surface. It
is also possible users would follow spraying with wiping, which could lead to some duration of dermal
contact with impeded evaporation.
There is related uncertainty surrounding the application of exposure durations for such scenarios. The
exposure durations modeled are based on reported durations of product use and may not reflect
reasonable durations of such dermal contact with impeded evaporation. In many cases, the exposure
duration modeled could exceed a reasonable duration of such dermal contact with a wet rag, for
example. Therefore, dermal exposure results based on the higher-end durations (i.e., those associated
with the moderate- and high-intensity user scenarios) may overestimate dermal exposure. Another
source of potential overestimation is the application of a single formulation density to scenarios covering
a range of specific TCE-containing products with a range of formulation densities. For such scenarios, a
single (highest) density was chosen to convert the mass used input obtained from the Westat (1987)
survey from ounces of product to grams of product. For some scenarios, this may have driven up the
mass used, though the degree of this impact is dependent on he broadness of the density range for that
condition of use.
In the evaluation of consumer dermal exposure, P_DER2b utilizes a measured dermal permeability
coefficient (Kp). EPA selected a Kp of 0.019 cm/hr from Poet (2000) obtained from a water patch test on
human skin using TCE in aqueous solution. While it is within range of other, predicted Kp values -
CEM predicts a Kp of 0.028 cm/hr and the NIOSH Skin Notation Profile for TCE calculates a Kp of
0.01197 cm/hr (Hudson and Dotson. , ) - it is a key parameter and there is some uncertainty
surrounding the impact of applying an aqueous Kp for the prediction of dermal flux for formulations of
TCE-containing consumer products, some of which contain nearly 100% TCE. While neat TCE would
be estimated to have a lower Kp based on relatively low water solubility (
Table 1-1) compared to its density, TCE is an irritant that would be expected to disrupt the stratum
corneum and lead to greatly increased absorption over time.
Inhalation 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
Page 178 of 748
-------
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
adjusting Zone 1 parameters (which represents the room of use or use environment). In modeling pepper
spray, the garage was selected as the room of use, but the room volume was changed to 16 m3 to
represent a half-dome chemical cloud around the person using the product. Additionally, the air
exchange rate for Zone 1 was set to 100 to reflect the high rate between the cloud and the rest of outside.
The interzonal ventilation rate was set to 0, which effectively blocks the exchange of air between Zone 1
and the rest of the house. Thus, the concentrations users are exposed to inside the home after product use
is zero. In the outside scenario, bystanders in the home are assumed to have zero exposures. However,
bystanders in the outdoor environment were not modeled, but could potentially be exposed to similar
levels as the user.
2.3.2.7.2 Data Uncertainties
Product Data
The products and articles assessed in this risk evaluation are largely based on EPA's 2017 Use and
Market Profile for TCE, as well as EPA's Use Report and Preliminary Information on Manufacturing,
Processing, Distribution, Use, and Disposal: TCE, which provide information on commercial and
consumer products available in the US marketplace at that time (\ c. < ^ \ •, 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 the NIH Household Product Survey and EPA's Chemical and
Products Database (CPDat), as well as available product labels and safety data sheets (SDSs). However,
it is possible that the entire universe of products may not have been identified, or that certain changes in
the universe of products may not have been captured, due to market changes or research limitations.
Use Patterns
A comprehensive survey of consumer use patterns in the 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,
Page 179 of 748
-------
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
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
emission rate based on chemical properties and emission profiles matching a spray or liquid application.
2,3,2,8 Confidence in Consumer Exposure Scenarios
The considerations and confidence ratings for the acute inhalation consumer exposure scenarios are
displayed in Table 2-71 with detailed explanations of rationale for the parameters in the footnotes.
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 ( ). 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 ( 2). 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 equation14 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 ( 87) 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.
However, 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.
There is a medium 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 2-72 with detailed explanations of rationale for the parameters in the footnotes.
14 The value of k is determined from an empirical relationship, developed by (Chinn. .1.98.1.1. 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.
Page 180 of 748
-------
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Overall, there is a low to moderate confidence in the consumer dermal exposure modeling approach and
results. The same model is employed to estimate dermal exposures; however, there is greater uncertainty
related to the potential for dermal contact with impeded evaporation (i.e., dermal exposure scenarios
wherein volatilization from the skin surface is inhibited); this contributes to the lower overall confidence
in the dermal results. The dermal permeability approach was selected for modeling instead of the
fraction absorbed method. Based on rationale provided in the problem formulation, EPA determined that
only dermal exposures with impeded evaporation would be evaluated for consumer conditions of use.
This is based on the expectation that, if not inhibited from volatilizing, inhalation exposure would
account for the preponderance of exposure from consumer uses. An example of dermal contact with
impeded evaporation for consumer applications would be having a TCE-soaked rag pressed firmly
against a user's fingers or hands for a period of time. Therefore, the permeability approach was deemed
more reflective of this type of dermal exposure scenario, as it does not account for losses due to
volatilization and assumes a constant flux of TCE for the duration of the use event. In modeling these
scenarios, the same use durations sourced from the Westat survey ( 87) are applied;
however, in doing so, the model assumes that there are no losses throughout the entire use duration. It is
unlikely that dermal contact would involve impeded evaporation for the entire use duration, particularly
for central-tendency and high-end use durations. It is more likely that such contact would be intermittent
throughout longer use durations and not constant. This leads to an overall low confidence in that input;
however, there would be greater confidence in the results obtained from the low-end use duration inputs
for any weight fraction modeled.
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 varied 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.
Page 181 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3079
3080
Table 2-71. 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 Inputs3
Overall
Category
Subcategory
Form
Default
Values2
Mass
Used4
Use
Duration5
Weight
Fraction6
Room of
Use7
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
and
Scrubber
to High
Degreasing
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
Page 182 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Confidence in User-Selected Varied Inputs3
Overall
Category
Subcategory
Form
Default
Values2
Mass
Used4
Use
Duration5
Weight
Fraction6
Room of
Use7
Confidence
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
Other
Film Cleaner
Aerosol
High
High
Moderate
Moderate
High
Moderate
Moderate
Consumer
to High
Uses
Other
Hoof Polish
Aerosol
High
NA
Moderate
Moderate
High
High
Moderate
Consumer
to High
Uses
Other
Pepper
Aerosol
High
NA
High
High
High
Moderate
Moderate
Consumer
Spray
to High
Uses
Other
Toner Aid
Aerosol
High
High
Moderate
Moderate
High
Moderate
Moderate
Consumer
to High
Uses
Confidence in Model Used considers whether model has been peer reviewed, as well as whether it is being applied in a manner
appropriate to its design and objective. The model used (CEM 2.1) has been peer reviewed, is publicly available, and has been
applied in a manner intended - to exposures associated with uses of household products and/or articles.
Confidence in Model Default Values considers default value data source(s) 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 (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 TSCA Work Plan Chemical Risk
Assessment for TCE (U.S. EPA. 2014b'). this parameter was varied from 1% to 25% and resulted in almost no difference in the
modeled peak air concentration.
Confidence in User-Selected Varied Inputs considers the quality of their data sources, as well as relevance of the inputs for the
selected consumer condition of use.
Page 183 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in User-Selected Varied Inputs3
Overall
Confidence
Category
Subcategory
Form
Mass
Used4
Use
Duration5
Weight
Fraction6
Room of
Use7
'Mass Used is primarily sourced from the Westat (.1.9871 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).
5Use Duration is primarily sourced from the Westat (.1.9871 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.
6Weight fraction of TCE in products is sourced from product Safety Data Sheets (SDSs), which were not reviewed as part of
systematic review but were taken as authoritative sources on a product's ingredients.
Room 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.
3081
3082
3083
Table 2-72. Confidence Ratings for Acute Dermal Consumer Exposure Modeling Scenarios
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in
Assumption of
Dermal
Contact with
Impeded
Evaporation3
Confidence in User-Selected
Varied Inputs4
Overall
Confidence
Category
Subcategory
Form
Kp5
Use
Duration6
Weight
Fraction7
Solvents for
Cleaning and
Degreasing
Brake & Parts
Cleaner
Aerosol
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Electronic
Degreaser/
Cleaner
Liquid
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Spray
Degreaser/
Cleaner
Aerosol
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Liquid
Degreaser/
Cleaner
Liquid
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Gun Scrubber
Aerosol
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Gun Scrubber
Liquid
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Tire Cleaner
Aerosol
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Solvents for
Cleaning and
Degreasing
Tire Cleaner
Liquid
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Cleaning and
Furniture Care
Products
Carpet
Cleaner
Liquid
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Cleaning and
Furniture Care
Products
Spot Remover
Aerosol
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Page 184 of 748
-------
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Consumer
Condition of User
Confidence
in Model
Used1
Confidence
in Model
Default
Values2
Confidence in
Assumption of
Dermal
Contact with
Impeded
Evaporation3
Confidence in User-Selected
Varied Inputs4
Overall
Confidence
Category
Subcategory
Form
Kp5
Use
Duration6
Weight
Fraction7
Cleaning and
Furniture Care
Products
Spot Remover
Liquid
Low to
Moderate
High
Moderate
Moderate
Low
High
Low to
Moderate
Apparel and
Footwear Care
Products
Shoe Polish
Aerosol
Low to
Moderate
High
Low
Moderate
Low
High
Low to
Moderate
Confidence in Model Used considers whether model has been peer reviewed, as well as whether it is being applied in a
manner appropriate to its design and objective. The model used (CEM 2.1) has been peer reviewed, is publicly available, and
has been applied in a manner intended - to estimate exposures associated with uses of household products and/or articles. For
the purposes of dermal exposure, this confidence rating also considers the appropriateness of the dermal permeability model
within CEM 2.1 for estimating dermal exposures with impeded evaporation and known sources of uncertainty.
Confidence in Model Default Values considers default value data source(s) such as surface area to body weight ratios for the
dermal contact area. These default values are all central tendency values (i.e., mean or median values) sourced from EPA's
Exposure Factors Handbook (U.S. EPA. 2011c').
Confidence in Assumption of Dermal Contact with Impeded Evaporation characterizes the uncertainty surrounding whether
or not occluded contact is even possible or likely. Certain conditions of use have greater uncertainty over whether or not any
occluded contact is expected, i.e., the spray scenarios. The liquid formulations are likely to result in some dermal contact with
a rag; however, there remains uncertainty related to the degree to which such contact would be occluded.
Confidence in User-Selected Varied Inputs considers the quality of their data sources, as well as relevance of the inputs for
the selected consumer condition of use.
5The dermal permeability coefficient (Kp) used (0.019 cm/hr) from Poet (2000) came from a water patch test on human skin
using TCE in an aqueous solution. While it is within range of other, predicted Kp values (CEM 2.1 predicts 0.028 cm/hr and
NIOSH calculates 0.01197 cm/hr), it is a key parameter and there is uncertainty surrounding the impact of applying an
aqueous Kp for prediction of dermal flux for formulations of TCE-containing consumer products with nearly 100% TCE.
6Use 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. The dermal modeling receives a "low" confidence for this criterion due
to the uncertainty associated with the period of time during which a dermal exposure duration is likely to be occluded, not due
to relevance or data source.
'Weight fraction of TCE in products is sourced from product Safety Data Sheets (SDSs) and were taken as authoritative
sources on a product's ingredients.
2.3.3 Potentially Exposed or Susceptible Subpopulations
TSCA requires that a risk evaluation "determine whether at chemical substance presents an
unreasonable risk of injury to health or the environment, without consideration of cost or other non-risk
factors, including an unreasonable risk to a potentially exposed or susceptible subpopulation identified
as relevant to the risk evaluation by the Administrator, under the conditions of use." TSCA § 3(12)
states that "the term 'potentially 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 ( 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.
Page 185 of 748
-------
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
In developing the draft risk evaluation, the 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 higher age and route specific intake rates compared to
the general population.
Of the human receptors identified in the previous sections, EPA identifies the following as potentially
exposed or susceptible subpopulations due to their greater exposure to TCE and considered them in the
risk evaluation:
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 users (males and female workers of reproductive age) exposed to TCE as well as
non-users or workers exposed to TCE indirectly by being in the same work area of the building. Also,
adolescents and female workers of reproductive age (>16 to less than 50 years old) were also considered
as a potentially exposed or susceptible subpopulations
Consumers/product users and bystanders associated with consumer use. TCE has been identified as
being used in products available to consumers. Sections 2.3.2.1 and 2.3.2.2 provide an overview of
exposure pathways considered for the consumer assessment. Furthermore, EPA identified consumers
and bystanders associated with use of TCE-containing consumer products as a potentially exposed and
susceptible subpopulation due to greater exposure as described in Section 2.3.2.3. For example, higher-
intensity users (i.e., those using consumer products for longer durations and in greater amounts) were
considered and evaluated. In addition, consumers are considered to include children and adults over age
11, but bystanders in the home exposed via inhalation are considered to include any age group, from
infant to adult, including pregnant women and/or women of reproductive age. However, only some
individuals within the general population may use these products. Therefore, those who do use these
products are a potentially exposed or susceptible subpopulation due to greater exposure. Exposures for
these subpopulations are considered and/or evaluated in Section 2.3.2.6 (Table 2-32 through Table
2-68).
In developing dermal exposure scenarios, EPA quantified age and gender-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 (\ c. « ^ \ 101 I. ) to inform body weights, intake rates, and body surface
areas for children and adults. Distinct dermal exposure estimates are provided for for adults (including
women of reproductive age) and children (Section 2.3.2.6.1).
For occupational exposures, EPA assessed exposures to workers and ONUs from all TCE conditions of
use. Table 2-73 presents the percentage of employed workers and ONUs whom 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 (115 JILL
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
Page 186 of 748
-------
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 2-73, 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 2-74 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 2-73. 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."
Table 2-74. Percentage o
' 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
Page 187 of 748
-------
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. Also - 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.
Page 188 of 748
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3 HAZARDS
3.1 Environmental Hazards
3.1.1 Approach and Methodology
During scoping and problem formulation ( |), 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 ( ) Environment
and Climate Change Canada (ECCC) Risk Assessment for Trichloroethylene (Environment Canada and
Health Can a 2) and Ecological Hazard Literature Search Results in Trichloroethylene (CASRN
79-01-6) Bibliography: Supplemental File for the TSCA Scope Document (U.S. EPA. 20171).
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 ( 2018b). Studies were rated high, medium, or
low for quality. The data quality evaluation results are outlined in the [Data Quality Evaluation of
Environmental Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] and indicate that most of the
acceptable studies for TCE were rated high and moderate for quality. With the 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. Mechanistsic 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 (Geiger 5);
(Broderius et al. 2005: Smith et al. 1991; Ward 5; Buccafusco et ai. 1981; Alexander et al..
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 of the range of values for freshwater species, and because
baseline narcosis is the expected mode of action for TCE in both freshwater and saltwater fish
( \lc\ander et al rs \); (Ward et al.. 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
EC50 (the concentration at which 50% of test organisms exhibit an effect) of 21.9 mg/L for loss of
Page 189 of 748
-------
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
equilibrium in a freshwater species, fathead minnows ( Ucwider et al r" \). This study was rated high
for quality.
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 {Jordanellafloridae). 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 quanitatively.
Chronic fish data for TCE were identified in two acceptable studies representing two freshwater species,
American flagfish (,Jordanella floridae) and fathead minnows (Pimephales promelas). 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 ECso 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 quantitiatively.
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 qualitatilvey in this assessment. Alexander et al. ( 5) 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 EC50S 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 (McDamiel et al.. ,\V I; h
-------
94
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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
[Xenopus laevis], American toad \Bufo americanus], and spotted salamander [Ambystoma maculatum]).
These studies reported 96-hr EC so values for developmental effects ranging from 22 mg/L to > 85 mg/L
(McDaniel et al. 2004; Fort et al. 2001; Fort et al. 199 etal C'M). 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. 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 al.. 2004; Fort et al..
1993; Fort et al.. 1991). As stated previously, McDaniel et al. (2004) reported signs of narcosis in green
and wood frog tadpoles.
Limited chronic data were also available for amphibians. McDaniel et al., (2004) included a chronic
toxicity test for amphibians on 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 or ECso values rated
high or medium for quality; these values ranged from 7.75 mg/L to 43.14 mg/L for Daphnia magna,
Ceriodaphnia dubia, andMysidopsis bahia (Dobaradaran et al.. 2012; Niederlehner et al.. 1998;
Abernethy et al.. 1986; Ward et al.. 1986; LeBlanc. 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 LCso 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 (ICso) of 11 mg/L, the concentration at which
the mean number of young decreased by 50%. EPA used these data quantitatively.
Page 191 of 748
-------
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 a significant change in protein activity related to phase I
metabolism. Results indicated a NOEC of 1.2 mg/L and a LOEC 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 this mechanistic data is not directly linked to a population-level response, this
data was used qualitatively rather than quantitiatively.
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 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.. 2011; 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 EC so of 95 mg/L. This
value is within the of the range of values for freshwater species, so saltwater and freshwater species
were integrated together. EPA derived a geometric mean of 242 mg/L from the high and medium quality
ECsos. A 72-hour EC 10 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 methylene chloride to
V. steinii1 s high metabolism. For several reasons explained in Section 3.1.4 Weight of the Scientific
Evidence, these data were considered less biologically relevant than values from other studies and were
not used quantitatively during data integration.
Page 192 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
192
193
194
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
LC50
(freshwater)
28.28-66.8
42
Mortality
(Geieeret al. 1985) (hieh);
(Alexander et al. 1978)
(hiah): (Smith et al. 1991)
(hiuh): (Broderius et al.
2005) (hieh); (Buccafiisco et
al. 1981) (medium)
LC50
(saltwater)
52
(Ward et al. 1986) (medium)
EC50
(freshwater)
21.9
Immobilization
(Alexander et al, 1978)
(high)
Amphibian
LC50
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) (hieh);
Niederlehneret al, 1998)
high); (Abernethv et al,
986) (medium);
Dobaradaran et al. 2012)
medium)
LC50
(saltwater)
14
(Ward et al. 1986) (medium)
Subchronic
/Chronic3
Fish
EC20
7.88
Growth
(B rode rius et al, 2005)
(high)
EC50
11.8
Growth
NOEC
LOEC
ChV
10.568
20.915
14.87
Fry Survival
(Smith et al. 1991) (hieh)
NOEC
LOEC
ChV
(subchronic)
5.758
21.233
11
Fry Survival
LC50
(subchronic)
82
Mortality
CSchell. 1987) (hiah)
Amphibians
NOEC
4
Tadpole
Survival
(McDaniel et al, 2004)
(medium)
EC50
(subchronic)
22 - >85
34
Deformities
(
(
(
(
a
Fort et al. 2001) (medium);
Fort et al.. 1991) (medium);
Fort et al.. 1993) (hieh);
McDaniel et al, 2004) (high
ind medium)
Aquatic
invertebrates
NOEC
LOEC
ChV
7.1
12
9.2
Reproduction
(Niederlehneret al.. 1998)
(high)
IC50
11
Algae4
EC50
(freshwater)
26.24 - 820
242
Growth
(Brack and Rottler. 1994)
(high); (Tsai and Chen, 2007)
(hieh); (Labra et al. 2010)
(medium); (Ando et al.
2003) (medium); (Lukavskv
et al. 2011) (medium)
Page 193 of 748
-------
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EC50
(saltwater)
95
(Ward et al.. 1986) (medium)
EC10
12.3
Growth
(Brack and Rottler. 1994)
(high)
NOEC
LOEC
ChV
0.02
0.05
0.03
Growth
(Labra et al.. 2010) (medium)
'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-lirs to 96-lirs).
Note: 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-HO-OPPT-2019-0500],
3.1.3 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 species.
This hazardous concentration is represented as an HCP, where p is the percent of species.
As stated previously, there were a wide range of toxicity values reported in the literature for algae
exposed to TCE. EC50s 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 3-1 shows the
SSD for algae created using EPA's SSD Toolbox (Etterson. 2019). 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
assessed together, because the only saltwater species, Skeletonemci costatum, had an EC50 within that of
the range of values for freshwater species.
An HC05 (Hazardous Concentration threshold for 5% of species) for algae of 52 mg/L was derived from
this SSD.
Page 194 of 748
-------
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Figure 3-1. Species Sensitivity Distribution (SSD) for Algae Species Using ECsos (Etterson, 2019)
Toxicity Value (Log 10[EC50]) mg/L
Note: The data in this figure includes ECsos measuring growth from medium- or high-quality studies. A black dot indicates
the toxicity value used for that species. The red diamond indicates an HCos. The SSD was created using a triangular
distribution and fit using graphical methods (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 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.
An SSD was also created using the acute hazard data, including LCso and ECso data for fish, amphibians,
and invertebrates (Figure 3-2) (Etterson. 2019). The input data for Figure 3-2 included ECsos and LCsos
available in the literature representing four species of fish (LCsos), one species of amphibian (LCsos),
and three species of invertebrates (LCsos/ECsos). As stated previously, freshwater and saltwater species
were assessed together, because the saltwater values were within the of the range freshwater species in
Page 195 of 748
-------
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
the same taxonomic group. Additionally, for fish and invertebrates, the mode of action for freshwater
and saltwater species expected to be the same (Broderius et al.. 2005; Ward et al.. 1986; Alexander et
al.. 1978V
For the HCos for acute hazard data, EPA used a model average of the Gumbel, triangular, normal, and
logistic distributions (Figure 3-2). The model-averaged HCos from all three distributions was 9.9 mg/L,
which estimates a concentration that is hazardous for 5% of aquatic species. The SSDs showed aquatic
invertebrates were the most sensitive species.
Figure 3-2. Species Sensitivity Distributions (SSDs) for Acute Hazard Data Using LCsos or ECsos
Note: The data in this figure includes LCsoS 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, and Gumble distributions using the maximum likelihood fitting method (Appendix E.l).
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
(Jordcmellafloridae) the most sensitive fish.
A chronic SSD for aquatic species was not created due to insufficient data.
Page 196 of 748
-------
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
304
305
306
307
308
309
310
311
312
313
314
315
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( ):
- 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. ( )
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 only used studies assigned an overall
quality level of high or medium to derive COCs or HCoss for each taxonomic group. EPA derived
geometric means 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). To calculate
HCoss, EPA created SSDs for algae species using comparable data (e.g., ECsos measuring growth) and
for all species (e.g., ECsos and LCsos measuring population effect measures, like growth, mortality,
immobilization, and deformities). Non-definitive toxicity values (e.g., ECso >85 mg/L) were not used to
derive geometric means or HCoss.
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 geometric means were calculated as shown in Table 3-1. The geometric mean for
aquatic invertebrates, 16 mg/L, represented the lowest toxicity value derived from each of the four
taxonomic groups. The SSD in Figure 3-2 shows that the three most sensitive species in the distribution
are aquatic invertebrates, further substantiating that this is the most sensitive taxonomic group to acute
exposures.
To assess aquatic toxicity from chronic exposures, data for three taxonomic groups were described in the
acceptable literature: fish, amphibians, and aquatic invertebrates. However, for amphibians, only a
NOEC was established. Therefore, the endpoints for fish and aquatic invertebrates (ChVs, an EC20, and
an ECso) were more biologically relevant, because they measured a toxic effect. Of these values, the
most sensitive was the EC20 measuring growth in fish at 7.88 mg/L.
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
Page 197 of 748
-------
316
317
318
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
351
352
353
354
355
356
357
358
359
360
361
362
363
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 in 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.
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 (H.S.
B1 \ Av16i, JO I .V). 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 ( , 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. 2012c).
To derive an acute COC for TCE, EPA used the geometric mean of the EC so and LCsos for aquatic
invertebrates from five different studies, all rated high or medium for quality (Dobaradaran et al.. 2012;
Niederlehner et al.. 1998; Abernethy et al.. 1986; Ward et al.. 1986; LeBlanc. 1980). The geometric
mean for aquatic invertebrates represented the lowest acute value from all four taxonomic groups of
aquatic species from the integrated data for TCE. The data used to calculate the geometric mean
represent toxicity data for three species, Daphnia magna, Ceriodaphnia dubia, and Mysidopsis bahia.
To calculate an acute COC, the geometric mean, 16 mg/L, was divided by the AF of 5 for aquatic
invertebrates and multiplied by 1,000 to convert mg/L to |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 for TCE is 3,200 ppb.
To derive a chronic COC, EPA used the lowest chronic toxicity value from the integrated data, an EC20
for growth in fish (fathead minnows) from a study rated high for quality (Broderius et al.. 2005). This
value, 7.88 mg/L was divided by an assessment factor of 10, and then multiplied by 1,000 to convert
from mg/L to |ig/L (or ppb).
Page 198 of 748
-------
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 a geometric mean of a LOEC and a NOEC for growth in
Raphidocelis subcapitata (Labra et ai. 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 for TCE is 3 ppb.
Additionally, EPA used algae data representing nine species to produce an SSD, which was used to
calculate an HCos of 52 mg/L (or 52,000 ppb). As stated previously, this HCos estimates a concentration
that is hazardous for 5% of species. The HCos can be used in addition to the COC for algae, estimating
the concentration of TCE that is expected to protect 95% of algae species.
The algal HCos for TCE is 52,000 ppb.
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
(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. 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).
The COCs derived for aquatic organisms are summarized in Table 3-2. EPA calculated the 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 ci M h">l>8;
Abernethy et at.. 1986; Ward et at.. 1986; LeBlanc. 1980). EPA calculated the chronic COC for TCE at
788 ppb, based on an EC20 for fathead minnows from Broderius et al. (2005). rated high for quality.
As stated previously, algae were assessed separately from other aquatic organisms, because durations
normally considered acute for other species (e.g., 96 hours) can encompass several generations of algae.
EPA calculated an algal COC for TCE at 3 ppb, based on a geometric mean of a LOEC and NOEC for
growth in Raphidocelis subcapitata from Labra et al. (2010). a study rated medium for quality. EPA also
calculated an HCos of 52,000 ppb for algae based on the ECsos for nine species, from studies rated
medium and high for quality.
Table 3-2 Concentrations of Concern (COCs) for Environmental Toxicity
1 ji\iionmcntal Aquatic Toxicity
Concent rati 011 of Concern
Toxicity from Acute Exposure
3,200 ppb
Toxicity from Chronic Exposure
788 ppb
Toxicity for Algae: COC based on the lowest toxicity value
HCos based on ECsos
3 ppb
52,000 ppb
Page 199 of 748
-------
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
436
437
438
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3.1.7 Assumptions and Key Uncertainties for Environmental Hazard Data
While EPA determined that there was sufficient environmental hazard data to characterize
environmental hazards of TCE, there are uncertainties. 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 one fish species, than
the acute hazard values, which are based on data from multiple species of aquatic invertebrates.
However, a few lines of evidence mitigate the uncertainty in the chronic data. For example, the fish
toxicity value 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 EPA had around 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 HCos is derived using data from nine species rather than just one, and is therefore representative of a
larger portion species in the environment.
Page 200 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
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 ( J.S. EPA 201 le) and an ATSDR Toxicological Profile ((AT SDR. 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 (U.S. 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.
Page 201 of 748
-------
465
466
467
468
469
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The new literature was screened against inclusion criteria in the PECO statement and the relevant
studies (e.g., useful for dose-response)15 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 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) 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-HQ-OPPT-2019-0500].
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).16 PODs were adjusted as appropriate to conform to
the specific exposure scenarios evaluated.
15 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.
16 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.
Page 202 of 748
-------
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
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 3.2.2.1 and [PBPKModelandReadMe (zipped). Docket: EPA-HQ-OPPT-2019-0500]). 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 ( 1).
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 (Setgrade and Gilmour. 2010).
which was not included in the 2014 TCE Risk Assessment ( 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.
).
3.2.2 Toxicokinetics
The toxicokinetics and PBPK modeling of TCE were thoroughly discussed in the 2014 Risk Assessment
(I £014b). This discussion is summarized below.
TCE is fat soluble (lipophilic) and easily crosses biological membranes. Though there are
quantitative differences across species and routes, TCE is readily absorbed into the body
following oral, dermal, or inhalation exposure. Because of its lipophilicity, TCE can cross the
placenta and also passes into breast milk ( ).
Absorption following inhalation of TCE is rapid and the inhaled absorbed dose is proportional to the
exposure concentration, duration of exposure, and lung ventilation rate. Therefore, for this risk
evaluation absorption of TCE is assumed to be 100% via inhalation. Likewise, TCE is
rapidly absorbed from the gastrointestinal tract into the systemic circulation (i.e., blood)
following oral ingestion. Oral absorption of TCE has been shown to be influenced by dose of the
chemical, the dosing vehicle and stomach contents. Absorbed TCE is first transported to the
liver where it is metabolized for eventual elimination (i.e., "first-pass effect") ( ).
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) ( ). 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). Consumer exposure was only evaluated for scenarios that may
involve dermal contact with impeded evaporation using a skin permeability model with a dermal
permeability coefficient of 0.019 cm/hr (Section 2.3.2.4.1).
Regardless of the route of exposure, TCE is widely distributed throughout the body. TCE levels
can be found in many different human and rodent tissues including: brain, muscle, heart,
Page 203 of 748
-------
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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).
The metabolism of TCE has been extensively studied in humans and rodents (U.S. EPA. 201 le).
Animals and humans metabolize TCE to metabolites to varying degrees. These metabolites are known to
play a key role in causing TCE-associated toxic effects. TCE metabolites are known to target the liver
and kidney. The two major metabolic pathways are (1) oxidative metabolism via the cytochrome P450
(CYP) mixed function oxidase system and (2) glutathione (GSH) conjugation followed by further
biotransformations and processing with other enzymes. The liver is the major tissue for the oxidative
and GSH conjugation metabolic pathways. Both pathways are saturable, and above the saturable
concentration/dose, TCE is excreted unchanged in expired air. Table 3-3 presents the important
metabolites formed following both the CYP (oxidation) and GSH (conjugation) pathways in humans and
animals. The amount and types of metabolites formed are important for understanding the toxicity of
TCE in both animals and humans.
These major TCE metabolites as well as a number of minor metabolites are also observed in the
metabolic pathway 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 (U.S. EPA. 201 le).
Table 3-3 TCE Metabolites Identified by Pathwa'
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).
Page 204 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Metabolites^^^
Tetrachloro-
ethane
Trichloro-
ethane
Dichloro-
ethylene
Dichloro-
ethane
Oxalic acid
X
X
X
Chloral
X
X
Chloral hydrate
(CH)
X
X
Monochloroacetic
acid
X
X
X
X
X
X
Dichloro acetic
acid (DCA)
X
X
X
X
Dichloro acetic
acid (TCA)
X
X
X
X
Trichloroethanol
(TCOH)
X
X
X
X
Trichloroethanol-
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. 201 le).
Compared to the CYP oxidation pathway, there appear to be more significant sex and species
differences in TCE metabolism via the GSH pathway (U.S. EPA. 201 le). Animal data show that rates of
TCE GSH conjugation in male rats/mice are higher than females. According to some in vitro data, the
rates of DCVG production in liver/kidney cytosol are highest in humans, followed by mice, and then
rats. In vitro data also suggest that y-glutamyl transpeptidase (i.e., GGT, an enzyme involved in DCVC
production) activity in kidneys seems to be highest in rats, then humans, and then mice (U.S. EPA.
201 le). Furthermore, species-dependent enzymatic activities have been reported for the P-lyase and
FM03 enzymes (U.S. EPA. 201 leV
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 (U.S. EPA. 201 le).
Varying rates of TCE pulmonary excretion in humans have been observed in different studies (Chiu et
al.. 2007; Opdam. 1989; Sato et al.. 1977). The relatively long terminal half4ives observed (up to 44
hours) suggest that the lungs require considerable time to completely eliminate TCE, primarily due to
high partitioning to adipose tissues (U.S. EPA. 201 le). 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
Page 205 of 748
-------
616
617
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
elimination half-lives of just over 50 hours in humans and only approximately 16 hours in rats (Ikeda
and Imarrmra. 1973).
3,2,2,1 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 ( 006). Predictions of internal dose in chemical risk assessments are achieved by
employing PBPK modeling.
PBPK models use a series of mathematical representations to describe the absorption, distribution,
metabolism and excretion 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. 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.
06).
U.S. EPA developed a peer-reviewed comprehensive Bayesian PBPK model-based analysis of TCE and
its metabolites in mice, rats and humans ( ). 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
model files, including inputs and outputs of all model runs, see [PBPKModel andReadMe (zipped).
Docket: EPA-HQ-OPPT-2019-0500].
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 ( ). 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).
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.
Table 3-5 List of All of the PBPK-Modeled Dose Metrics Used in the TCE IRIS Assessment
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
Page 206 of 748
-------
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 W3 4
Total amount of TCE oxidized per unit adjusted body weight
TotTCAInBW
Total amount of TCA produced
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. 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 ( ).
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 dose Points of
Departure (idPODs) for the applied dose PODs (i.e., LOAEL, NOAEL, or BMDL) that were identified
in the TCE IRIS assessment. 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 ( ).
Page 207 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
675
676
677
678
679
Distribution (separate
uncertainty and variability)
Figure 3-4 Dose-Response Analyses of Rodent Non-Cancer Effects Using the Rodent and Human
PBPK Models
Notes: Figure adapted from Figure 5-2 (Chapter 5, TCE IRIS assessment) ( IS. EPA. 201 le). Square nodes indicate point
values, circle nodes indicate distributions and the inverted triangle indicates a (deterministic) functional relationship.
Page 208 of 748
-------
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
Rodent internal
dose
Uncertain *
variant*
¦ d,sW^llon
Human internal
dose'
Uncertainty &
variability
distribution
id POD J
Human inhalation
^exposure (ppm)
, Lower 99*
percentile
=HECc
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.
Figure 3-5 Example of HEC99 Estimation through Interpecies, Intraspecies and Route-to- Route
Extrapolation from a Rodent Study LQAEL/NOAEL
The rodent population model was designed to characterize study-to-study variation and used median
values of dose-metrics to generate idPODs. The rodent PBPK model did not characterize variation
within studies and assumed that the rodent idPODs were for pharmacokinetically identical animals. The
basis of that assumption was that animals with the same sex/species/strain combination were considered
pharmacokinetically identical and represented by the group average. In practice, the use of median or
mean internal doses for rodents did not make much difference except when the uncertainty in the rodent
dose-metric was high ( J.S. EPA. 2011 e).
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 ITEC/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 forgiven administered doses of TCE. This risk evaluation presents both HEC/HED50 and
HEC/HED99 POD values.
Page 209 of 748
-------
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
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3.2.3 Hazard Identification
3.2.3.1 Non-Cancer Hazards
EPA previously identified human health hazard for the below endpoints in ( ) and (
E ). 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 ( ). 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-HQ-OPPT-2019-0500],
3,2,3.1.1 Liver toxicity
Animals and humans exposed to TCE consistently experience liver toxicity. Specific effects include
the following structural changes: increased liver weight, increase in deoxyribonucleic acid (DNA)
synthesis (transient), enlarged hepatocytes, enlarged nuclei, and peroxisome proliferation.
The role of metabolites is important but not well understood. Many investigators have dosed animals
with TCE, as well as with many of its metabolites to 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.
1 ).
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,
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 (\ v < < \ IVI 1^). 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 ( ). A case study published after the 2011 IRIS Assessment reported
TCE hypersensitivity-induced liver damage (Jung et ai.! ).
Animal Data
The 2014 TSCA Work Plan Chemical Risk Assessment ( >) 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 ( ). 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'Flaht 85) and for up to 120 days via inhalation (Woolhiser et ai. 2006;
Kiellstrand et ai. 1983). Hypertrophy, histopathology, cytotoxicity, and altered serum biochemistry
were also observed in mice in (Buben and O'Flal 985) and (Kiellstrand et ai. 1983). Increased
liver weight was additionally observed in (Boverhof et ai. 2013). identified in the EPA literature
Page 210 of 748
-------
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
search, following 6hr/day inhalation exposure to a single concentration level (lOOOppm) 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 ( ). 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 ( ).
EPA did not identify new any 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.
E 14b).
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 ( )
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 confounding mortality. 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
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, with possible roles for TCOH and TCA (I v H \ JO I I ^). Increased
relative kidney weight compared to body weight was also observed in both mice and rats following
inhalation exposure over several weeks to months (Boverhof et al. JO I Woolhiser et ai. 2006;
Kiellstrand et al.. 1983).
3.2.3.1.3 Neurotoxicity
Neurotoxicity has been demonstrated in animal and human studies under both acute and chronic
exposure conditions ( ). 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 al. ^ ) 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 ( 14b). Therefore, EPA primarily relied on the previous hazard conclusions.
Page 211 of 748
-------
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (
201leV
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 al. 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.
* * \
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 ( ). Additionally, several newer
epidemiological studies have found an association between TCE exposure and neurodegenerative
disorders such as Amyotrophic Lateral Sclerosis (Bove et al.. 2014a) and Parkinson's disease (Bove et
al.. 2014b; Goldman et al.. ).
Animal Data
The 2014 TSCA Work Plan Chemical Risk Assessment ( i) 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
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 (Arito et al.. 1994) and demyelination of the
hippocampus following 8 weeks of drinking water exposure (Isaacson et z 3) in rats. Neuronal
degeneration (Gash et al.. 2008) and diminished sciatic nerve regeneration (Kiellstrand et al.. 1987)
were also observed following TCE exposure in rodents, however those studies scored Low and
Unacceptable, respectively in data quality evaluation. More recent studies have observed both sedative
(Wilmer et al.. 2014) and stimulatory effects (Shelton and Nicholson. 2014) 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 al.. 2010).
3.2.3.1.4 Immunotoxicity (including sensitization)
Immune-related effects following TCE exposures have been observed in both animal and human
studies. In general, these effects were associated with inducing enhanced immune responses as
opposed to immunosuppressive effects. Of concern are the immune-related and inflammatory effects
reported in TCE-exposed animals and humans. These effects may influence a variety of other
conditions of considerable public health importance, such as cancer and atherosclerosis (U.S. EPA.
201 le).
Page 212 of 748
-------
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EPA's literature search identified a single acute inhalation study in rats that identified a novel endpoint
for impaired response to infection (Selerade and Gilmour. 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 ( ). Therefore, EPA primarily relied on the previous hazard
conclusions for all other endpoints.
Human Studies
Studies have reported a relationship between systemic autoimmune diseases, such as scleroderma, and
occupational exposure to TCE. The TCE IRIS assessment (\ v < < \ JO I I 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 gender 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 gender-related
differences in exposure prevalence, the reliability of the exposure assessment, gender-related
differences in susceptibility to TCE toxicity or chance (\ c. < ^ \ JO i I ^).
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. 201 le). These findings
were supported by studies in mice (described below) in which short exposures to TCE resulted in
increased levels of inflammatory cytokines.
The epidemiological database also provides evidence of immunosuppression based on reduced IgG
antibody levels in TCE-exposed workers (Zhang et at.. 2013).
Animal Data
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 ( ). Key studies identified evidence of autoimmunity from chronic
TCE exposure in both non-autoimmune prone (Keil et at.. 2009) and autoimmune prone (Kaneko et at..
2000) mice. Evidence of localized immunosuppression has also been reported in mice and rats
(Boverhof et at.. 2013; Woolhiser et at.. 2006; Sanders et at.. 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 (Kelt et at.. 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 (S el grade and Gitmour. 2010).
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 in 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
Page 213 of 748
-------
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
effects). 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 (U.S. EPA. ).
3.2.3.1.5 Reproductive toxicity
Both the epidemiological and animal studies provide suggestive, but limited, evidence of adverse
outcomes to female reproductive outcomes. However, much more extensive evidence exists in support
of an association between TCE exposures and male reproductive toxicity ( ).
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 ( ).
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.
).
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 (I v << \ JO lie).
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 (I v << \ ). 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 (Puteaux 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 ( ). 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).
Page 214 of 748
-------
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3.2.3.1.6 Developmental Toxicity
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 assessment and more
recent studies.
For developmental toxicity other than congenital heart defects EPA did not identify any repeat-dose
experimental studies in animals or human epidemiological studies that would contribute significant
additional information for this hazard. Therefore, EPA relied primarily on conclusions from the 2014
TSCA Work Plan Chemical Risk Assessment ( b) for these other endpoints. For
congenital heart defects, EPA evaluated more recent epidemiological studies, mechanistic studies, and
a single experimental animal study that provide conflicting evidence for this endpoint.
Human Data
The 2014 TSCA Work Plan Chemical Risk Assessment ( •) 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 (\ ^ \ 1:0 i 1^).
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 ( ).
There have been some epidemiological studies that have consistently reported an increased incidence of
birth defects in TCE-exposed populations. For instance, ATSDR has conducted studies at Camp
Lejeune, North Carolina, where individuals were exposed to VOC-contaminated drinking water
(Ruckart et ai. 2014. 2013). TCE was one of the main contaminants found in the drinking water.
Ruckart et al. found an association between neural tube defects and TCE exposure above 5 ppb during
the first trimester of pregnancy, however either negative or null associations were identified between
TCE exposure and other developmental effects (e.g., reduced birth weight, oral cleft defects). Yauck et
al. (2004) observed a strong relative risk estimate for cardiac malformations in infants from Milwaukee,
Wisconsin born to TCE-exposed mothers aged 38 years or older. In addition to older age, increased risk
was also independently associated with other confounders including alcohol use, hypertension, and
diabetes. Forand et al., ( ) (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
Page 215 of 748
-------
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
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
developmental effects. One study reported increased risk of spina bifida to offspring of TCE-exposed
mothers (Swartz et al. 2015). and both statistically significant and non-significant associations have
been observed between exposure to the TCE metabolites trichloracetic acid and trichloroethanol with
various outcomes including oral clefts, urinary tract malformations, and limb defects (Cordier et al..
2012). In contrast, (Brender et al.. 2014) found no statistically significant association with neural tube
defects, spina bifida, anenocephaly, any oral cleft, cleft palate, cleft lip with or without cleft palate, any
limb deficiency, or longitudinal or transverse limb deficiencies. The study did identify an increased risk
of septal heart defects (see below section) in older mothers, however. 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 birth defects and
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 ( '1 le).
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.. 200.'; Riwsom et al..
1993). Healy et al. (1982) observed increased resorptions, skeletal abnormalities, and decreased fetal
weight, but the study scored Unacceptable in data quality evaluation. Some of the observed effects
appear to be strain-specific ( ). Among newer studies identified in the EPA literature
search, 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.
Congenital Heart Defects
In vivo animal studies in rats and chicks have identified an association between TCE exposures and
cardiac defects17 in the developing embryo and/or fetus ( ). The 2014 TSCA Work
Plan Chemical Risk Assessment (U.S. EPA. 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 TCE
exposure (Brender et al JO I I; I < irand et al.. 2012; Goldberg et al.. 1990). 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
17 "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.
Page 216 of 748
-------
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
mi
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
1079
1080
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
cardiac and cardiovascular effects, there was a very high background incidence of cardiovascular defects
in soybean oil-control rats and the authors did observe a 19% increase in cardiac-specific defects (per-
litter, 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. 2019). Several epidemiological studies also report either negative (Lagakos et ai. 1986) or
equivocal (Yauck et al. 200 i; Hove et al. 1995) statistical associations between TCE exposure and
heart defects. Gilboa et al. (2012) identified a statistically significant association of perimembranous
ventricular septal defects with exposure to chlorinated solvents as a class, but not to TCE alone.
In previous assessments EPA concluded that the weight of evidence supports TCE exposure posing a
potential hazard for congenital malformations, including cardiac defects in offspring (Makris et al.. 2016;
I v i i \ ^ n lb, 201 I ^). Given both the conflicting results and the publication of newer animal,
epidemiological, and in vitro studies since the completion of the 2014 TCE Risk Evaluation, EPA re-
evaluated the weight of evidence for congenital heart defects (see Section 3.2.4.1.6 and Appendix G).
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).
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. ( ).
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,
Page 217 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
blistering, and burning sensations. Eye effects and CNS effects also resulted following simultaneous
vapor inhalation along with percutaneous penetration. Skin irritation potential varied greatly among
individuals in volunteer studies, with some exhibiting extreme pain and others hardly reporting any
effects. Studies on both humans and animals demonstrate that TCE is a moderate skin sensitizer, with
hypersensitivity reactions observed following exposure to both TCE and various metabolites.
3.2,3.2 Genotoxicity and Cancer Hazards
3.2.3.2.1 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
(IARC. 2014). 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,( ). 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 ( ).
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 (\ v < < \ JO I I ^).
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 ( )
3.2.3.2.2 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
increase in risk". Multiple TCE metabolites (i.e., and thus pathways) likely contribute to TCE-induced
liver tumors ( ).
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 ( )) 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 ( ).
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 ( ).
Page 218 of 748
-------
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
3.2.3.2.3 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 (I v < < \ 201 I e).
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) ( ). 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 (I. c. < ^ \ 201 I ^).
3.2.3.2.4 Other cancers
Reproductive System
The effects of TCE on cancers of the reproductive system have been examined for males
and females in both epidemiological and experimental animal studies. The epidemiological
literature includes data on prostate in males and cancers of the breast and cervix in females. The
experimental animal literature includes data on prostate and testes in male rodents; and uterus,
ovary, mammary gland, vulva, and genital tract in female rodents. The evidence for these cancers is
generally not robust ( ).
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 ( ).
3.2.4 Weight of Scientific Evidence
3.2.4.1 Non-Cancer Hazards
The EPA literature search ( ) 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 ( ) and the 2014 TSCA Work Plan
Chemical Risk Assessment ( 3).
3.2.4.1.1 Liver toxicity
The EPA literature search ( ) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
Page 219 of 748
-------
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 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 ( ) 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 consistently observe induction of kidney
toxicity (e.g., damage to renal tubules and nephropathy) and progression of existing kidney disease.
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 ( ) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
In addition to anesthetic effects at high concentrations, human evidence concludes that TCE exposure
induces abnormalities in trigeminal nerve function, and TCE exposure has also been associated with
neurodegenerative disorders. These effects have been confirmed in animal studies which additionally
demonstrate a variety of neurological effects from TCE exposure. Overall, neurotoxicity following TCE
exposure is supported by the weight of evidence. Therefore, this hazard was carried forward for dose-
response analysis.
3.2.4.1.4 Immunotoxicity
The EPA literature search ( ) did not identify any new evidence that significantly
contributes to or challenges the previously established weight of evidence (WOE) for this hazard.
Both animal and human studies demonstrate that TCE exposure can result in either autoimmune
responses or immunosuppression. There is also evidence of both systemic and localized hypersensitivity
resulting in skin sensitization and autoimmune hepatitis. Selgrade et al (2010) demonstrated reduced
response to respiratory infection. There are no other reasonably available studies that examined respiratory
immunotoxicity, however this endpoint is consistent with other data on immunosuppression. Overall,
immunotoxicity following TCE exposure is supported by the weight of evidence. Therefore, this hazard
was carried forward for dose-response analysis, including both systemic and respiratory endpoints.
There is only qualitative information available for sensitization and hypersensitivity, so this hazard was
not carried forward for dose-response analysis.
3.2.4.1.5 Reproductive toxicity
The EPA literature search ( ) 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 strong evidence for male reproductive effects from TCE. Effects
observed include effects on sperm, male reproductive organs, hormone levels, and sexual behavior.
There is insufficient evidence for determining whether TCE contributes to female reproductive toxicity.
Page 220 of 748
-------
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Overall, male reproductive toxicity following TCE exposure is supported by the weight of evidence.
Therefore, this hazard was carried forward for dose-response analysis.
3.2.4.1.6 Developmental Toxicity
The EPA literature search ( ) 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.
Overall, based on suggestive epidemiologic data and fairly consistent laboratory animal data,
developmental toxicity following TCE exposure is supported by the weight of evidence. Therefore, this
hazard was carried forward for dose-response analysis.
Developmental toxicity endpoints will be considered for both acute and chronic scenarios. Although
developmental studies typically involve multiple exposures, they are considered relevant for evaluating
single exposures because 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 ai. 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 ( ). 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 (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
Page 221 of 748
-------
1262
1263
1264
1265
1266
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
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
raw scoring data; however, many of those concerns have been alleviated by subsequent communications
to EPA (Johnsoi 08). The positive findings reported in (Dawson etal.. 1993) and (Johnson et.
al.. 2003) have not been confirmed by another laboratory, so controversy over the results remains. When
considering the totality of information provided (not only what was in the initial publications), both
(Dawson etal.. 1993) and (Johnson et. al.. 2003) received a Medium in data quality evaluation.
EPA previously published weight of evidence (WOE) analyses both as part of the 2014 TCE Risk
Assessment and as a peer-reviewed journal article (Makris et al. 2016). which concluded that the
totality of data does support congenital heart defects as a human health hazard for TCE. These WOE
analyses utilized modified Bradford-Hill criteria (Hill. 1965) to evaluate the overall evidence for
causality following study quality review. Recently, (Wikoff et al.. 2018) published a WOE analysis
focusing only on animal and epidemiological data that came to the opposite conclusion using a Risk of
Bias assessment for internal study validity. During the development of this risk evaluation, EPA
received a study sponsored by the Halogenated Solvents Industry Alliance (HSIA) (Charles River
Laboratories. 2019) that attempted to replicate the (Johnson et al.. 2003) study, examining the incidence
of developmental cardiac defects following administration of TCE to rats via drinking water. This study
was subsequently peer reviewed and published in the scientific literature.
Charles River Study
Charles River Laboratories (2019) performed a developmental toxicity study according to principles of
Good Laboratory Practice. The study authors administered TCE to pregnant Sprague-Dawley rats via
drinking water at concentrations of 0 ppm, 0.25 ppm, 1.5 ppm, 500 ppm, and 1000 ppm in reverse
osmosis-filtered water from gestation day 1 through 21. Retinoic acid (RA) served as the positive
control and was administered via gavage (3mg/ml, 5mg/kg-bw) on gestation days 6-15. The study
authors did not observe a statistically significant increase of interventricular septal defects in TCE-
treated fetuses (2.4% in negative control, 3.7% at highest dose) or any other types of cardiac defects
identified in the study.
While the results of the Charles River study ( ) results appear to contradict the results observed by
(Johnson et al.. 2003) and (Dawson et al.. 1993). EPA concludes that the Charles River study
methodology was likely of reduced sensitivity and therefore does not entirely replicate the study
conditions of those earlier studies. 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 al.. 2003). Considering total VSDs, 3.5% of fetuses showed a VSD in
Charles River vs 3.8% in Johnson at the highest dose, with 1.5% in Charles River vs 2.2% in Johnson at
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.
Page 222 of 748
-------
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
As further indication of the potentially limited 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 ai. 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 at.. 2003) and (Dawson etai. 1993)
(including atrial septal and valve defects). Therefore, (Charles River Laboratories. 2019) insufficiently
replicates the methodology of (Johnson et ai. 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. ) received a Medium in data quality evaluation, the same as (Dawson et ai.
1993) and (Johnson et ai. 2003). For a more detailed analysis of the (Charles River Laboratories. 2019)
study, see Appendix G. 1.
While (Charles River Laboratories. 2019) was not considered a close enough replication to (Johnson et
ai. 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.
WOE Analysis
In order to address the conflicting results of the previous WOE assessments ( Jkris
et ai. 2016; Wikoff et ai. 2018). in support of this risk evaluation EPA performed another WOE
analysis. This analysis included all relevant primary literature cited in (Makris et ai. 2016). the 2014
TCE Risk Assessment (U.S. EPA. 2014b). and any additional on-topic studies identified in the
systematic review literature search ( i). Additionally, EPA also incorporated any newer
studies published after the end date of the literature search, including an in vitro mechanistic study
(Harris et ai. 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. ( 2016i)1. which advocates presenting evidence on a semiqualitative scale
on the basis of three evidence areas: reliability, outcome/strength, and relevance (see Appendix G.2.1 for
more details on selection of approach and methodological details).
In short, the overall grade for each study was defined by the lowest-amplitude score of each evidence
area, and those overall study grades were integrated to select a representative overall summary score for
each line of evidence (epidemiological, in vivo, or mechanistic). Independently, the area scores of each
study were averaged to obtain integrated areas scores for each line of evidence, however these were not
used to determine the overall summary score. Functionally, this scoring methodology is similar to that
used by (Wikoff et ai. 2018). although that analysis focused on data quality reliability through a risk of
bias assessment. Importantly, (Wikoff et ai. 2018) did not evaluate any mechanistic data, which may
explain the different overall conclusions between that study and this analysis. Importantly, this WOE
assessment also incorporated data on TCE metabolites, which are believed to be the toxicologically
active agent for many of the observed cardiac effects as well as other developmental outcomes.
The overall weight-of-evidence for TCE-induced congenital cardiac defects is presented in Table 3-6.
Epidemiological, toxicological and mechanistic studies were available. The epidemiology studies as a
group provide suggestive evidence for an effect of TCE on cardiac defects in humans (summary score of
+). Oral in vivo studies provided ambiguous to weakly positive (0/+) results for TCE itself, but positive
results for its TCA and DCA metabolites (+), while inhalation studies contributed negative evidence (-).
Page 223 of 748
-------
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ++).
The database overall was determined to be both reliable and relevant. Integration of the three evidence
areas resulted in an overall summary score of (+), demonstrating positive overall evidence that TCE may
produce cardiac 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 G.2 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-HO-OPPT-2019-0500].
Table 3-6. Overall Summary Scores by Line of Evidence for Cart
Evidence Area
Summary
Score
Epidemiology studies
+
In vivo animal toxicity studies
0
Mechanistic studies
++
Overall
+
iac Defects from TCE
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).
Mode of Action
A number of studies have been conducted to elucidate the mode of action for TCE-related cardiac
teratogenicity. During early cardiac morphogenesis, 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
2+
following TCE exposure. For example, studies have reported TCE-related alterations in cellular Ca
fluxes during cardiac development (Caldwell et al.. 2008; Selmin et al.. 2008; Collier et al.. 2003).
Of note, early stages of cardiac development are quite similar across various species (Makris et al.. 2016).
Therefore, these mechanistic data provide support to the plausibility of TCE-related cardiac effects in
humans (U.S. EPA. 201 le). EPA also notes that 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.
Page 224 of 748
-------
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ai. 2010). Specifically, TCE exposure induced expression of oxidative stress genes (Makwana et al..
2010) and increased DNA hypermethylation of a calcium-ATP pump promoter in developing cardiac
tissue (Palbykin et al.. 2011) only at lower and not higher doses, resulting in multimodal calcium
responses (Caldwell et al.. 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 al.. 2013)). In (Harris et al.. 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 al.. 2003). whereby toxicological outcomes present at different doses equating to either
inhibition or activation of particular gene expression (Harris et al.. ). This differential gene
expression would in turn lead to dose-specific downstream metabolic and phenotypic effects.
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 al..
2016). Therefore, this endpoint was carried forward for dose-response analysis.
3.2.4.1.7 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 Gilmour. 2010). Other acute studies described
above were not selected for assessing acute risks due to a lack of sufficient dose-response information.
3.2.4.2 Cancer Hazards
Meta-analyses were performed in the 2011 EPA TCE IRIS Assessment (Appendix C, (
2( )) 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 ( ) also cited other lines of supporting evidence
for TCE carcinogenicity in humans by all routes of exposure:
Page 225 of 748
-------
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
"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. "
"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 ( >). 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 ( ). 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 ( ), the results presented below
represent a standalone, new analysis. See Appendix H for full details and results.
3.2.4.2.1 Meta-Analysis Results
The 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.Q ) 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.
Page 226 of 748
-------
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 at.. 2013). Studies that scored
high showed no heterogeneity of effects for NHL and kidney cancer, but moderate heterogeneity
remained for liver cancer.
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. 2014) 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, cancer was 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 (U.S. EPA. 2 ).
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. Therefore, a causal or predictive link between
cytotoxicity and carcinogenicity cannot be established. There is inadequate experimental support for
other potential MO As such as peroxisome proliferator activated receptor alpha (PPARa) induction, a2\i-
globulin nephropathy, and formic acid-related nephrotoxicity (\ c. < ^ \ JO I 1^).
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 (I v << \ _\"W 1^).
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
Page 227 of 748
-------
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
vitro and in vivo. 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. (
201leV
PPARq receptor activation
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, and some data suggests that PPARa activation may not be sufficient for
carcinogenesis. The reasonably available data clearly supports a role of PPARa activation in liver
turn oogenesis, however any key causal effects are likely mediated by multiple mechanisms and neither
causality, sufficiency, or necessity of PPARa signaling in liver carcinogenicity can be established (U.S.
* * \ \^).
Other mechanisms
There is limited evidence for a tumorigenic role of increased liver weight, growth selection, cytotoxicity,
oxidative stress, and/or glycogen accumulation. Heritable epigenetic changes such as altered DNA
methylation patterns, which disrupt the balance of gene expression and may lead to over- or 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 based on choline/methionine deficiency studies, however the applicability of
this mechanism to TCE-induced carcinogenesis is unknown as these changes could either be causally or
consequentially related to carcinogenicity (U.S. EPA. 201 le).
Conclusions
The reasonably available data is inadequate to support any singular MOA. TCE-induced liver
carcinogenesis appears to be very complex and likely involves multiple contributing mechanisms. The
strongest evidence exists for involvement of both genotoxicity and PPARa activation, however a causal
relationship cannot be established because the dose levels required to elicit outcomes through both
MO As are higher than those demonstrating tumorigenic activity ( ).
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 ( 05), 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 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).
Page 228 of 748
-------
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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 ai. 2009; Van Raaii 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
Page 229 of 748
-------
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(Selerade 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 ( ) determined that the studies
of (Woolhiser et ai. 2006; Bub en and O'Flaherty. 1985; 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'Flal 985) 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.
Docket: EPA-HQ-OPPT-2019-0500] 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 al.. 1986).
(NCI. 1976) and (NTP. 1988) reported histological changes in the kidney, whereas (Kiellstrand et al..
1983) and (Woolhiser et al.. 2006) reported increased kidney/body weight ratios (\ c. \ 1* \ JO I I ^).
NCI (1976) scored Unacceptable in EPA's data quality evaluation [Data Quality Evaluation of Human
Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] 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 al.. 1991) was the only epidemiological study that the IRIS
program deemed suitable for further evaluation in the TCE's dose-response assessment for
neurotoxicity. Only the following four animal studies were considered suitable for dose-response
analysis for the neurotoxicity endpoint in the 2014 TSCA Work Plan Chemical Risk Assessment (U.S.
EP \ .014b): ( \iiio et al.. 1994). (Isaacson et al.. 1990). (Gash et al.. 2008). and (Kiellstrand et al..
1987). Kj ell strand (1987) scored Unacceptable in in EPA's data quality evaluation [Data Quality
Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] and therefore was
excluded from dose-response analysis. Gash et al. (2008) 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.
3.2.5.1.4 Inuminotoxicity
Only the following four animal studies were suitable for the 2014 TSCA Work Plan Chemical Risk
Assessment ( 14b) non-cancer dose-response analysis for the immunotoxicity endpoint:
(Keil et al.. 2009). (Kaneko et al.. 2000). (Sanders et al.. 1982). and (Woolhiser et al.. 2006). For this
Risk Evaluation, EPA also assessed the endpoint of acute immunosuppression observed in (S el grade
and Gilmour. 2010). In Selgrade et al (2010). mice were infected via respiration with aerosolized S.
zooepidemicus bacteria following 3h TCE exposure. Mortality, bacterial, clearance from the lung,
Page 230 of 748
-------
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
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
1728
1729
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
percent of mice infected, and phagocytic index were assessed following co-exposure. Mortality was
selected as the most statistically sensitive endpoint due to a larger numbers of mice per exposure group
and more dose groups, however "percent of mice infected" was also considered for dose-response
analysis (Appendix F.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-HQ-OPPT-2019-0500] 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 ( b): (Kumar et al.. 2000). (Kumar et al..
2001). (Kan et al.. 2007). (Xu et al.. 2004). (Narotskv et al.. 1995). (George et al.. 1986). (Duteaux et
al.. 2004). and (Forkert et al.. 2002). Forkert et al. (2002) scored Unacceptable in EPA's data quality
evaluation and therefore was excluded from dose-response analysis, however it had the same POD as
(Kan et al.. 2007). which scored Medium. Duteaux et al. (2004) scored a Low for data quality and was
not carried forward to dose-response analysis given the other, higher quality studies available. The
remaining studies all scored Medium or High for data quality [Data Quality Evaluation of Human
Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] and were therefore utilized for 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).
Although the focus of the discussion below is on these 5 studies and corresponding endpoints, it is
important to mention that developmental immunotoxicity has also been demonstrated in TCE-treated
animals. The most sensitive immune system response was reported by (Peden-Adams et al.. 2006). 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 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 ( ).
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-HQ-OPPT-2019-0500] due to concerns over statistical reliability and dose
precision (difficult to calculate precise dosage). Additionally, it could not be accurately PBPK modeled
because exposure occurred in utero, through nursing, and after weaning. Therefore, this study was not
considered further for dose-response assessment, although developmental immunotoxicity will still be
considered qualitatively.
Pre- and Postnatal Mortality and Growth
The following two studies were considered suitable for non-cancer dose-response analysis for pre- and
Page 231 of 748
-------
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
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
postnatal mortality and growth effects in the 2014 TSCA Work Plan Chemical Risk Assessment (H.S.
B1 \ .014b): (Healv et ai. 1982) and (Narot> i v\ ^ rs°5). Healy et al. (1982) scored Unacceptable
in in EPA's data quality evaluation [Quality Evaluation of Human Health Hazard Studies. Docket:
EPA-HQ-OPPT-2019-0500] and therefore was excluded from dose-response analysis. (Narotsky et al..
1995) scored a High and was therefore utilized for dose-response analysis.
Developmental Neurotoxicity
There is evidence of alterations in animal brain development and in behavioral parameters (e.g.,
spontaneous motor activity and social behaviors) following TCE exposure during the development of
the nervous system. Among all of the 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-HQ-OPPT-2019-0500] 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
effects domains evaluated in the TCE IRIS assessment. Johnson et al. (Johnson et al.. 2003) reported
data from different experiments over a several-year period in which pregnant Sprague-Dawley rats (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).
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 was 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-HQ-OPPT-2019-0500], which incorporated all available information on the two studies,
including subsequent errata and communications to EPA (Johnson et al.. 2014: Johnson. 2014. 2008:
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
- protocol for unanimous confirmation of any observed cardiac defects by the three
principle investigators
Page 232 of 748
-------
1778
1779
1780
1781
1782
1783
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
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Because both studies passed data evaluation with the same score and statistics could only be
performed using a pup as the statistical unit for (Dnwson et ai. 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 H) 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 ( v «« \ . ' i i ) and cited in the 2014 TSCA Work Plan Chemical
Risk Assessment ( 014b). A linear non-threshold assumption was applied to the TCE cancer
dose-response analysis because there is sufficient evidence that TCE-induced kidney cancer operates
primarily through a mutagenic mode of action while it cannot be ruled out for the other two cancer types.
The 2011 IRIS Assessment (U.S. EPA. 201 le) selected the epidemiological kidney cancer data
Charbotel et al (2006) as the best representative dose-response data for derivation of an oral slope factor
and inhalation unit risk value as a case-control study with quantitative cumulative exposure estimates
based on a task-exposure matrix based on decades of measurement. Charbotel et al (2006) received a
High score for 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-OPPT-2019-0500]. 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 ( 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 susceptible to exposure to TCE. Factors
affecting susceptibility examined in the available studies on TCE include lifestage, gender, genetic
polymorphisms, race/ethnicity, preexisting health status, lifestyle factors, and nutrition status. Factors
Page 233 of 748
-------
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
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
that affect early lifestage susceptibility include exposures during gestation, such as transplacental
transfer, and during infancy, such as breast milk ingestion, early lifestage-specific toxicokinetics, and
early lifestage-specific health outcomes including developmental cardiac defects. Gender-specific
differences also exist in toxicokinetics (e.g., cardiac outputs, percent body fat, expression of
metabolizing enzymes) and susceptibility to toxic endpoints (e.g., gender-specific effects on the
reproductive system, gender differences in baseline risks to endpoints such as scleroderma or liver
cancer). Genetic variation likely has an effect on the toxicokinetics of TCE. Pre-existing diminished
health status may alter the response to TCE exposure. Individuals with increased body mass may have
an altered toxicokinetic response due to the increased uptake of TCE into fat. Other conditions that may
alter the response to TCE exposure include diabetes and hypertension, and lifestyle and nutrition factors
such as alcohol consumption, tobacco smoking, nutritional status, physical activity, and socioeconomic
status ( ). 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 (Braider 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 (U.S. EPA. 201 le). 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 (\ v < < \ _:01 I e). 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 ( ). In the 2014 TCE risk
evaluation (U.S. EPA. 2014b). 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 evaluation, and EPA
utilized the same analysis for this assessment.
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 ( ) Note that the effects within the same health
effect domain were generally assumed to have the same relevant internal dose-metrics, with some
Page 234 of 748
-------
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exceptions. Compared to the 2014 TSCA Work Plan Chemical Risk Assessment, an additional POD
from Selgrade (2010) has also been added for acute exposure scenarios.
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 O 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 studies
were selected from each health domain /organ system that best represent 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 representative 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. 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 ) for more details on dose-metric and
benchmark response (BMR) determinations for all endpoints except that 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 at.. 2009; Van Raaii et at.. 2003; ) 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. Therefore, developmental endpoints were considered relevant for calculating risks associated
with acute occupational or consumer exposure. Single-exposure studies identifying a dose-responsive
specific health outcome were also considered for deriving PODs representative of risks following acute
exposures.
HECs for developmental toxicity were adjusted to reflect a 24-hr value, consistent with both
occupational and consumer exposure values. The POD from Selgrade (2010). a 3hr acute inhalation
study, was adjusted to a 24hr HEC value for occupational risk estimates due to limited 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 time periods.
Page 235 of 748
-------
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
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
1962
1963
1964
1965
1966
1967
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Developmental Toxicity Endpoints
— Prenatal Mortality
(Narotsky et al. 1995) was also discussed above in the reproductive toxicity section, but also
identified mortality to the developing fetus following in utero TCE exposure. F344 timed-pregnant
rats (8-12 dams/group) were treated with TCE by gavage during GD 6 to 15. The BMDLoi for
increased resorptions was 32.2 mg/kg-bw/day ( ).
— Developmental Neurotoxicity
(Fredriksson et al. 1993) treated male NMRI mouse pups (12/group, selected from 3-4 litters) with
TCE via gavage (0, 50, or 290 mg/kg-bw/day) during postnatal days (PND) 10 to 16. Locomotor
behavior was evaluated at PND 17 and 60. TCE-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 £014b). 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
G. 1.2.2).
The BMR selection from the 2014 TSCA Work Plan Chemical Risk Assessment ( )
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
4. Confidence in the model fit and variance
Page 236 of 748
-------
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( ). 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
ai. 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. The original reported p-value for the model fit was poor (p = 0.0129).
However, there were limitations in the way BMDS calculated p-values at that time (i.e., subgrouping
individual litter results) and limitations in the fitting of inter-litter correlations in the 2011 version of
BMDS. Accordingly, EPA conducted further modeling with this data in the original 2011 assessment
and with the latest version of BMDS:
• 2011 Re-analysis: An R program was applied which demonstrated an adequate model
fit (Appendix F in ( )). 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.
• New BMDS Analysis (2019): BMD modeling was re-run on the (Johnson et ai. 2003)
dataset using the latest version of the BMDS nested models (v3.1.1), which no longer
requires subgrouping litter data to calculate p-values. The resulting BMDLs and AICs
agreed with results in the 201 1 IRIS Assessment (U.S. EPA. 2 ). However, the p-
value of = 0.661 from the updated BMDS nested model run (Appendix N) is
significantly improved, demonstrating strong model fit and confirming 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 N.
Immunotoxicitv
— Immunosuppression (diminished response to infection)
In addition to the previously described developmental toxicity studies, (S el grade and Gilmour.: )
was deemed suitable for dose-response analysis of immunotoxicity based on observed decreased
response to infection. In Selgrade et al (2010). 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
Page 237 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2014 to this risk evaluation and was not previously evaluated for dose-response analysis in the 2014 TSCA
2015 Work Plan Chemical Risk Assessment (U.S. EPA. 2014b). This study was discussed in the 201 1 IRIS
2016 Assessment ( ) but was excluded from the 2014 Risk Assessment in an oversight.
2017
2018 For (S el grade and Gilmour. 20101 BMD modeling was performed on the endpoints of mortality and
2019 percentage of mice infected (see [Personal Communication to OPPT. Raw Data Values from Selgrade
2020 and Gilmour, 2010. Docket: EPA-HQ-OPPT-2019-0500]). A reliable BMDL could not be obtained from
2021 the percentage infected data because BMDs and BMDLs from all models were well below the lowest
2022 data point and cannot be considered reliable. For mortality, a BMR of 1% increase was selected due to
2023 the severity of the effect. Based on evidence of systemic chronic immunosuppression (Sanders et al.
2024 1982; Woolhiser et al.. 20061 this acute endpoint was applied to systemic exposure. Based on assumed
2025 ppm equivalence across species ( ), the BMDLi also serves as the HEC for 3hr
2026 exposure, while 1.74 ppm is the HEC for 24hr exposure. Route-to-route extrapolation and allometric
2027 scaling based on values from ( ;88) and subsequent allometric scaling results in a dermal
2028 HED of 2.74 mg/kg.
2029
2030 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
HECS0
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Develop-
mental
Effects
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
(Narotskv et
al, 1995)
High
(1.3)
Rat
(female)
22 days
throughout
gestation
(gestational
days 0 to 22)
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. 2003)
Medium
(1.9)
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=1;UFA=3;
UFh=3; UFl=10;
Total UF=100
(Fredriksson
et al, 1993)
Medium
(1.7)
Immune
System
Rat
(female)
3hr/day, single
dose; followed
by respiratory
infection
BMDL01 =
13.9 ppm
Immuno-
suppression
N/A4
N/A4
1.744
N/A4
2.744'5
UFS=1;UFA=3;
UFH=10; UFL=1;
Total UF=30
(Sekrade and
Gilmour,
2010)
High
(1.6)
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 UFS=subchronic to chronic LTF; UFA=interspecies UF; UFH=intraspecies LTF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] for full evaluation by metric.
4Data from Sekrade and Gilmour. 2010) was not subject to PBPK modeling due to uncertainty concerning the most appropriate dose metric. The BMDL value
adjusted for a 24hr exposure will be used as the POD for occupational risk estimates, while the 3hr value will be used for consumer risk estimates. This value is
presented in the HEC99 column but does not represent any particular percentile since it was not PBPK-modeled.
5 A dermal LIED was obtained through route-to-route extrapolation using breathing rate and body weight data on male CD-I mice (insufficient female data was
reasonably available) from ("U.S. EPA, 1988) and allometric scaling based on (U.S. EPA, 201 Id) using a dosimetric adjustment factor of 0.14 for mice.
2031
2032 Table 3-7 presents the derived PODs from all studies considered for dose-response analysis of acute
2033 exposure scenarios. EPA selected studies representative of the distinct endpoints of prenatal mortality,
2034 congenital defects, developmental neurotoxicity, and response to infection. Most of the developmental
2035 toxicity studies utilized the PBPK dose metric of TotMetabBW34, or the total amount TCE metabolized
2036 per unit adjusted body weight. This dose metric was selected because for these endpoints there is
2037 insufficient information for site-specific or mechanism-specific determinations of an appropriate dose-
2038 metric, however in general TCE toxicity is associated with metabolites rather than the parent compound.
2039 TotOxMetab34, or the total amount TCE oxidized per unit adjusted body weight, was used for deriving
2040 HEC/HED values for congenital heart defects because evidence demonstrating effects from TCA and
Page 238 of 748
-------
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
DCA (see Section 3.2.4.1.6) suggests that oxidative metabolism is important for TCE-induced heart
malformations.
The LogProbit model was selected for BMD modeling results of (Selerade and Gilmour. 2010) data
because it was the model with the lowest AIC, using a BMR of 1% based on the endpoint of mortality.
Data from (Narotsky et al. 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
3.2.2.1 and (U.S. EPA. ) 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 (S el grade and Gilmour. 2010) 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 was 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.. 2003) 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 (Selerade 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 a chronic study (Keil et al.. 2009).
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
2.3.2.7.1). Non-cancer endpoints selected as most relevant for calculating risks associated with chronic
(repeated) occupational exposures to TCE included effects on the to the liver, kidney, nervous system,
immune system, reproductive system, and development, with all HECs adjusted to reflect a 24-hr value,
consistent with calculated occupational exposure values.
Liver toxicity
— Increased liver weight and cytotoxicity/hyper trophy
(Ki ell strand 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). 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 cytotoxicity and histopathology also observed.
(Buben and O'Flahe 35) 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
Page 239 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2089 weeks. A BMDLio of 82 mg/kg-bw/day was identified as the POD for increased liver/body weight
2090 ratios, with cytotoxicity and histopathology also observed.
2091
2092 In (Woolhiser et at.. 20061 Sprague-Dawley female rats (16/group) were exposed to TCE via
2093 inhalation at concentrations of 0, 100, 300, or 1,000 ppm for 6 hrs/day, 5 days/week for 4 weeks. A
2094 BMDLio of 25 ppm was estimated for increased liver/body weight ratio.
2095
5 Table 3-J
I: Dose-response analysis of select
ed studies considered for evalual
ion of liver toxicity
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
HECS0
(ppm)
HEC99
(ppm)
HED50
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
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
BMDLio= 82
mg/kg-bw/day
AmetLivl
BW34
32
11
12
10
UFS=1;UFA=3;
UFh=3;UFl=1;
Total UF=10
(Buben and
O'Flahertv.
1985)
High
(1.3)
Rat
(female)
6 hr/day, 5
days/week for 4
weeks
BMDLio= 25
ppm
Increased
liver/body
weight ratio
AmetLivl
BW34
53
19
19
16
UFS=1;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 Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] for full evaluation by metric. * Woolhiser 2006
was downgraded from a Fligh, with calculated score =1.3.
2097
2098 Table 3-8 presents the derived PODs from all studies considered for dose-response analysis. Increased
2099 liver/body weight ratio was the only endpoint modeled from all studies based on the dose metric
2100 AMetLivlBW34, or the amount of TCE oxidized in liver per unit adjusted body weight. This dose metric
2101 was selected because evidence suggests that hepatic oxidative metabolism is involved in TCE liver
2102 toxicity and dose-response relationships using this metric showed greater consistency than other
2103 considered metrics. All studies were BMDL modeled. A BMR of 10% RD was used to represent a
2104 minimal, biologically significant amount of change in relative liver weight. See Section 3.2.2.1 and (
2105 ) for more details on TCE PBPK modeling, dose metric selection, and BMR selection.
2106
2107 Differences from standard UF values are explained below:
2108 All three studies were assigned UFs = 1 despite shorter exposure duration because although the studies
2109 were subchronic, hepatomegaly (enlarged liver) occurs rapidly with TCE exposure, and no differences
2110 were observed in severity of relative kidney weight increases between 30 and 120 days in (Kiellstrand et
2111 a I).
2112
2113 The data from (Kiellstrand et al.. 1983) was selected to represent the liver toxicity hazard. (Woolhiser et
2114 al.. 2006) was excluded from further consideration because additional signs of toxicity were not
2115 observed, indicating that the increased liver weight was likely merely adaptive. (Kiellstrand et al.. 1983)
2116 was selected over (Buben and O'Flah )85) because it covered up to 120 days exposure as opposed
2117 to only 42 days. Additionally, (Kiellstrand et al.. 1983) utilized the widest dose range of any study,
2118 imparting more precision in the POD estimate.
2119
2120 Kidney toxicity
2121 — Kidney Pathology
Page 240 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
(Maltoni et ai. 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
( ). 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 ai. 1986).
In another oral (gavage) study CNTP. 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 ( ).
— Increased Relative Kidney Weight
(Woolhiser et ai. 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 ( ).
Increased kidney/body weight ratios were also seen in (Kiellstrand et ai. 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
HECS0
(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
BMDL05 = 9.45
mg/kg-bw/day
Toxic nephropathy
ABioact
DCVC
BW34
0.042
0.0056
0.033
0.0034
UFs=l;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 19861
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=l;UFA= 3;
UFH=3;UFL=1;
Total UF=10
(Maltoni et
aL 1986)
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=l;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=l;UFA= 3;
UFH=3;UFL=1;
Total UF=10
(Kiellstrand
et aL. 1983)
Medium
(1.8)
1 POD type can be NOAEL, LOAEL, or BMDL. EPA adjusted all values to continuous exposure.
2 UFS=subchronic to chronic UF; LTFA=interspecies UF; UFH=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-OPPT-2019-0500] 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.
Page 241 of 748
-------
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 3-9 presents the derived PODs from all studies considered for dose-response analysis. The studies
considered for dose-response analysis identified either indications of kidney pathology or increase
kidney/body weight ratio. All rat studies utilized ABioactDCVCBW34, or the amount of DCVC
bioactivated in the kidney per unit adjusted body weight, because GSH-conjugative bioactivation of
TCE into metabolites such as DCVC in the kidney is expected to be responsible for kidney toxicity.
AMetGSHBW34, or the amount of TCE conjugated with GSH per unit adjusted body weight, was
utilized for mice studies because PBPK information on DCVC activation in mice is not reasonably
available. All studies were BMDL modeled. A BMR of 5% ER was used for ( 5) because toxic
nephropathy is a severe toxic effect. (Maltoni etai. 1986) used a BMR of 10% ER because
meganuclocytosis is considered minimally adverse, while both studies examining increased relative
kidney weight used a standard BMR of 10% RD. See Section 3.2.2.1 and ( ) for more
details on TCE PBPK modeling, dose metric selection, and BMR selection.
Differences from standard UF values are explained below:
(Woolhiser et al. 2006) and (Kiellstrand et al.. 1983) were assigned UFs = 1 despite shorter exposure
duration because no differences were observed in severity of relative kidney weight increases between 30
and 120 days in (Kiellstrand et al.. 1983).
EPA determined that kidney pathology was a better indicator of adverse kidney effects than increased
relative organ weight and therefore only that endpoint was selected to represent kidney toxicity. While
there are concerns about the procedure of continuing observation until spontaneous death in (Maltoni et
al.. 1986) due to the potential for confounding effects from autophagy or infection, there are unlikely to
be significant artifacts from this methodology affecting the interpretation of kidney lesions. There was
random allocation to study groups and kidney lesions were not observed in the control or lowest dose
group. Therefore, background false positives were not an issue and the observed dose-response is
expected to be independent of this confounder. Additionally, a 2011 review of pathology results from
other cancer studies performed in this laboratory (Ramazzini Institute) by the NTP Pathology Working
Group (Malarkey and Bud ) found good agreement on the interpretation of most solid tumors
and only identified significant differences among inflammatory cancers of the blood and respiratory
tract.
Both (Maltoni et al.. 1986) and (N 38) scored a Medium in data quality, however (Maltoni et al..
1986) tested exposure over a sufficiently similar duration with a more appropriate dose range. The
elevated doses in (NTP. 1988) resulted in massive nephrotoxicity and introduce large uncertainty in
BMD modeling the effects at low doses well below the tested doses with a BMR well below the
observed effect incidence in the study. Therefore, the BMDL and resulting HEC/HED from (Maltoni et
al.. 1986) was considered more reliable. Among the inhalation and oral results from (Maltoni et al..
1986). with few other differences among the data the lower resulting oral POD was selected to represent
the endpoint in order to be health-protective. Of note, this represents a change from the 2014 TSCA Work
Plan Chemical Risk Assessment ( 2014b). which selected the POD from (NTP. 1988) to
represent kidney toxicity.
Neurotoxicity
— CNS Depression
(Arito et al.. 1994) exposed Wistar male rats (5/group) to TCE via inhalation to concentrations of 0,
50, 100, or 300 ppm for 8 hrs/day, 5 days/week for 6 weeks. Exposure to all of the TCE concentrations
significantly decreased the amount of time spent in wakefulness during the exposure period. Some
carry over was observed in the 22 hr-post exposure period, with significant decreases in wakefulness
seen at 100 ppm TCE. Significant changes in wakefulness- sleep elicited by the long-term exposure
Page 242 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2198 appeared at lower exposure levels. The LOAEL for sleep changes was 12 ppm (i.e., LOAEL, adjusted
2199 for continuous exposure) (U.S. EPA. 201 le).
2200
2201 — Trigeminal nerve effects
2202 (Ruiiten et at.. 1991) evaluated the TCE exposures and possible health effects of 31 male printing
2203 workers (mean age: 44 yrs) and 28 unexposed control subjects (mean age: 45 yrs). The exposure
2204 duration was expressed as "cumulative exposure" (concentration x time). Using historical monitoring
2205 data, mean exposures were calculated as 704 ppm x number of years worked, where the mean number
2206 of years was 16 (range: 160-2,150 ppm x yr) ( ). The study measured the trigeminal
2207 nerve function by using the blink reflex, but no abnormal findings were observed. However, the study
2208 found a statistically significant average increase in the latency response time in TCE-exposed workers
2209 on the masseter reflex test, another test commonly used to measure the integrity of the trigeminal
2210 nerve. The POD derived from the dataset was a LOAEL of 14 ppm (U.S. EPA. ^ ).
2211
2212 — Neuronal demyelination
2213 (Isaacson etal. 1990) dosed weanling Sprague-Dawley male rats (12/dose group) via the oral route
2214 (drinking water) in an experimental protocol for an 8-week period. The control group had unexposed
2215 rats for 8 weeks. The experimental group#l exposed rats to 47 mg/kg-bw/day TCE for 4 weeks and
2216 then no TCE exposure for 4 weeks. The experimental group#2 exposed rats to 47 mg/kg-bw/day TCE
2217 for 4 weeks, no TCE exposure for the following 2 weeks, and then 24 mg/kg-bw/day TCE for the final
2218 2 weeks. Rats in group#2 reported a decreased latency to find the platform in the Morris water maze
2219 test. While these results actually suggest increased cognitive performance, all of the TCE-treated groups
2220 exhibited hippocampal demyelination, with effects more severe in the twice-exposed group. The
2221 LOAEL for neurodegenerative effects (i.e., demyelination in the hippocampus) was 47 mg/kg-bw/day
2222 ( ).
2223
224 1
"able 2
i-10: Dose-response analysis of se
ected studies considered for evaluation of neurological ei
'fects
Target
Organ
System
Species
Duration
POD Type 1
(applied dose)
Effect
Dose
Metric
HECS0
(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
w akefulness
TotMetab
BW34
13
4.8
6.6
6.5
UFS=3;UFA=3;
UFh=3; UFL=10;
Total UF=300
(Arito et al,
1.994)
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=1; 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
UFS=10;UFA=3;
LTFH=3; UFL=10;
Total UF=1000
(Isaacson et
al. 1990)
Medium
(2)*
1 POD type can be NOAEL, LOAEL, or BMDL. EPA 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-HQ-OPPT-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
2225
2226 Table 3-10 presents the derived PODs from all studies considered for dose-response analysis. The
2227 reasonably available datasets for considering neurotoxicity included single studies for each of the three
2228 endpoints of central nervous system (CNS) depression, trigeminal nerve effects, and neuronal
2229 demyelination. The TotMetabBW34 dose metric, or the total amount TCE metabolized per unit adjusted
2230 body weight, was used for all three studies. Thise dose metric was selected because for these endpoints
2231 there is insufficient information for site-specific or mechanism-specific determinations of an appropriate
Page 243 of 748
-------
2232
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
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
dose-metric, however in general TCE toxicity is associated with metabolites rather than the parent
compound. LOAELs were used as PODs for all studies, and none were BMD modeled. See Section
3.2.2.1 and (U.S. EPA. ) for more details on TCE PBPK modeling and dose metric selection.
Differences from standard UF values are explained below:
(Artto et al. 1994) was assigned UFs = 3 (instead of 10) despite being only a 6 week study because
effects observed at 6 weeks exposure were only minimally different than effects at 2 weeks (differences
observed post-exposure).
(Ruiiten et al.. 1991) was assigned UFs = 1 because the data was based on a mean of 16 years of human
exposure. UFl = 3 (instead of 10) due to the observed effect being an early marker and representing a
minimal degree of change.
EPA did not select (Isaacson et al.. 1990). demonstrating demyelination of the hippocampus, to
represent the neurotoxicity hazard because dosing during the study was not continuous and the resulting
POD was subject to a large cumulative uncertainty factor (1000). (Arito et al.. 1994) and (Ruiiten et al..
1991) were both considered for use in quantitative risk estimation as they were relatively well-conducted
studies examining independent endpoints within the hazard of neurological effects.
Immunotoxicitv
— Thymus Effects / Autoimmunity
(Keil et al.. 2009) exposed B6C3F1 mice (10/group), a standard test strain not genetically prone to
develop autoimmune disease, to TCE via drinking water for 27 or 30 weeks at concentrations in water
of 0, 1.4, or 14 ppm (0.35 or 3.5 mg/kg-bw/day). The study reported a significant decrease in thymus
weight concentrations and thymic cellularity as well as an increase in autoantibodies to ssDNA and
dsDNA. A LOAEL of 0.35 mg/kg-bw/day was identified as the POD for the thymic and autoimmune
effects ( ).
— Autoimmunity
(Kaneko et al.. 2000) exposed auto-immune prone mice (5/group) to TCE via inhalation at
concentrations of 0, 500, 1,000, or 2,000 ppm for 4 hrs/day, 6 days/week, for 8 weeks. At
concentrations > 500 ppm, mice exhibited dose-related liver inflammation, splenomegaly and
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 ( ).
— Immunosuppression
In (Sanders et al.. 1982). male and female CD-1 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 " \ \ \ :0 lie).
Another study that was previously discussed for liver and kidney effects (Woolhiser et al.. 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.
Page 244 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2281 A BMDLisd of 24.9 ppm was identified for this immunosuppressive effect (\ v << \ _\"W 1^).
2282
2283 Table 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)
HED99
(mg/kg)
Uncertainty
Factors (UFs) 2
Reference
Data
Quality 3
Immune
system
Mouse
(female)
27-30 weeks
LOAEL = 0.35
mg/kg-bw/day
Decrease in
thymus weight
and thymus
cellularity
TotMetab
BW34
0.092
0.033
0.049
0.048
UFS=1;UFA=3;
UFh=3;UFl=10;
Total UF=100 4
(Keil et al.,
2009)
High
(1.6)
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=30 4
(Keil et al.,
2009)
High
(1.6)
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; UFA= 3;
UFH=1;UFL=10;
Total UF=300
(Kaneko et
al. 2000)
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, 1982)
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
fWoolliiser
et al, 2006)
High
(1.1)
1 POD type can be NOAEL, LOAEL, or BMDL. The IRIS program adjusted all values to continuous exposure.
2UFS=subchronic to chronic UF; UFA=interspecies UF; UFFI=intraspecies UF; UFL=LOAEL to NOAEL UF.
3 See [Data Quality Evaluation of Human Health Hazard Studies. Docket: EPA-HQ-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).
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
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
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.. 2006). 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 3.2.2.1 and ( ) 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) due to the observed effect being considered an
early, subclinical or pre-clinical early marker of disease.
Decreased thymus weight and eellularity as observed in (Kelt et at.. 2009) was not considered for use in
risk estimation because EPA determined that this effect is insufficiently adverse compared to the other
endpoints. Of note, elimination of this endpoint and corresponding change in total UF represents a change
from the 2014 TSCA. Work Plan Chemical Risk Assessment ( 1014b). The data from (Keil et
at.. 2009) was selected to represent autoimmunity however, because the study was of longer duration than
Page 245 of 748
-------
2305
2306
2307
2308
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
2349
2350
2351
2352
2353
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
(Kaneko et ai. 2000) with a smaller cumulative uncertainty factor. (Sanders et ai. 1982) was selected to
represent immunosuppression because the study was of a much longer duration than (Woolhiser et al..
2006).
Reproductive toxicity
— Male Reproductive Effects
(Chia et al. 1996) examined a cohort of 85 workers in an electronics factory. The workers provided
urine, blood, and sperm samples. The mean urine TCA level was 22.4 mg/g creatinine (range: 0.8-
136.4 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-1 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 ( ).
(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:
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 al.. 2000) reported altered testicular histopathology, increased sperm abnormalities, and
significantly increased pre- and/or postimplantation loss in litters in the groups with 2 or 10 weeks of
exposure, or 5 weeks of exposure with 2 of weeks rest. Multiple sperm effects were observed in another
study by Kumar (2001). After 12 weeks 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 ( ).
(Kan et al.. 2007) also provided evidence for the damage to the epididymis epithelium and sperm.
CD-I male mice (4/group) were exposure by 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.
Page 246 of 748
-------
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
— 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 (highest dose only
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 247 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2370 Table 3-]
2: Dose-res
ponse analysis of selected studies consideret
for evaluation of reproductive effects
Target
Organ
System
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
Reproductive
system
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,
1996)
Medium
(1.8)
Rat
(male)
4 hrs/day, 5
days/week, 2-10
weeks exposed,
2-8 weeks
unexposed
LOAEL = 45
ppm
Sperm effects and
male reproductive
tract effects
TotMetab
BW34
32
13
16
16
UFS=10; UFA= 3;
UFH=3; UFl=10;
Total UF=1000
(Kumar et
al.2000)
Medium
(1.7)
4 hrs/day, 5
days/week for
12 or 24 weeks
(Kumar et
al. 200D
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
80
73
UFS=10; UFA= 3;
UFH=3; UFl=10;
Total UF=1000
(Kan et al.
2007)
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
80
73
UFS=10; UFA= 3;
UFH=3; UFl=10;
Total UF=1000
(Xu et al.
2004)
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
(Narotskv
et al,
1995)
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. 1986)
High
(1.1)
1 POD type can be NOAEL, LOAEL, or BMDL. The IRIS program adjusted all values to continuous exposure.
2 UFS=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-HQ-OPPT-2019-0500] for full evaluation by metric. *Kan 2007 was
downgraded from a High, with calculated score =1.6.
2371
2372 Table 3-12 presents the derived PODs from all studies considered for dose-response analysis. The
2373 majority of studies identified effects indicative of male reproductive toxicity, with one study
2374 demonstrating female reproductive toxicity. The TotMetabBW34 dose metric, or the total amount TCE
2375 metabolized per unit adjusted body weight, was used for all three studies. This dose metric was selected
2376 because for these endpoints there is insufficient information for site-specific or mechanism-specific
2377 determinations of an appropriate dose-metric, however in general TCE toxicity is associated with
2378 metabolites rather than the parent compound. For (Chia et at.. 1996). the 2011 IRIS Assessment (U.S.
2379 ) notes some additional uncertainty in the dose estimate because exposure groups were
2380 defined by ranges and exposure was estimated by conversion of urinary TCA. LOAELs were used as
2381 PODs for all studies except (Chia et 6), which was BMD modeled with a standard BMR of 10%
2382 extra risk. The 2011 IRIS Assessment (1 c. 1 ^ \ JO I I e) indicates some uncertainty in the biological
2383 signficance of this BMR because the study used a lower cutoff to define hyperzoospermia than other
2384 studies. See Section 3.2.2.1 and (U.S. EPA. ) for more details on TCE PBPK modeling, dose
2385 metric selection, and BMR selection.
2386
Page 248 of 748
-------
2387
2388
2389
2390
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
2427
2428
2429
2430
2431
2432
2433
2434
2435
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
For male reproductive toxicity, (Chia et ai. 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 ( 05), 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 ( >) and also provides consistency
across assessments.
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
Page 249 of 748
-------
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
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
2482
2483
2484
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( 05), 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 |), 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 H) confirming a positive association between TCE
exposure and all three cancer sites, the derived PODs will remain the same as for ( ) and
(I )•
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 [j,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
Hg/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 short, extra lifetime cancer risks were summed across the three cancer types and
the ratio of the sum of the extra risks to the extra risk for kidney alone was derived. EPA calculated this
ratio using two sets of data: the summary RR estimates from the 2011 meta-analyses for NHL, kidney
cancer, and liver cancer, and the SIR estimates for all three cancer types from the Raaschou-Nielsen et
al. (2003) study. The value for the ratio of the sum of the extra risks to the extra risk for RCC alone was
3.28 from the first calculation (using meta-analysis results) and 4.36 from the second calculation (using
Raaschou-Nielsen et al. data). The geometric and arithmetic mean of these two values is 3.8, and EPA
decided to round up to 4 based on the imprecision of the adjustment factor.
The oral slope factor (OSF) for TCE is defined as a plausible upper bound lifetime extra risk of
cancer from chronic ingestion of TCE per mg/kg/day oral dose. The estimate of the oral slope factor is
4.64 x 10"2 per mg/kg/day (5 x 10"2 per mg/kg/day rounded to one significant figure), resulting from
PBPK model-based route-to-route extrapolation of the inhalation unit risk estimate based on the human
kidney cancer risks reported in Charbotel et al. (2006) and adjusted 5-fold upward for potential risk for
NHL and liver cancer. For this adjustment, individual IUR estimates were first obtained for each site
based on the ratios of extra risk relative to kidney. Those site-specific IUR estimates were then
Page 250 of 748
-------
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
extrapolated to the equivalent OSFs using site-specific dose metrics,18 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.
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 plan risk assessment for TCE does not estimate excess cancer
risks for acute exposures because the relationship between a single short-term exposure to TCE and the
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)
(I ) 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, however uncertainty was similar as to
other metrics for rat and human data ( ). 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.
18 Kidney: ABioactDCVCBW34; NHL: TotMetabBW34; Liver: AMetLivlBW34
Page 251 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2520 Table 2
i-13: Dose-response ana
ysis of selected stut
ies considered for acute exposure scenarios
()r»;ui/
S\ skill
Spwics
Duniliiin
POD T\ pi-
feipplkildiiM-)
I.IIWl
IW
Miirii'
II Ms.
(|>|>m>
MIX,.,
(ppm)
111:1).;.,
INI).'.
(m»/k»)
I luiThiiim
l-'iii'iiirs (I I s)
kll'l'IVIHl'
Diilii
Qu;ilil\
Develop-
mental
Effects
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
(Narotskv et
al, 1995)
High
Rat
(female)
22 days
throughout
gestation
(gestational days
0 to 22)
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. 2003)
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
(Tredriksson
et al. 1993)
Medium
Immune
System
Rat
(female)
3hr/day, single
dose; followed
by respiratory
infection
BMDL01 =
13.9 ppm
Immuno-
suppression
N/A1
N/A1
1.74 1
N/A1
2.74 1'2
UFS=1;UFA=3;
UFH=10; UFL=1;
Total UF=30
(Selarade and
Gilmour,
2010)
High
1 Data from (Sel grade and Gilmour, 2010) was not subject to PBPK modeling due to uncertainty concerning the most appropriate dose metric. The BMDL value
adjusted for a 24hr exposure will be used as the POD for occupational risk estimates, while the 3hr value will be used for consumer risk estimates. This value is
presented in the HEC99 column but does not represent any particular percentile since it was not PBPK-modeled.
2 A dermal HED was obtained through route-to-route extrapolation using breathing rate and body weight data on male CD-1 mice (insufficient female data was
reasonably available) from ("U.S. EPA, 1988) and allometric scaling based on ("U.S. EPA, 201 Id) using a dosimetric adjustment factor of 0.14 for mice.
2521
Page 252 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
able 3-14: Dose-response ana
Target
Organ
System
Species
Duration
POD Type
(applied dose)
Effect
Dose Metric
hec50
(ppm)
hec99
(ppm)
HEDso
(mg/kg)
HED99
(mg/kg)
Uncertainty
Factors (UFs)
Reference
Data
Quality
Liver
Mouse
(male)
Continuous and
intermittent
exposures, variable
time periods for 30-
120 days
BMDLio=
21.6 ppm
Increased liver/body
weight ratio and
cytotoxicity/
hypertrophy
AMetLivl
BW34
25
9.1
9.0
7.9
UFs=l;UFA= 3;
UFh=3; UFl=1;
Total UF=10
(Kiellstrand et
al., 1983)
Medium
Kidney
Rat
(male)
- Oral
4-5 days/week for
52 weeks
BMDL10 = 34
mg/kg-bw/day
Pathology changes in
renal tubule
ABioact
DCVCBW34
0.19
0.025
0.15
0.015
UFS=1;UFA=3;
UFh=3; UFl=1;
Total UF=10
(Maltoni et al..
1986)
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..
1994)
Medium
Human
(bo tli
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=1;UFA= 1;
UFH=3; UFL=3;
Total UF=10
(Ruiiten et al..
1991)
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;
UFH=3; UFL=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=l;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
BMDL10 =
1.4 ppm
Decreased normal
sperm morphology
and hyperzoospennia
TotMetab
BW34
1.4
0.5
0.74
0.73
UFS=10; UFA= 1;
UFH=3;UFL=1;
Total UF=30
(Chia 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
(Narotskv et al..
1995)
High
Develop-
mental
Effects
Rat
(female)
Gestational days 6 to
15
BMDL01= 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
(gestational days
0-22)
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 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.. 1993)
Medium
2523
Page 253 of 748
-------
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
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
2563
2564
2565
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 3-15: Cancer Points of Departure for Lifetime Exposure Scenarios
POD Type
Oral Slope I'aclor
Inhalation I nil Risk
1 Alia Risk Ik-nchmaik
POD (extra risk per
dose/concentration)
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 at.. 2006) and the combined estimates account for the
additional relative contribution from the other two cancers.
For TCE, EPA, consistent with OSHA (878 F.2d 389 (D.C. Cir. 1989) and 2016 NIOSH guidance
(Whittaker et at.. 2016). 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
Act (OSHA) 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).
3.2.6 Assumptions and Key Sources of Uncertainty for Human Health Hazard
3.2.6.1 Confidence in Hazard Identification and Weight of Evidence
There is high confidence in the database for human health hazard. All studies considered for dose-
response analysis scored either Medium or High in data quality evaluation and were determined to be
highly relevant to the pertinent health outcome. EPA selected the best representative study for each
identified endpoint from among a broad selection of studies, taking into account factors such as data
quality evaluation score, species, exposure duration, dose range, cumulative uncertainty factor, and
relevance. The only identified study that examined developmental immunotoxicity fPeden-Adams et at..
2006) scored a Low in data evaluation and a POD could not be sufficiently derived.
EPA has high confidence in the overall weight of scientific evidence. EPA did not identify any
information that would question the previous WOE regarding the evaluation of liver, kidney,
neurological, immunological, reproductive toxicity, and developmental toxicity (other than cardiac
malformations). For cancer, EPA performed an updated meta-analysis that found positive statistical
associations between human TCE exposure and cancer of kidney, liver, and NHL types, in agreement
with the previous meta-analyses performed in 2011 (Appendix C, ( ). For congenital
heart defects, EPA performed a thorough WOE assessment (Appendix G.2), examining all pertinent
studies in the reasonably available literature. While some uncertainty remains in the dose-response
analysis of the (Johnson et at.. 2003) study and the resulting POD, there is medium confidence in the
qualitative relevance of the endpoint to human toxicity based on the results of the WOE.
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
Page 254 of 748
-------
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
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
study because of experimental limitations (e.g., the ability to detect an effect, the doses used and dose
spacing, measurement errors, etc.), and POD approximations like the doses used (i.e., a NOAEL) an
exposure level which is modeled from the reasonably available doses used (i.e., BMDL) are used. 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.
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 ai.
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.
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 of oxidative metabolism flux .There remains substantial uncertainty in the 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
overestimate kidney toxicity for a proportion of the population, however use of these values are
expected to sufficiently account for the majority of human toxicokinetic variability, including increased
biological susceptibility (see Section 3.2.5.2). Of note, there was significantly less uncertainty for
extrapolation of rat GSH conjugation data, which was used for the selected kidney PODs, compared to
data from mice. Despite any limitations of the model, overall uncertainty for the selected PODs is
reduced by the use of a PBPK model. Use of the PBPK model resulted in data-derived HEC/HED99
Page 255 of 748
-------
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 for both interspecies and intraspecies
toxicokinetic variability. Data-derived values are always preferred to default uncertainty adjustments
and improve confidence in the adjusted PODs.
3.2.6.3 Cancer Dose Response
Potential sources of uncertainty associated with Charbotel et al. (2006) include the modest sample size
of the study and localized population (86 kidney cancer cases, 37 associated with TCE exposure from a
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. (2006) was a well conducted. High quality
study that used a comprehensive exposure assessment with a detailed occupational questionnaire and
sensitivity and regression analyses found no statistical effect on the cancer POD from a sensitivity
analysis adjusting for exposure to other chemicals (I v << \ 1:011^).
The two major sources of uncertainty in quantitative cancer risk estimates are generally interspecies
extrapolation and high-dose to low-dose extrapolation. The unit risk estimate for kidney cancer
incidence derived from the Charbotel et al. (2006) results is not subject to interspecies uncertainty
because it is based on human data. A major uncertainty remains in the extrapolation from occupational
exposures to lower environmental exposures. There was some evidence of a contribution to increased
kidney cancer risk from peak exposures; however, there remained an apparent dose-response
relationship for RCC risk with increasing 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 ( 2005). The weight of evidence also supports
involvement of processes of cytotoxicity and regenerative proliferation in the carcinogenicity of TCE,
although not with the extent of support as for a mutagenic mode of action. In particular, data linking
TCE-induced proliferation to increased mutation or clonal expansion are lacking, as are data informing
the quantitative contribution of cytotoxicity. Because any possible involvement of a cytotoxicity mode
of action would be additional to mutagenicity, the dose-response relationship would nonetheless be
expected to be linear at low doses. Therefore, the additional involvement of a cytotoxicity mode of
action does not provide evidence against the use of linear extrapolation from the POD.
The upward adjustment of the cancer PODs based on additional contributions from liver and NHL
cancer was based on peer-reviewed methodology as explained in the 2011 IRIS Assessment (
201 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
Representative 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-
Page 256 of 748
-------
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 at..
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 ( 96: U.S. EPA.
1991). however this may possibly result in an overestimation of risk for some scenarios. For the acute
immunotoxicity study (Belgrade a a our. 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 2; Woolhiser et ai. 2006).
Confidence is raised from the robust WOE analysis performed on the congenital heart defects endpoint
(see Appendix G), the presence of a variety of endpoints including a study using acute TCE
administration, and reduced uncertainty factors due to the use of a PBPK model or allometric scaling.
Representative Acute Non-Cancer Endpoint
Based on the following considerations, the POD for mortality due to immunosuppression from (S el grade
and Gilmour. 2010) is considered to be the most robust and best representative 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
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
6) The endpoint is severe
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.
Representative Chronic Non-Cancer Endpoint
Based on the following considerations, the POD for autoimmunity from (Keil et ai. 2009) is considered
to be the most robust and best representative 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 both functional immunological markers (increased anti-self
antibodies) and immunological organ changes (thymus weight and cellularity)
Page 257 of 748
-------
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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 al. 2006) on kidney
cancer, with the PODs adjusted upward to account for the additional two cancer sites. Confidence is
slightly reduced due to some uncertainty over the precision of the dose-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.
Page 258 of 748
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
4 RISK CHARACTERIZATION
4.1 Environmental Risk
EPA took fate, exposure, and environmental hazard into consideration to characterize 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 sediment for aquatic and 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 organisms are assessed and presented in
this draft 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-2019-0500]). The [Data Quality Evaluation of Environmental Hazard Studies. Docket: EPA-HQ-
OPPT-2019-0500] 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 ( 1),
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.waterqiialitydata.iis) and reasonably available literature, to characterize the risk of TCE to
Page 259 of 748
-------
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. ( 0
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
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 only 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, indicating no 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.
Assuming 20 days of releases, Praxair Technology Center in Tonawanda, NY had a chronic RQs of 3.81
Page 260 of 748
-------
93
94
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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
with 20 days of exceedance, and an algae COCs representing the most sensitive species of algae of
1,000 with 20 days of exceedance. In other words, the surface water concentration modeled for this
facility was 3.81 times higher than the COC for chronic exposures, and 1,000 times higher than the COC
for the most sensitive species of algae. Assuming 260 days of releases from the facility, the algae RQ
representing the most sensitive species was 56.33 with 350 days of exceedance. However, for algae
species as a whole, RQs for this site were 0.06 assuming 20 days of release and 0.00 assuming 350 days
of release, meaning the concentration did not exceed the COC of 52,000 ppb which represents nine
different species of algae. Therefore, there may be risk for some of the most sensitive species of algae at
this site, but not for algae species as a whole. Risks were identified at this site for other aquatic
organisms for 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 alge as high as 113.04 with 20 days of exceedance. Assuming this facility released
TCE for 250 days per year, the RQ is 9.06 with 194 days of exceedance. However, for algae species as a
whole, RQs for this site were 0.01 for 20 days of releases, and 0.00 for 250 days, meaning the
concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.
Therefore, there may be risk for some of the most sensitive species of algae at these sites, but not for
algae species as a whole. No risks were identifiedfor other aquatic organisms in this condition of use.
Open-top Vapor Degreasing:
Of the 64 open-top vapor degreasing facilities, three sites had acute RQs > 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 3.11, a chronic RQs of 12.61 with 20 days of exceedance,
and an algae COCs representing the most sensitive species of algae of 3,312.50 with 20 days of
exceedance. Assuming 260 days of relese from the facility, the algae RQ representing the most sensitive
species was 255.21 with 260 days of exceedance. However, for algae species as a whole, RQs for this
site were 0.01 assuming 260 days of release, and 0.19 assuming 20 days of release, meaning the
concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.
Therefore, there may be risk for some of the most sensitive species of algae at this site, but not 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 3.11 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 for this site, meaning the
concentration did not exceed the COC of 52,000 ppb which represents nine different species of algae.
Therefore, there may be risk for some of the most sensitive species of algae at this site, but not for algae
species as a whole.
Akebono Elizabethtown Plant in Elizabethtown, KY had an RQ for the most sensitive species of algae
of 1.62 with 27 days of exceedance, assuming 260 days of release per year. However, for algae species
as a whole, RQs for this facility were 0.00 for this site, meaning the concentration did not exceed the
Page 261 of 748
-------
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
COC of 52,000 ppb which represents nine different species of algae. Therefore, there may be risk for
some of the most sensitive species of algae at this site, but not 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 alge as high as 44.44, assuming 20 days of release per year. In other words, the
surface water concentration modeled for this facility was 44.44 times higher than the COC for the most
sensitive species of algae (3 ppb). Additionally, this COC was exceeded for 20 days. Assuming this
facility released TCE for 250 days per year, the RQ is 3.61 with 250 days of exceedance. However, for
algae species as a whole, RQs for this facility were 0.00, meaning the concentration did not exceed the
COC of 52,000 ppb which represents nine different species of algae. Therefore, there may be risk for
some of the most sensitive species of algae at this site, but not for algae species as a whole. No risks
were identifiedfor 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 alge of 3.01, assuming 250 days of release per year. In other words, the surface
water concentration modeled for this facility was 3.01 times higher than the COC for the most sensitive
species of algae (3 ppb). Additionally, this COC was exceeded for 35 days. Washington Penn Plastics in
Frankfort, KY had an RQ for the most sensitive species of alge of 2.51, assuming 250 days of release
per year. Additionally, this COC was exceeded for 22 days. Keeshan and Bost Chemical Co., Inc. in
Manvel, TX had an RQ for the most sensitive species of algae of 66.67 with 20 days of exceedance,
assuming 20 days of release per year. Assuming 350 days of release, this site has an RQ for the most
sensitive species of algae of 3.17 with 350 days of exceedance. However, for algae species as a whole,
RQs for these facilities were 0.00, meaning the concentration did not exceed the COC of 52,000 ppb
which represents nine different species of algae. Therefore, there may be risk for some of the most
sensitive species of algae at these sites, but not for algae species as a whole. No risks were identifiedfor
other aquatic organisms 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, meaning the concentration did not exceed the COC of 52,000 ppb which
represents nine different species of algae. Therefore, there may be risk for some of the most sensitive
species of algae at this site, but not for algae species as a whole. No risks were identifiedfor other
aquatic organisms for this condition of use.
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
Page 262 of 748
-------
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
sensitive species of algae (3 ppb). Additionally, this COC was exceeded for 20 days. Assuming this
facility released TCE for 250 days per year, the RQ is 3.00 with 250 days of exceedance. However, for
algae species as a whole, RQs for this facility were 0.00, meaning the concentration did not exceed the
COC of 52,000 ppb which represents nine different species of algae. Therefore, there may be risk for
some of the most sensitive species of algae at this site, but not for algae species as a whole. No risks
were identifiedfor other aquatic organisms in this condition of use.
Process Solvent Recycling and Worker Handling of Wastes:
Of the five facilities with other commercial uses 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 alge as high as 46.08 with 20 days of
exceedance. Assuming this facility released TCE for 250 days per year, the RQ is 3.92 with 250 days of
exceedance. Assuming 20 days of release, Veolia Es Technical Solutions LLC in Middlesex, NJ had an
RQ for the most sensitive species of alge of 11.91 with 20 days of exceedance. And assuming 250 days
of releases, Clean Harbors Deer Park LLC in La Porte, TX had an RQ for the most sensitive species of
alge of 2.86 with 110 days of exceedance. However, for algae species as a whole, RQs for at all three
facilities were 0.00, meaning the concentration did not exceed the COC of 52,000 ppb which represents
nine different species of algae. Therefore, there may be risk for some of the most sensitive species of
algae at these sites, but not for algae species as a whole. No risks were identifiedfor other aquatic
organisms in this condition of use.
Wastewater Treatment Plants (WWTPs):
Of the nine WWTPs, one site had algae RQs > 1 with 20 days or more of exceedances. New Rochelle
STP in New Rochelle, NY had an RQ for the most sensitive species of alge of 4.26, assuming 20 days of
release per year. This means that the surface water concentration modeled for this facility was 4.26 times
higher than the COC for the most sensitive species of algae (3 ppb). Additionally, this COC was
exceeded for 20 days. Assuming this facility released TCE for 365 days per year, the RQ is only 0.23
with 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, no risks to aquatic species were for this facility or
condition of use.
Page 263 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
220 Table 4-1. Environmental Risk Quotients for Facilities Releasing TCE to Surface Water as Modeled in E-FAST (RQs > 1 in bold)
Name, Location, and ID of
Active Releaser Facility a
Release
Media b
Modeled Facility or
Industry Sector in
EFAST c
EFAST
Waterbody
Type d
Days of
Release
e
Release
(kg/day)f
7Q10
SWC
(ppb)g
COC Type
COC
(ppb)
Days of
Exceedance
(days/year)
h~
Risk
Quotient
OES: Processing as a Reactant
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
Surface
Water
NPDES NY0000281
Still body
350
0.00169
169
Acute
3,200
NA
0.05
Chronic
788
0
0.21
Algae
3
350
56.33
Algae (HCos)
52,000
0
0.00
20
0.03
3000
Acute
3,200
NA
0.94
Chronic
788
20
3.81
Algae
3
20
1,000.00
Algae (HCos)
52,000
0
0.06
OES: Repackaging
Hubbard-Hall Inc,
Waterbury, CT
NPDES: Unknown
Off-site
Waste-
water
Treatme
nt
Receiving Facility:
Recycle Inc.; POTW
(Ind.)
Surface water
250
1.108
27.18
Acute
3,200
NA
0.01
Chronic
788
0
0.03
Algae
3
194
9.06
Algae (HCos)
52,000
0
0.00
20
13.85
339.11
Acute
3,200
NA
0.11
Chronic
788
1
0.43
Algae
3
20
113.04
Algae (HCos)
52,000
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
3,200
NA
0.24
Chronic
788
0
0.97
Algae (COC)
3
260
255.21
Algae (HCos)
52,000
0
0.01
20
25.44
9937.5
Acute
3,200
NA
3.11
Chronic
788
20
12.61
Algae
3
20
3,312.50
Algae (HCos)
52,000
0
0.19
GM Components Holdings
LLC,
Lockport, NY
NPDES: NY0000558
Surface
Water
NPDES NY0000558
Surface water
260
0.13
10.97
Acute
3,200
NA
0.00
Chronic
788
0
0.01
Algae (COC)
3
117
3.66
Algae (HCos)
52,000
0
0.00
20
1.71
144.47
Acute
3,200
NA
0.05
Chronic
788
0
0.18
Algae
3
20
48.16
Algae (HCos)
52,000
0
0.00
Akebono Elizabethtown Plant,
Surface water
260
0.07
4.87
Acute
3,200
NA
0.00
Page 264 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name. 1 Av;ilkiii. and II) of
\cli\e Releaser l'acilil>
Release
Media
Modeled 1 aeilils or
liidiisii's Secloi'iii
1 1 \S 1
I I AST
\\aleihod>
T\ pe
1 )a> s of
Release
Release
(ku da>)
"Old
S\V(
ipph)
('()(' T\pe
COC
(pph)
1 )a\ s of
1 Aceedaiice
s \ean
Risk
Ouoiienl
Elizabethtown, KY
Chronic
788
0
0.01
NPDES: KY0089672
Algae (COC)
3
27
1.62
Surface
Water
Surrogate NPDES
KY0022039
Algae (HC05)
52,000
0
0.00
Acute
3,200
NA
0.02
20
0.897
62.38
Chronic
788
0
0.08
Algae
3
16
20.79
Algae (HC05)
52,000
0
0.00
OES: Adhesives, Sealants, Paints, and Coatings
Acute
3,200
NA
0.00
250
0.013
10.83
Chronic
788
0
0.01
Algae (COC)
3
250
3.61
Surface
Water
Algae (HC05)
52,000
0
0.00
NPDES RI0000281
Acute
3,200
NA
0.04
Raytheon Company,
Chronic
788
0
0.17
Portsmouth, RI
Still body
20
0.160
133.33
Algae (COC)
3
20
44.44
NPDES: RI0000281
Algae (HC05)
52,000
0
0.00
No info on receiving
facility; Adhesives
and Sealants Manuf.
Acute
3,200
NA
0.00
POTW
250
0.013
0.32
Chronic
788
0
0.00
Algae (COC)
3
0
0.11
Algae (HC05)
52,000
0
0.00
OES: Other Industrial Uses
Acute
3,200
NA
0.00
250
1.553
9.03
Chronic
788
0
0.01
Eli Lilly And Company-
Algae (COC)
3
35
3.01
Lilly Tech Ctr,
Surface
NPDES IN0003310
Surface water
Algae (HC05)
52,000
0
0.00
Indianapolis, IN
Water
Acute
3,200
NA
0.04
NPDES: IN0003310
20
19.410
113.09
Chronic
788
0
0.14
Algae
3
17
37.70
Algae (HC05)
52,000
0
0.00
Acute
3,200
NA
0.00
250
0.032
7.53
Chronic
788
0
0.01
Washington Penn Plastics,
Surface
Water
Surrogate NPDES
KY0028410
Algae (COC)
3
22
2.51
Frankfort, KY
Surface water
Algae (HC05)
52,000
0
0.00
NPDES: KY0097497
Acute
3,200
NA
0.03
20
0.399
94.12
Chronic
788
0
0.12
Algae
3
13
31.37
Page 265 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility a
Release
Media b
Modeled Facility or
Industry Sector in
EFAST c
EFAST
Waterbody
Type d
Days of
Release
e
Release
(kg/day)f
7Q10
SWC
(ppb)g
COC Type
COC
(ppb)
Days of
Exceedance
(days/year)
h"
Risk
Quotient
Algae (HCos)
52,000
0
0.00
Acute
3,200
NA
0.00
350
0.000095
9.50
Chronic
788
0
0.01
Keeshan and Bost Chemical
Algae
3
350
3.17
Co., Inc.,
Surface
NPDES TX0072168
Still body
Algae (HCos)
52,000
0
0.00
Manvel, TX
Water
Acute
3,200
NA
0.06
NPDES: TX0072168
20
0.002
200.00
Chronic
788
0
0.25
Algae
3
20
66.67
Algae (HCos)
52,000
0
0.00
OES: Industrial Processing Aid
Acute
3,200
NA
0.00
Off-site
Waste-
water
Treatme
nt
300
0.38
9.3
Chronic
788
0
0.01
Entek International LLC,
Lebanon OR
NPDES: N/A
Algae (COC)
3
140
3.10
No info on receiving
Surface water
Algae (HCos)
52,000
0
0.00
facility; POTW (Ind.)
Acute
3,200
0
0.04
20
5.65
138.34
Chronic
788
0
0.18
Algae (COC)
3
20
46.11
Algae (HCos)
52,000
0
0.00
OES: Other Commercial Uses
Acute
3,200
NA
0.00
250
0.00027
9
Chronic
788
0
0.01
Park Place Mixed Use
Algae (COC)
3
250
3.00
Development,
Surface
Surrogate NPDES
Still body
Algae (HCos)
52,000
0
0.00
Annapolis, MD
Water
MD0052868
Acute
3,200
NA
0.03
NPDES: MD0068861
20
0.00334
110
Chronic
788
0
0.14
Algae (COC)
3
20
36.67
Algae (HCos)
52,000
0
0.00
OES: Process Solvent Recycling and Worker Handling of Wastes
Acute
3,200
NA
0.00
250
0.004
11.76
Chronic
788
0
0.01
Clean Water Of New York
Algae (COC)
3
250
3.92
Inc,
Surface
Surrogate NPDES
Still body
Algae (HCos)
52,000
0
0.00
Staten Island, NY
Water
NJ0000019
Acute
3,200
NA
0.04
NPDES: NY0200484
20
0.047
138.24
Chronic
788
0
0.18
Algae
3
20
46.08
Algae (HCos)
52,000
0
0.00
Still body
250
24.1
2.85
Acute
3,200
NA
0.00
Page 266 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility a
Release
Media b
Modeled Facility or
Industry Sector in
EFAST c
EFAST
Waterbody
Type d
Days of
Release
e
Release
(kg/day)f
7Q10
SWC
(ppb)g
COC Type
COC
(ppb)
Days of
Exceedance
(days/year)
h"
Risk
Quotient
Chronic
788
0
0.00
Veolia Es Technical Solutions
LLC,
Middlesex, NJ
NPDES: NJ0020141
Off-site
Algae (COC)
3
0
0.95
Waste-
Receiving Facility:
Algae (HCos)
52,000
0
0.00
water
Middlesex Cnty UA;
Acute
3,200
NA
0.01
Treatme
NPDES NJ0020141
20
301.78
35.72
Chronic
788
0
0.05
nt
Algae
3
20
11.91
Algae (HCos)
52,000
0
0.00
Acute
3,200
NA
0.00
Off-site
Waste-
water
Treatme
nt
250
0.35
8.57
Chronic
788
0
0.01
Clean Harbors Deer Park
Algae (COC)
3
110
2.86
LLC,
POTW (Ind.)
Surface water
Algae (HCos)
52,000
0
0.00
La Porte, TX
Acute
3,200
NA
0.03
NPDES: TX0005941
20
4.36
106.75
Chronic
788
0
0.14
Algae
3
19
35.58
Algae (HCos)
52,000
0
0.00
OES: Wastewater Treatment Plants (WWTP)
Acute
3,200
NA
0.00
365
0.043
0.7
Chronic
788
0
0.00
New Rochelle STP,
New Rochelle, NY
NPDES: NY0026697
Algae (COC)
3
0
0.23
Surface
NPDES NY0026697
Still body
Algae (HCos)
52,000
0
0.00
Water
Acute
3,200
NA
0.00
20
0.786
12.79
Chronic
788
0
0.02
Algae (COC)
3
20
4.26
Algae (HCos)
52,000
0
0.00
221 a. Facilities actively releasing tricliloroethylene were identified via DMR, TRI, and CDR databases for the 2016 reporting year.
222 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
223 non-POTW WWTP facility). A wastewater treatment removal rate of 81% is applied to all indirect releases, as well as direct releases from WWTPs.
224 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
225 on location) or a representative generic industry sector. The name of the indirect releaser is provided, as reported in TRI.
226 d.EFAST uses ether the "surface water" model, for rivers and streams, or the "still water" model, for lakes, bays, and oceans.
227 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.
228 f. The daily release amount was calculated from the reported annual release amount divided by the number of release days per year.
229 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.
230 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
231 predicted surface water concentration exceeds the COC. Otherwise, the days of exceedance can be assumed to be zero.
Page 267 of 748
-------
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
265
266
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
US study (U.S. EPA. 1977) that had measurements reflecting near-facility monitoring data. The other
monitored data collected in the US reflect ambient concentrations.
Monitored data from one US study (U.S. EPA. 1977) in the published literature reporting near-facility
concentrations 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
encompases the range of the modeled estimates across all OES with the exception of two sites, that
release to still water bodies.
EPA also had monitored data reflecting ambient water concentrations. EPA's Storage and Retrieval
(STORET) data and USGS's National Water Information System (NWIS) data were extracted on Oct
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 Calculated using Monitored Environmen
Monitored Surface Water
Concentrations (ppb) from
2013-2017
Algae RQ
RQ using Acute
COC of 3,200
ppb
RQ using
Chronic COC of
788 ppb
using COC
of 3 ppb
using HC05 of
52,000 ppb
Mean (Standard Deviation):
0.33 (0.29) ppb
0.11
0.0
0.0
0.0
Maximum: 2 ppb
0.67
0.0
0.0
0.0
al Concentrations from WQX/WQP
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
al.. 1983; USGS. 2003; Robinson et al.. 2004). The maximum concentration was collected from the
Charles River in Boston, Massachusetts (an urban area) between 1998 and 2000 (Robinson et al.. 2004).
The next highest TCE concentration was 2.0 |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 an 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 268 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-3. RQs Calculated using Monitored Environmental Concentrations from Published
Literature
Monitored Surface
Water Concentrations
(ppb) from 2013-2017
Algae RQ
RQ using Acute
COC of 3,200
ppb
RQ using
Chronic COC of
788 ppb
using COC of 3
ppb
using HC05 of
52,000 ppb
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 4-1 and Figure 4-2 (east and west US) for the maximum days of
release scenario, and in Figure 4-3 and Figure 4-4 (east and west US) for the 20-days of release scenario.
The co-location of TCE releasing facilities and monitoring stations in a HUC is shown in Figure 4-5. for
HUCs in North Carolina and in Figure 4-5 for the HUC in New Mexico. The modeled estimates are only
shown in Figure 4-5 and Figure 4-6 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 4-1 to Figure 4-6 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 was limited geographically and temporally.
Page 269 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
292 Figure 4-1. Concentrations of Trichloroethylene from Releasing Facilities (Higher Release
293 Frequency Scenarios) and WQX Monitoring Stations: Year 2016, East US.
294 [Note: All indirect releases are mapped at the receiving facility unless the receiving facility is unknown.]
3 - 787 jjg/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
El A Days of exceedance £ 20 days
States with no modeled or measured
concentrations
300
¦i Miles
295
296
Page 270 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
297
298
299
300
301
Figure 4-2. Concentrations of Trichloroethylene from Releasing Facilities (Higher Release
Frequency Scenarios) and WQX Monitoring Stations: Year 2016, West US.
[Note: All indirect releases are mapped at the receiving facility unless the receiving facility is unknown.]
Jj
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 2 20 days
1/ > States with no modeled or measured
concentrations
Page 271 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
302 Figure 4-3. Concentrations of Trichloroethylene from Releasing Facilities (20 Days of Release
303 Scenario) and WQX Monitoring Stations: Year 2016, East US.
304 [Note: All indirect releases are mapped at the receiving facility unless the receiving facility is unknown.]
305
Concentration Levels Concentration Type
¦ 788 - 51.999 (jg/L ~ Modeled - Direct Release (20 days/yr)
¦ 3 - 787 pg/L o Measured - NWIS/STORET Monitoring Sites
¦ < 3 pg/L (below all COCs) 21 Days of exceedance 2 20 days
¦ Not detected \y States with no modeled or measured
concentrations
Page 272 of 748
-------
306
307
308
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Figure 4-4. Concentrations of Trichloroethvlene Releasing Facilities (20 Days of Release Scenario)
and WQX Monitoring Stations: Year 2016, West US.
[Note: All indirect re
309
310
eases are mapped at the receiving facility unless the receiving facility is unknown.]
Concentration Levels Concentration Type
¦ 3 - 787 pg/L ~ Modeled - Direct Release (250 - 365 days/yr)
¦ < 3 yg/L (below all COCs) Measured - NWIS/STORET Monitoring Sites
¦ Not detected ~ Days of exceedance a 20 days
States with no modeled or measured
concentrations
Page 273 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
311 Figure 4-5. Co-location of Triehloroethylene-Releasing Facilities and WQX Monitoring Stations at
312 the HUC 8 Level in NC
313
Upper Dan
03010103
11(1000900446
110031424233
smith '¦¦;y
Mountain -¦ j
Late s /
II0001501492
110001489050
Haw
03030002
Upper Yadkin
03040101
110000345779
Upper Catawba
03050101
lohn hpKrlhteerfair Upper Tar
JhW 03020101
II0001504747
1110031398707
I
Upper Neuse
03020201
N( 0089494
Deep
03030003
Upper French Broad
06010105
Lower Yadkin
03040103
110007119974
110000345939
v , cm
Concentrations
Measured - NWIS/STORET Monitoring Sites
• Not detected
Modeled - Indirect Release (250 - 365 days/yr)
A Below all COC
Modeled - Direct Release (250 - 365 days/yr)
¦ Below all COC
L_JHUC-8 boundary
Northeast Cape Fear
03030007
100
i i Miles
\ i
3S TKej^ational Map: Najbnal Hydrography Dataset. Data refreshed October, 2018
Green
SiKrmn.
N( 00012281
Page 274 of 748
-------
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Figure 4-6. Co-location of Trichloroethylene-Releasing Facilities and WQX Monitoring Stations at
4.1.3 Risk Estimation for Sediment
EPA did not quantitatively assess exposure to sediment organisms, because TCE is not expected to
partition to sediment, based on physical-chemical properties. TCE is expected to remain in aqueous
phases and not adsorb to sediment due to its water solubility (> 1280 m g/L) and low partitioning to
organic matter (log Koc = 1.8-2.17). Limited sediment monitoring data for TCE that are available
suggest that TCE is present in sediments, but because TCE has relatively low partition to organic matter
(log Koc = 1.802.17) and biodegrades slowly [19% biodegradation in 28 days (ECB2004)],
TCE concentrations in sediment pore water are expected to be similar to the concentrations in the
overlying water or lower in the deeper part of sediment which anaerobic condition prevails. Thus, the
TCE detected in sediments is likely from the pore water.
4.1.4 Risk Estimation for Terrestrial
EPA did not quantitatively assess exposure to terrestrial organisms through soil, water, or biosolids.
TCE is not expected to partition to soil but is expected to volatilize to air, based on its physical-chemical
properties. Review of hazard data for terrestri al organisms shows potential hazard; however, physical-
chemical properties do not support an exposure pathway through water and soil pathways to terrestrial
organisms.
Page 275 of 748
-------
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. To further support this analysis, TCE was not detected in EPA's
Targeted National Sewage Sludge Survey (TNSSS) nor was it reported in biosolids during EPA's
Biennial Reviews for Biosolids, a robust biennial literature review conducted by EPA's Office of Water
{U.S. EPA, 2019, 5933985}. Furthermore, TCE is not anticipated to remain in soil, as it is expected to
either volatilize into air or migrate through soil into groundwater.
TCE is expected to volatilize to air, based on physicochemical properties. However, the emission
pathways to ambient air from commercial and industrial stationary sources or associated inhalation
exposure of terrestrial species were out of the scope of the risk evaluation because stationary source
releases of TCE to ambient air are adequately assessed and any risks effectively managed when under
the jurisdiction of the Clean Air Act (CAA).
Page 276 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
350 4.2 Human Health Risk
351
352
353
354
355
356
4.2.1 Risk Estimation Approach
The use scenarios, populations of interest and toxicological endpoints used for acute and chronic
exposures are are presented in Table 4-4.
Table 4-4. Use Scenarios, Populations of Interest and Toxicological Endpoints Used for Acute and
Chronic Exposures
Population of Interest and
Exposure Scenario
Workers: 1
Acute- Adolescent (>16 years old) and adult workers exposed to TCE for
a single 8-hr exposure
Chronic- Adolescent (>16 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) and adult worker exposed
to TCE indirectly by being in the same work area of the building
Consumers 1
Acute- Children (>11 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.
Health Effects,
Concentration and Time
Duration
Non-Cancer Point of Departures (POD):
HEC- ppm;
POD HECs represent 24hr values and exposure concentrations have been
adjusted to match the time duration for inhalation exposure.
Note: Selgrade 2010 POD is a 3h acute value that has been adjusted to
match the 24hr exposure value for workers (3h exposure values were
used for consumers to match available 3hr exposure estimates from
CEM).
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)
used in Non-Cancer Margin
of Exposure (MOE)
calculations
Benchmark MOEs: Vary by endpoint
Benchmark MOE = (UFS) x (UFA) x (UFH) x (UFL)5
1 Adult workers (>16 years old) include both healthy 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 4-4. Use could be extended to all users.
Page 277 of 748
-------
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
shown in Table 4-5 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.
5 UFs=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 assessing non-cancer risk. The MOE is the
ratio of the point of departure (POD) dose divided by the human exposure dose. The MOE is compared
to the benchmark MOE. If the MOE exceeds the benchmark MOE, this indicates the potential for risk to
human health.
Acute or chronic MOEs (MOEaCute or 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 J for more details on the derivation of chronic exposure values from acute
concentrations/doses.
Consumer risks via inhalation were calculated based on maximum Time-Weighted Average (TWAs) for
either 3h or 24h periods and consumer risks via dermal exposure were calculated based on Acute Dose
Rate (ADR). See Section 2.3.1.3.1 for more details on consumer exposure).
EPA used margin of exposures (MOEs) to estimate acute or chronic risks for non-cancer based on the
following:
• the most sensitive and robust HEDs within each health effects domain reported in the literature;
• the endpoint/study-specific UFs applied to the HEDs per EPA RfD Guidance ("U.S. EPA. 2002); and
• the exposure estimates calculated for TCE uses examined in this risk assessment (see Section 2.3 -
Human Exposures).
Page 278 of 748
-------
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
436
437
438
439
440
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 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 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.
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)
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 ( ). The volatile properties of TCE
Page 279 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (Kasting 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 Representative Points of Departure for Use in Risk Estimation
All PODs listed in Table 3-13 will be used for risk estimation of acute exposure scenarios. For chronic
exposure scenarios, due to the large number of relevant endpoints, risks will be assessed using a single
endpoint representative of each health domain. EPA considers all of the endpoints identified in Table
3-14 to be similarly relevant to human health 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
4-5. For complete MOE tables displaying risk estimates for all chronic endpoints, see [Risk Calculator
for Occupational Exposures. Docket: EPA-HQ-OPPT-2019-0500].
As described in (Section 3.2.6.4), EPA considers the POD for immunosuppression from (S el grade and
Gitmouf. 2010) to be the best overall representative endpoint for acute scenarios and autoimmunity from
(Keil et at.. 2009) to be the best overall representative 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 4-5: Most Sensitive Endpoints from Each Health Domain for Risk Estimation
of Chronic Exposure Scenarios
Target Organ/
System
POD Type
Effect
HEC99
(ppm)
HED99
(mg/kg)
Uncertainty
Factors (UFs)
Reference
Data
Quality
Developmental
Effects
BMDL01 —
0.0207mg/kg-
bw/day
Congenital heart defects
0.0037
0.0052
UFS=1;UFA=3;
UFh=3;UFl=1;
Total LTF= 10
(Johnson et aL,
2003)
Medium
Kidney
BMDL10 = 34
mg/kg-bw/day
Pathology changes in
renal tubule
0.025
0.015
UFS=1;UFA=3;
UFh=3;UFl=1;
Total UF=10
(Maltoni et aL,
1986)
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; UFL=3;
Total UF=30
(Keil et aL,
2009)
High
Reproductive
System
BMDL10 = 1.4
ppm
Decreased normal sperm
morphology and hyper-
zoospermia
0.5
0.73
LTFS=10;LTFA= 1;
UFH=3;UFL=1;
Total UF=30
(Chia et aL,
1996)
Medium
Nervous System
LOAEL = 12
ppm
Significant decreases in
wakefulness
4.8
6.5
LTFS=3; IIFA= 3;
LTFH=3; UFL=10;
Total UF=300
(Arito et aL,
1994)
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,1983)
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
Page 280 of 748
-------
470
471
472
473
474
475
476
All
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 either monitoring or modeling exposure data. Non-cancer endpoints were applied to acute and
chronic exposures while cancer risk estimates are provided for adjusted lifetime exposure. Both are
presented for exposure scenarios where both data types are reasonably available. All dermal risk
estimates are based on modeling data as discussed in Section 2.3.1.2.5. Although generally ONU
exposures are expected to be less than workers, when sufficient data was 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 exposure modeling data. 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-OPPT-2019-0500].
Page 281 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
498
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
4.3E-03
4.3E-02
0.21
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.0E-02
0.30
1.5
3.0E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.5
34.8
173.9
-
1.8
8.9
17.8
35.6
Central Tendency
24.0
239.9
1,199.4
24.0
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
26.7
266.6
1,333.0
-
12.2
60.8
121.5
243.0
Central Tendency
183.9
1,839.1
9,195.6
183.9
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
30
High End
2.0
20.2
100.8
-
1.2
5.9
11.9
23.8
Central Tendency
13.9
139.1
695.7
13.9
3.6
17.8
35.7
71.3
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
15.4
154.0
770.0
-
5.0
25.0
50.1
100.1
Central Tendency
106.2
1,062.4
5,311.8
106.2
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.2E-02
0.42
2.1
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.29
2.9
14.6
0.29
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.1
81.2
406.2
-
4.1
20.6
41.2
82.4
Central Tendency
56.0
560.4
2,801.8
56.0
12.4
61.8
123.5
247.1
Immunotoxicity
(Keil et al.. 2009)
30
High End
5.6E-02
0.56
2.8
-
3.0E-02
0.15
0.30
0.61
Central Tendency
0.39
3.9
19.3
0.39
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 1996)
30
High End
0.85
8.5
42.3
-
0.46
2.3
4.6
9.2
Central Tendency
5.8
58.4
291.9
5.8
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.3E-03
6.3E-02
0.31
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
4.3E-02
0.43
2.2
4.3E-02
9.9E-03
4.9E-02
9.9E-02
0.20
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 considered plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.
Page 282 of 748
-------
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Manufacturing utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table 4-6.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. 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. 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. 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 283 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
521
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
4.3E-03
4.3E-02
0.21
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.0E-02
0.30
1.5
3.0E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.5
34.8
173.9
-
1.8
8.9
17.8
35.6
Central Tendency
24.0
239.9
1,199.4
24.0
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
26.7
266.6
1,333.0
-
12.2
60.8
121.5
243.0
Central Tendency
183.9
1,839.1
9,195.6
183.9
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
30
High End
2.0
20.2
100.8
-
1.2
5.9
11.9
23.8
Central Tendency
13.9
139.1
695.7
13.9
3.6
17.8
35.7
71.3
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
15.4
154.0
770.0
-
5.0
25.0
50.1
100.1
Central Tendency
106.2
1,062.4
5,311.8
106.2
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.2E-02
0.42
2.1
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.29
2.9
14.6
0.29
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.1
81.2
406.2
-
4.1
20.6
41.2
82.4
Central Tendency
56.0
560.4
2,801.8
56.0
12.4
61.8
123.5
247.1
Immunotoxicity
(Keil et al.. 2009)
30
High End
5.6E-02
0.56
2.8
-
3.0E-02
0.15
0.30
0.61
Central Tendency
0.39
3.9
19.3
0.39
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 1996)
30
High End
0.85
8.5
42.3
-
0.46
2.3
4.6
9.2
Central Tendency
5.8
58.4
291.9
5.8
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.3E-03
6.3E-02
0.31
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
4.3E-02
0.43
2.2
4.3E-02
9.9E-03
4.9E-02
9.9E-02
0.20
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 considered plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.
Page 284 of 748
-------
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Processing as a Reactant utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table
4-7.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. 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. 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. 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 285 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
545
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)
30
High End
6.7E-02
0.67
3.4
0.57
1.2
5.9
11.9
23.8
Central Tendency
0.38
3.8
18.9
4.7
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
1.9E-03
1.9E-02
9.3E-02
1.6E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
1.0E-02
0.10
0.52
0.13
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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 286 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
546
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)
30
High End
1.3E-02
0.13
0.67
2.2E-02
1.2
5.9
11.9
23.8
Central Tendency
0.15
1.5
7.5
0.29
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
3.7E-04
3.7E-03
1.9E-02
6.1E-04
3.0E-02
0.15
0.30
0.61
Central Tendency
4.1E-03
4.1E-02
0.21
8.0E-03
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
547
Page 287 of 748
-------
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Batch Open Top Vapor l)egreasing utilized both monitoring and modeling inhalation exposure data (with dermal modeling).
Results are presented in Table 4-8 and Table 4-9.
Acute Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for all endpoints at both high-end and
central tendency exposure levels via both inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple
endpoints based on monitoring and for all endpoints based on modeling at both high-end and central tendency inhalation exposure levels.
Based on both monitoring and modeling data, MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure
levels even when assuming the highest plausible APF. MOEs remained below the benchmark MOE for 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 25.92ppm to 19.23 ppm and the central tendency acute exposure estimate from 4.60
ppm to 4.26 ppm. Chronic high-end and central tendency exposures are reduced from 17.75 ppm and 3.15 ppm to 13.17 ppm and 2.92 ppm,
respectively. Lifetime exposures are reduced from 9.10 ppm and 1.25 ppm to 6.75 ppm and 1.15 ppm, respectively. The reduced exposures do
not significantly affect the risk estimates, since exposures were only reduced by up to -30%. Based on PEL-capped exposure estimates, the
acute and chronic central tendency MOEs for the congenital heart defects endpoint (with benchmark MOE = 10) are 8.7E-04 and 1.3E-03,
respectively. The central tendency cancer extra risk (benchmark = 1E-04) is 2.6E-02. Therefore, the MOEs remains orders of magnitude
below the benchmark MOE (or above the benchmark for cancer risk) when using only PEL-capped exposure estimates. Full details are
provided in [Occupational Risk Estimate Calculator. Docket # EPA-HQ-OPPT-2019-0500].
Page 288 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
586
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)
30
High End
3.6
35.9
179.5
-
1.2
5.9
11.9
23.8
Central Tendency
11.4
114.0
570.1
11.4
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
9.9E-02
0.99
5.0
-
3.0E-02
0.15
0.30
0.61
Central Tendency
0.32
3.2
15.8
0.32
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
Page 289 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Batch Closed-Loop Vapor Degreasing 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. 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. 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. 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 290 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
613 Table 4-11. Occupational I
lisk Estimation - Conveyorized Vapor Degreasing - Inhalation IV
onitoring 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)
30
High End
0.11
1.1
5.4
-
1.2
5.9
11.9
23.8
Central Tendency
0.16
1.6
8.1
0.16
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
3.0E-03
3.0E-02
0.15
-
3.0E-02
0.15
0.30
0.61
Central Tendency
4.5E-03
4.5E-02
0.22
4.5E-03
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Chiaetal.. 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
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.
Page 291 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
614 Table 4-12. Occupational I
lisk Estimation - Conveyorized Vapor Degreasing - Inhalation IV
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)
30
High End
1.7E-03
1.7E-02
8.6E-02
2.8E-03
1.2
5.9
11.9
23.8
Central Tendency
0.13
1.3
6.4
0.22
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
4.7E-05
4.7E-04
2.4E-03
7.7E-05
3.0E-02
0.15
0.30
0.61
Central Tendency
3.5E-03
3.5E-02
0.18
6.2E-03
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
615
Page 292 of 748
-------
616
617
618
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
646
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Conveyorized Vapor Degreasing utilized both monitoring and modeling inhalation exposure data (with dermal modeling).
Results are presented in Table 4-11 and Table 4-12.
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. 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. 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. 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 293 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
647
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)
30
High End
0.37
3.7
18.5
0.55
1.2
5.9
11.9
23.8
Central Tendency
0.88
8.8
43.9
1.7
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
1.0E-02
0.10
0.51
1.5E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
2.4E-02
0.24
1.2
4.6E-02
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
648
Page 294 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Web Vapor I)egreasing utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-13.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at 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 295 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
675
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)
30
High End
9.1E-02
0.91
4.6
0.15
1.2
5.9
11.9
23.8
Central Tendency
1.6
15.7
78.4
2.8
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
2.5E-03
2.5E-02
0.13
4.2E-03
3.0E-02
0.15
0.30
0.61
Central Tendency
4.3E-02
0.43
2.2
7.9E-02
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
676
677
Page 296 of 748
-------
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Cold Cleaning utilized modeling 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 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 297 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
700
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)
30
High End
0.22
2.2
10.9
5.0
0.76
3.8
7.6
15.1
Central Tendency
0.68
6.8
34.2
37.3
2.3
11.4
22.7
45.4
CHRONIC NON-CANCER
Liver
(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
Immunotoxicity
(Keil et al.. 2009)
30
High End
6.0E-03
6.0E-02
0.30
0.14
1.9E-02
9.7E-02
0.19
0.39
Central Tendency
1.9E-02
0.19
0.95
1.0
5.8E-02
0.29
0.58
1.2
Reproductive Toxicity
(Cilia etal.. 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
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.
701
702
Page 298 of 748
-------
703
704
705
706
707
708
709
710
711
712
713
714
715
716
111
718
719
720
721
722
723
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Aerosol Applications utilized modeling inhalation exposure data (with dermal modeling) and are presented in Table 4-15.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via inhalation
and 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 299 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
724
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)
30
High End
1.8
18.3
91.5
-
0.76
3.8
7.6
Central Tendency
13.6
136.3
681.7
13.6
2.3
11.4
22.7
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
4.9E-02
0.49
2.5
-
1.7E-02
8.3E-02
0.17
Central Tendency
0.37
3.7
18.3
0.37
5.6E-02
0.28
0.56
Reproductive Toxicity
(Cilia etal.. 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
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. The highest PPE scenarios displayed are plausible for this exposure scenario.
1 EPA is unable to estimate ONU exposures separately from workers.
2 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
Page 300 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
725
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)
30
High End
1.9
18.8
94.2
3.0
0.76
3.8
7.6
Central Tendency
5.4
54.3
271.6
10.9
2.3
11.4
22.7
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
5.1E-02
0.51
2.5
8.0E-02
1.7E-02
8.3E-02
0.17
Central Tendency
0.15
1.5
7.3
0.29
5.6E-02
0.28
0.56
Reproductive Toxicity
(Cilia etal.. 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
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. 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).
726
Page 301 of 748
-------
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
754
755
756
757
758
759
760
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 4-16 and Table 4-17.
Acute Non-Cancer Risk Estimates:
Based on both monitoring and modeling data, MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end
and central tendency exposure levels via both inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from
workers based on monitoring data. ONU risk estimates were below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels based on modeling data. Based on both monitoring and modeling data, MOEs remained below the
benchmark MOE for 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. 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. 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.
Therefore, the use of respirators is unlikely for workers in these facilities.
Page 302 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
761
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)
30
High End
4.6
45.8
228.9
-
1.2
5.9
11.9
23.8
Central Tendency
10,546.0
105,459.9
527,299.6
10,546.0
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
0.13
1.3
6.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
292.0
2,920.1
14,600.7
292.0
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
762
Page 303 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Formulation of Aerosol and Non-Aerosol Products utilized monitoring 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 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. 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 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. 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. 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 304 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
791
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)
30
High End
4.6
45.8
228.9
-
1.2
5.9
11.9
23.8
Central Tendency
10,546.0
105,459.9
527,299.6
10,546.0
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
0.13
1.3
6.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
292.0
2,920.1
14,600.7
292.0
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
792
Page 305 of 748
-------
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Repackaging utilized monitoring 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 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. MOEs were below the benchmark MOE for multiple endpoints at both dermal exposure levels. MOEs remained below the
benchmark MOE for multiple endpoints at high-end inhalation exposure even when assuming the highest plausible APF. MOEs remained
below the benchmark MOE for 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 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. 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. Risk
estimates were above the benchmark at both dermal exposure levels. Risk estimates were not above the benchmark for high tendency
inhalation exposure when assuming APF = 50. Risk estimates remained above the benchmark for cancer at both dermal exposure levels even
when assuming the highest plausible glove PF protection.
Page 306 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
828 Table 4-20. Occupational I
iisk Estimation - Metalworking Fluids - Inhalation Monitoring I
>ata
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)
30
High End
6.9E-02
0.69
3.5
-
1.5
7.4
14.9
29.7
Central Tendency
7.5E-02
0.75
3.7
7.5E-02
4.5
22.3
44.6
89.2
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
1.9E-03
1.9E-02
9.6E-02
-
3.8E-02
0.19
0.38
0.76
Central Tendency
2.1E-03
2.1E-02
0.10
2.1E-03
0.11
0.57
1.1
2.3
Reproductive Toxicity
(Cilia etal.. 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
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.
Page 307 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
829
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)
30
High End
20.1
200.8
1,003.8
-
1.5
7.4
14.9
29.7
Central Tendency
74.6
745.7
3,728.6
74.6
4.5
22.3
44.6
89.2
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
0.55
5.5
27.5
-
3.8E-02
0.19
0.38
0.76
Central Tendency
2.1
20.6
103.1
2.1
0.11
0.57
1.1
2.3
Reproductive Toxicity
(Cilia etal.. 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
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.
830
Page 308 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE calculations for Metalworking Fluids utilized both monitoring and modeling inhalation exposure data (with dermal modeling). Results
are presented in Table 4-20 and Table 4-21.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints based on monitoring and for 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. 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 based on monitoring data.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints based on monitoring and for multiple endpoints based on modeling at
both high-end and central tendency exposure levels via inhalation. Based on both monitoring and modeling data, EPA is unable to estimate
ONU exposures separately from workers. 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. 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 309 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
861
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)
30
High End
0.13
1.3
6.6
5.2
1.3
6.6
13.2
26.4
Central Tendency
1.1
11.3
56.3
5.5
4.0
19.8
39.6
79.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
3.7E-03
3.7E-02
0.18
0.14
3.4E-02
0.17
0.34
0.68
Central Tendency
3.1E-02
0.31
1.6
0.15
0.10
0.51
1.0
2.0
Reproductive Toxicity
(Cilia etal.. 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
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 310 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Adhesives, Sealants, Paints, and Coatings (Industrial Setting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-22. Inhalation exposures are estimated to be identical for industrial and commercial workers.
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
even when assuming the highest plausible glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for all endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when assuming the highest plausible APF and glove PF protection.
Cancer Risk Estimates:
Extra risk estimates for workers were above the benchmark level for cancer at both high-end and central tendency exposure levels via both
inhalation and dermal routes. Risk estimates for ONUs were also above the benchmark for cancer at both high-end and central tendency
inhalation exposure levels. Risk estimates remained above the benchmark for cancer at both exposure levels via dermal and inhalation routes
even when assuming the highest plausible APF and glove PF protection.
Page 311 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
888
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)
30
High End
0.13
1.3
6.6
5.2
0.84
4.2
8.4
Central Tendency
1.1
11.3
56.3
5.5
2.5
12.6
25.2
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
3.7E-03
3.7E-02
0.18
0.14
2.2E-02
0.11
0.22
Central Tendency
3.1E-02
0.31
1.6
0.15
6.5E-02
0.32
0.65
Reproductive Toxicity
(Cilia etal.. 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
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 under a rigorous PPE program.
1 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
889
Page 312 of 748
-------
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Adhesives, Sealants, Paints, and Coatings (Commercial Setting) utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-23. Inhalation exposures are estimated to be identical for industrial and commercial settings.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via
inhalation and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central
tendency inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both exposure levels via dermal
and inhalation routes even when 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 313 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
915
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)
30
High End
0.27
2.7
13.6
1.2
1.2
5.9
11.9
23.8
Central Tendency
0.82
8.2
40.9
2.7
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
7.5E-03
7.5E-02
0.38
3.3E-02
3.0E-02
0.15
0.30
0.61
Central Tendency
2.3E-02
0.23
1.1
7.3E-02
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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.
916
917
Page 314 of 748
-------
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Industrial Processing Aid utilized 12hr monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-24.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for most endpoints at both high-end and central tendency exposure levels via inhalation
and dermal routes. MOEs for ONUs were also below the benchmark MOE for multiple endpoints at both high-end and central tendency
inhalation exposure levels. MOEs remained below the benchmark MOE for multiple endpoints at both inhalation exposure levels even when
assuming the highest plausible APF. MOEs remained below the benchmark MOE for 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 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 315 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
946
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)
30
High End
2.5
24.9
124.6
-
2.2
10.8
21.6
Central Tendency
61.4
614.1
3,070.6
61.4
6.5
32.5
64.9
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
6.9E-02
0.69
3.4
-
5.5E-02
0.28
0.55
Central Tendency
1.7
17.0
85.0
1.7
0.17
0.83
1.7
Reproductive Toxicity
(Cilia etal.. 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
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. The highest PPE scenarios displayed are plausible for this exposure scenario under a rigorous PPE program.
1 EPA is unable to estimate ONU exposures separately from workers.
2 Glove PF =20 is only applicable to industrial settings (See Section 2.3.1).
947
Page 316 of 748
-------
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Commercial Printing and Copying utilized monitoring inhalation exposure data (with dermal modeling) and are presented in
Table 4-25.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE 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. 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. 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. 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 317 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
977
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
4.3E-03
4.3E-02
0.21
-
2.3E-03
1.1E-02
2.3E-02
4.5E-02
Central Tendency
3.0E-02
0.30
1.5
3.0E-02
6.8E-03
3.4E-02
6.8E-02
0.14
Developmental -
Neurotoxicity
(Fredriksson et al.. 1993)
100
High End
3.5
34.8
173.9
-
1.8
8.9
17.8
35.6
Central Tendency
24.0
239.9
1,199.4
24.0
5.3
26.7
53.4
106.7
Developmental -
Mortality
(Narotskv et al.. 1995)
10
High End
26.7
266.6
1,333.0
-
12.2
60.8
121.5
243.0
Central Tendency
183.9
1,839.1
9,195.6
183.9
36.5
182.3
364.5
729.0
Immunotoxicity -
Immunosuppression
(Selarade and Gilmour. 2010)
100
High End
2.0
20.2
100.8
-
1.2
5.9
11.9
23.8
Central Tendency
13.9
139.1
695.7
13.9
3.6
17.8
35.7
71.3
CHRONIC NON-CANCER
Liver
(Kiellstrand et al.. 1983)
10
High End
15.4
154.0
770.0
-
5.0
25.0
50.1
100.1
Central Tendency
106.2
1,062.4
5,311.8
106.2
15.0
75.1
150.2
300.3
Kidney
(Maltoni et al.. 1986)
10
High End
4.2E-02
0.42
2.1
-
9.5E-03
4.8E-02
9.5E-02
0.19
Central Tendency
0.29
2.9
14.6
0.29
2.9E-02
0.14
0.29
0.57
Neurotoxicity
(Arito et al.. 1994)
300
High End
8.1
81.2
406.2
-
4.1
20.6
41.2
82.4
Central Tendency
56.0
560.4
2,801.8
56.0
12.4
61.8
123.5
247.1
Immunotoxicity
(Keil et al.. 2009)
30
High End
5.6E-02
0.56
2.8
-
3.0E-02
0.15
0.30
0.61
Central Tendency
0.39
3.9
19.3
0.39
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 1996)
30
High End
0.85
8.5
42.3
-
0.46
2.3
4.6
9.2
Central Tendency
5.8
58.4
291.9
5.8
1.4
6.9
13.9
27.7
Developmental Toxicity
(Johnson et al.. 2003)
10
High End
6.3E-03
6.3E-02
0.31
-
3.3E-03
1.6E-02
3.3E-02
6.6E-02
Central Tendency
4.3E-02
0.43
2.2
4.3E-02
9.9E-03
4.9E-02
9.9E-02
0.20
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.
Page 318 of 748
-------
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Other Industrial Uses utilized monitoring inhalation exposure data (with dermal modeling) and are presented in Table 4-26.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at both high-end and central tendency exposure levels via both
inhalation and dermal routes. EPA is unable to estimate ONU exposures separately from workers. 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
inhalation and for all endpoints via the dermal route. EPA is unable to estimate ONU exposures separately from workers. 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. EPA is unable to estimate ONU exposures separately from workers. 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.
Page 319 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
999
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)
100
High End
4.6
45.8
228.9
-
1.2
5.9
11.9
23.8
Central Tendency
10,546.0
105,459.9
527,299.6
10,546.0
3.6
17.8
35.7
71.3
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
Immunotoxicity
(Keil et al.. 2009)
30
High End
0.13
1.3
6.3
-
3.0E-02
0.15
0.30
0.61
Central Tendency
292.0
2,920.1
14,600.7
292.0
9.1E-02
0.46
0.91
1.8
Reproductive Toxicity
(Cilia etal.. 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
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 320 of 748
-------
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE results for Process Solvent Recycling and Worker Handling of Wastes utilized monitoring inhalation exposure data (with dermal
modeling) and are presented in Table 4-27.
Acute Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. 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 and at both dermal exposure levels even when assuming the
highest plausible APF and glove PF protection.
Chronic Non-Cancer Risk Estimates:
MOEs for workers were below the benchmark MOE for multiple endpoints at high-end inhalation exposures, but MOEs were above the
benchmark MOE for all endpoints at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from
workers. 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 above
the benchmark MOE for cancer at central tendency inhalation exposures. EPA is unable to estimate ONU exposures separately from workers.
Risk estimates were not above the benchmark at central tendency inhalation exposure when assuming APF = 50. Risk estimates remained
above the benchmark at both dermal exposure levels even when assuming the highest plausible glove PF protection.
Page 321 of 748
-------
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 2.3.2.6.1 for
more details on the characterization of consumer exposure and [CEMModeling Results and Risk
Estimates. Docket # EPA-HQ-OPPT-2019-0500] for MOE estimates of all modeled scenarios.
As discussed in Section 2.3.2.2, 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 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 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. Therefore, based on reasonably available information, EPA did not develop risk estimates for
this population.
Page 322 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1075 Table 4-28. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Brake and Parts
1076 Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
3.5E-02
Bystander
2.2E-04
1.8E-01
1.4
0.14
Moderate-
Intensity
User
User
4.1E-04
0.33
2.5
0.21
Bystander
1.6E-03
1.3
10
0.94
Low-
Intensity
User
User
5.2E-03
4.2
32
2.7
Bystander
2.0E-02
17
127
12
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
6.8E-05
5.4E-02
0.37
3.6E-02
Children (16-20 years)
7.3E-05
5.7E-02
0.39
3.8E-02
Children (11-15 years)
6.7E-05
5.3E-02
0.36
3.5E-02
Moderate-
Intensity
User
Adult (>21 years)
9.1E-04
0.72
4.9
0.48
Children (16-20 years)
9.7E-04
0.77
5.2
0.51
Children (11-15 years)
8.9E-04
0.70
4.8
0.47
Low-
Intensity
User
Adult (>21 years)
4.1E-02
32
220
22
Children (16-20 years)
4.4E-02
34
235
23
Children (11-15 years)
4.0E-02
32
215
21
1077
1078 MOE results for Brake and Parts Cleaner are presented in Table 4-28.
1079
1080 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1081 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1082 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1083 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1084 levels.
Page 323 of 748
-------
1085
1086
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-29. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al.. 19931
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
5.0E-02
Bystander
4.9E-04
0.40
3.0
0.28
Moderate-
Intensity
User
User
2.3E-03
1.9
15
1.2
Bystander
1.3E-02
10
78
7.1
Low-
Intensity
User
User
6.7E-02
54
414
33
Bystander
0.34
277
2123
193
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
MOE results for Aerosol Electronic Degreaser Cleaner are presented in Table 4-29.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for congenital heart defects at high, medium, and low-intensity user
inhalation exposure levels and for multiple endpoints at high and medium-intensity exposure levels.
Page 324 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1103 Table 4-30. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Electronic
1104 Degreaser/Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
1.0E-04
8.3E-02
0.64
5.2E-02
Bystander
5.1E-04
0.41
3.2
0.29
Moderate-
Intensity
User
User
1.6E-03
1.3
9.9
0.79
Bystander
8.5E-03
6.9
53
4.8
Low-
Intensity
User
User
2.1E-02
17
132
11
Bystander
0.11
88
674
61
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
1.2E-04
9.5E-03
0.65
6.4E-02
Children (16-20 years)
1.3E-04
0.10
0.70
6.8E-02
Children (11-15 years)
1.2E-04
9.3E-02
0.64
6.2E-02
Moderate-
Intensity
User
Adult (>21 years)
1.8E-03
1.4
9.7
9.5E-01
Children (16-20 years)
1.9E-03
1.5
10
1.0
Children (11-15 years)
1.8E-03
1.4
9.6
9.4E-01
Low-
Intensity
User
Adult (>21 years)
7.3E-03
5.7
39
3.8
Children (16-20 years)
7.8E-03
6.1
42
4.1
Children (11-15 years)
7.1E-03
5.6
38
3.7
1105
1106 MOE results for Liquid Electronic Degreaser/Cleaner are presented in Table 4-30.
1107
1108 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1109 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1110 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1111 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1112 levels.
1113
Page 325 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1114 Table 4-31. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Spray
5 Degreaser/Cleaner
Benchmark
10
100
10
30
Scenario
Consumer
Receptor
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
2.3E-05
1.8E-02
0.14
1.2E-02
Bystander
7.9E-05
6.4E-02
0.49
4.9E-02
Moderate-
Intensity
User
User
9.0E-05
7.3E-02
0.56
4.6E-02
Bystander
3.6E-04
0.29
2.2
0.21
Low-
Intensity
User
User
6.0E-04
0.48
3.7
0.31
Bystander
2.5E-03
2.0
15
1.4
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
7.3E-05
5.7E-02
0.39
3.8E-02
Children (16-20 years)
7.8E-05
6.1E-02
0.42
4.1E-02
Children (11-15 years)
7.1E-05
5.6E-02
0.38
3.7E-02
Moderate-
Intensity
User
Adult (>21 years)
5.8E-04
0.46
3.1
0.31
Children (16-20 years)
6.2E-04
0.49
3.3
0.33
Children (11-15 years)
5.7E-04
0.45
3.1
0.30
Low-
Intensity
User
Adult (>21 years)
2.9E-03
2.3
16
1.5
Children (16-20 years)
3.1E-03
2.4
17
1.6
Children (11-15 years)
2.8E-03
2.2
15
1.5
6
1117 MOE results for Aerosol Spray Degi'eciser Cleaner are presented in Table 4-31.
1118
1119 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1120 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1121 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1122 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1123 levels.
1124
Page 326 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1125 Table 4-32. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid
1126 Degreaser/Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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-05
2.0E-02
0.16
1.3E-02
Bystander
1.0E-04
8.3E-02
0.64
6.1E-02
Moderate-
Intensity
User
User
2.4E-04
0.19
1.5
0.12
Bystander
1.2E-03
1.0
7.8
0.70
Low-
Intensity
User
User
1.4E-03
1.2
8.8
0.71
Bystander
7.6E-03
6.2
47
4.3
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
3.0E-05
2.4E-02
0.16
1.6E-02
Children (16-20 years)
3.2E-05
2.6E-02
0.17
1.7E-02
Children (11-15 years)
3.0E-05
2.3E-02
0.16
1.6E-02
Moderate-
Intensity
User
Adult (>21 years)
2.4E-04
0.19
1.3
0.13
Children (16-20 years)
2.6E-04
0.20
1.4
0.14
Children (11-15 years)
2.4E-04
0.19
1.3
0.13
Low-
Intensity
User
Adult (>21 years)
1.8E-03
1.4
9.8
0.96
Children (16-20 years)
1.9E-03
1.5
10
1.0
Children (11-15 years)
1.8E-03
1.4
9.6
0.94
1127
1128 MOE results for Liquid Degreaser/Cleaner are presented in Table 4-32.
1129
1130 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1131 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1132 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1133 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1134 levels.
1135
Page 327 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1136 Table 4-33. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Gun
1137 Scrubber
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
5.0E-02
40
309
26
Bystander
0.20
164
1255
120
Moderate-
Intensity
User
User
4.7E-02
38
294
24
Bystander
0.25
202
1551
141
Low-
Intensity
User
User
8.1E-02
66
506
41
Bystander
0.44
354
2715
247
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
7.5E-05
5.9E-02
0.41
4.0E-02
Children (16-20 years)
8.1E-05
6.4E-02
0.43
4.2E-02
Children (11-15 years)
7.4E-05
5.8E-02
0.40
3.9E-02
Moderate-
Intensity
User
Adult (>21 years)
6.0E-04
0.48
3.2
0.32
Children (16-20 years)
6.4E-04
0.51
3.5
0.34
Children (11-15 years)
5.9E-04
0.46
3.2
0.31
Low-
Intensity
User
Adult (>21 years)
7.5E-03
5.9
41
4.0
Children (16-20 years)
8.0E-03
6.3
43
4.2
Children (11-15 years)
7.3E-03
5.8
40
3.9
1138
1139 MOE results for Aerosol Gun Scrubber are presented in Table 4-33.
1140
1141 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1142 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1143 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1144 benchmark MOE for congenital heart defects at high, medium, and low-intensity user inhalation
1145 exposure levels.
Page 328 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1146 Table 4-34. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Gun
7 Scrubber
Benchmark
10
100
10
30
Scenario
Consumer
Receptor
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
5.8E-02
47
361
30
Bystander
0.24
191
1465
140
Moderate-
Intensity
User
User
5.5E-02
45
343
28
Bystander
0.29
236
1809
164
Low-
Intensity
User
User
5.9E-02
48
370
30
Bystander
0.30
247
1893
172
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
3.3E-05
2.6E-02
0.18
1.7E-02
Children (16-20 years)
3.5E-05
2.7E-02
0.19
1.8E-02
Children (11-15 years)
3.2E-05
2.5E-02
0.17
1.7E-02
Moderate-
Intensity
User
Adult (>21 years)
2.6E-04
0.21
1.4
0.14
Children (16-20 years)
2.8E-04
0.22
1.5
0.15
Children (11-15 years)
2.5E-04
0.20
1.4
0.13
Low-
Intensity
User
Adult (>21 years)
1.9E-03
1.5
10
1.0
Children (16-20 years)
2.1E-03
1.6
11
1.1
Children (11-15 years)
1.9E-03
1.5
10
1.0
48
1149 MOE results for Liquid Gun Scrubber are presented in Table 4-34.
1150
1151 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1152 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1153 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1154 benchmark MOE for congenital heart defects at high, medium, and low-intensity user inhalation
1155 exposure levels.
1156
1157
1158
Page 329 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
2.3E-04
0.18
1.4
0.11
Bystander
1.1E-03
0.91
7.0
0.64
Moderate-
Intensity
User
User
2.1E-03
1.7
13
1.1
Bystander
1.1E-02
9.2
71
6.4
Low-
Intensity
User
User
2.1E-02
17
130
11
Bystander
0.11
87
667
61
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1160
1161
1162
1163
1164
1165
1166
1167
1168
MOE results for Mold Release are presented in Table 4-35.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 330 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1169 Table 4-36. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Aerosol Tire
1170 Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
2.4E-04
0.19
1.5
0.13
Bystander
5.4E-04
0.44
3.4
0.32
Moderate-
Intensity
User
User
8.9E-04
0.72
5.5
0.46
Bystander
3.6E-03
2.9
22
2.0
Low-
Intensity
User
User
6.4E-03
5.2
40
3.3
Bystander
2.6E-02
21
164
15
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
3.3E-04
0.26
1.8
0.17
Children (16-20 years)
3.5E-04
0.28
1.9
0.19
Children (11-15 years)
3.2E-04
0.26
1.7
0.17
Moderate-
Intensity
User
Adult (>21 years)
1.3E-03
1.0
7.1
0.70
Children (16-20 years)
1.4E-03
1.1
7.6
0.74
Children (11-15 years)
1.3E-03
1.0
6.9
0.68
Low-
Intensity
User
Adult (>21 years)
5.7E-03
4.5
31
3.0
Children (16-20 years)
6.0E-03
4.8
33
3.2
Children (11-15 years)
5.5E-03
4.4
30
2.9
1171
1172 MOE results for Aerosol Tire Cleaner are presented in Table 4-36.
1173
1174 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1175 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1176 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1177 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1178 levels.
1179
Page 331 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1180 Table 4-37. Consumer Risk Estimation - Solvents for Cleaning and Degreasing - Liquid Tire
1181 Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
7.8E-05
6.3E-02
0.48
4.2E-02
Bystander
2.4E-04
0.20
1.5
0.14
Moderate-
Intensity
User
User
4.0E-04
0.32
2.5
0.21
Bystander
1.6E-03
1.3
9.9
0.92
Low-
Intensity
User
User
2.0E-03
1.6
12
1.0
Bystander
8.3E-03
6.7
51
4.7
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
5.9E-05
4.7E-02
0.32
3.1E-02
Children (16-20 years)
6.3E-05
5.0E-02
0.34
3.3E-02
Children (11-15 years)
5.8E-05
4.6E-02
0.31
3.0E-02
Moderate-
Intensity
User
Adult (>21 years)
2.4E-04
0.19
1.3
0.12
Children (16-20 years)
2.5E-04
0.20
1.4
0.13
Children (11-15 years)
2.3E-04
0.18
1.2
0.12
Low-
Intensity
User
Adult (>21 years)
7.1E-04
0.56
3.8
0.37
Children (16-20 years)
7.6E-04
0.60
4.1
0.40
Children (11-15 years)
6.9E-04
0.55
3.7
0.37
1182
1183 MOE results for Liquid Tire Cleaner are presented in Table 4-37.
1184
1185 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1186 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1187 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1188 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1189 levels.
1190
Page 332 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson 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
0.13
Bystander
1.3E-03
1.0
7.8
0.71
Moderate-
Intensity
User
User
2.4E-03
1.9
15
1.2
Bystander
1.3E-02
10
79
7.1
Low-
Intensity
User
User
1.3E-02
11
83
6.8
Bystander
4.3E-02
35
270
28
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1192
1193
1194
1195
1196
1197
1198
1199
MOE results for Tap and Die Fluid are presented in Table 4-38.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 333 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
0 Table 4-39. Consumer I
lisk Estimation - Lubricants and Greases - Penetrating Lubricant
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson 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
0.16
Bystander
1.6E-03
1.3
9.8
0.89
Moderate-
Intensity
User
User
5.4E-03
4.4
33
2.7
Bystander
2.9E-02
23
179
16
Low-
Intensity
User
User
0.17
139
1065
86
Bystander
0.88
712
5460
496
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1202 MOE results for Penetrating Lubricant are presented in Table 4-39.
1203
1204 MOEs for consumer users were below the benchmark MOE for congenital heart defects at high,
1205 medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and medium-
1206 intensity exposure levels. Dermal exposure was not quantified. MOEs for bystanders were below the
1207 benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
1208 defects at all user intensity inhalation exposure levels.
1209
Page 334 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1210 Table 4-40. Consumer Risk Estimation - Adhesives and Sealants - Solvent-Based Adhesive and
Sealant
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
1.1E-04
9.3E-02
0.71
5.6E-02
Bystander
9.1E-04
0.74
5.7
0.52
Moderate-
Intensity
User
User
3.7E-03
3.0
23
1.8
Bystander
3.6E-02
29
223
20
Low-
Intensity
User
User
0.42
340
2604
207
Bystander
2.8
2300
17636
1602
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1212
1213 MOE results for Solvent-Based Adhesive and Sealant are presented in Table 4-40.
1214
1215 MOEs for consumer users were below the benchmark MOE for congenital heart defects at high,
1216 medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and medium-
1217 intensity exposure levels. Dermal exposure was not quantified. MOEs for bystanders were below the
1218 benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
1219 defects at all user intensity inhalation exposure levels.
1220
Page 335 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-41. Consumer I
lisk Estimation - Adhesives and Sealants - Mirror Edge Sealant
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
1.1E-03
0.90
6.9
0.57
Bystander
4.7E-03
3.8
29
2.7
Moderate-
Intensity
User
User
3.3E-03
2.7
21
1.7
Bystander
1.8E-02
15
114
10
Low-
Intensity
User
User
0.17
134
1028
83
Bystander
0.91
737
5651
513
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1222
1223 MOE results for Mirror Edge Sealant are presented in Table 4-41.
1224
1225 MOEs for consumer users were below the benchmark MOE for congenital heart defects at high,
1226 medium, and low-intensity inhalation exposure levels and for multiple endpoints at high and medium-
1227 intensity exposure levels. Dermal exposure was not quantified. MOEs for bystanders were below the
1228 benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
1229 defects at all user intensity inhalation exposure levels.
1230
Page 336 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-42. Consumer I
lisk Estimation - Adhesives and Sealants - Tire Repair Cement / Sealer
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
3.1E-04
0.25
1.9
0.17
Bystander
9.7E-04
0.79
6.1
0.57
Moderate-
Intensity
User
User
5.6E-03
4.5
35
2.9
Bystander
2.3E-02
18
141
13
Low-
Intensity
User
User
6.2E-02
50
385
32
Bystander
0.23
188
1444
133
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1232
1233 MOE results for Tire Repair Cement Sealer are presented in Table 4-42.
1234
1235 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1236 low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
1237 were below the benchmark MOE for multiple endpoints for high and medium-intensity users and for
1238 congenital heart defects at all user intensity inhalation exposure levels.
1239
1240
Page 337 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1 Table 4-43. Consumer I
lisk Estimation - Cleaning and Furniture Care Products - Carpet Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
7.0E-05
5.7E-02
0.44
3.6E-02
Bystander
3.2E-04
0.26
2.0
0.18
Moderate-
Intensity
User
User
5.8E-04
0.47
3.6
0.29
Bystander
2.9E-03
2.4
18
1.7
Low-
Intensity
User
User
3.4E-03
2.7
21
1.7
Bystander
1.6E-02
13
99
9.0
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
1.1E-04
8.8E-02
0.60
5.9E-02
Children (16-20 years)
1.2E-04
9.4E-02
0.64
6.3E-02
Children (11-15 years)
1.1E-04
8.6E-02
0.59
5.7E-02
Moderate-
Intensity
User
Adult (>21 years)
6.7E-04
0.53
3.6
0.35
Children (16-20 years)
7.1E-04
0.56
3.8
0.38
Children (11-15 years)
6.6E-04
0.52
3.5
0.35
Low-
Intensity
User
Adult (>21 years)
1.3E-02
11
72
7.1
Children (16-20 years)
1.4E-02
11
77
7.5
Children (11-15 years)
1.3E-02
10
70
6.9
42
1243 MOE results for Carpet Cleaner are presented in Table 4-43.
1244
1245 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1246 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1247 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1248 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1249 levels.
1250
Page 338 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1251 Table 4-44. Consumer Risk Estimation - Cleaning and Furniture Care Products - Aerosol Spot
1252 Remover
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
1.1E-04
9.3E-02
0.71
5.6E-02
Bystander
1.1E-03
0.87
6.7
0.61
Moderate-
Intensity
User
User
9.8E-04
0.79
6.1
0.47
Bystander
9.9E-03
8.0
61
5.6
Low-
Intensity
User
User
6.5E-03
5.3
41
3.2
Bystander
5.4E-02
43
333
30
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
9.4E-04
0.74
5.1
0.50
Children (16-20 years)
1.0E-03
0.79
5.4
0.53
Children (11-15 years)
9.2E-04
0.73
5.0
0.49
Moderate-
Intensity
User
Adult (>21 years)
5.7E-03
4.5
31
3.0
Children (16-20 years)
6.0E-03
4.8
33
3.2
Children (11-15 years)
5.5E-03
4.4
30
2.9
Low-
Intensity
User
Adult (>21 years)
5.7E-02
45
305
30
Children (16-20 years)
6.0E-02
48
325
32
Children (11-15 years)
5.5E-02
44
297
29
1253
1254 MOE results for Aerosol Spot Remover are presented in Table 4-44.
1255
1256 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1257 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1258 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1259 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1260 levels.
1261
Page 339 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1262 Table 4-45. Consumer Risk Estimation - Cleaning and Furniture Care Products - Liquid Spot
1263 Remover
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
9.3E-05
7.5E-02
0.58
4.7E-02
Bystander
4.6E-04
0.37
2.9
0.26
Moderate-
Intensity
User
User
7.8E-04
0.63
4.9
0.39
Bystander
4.2E-03
3.4
26
2.4
Low-
Intensity
User
User
6.8E-03
5.5
42
3.4
Bystander
3.4E-02
28
214
19
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
1.6E-04
0.13
0.87
8.5E-02
Children (16-20 years)
1.7E-04
0.14
0.93
9.1E-02
Children (11-15 years)
1.6E-04
0.13
0.85
8.4E-02
Moderate-
Intensity
User
Adult (>21 years)
9.8E-04
0.77
5.3
0.51
Children (16-20 years)
1.0E-03
0.82
5.6
0.55
Children (11-15 years)
9.5E-04
0.75
5.1
0.50
Low-
Intensity
User
Adult (>21 years)
1.5E-02
12
79
7.7
Children (16-20 years)
1.6E-02
12
84
8.2
Children (11-15 years)
1.4E-02
11
77
7.5
1264
1265 MOE results for Liquid Spot Remover are presented in Table 4-45.
1266
1267 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1268 low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
1269 benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
1270 benchmark MOE for multiple endpoints at high, medium, and low-intensity user inhalation exposure
1271 levels.
1272
Page 340 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1273 Table 4-46. Consumer Risk Estimation - Arts, Crafts, and Hobby Materials - Fixatives and
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
4.0E-04
0.32
2.5
0.20
Bystander
1.6E-03
1.3
10
0.92
Moderate-
Intensity
User
User
2.5E-03
2.0
15
1.2
Bystander
1.3E-02
11
83
7.6
Low-
Intensity
User
User
1.3E-02
10
79
6.4
Bystander
6.5E-02
53
407
37
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1275
1276
1277
1278
1279
1280
1281
1282
MOE results for Fixatives and Finishing Spray Coatings are presented in Table 4-46.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 341 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
1.1E-03
0.89
6.8
0.55
Bystander
5.5E-03
4.4
34
3.1
Moderate-
Intensity
User
User
1.1E-02
8.8
67
5.4
Bystander
5.9E-02
48
366
33
Low-
Intensity
User
User
6.2E-02
50
386
31
Bystander
3.2E-01
258
1977
180
Dermal Exposure
High-
Intensity
User
Adult (>21 years)
1.7E-03
1.4
9.3
0.91
Children (16-20 years)
1.8E-03
1.45
9.9
0.97
Children (11-15 years)
1.7E-03
1.3
9.1
0.89
Moderate-
Intensity
User
Adult (>21 years)
1.0E-02
8.2
56
5.5
Children (16-20 years)
1.1E-02
8.7
60
5.8
Children (11-15 years)
1.0E-02
8.0
54
5.3
Low-
Intensity
User
Adult (>21 years)
0.10
82
560
55
Children (16-20 years)
0.11
87
596
58
Children (11-15 years)
0.10
80
545
53
1284
1285
1286
1287
1288
1289
1290
1291
1292
MOE results for Shoe Polish are presented in Table 4-47.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity exposure levels via both inhalation and dermal routes. Dermal MOEs were below the
benchmark MOE for multiple endpoints and all age groups. MOEs for bystanders were below the
benchmark MOE for multiple endpoints for high and medium-intensity users and for congenital heart
defects at all user intensity inhalation exposure levels.
Page 342 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
5.8E-05
0.12
0.91
7.2E-02
Bystander
2.4E-04
0.94
7.2
0.66
Moderate-
Intensity
User
User
3.6E-04
0.72
5.5
0.43
Bystander
1.9E-03
7.3
56
5.1
Low-
Intensity
User
User
1.9E-03
4.1
31
2.5
Bystander
9.5E-03
33
251
23
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1294
1295
1296
1297
1298
1299
1300
1301
MOE results for Fabric Spray are presented in Table 4-48.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 343 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1302 Table 4-49. Consumer Risk Estimation - Other Consumer Uses - Film Cleaner
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
5.8E-05
4.7E-02
0.36
3.0E-02
Bystander
2.4E-04
0.19
1.5
0.13
Moderate-
Intensity
User
User
3.6E-04
0.29
2.2
0.18
Bystander
1.9E-03
1.6
12
1.1
Low-
Intensity
User
User
1.9E-03
1.5
12
0.93
Bystander
9.5E-03
7.7
59
5.4
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1303
1304
1305
1306
1307
1308
1309
1310
MOE results for Fabric Spray are presented in Table 4-49.
MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
inhalation exposure levels.
Page 344 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-50. Consumer I
lisk Estimation - Other Consumer Uses - Hoof Polish
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
Developmental Effects
Congenital
Heart Defects
(Johnson et al.. 2003)
Developmental Effects
Developmental
Neurotoxicity
(Frcdriksson et al..
Developmental Effects
Increased Resorptions
(Narotskv et al.. 1995)
Acute
Immunotoxicity
Immunosuppression
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
1.7E-03
1.4
10
0.79
Bystander
0.34
272
2084
157
Moderate-
Intensity
User
User
1.7E-02
14
106
8.0
Bystander
7.8
6307
48351
3653
Low-
Intensity
User
User
0.12
97
747
56
Bystander
48
38519
295309
22309
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1312
1313 MOE results for Hoof Polish are presented in Table 4-50.
1314
1315 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1316 low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
1317 were below the benchmark MOE for congenital heart defects only for high and medium-intensity users.
1318 MOEs for bystanders were not below the benchmark MOE for any endpoint at low-intensity inhalation
1319 exposure levels.
1320
Page 345 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-51. Consumer I
lisk Estimation - Other Consumer Uses - Pepper Spray
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
Single
Scenario
User
0.21
169
1297
98
Bystander
Not modeled due to simulated outdoor scenario - can be considered equal to user.
Dermal exposures were not quantified for this scenario, as dermal exposure with impeded evaporation is
not expected.
1322
1323 MOE results for Pepper Spray are presented in Table 4-51.
1324
1325 MOEs for consumer users were below the benchmark MOE for congenital heart defects. Dermal
1326 exposure was not quantified. MOEs for bystanders are expected to be equivalent to users.
1327
1328
1329 Table 4-52. Consumer Risk Estimation - Other Consumer Uses - Toner Aid
Scenario
Consumer
Receptor
Benchmark
10
100
10
30
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
Response to Infection
(Selerade and Gilmour.
1993)
2010)
Inhalation Exposure
High-
Intensity
User
User
4.2E-04
0.34
2.6
0.21
Bystander
1.7E-03
1.4
11
0.97
Moderate-
Intensity
User
User
2.6E-03
2.1
16
1.3
Bystander
1.4E-02
11
88
8.0
Low-
Intensity
User
User
1.4E-02
11
84
6.8
Bystander
6.9E-02
56
431
39
Dermal exposures were not quantified for this scenario, as dermal exposure with impec
not expected.
ed evaporation is
1330
1331 MOE results for Toner Aid are presented in Table 4-52.
1332
1333 MOEs for consumer users were below the benchmark MOE for multiple endpoints at high, medium, and
1334 low-intensity inhalation exposure levels. Dermal exposure was not quantified. MOEs for bystanders
1335 were below the benchmark MOE for multiple endpoints at high, medium, and low-intensity user
1336 inhalation exposure levels.
Page 346 of 748
-------
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. In some ways the E-FAST estimates are
underestimating exposure, because data used in E-FAST include TRI and DMR data. TRI does not
include smaller facilities with fewer than 10 full time employees, nor does it cover certain sectors, which
may lead to underestimates in total TCE releases to the environment. 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.
In other ways the E-FAST estimates are overestimating exposure, because TCE is a volatile chemical,
and E-FAST doesn't take volatilization 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.
The reasonably available monitored data was limited temporally and geographically. Aquatic
environmental conditions such as temperature and composition (i.e., total organic carbon, water
hardness, dissolve oxygen, and pH) can fluctuate with the seasons, which could affect TCE
concentrations in water and sediment pore water. In addition, TCE monitoring data was collected only in
certain areas, and within a limited number of states in the U.S. There were no measurements 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
Page 347 of 748
-------
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
percentiles of the actual distributions. However, these substitutes are uncertain and are not as reliable as
the true percentiles. For instance, in the few cases where enough data were found to determine statistical
means and 95th percentiles, the associated substitutes (i.e., medians and high ends of ranges) were
shown to overestimate exposures, sometimes significantly. While it most air concentration data
represent real exposure levels, EPA cannot determine whether these concentrations are representative of
the statistical distributions of actual air concentrations to which workers are exposed. It is unknown
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. 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.
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.
43,2.2 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. 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 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
Page 348 of 748
-------
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
clustered 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.
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
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.
See Section 2.3.2.7 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 estmating dermal
absorption. As discussed in Section 2.3.2.5.1, the occupational exposure assessment used a fractional
absorption model that accounts for evaporation of volatile chemicals such as TCE. In contrast, the
consumer assessment used a permeability model that incorporates duration of use and was only applied
to exposure scenarios where evaporation was believed to be impeded. There are several parameters that
must be estimated for each of the respective models, including quantity deposited on skin, surface area
of contact, evaporative flux, film thickness, and exposure duration. Many of these are 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 permeability model can be used for these exposure scenarios with greater confidence. Overall, the
two 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
Page 349 of 748
-------
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
m
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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).
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. 2006).
Consumer Exposure Scenarios
There is medium to high confidence in consumer inhalation exposure modeling (see Section 2.3.2.8),
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 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 both 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:
Acute Non-Cancer Inhalation Occupational Risk (workers): Medium
Acute Non-Cancer Dermal Occupational Risk (workers): Medium
Acute Non-Cancer Inhalation Occupational Risk (ONUs): Medium (Low19 when based on central
tendency of workers without ONU-specific data)
19 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) does not change if ONU
chronic exposure values are varied by lOx in either direction.
Page 350 of 748
-------
1521
1522
1523
mt
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (Low19 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 (Low19 when based on central
tendency of workers without ONU-specific data)
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
Page 351 of 748
-------
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-OPPT-2019-0500], based on differences in delivered dose accounting for
differing body weight and hand size. Exposures differ by only -10% between these groups, so this
difference is relatively insignificant considering the magnitude of risk estimates relative to the
benchmark MOE. Accordingly, the risk characterization section only presents dermal risk estimates for
average adult workers (Section 4.2.2). Similarly, risk estimates were provided for each of the three
lifestages that are expected to potentially be directly exposed through consumer use, namely 11-15 year
olds, 16-20 year olds, and adults 21 and over (Section 4.2.3). These risk estimates also only varied by a
small percentage relative to the magnitude of risk estimates relative to the benchmark MOE. EPA
determined that bystanders may include lifestages of any age.
For inhalation exposures, risk estimates did not differ between genders or across lifestages because both
exposures and inhalation hazard values are expressed as an air concentration. EPA expects that
variability in human physiological factors (e.g., breathing rate, body weight, tidal voume) which may
affect internal delivered concentration or dose is sufficiently accounted for in the PBPK model, although
some differences among lifestages or between working and at-rest individuals may not have been
accounted for. The use of HEC/HED99 values is expected to account for the vast majority of
physiological differences among individuals.
EPA identified lifestage, gender, 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. The (S el grade and Gilmour.
2010) study accounts for pre-existing infection concurrent with TCE exposure, representing a
susceptible status that applies intermittently to the entire population. Cardiac malformations are most
strongly associated with offspring of older mothers (Brender et ai. 2014; Yauck et al. 2004). While
inconsistencies in the data on cardiac malformations (Appendix G.2) suggest that there may nor be a risk
for all individuals, inclusion of risk estimates for cardiac malformations is protective of susceptible
mothers (Jenkins et al.. 2007) and their offspring.
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 §
702.33). In this risk evaluation, EPA determined that aggregating dermal and inhalation exposure for
risk characterization was not appropriate due to uncertainties in quantifying the relative contribution of
dermal vs inhalation exposure, since dermally applied dose could evaporate and then be inhaled.
Aggregating exposures from multiple routes could therefore inappropriately overestimate total exposure,
Page 352 of 748
-------
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
as simply adding exposures from different routes without an available PBPK model for those routes
would compound uncertainties. EPA also did not consider aggregate exposure among individuals who
may be exposed both in an occupational and consumer context because there is insufficient information
reasonably available as to the likelihood of this scenario or the relative distribution of exposures from
each pathway.
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 § 702.33). In terms of this risk evaluation, EPA considered sentinel exposures by
considering risks to populations who may have upper bound exposures - for example, workers and
ONUs who perform activities with higher 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. EPA characterized high-end exposures in evaluating exposure using both
monitoring data and modeling approaches. Where statistical data are reasonably available, EPA typically
uses the 95th percentile value of the reasonably available dataset to characterize high-end exposure for a
given condition of use. For consumer and bystander exposures, EPA characterized sentinel exposure
through a "high-intensity use" category based on both product and user-specific factors.
Page 353 of 748
-------
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 4-53). 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 521 facilities (with 20 days or more of
exceedances for 461 of these facilities, and more than 100 days exceedances for 10 facilities); however,
as a taxonomic group, 95% of algae species not experience risk. (They had RQs > 1 using the algae
COC of 3 ppb but RQs < 1 using the algae HCos of 52,000 ppb.) These facilities are not included in
Table 4-53 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 52,000 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
encompasses the range of the modeled estimates across all OES from E-FAST.
Open-top Vapor Degreasing:
One out of 64 open-top vapor degreasing facilities had releases of TCE to surface water that indicate
risk to aquatic organisms. U.S. NASA Michoud Assembly Facility in New Orleans, LA had an acute RQ
> 1 (RQ = 3.11). In other words, the surface water concentration modeled for this facility was 3.11 times
higher than the acute COC of 3,200 ppb, indicating risk to aquatic organisms from acute exposures. The
facility also had a chronic RQ of 12.61 with 20 days of exceedance. In other words, 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.
Processing as a Reactant:
One out of 443 facilties (including 440 unknown sites modeled in E-FAST) that process TCE as a
reactant had releases of TCE to surface water that indicate risk to aquatic organisms like fish and
invertebrates. Praxair Technology Center in Tonawanda, NY had a chronic RQs of 3.81 with 20 days of
exceedance. In other words, the surface water concentration modeled for this facility was 3.81 times
higher than the COC for chronic exposures. Therefore, EPA identified risk to aquatic organisms at this
site for chronic exposures to TCE.
Page 354 of 748
-------
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table 4-53. Facilities with Acute or Chronic Risk Identified for Aquatic Organisms (RQs > 1 in bold)
Name, Location, and
ID of Active Releaser
Facility a
Release
Media b
Modeled Facility or
Industry Sector in
EFAST c
EFAST
Waterbody
Type d
Days of
Release
e
Release
(kg/day)f
7Q10
SWC
(ppb)g
COC Type
COC
(ppb)
Days of
Exceedance
(days/year)
h"
Risk
Quotient
OES: Processing as a Reactant
Acute
3,200
NA
0.05
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 (HCos)
52,000
0
0.00
Water
Acute
3,200
NA
0.94
20
0.03
3000
Chronic
788
20
3.81
Algae
3
20
1,000.00
Algae (HCos)
52,000
0
0.06
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
Acute
3,200
NA
0.24
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 (HCos)
52,000
0
0.01
NPDES: LA0052256
Water
LA0003280
Acute
3,200
NA
3.11
20
25.44
9937.5
Chronic
788
20
12.61
Algae
3
20
3,312.50
Algae (HCos)
52,000
0
0.19
a. Facilities actively releasing tricliloroethylene 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, as well as direct releases from WWTPs.
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.
Page 355 of 748
-------
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
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
1693
1694
1695
1696
1697
1698
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
4.5.2 Human Health Risk Conclusions
4.5.2.1 Summary of Risk Estimates for Workers and ONUs
Table 4-54 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 4-54 in the
Occupational Exposure Scenario column.
Of note, 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 (Selerade 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 (Section 3.2.6.4). 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 4-54 with "upper limit" 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
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
Page 356 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1699 • Repackaging
1700 • Process Solvent Recycling and Worker Handling of Wastes
1701
1702 Dermal Exposure
1703 For acute and chronic exposures via dermal contact without PPE (i.e. no gloves) there are risks to
1704 workers for both non-cancer effects and cancer (ONUs are assumed to not have direct dermal contact
1705 with TCE) at both high-end and central-tendency exposure levels for all OES. Risks are still identified
1706 for all exposure scenarios (at high-end exposure levels following acute exposure and at both exposure
1707 levels following chronic exposure) when gloves are worn even when assuming the maximum applicable
1708 glove protection (either PF 10 or 20).
Page 357 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1709 Table 4-54. Occupational Risk Summary Table
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Inhalation
High-
End
2.0
5.6E-02
6.7E-03
100.8
(APF 50)
2.8
(APF 50)
1.3E-04
(APF 50)
Worker
Central
Tendency
13.9
0.39
7.5E-04
139.1
(APF 10)
19.3
(APF 50)
7.5E-05
(APF 10)
Manufacture -
Domestic
manufacture
Domestic manufacture
Manufacturing -
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Table 4-6
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
13.9
0.39
7.5E-04
N/A
Inhalation
High-
End
4.6
0.13
2.9E-03
45.8
(APF 10)
6.3
(APF 50)
5.9E-05
(APF 50)
Worker
Central
Tendency
10546
292
9.9E-07
105460
(APF 10)
2920
(APF 10)
9.9E-08
(APF 10)
Manufacture -
Import
Repackaging -
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Import
Table 4-19
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
10546
292
9.9E-07
N/A
Processing -
Processing as a
Intermediate in industrial
gas manufacturing (e.g..
Processing as a
Reactant -
Table 4-7
Worker
Inhalation
High-
End
2.0
5.6E-02
6.7E-03
100.8
(APF 50)
2.8
(APF 50)
1.3E-04
(APF 50)
reactant/
intermediate
manufacture of
fluorinated gases used as
Central
Tendency
13.9
0.39
7.5E-04
139.1
(APF 10)
19.3
(APF 50)
7.5E-05
(APF 10)
Page 358 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle
Stage/
Category
Subcategory
refrigerants, foam
blowing agents and
solvents)
Occupational
Exposure
Scenario
Population
Exposure
Route and
Duration
Exposure
Level
Risk Estimates for No PPE
Risk Estimates with PPE
Acute Non-
Cancer
(benchmark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
13.9
0.39
7.5E-04
N/A
Processing -
Incorporation
into formulation,
mixture or
reaction product
Processing -
incorporated
into articles
Solvents (for cleaning or
degreasing)
Formulation of
Aerosol and Non-
Aerosol Products ¦
Table 4-18
Worker
Inhalation
High-
End
4.6
0.13
2.9E-03
45.8
(APF 10)
6.3
(APF 50)
5.9E-05
(APF 50)
Adhesives and sealant
chemicals
Central
Tendency
10546
292
9.9E-07
105460
(APF 10)
2920
(APF 10)
9.9E-08
(APF 10)
Solvents (which become
part of product
formulation or mixture)
(e.g., lubricants and
greases, paints and
coatings, other uses)
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04 (PF
20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Solvents (becomes an
integral component of
articles)
Central
Tendency
10546
292
9.9E-07
N/A
Processing -
Repackaging
Solvents (for cleaning or
degreasing)
Repackaging -
Table 4-19
Worker
Inhalation
High-
End
4.6
0.13
2.9E-03
45.8
(APF 10)
6.3
(APF 50)
5.9E-05
(APF 50)
Central
Tendency
10546
292
9.9E-07
105460
(APF 10)
2920
(APF 10)
9.9E-08
(APF 10)
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
Page 359 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
10546
292
9.9E-07
N/A
Inhalation
High-
End
4.(i
0.13
2.91-113
45.8
(APF 10)
(>.3
(API- 50)
5.9E-05
(APF 50)
Workers
Central
Tendency
10546
292
9.9E-07
105460
(APF 10)
(APF 10)
9.9E-08
(APF 10)
Processing -
Recycling
Process Solvent
Recycling and
Worker Handling
of Wastes -
Table 4-27
Dermal
High-
End
1.2
3.0I-.-02
3.XI-.-02
23.S
(PI- 20)
0.(,l
(PI" 20)
i.'h:-o3
(P1- 20)
Recycling
Central
Tendency
3.(.
'ur.-o2
«>.T.-03
35.7
(PI' 1U)
I.X
(PI" 20)
4.*>i:-o4
(PI" 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
10546
292
9.9E-07
N/A
Distribution in
commerce -
Distribution
Distribution
Distribution
Distribution is accounted for as part of other COUs
Industrial/
commercial use ¦
Solvents (for
Inhalation
(Monitoring
Data)3
High-
End
(..T.-02
i.')i:-o3
0.20
3.4
(API- 50)
'Ui:-o2
(API- 50)
4.0T.-03
(API- 50)
Batch vapor degreaser
(e.g., open-top, closed-
loop)
Batch Open-Top
Vapor Degreasing
- Table 4-8
Workers
Central
Tendency
0.3X
i.oi:-o2
2.XI-.-02
ix.y
(API- 50)
0.52
(API- 50)
5.51.-04
(API- 50)
cleaning or
degreasing)
Dermal
High-
End
1.2
3.0T.-02
3.XI-.-02
23.X
(PI- 20)
0.(>l
(PI" 20)
l.«>l-'.-03
(PI- 20)
Central
Tendency
3.(i
'XII.-112
9.T.-03
35.7
l-'.-04
(PI- 20)
Page 360 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
ONU
Inhalation
(Monitoring
High-
End
0.57
1.6E-02
2.3E-02
N/A
Data)3
Central
Tendency
4.7
0.13
2.2E-03
N/A
Inhalation
High-
End
3.6
9.9E-02
3.7E-03
35.9
(APF 10)
5.0
(APF 50)
7.5E-05
(APF 50)
Workers
Central
Tendency
11.4
0.32
9.1E-04
114.0
(APF 10)
15.8
(APF 50)
9.1E-05
(APF 10)
Batch Closed-
Loop Vapor
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Industrial/
commercial use ¦
Solvents (for
Degreasing -
Table 4-10
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
cleaning or
degreasing)
Central
Tendency
11.4
0.32
9.1E-04
N/A
Inhalation
(Monitoring
Data)a
High-
End
0.11
3.0E-03
0.12
5.4
(APF 50)
0.15
(APF 50)
2.5E-03
(APF 50)
Workers
Central
Tendency
0.16
4.5E-03
6.5E-02
8.1
(APF 50)
0.22
(APF 50)
1.3E-03
(APF 50)
In-line vapor degreaser
(e.g., conveyorized, web
cleaner)
Conveyorized
Vapor Degreasing
-Table 4-11
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
ONU
(upper
limit)
Inhalation
(Monitoring
Data)3
High-
End
-
-
-
-
Central
Tendency
0.16
4.5E-03
6.5E-02
N/A
Page 361 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Industrial/
commercial use ¦
Solvents (for
cleaning or
degreasing)
Web Vapor
Degreasing -
Table 4-13
Workers
Inhalation
High-
End
0.3"7
i.oi:-o2
2.91.-02
IS.5
(API- 50)
0.51
(API- 50)
5.XI.-04
(API- 50)
Central
Tendency
O.XX
2.41-'.-02
I.I 1.-02
4'
( \H' 5(1)
1.2
(API- 50)
2.3I.-04
(API- 50)
Dermal
High-
End
1.2
3.01-'.-02
3.XI-.-02
23.S
(i»r 20)
0.(.l
(PI" 20)
l.«>l-'.-03
(PI- 20)
Central
Tendency
3.(i
'XII.-112
«j.",i:-o3
35.7
.ii-:-o2
2.51-'.-03
0.11
4.(i
(API- 50)
0.13
(API- 50)
2.3E-03
(API- 50)
Central
Tendency
1.6
4.31-'.-02
(..21.-03
~S 4
( \H' 5(1)
2.2
(API- 50)
I.2T.-04
(API- 50)
Dermal
High-
End
1.2
3.01-'.-02
3.XE-02
23.S
(PI- 20)
0.(>l
(PI" 20)
l.«)l-'.-03
(PI- 20)
Central
Tendency
3.(i
«).ir.-o2
«>.7l-'.-03
35.7
il'l' Id)
I.S
(PI" 20)
4.') 1.-04
(PI- 20)
ONU
Inhalation
High-
End
0.15
4.T.-04
(..«>i-:-o2
N/A
Central
Tendency
2.S
s.si:-o3
3.3I.-03
N/A
Aerosol spray
degreaser/cleaner
Aerosol
Applications -
Table 4-15
Worker
Inhalation
High-
End
0.22
(>.oi:-o3
4.«)l-'.-02
io.y
(API- 50)
0.30
(API- 50)
^T.-O-t
(API- 50)
Central
Tendency
0.(.X
i.')i:-o2
I.4I-.-02
u:
( \H' 5(1)
0.«>5
(API- 50)
2.«)l-:-04
(API- 50)
Page 362 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Dermal
High-
End
o.^r.
i.')i:-o2
5.') 1.-02
15.1
(PI- 20)
0.3')
(PI- 20)
2.9I.-03
(PI- 20)
Mold release
Central
Tendency
2.3
5.XI-.-02
1.51.-02
45 4
5
( \H' 5(1)
Central
Tendency
"4 (>
2.1
1.31.-04
"45 "
(\\>\: III)
|Ui |
( \H' 5(1)
: (.i:-()(.
( \H' 5(1)
Dermal
High-
End
1.5
3.Sl-:-02
3.0T-02
2')."'
(PI- 20)
O.^f.
(PI" 20)
1.51.-03
(PI- 20)
Central
Tendencs
4.5
0.11
¦'.si:-o3
o.r
(PI- 20)
2.3
(PI- 20)
3.91-!-04
(P1- 20)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
"4 (>
2.1
1.31.-04
N/A
Penetrating lubricant
Aerosol
Applications -
Table 4-15
Worker
Inhalation
High-
End
0.22
r..oi:-o3
4.9I-.-02
10.9
(API- 50)
0.30
(API- 50)
9.T.-04
(API- 50)
Central
Tendency
0.(.X
1.91.-112
1.41.-02
u:
( \H' 5(1)
0.95
(API- 50)
2.9I-.-04
(API- 50)
Dermal
High-
End
O.-'f,
i.yi:-o2
5.91-112
15.1
(PI- 20)
0.39
(PI" 20)
2.9I.-03
(P1- 20)
Central
Tendency
2.3
5.XI-.-02
I.5I-.-02
45 4
il'l' :<))
1.2
(PI" 20)
¦'.(.ixm
(P1- 20)
Page 363 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Industrial/
commercial use
- Lubricants and
greases/
lubricants and
lubricant
additives
ONU
Inhalation
High-
End
5.0
0.14
2.0E-03
N/A
Central
Tendency
37.3
1.0
2.6E-04
N/A
Solvent-based adhesives
and sealants
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-22 and
Table 4-23
Worker
Inhalation
High-
End
0.13
3.7E-03
0.10
6.6
(APF 50)
0.18
(APF 50)
2.0E-03
(APF 50)
Central
Tendency
1.1
3.1E-02
9.3E-03
56.3
(APF 50)
1.6
(APF 50)
1.9E-04
(APF 50)
Dermal
(Industrial)
High-
End
1.3
3.4E-02
3.4E-02
26.4
(PF 20)
0.68
(PF 20)
1.7E-03
(PF 20)
Tire repair cement/
Sealer
Central
Tendency
4.0
0.10
8.7E-03
39.6
(PF 10)
2.0
(PF 20)
4.4E-04
(PF 20)
Dermal
(Commercial)
High-
End
0.84
2.2E-02
5.3E-02
8.4
(PF 10)
0.22
(PF 10)
5.3E-03
(PF 10)
Central
Tendency
2.5
6.5E-02
1.4E-02
25.2
(PF 10)
0.65
(PF 10)
1.4E-03
(PF 10)
Mirror edge sealant
ONU
Inhalation
High-
End
5.2
0.14
2.6E-03
N/A
Central
Tendency
5.5
0.15
1.9E-03
N/A
Industrial/
commercial use -
Functional fluids
(closed systems)
Heat exchange fluid
Other Industrial
Uses -
Table 4-26
Worker
Inhalation
High-
End
2.0
5.6E-02
6.7E-03
100.8
(APF 50)
2.8
(APF 50)
1.3E-04
(APF 50)
Central
Tendency
13.9
0.39
7.5E-04
139.1
(APF 10)
19.3
(APF 50)
7.5E-05
(APF 10)
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
Page 364 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
13.9
0.39
7.5E-04
N/A
Inhalation
High-
End
0.13
3.7E-03
0.10
6.6
(APF 50)
0.18
(APF 50)
2.0E-03
(APF 50)
Central
Tendency
1.1
3.1E-02
9.3E-03
56.3
(APF 50)
1.6
(APF 50)
1.9E-04
(APF 50)
Worker
Dermal
High-
End
1.3
3.4E-02
3.4E-02
26.4
(PF 20)
0.68
(PF 20)
1.7E-03
(PF 20)
Industrial/
commercial use ¦
Diluent in solvent-based
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-22 and
Table 4-23
(Industrial)
Central
Tendency
4.0
0.10
8.7E-03
39.6
(PF 10)
2.0
(PF 20)
4.4E-04
(PF 20)
Paints and
coatings
paints and coatings
Dermal
High-
End
0.84
2.2E-02
5.3E-02
8.4
(PF 10)
0.22
(PF 10)
5.3E-03
(PF 10)
(Commercial)
Central
Tendency
2.5
6.5E-02
1.4E-02
25.2
(PF 10)
0.65
(PF 10)
1.4E-03
(PF 10)
ONU
Inhalation
High-
End
5.2
0.14
2.6E-03
N/A
Central
Tendency
5.5
0.15
1.9E-03
N/A
Carpet cleaner
Inhalation
(Modeling
Data)b
High-
End
1.9
5.1E-02
5.8E-03
94.2
(APF 50)c
2.5
(APF 50)c
1.2E-04
(APF 50)c
Industrial/
commercial use -
Cleaning and
furniture care
products
Spot Cleaning
and Wipe
Worker
Central
Tendency
5.4
0.15
1.8E-03
54.3
(APF 10)c
7.3
(APF 50)c
3.7E-05
(APF 10)c
Wipe cleaning
Cleaning0 -
Table 4-17
Dermal
High-
End
0.76
1.7E-02
6.9E-02
7.6
(PF 10)
0.17
(PF 10)
6.9E-03
(PF 10)
Central
Tendency
2.3
5.6E-02
1.6E-02
22.7
(PF 10)
0.56
(PF 10)
1.6E-03
(PF 10)
Page 365 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Industrial/
commercial use -
Laundry and
dishwashing
products
Spot remover
ONU
Inhalation
(Modeling
Data)b
High-
End
3.0
8.0E-02
3.6E-03
N/A
Central
Tendency
10.9
0.29
9.2E-04
N/A
Industrial/
commercial use -
Arts, crafts and
hobby materials
Fixatives and finishing
spray coatings
Adhesives,
Sealants, Paints,
and Coatings -
Table 4-22 and
Table 4-23
Worker
Inhalation
High-
End
0.13
3.7E-03
0.10
6.6
(APF 50)
0.18
(APF 50)
2.0E-03
(APF 50)
Central
Tendency
1.1
3.1E-02
9.3E-03
56.3
(APF 50)
1.6
(APF 50)
1.9E-04
(APF 50)
Dermal
(Industrial)
High-
End
1.3
3.4E-02
3.4E-02
26.4
(PF 20)
0.68
(PF 20)
1.7E-03
(PF 20)
Central
Tendency
4.0
0.10
8.7E-03
39.6
(PF 10)
2.0
(PF 20)
4.4E-04
(PF 20)
Dermal
(Commercial)
High-
End
0.84
2.2E-02
5.3E-02
8.4
(PF 10)
0.22
(PF 10)
5.3E-03
(PF 10)
Central
Tendency
2.5
6.5E-02
1.4E-02
25.2
(PF 10)
0.65
(PF 10)
1.4E-03
(PF 10)
ONU
Inhalation
High-
End
5.2
0.14
2.6E-03
N/A
Central
Tendency
5.5
0.15
1.9E-03
N/A
Industrial/
commercial use -
Corrosion
inhibitors and
anti-scaling
agents
Corrosion inhibitors and
anti-scaling agents
Industrial
Processing Aid -
Table 4-24
Worker
Inhalation
High-
End
0.27
7.5E-03
4.9E-02
13.6
(APF 50)
3.0E-02
(APF 50)
9.9E-04
(APF 50)
Central
Tendency
0.82
2.3E-02
1.3E-02
40.9
(APF 50)
9.1E-02
(APF 50)
2.5E-04
(APF 50)
1710
Page 366 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= lO"4)
Process solvent used in
Dermal
High-
End
1.2
3.0E-02
3.8E-02
23.8
(PF 20)
0.61
(PF 20)
1.9E-03
(PF 20)
battery manufacture
Central
Tendency
3.6
9.1E-02
9.7E-03
35.7
(PF 10)
1.8
(PF 20)
4.9E-04
(PF 20)
Industrial/
commercial use -
Processing aids
Process solvent used in
polymer fiber spinning,
fluoroelastomer
manufacture and
Alcantara manufacture
Industrial
Processing Aid -
Table 4-24
ONU
Inhalation
High-
End
1.2
3.3E-02
1.1E-02
N/A
Extraction solvent used in
caprolactam manufacture
Central
2.7
7.3E-02
3.9E-03
N/A
Precipitant used in beta-
cyclodextrin manufacture
Tendency
Inhalation
High-
End
2.5
6.9E-02
5.4E-03
124.6
(APF 50)c
3.4
(APF 50)c
1.1E-04
(APF 50)c
Workers
Central
Tendency
61.4
1.7
1.7E-04
614.1
(APF 10)c
85.0
(APF 50)c
1.7E-05
(APF 10)c
Industrial/
commercial use -
Toner aid
Commercial
Printing and
Copyingc -
Table 4-25
Dermal
High-
End
2.2
5.5E-02
2.1E-02
21.6
(PF 10)
0.55
(PF 10)
2.1E-03
(PF 10)
Ink, toner and
colorant
products
Central
Tendency
6.5
0.17
5.3E-03
32.5
(PF 5)
1.7
(PF 10)
5.3E-04
(PF 10)
ONU
(upper
limit)
Inhalation
High-
End
-
-
-
-
Central
Tendency
61.4
1.7
1.7E-04
N/A
Industrial/
commercial use -
Brake and parts cleaner
Aerosol
Applications -
Table 4-15
Workers
Inhalation
High-
End
0.22
6.0E-03
4.9E-02
10.9
(APF 50)
0.30
(APF 50)
9.7E-04
(APF 50)
Automotive care
products
Central
Tendency
0.68
1.9E-02
1.4E-02
34.2
(APF 50)
0.95
(APF 50)
2.9E-04
(APF 50)
Page 367 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= 10"4)
Acute Non-
Cancer
(benclunark
MOE = 30)
Chronic
Non-Cancer
(benclunark
MOE = 30)
Cancer
(benclunark
= lO"4)
Dermal
High-
End
0.76
1.9E-02
5.9E-02
15.1
(PF 20)
0.39
(PF 20)
2.9E-03
(PF 20)
Central
Tendency
2.3
5.8E-02
1.5E-02
45.4
(PF 20)
1.2
(PF 20)
7.6E-04
(PF 20)
ONU
Inhalation
High-
End
5.0
0.14
2.0E-03
N/A
Central
Tendency
37.3
1.0
2.6E-04
N/A
Industrial/
commercial use ¦
Apparel and
footwear care
products
Shoe polish
Other Commercial
Uses
(Spot Cleaning
and Wipe
Cleaning)0 -
Table 4-17
Worker
Inhalation
(Modeling
Data)b
High-
End
1.9
5.1E-02
5.8E-03
94.2
(APF 50)c
2.5
(APF 50)c
1.2E-04
(APF 50)c
Central
Tendency
5.4
0.15
1.8E-03
54.3
(APF 10)c
7.3
(APF 50)c
3.7E-05
(APF 10)c
Dermal
High-
End
0.76
1.7E-02
6.9E-02
7.6
(PF 10)
0.17
(PF 10)
6.9E-03
(PF 10)
Central
Tendency
2.3
5.6E-02
1.6E-02
22.7
(PF 10)
0.56
(PF 10)
1.6E-03
(PF 10)
Industrial/
commercial use ¦
Other uses
Hoof polishes
Gun Scrubber
ONU
Inhalation
(Modeling
Data)b
High-
End
3.0
8.0E-02
3.6E-03
N/A
N/A
Pepper spray
Other miscellaneous
industrial and commercial
uses
Central
Tendency
10.9
0.29
9.2E-04
N/A
1711
Page 368 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Acute Non-
Cancer
(benchmark
MOE = 30)
Chronic
Non-Cancer
(benchmark
MOE = 30)
Cancer
(benchmark
= 10"4)
Disposal
Industrial pre-treatment
Process Solvent
Recycling and
Worker Handling
of Wastes -
Table 4-27
Workers
Inhalation
High-
End
4.(i
0.13
2.91-113
45.8
(APF 10)
(..3
(API- 50)
5.9E-05
(APF 50)
Central
Tendency
10546
292
9.9E-07
105460
(APF 10)
2920
(APF 10)
9.9E-08
(APF 10)
Industrial wastewater
treatment
Dermal
High-
End
1.2
3.01.-02
3.Sl-:-02
23.X
(PI- 20)
0.(»l
(PI- 20)
I.S
(PI- 20)
I.1'>1.-03
(PI- 20)
4.'>l-:-04
(PI- 20)
Central
Tendencs
3.(i
'XII.-112
«>.T.-03
35.7
-------
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
4.5.2.2 Summary of Risk Estimates for Consumers and Bystanders
Table 4-55 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 holding 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 4-55 in the
Consumer Condition of Use Scenario column.
Of note, 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 (Selgrade and Gilmour. 2010) as the best overall representative acute endpoint
(Section 3.2.6.4). 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 where
dermal exposure is expected at both medium and high-intensity user exposure levels (and for most
COUs at low-intensity).
Table 4-55. Consumer Risk Summary Table
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Group
Acute Non-Cancer
(benchmark MOE = 30)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Consumer Use -
Solvents (for
cleaning or
degreasing)
Brake and Parts
Cleaner -
Table 4-28
User
Inhalation
Alla
3.5E-02
0.21
2.7
Dermal
21+
3.6E-02
0.48
22
16-20
3.8E-02
0.51
23
11-15
3.5E-02
0.47
21
Bystander
Inhalation
All
0.14
0.94
12
Aerosol electronic
degreaser/cleaner -
Table 4-29
User
Inhalation
All
5.0E-02
1.2
33
Bystander
Inhalation
All
0.28
7.1
193
Liquid electronic
degreaser/cleaner -
Table 4-30
User
Inhalation
All
5.2E-02
0.79
11
Dermal
21+
6.4E-02
9.5E-01
3.8
16-20
6.8E-02
1.0
4.1
11-15
6.2E-02
9.4E-01
3.7
Bystander
Inhalation
All
0.29
4.8
61
Page 370 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Population
Exposure
Route and
Duration
Age
Group
Acute Non-Cancer
(benchmark MOE = 30)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Aerosol spray
degreaser/cleaner -
Table 4-31
User
Inhalation
All
1.2E-02
4.6E-02
0.31
Dermal
21+
3.8E-02
0.31
1.5
16-20
4.1E-02
0.33
1.6
11-15
3.7E-02
0.30
1.5
Bystander
Inhalation
All
4.9E-02
0.21
1.4
Liquid
degreaser/cleaner -
Table 4-32
User
Inhalation
All
1.3E-02
0.12
0.71
Dermal
21+
1.6E-02
0.13
0.96
16-20
1.7E-02
0.14
1.0
11-15
1.6E-02
0.13
0.94
Bystander
Inhalation
All
6.1E-02
0.70
4.3
Aerosol gun
scrubber -
Table 4-33
User
Inhalation
All
26
24
41
Dermal
21+
4.0E-02
0.32
4.0
16-20
4.2E-02
0.34
4.2
11-15
3.9E-02
0.31
3.9
Bystander
Inhalation
All
120
141
247
Liquid gun
scrubber -
Table 4-34
User
Inhalation
All
30
28
30
Dermal
21+
1.7E-02
0.14
1.0
16-20
1.8E-02
0.15
1.1
11-15
1.7E-02
0.13
1.0
Bystander
Inhalation
All
140
164
172
Mold Release -
Table 4-35
User
Inhalation
All
0.11
1.1
11
Bystander
Inhalation
All
0.64
6.4
61
Aerosol Tire Cleaner
- Table 4-36
User
Inhalation
All
0.13
0.46
3.3
Dermal
21+
0.17
0.70
3.0
16-20
0.19
0.74
3.2
11-15
0.17
0.68
2.9
Bystander
Inhalation
All
0.32
2.0
15
Liquid Tire Cleaner -
Table 4-37
User
Inhalation
All
4.2E-02
0.21
1.0
Dermal
21+
3.1E-02
0.12
0.37
16-20
3.3E-02
0.13
0.40
11-15
3.0E-02
0.12
0.37
Bystander
Inhalation
All
0.14
0.92
4.7
Consumer Use -
Lubricants and
greases
Tap and Die Fluid -
Table 4-38
User
Inhalation
All
0.13
1.2
6.8
Bystander
Inhalation
All
0.71
7.1
28
User
Inhalation
All
0.16
2.7
86
Page 371 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Penetrating lubricant
- Table 4-39
Population
Exposure
Route and
Duration
Age
Group
Acute Non-Cancer
(benchmark MOE = 30)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Bystander
Inhalation
All
o.x9
l(.
4 <><¦
Consumer Use -
Adhesives and
sealants
Solvent-based
adhesives and
sealants -
Table 4-40
User
Inhalation
All
5.r. i-:-o2
I.S
:
si
Bystander
Inhalation
All
2.7
10
51'
Tire repair cement/
sealer -
Table 4-42
User
Inhalation
N/A
0.17
2.9
Bystander
Inhalation
N/A
0.57
13
1 v.
Consumer use -
Cleaning and
furniture care
products
Carpet cleaner -
Table 4-43
User
Inhalation
All
3.(.i:-02
0.29
\r
Dermal
21+
5.91.-02
0.35
7.1
16-20
(..3i:-02
0.3S
7.5
11-15
5.T.-02
0.35
(..9
Bystander
Inhalation
All
O.IS
\.->
9.0
Aerosol Spot
Remover -
Table 4-44
User
Inhalation
All
5.(.i:-02
0.4"7
3.2
Dermal
21+
0.50
3.0
30
16-20
0.53
3.2
11-15
0.49
2.9
29
Bystander
Inhalation
All
0.(.l
5.(>
}<)
Liquid Spot
Remover -
Table 4-45
User
Inhalation
All
4.7i:-02
0.39
3.4
Dermal
21+
X.5I-.-02
0.51
7.7
16-20
9.11-:-02
0.55
S.2
11-15
S.4i:-02
0.50
7.5
Bystander
Inhalation
All
0.2(»
2.4
19
Consumer use -
Arts, crafts, and
hobby materials
Fixatives and
finishing spray
coatings -
Table 4-46
User
Inhalation
All
0.20
1.2
(..4
Bystander
Inhalation
All
0.92
37
Consumer use -
Apparel and
footwear care
products
Shoe polish -
Table 4-47
User
Inhalation
All
0.55
5.4
'1
Dermal
21+
0.91
5.5
55
16-20
0.97
5.S
5X
11-15
0.S9
5.3
53
Bystander
Inhalation
All
3.1
33
ISO
Consumer use -
Other consumer
uses
Fabric spray -
Table 4-48
User
Inhalation
All
"7.2l-:-02
0.43
2.5
Bystander
Bystander
All
0.(.(.
5.1
23
Film cleaner -
User
Inhalation
All
3.0T-02
O.IS
0.93
Page 372 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle
Stage/
Category
Subcategory/
Consumer
Condition of Use
Scenario
Table 4-49
Population
Exposure
Route and
Duration
Age
Group
Acute Non-Cancer
(benchmark MOE = 30)
High-Intensity
User
Moderate-
Intensity User
Low-Intensity
User
Bystander
Bystander
All
0.13
I.I
5.4
Hoof polish -
Table 4-50
User
Inhalation
All
o.-»>
s.o
5<>
Bystander
Bystander
All
157
3653
22309
Pepper spray -
Table 4-51
User
Inhalation
All
98
Bystander
Toner aid -
Table 4-52
User
Inhalation
All
0.21
1.3
(..X
Bystander
Bystander
All
o.y
S.O
V)
a Inhalation exposures are based on a 2-zone model of air concentrations (Section \ V \4 1; lli.il ;nv nukpciuknl of any age-
specific exposure factors.
1739
Page 373 of 748
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5 RISK DETERMINATION
5.1 Unreasonable Risk
5.1.1 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).20
Under TSCA, conditions of use are defined as the circumstances, as determined by the Administrator,
under which the substance is intended, known, or reasonably foreseen to be manufactured, processed,
distributed in commerce, used, or disposed of. TSCA §3(4).
An unreasonable risk may be indicated when health risks under the conditions of use are identified by
comparing the estimated risks with the risk benchmarks and where the risks affect the general
population or PESS, identified as relevant. For workers (which are one example of PESS), an
unreasonable risk may be indicated when risks are not adequately addressed through expected use of
workplace practices and exposure controls, including engineering controls or use of personal protective
equipment (PPE). An unreasonable risk may be indicated when environmental risks under the conditions
of use are greater than environmental risk benchmarks. The risk estimates contribute to the evidence
EPA uses to determine unreasonable risk.
EPA uses the term "indicates unreasonable risk" to indicate EPA concern for potential unreasonable
risk. For non-cancer endpoints, "less than MOE benchmark" is used to indicate potential unreasonable
risk; this occurs if an MOE value is less than the benchmark MOE (e.g.,, MOE 0.3 < benchmark MOE
30). For cancer endpoints, EPA uses the term "greater than risk benchmark" to indicate potential
unreasonable risk; this occurs, for example, if the lifetime cancer risk value is greater than 1 in 10,000
(e.g.,, cancer risk value is 5xl0"2 which is greater than the standard range of acceptable cancer risk
benchmarks of lxlO"4 to lxlO"6). For environmental endpoints, to indicate potential unreasonable risk
EPA uses a risk quotient (RQ) value "greater than 1" (i.e., RQ >1). Conversely, EPA uses the term
"does not indicate unreasonable risk" to indicate that it is unlikely that EPA has a concern for potential
unreasonable risk. More details are described below.
20 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 374 of 748
-------
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
77
78
79
80
81
82
83
84
85
86
87
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The degree of uncertainty surrounding the MOEs, cancer risk or RQs is a factor in determining whether
or not unreasonable risk is present. Where uncertainty is low, and EPA has high confidence in the
hazard and exposure characterizations (for example, the basis for the characterizations is measured or
monitoring data or a robust model and the hazards identified for risk estimation are relevant for
conditions of use), the Agency has a higher degree of confidence in its risk determination. EPA may also
consider other risk factors, such as severity of endpoint, reversibility of effect, or exposure-related
considerations, such as magnitude or number of exposures, in determining that the risks are
unreasonable under the conditions of use. Where EPA has made assumptions in the scientific evaluation,
whether or not those assumptions are protective will also be a consideration. Additionally, EPA
considers the central tendency and high-end scenarios when determining the unreasonable risk. High-
end risk estimates (i.e., 95th percentile) are generally intended to cover individuals or sub-populations
with greater exposure (PESS) and central tendency risk estimates are generally estimates of average or
typical exposure.
EPA may make a no unreasonable risk determination for conditions of use where the substance's hazard
and exposure potential, or where the risk-related factors described previously, lead EPA to determine
that the risks are not unreasonable.
5.1,2 Risks to Human Health
5,1.2,1 Determining Non-Cancer Risks
Margins of exposure (MOEs) are used in EPA's risk evaluations as a starting point to estimate non-
cancer risks for acute and chronic exposures. The non-cancer evaluation refers to potential adverse
health effects associated with health endpoints other than cancer, including to the body's organ systems,
such as reproductive/developmental effects, cardiac and lung effects, and kidney and liver effects. The
MOE is the point of departure (POD) (an approximation of the no-observed adverse effect level
(NOAEL) or benchmark dose level (BMDL)) for a specific health endpoint divided by the exposure
concentration for the specific scenario of concern. The benchmark for the MOE that is used accounts for
the total uncertainty in a POD, including, as appropriate: (1) the variation in sensitivity among the
members of the human population (i.e., intrahuman/intraspecies variability); (2) the uncertainty in
extrapolating animal data to humans (i.e., interspecies variability); (3) the uncertainty in extrapolating
from data obtained in a study with less-than-lifetime exposure to lifetime exposure (i.e., extrapolating
from subchronic to chronic exposure); and (4) the uncertainty in extrapolating from a lowest observed
adverse effect level (LOAEL) rather than from a NOAEL. MOEs can provide a non-cancer risk profile
by presenting a range of estimates for different non-cancer health effects for different exposure scenarios
and are a widely recognized point estimate method for evaluating a range of potential non-cancer health
risks from exposure to a chemical.
A calculated MOE that is less than the benchmark MOE indicates the possibility of risk to human health.
Whether those risks are unreasonable will depend upon other risk-related factors, such as severity of
endpoint, reversibility of effect, exposure-related considerations (e.g.,, duration, magnitude, frequency
of exposure, population exposed), and the confidence in the information used to inform the hazard and
exposure values. If the calculated MOE is greater than the benchmark MOE, generally it is less likely
that there is risk.
Uncertainty factors (UFs) also play an important role in the risk estimation approach and in determining
unreasonable risk. A lower benchmark MOE (e.g., 30) indicates greater certainty in the data (because
fewer of the default UFs relevant to a given POD as described above were applied). A higher benchmark
Page 375 of 748
-------
88
89
90
91
92
93
94
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
MOE (e.g., 1000) would indicate more uncertainty in risk estimation and extrapolation for the MOE for
specific endpoints and scenarios. However, these are often not the only uncertainties in a risk evaluation.
5,1.2,2 Determining Cancer Risks
EPA estimates cancer risks by determining the incremental increase in probability of an individual in an
exposed population developing cancer over a lifetime (excess lifetime cancer risk (ELCR)) following
exposure to the chemical under specified use scenarios. Standard cancer benchmarks used by EPA and
other regulatory agencies are an increased cancer risk above benchmarks ranging from 1 in 1,000,000 to
1 in 10,000 (i.e., lxlO"6 to lxlO"4) depending on the subpopulation exposed. Generally, EPA considers 1
x 10"6 to lx 10"4 as the appropriate benchmark for the general population, consumer users, and non-
occupational PESS.21
For the subject chemical substance, the EPA, consistent with case law and 2017 NIOSH guidance,22
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 Act (OSHA)
requirements. It is important to note that lxlO"4 is not a bright line and EPA has discretion to make risk
determinations based on other benchmarks as appropriate. It is important to note that exposure-related
considerations (duration, magnitude, population exposed) can affect EPA's estimates of the excess
lifetime cancer risk.
5,1,3 Determining Environmental Risk
To assess environmental risk, EPA identifies and evaluates environmental hazard data for aquatic,
sediment-dwelling, and terrestrial organisms exposed under acute and chronic exposure conditions. The
environmental risk includes any risks that exceed benchmarks to the aquatic environment from levels of
the evaluated chemical released to the environment (e.g.,, surface water, sediment, soil, biota) under the
conditions of use, based on the fate properties, release potential, and reasonably available environmental
monitoring and hazard data.
Environmental risks are estimated by calculating a RQ. The RQ is defined as:
RQ = Environmental Concentration / Effect Level
An RQ equal to 1 indicates that the exposures are the same as the concentration that causes effects. If the
RQ is greater than 1, the exposure is greater than the effect concentration and there is potential for risk
presumed. If the RQ is less than 1, the exposure is less than the effect concentration and unreasonable
risk is not likely. The Concentrations of Concern (COC) or hazard value for certain aquatic organisms
are used to calculate RQs for acute and chronic exposures. For environmental risk, EPA is more likely to
21 As an example, whenEPA'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. 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).
22 International Union, UAW v. Pendergrass, 878 F.2d 389 (D.C. Cir. 1989), citing Industrial Union Department, AFL-CIO v.
American Petroleum Institute, 448 U.S. 607 (1980) ("Benzene decision"), in which it was found that a lifetime cancer risk of
1 in 1,000 was found to be clearly significant; and NIOSH (Whittaker et at. 20.1.6). Current intelligence bulletin 68: NIOSH
chemical carcinogen policy, available at https://www.cdc. gov/n.iosIi/docs/20.1.7-.1.00/p(lK ).pdf.
Page 376 of 748
-------
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
determine that there is unreasonable risk if the RQ exceeds 1 for the conditions of use being evaluated.
Consistent with EPA's human health evaluations, the RQ is not treated as a bright line and other risk-
based factors may be considered (e.g., exposure scenario, uncertainty, severity of effect) for purposes of
making a risk determination.
5,2 Risk Determinations for TCE
EPA's preliminary determinations of unreasonable risk for specific conditions of use of TCE listed
below are based on health risks to workers and occupational non-users (ONUs) during occupational
exposures, and to consumers and bystanders during exposures to consumer uses.
As described in section 4, significant risks associated with more than one adverse effect (e.g.,
developmental toxicity, reproductive toxicity, liver toxicity, kidney toxicity, immunotoxicity,
neurotoxicity, and cancer) were identified for particular conditions of use. While congenital heart
defects were the most sensitive endpoint for TCE, for the purpose of the draft risk determination, there
are uncertainties which decrease EPA's confidence in this endpoint. Section 26 of TSCA requires that
EPA make decisions consistent with the "best available science." Section 26 also requires other
scientific considerations including consideration of the "extent of independent verification" and "weight
of the scientific evidence." As described in EPA's framework rule for risk evaluation [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 require 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. Based on these considerations, EPA is relying upon
immunosuppression for acute inhalation and dermal exposures, and autoimmunity for chronic inhalation
and dermal exposures. In Table 5-1 and Section 5.3 below, EPA has identified immunosuppression and
autoimmunity as the critical endpoints for determining whether or not a condition of use presents
unreasonable risks. EPA has the most confidence in these endpoints and it is expected that addressing
risks for these effects would address other identified risks. For the majority of the occupational and
consumer conditions of use, unreasonable risk determinations were consistent whether based on
congenital heart defects (an endpoint for which EPA has lower confidence) or immunosuppression and
autoimmunity endpoints.
• Workers: EPA evaluated workers' acute and chronic inhalation and dermal exposures for cancer
and non-cancer risks and determined whether any risks are unreasonable. The drivers for EPA's
determination of unreasonable risk for workers are immunosuppression resulting from acute
inhalation and dermal exposures, autoimmunity resulting from chronic inhalation and dermal
exposures, and cancer resulting from chronic inhalation and dermal exposure. The
determinations reflect the severity of the effects associated with the occupational exposures to
TCE and incorporate consideration of expected PPE. EPA expects there is compliance with
federal and state laws, such as worker protection standards, unless case-specific facts indicate
otherwise, and therefore existing OSHA regulations for worker protection and hazard
communication will result in use of appropriate PPE consistent with the applicable SDSs.
Estimated numbers of workers are in Section 2.3.1.2.7.
• Occupational Non-Users (ONUs): EPA evaluated ONU acute and chronic inhalation exposures
for cancer and non-cancer risks and determined whether any risks are unreasonable. EPA
considers occupational non-users to be a subset of workers for whom the potential inhalation
exposures may differ based on proximity to the exposure source. The drivers for EPA's
determination of unreasonable risks to ONUs are immunosuppression resulting from acute
Page 377 of 748
-------
170
171
172
173
174
175
176
111
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
inhalation exposures, autoimmunity resulting from chronic inhalation exposures, and cancer
resulting from chronic inhalation exposure. The determinations reflect the severity of the effects
associated with the occupational exposures to TCE and the expected absence of PPE for ONUs.
For dermal exposures, because ONUs are not expected to be dermally exposed to TCE, dermal
risks to ONUs generally were not evaluated. For inhalation exposures, EPA, where possible,
used monitoring or modeling information to estimate ONU exposures and to describe the risks
separately from workers directly exposed. For some conditions of use, EPA did not separately
calculate risk estimates for ONUs and workers. For these conditions of use, there is uncertainty
in the ONU risk estimates since the data or modeling did not distinguish between worker and
ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower than
inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this
uncertainty, EPA considered the central tendency risk estimate when determining ONU risk for
those conditions of use for which ONU exposures were not separately estimated. Estimated
numbers of occupational non-users are in Section 2.3.1.2.7.
• Consumers: EPA evaluated consumer acute inhalation and dermal exposures for non-cancer risks
and determined whether any risks are unreasonable. The driver for EPA's determination of
unreasonable risk is immunosuppression from acute inhalation and dermal exposures. Generally,
risks for consumers were indicated by acute inhalation and dermal exposure at low, medium, and
high intensity use. Estimated numbers of consumers are in Section 2.3.1.2.7.
• Bystanders (from consumer uses): EPA evaluated bystander acute inhalation exposures for non-
cancer risks and determined whether any risks are unreasonable. The driver for EPA's
determination of unreasonable risk is immunosuppression from acute inhalation exposure.
Generally, risks for bystanders were indicated by acute inhalation exposure scenarios at low,
medium, and high intensity use. Because bystanders are not expected to be dermally exposed to
TCE, dermal non-cancer risks to bystanders were not evaluated. Estimated numbers of
bystanders are in Section 2.3.1.2.7.
As described below, risks to the environment and general population either were not relevant for these
conditions of use or were evaluated and not found to be unreasonable. For the conditions of use where
EPA found no unreasonable risk, EPA describes the estimated risks in Section 4.5.2 (Table 4-54 and
Table 4-55).
• Environmental risks: EPA concluded that environmental exposures are expected for aquatic
species for the conditions of use within the scope of the evaluation. EPA identified risks from acute
and chronic exposures for aquatic organisms like aquatic invertebrates and fish near two facilities
releasing TCE to surface water and risks to the most sensitive algae species near over 400 facilities.
EPA did not identify any additional scenarios indicating unreasonable risk for aquatic organisms
from exposures to TCE in surface waters. For aquatic organisms like aquatic invertebrates and fish,
one facility had an acute RQ greater than 1 (RQ = 3.11), exceeding the acute COC of 3,200 ppb and
indicating risk to aquatic organisms from acute exposures. This facility is one of 59 facilities
modeled by EPA that use TCE for open-top vapor degreasing (see Section 4.5.1). Another facility
had an acute RQ of 0.94 indicating some uncertainty about whether it would also pose risks to
aquatic organisms from acute exposures. This facility is one of 11 facilities modeled by EPA that
process TCE as a reactant (see Section 4.5.1). Both facilities had chronic RQs greater than 1,
exceeding the chronic COC of 788 ppb for 20 days. The over 400 facilities with potential risks to the
Page 378 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
217 most sensitive algae species (exceeding the algae COC of 3 ppb) did not show risks for algae species
218 as a whole, as they showed no risks for 95% of algae species (no exceedances of the algae COC of
219 52,000 ppb). Monitored data from the Water Quality Portal and grey literature show no exceedances
220 of the acute COC and the chronic COC in ambient water. Monitored data from literature showed
221 some exceedances of the algae COC of 3 ppb in ambient water; however, the data show no
222 exceedances of the algae COC of 52,000 ppb. Therefore, EPA did not identify risks for acute or
223 chronic exposure durations in ambient water for areas where monitored data were reasonably
224 available. Given the uncertainties in the modeling data and exceedance of the acute RQ for only one
225 data point and of the chronic RQ for only two out of 70 facilities modeled, EPA does not consider
226 these risks unreasonable (see Section 4.5.2).
227
228 • General population: Exposure pathways to the general population are covered by other statutes and
229 consist of: the ambient air pathway (i.e., TCE is listed as a HAP in the Clean Air Act (CAA)), the
230 drinking water pathway (i.e., National Primary Drinking Water Regulations (NPDWRs) are
231 promulgated for TCE under the Safe Drinking Water Act), ambient water pathways (i.e., TCE is a
232 priority pollutant with recommended water quality criteria for protection of human health under the
233 CWA), the biosolids pathway (i.e., the biosolids pathway for TCE is currently being addressed in the
234 CWA regulatory analytical process), disposal pathways (TCE disposal is managed and prevented
235 from further environmental release by RCRA and SDWA regulations). As described above, other
236 environmental statutes administered by EPA adequately assess and effectively manage these
237 exposures. EPA believes that the TSCA risk evaluation should focus on those exposure pathways
238 associated with TSCA conditions of use that are not subject to the regulatory regimes discussed
239 above because those pathways are likely to represent the greatest areas of concern to EPA.
240 Therefore, EPA did not evaluate hazards or exposures to the general population in this risk
241 evaluation, and there is no risk determination for the general population (U.S. EPA. 2018d).
242
243 Table 5-1 below presents an overview of risk determinations by condition of use. An in-depth
244 explanation of each determination follows the table, in Section 5.3.
245
246 Table 5-1. Summary of Unreasonable Risk Determinations by Condition of Use
Condition of Use
Unreasonable Risk Determination
Manufacture - Domestic Manufacture
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Manufacture - Import (includes repackaging and
loading/unloading)
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Processing - Processing as a reactant/intermediate in
industrial gas manufacturing (e.g., manufacture of
fluorinated gases used as refrigerants, foam blowing
agents and solvents)
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Processing - Incorporation into formulation, mixture or
reaction product - Solvents (for cleaning or degreasing);
adhesives and sealant chemicals; solvents (which
become part of product formulation or mixture) (e.g.,
lubricants and greases, paints and coatings, other uses)
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Page 379 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Condition of Use
Unreasonable Risk Determination
Processing - Incorporation into articles - Solvents
(becomes an integral components of articles)
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Processing - Repackaging - Solvents (for cleaning or
degreasing)
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Processing - Recycling
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Distribution in Commerce
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Batch vapor degreaser (open-top)
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Batch vapor degreaser (closed-loop)
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - In-line vapor degreaser (conveyorized)
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - In-line vapor degreaser (web cleaner)
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Cold cleaner
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Solvents (for cleaning or
degreasing) - Aerosol spray degreaser/cleaner; mold
release
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Lubricants and
greases/lubricants and lubricant additives - Tap and die
fluid
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Lubricants and
greases/lubricants and lubricant additives - Penetrating
lubricant
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Adhesives and sealants -
Solvent-based adhesives and sealants; tire repair
cement/sealer; mirror edge sealant
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Functional fluids (closed
systems) - Heat exchange fluid
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Paints and coatings -
Diluent in solvent-based paints and coatings
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Page 380 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Condition of Use
Unreasonable Risk Determination
Industrial/Commercial Use - Cleaning and furniture
care products - Carpet cleaner; wipe cleaner
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Laundry and dishwashing
products - Spot remover
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Arts, crafts and hobby
materials - Fixatives and finishing spray coatings
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Corrosion inhibitors and
anti-scaling agents - Corrosion inhibitors and anti-
scaling agents
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
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
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Ink, toner and colorant
products - Toner aid
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Automotive care products
- Brake and parts cleaners
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Apparel and footwear care
products - Shoe polish
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Industrial/Commercial Use - Other commercial uses -
Hoof polishes; gun scrubber; pepper spray; other
miscellaneous industrial and commercial uses
Presents an unreasonable risk of injury to health
(workers and occupational non-users)
Disposal
Presents an unreasonable risk of injury to health
(workers)
Does not present an unreasonable risk of injury to
health (occupational non-users)
Consumer Use - Solvents (for cleaning or degreasing) -
Brake and parts cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Aerosol electronic degreaser/cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Liquid electronic degreaser/cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Aerosol spray degreaser/cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Liquid degreaser/cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Page 381 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Condition of Use
Unreasonable Risk Determination
Consumer Use - Solvents (for cleaning or degreasing) -
Aerosol gun scrubber
Presents an unreasonable risk of injury to health
(consumers)
Does not present an unreasonable risk of injury to
health (bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Liquid gun scrubber
Presents an unreasonable risk of injury to health
(consumers)
Does not present an unreasonable risk of injury to
health (bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Mold release
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Aerosol tire cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Solvents (for cleaning or degreasing) -
Liquid tire cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Lubricants and greases - Tap and die
fluid
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Lubricants and greases - Penetrating
lubricant
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Adhesives and sealants - Solvent-
based adhesive and sealant
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Adhesives and sealants - Mirror edge
sealant
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Adhesives and sealants - Tire repair
cement/sealer
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Cleaning and furniture care products -
Carpet cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Cleaning and furniture care products -
Aerosol spot remover
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Cleaning and furniture care products -
Liquid spot remover
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Arts, crafts, and hobby materials -
Fixatives and finishing spray coatings
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Apparel and footwear care products -
Shoe polish
Presents an unreasonable risk of injury to health
(consumers)
Does not present an unreasonable risk of injury to
health (bystanders)
Consumer Use - Other consumer uses - Fabric spray
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Page 382 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Condition of Use
Unreasonable Risk Determination
Consumer Use - Other consumer uses - Film cleaner
Presents an unreasonable risk of injury to health
(consumers and bystanders)
Consumer Use - Other consumer uses - Hoof polish
Presents an unreasonable risk of injury to health
(consumers)
Does not present an unreasonable risk of injury to
health (bystanders)
Consumer Use - Other consumer uses - Pepper spray
Does not present an unreasonable risk of injury to
health (consumers)
Consumer Use - Other consumer uses - Toner aid
Presents an unreasonable risk of injury to health
(consumers and bystanders)
247 5.3 Detailed Risk Determinations by Condition of Use
248 5.3.1 Manufacture - Domestic manufacture
249
250 Section 6(b)(4)(A) unreasonable risk determination for domestic manufacture of TCE:
251 • Presents an unreasonable risk of injury to health (workers and occupational non-users
252 (ONUs)).
253
254 Unreasonable risk driver - workers:
255 • Immunosuppression resulting from acute dermal exposures.
256 • Autoimmunity resulting from chronic inhalation and dermal exposures.
257 • Cancer resulting from chronic inhalation and dermal exposures.
258
259 Unreasonable risk driver - ONUs:
260 ~ Immunosuppression resulting from acute inhalation exposures.
261 • Autoimmunity resulting from chronic inhalation exposures.
262 • Cancer resulting from chronic inhalation exposures.
263
264 Driver benchmarks - workers and ONUs:
265 • Immunosuppression: Benchmark MOE = 30.
266 • Autoimmunity: Benchmark MOE = 30.
267 • Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
268
269 Risk estimate - workers:
270 • Immunosuppression:
271 o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-6)
272 • Autoimmunity:
273 o Chronic inhalation MOEs 19.3 and 2.8 (central tendency and high-end) with PPE
274 (respirator APF 50).
275 o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
276 20). (Table 4-6)
277 • Cancer:
278 o Inhalation: 1.3E-04 (high-end) with PPE (respirator APF 50).
Page 383 of 748
-------
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
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-6)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 13.9 (central tendency). (Table 4-6)
• Autoimmunity:
o Chronic inhalation MOE 0.39 (central tendency). (Table 4-6)
• Cancer:
o Inhalation: 7.5E-04 (central tendency). (Table 4-6)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while non-cancer risk estimates for acute
inhalation exposures do not indicate risks with expected respiratory protection (APF 50), all other risk
estimates indicate risk even with expected respiratory and dermal protection. EPA did not separately
calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the
data did not distinguish between worker and ONU inhalation exposure estimates. ONU inhalation
exposures are expected to be lower than inhalation exposures for workers directly handling the chemical
substance; however, the relative exposure of ONUs to workers in these cases cannot be quantified. To
account for this uncertainty, EPA considered the central tendency estimate when determining ONU risk.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA assessed inhalation exposures during manufacturing using monitoring data
submitted by the Halogenated Solvents Industry Alliance (HSIA). EPA estimated dermal exposures
using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably
available for the condition of use.
Life Cycle Stage
Category
Subcategory
Manufacture
Domestic Manufacture
Domestic manufacture
5.3.2 Manufacture - Import (includes repackaging and loading/unloading)
Section 6(b)(4)(A) unreasonable risk determination for import of TCE:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
Page 384 of 748
-------
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
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-19)
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-19)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-19)
Risk Considerations: For workers, while non-cancer risk estimates for acute inhalation exposures and
cancer risk estimates for inhalation exposures do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
Acute and chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not
indicate risks in the absence of PPE. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
assessed inhalation exposures during import using the repackaging exposure scenario. EPA estimated
dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Manufacture
Import
Import
5,3,3 Processing - Processing as a reactant/intermediate in industrial gas manufacturing
(e.g., manufacture of fluorinated gases used as refrigerants, foam blowing agents
and solvents)
Section 6(b)(4)(A) unreasonable risk determination for processing of TCE as a reactant/intermediate:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
Page 385 of 748
-------
367
368
369
370
371
372
373
374
375
376
377
378
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-7)
• Autoimmunity:
o Chronic inhalation MOEs 19.3 and 2.8 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-7)
• Cancer:
o Inhalation: 1.3E-04 (high-end) with PPE (respirator APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-7)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 13.9 (central tendency). (Table 4-7)
• Autoimmunity:
o Chronic inhalation MOE 0.39 (central tendency). (Table 4-7)
• Cancer:
o Inhalation: 7.5E-04 (central tendency). (Table 4-7)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while non-cancer risk estimates for acute
inhalation exposures do not indicate risks with expected respiratory protection (APF 50), all other risk
estimates indicate risk even with expected respiratory and dermal protection (PF = 20). EPA did not
separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk estimate
since the data did not distinguish between worker and ONU inhalation exposure estimates. ONU
inhalation exposures are expected to be lower than inhalation exposures for workers directly handling
the chemical substance; however, the relative exposure of ONUs to workers in these cases cannot be
quantified. To account for this uncertainty, EPA considered the central tendency estimate when
determining ONU risk. The high volatility of TCE and potentially severe effects from short term
exposure are factors when weighing uncertainties. EPA did not identify inhalation exposure monitoring
data related to processing TCE as a reactant. Therefore, EPA used monitoring data from the manufacture
of TCE as surrogate data for the processing condition of use. EPA believes the handling and TCE
concentrations for both conditions of use to be similar. EPA estimated dermal exposures using the
Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available
for the condition of use.
Page 386 of 748
-------
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
436
437
438
439
440
441
442
443
444
445
446
447
448
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category
Subcategory
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)
5.3.4 Processing - Incorporation into formulation, mixture or reaction product - Solvents
(for cleaning or degreasing); adhesives and sealant chemicals; solvents (which
become part of product formulation or mixture) (e.g., lubricants and greases, paints
and coatings, other uses)
Section 6(b)(4)(A) unreasonable risk determination for incorporation of TCE into formulation, mixture,
reaction product, or articles:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-18)
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-18)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-18)
Risk Considerations: For workers, while non-cancer risk estimates for acute inhalation exposures and
cancer risk estimates for inhalation exposures do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
Acute and chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not
indicate risks in the absence of PPE. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
Page 387 of 748
-------
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify inhalation exposure monitoring data related to using TCE when formulating aerosol and
non-aerosol products. Therefore, EPA used monitoring data from repackaging as a surrogate. EPA
estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal
exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Processing
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)
5.3.5 Processing - Incorporation into articles - Solvents (becomes an integral components
of articles)
Section 6(b)(4)(A) unreasonable risk determination for incorporation of TCE into articles as solvents
that become integral components of articles:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-18)
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-18)
• Cancer:
Page 388 of 748
-------
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
513
514
515
516
517
518
519
520
521
522
523
524
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-18)
Risk Considerations: For workers, while non-cancer risk estimates for acute inhalation exposures and
cancer risk estimates for inhalation exposures do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
Acute and chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not
indicate risks in the absence of PPE. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify inhalation exposure monitoring data related using TCE when formulating aerosol and non-
aerosol products. Therefore, EPA used monitoring data from repackaging as a surrogate. EPA estimated
dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Processing
Processing - incorporated
into articles
Solvents (becomes an integral
components of articles)
5.3.6 Processing - Repackaging - Solvents (for cleaning or degreasing)
Section 6(b)(4)(A) unreasonable risk determination for processing and repackaging of TCE as a solvent
for cleaning or degreasing:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-19)
Page 389 of 748
-------
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-19)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-19)
Risk Considerations: For workers, while non-cancer risk estimates for acute inhalation exposures and
cancer risk estimates for inhalation exposures do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
Acute and chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not
indicate risks in the absence of PPE. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
assessed inhalation exposures during import using the repackaging exposure scenario. EPA estimated
dermal exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data
was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Processing
Processing - repackaging
Solvents (for cleaning or degreasing)
5,3,7 Processing - Recycling
Section 6(b)(4)(A) unreasonable risk determination for recycling of TCE:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-27)
Page 390 of 748
-------
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-27)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-27)
Risk Considerations: For workers, while non-cancer risk estimates for acute inhalation exposures and
cancer risk estimates for inhalation exposures do not indicate risks with expected respiratory protection
(APF 50), all other risk estimates indicate risk even with expected respiratory and dermal protection.
Acute and chronic inhalation exposures at the central tendency for cancer and non-cancer effects do not
indicate risks in the absence of PPE. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify inhalation exposure monitoring data related to using TCE when formulating aerosol and
non-aerosol products. Therefore, EPA used monitoring data from repackaging as a surrogate for
recycling. EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Processing
Recycling
Recycling
5.3.8 Distribution in Commerce
Section 6(b)(4)(A) unreasonable risk determination for distribution of TCE:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Risk Considerations: A quantitative evaluation of the distribution of TCE was not included in the risk
evaluation because exposures and releases from distribution were considered within each condition of
use.
Life Cycle Stage
Category
Subcategory
Distribution in commerce
Distribution
Distribution in commerce
5.3.9 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor
degreaser (open-top)
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for batch vapor degreasing (open-top):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Page 391 of 748
-------
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 18.9 and 3.4 (central tendency and high-end) with PPE
(respirator APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-8)
• Autoimmunity:
o Chronic inhalation MOEs 0.52 and 9.3E-02 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-8)
• Cancer:
o Inhalation: 5.5E-04 and 4.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-8)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 4.7 (central tendency). (Table 4-8)
• Autoimmunity:
o Chronic inhalation MOE 0.13 (central tendency). (Table 4-8)
• Cancer:
o Inhalation: 2.2E-03 (central tendency). (Table 4-8)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from NIOSH investigations at twelve sites using TCE as a degreasing solvent in
OTVDs. Due to the large variety in shop types that may use TCE as a vapor degreasing solvent, it is
unclear how representative these data are of a "typical" shop. Therefore, EPA supplemented the
Page 392 of 748
-------
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
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
identified monitoring data using the Open-Top Vapor Degreasing Near-Field/Far-Field Inhalation
Exposure Model. EPA's inhalation exposure modeling is based on a near-field/far-field approach, where
a vapor generation source located inside the near-field diffuses into the surrounding environment.
Workers are assumed to be exposed to TCE vapor concentrations in the near-field, while occupational
non-users are exposed at concentrations in the far-field. These estimates were used for determining
worker and ONU risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to
Volatile Liquids Model because dermal exposure data was not reasonably available for the condition of
use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
Batch vapor degreaser (open-top)
5.3.10 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Batch vapor
degreaser (closed-loop)
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for batch vapor degreasing (closed-loop):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-10)
• Autoimmunity:
o Chronic inhalation MOEs 15.8 and 5.0 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-10)
• Cancer:
Page 393 of 748
-------
697
698
699
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
728
729
730
731
732
733
734
735
736
737
738
739
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-10)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 11.4 (central tendency). (Table 4-10)
• Autoimmunity:
o Chronic inhalation MOE 0.32 (central tendency). (Table 4-10)
• Cancer (liver, kidney, NHL):
o Inhalation: 9.1E-04 (central tendency). (Table 4-10)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while non-cancer risk estimates for acute
inhalation exposures and cancer risk estimates for inhalation exposures do not indicate risks with
expected respiratory protection (APF 50), all other risk estimates indicate risk even with expected
respiratory and dermal protection. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
identified inhalation exposure monitoring data from a European Chemical Safety report using TCE in
closed degreasing operations. EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
Batch vapor degreaser (closed-loop)
5.3.11 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor
degreaser (conveyorized)
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for in-line vapor degreasing (conveyorized):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Page 394 of 748
-------
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 8.1 and 5.4 (central tendency and high-end) with PPE (respirator
APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-11)
• Autoimmunity:
o Chronic inhalation MOEs 0.22 and 0.15 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-11)
• Cancer (liver, kidney, NHL):
o Inhalation: 1.3E-03 and 2.5E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-11)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 0.16 (central tendency). (Table 4-11)
• Autoimmunity:
o Chronic inhalation MOE 4.5E-03 (central tendency). (Table 4-11)
• Cancer (liver, kidney, NHL):
o Inhalation: 6.5E-02 (central tendency). (Table 4-11)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF 50 and PF = 20). The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA
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. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for this condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
In-line vapor degreaser (conveyorized)
Page 395 of 748
-------
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5,3,12 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - In-line vapor
degreaser (web cleaner)
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for in-line vapor degreaser (web cleaner):
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 18.5 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-13)
• Autoimmunity:
o Chronic inhalation MOEs 1.2 and 0.51 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-13)
• Cancer:
o Inhalation: 2.3E-04 and 5.8E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-13)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 1.7 (central tendency). (Table 4-13)
• Autoimmunity:
o Chronic inhalation MOE 4.6E-02 (central tendency). (Table 4-13)
• Cancer:
o Inhalation: 5.9E-03 (central tendency). (Table 4-13)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
Page 396 of 748
-------
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
866
867
868
869
870
871
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
expected respiratory and dermal protection (APF 50 and PF = 20). The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify any inhalation exposure monitoring data related to the use of TCE in web degreasing.
Therefore, EPA assessed inhalation exposures during web degreasing using the Web Degreasing Near-
Field/Far-Field Inhalation Exposure Model. EPA's inhalation exposure modeling is based on a near-
field/far-field approach, where a vapor generation source located inside the near-field diffuses into the
surrounding environment. Workers are assumed to be exposed to TCE vapor concentrations in the near-
field, while occupational non-users are exposed at concentrations in the far-field. These estimates were
used for determining worker and ONU risks. For workers, EPA estimated dermal exposures using the
Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available
for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
In-line vapor degreaser (web cleaner)
5.3.13 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Cold cleaner
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for cold cleaning:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 4.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-14)
• Autoimmunity:
o Chronic inhalation MOEs 2.2 and 0.13 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-14)
Page 397 of 748
-------
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
904
905
906
907
908
909
910
911
912
913
914
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Cancer:
o Inhalation: 1.2E-04 and 2.3E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-14)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 2.8 (central tendency). (Table 4-14)
• Autoimmunity:
o Chronic inhalation MOE 7.9E-02 (central tendency). (Table 4-14)
• Cancer:
o Inhalation: 3.3E-03 (central tendency). (Table 4-14)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA did not identify inhalation
exposure monitoring data for the Cold Cleaning condition of use. Therefore, EPA used the Cold
Cleaning Near-Field/Far-Field Inhalation Exposure Model to estimate exposures to workers and ONUs.
EPA's inhalation exposure modeling is based on a near-field/far-field approach, where a vapor
generation source located inside the near-field diffuses into the surrounding environment. Workers are
assumed to be exposed to TCE vapor concentrations in the near-field, while occupational non-users are
exposed at concentrations in the far-field. These estimates were used for determining worker and ONU
risks. For workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids
Model because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
Cold cleaner
5,3.14 Industrial/Commercial Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner; mold release
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a solvent
for aerosol spray degreaser/cleaner and for mold release:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
Page 398 of 748
-------
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 10.9 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 15.1 (high-end) with PPE (gloves PF 20). (Table 4-15)
• Autoimmunity:
o Chronic inhalation MOEs 0.95 and 0.30 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.2 and 0.39 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-15)
• Cancer:
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-15)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.0 (high-end). (Table 4-15)
• Autoimmunity:
o Chronic inhalation MOE 1.0 (central tendency). (Table 4-15)
• Cancer:
o Inhalation: 2.6E-04 (central tendency). (Table 4-15)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF=50 and PF=20). EPA estimated ONU exposures could
be as high as worker exposures as a high-end estimate. The high volatility of TCE and potentially severe
effects from short term exposure are factors when weighing uncertainties. EPA did not identify
inhalation exposure monitoring data related to the use of TCE in aerosol degreasers. Therefore, EPA
estimated inhalation exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA's
inhalation exposure modeling is based on a near-field/far-field approach, where a vapor generation
source located inside the near-field diffuses into the surrounding environment. Workers are assumed to
be exposed to TCE vapor concentrations in the near-field, while occupational non-users are exposed at
concentrations in the far-field. These estimates were used for determining worker and ONU risks. For
workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Page 399 of 748
-------
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
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Solvents (for cleaning or
degreasing)
• Aerosol spray degreaser/cleaner
• Mold release
5.3.15 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant
additives - Tap and die fluid
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a lubricant
grease/lubricant, and lubricant additive in tap and die fluid:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Unreasonable risk driver - ONUs:
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 29.7 (high-end) with PPE (gloves PF 20). (Table 4-21)
• Autoimmunity:
o Chronic inhalation MOE 27.5 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 2.3 and 0.76 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-21)
• Cancer:
o Dermal: 3.9E-04 and 1.5E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-21)
Risk estimate - ONUs:
• Autoimmunity:
o Chronic inhalation MOE 2.1 (central tendency). (Table 4-21)
• Cancer:
o Inhalation: 1.3E-04 (central tendency). (Table 4-21)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE, with the exception of acute inhalation exposures at the central
tendency. For workers, while non-cancer risk estimates for acute inhalation exposures and cancer risk
Page 400 of 748
-------
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
estimates from inhalation exposures do not indicate risks with expected respiratory protection (APF 50),
all other risk estimates indicate risk even with expected respiratory and dermal protection. EPA did not
separately calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk estimate
since the data did not distinguish between worker and ONU inhalation exposure estimates. ONU
inhalation exposures are expected to be lower than inhalation exposures for workers directly handling
the chemical substance; however, the relative exposure of ONUs to workers in these cases cannot be
quantified. To account for this uncertainty, EPA considered the central tendency estimate when
determining ONU risk. The high volatility of TCE and potentially severe effects from short term
exposure are factors when weighing uncertainties. EPA identified inhalation exposure monitoring data
from OSHA facility inspections at two sites using TCE in metalworking fluids. EPA estimated dermal
exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not
reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Lubricants and
greases/lubricants and
lubricant additives
Tap and die fluid
5.3.16 Industrial/Commercial Use - Lubricants and greases/lubricants and lubricant
additives - Penetrating lubricant
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as penetrating
lubricant:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 10.9 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 15.1 (high-end) with PPE (gloves PF 20). (Table 4-15)
• Autoimmunity:
Page 401 of 748
-------
1044
1045
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
1079
1080
1081
1082
1083
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Chronic inhalation MOEs 0.95 and 0.30 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.2 and 0.39 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-15)
• Cancer:
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-15)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.0 (high-end). (Table 4-15)
• Autoimmunity:
o Chronic inhalation MOE 1.0 (central tendency). (Table 4-15)
• Cancer:
o Inhalation: 2.6E-04 (central tendency). (Table 4-15)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. EPA estimated ONU exposures could be as high as worker
exposures as a high-end estimate. The high volatility of TCE and potentially severe effects from short
term exposure are factors when weighing uncertainties. EPA did not identify inhalation exposure
monitoring data related to the use of TCE in aerosol degreasers. Therefore, EPA estimated inhalation
exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA estimated dermal
exposures using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not
reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Lubricants and
greases/lubricants and
lubricant additives
Penetrating lubricant
5.3.17 Industrial/Commercial Use - Adhesives and sealants - Solvent-based adhesives and
sealants; tire repair cement/sealer; mirror edge sealant
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as an adhesive
and sealant in solvent-based adhesives and sealants, tire repair cement/sealer, and mirror edge sealant:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
Page 402 of 748
-------
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
1127
1128
1129
1130
1131
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 6.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 25.2 and 8.4 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOEs 1.6 and 0.18 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 0.65 and 0.22 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-04 and 2.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 1.4E-03 and 5.3E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-23)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.5 (central tendency). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOE 0.15 (central tendency). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-03 (central tendency). (Table 4-23)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a NIOSH Health Hazard Evaluation report (Chrostek, 1981) using TCE in coating
applications and from OSHA facility inspections (OSHA, 2017) at three sites using TCE in adhesives
and coatings. The OSHA data also provided two data points where the worker job description was
"foreman." EPA assumed this data is applicable to ONU exposure. EPA estimated dermal exposures
using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably
available for the condition of use.
Page 403 of 748
-------
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Adhesives and sealants
• Solvent-based adhesives and
sealants
• Tire repair cement/sealer
• Mirror edge sealant
5.3.18 Industrial/Commercial Use - Functional fluids (closed systems) - Heat exchange
fluid
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as a functional
fluid (closed systems) for heat exchange fluid:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-26)
• Autoimmunity:
o Chronic inhalation MOEs 19.3 and 2.8 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-26)
• Cancer:
o Inhalation: 1.3E-04 (high-end) with PPE (respirator APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-26)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 13.9 (central tendency). (Table 4-26)
Page 404 of 748
-------
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Autoimmunity:
o Chronic inhalation MOE 0.39 (central tendency). (Table 4-26)
• Cancer:
o Inhalation: 7.5E-04 (central tendency). (Table 4-26)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while non-cancer risk estimates for acute
inhalation exposures do not indicate risks with expected respiratory protection (APF 50), all other risk
estimates indicate risk even with expected respiratory and dermal protection. EPA did not separately
calculate risk estimates for ONUs and workers. There is uncertainty in the ONU risk estimate since the
data did not distinguish between worker and ONU inhalation exposure estimates. ONU inhalation
exposures are expected to be lower than inhalation exposures for workers directly handling the chemical
substance; however, the relative exposure of ONUs to workers in these cases cannot be quantified. To
account for this uncertainty, EPA considered the central tendency estimate when determining ONU risk.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA did not identify inhalation exposure monitoring data related to using TCE
for other industrial uses. Therefore, EPA used monitoring data from loading/unloading TCE during
manufacturing as a surrogate for this condition of use. EPA estimated dermal exposures using the
Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available
for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Functional fluids (closed
systems)
Heat exchange fluid
5.3.19 Industrial/Commercial Use - Paints and coatings - Diluent in solvent-based paints
and coatings
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in paints and
coatings as a diluent in solvent-based paint and coatings:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
• Does not present an unreasonable risk of injury to the environment (aquatic, sediment dwelling
and terrestrial organisms).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Page 405 of 748
-------
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 6.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 25.2 and 8.4 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOEs 1.6 and 0.18 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 0.65 and 0.22 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-04 and 2.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 1.4E-03 and 5.3E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-23)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.5 (central tendency). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOE 0.15 (central tendency). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-03 (central tendency). (Table 4-23)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a NIOSH Health Hazard Evaluation report (Chrostek, 1981) using TCE in coating
applications and from OSHA facility inspections (OSHA, 2017) at three sites using TCE in adhesives
and coatings. The OSHA data also provided two data points where the worker job description was
"foreman." EPA assumed this data is applicable to ONU exposure. EPA estimated dermal exposures
using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably
available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Paints and coatings
Diluent in solvent-based paints and
coatings
Page 406 of 748
-------
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5.3,20 Industrial/Commercial Use - Cleaning and furniture care products - Carpet
cleaner; wipe cleaning
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in cleaning
and furniture care products for carpet cleaning and wipe cleaning, and in laundry and dishwashing
products as a spot remover:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 5.4 and 1.9 (central tendency and high-end) without respiratory
PPE.
o Acute dermal MOEs 22.7 and 7.6 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOEs 0.15 and 5.1E-02 (central tendency and high-end) without
respiratory PPE.
o Chronic dermal MOEs 0.56 and 0.17 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-17)
• Cancer:
o Inhalation: 1.8E-03 and 5.8E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-17)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 10.9 (central tendency). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOE 0.29 (central tendency). (Table 4-17)
• Cancer:
o Inhalation: 9.2E-04 (central tendency). (Table 4-17)
Page 407 of 748
-------
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. While workers are unlikely to wear respiratory protection for
this condition of use, all other risk estimates indicate risk even with respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA identified minimal 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. Workers are assumed to be exposed to TCE vapor concentrations in the near-field, while
occupational non-users are exposed at concentrations in the far-field. These estimates were used for
determining worker and ONU risks. For workers, EPA estimated dermal exposures using the Dermal
Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available for the
condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Cleaning and furniture care
products
• Carpet cleaner
• Wipe cleaning
5.3.21 Industrial/Commercial Use - Laundry and dishwashing products - Spot remover
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in laundry and
dishwashing products as a spot remover:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 5.4 and 1.9 (central tendency and high-end) without respiratory
PPE.
Page 408 of 748
-------
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Acute dermal MOEs 22.7 and 7.6 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOEs 0.15 and 5.1E-02 (central tendency and high-end) without
respiratory PPE.
o Chronic dermal MOEs 0.56 and 0.17 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-17)
• Cancer:
o Inhalation: 1.8E-03 and 5.8E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-17)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 10.9 (central tendency). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOE 0.29 (central tendency). (Table 4-17)
• Cancer:
o Inhalation: 9.2E-04 (central tendency). (Table 4-17)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. While workers are unlikely to wear respiratory protection for
this condition of use, all other risk estimates indicate risk even with respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA identified minimal 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. Workers are assumed to be exposed to TCE vapor concentrations in the near-field, while
occupational non-users are exposed at concentrations in the far-field. These estimates were used for
determining worker and ONU risks. For workers, EPA estimated dermal exposures using the Dermal
Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available for the
condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Laundry and dishwashing
products
Spot remover
5.3.22 Industrial/Commercial Use - Arts, crafts and hobby materials - Fixatives and
finishing spray coatings
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in arts, crafts
and hobby materials as a fixative and finishing spray coating:
Page 409 of 748
-------
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 6.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOEs 25.2 and 8.4 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOEs 1.6 and 0.18 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 0.65 and 0.22 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-04 and 2.0E-03 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 1.4E-03 and 5.3E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-23)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.5 (central tendency). (Table 4-23)
• Autoimmunity:
o Chronic inhalation MOE 0.15 (central tendency). (Table 4-23)
• Cancer:
o Inhalation: 1.9E-03 (central tendency). (Table 4-23)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a 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
Page 410 of 748
-------
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
and coatings. The OSHA data also provided two data points where the worker job description was
"foreman." EPA assumed this data is applicable to ONU exposure. EPA estimated dermal exposures
using the Dermal Exposure to Volatile Liquids Model because dermal exposure data was not reasonably
available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Arts, crafts and hobby
materials
Spot remover
5.3.23 Industrial/Commercial Use - Corrosion inhibitors and anti-scaling agents -
Corrosion inhibitors and anti-scaling agents
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as corrosion
inhibitor, and anti-scaling agent:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 13.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-24)
• Autoimmunity:
o Chronic inhalation MOEs 1.1 and 0.38 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-24)
• Cancer:
o Inhalation: 2.5E-04 and 9.9E-04 (central tendency and high-end) with PPE (respirator
APF 50).
Page 411 of 748
-------
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-24)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 2.7 (central tendency). (Table 4-24)
• Autoimmunity:
o Chronic inhalation MOE 7.3E-02 (central tendency). (Table 4-24)
• Cancer:
o Inhalation: 3.9E-03 (central tendency). (Table 4-24)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a European Commission (EC) Technical Report (European Commission, 2014,
3970806). The data was supplied to the EC as supporting documentation in an application for continued
use of TCE under the REACH Regulation. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dermal exposure data was not reasonably available for the condition
of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Corrosion inhibitors and
anti-scaling agents
Corrosion inhibitors and anti-scaling
agents
5.3,24 Industrial/Commercial Use - Processing aids - Process solvent used in battery
manufacture; process solvent used in polymer fiber spinning, fluoroelastomer
manufacture, and Alcantara manufacture; extraction solvent used in caprolactam
manufacture; precipitant used in beta-cyclodextrin manufacture
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in processing
aids as a process solvent used in battery manufacture, polymer fiber spinning, fluoroelastomer
manufacture, and Alcantara manufacture, as an extraction solvent used in caprolactam manufacture, and
as a precipitant used in beta-cyclodextrin manufacture:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
Page 412 of 748
-------
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 13.6 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-24)
• Autoimmunity:
o Chronic inhalation MOEs 1.1 and 0.38 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-24)
• Cancer:
o Inhalation: 2.5E-04 and 9.9E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-24)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 2.7 (central tendency). (Table 4-24)
• Autoimmunity:
o Chronic inhalation MOE 7.3E-02 (central tendency). (Table 4-24)
• Cancer:
o Inhalation: 3.9E-03 (central tendency). (Table 4-24)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection. The high volatility of TCE and potentially severe effects
from short term exposure are factors when weighing uncertainties. EPA identified inhalation exposure
monitoring data from a European Commission (EC) Technical Report (European Commission, 2014,
3970806). The data was supplied to the EC as supporting documentation in an application for continued
use of TCE under the REACH Regulation. EPA estimated dermal exposures using the Dermal Exposure
to Volatile Liquids Model because dermal exposure data was not reasonably available for the condition
of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Processing aids
• Process solvent used in battery
manufacture
Page 413 of 748
-------
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category
Subcategory
• Process solvent used in polymer
fiber spinning, fluoroelastomer
manufacture, and Alcantara
manufacture
• Extraction solvent used in
caprolactam manufacture
• Precipitant used in beta-
cyclodextrin manufacture
5.3.25 Industrial/Commercial Use - Ink, toner, and colorant products - Toner aid
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE as an ink,
toner, and colorant product as a toner aid:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOE 2.5 (high-end) without respiratory PPE.
o Acute dermal MOE 21.6 (high-end) with PPE (gloves PF 20). (Table 4-25)
• Autoimmunity:
o Chronic inhalation MOEs 1.7 and 6.9E-02 (central tendency and high-end) without
respiratory PPE.
o Chronic dermal MOEs 1.7 and 0.55 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-25)
• Cancer:
o Inhalation: 1.7E-04 and 5.4E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 5.3E-04 and 2.1E-03 (central tendency and high-end) with PPE (gloves PF=10).
(Table 4-25)
Page 414 of 748
-------
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk estimate - ONUs:
• Autoimmunity:
o Chronic inhalation MOE 1.7 (central tendency). (Table 4-25)
• Cancer:
o Inhalation: 1.7E-04 (central tendency). (Table 4-25)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. While workers are unlikely to wear respiratory protection for
this condition of use, all other risk estimates indicate risk even with respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA identified inhalation exposure monitoring data from a European
Commission (EC) Technical Report (European Commission, 2014, 3970806). The data was supplied to
the EC as supporting documentation in an application for continued use of TCE under the REACH
Regulation. EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Ink, toner and colorant
products
Toner aid
5,3.26 Industrial/Commercial Use - Automotive care products - Brake and parts cleaners
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE for automotive
care products as a brake and part cleaner:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
Page 415 of 748
-------
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Immunosuppression:
o Acute inhalation MOE 10.9 (high-end) with PPE (respirator APF 50).
o Acute dermal MOE 15.1 (high-end) with PPE (gloves PF 20). (Table 4-15)
• Autoimmunity:
o Chronic inhalation MOEs 0.95 and 0.30 (central tendency and high-end) with PPE
(respirator APF 50).
o Chronic dermal MOEs 1.2 and 0.39 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-15)
• Cancer:
o Inhalation: 2.9E-04 and 9.7E-04 (central tendency and high-end) with PPE (respirator
APF 50).
o Dermal: 7.6E-04 and 2.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-15)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 5.0 (high-end). (Table 4-15)
• Autoimmunity:
o Chronic inhalation MOE 1.0 (central tendency). (Table 4-15)
• Cancer:
o Inhalation: 2.6E-04 (central tendency). (Table 4-15)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, all risk estimates indicate risk even with
expected respiratory and dermal protection (APF=50 and PF=20). EPA estimated ONU exposures could
be as high as worker exposures as a high-end estimate. The high volatility of TCE and potentially severe
effects from short term exposure are factors when weighing uncertainties. EPA did not identify
inhalation exposure monitoring data related to the use of TCE in aerosol degreasers. Therefore, EPA
estimated inhalation exposures using the Brake Servicing Near-field/Far-field Exposure Model. EPA's
inhalation exposure modeling is based on a near-field/far-field approach, where a vapor generation
source located inside the near-field diffuses into the surrounding environment. Workers are assumed to
be exposed to TCE vapor concentrations in the near-field, while occupational non-users are exposed at
concentrations in the far-field. These estimates were used for determining worker and ONU risks. For
workers, EPA estimated dermal exposures using the Dermal Exposure to Volatile Liquids Model
because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Automotive care products
Brake and parts cleaners
5.3.27 Industrial/Commercial Use - Apparel and footwear care products - Shoe polish
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in apparel and
footwear care products as a shoe polish:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Page 416 of 748
-------
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 5.4 and 1.9 (central tendency and high-end) without respiratory
PPE.
o Acute dermal MOEs 22.7 and 7.6 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOEs 0.15 and 5.1E-02 (central tendency and high-end) without
respiratory PPE.
o Chronic dermal MOEs 0.56 and 0.17 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-17)
• Cancer:
o Inhalation: 1.8E-03 and 5.8E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-17)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 10.9 (central tendency). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOE 0.29 (central tendency). (Table 4-17)
• Cancer:
o Inhalation: 9.2E-04 (central tendency). (Table 4-17)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. While workers are unlikely to wear respiratory protection for
this condition of use, all other risk estimates indicate risk even with respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA identified minimal 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
Page 417 of 748
-------
1723
1724
1725
1726
1727
1728
1729
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
1759
1760
1761
1762
1763
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
approach, where a vapor generation source located inside the near-field diffuses into the surrounding
environment. Workers are assumed to be exposed to TCE vapor concentrations in the near-field, while
occupational non-users are exposed at concentrations in the far-field. These estimates were used for
determining worker and ONU risks. For workers, EPA estimated dermal exposures using the Dermal
Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available for the
condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Apparel and footwear care
products
Shoe polish
5.3.28 Industrial/Commercial Use - Hoof polishes; gun scrubber; pepper spray; other
miscellaneous industrial and commercial uses
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE in other
commercial uses for hoof polishes, sun scrubber, pepper spray, and other miscellaneous industrial and
commercial uses:
• Presents an unreasonable risk of injury to health (workers and occupational non-users).
Unreasonable risk driver - workers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic inhalation and dermal exposures.
Unreasonable risk driver - ONUs:
• Immunosuppression resulting from acute inhalation exposures.
• Autoimmunity resulting from chronic inhalation exposures.
• Cancer resulting from chronic inhalation exposures.
Driver benchmarks - workers and ONUs:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute inhalation MOEs 5.4 and 1.9 (central tendency and high-end) without respiratory
PPE.
o Acute dermal MOEs 22.7 and 7.6 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOEs 0.15 and 5.1E-02 (central tendency and high-end) without
respiratory PPE.
Page 418 of 748
-------
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1111
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Chronic dermal MOEs 0.56 and 0.17 (central tendency and high-end) with PPE (gloves
PF 20). (Table 4-17)
• Cancer:
o Inhalation: 1.8E-03 and 5.8E-03 (central tendency and high-end) without respiratory
PPE.
o Dermal: 1.6E-03 and 6.9E-03 (central tendency and high-end) with PPE (gloves PF =
10). (Table 4-17)
Risk estimate - ONUs:
• Immunosuppression:
o Acute inhalation MOE 10.9 (central tendency). (Table 4-17)
• Autoimmunity:
o Chronic inhalation MOE 0.29 (central tendency). (Table 4-17)
• Cancer:
o Inhalation: 9.2E-04 (central tendency). (Table 4-17)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. While workers are unlikely to wear respiratory protection for
this condition of use, all other risk estimates indicate risk even with respiratory and dermal protection.
The high volatility of TCE and potentially severe effects from short term exposure are factors when
weighing uncertainties. EPA identified minimal 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. Workers are assumed to be exposed to TCE vapor concentrations in the near-field, while
occupational non-users are exposed at concentrations in the far-field. These estimates were used for
determining worker and ONU risks. For workers, EPA estimated dermal exposures using the Dermal
Exposure to Volatile Liquids Model because dermal exposure data was not reasonably available for the
condition of use.
Life Cycle Stage
Category
Subcategory
Industrial/commercial use
Other commercial uses
• Hoof polishes
• Gun scrubber
• Pepper spray
• Other miscellaneous industrial
and commercial uses
5.3.29 Disposal
Section 6(b)(4)(A) unreasonable risk determination for industrial/commercial use of TCE for disposal:
• Presents an unreasonable risk of injury to health (workers).
• Does not present an unreasonable risk of injury to health (occupational non-users).
Unreasonable risk driver - workers:
Page 419 of 748
-------
1803
1804
1805
1806
1807
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Immunosuppression resulting from acute dermal exposures.
• Autoimmunity resulting from chronic inhalation and dermal exposures.
• Cancer resulting from chronic dermal exposures.
Driver benchmarks - workers:
• Immunosuppression: Benchmark MOE = 30.
• Autoimmunity: Benchmark MOE = 30.
• Cancer (liver, kidney, NHL): Benchmark = lxlO"4.
Risk estimate - workers:
• Immunosuppression:
o Acute dermal MOE 23.8 (high-end) with PPE (gloves PF 20). (Table 4-27)
• Autoimmunity:
o Chronic inhalation MOE 6.3 (high-end) with PPE (respirator APF 50).
o Chronic dermal MOEs 1.8 and 0.61 (central tendency and high-end) with PPE (gloves PF
20). (Table 4-27)
• Cancer:
o Dermal: 4.9E-04 and 1.9E-03 (central tendency and high-end) with PPE (gloves PF =
20). (Table 4-27)
Risk Considerations: For workers and ONUs, all pathways of occupational exposure for this condition
of use indicate risk in the absence of PPE. For workers, while non-cancer risk estimates for acute
inhalation exposures and cancer risk estimates for inhalation exposures do not indicate risks with
expected respiratory protection (APF 50), all other risk estimates indicate risk even with expected
respiratory and dermal protection. EPA did not separately calculate risk estimates for ONUs and
workers. There is uncertainty in the ONU risk estimate since the data did not distinguish between
worker and ONU inhalation exposure estimates. ONU inhalation exposures are expected to be lower
than inhalation exposures for workers directly handling the chemical substance; however, the relative
exposure of ONUs to workers in these cases cannot be quantified. To account for this uncertainty, EPA
considered the central tendency estimate when determining ONU risk. The high volatility of TCE and
potentially severe effects from short term exposure are factors when weighing uncertainties. EPA did
not identify inhalation exposure monitoring data related to using TCE for other industrial uses.
Therefore, EPA used monitoring data from loading/unloading TCE during manufacturing as a surrogate
for this condition of use. EPA estimated dermal exposures using the Dermal Exposure to Volatile
Liquids Model because dermal exposure data was not reasonably available for the condition of use.
Life Cycle Stage
Category
Subcategory
Disposal
Disposal
• Industrial pre-treatment
• Industrial wastewater treatment
• Publicly owned treatment works
(POTW)
5.3.30 Consumer Use - Solvents (for cleaning or degreasing) - Brake and parts cleaner
Page 420 of 748
-------
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for brake and
parts cleaners:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.21 (moderate intensity user),
o Acute dermal MOE 0.48 (moderate intensity user). (Table 4-28)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 0.94 (moderate intensity user). (Table 4-28)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders, the risk estimates for the medium
intensity use scenario of acute inhalation indicate risk. Because bystanders are not expected to be
dermally exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure
scenario for bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to
estimate exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air
concentrations a user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Brake and Parts cleaner
5,3,31 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol electronic
degreaser/cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for aerosol
electronic degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Page 421 of 748
-------
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.2 (moderate intensity user). (Table 4-29)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 7.1 (moderate intensity user). (Table 4-29)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Aerosol electronic degreaser/cleaner
5,3.32 Consumer Use - Solvents (for cleaning or degreasing) - Liquid electronic
degreaser/cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for liquid
electronic degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
Page 422 of 748
-------
1927
1928
1929
1930
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
1962
1963
1964
1965
1966
1967
1968
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
o Acute inhalation MOE 0.79 (moderate intensity user),
o Acute dermal MOE 9.5E-01 (moderate intensity user). (Table 4-30)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 4.8 (moderate intensity user). (Table 4-30)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Liquid electronic degreaser/cleaner
5.3.33 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol spray
degreaser/cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for aerosol
spray degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 4.6E-02 (moderate intensity user),
o Acute dermal MOE 0.31 (moderate intensity user). (Table 4-31)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 0.21 (moderate intensity user). (Table 4-31)
Page 423 of 748
-------
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Aerosol spray degreaser/cleaner
5.3,34 Consumer Use - Solvents (for cleaning or decreasing) - Liquid degreaser/cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for liquid
degreaser/cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE =10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.12 (moderate intensity user),
o Acute dermal MOE 0.13 (moderate intensity user). (Table 4-32)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 0.70 (moderate intensity user). (Table 4-32)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
Page 424 of 748
-------
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Liquid degreaser/cleaner
5,3,35 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol gun scrubber
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for aerosol
gun scrubber:
• Presents an unreasonable risk of injury to health (consumers).
• Does not present an unreasonable risk of injury to health (bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Driver benchmarks - consumers:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 24 (moderate intensity user),
o Acute dermal MOE 0.32 (moderate intensity user). (Table 4-33)
Risk Considerations: All pathways of consumer exposure for this condition of use indicate risk.
Consumer risk determinations reflect the severity of the effects associated with acute exposures. Risk
estimates for consumer users at the medium intensity use scenarios of acute inhalation and dermal
exposures indicate risk. For bystanders the risk estimates for the medium intensity use scenario of acute
inhalation do not indicate risk. Because bystanders are not expected to be dermally exposed to TCE,
dermal risks to bystanders were not evaluated. For the consumer exposure scenario for bystanders,
inhalation exposures were estimated using the same model (CEM 2.1) used to estimate exposure to
users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a user and
bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Aerosol gun scrubber
5.3.36 Consumer Use - Solvents (for cleaning or degreasing) - Liquid gun scrubber
Page 425 of 748
-------
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for liquid gun
scrubber:
• Presents an unreasonable risk of injury to health (consumers).
• Does not present an unreasonable risk of injury to health (bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Driver benchmarks - consumers:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 28 (moderate intensity user),
o Acute dermal MOE 0.14 (moderate intensity user). (Table 4-34)
Risk Considerations: All pathways of consumer exposure for this condition of use indicate risk.
Consumer risk determinations reflect the severity of the effects associated with acute exposures. Risk
estimates for consumer users at the medium intensity use scenarios of acute inhalation and dermal
exposures indicate risk. For bystanders the risk estimates for the medium intensity use scenario of acute
inhalation do not indicate risk. Because bystanders are not expected to be dermally exposed to TCE,
dermal risks to bystanders were not evaluated. For the consumer exposure scenario for bystanders,
inhalation exposures were estimated using the same model (CEM 2.1) used to estimate exposure to
users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a user and
bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Liquid gun scrubber
5.3.37 Consumer Use - Solvents (for cleaning or degreasing) - Mold release
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for mold
release:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Page 426 of 748
-------
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
2127
2128
2129
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.1 (moderate intensity user). (Table 4-35)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 6.4 (moderate intensity user). (Table 4-35)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Mold release
5.3.38 Consumer Use - Solvents (for cleaning or degreasing) - Aerosol tire cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for aerosol tire
cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.46 (moderate intensity user),
o Acute dermal MOE 0.70 (moderate intensity user). (Table 4-36)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 2.0 (moderate intensity user). (Table 4-36)
Page 427 of 748
-------
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Aerosol tire cleaner
5.3.39 Consumer Use - Solvents (for cleaning or degreasing) - Liquid tire cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a solvent for liquid tire
cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.21 (moderate intensity user),
o Acute dermal MOE 0.12 (moderate intensity user). (Table 4-37)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 0.92 (moderate intensity user). (Table 4-37)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
Page 428 of 748
-------
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Solvents (for cleaning or
degreasing)
Liquid tire cleaner
5.3.40 Consumer Use - Lubricants and greases - Tap and die fluid
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a lubricant and grease
in tap and die fluid:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.2 (moderate intensity user). (Table 4-38)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 7.1 (moderate intensity user). (Table 4-38)
Risk Considerations: Consumer and bystander risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity use
scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure for scenario bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrations a user and bystander(s) would be
exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Lubricants and greases
Tap and die fluid
Page 429 of 748
-------
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5.3.41 Consumer Use - Lubricants and greases - Penetrating lubricant
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE as a penetrating lubricant:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 2.7 (moderate intensity user). (Table 4-39)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 16 (moderate intensity user). (Table 4-39)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Lubricants and greases
Penetrating lubricant
5.3.42 Consumer Use - Adhesives and sealants - Solvent-based adhesive and sealant
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in adhesives and sealants
as solvent-based adhesive and sealant:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Page 430 of 748
-------
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.8 (moderate intensity user). (Table 4-40)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 20 (moderate intensity user). (Table 4-40)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Adhesives and sealants
Solvent-based adhesive and sealant
5.3.43 Consumer Use - Adhesives and sealants - Mirror edge sealant
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in adhesives and sealants
as mirror edge sealant:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Page 431 of 748
-------
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.7 (moderate intensity user). (Table 4-41)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 10 (moderate intensity user). (Table 4-41)
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Adhesives and sealants
Mirror edge sealant
5,3.44 Consumer Use - Adhesives and sealants - Tire repair cement/sealer
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in adhesives and sealants
as tire repair cement/sealer:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE =10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 2.9 (moderate intensity user). (Table 4-42)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 13 (moderate intensity user). (Table 4-42)
Page 432 of 748
-------
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk Considerations: Risk estimates for consumer users and bystanders at the medium intensity use
scenarios of acute inhalation exposures indicate risk. Consumer and bystander risk determinations
reflect the severity of the effects associated with acute exposures. Dermal exposures were not quantified
for this scenario, as consumer dermal exposure with impeded evaporation is not expected, and
bystanders are not expected to be dermally exposed to TCE. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Adhesives and sealants
Tire repair cement/sealer
5.3.45 Consumer Use - Cleaning and furniture care products - Carpet cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in cleaning and furniture
care products as carpet cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.29 (moderate intensity user),
o Acute dermal MOE 0.35 (moderate intensity user). (Table 4-43)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 1.7 (moderate intensity user). (Table 4-43)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
Page 433 of 748
-------
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Cleaning and furniture care
products
Carpet cleaner
5.3.46 Consumer Use - Cleaning and furniture care products - Aerosol spot remover
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in cleaning and furniture
care products as aerosol spot remover:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.47 (moderate intensity user),
o Acute dermal MOE 3.0 (moderate intensity user). (Table 4-44)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 5.6 (moderate intensity user). (Table 4-44)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Page 434 of 748
-------
2420
2421
2422
2423
2424
2425
2426
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
2454
2455
2456
2457
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Life Cycle Stage
Category
Subcategory
Consumer use
Cleaning and furniture care
products
Aerosol spot remover
5,3,47 Consumer Use - Cleaning and furniture care products - Liquid spot remover
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in cleaning and furniture
care products as liquid spot remover:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.39 (moderate intensity user),
o Acute dermal MOE 0.51 (moderate intensity user). (Table 4-45)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 2.4 (moderate intensity user). (Table 4-45)
Risk Considerations: All pathways of consumer and bystander exposure for this condition of use
indicate risk. Consumer and bystander risk determinations reflect the severity of the effects associated
with acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation and dermal exposures indicate risk. For bystanders the risk estimates for the medium intensity
use scenario of acute inhalation indicate risk. Because bystanders are not expected to be dermally
exposed to TCE, dermal risks to bystanders were not evaluated. For the consumer exposure scenario for
bystanders, inhalation exposures were estimated using the same model (CEM 2.1) used to estimate
exposure to users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a
user and bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Cleaning and furniture care
products
Liquid spot remover
Page 435 of 748
-------
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5.3,48 Consumer Use - Arts, crafts, and hobby materials - Fixatives and finishing spray
coatings
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in arts, crafts, and hobby
materials as fixative and finishing spray coating:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.2 (moderate intensity user). (Table 4-46)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 7.6 (moderate intensity user). (Table 4-46)
Risk Considerations: Consumer and bystander risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity use
scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrations a user and bystander(s) would be
exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Arts, crafts, and hobby
materials
Fixatives and finishing spray coatings
5.3.49 Consumer Use - Apparel and footwear care products - Shoe polish
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in apparel and footwear
care products in shoe polish:
• Presents an unreasonable risk of injury to health (consumers).
Page 436 of 748
-------
2499
2500
2501
2502
2503
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
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
• Does not present an unreasonable risk of injury to health (bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation and dermal exposures.
Driver benchmarks - consumers:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 5.4 (moderate intensity user),
o Acute dermal MOE 5.5 (moderate intensity user). (Table 4-47)
Risk Considerations: All pathways of consumer exposure for this condition of use indicate risk.
Consumer risk determinations reflect the severity of the effects associated with acute exposures. Risk
estimates for consumer users at the medium intensity use scenarios of acute inhalation and dermal
exposures indicate risk. For bystanders the risk estimates for the medium intensity use scenario of acute
inhalation do not indicate risk. Because bystanders are not expected to be dermally exposed to TCE,
dermal risks to bystanders were not evaluated. For the consumer exposure scenario for bystanders,
inhalation exposures were estimated using the same model (CEM 2.1) used to estimate exposure to
users. CEM 2.1 is a two-zone model that allows for the estimation of air concentrations a user and
bystander(s) would be exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Apparel and footwear care
products
Shoe polish
5.3.50 Consumer Use - Other consumer uses - Fabric spray
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in fabric spray:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.43 (moderate intensity user). (Table 4-48)
Page 437 of 748
-------
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 5.1 (moderate intensity user). (Table 4-48)
Risk Considerations: Consumer and bystander risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity use
scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrations a user and bystander(s) would be
exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Other consumer uses
Fabric spray
5.3.51 Consumer Use - Other consumer uses - Film cleaner
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in film cleaner:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 0.18 (moderate intensity user). (Table 4-49)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 1.1 (moderate intensity user). (Table 4-49)
Risk Considerations: Consumer and bystander risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
Page 438 of 748
-------
2583
2584
2585
2586
2587
2588
2589
2590
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
2619
2620
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity use
scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation
exposures were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1
is a two-zone model that allows for the estimation of air concentrations a user and bystander(s) would be
exposed to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Other consumer uses
Film cleaner
5,3.52 Consumer Use - Other consumer uses - Hoof polish
Section 6(b)(4)(A) unreasonable risk determination for 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).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 8.0 (moderate intensity user). (Table 4-50)
Risk Considerations: Consumer risk determinations reflect the severity of the effects associated with
acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation indicate risk. For bystanders the risk estimates for the medium intensity use scenario of acute
inhalation do not indicate risk. Dermal exposures were not quantified for this scenario, as consumer
dermal exposure with impeded evaporation is not expected, and bystanders are not expected to be
dermally exposed to TCE. For the consumer exposure scenario for bystanders, inhalation exposures
were estimated using the same model (CEM 2.1) used to estimate exposure to users. CEM 2.1 is a two-
zone model that allows for the estimation of air concentrations a user and bystander(s) would be exposed
to following an exposure event.
Life Cycle Stage
Category
Subcategory
Consumer use
Other consumer uses
Hoof polish
Page 439 of 748
-------
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
5,3.53 Consumer Use - Other consumer uses - Pepper spray
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in pepper spray:
• Does not present an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 98 (moderate intensity user). (Table 4-51)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 98 (moderate intensity user). (Table 4-51)
Risk Considerations: Consumer risk determinations reflect the severity of the effects associated with
acute exposures. Risk estimates for consumer users at the medium intensity use scenarios of acute
inhalation do not indicate risk. For bystanders, MOEs are expected to be equivalent to consumers.
Dermal exposures were not quantified for this scenario, as consumer dermal exposure with impeded
evaporation is not expected, and bystanders are not expected to be dermally exposed to TCE.
Life Cycle Stage
Category
Subcategory
Consumer use
Other consumer uses
Pepper spray
5.3,54 Consumer Use - Other consumer uses - Toner aid
Section 6(b)(4)(A) unreasonable risk determination for consumer use of TCE in toner aid:
• Presents an unreasonable risk of injury to health (consumers and bystanders).
Unreasonable risk driver - consumers:
• Immunosuppression resulting from acute inhalation exposures.
Unreasonable risk driver - bystanders:
• Immunosuppression resulting from acute inhalation exposures.
Page 440 of 748
-------
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Driver benchmarks - consumers and bystanders:
• Immunosuppression: Benchmark MOE = 10.
Risk estimate - consumers:
• Immunosuppression:
o Acute inhalation MOE 1.3 (moderate intensity user). (Table 4-52)
Risk estimate - bystanders:
• Immunosuppression:
o Acute inhalation MOE 8.0 (moderate intensity user). (Table 4-52)
Risk Considerations: Consumer and bystander risk determinations reflect the severity of the effects
associated with acute exposures. Risk estimates for consumer users at the medium intensity use
scenarios of acute inhalation indicate risk. For bystanders the risk estimates for the medium intensity use
scenario of acute inhalation indicate risk. Dermal exposures were not quantified for this scenario, as
consumer dermal exposure with impeded evaporation is not expected, and bystanders are not expected
to be dermally exposed to TCE.
Life Cycle Stage
Category
Subcategory
Consumer use
Other consumer uses
Toner Aid
Page 441 of 748
-------
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
REFERENCES
Aberneth\ S, Rohu \\L Mmi W vt , W ciK 1'u, MacLh 1 > (1986). Acute lethal toxicity of
hydrocarbons and chlorinated hydrocarbons to two planktonic crustaceans the key role of
organism-water partitioning. Aquat Toxicol AMST: 163-174.
Adgate ,11 , Church. TR; Ryan. AD; Ramachandran. t ledrickseii \< C4(»ck. TH; Morandi. MT;
Sexton. K. (2004). Outdoor, indoor, and personal exposure to VOCs in children. Environ Health
Perspect 112: 1386-1392. http://dx.doi.org/10.1289/ehp
AIHA. (2009). Mathematical models for estimating occupational exposure to chemicals. In CB Keil; CE
Simmons; TR Anthony (Eds.), (2nd ed.). Fairfax, VA: AIHA Press.
Alanee. S; demons. J; Zahnd. W; Sadowski. nda. D. (2015). Trichloroethylene Is Associated with
Kidney Cancer Mortality: A Population-based Analysis. Anticancer Res 35: 4009-4013.
Alexander. HC; McCartv. WM; Bartlett. EA. (1978). Toxicity of perchloroethylene, trichloroethylene,
1,1,1-trichloroethane, and methylene chloride to fathead minnows. Bull Environ Contam Toxicol
20: 344-352. http://dx.doi.org/ 531
An do jtuka. S; Nishiyama. M; Senoo. K; Watanabe. MM; Matsumoto. S. (2003). Toxic Effects of
Dichloromethane and Trichloroethylene on the Growth of Planktonic Green Algae, Chlorella
vulgaris NIES227, Selenastrum capricornutum NIES35, and Volvulina steinii NIES545. 18: 43-
46.
Anttil :ala. E; Sallmen. M; Hernberg. S; Hemminki. K. (1995). Cancer incidence among
Finnish workers exposed to halogenated hydrocarbons. J Occup Environ Med 37: 797-806.
Arito. H; Takahashi. M; Ishikawa. T. (1994). Effect of subchronic inhalation exposure to low-level
trichloroethylene on heart rate and wakefulness-sleep in freely moving rats. Sangyo Igaku 36: 1-
8.
AT SDR. (2019). Toxicological Profile for T ri chl oroethyl ene: CAS # 79-01-6. Atlanta, GA.
https://www.atsdr.cdc.gov/ToxProfiles/tpl9.pdf
Axelson. O; S el den. A; Andersson. K; Hogstedt. C. (1994). Updated and expanded Swedish cohort
study on trichloroethylene and cancer risk. J Occup Med 36: 556-562.
Bahr. DE: Aldrich. TE; Seidu. D; Brion. GM; Toilet int. PGP. (2011). OCCUPATIONAL
EXPOSURE TO TRICHLOROETHYLENE AND CANCER RISK FOR WORKERS AT THE
PADUCAH GASEOUS DIFFUSION PLANT. Int J Occup Med Environ Health 24: 67-77.
http://dx.doi.org/10.2478/sl33
Baldwin. PE; Mayn (1998a). A survey of wind speed in indoor workplaces. Ann Occup Hyg
42: 303-313. http://dx.doi.org/lO 101 ' 0003-4878(98)000 < I
Baneriee. S; Yalkowskv. S^ /ani. SC. (1980). Water solubility and octanol-water partition-
coefficients of organics - limitations of the solubility-partition coefficient correlation. Environ
Sci Technol 14: 1227-1229. http://dx.doi.org/ i 0. i 02 i /es60170,tO I
Barrows. ME; Petrocelli. SR; Macek. rroll. II. (1980). Bioconcentration and elimination of
selected water pollutants by bluegill sunfish (Lepomis macrochirus). In R Haque (Ed.),
Dynamics, exposure and hazard assessment of toxic chemicals (pp. 379-392). Ann Arbor, MI:
Ann Arbor Science.
Bassiv !' \ h tng. L; Vermeulen. R; Tai^ \ tin. W. ei; Guo. W; Purdue. MP; Yin. S;
Rappaport. SM; Shen. M. in; Ji. Z; Qiu t ^ \ ^ 1 > r% \\ u Tn \t^ N « t «
Y\i^ t ei; Freeman. L.EB; Btan \ i Li\ es. RB; Huang. H; Smith. MT; Rothmari 'x Uiii
(2016). Comparison of hematological alterations and markers of B-cell activation in workers
exposed to benzene, formaldehyde and trichloroethylene. Carcinogenesis 37: 692-700.
http://dx.doi.org/10.1093/carcin/bgw053
Page 442 of 748
-------
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Bertilssoii J. Pctersson !;. IVodriksson Pi, Magnusson. M; Fransson. PA. (2017). Use of pepper spray
in policing: retrospective study of situational characteristics and implications for violent
situations. 18: 391-406. http://dx.doi.org/i0J080/15614263.2017.12KN I l»
Blossom. SI; Coonev. CA; Melnyk. SB; Ran. XL; Swearingen. CI; Wessinger. WD. (2013). Metabolic
changes and DNA hypomethylation in cerebellum are associated with behavioral alterations in
mice exposed to trichloroethylene postnatally. Toxicol Appl Pharmacol 269: 263-269.
http://dx.doi.org/10 J 016/i .taap.2013.03.025
Boice < 1} h Xtaraiii' 1 * i1 >. :ek. J; Sadler. C; Mclaughlin. IK. (1999). Mortality among aircraft
manufacturing workers. Occup Environ Med 56: 581-597.
http://dx.doi.ore >em.56.9.581
Boice M* \Uun<< IH 1 oh en. SS: Mum ma. MT; Blot. WJ; Brill. h\ ek. IP; Henderson. BE;
Mclaughlin. IK. (2006). Mortality among Rocketdyne workers who tested rocket engines, 1948-
1999. J Occup Environ Med 48: 1070-1092.
http://dx.doi.org/10.1097/01 .join.0000.. 10 I '< '< 11'< h ^
Bouwer. El; McCartv. PL. (1983). Transfomiations of 1- and 2-carbon halogenated aliphatic organic
compounds under methanogenic conditions. Appl Environ Microbiol 45: 1286-1294.
Bove. FJ. (1996). Public drinking water contamination and birthweight, prematurity, fetal deaths, and
birth defects. Toxicol Ind Health 12: 255-266.
Bove t i I ulcomer. MC; Klot J11. Esm.tn i Oufficv. EM; Savriii Ji' (1995). Public drinking water
contamination and birth outcomes. Am J Epidemiol 141: 850-862.
Bove. ickart. PZ; Maslia. M; Larson. TC. (2014a). Evaluation of mortality among marines and
navy personnel exposed to contaminated drinking water at USMC base Camp Lejeune: a
retrospective cohort study. Environ Health 13: 10. http://dx.doi.on 10 I 186/1 I \ l'< 10
B ickart. PZ; Maslia. M; Larson. TC. (2014b). Mortality study of civilian employees exposed
to contaminated drinking water at USMC Base Camp Lejeune: a retrospective cohort study.
Environ Health 13: 68. http://dx.doi.org/10.1 186 I I 069X-13-68
Boverhof. PR; Krieger. SM; Hotchkis*¦ ,< 'Mebbins. r ! < faomas. J; Woolhiser. MR. (2013).
Assessment of the immunotoxic potential of trichloroethylene and perchloroethylene in rats
following inhalation exposure. J Immunotoxicol 10: 311-320.
http://dx.doi.org/10.3109/1 •» i
Bove' nch. W; Run van. R (2000). T ri chl oroethyl ene inhibits development of embryonic heart
valve precursors in vitro. Toxicol Sci 53: 109-1 17. http://dx.doi.org/10.1093/tox; 9
Brack. W; Rottler. H. (1994). Toxicity testing of highly volatile chemicals with green algae: A new
assay. Environ Sci PollutRes Int 1: 223-228.
Brender. IP; Shinde. Mil; Zha ig. X; Langlois. PH. (2014). Maternal residential proximity to
chlorinated solvent emissions and birth defects in offspring: a case-control study. Environ Health
13: 96. hut; »t\.doi.on 10 I 186/J I 0;^\ I '\
Bridges. J; Sauer. UG; Buesen. R; Pefem > '1 1 i o U efsen. KE; Tralau. T; van Ravenzwa;^ Ji Poo I'x \
Pemberton. M. (2017). Framework for the quantitative weight-of-evidence analysis of 'omics
data for regulatory purposes. Regul Toxicol Pharmacol 91: S46-S60.
http://dx.doi.org/10 J 016/i.vrtph.201 10 010
Bridges. J; Solomon. KR. (2016). Quantitative weight-of-evidence analysis of the persistence,
bioaccumulation, toxicity, and potential for long-range transport of the cyclic volatile methyl
siloxanes [Review], J Toxicol Environ Health B Crit Rev 19: 345-379.
http://dx.doi.orE 30/10937404.2016.1200505
Broderius. SI; Kahl. MP; El on en. GE; Hammermeister. DE; Hoglund. MP. (2005). A Comparison of
the Lethal and Sublethal Toxicity of Organic Chemical Mixtures to the Fathead Minnow
(Pimphales promelas). 24: 3117-3127.
Page 443 of 748
-------
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Brtlni»y I i ;h. B; Wiesenhuttei M kab stein. S; Lam inert. M; Baumuller. A; Bolt. H. (2003). Renal
cell cancer risk and occupational exposure to trichloroethylene: Results of a consecutive case-
control study in Arnsberg, Germany. Am J Ind Med 43: 274-285.
http://dx.doi.org/10.1002/aiim. 10185
Bube rty. El. (1985). Delineation of the role of metabolism in the hepatotoxicity of
trichloroethylene and perchloroethylene: A dose-effect study. Toxicol Appl Pharmacol 78: 105-
122.
Buccafus s. SJ: LeBlanc. GA. (1981). Acute toxicity of priority pollutants to bluegill (Lepomis
macrochirus). Bull Environ Contain Toxicol 26: 446-452. http://dx.doi.orv 10 100 «H 01 2 J I IS
Buhagen. M; Grenskag. A: Raede. SF: H. (2016). Association between kidney cancer and
occupational exposure to trichloroethylene. J Occup Environ Med 58: 957-959.
http://dx.doi.orE [.0000000000000838
Burnham. KP; Anderson. DR. (2002). Model selection and multimodel inference: a practical
information-theoretic approach (2nd ed.). New York: Springer.
http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-95364-9
Caldwell. PT; Thorn ' P\ u»lmson. PD: Boitano v Kunyan. RB: Selmin. O (2008). Trichloroethylene
disrupts cardiac gene expression and calcium homeostasis in rat myocytes. Toxicol Sci 104: 135-
143. http://dx.doi.org/10.1093/toxsci/kfn078
CalEPA. (2009). Public health goals for chemicals in drinking water: Trichloroethylene. Sacramento,
CA. https://oehha.ca.gov/media/downloads/water/chemicals/phg/tcephg0709' if
CARB. (2000). Initial statement of reasons for the proposed airborne toxic control measure for
emissions of chlorinated toxic air contaminants from automotive maintenance and repair
activities. California Air Resources Board.
C (2006). California Dry Cleaning Industry Technical Assessment Report. Stationary Source
Division, Emissions Assessment Branch.
https://www.arb.ca.gov/toxics/drvclean/finaldrvcleantechreport.pdf
Carat i or said. BA; Dugard. PH; Zablotny. CL. (2006). Developmental toxicity studies in
Crl:CD (SD) rats following inhalation exposure to trichloroethylene and perchloroethylene. Birth
Defects Res B Dev Reprod Toxicol 77: 405-412. http://dx.doi.org/ irb.20091
Chan. CC; Yam lartin. JW: Williams. DT. (1990). Determination of organic contaminants in
residential indoor air using an adsorption-thermal desorption technique. J Air Waste Manag
Assoc 40: 62-67.
Charbote te. J: Hours. M; Martin. XL; Bergeret. A. (2006). Case-control study on renal cell
cancer and occupational exposure to trichloroethylene. Part II: Epidemiological aspects. Ann
Occup Hyg 50: 777-787. http://dx.doi.oo 93/annhyg/mel039
Charles River Laboratories. (2019). An oral (drinking water) study of the effects of trichloroethylene
(TCE) on fetal heart development in Sprague Dawley rats: Laboratory Project ID 00459506.
(EPA-HQ-OPPT-2016-073 7-0120).
Chia. SE; Ong. CN: Tsakok. MF; Ho. A. (1996). Semen parameters in workers exposed to
trichloroethylene. Reprod Toxicol 10: 295-299. http://dx.doi.org/10.1016/0890-6238(96)00058-5
Chin. >dwin. C; Parker. E; Robins. T; Lewis. T; Harbin. P; Batterman. S. (2014). Levels and
sources of volatile organic compounds in homes of children with asthma. Indoor Air 24: 403-
415. http://dx.doi.oi /ina. 12086
Chinn, KSK. (1981). A simple model for predicting chemical agent evaporation. Alexandria, VA: U.S.
Department of Defense, Defense Technical Information Center, Cameron Station.
http://www.epa.gov/opptintr/exposure/presentations/efast/chinn 1 simple method for pr
edicting.pdf
Page 444 of 748
-------
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Chiu. WA; Micallef. S; Monster \C, Hois l:\ (2007). Toxicokinetics of inhaled trichloroethylene and
tetrachloroethylene in humans at 1 ppm: Empirical results and comparisons with previous
studies. Toxicol Sci 95: 23-36. http://dx.doi.ore/ 3/toxsci/kfll29
Christensen. KY; Vizcava. D; Richardson. H; Lav one. J; Aronson. K; Siemiatvcki. _L (2013). Risk of
selected cancers due to occupational exposure to chlorinated solvents in a case-control study in
Montreal. J Occup Environ Med 55: 198-208. http://dx.doi.org' ' QM.0b013e3182728eab
Clayton. CA; Pellizzari. ED; Whitmore. RW; Perritt. RL; Ouackenboss. II. (1999). National Human
Exposure Assessment Survey (NHEXAS): Distributions and associations of lead, arsenic, and
volatile organic compounds in EPA Region 5. J Expo Anal Environ Epidemiol 9: 381-392.
http://dx.doi.ore ?8/si.iea.7500Q55
Cocco. P; T'Mannetie. A; Fadda. D; Melis. M; Becker. N: de Saniose.. S: Foretova. L; Mareckova. J;
Staines. A; Kleefeld. S: Mavnadie. M; Nietei nan. P; Boffetta. P. (2010). Occupational
exposure to solvents and risk of lymphoma subtypes: results from the Epilymph case-control
study. Occup Environ Med 67: 341-347. http://dx.doi.oi /oem.2009.046839
Cocco. P; Vermeuleii k I It no. V: Nonne I' 1 ampagna. M; Purdue. M; Blair. A; Monnereau. A; Or si.
L; Clavei < rocker. N: de Saniose. S: Foretova. L; Staines. A; Mavnadie. M; Nietei^ \ \liligi.
L; 'T Mannetie. A; Kricket ennan. P; Boffetta. P: Lan. Q; Rothman. N. (2013).
Occupational exposure to trichloroethylene and risk of non-Hodgkin lymphoma and its major
subtypes: a pooled IinterLlymph analysis. Occup Environ Med 70: 795-802.
http://dx.doi.org 10 I l'< < iemed-2 01'< ! 01 ^ I
Collier. JM; Selmin. O; Johnson. PD; Runyan. RB. (2003). Trichloroethylene effects on gene expression
during cardiac development. Birth Defects Res A Clin Mol Teratol 67: 488-495.
http://dx.doi.org/10.1002/bdra. 10073
Cordier. S: Garlantezec. R; Lakn 1 Rouget. F; Monfon Honvallot. N: Roig. B; Pulkkinen. J;
Chevrier. C; Multigner. L. uc. (2012). Exposure during pregnancy to glycol ethers and
chlorinated solvents and the risk of congenital malformations. Epidemiology 23: 806-812.
http://dx.doi.orE L0b013e31826c2bd8
Daub inner. RP. (1989). Physical and thermodynamic properties of pure chemicals: Data
compilation. Washington, DC: Taylor & Francis.
Daub inner. RP. (1995). Physical and thermodynamic properties of pure chemicals: Data
compilation. Washington DC: Taylor and Francis.
Davis. A; Gift. IS; Woodall. GM; Narotsky. MG; Fourman. GL. (2009). The role of developmental
toxicity studies in acute exposure assessments: analysis of single-day vs. multiple-day exposure
regimens. Regul Toxicol Pharmacol 54: 134-142. http://dx.doi.org/10.1016/i.yrtph.2009.03.006
Dawson. B; Johnson. P; Goldberg. S: Ulreich. J. (1990). Cardiac teratogenesis of trichloroethylene and
dichloroethylene in a mammalian model. J Am Coll Cardiol 16: 1304-1309.
Dawson. B; Johnson. P; Goldberg. S: Ulreich. J. (1993). Cardiac teratogenesis of halogenated
hydrocarbon-contaminated drinking water. J Am Coll Cardiol 21: 1466-1472.
http://dx.doi.org/10.1016/0735-1097(93)90325-11
Dekant. W; Bridges. J. (2016). Assessment of reproductive and developmental effects of DIN P, DnHP
and DCHP using quantitative weight of evidence. Regul Toxicol Pharmacol 81: 397-406.
http://dx.doi.org 10 101 i.yrtph.20 i 0° 0
Dement >1 U^Hweg. S: Wilson. MP; Hammom! r c . cKone. TH. (2009). Evaluating indoor exposure
modeling alternatives for LCA: A case study in the vehicle repair industry. Environ Sci Technol
43: 5804-5810. http://dx.doi.org/10.1021/es8
Di Toro. DM. (1984). Probability Model of Stream Quality Due to Runoff. ASCE. J Environ Eng 1 10:
607-628.
Page 445 of 748
-------
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Dierickx. PI. (1993). Comparison between fish lethality data and the in vitro cytotoxicity of lipophilic
solvents to cultured fish cells in a two-compartment model. Chemosphere 27: 1511-1518.
Pilling. WL; Tefertiller. NB; Kallos. GJ. (1975). Evaporation rates and reactivities of methylene
chloride, chloroform, 1,1,1-trichloroethane, trichloroethylene, tetrachloroethylene, and other
chlorinated compounds in dilute aqueous solutions. Environ Sci Technol 9: 833-838.
http://dx.doi.ore/10.102 l/es60107a008
Dobaradaran. S: Mahvi. AH; Nabizadeh. R; Ramavandi. B; Nazmara. S: Zarei. S. (2012). BIO AS SAY
COMPARISON OF TRICHLOROETHYLENE (TCE) TOXICITY ON DAPHNIA MAGNA
(D. MAGNA) BEFORE AND AFTER ULTRASOUND AND PHOTOLYSIS PROCESSES.
Fresen Environ Bull 21: 1533-1538.
Dodson. RT, I cm JI %>cmglei JD. Strips i*1 :' *mctt. 1)1 \. (2008). Influence of basements, garages,
and common hallways on indoor residential volatile organic compound concentrations. Atmos
Environ 42: 1569-1581. http://dx.doi.ore/10 101 i atmosenv.200 10 0S8
Dorfmueller. MA; Henne. SP; Yoi i. K Vrnschein hi \Unson. JM. (1979). Evaluation of
teratogenicity and behavioral toxicity with inhalation exposure of maternal rats to
trichloroethylene. Toxicology 14: 153-166. http://dx.doi.of>- 10 101 0300~483X(79)900 I I
Dosemeci. M; Cocco. P; Chow. WH. (1999). Gender differences in risk of renal cell carcinoma and
occupational exposures to chlorinated aliphatic hydrocarbons. Am J Ind Med 36: 54-59.
http://dx.doi.orE SICB1097-0274fl99907B6:K54::AID-AJIM8>3.0.CO:2-0
Dow. J; Green. T. (2000). Trichloroethylene induced vitamin B( 12) and folate deficiency leads to
increased formic acid excretion in the rat. Toxicology 146: 123-136.
http://dx.doi.ore/ i 0. i 0 i 6/S0300~483X(00)00156-6
Drake. V: Koprowski. S: Lough. J; Hu. N: Smith. S. (2006a). Trichloroethylene exposure during cardiac
valvuloseptal morphogenesis alters cushion formation and cardiac hemodynamics in the avian
embryo. Environ Health Perspect 114: 842-847. http://dx.doi.ore/10.1289/ehp.8781
Drake. VI; Koprowski. SL; Hu. N: Smith. SM; Loueh. J. (2006b). Cardiogenic effects of
trichloroethylene and trichloroacetic acid following exposure during heart specification of avian
development. Toxicol Sci 94: 153-162. http://dx.doi.off 93/toxsci/kfl083
Duteaux. SB; Berger. T; Hess. RA; Sartir Miller. MG. (2004). Male reproductive toxicity of
trichloroethylene: Sperm protein oxidation and decreased fertilizing ability. Biol Reprod 70:
1518-1526. http://dx.doi.ore/10.1095/biolreprod.103.02
EC. (2018). Memorandum on weight of evidence and uncertainties. Revision 2018. Scientific
Committee on Health, Environmental and Emerging Risks (SCHEER).
https://ec.europa.eu/health/sites/health/files/scientific committees/scheer/docs/schee if
ECB. (2000). IUCLID dataset: CAS No. 79-01-6: Trichloroethylene. Ispra, Italy: European Chemicals
Bureau, European Commission. Retrieved from https://echa.europa.eu/substance-information/-
/substancein 2
ECB. (2004). European Union risk assessment report: Trichloroethylene (pp. 1 -348). (EUR 21057 EN).
European Commission, https://echa.eiiropa.eii/documents/10162/83f0c99f-f6 f-a64b-
5fdc0
ECHA. (2004). Summary risk assessment report: Trichloroethylene. (1.04.29). Ispra, Italy: European
Commission Joint Research Centre, Institute for Health and Consumer Protection, European
Chemicals Bureau, https://echa.europa.eu/documents/10162/d30e53cc-89 )c0-
7ec216f84d48
ECHA. (2017). Registration dossier: Trichloroethylene. Reasonably available online at
https://echa.europa.eu/el/registration-dossier/-/registered-dossier/14485 (accessed October 1,
2018).
Page 446 of 748
-------
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EFSA. (2017). Guidance on the use of the weight of evidence approach in scientific assessments. EFSA
J 15: 1-69. http://dx.doi.Org/10.2903/i.efsa.2t
Engineers. USACo. (2018). Weight-of-Evidence Concepts: Introduction and Application to Sediment
Management, https://apps.dtic.mi1/dtic/tr/fulltext/u2/l048843.pdf
Environment Canada and Health Canada. (1993). Canadian Environmental protection act priority
substances list assessment report trichloroethylene. Ottawa Canada.
Epstein. PL; Nolen. GA; Randall. XL; Christ. SA; Read. EI; Stobei mith. MK. (1992).
Cardiopathic effects of dichloroacetate in the fetal Long-Evans rat. Teratology 46: 225-235.
http://dx.doi.org/10.1002/tera. 1420460306
Esmen. N: Corn. M; Hammad. Y; Whittier. D; Kotsko. N. (1979). Summary of measurements of
employee exposure to airborne dust and fiber in sixteen facilities producing man-made mineral
fibers. Am Ind Hyg Assoc J 40: 108-117.
Etterson. M. (2019). Species Sensitivity Distribution (SSD) Toolbox. Duluth, MN: US Environmental
Protection Agency.
European Solvents Industry Group (ES1G). (2012). SPERC fact sheet: Manufacture of substance -
industrial (solvent-borne). Brussels, Belgium, https://www.esig.org/reach-ges/environment/
Fishe annel. S; Eggers. J; Johnson. P; Macmahon. K; Goodyear. C; Sudberrv. G; Warren. D;
Latendresse. J; Graeter. L. (2001). Trichloroethylene, trichloroacetic acid, and dichloroacetic
acid: Do they affect fetal rat heart development. Int J Toxicol 20: 257-267.
http://dx.doi.ore 10 1030/10^ I a to I ^992
Fleeman. TL; Cappon. irtt. ME. (2004). Postnatal closure of membranous ventricular septal
defects in Sprague-Dawley rat pups after maternal exposure with trimethadione. Birth Defects
Res B Dev Reprod Toxicol 71: 185-190. http://dx.doi.org/10.1002/bdrb.2
Forand. SP; Lewis-Michl. EL; Gomez. MI. (2012). Adverse birth outcomes and maternal exposure to
trichloroethylene and tetrachloroethylene through soil vapor intrusion in New York State.
Environ Health Perspect 120: 616-621. http://dx.doi.org/10.1289/eiin I 10 ;\\4
Forkert. P; Lash. L; Nadeau. V; Tardif. R; Simmonds. A. (2002). Metabolism and toxicity of
trichloroethylene in epididymis and testis. Toxicol Appl Pharmacol 182: 244-254.
Fou 1 >, Ravbum j. Ooyou^ < ? ' "'tie. J. (1991). Assessing the efficacy of an Aroclor 1254-induced
exogenous metabolic activation system for FETAX. Drug Chem Toxicol 14: 143-160.
http://dx.doi.org/10.3109/01480549109Q17873
Fj Rogers. R; Stover. E; Finch. R (2001). Optimization of an exogenous metabolic activation
system for FETAX. I. Post-isolation rat liver microsome mixtures. Drug Chem Toxicol 24: 103-
115. http://dx.doi.org/10.1081/DC 304
Fj u i v > >ver. EL; Ravburn. JR; Hull. M; Bant!-' < \ (1993). Evaluation of the developmental
toxicity of trichloroethylene and detoxification metabolites using Xenopus. Birth Defects Res B
Dev Reprod Toxicol 13: 35-45.
Fredriksson. \. Onnielsson * ! i iksson. P. (1993). Altered behaviour in adult mice orally exposed
to tri- and tetrachloroethylene as neonates. Toxicol Lett 66: 13-19.
http://dx.doi.orE >378-4274(93)90074-8
Gangwal. S; Reif. DM; Mosher. S; Egeghy. PP; Wambaugh J T, judson. RS; Hubal. EA. (2012).
Incorporating exposure information into the toxicological prioritization index decision support
framework. Sci Total Environ 435-436: 316-325.
http://dx.doi.org/10.1016/i.scitotenv.2« l _ « *-^6
Gash. D; Rutland. K; Hudson. N; Sullivan P v . s. W; Pand'\ j i < iu. M; Choi. D; Hunter. R;
Gerhardt. G; Smith. C; Slevin. ce. T. (2008). Trichloroethylene: Parkinsonism and
complex 1 mitochondrial neurotoxicity. Ann Neurol 63: 184-192.
http://dx.doi.ore )2/ana.21288
Page 447 of 748
-------
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Geieer. PL; Northcott ('!',('all 0,1, Brooke. LT. (1985). Acute toxicities of organic chemicals to
fathead minnows (Pimephales promelas): Volume II. Superior, WI: University of Wisconsin-
Superior, Center for Lake Superior Environmental Studies.
Geori-11 * ^ 1 ^ ; Myers. CB; Lawton. \P, 1 amb. JC. (1986). Trichloroethylene: Reproduction
and fertility assessment in F344 rats when administered in the feed (pp. 312 PP). (NTP-86-085).
Research Triangle Park, NC: National Institute of Environmental Health Sciences, National
Toxicology Program.
Gilboa. SM; Desrosiers I \ < son. C; Lupo. PI; Riehle-Colarusso. TJ; Stewart ^ \ van
Wiingaarden. E; Waters. N rrea. A; Stud. NBDP. (2012). Association between maternal
occupational exposure to organic solvents and congenital heart defects, National Birth Defects
Prevention Study, 1997-2002. Occup Environ Med 69: 628-635.
http://dx.doi.ore 10 I l'< < >emed-2 Oil 100 '<
Goldberg. SI; Lebowitz. MP; Graver. El; Hicks. S. (1990). An association of human congenital cardiac
malformations and drinking water contaminants. J Am Coll Cardiol 16: 155-164.
Goldman. SM; Quintan 3ss. GW; Marras. C; Mens. C; Bhudhikanok. GS; Comyns. K; Korell. M;
Chade. AR; Kasten. M; Priestl'\ r% i Ikhi t 1 i ^nnandez. HH; Camhi i 1 .m\ston. JW;
Tanner. CM. (2012). Solvent exposures and parkinson disease risk in twins. Ann Neurol 71: 776-
784. http://dx.doi.ore/10.1002/ana.22629
Golsteiin. L; Huizei tuck. M; van Zelm. R; Huiibregts. MA. (2014). Including exposure variability
in the life cycle impact assessment of indoor chemical emissions: the case of metal degreasing.
Environ Int 71: 36-45. http://dx.doi.oi /j.envint.2014.06.003
Gone (2007). Weight of Evidence: a framework for the appraisal of the quality and relevance of
evidence. 22: 213-228. http://dx.doi.ore/lO I0N0/02 I '.0701296189
Green i U<;/oem.200 '< 00 I '<
Greenland. S: Sal van. A; Weeman. PH.; Hallock. MF; Smith. TJ. (1994). A case-control study of cancer
mortality at a transformer-assembly facility. Int Arch Occup Environ Health 66: 49-54.
http://dx.doi.ore 0386579
Hansen. J; Raaschou-Nielsen. O; Christensen. JM; Johansen. I; Mclauehlin. IK; Lipworth. L; Blot. WJ;
Olsen. JH. (2001). Cancer incidence among Panish workers exposed to trichloroethylene. J
Occup Environ Med 43: 133-139.
Hansen. J; Sallmen. M; Selden nttila. A; Pukkala. E; Andersson. K; Bryngelsson. I; Raaschou-
Nielsen. O. le: Olsen. JH; Mclauehlin. IK. (2013). Risk of Cancer Among Workers Exposed to
Trichloroethylene: Analysis of Three Nordic Cohort Studies. J Natl Cancer Inst 105: 869-877.
http://dx.doi.ore B/inci/ditlO?
HardeH. L; Eriksson. M; Deeerman. A. (1994). Exposure to phenoxyacetic acids, chlorophenols, or
organic solvents in relation to histopathology, stage, and anatomical localization of non-
Hodgkin's lymphoma. Cancer Res 54: 2386-2389.
Hardin HO. Hond. GP; Sikov. MR; Andrew. FP; Beliles. RP; Niemeln (1981). Testing of
selected workplace chemicals for teratogenic potential. Scand J Work Environ Health 7: 66-75.
Harrr [sroait. Iv \ x unez. M; Martopullo. I; Lencinas. A; Selmiii n ^ ^'ainyan. KM (2018).
Trichloroethylene perturbs HNF4a expression and activity in the developing chick heart. Toxicol
Lett 285: 113-120. http://dx.doi.oiv 10 101 Itoxlet :0l 1: 0:
Hassoun. E; Kariya. C; Williams. F. (2005). Pichloroacetate-induced developmental toxicity and
production of reactive oxygen species in zebrafish embryos. J Biochem Mol Toxicol 19: 52-58.
http://dx.doi.ore/10.1002/ibt.20051
Page 448 of 748
-------
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Havashi. M; IJeda. T; IJveno. K; Wada. K; Kinae. N: Saotome. K; Tanaka. N: Takai. A; Sasaki \ I',
Asano. N: Sofuni. T; Ojima. Y. (1998). Development of genotoxicity assay systems that use
aquatic organisms. Mutat Res 399: 125-133.
Healv. TEJ; Poole. TR; Hopper. A. (1982). Rat fetal development and maternal exposure to
trichloroethylene 100 ppm. Br J Anaesth 54: 337-341.
Heavner. PL; Morgan. WT; Ogden. MW. (1995). Determination of volatile organic compounds and
ETS apportionment in 49 homes. Environ Int 21: 3-21. http://dx.doio: /0160-
(94)00018-3
Hellweg. S; Demon. E; Bruzzi. R; Meiier. A; Rosenbaum. RK; Huiibregts. MA; Mckone. TE. (2009).
Integrating human indoor air pollutant exposure within Life Cycle Impact Assessment [Review],
Environ Sci Technol 43: 1670-1679. http://dx.doi.org/10.1021 /es8
Higgins. JP; Thompson. SG: Peeks. II; Altaian. DG. (2003). Measuring inconsistency in meta-analyses
[Review], BMJ 327: 557-560. http://dx.doi.org/10.1136/bir
H (1965). The environment and disease: Association or causation? Proc R Soc Med 58: 295-300.
Horvath. AL; Getzen. FW; Maczynska. Z. (1999). IUPAC-NIST Solubility data series 67: Halogenated
ethanes and ethenes with water. J Phys Chem Ref Pata 28: 395-627.
http://dx.doi.org/10.1063/1.556039
Houde. M; Douville. M; Gagnon. P; Sproull. J: Cloutier. F. (2015). Exposure of Paphnia magna to
trichloroethylene (TCE) and vinyl chloride (VC): evaluation of gene transcription, cellular
activity, and life-history parameters. Ecotoxicol Environ Saf 116: 10-18.
http://dx.doi.ore . ecoen v. 2015.02.031
Hudson. NL; Dotson. GS. (2017). NIOSH Skin Notation (SK) Profile: Trichloroethylene (TCE) (CAS
No. 79-01-6).
Hunter. E; Rogers. E; Schn^ > 1 tun! \ (1996). Comparative effects of haloacetic acids in whole
embryo culture. Teratology 54: 57-64. http://dx.doi.org/ 2/(SICDlQ96~
9926C199606)54:2<57:: AIP-TERA1>3,0.CO:2-1
'U (2014). IARC Monographs on the evaluation of carcinogenic risks to humans: Trichloroethylene,
tetrachloroethylene, and some other chlorinated agents. Geneva, Switzerland: World Health
Organization, International Agency for Research on Cancer.
http://monographs.iarc.fr/ENG/Monographs/PPFs/index.php
Ikeda. M; Imamura. T. (1973). Biological half-life of trichloroethylene and tetrachloroethylene in human
subjects [Review], Int Arch Occup Environ Health 31: 209-224.
http://dx.doi.org 10 100 Kt 00 ¦> 11
IRTA. (2007). Spotting chemicals: Alternatives to perchloroethylene and trichloroethylene in the textile
cleaning industry. Prepared for: Cal/EPA's Pepartment of Toxic Substances Control and U.S.
Environmental Protection Agency Region IX.
http://www.irta.iis/reports/PTSC%20Spotting%20Chemical%20for%20Web.pdf
Isaacs. K. (2014). The consolidated human activity database - master version (CHAP-Master) technical
memorandum. Washington, PC: U.S. Environmental Protection Agency, National Exposure
Research Laboratory, https://www.epa.gov/sites/production/files/2Q15-
02/docum ents/ ch admaster
Isaacson. LG; Spoh ylor. DH. (1990). Trichloroethylene affects learning and decreases myelin
in the rat hippocampus. Neurotoxicol Teratol 12: 375-381. http://dx.doio /0892-
0362(90)90057-1
Javiock. MA. (2012). Engineering case report: Estimating overspray exposure potential from aerosol
sprayed products onto surfaces. J Occup Environ Hyg 9: P155-P160.
http://dx.doi.org/10.1080/15459624.201: 00191
Page 449 of 748
-------
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Jen kin *¦ lv i i < »rrea. \ I ^mstein. J A; Botto. L; Britt AE; Daniels. SR; Elixson. M; Warnes. CA; Webb.
CL. (2007). Noninherited risk factors and congenital cardiovascular defects: Current knowledge:
A scientific statement from the American Heart Association Council on cardiovascular disease in
the young. Circulation 115: 2995-3014.
http://dx.doi.ore 10 I I I t! ^ . \ I ^ Ul \ so ^ _i
Jia. C; Batterman. S: Godwin. C. (2008a). VOCs in industrial, urban and suburban neighborhoods, Part
1: Indoor and outdoor concentrations, variation, and risk drivers. Atmos Environ 42: 2083-2100.
http://dx.doi.org/10.1016/i .atmosenv.:.^' m1
Jia. CR: D'Souz; tterman. S. (2008b). Distributions of personal VOC exposures: A population-
based analysis. Environ Int 34: 922-931. http://dx.doi.org/10.1016/i .envint.20Q8.Q2.QQ2
Jiang. Y; Wang. D; Zha>^ Wa>^ l.n^ 1 • nen. T. (2015). Disruption of cardiogenesis in human
embryonic stem cells exposed to trichloroethylene. Environ Toxicol 31: 1372-1380.
http://dx.doi.org/10.1002/tox.22142
Johnson. P. (2008). Personal communication from Paula Johnson, University of Arizona, to Susan
Makris, U.S. EPA, 26 August 2008 [Personal Communication],
Johnson. P. (2014). [Personal communication from Paula Johnson, University of Arizona, to Susan
Makris, U.S. EPA, 21 February 2014] [Personal Communication],
Johnson. PD; Daws> jldberg. SJ. (1998). Cardiac teratogenicity of trichloroethylene
metabolites. J Am Coll Cardiol 32: 540-545. http://dx.doi.org/; i/S0735-1097(98'K)0232-0
Johnson. PD; Goldberg. SJ; Mays. MZ; Dawson. (2003). Threshold of trichloroethylene
contamination in maternal drinking waters affecting fetal heart development in the rat. Environ
Health Perspect 111: 289-292. http://dx.doix ^ ^. * )/etn> > I _ >
Johnson. PD; Goldberg. SJ; Mays. MZ; Dawson. BY (2005). Erratum: Threshold of trichloroethylene
contamination in maternal drinking waters affecting fetal heart development in the rat" (Johnson
et al. 2003) [Erratum], Environ Health Perspect 113: A18.
Johnson. PD; Goldberg. SJ; Mays. MZ; Dawson. (2014). Erratum: Erratum for Johnson et al.
[Environ Health Perspect 113:A18 (2005)] [Erratum], Environ Health Perspect 122: A94.
http://dx.doi.org/10.1289/eU' i-- \ I
Jung. H; Kim. H; Song. B; Kim. E. (2012). Trichloroethylene Hypersensitivity Syndrome: A Disease of
Fatal Outcome. Yonsei Med J 53: 231-235. http://dx.doi.org/10.3349/ymi.2012.53.1.231
Kan. FW; Forkert. PG; Wade. (2007). Trichloroethylene exposure elicits damage in epididymal
epithelium and spermatozoa in mice. Histol Histopathol 22: 977-988.
http://dx.doi.orE HH-22.977
Kaneko. T; Saegusa. M; Tasaka. K; Sato. A. (2000). Immunotoxicity of trichloroethylene: A study with
MRL-lpr/lpr mice. J Appl Toxicol 20: 471-475. http://dx.doi.org/10.10Q2/1099~
1263(201 ?)20:6<471:: A1D-J AT716>3.0.CQ;2-E
Kasti iller. MA. (2006). Kinetics of finite dose absorption through skin 2: Volatile
compounds. JPharm Sci 95: 268-280. http://dx.doi.org/10.1002/ips.20497
Keil. DE; Peden-Adams. MM; Wallace. S; Rm P dkeson. GS. (2009). Assessment of
trichloroethylene (TCE) exposure in murine strains genetically-prone and non-prone to develop
autoimmune disease. J Environ Sci Health A Tox Hazard Subst Environ Eng 44: 443-453.
http://dx.doi.org/ i 0. i 080/1093452090. r 'A
Kiellstrand. P; Holmqun r% \liu. P; Kanie. M; Romare. S; Jonsson. I; Mansson I , Hjerkemo. M.
(1983). Trichloroethylene: Further studies of the effects on body and organ weights and plasma
butyrylcholinesterase activity in mice. Acta Pharmacol Toxicol 53: 375-384.
http://dx.doi.orE )773.1983.tb03438.x
Page 450 of 748
-------
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Kiellstrand. P; Kanie. M; Bierkemo. M. (1987). Regeneration of the sciatic nerve in mice and rats
exposed to trichloroethylene. Toxicol Lett 38: 187-191. http://dx.doi.ore/ )/0378-
4274(87
Klein. P; Kurz. J. (1994a). [Reduction of Solvent Concentrations in Surroundings of Dry-Cleaning
Shops], Bonningheim, Germany: Hohenstein Physiological Institute on Clothing.
Klimisch. HI; Andreae. M; Tillmann. II. (1997). A systematic approach for evaluating the quality of
experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25: 1-5.
http://dx.doi.ore 10 1006/rtph.l99\ 10
Kumar. P; Prasad. A: Mani. U; Maii. B; Dutta. K. (2001). Trichloroethylene induced testicular toxicity
in rats exposed by inhalation. Hum Exp Toxicol 20: 585-589.
http://dx.doi.ore 10 I I'M 096032:01 IN620882
Kumar. P; Prasa > V v . ten a. DK; Manu ^ Vlaii. BK; Dutta. KK. (2000). Fertility and general
reproduction studies in trichloroethylene exposed rats. Indian Journal of Occupational Health 43:
117-126.
Labra. M; Mattim I (-'ernasconi. M; Bertaccln n F; Brum i t uterio. S. (2010). The Combined
Toxic and Genotoxic Effects of Chromium and Volatile Organic Contaminants to
Pseudokirchneriella subcapitata. Water Air Soil Pollut 213: 57-70.
http://dx.doi.orE 67-3
Laeakos. SW; Wes? ten. M. (1986). An analysis of contaminated well water and health effects
in Woburn, Massachusetts. J Am Stat Assoc 81: 583-596. http://dx.doi.ore/10.2307/2288982
LeBlanc. GA. (1980). Acute toxicity of priority pollutants to water flea (Daphnia magna). Bull Environ
Contam Toxicol 24: 684-691. http://dx.doi.ore/10 100 < H 01 ;0N i I
Leblanc. M; Allen. JG: Herrick. RF; Stev . (2018). Comparison of the near field/far field model
and the advanced reach tool (ART) model VI.5: exposure estimates to benzene during parts
washing with mineral spirits. Int J Hyg Environ Health 221: 231-238.
http://dx.doi.ore/10.1016/jiiheh.201 10 01
Leiehton. DT. Jr; Calo. J.M. (1981). Distribution coefficients of chlorinated hydrocarbons in dilute air-
water systems for groundwater contamination applications. Journal of Chemical and Engineering
Data 26: 382-585. http://dx.doi.ore/10.1021/ie0002C
Lide. DR. (2007). CRC handbook of chemistry and physics: A ready-reference book of chemical and
physical data. In DR Lide (Ed.), (88th ed.). Boca Raton, FL: CRC Press.
Lindstrom. AB; Proffitt rtune. CR. (1995). Effects of modified residential construction on indoor
air quality. Indoor Air 5: 258-269. http://dx.doi.oo 10 I I I I | I 00-06 s Ps's5.00005.x
Linkov. I; Massey. O; Keisler. J; Rusyn. I; Hartune. T. (2015). From "Weight of Evidence" to
Quantitative Data Integration using Multicriteria Decision Analysis and Bayesian Methods.
ALTEX 32: 3-8. http://dx.doi.ore/10 I I ¦> '> altex i \\:m
Lipworth. L; Son derm an. IS; Mum ma. MT; Tarone. RE; Marai lice. JD; Mclaughlin. IK.
(2011). Cancer mortality among aircraft manufacturing workers: an extended follow-up. J Occup
Environ Med 53: 992-1007. http://dx.doi.ore >7/JQM.0b013e31822e0940
Liu. M. ei; Choi . Hunter. RL; Pandyj <1* 1 ,rs. WA; Sullivan. PG: Kim. HC: Gash O,
(2010). Trichloroethylene induces dopaminergic neurodegeneration in Fisher 344 rats. J
Neurochem 112: 773-783. http://dx.doi.ore/l >009.06497.x
Loeber. C; Hendrix. M; Diez De Finos. S: Goldberg. S. (1988). Trichloroethylene: A cardiac teratogen
in developing chick embryos. Pediatr Res 24: 740-744. http://dx.doi.ore/10.1203/00006450-
198812000-00018
Lone. JL; Stensel. HP; Ferguson. JF; Strand. SE; Oneerth. JE. (1993). Anaerobic and aerobic treatment
of chlorinated aliphatic compounds. J Environ Eng 119: 300-320.
Page 451 of 748
-------
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Lukavsb J, I'urnadzhieva v < (2011). Toxicity of Trichloroethylene (TCE) on Some Algae
and Cyanobacteria. Bull Environ Contam Toxicol 86: 226-231.
http://dx.doi. org/10.1007/s00128-011 -0195-1
Lutz. WK; Gavlor. DW; Cono' ttz. RW. (2005). Nonlinearity and thresholds in dose-response
relationships for carcinogenicity due to sampling variation, logarithmic dose scaling, or small
differences in individual susceptibility [Review], Toxicol Appl Pharmacol 207: S565-S569.
http://dx.doi.ore .taap.2005.01.038
Makris. SL; Scon 1 t<»\. J; Knudsen. TB; Hotchkiss. AK; Arzuaea. \ < uling. SY; Powers. CM;
Jinot J; Hogan. KA; Abbo nter. ES; Narotskv. MG. (2016). A systematic evaluation of
the potential effects of tri chl oroethyl ene exposure on cardiac development [Review], Reprod
Toxicol 65: 321-358. http://dx.doi.org/10J016/i.reprotox.201 OS.014
Makwana. O; Ahles I . I cncinas. A; Selmin. < H Kan van. RB. (2013). Low-dose tri chl oroethyl ene
alters cytochrome P450-2C subfamily expression in the developing chick heart. Cardiovasc
Toxicol 13: 77-84. http://dx.doi.oi 4)
Makwana. O; King. NM; Ahles. L; Selmin. O; Granzier. HL; Runyan. (2010). Exposure to low-
dose tri chl oroethyl ene alters shear stress gene expression and function in the developing chick
heart. Cardiovasc Toxicol 10: 100-107. http://dx.doi.oo '66-y
Malarkev. DE; Bucher. JR. (2011). Summary report of the National Toxicology Program and
Environmental Protection Agency-sponsored review of pathology materials from selected
Ramazzini Institute rodent cancer bioassays [NTP], Research Triangle Park: National
Toxicology Program.
http://ntp.niehs.nih.gov/ntp/about ntp/partnerships/international/summarypwg report ri hioassa
vs.pdf
Maltoni. C; Lefemine. G; Cott (1986). Experimental research on tri chl oroethyl ene carcinogenesis.
In Experimental research on tri chl oroethyl ene carcinogenesis. Princeton, NJ: Princeton Scientific
Publishing.
M,,o r Om, J; Zhao. N; Shao \ < Mi. W; He. W; Cui. H: Lin. \ i\ i 1 ,mg. Z; Xu. S; Huang. H:
Zhou. M; Xu. X; Qiu. W; Liu. O; Zha (2017). Maternal folic acid supplementation and
dietary folate intake and congenital heart defects. PLoS ONE 1: 1-14.
http://dx.doi.ore oumat. pone. 0187996
Marquart. H; Franken. R; Goede. H; Fransman. W; Schinkel. J. (2017). Validation of the dermal
exposure model in ECETOC TRA. Annals of Work Exposures and Health 61: 854-871.
http://dx.doi. org/10.1093/ann weh/wxx05 9
McDaniel i. Martin. P; Ross. N; Brown. S; Lesage. S; Pauti H (2004). Effects of chlorinated solvents
on four species of North American amphibians. Arch Environ Contam Toxicol 47: 101-109.
Miligi. L; Costantini. AS; Benvenuti. A; Kriebet < * < lei a I U» lino. R; Ramazzotn \ Sella.
S; Stagnaro. E; Crosignani. P; Amadori. D; Mirabelli. D; Sommani. L; Belletti. I; Troschel. L;
Romeo. L; Miceti. G; Tozzi. GA; Mendico. I; Vineis. P. (2006). Occupational exposure to
solvents and the risk of lymphomas. Epidemiology 17: 552-561.
http://dx.doi.ore 10 10'" 01.ede.0000: '<1: 9.30988.4d
Mishima. N; Hoffman. S; Hill. EG; King. EL. (2006). Chick embryos exposed to trichloroethylene in an
ex ovo culture model show selective defects in early endocardial cushion tissue formation. Birth
Defects Res A Clin Mol Teratol 76: 517-527. http://dx.doi.org/10.1002/bdra.20283
Moore. LE; Boffetta. P; Kararoi. S; Brennan. P; Stewart. PS; Hung. R; Zaridze. D; Matv lout
V; Kollarova. H; Bencko. V; Navratilova. M; Szeszenia-Dabrowska. N; Mates. D; Gromiec. J;
Holcato\ erino. M; Chanock. S; Chow. WH; Rothman. N. (2010). Occupational
trichloroethylene exposure and renal carcinoma risk: Evidence of genetic susceptibility by
Page 452 of 748
-------
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
reductive metabolism gene variants. Cancer Res 70: 6527-6536. http://dx.doi.ors >008-
5472. CAN-09-4167
Morgan. RW; Kelsh. MA; Zhao. K; Herineer. S. (1998). Mortality of aerospace workers exposed to
trichloroethylene. Epidemiology 9: 424-431.
Morris. M; Wolf. K. (2005). Evaluation of New and Emerging Technologies for Textile Cleaning.
Institute for Research and Technical Assistance (IRTA).
http://wsppn.org/pdf/irta/Emereine Technoloeit lie %20€lea.pdf
NAC/AEGL. (2009). Trichloroethylene (CAS reg. no. 79-01-6): Interim acute exposure guidelines
levels (AEGLs) [AEGL], Washington, DC: National Advisory Committee for Acute Exposure
Guideline Levels, https://www.epa.eov/sites/production/files/2
08/docum ents/ tri chl oroeth yl en e interim dec 2008 vl.pdf
Narotsky. MG: Weller. EA; Chinchilli. VM; Kavlock. RJ. (1995). Nonadditive developmental toxicity
in mixtures of trichloroethylene, di(2-ethylhexyl) phthalate, and heptachlor in a 5 x 5 x 5 design.
Fundam Appl Toxicol 27: 203-216. http://dx.doi.oo 93/toxsci/27.2.203
NCI (1976). Carcinogenesis bioassay of trichloroethylene. (NC1-CG-TR-2). Bethesda, MD: U.S.
Department of Health, Education, and Welfare, Public Health Service, National Institutes of
Health, http://ntp.niehs.nih.eov/ntp/htdoc pts/tr002.pdf
Nicas. M. (2009). The near field/far field (two-box) model with a constant contamination emission rate.
In CB Keil; CE Simmons; TR Anthony (Eds.), (2nd ed., pp. 47-52). Fairfax, VA: AIHA Press.
NICNAS. (2000). Trichloroethylene: Priority existing chemical assessment report no. 8. (8). Sydney,
Australia. http://www.nicnas.eov.au/Publications/CAR/PEC/PEC8.asp
Niederlehner H, Cairn? < * luith. E. (1998). Modeling acute and chronic toxicity of nonpolar narcotic
chemicals and mixtures to Ceriodaphnia dubia. Ecotoxicol Environ Saf 39: 136-146.
http://dx.doi.ors 36/eesa. 1997.1621
NIQSH. (2001). Respirator Usage in Private Sector Firms. Washington D C.: United States Department
of Labor, Bureau of Labor Statistics and National Institute for Occupational Safety and Health.
https://www.cdc.eov/niosh/docs/respsurv/
Nordstrom. M; Hardell. L; Maenuson \. t laebere. H; Rask-Andersen. A. (1998). Occupational
exposures, animal exposure and smoking as risk factors for hairy cell leukaemia evaluated in a
case-control study. Br J Cancer 77: 2048-2052.
NRC. (2001). Standing Operating Procedures for Developing Acute Exposure Guideline Levels for
Hazardous Chemicals, http://www.nap.edu/cataloe.php7recoixl id: 101J J.
NRC. (2006). Assessing the human health risks of trichloroethylene: Key scientific issues. Washington,
DC: The National Academies Press, http://www.nap.edu/cataloe.php7record id=l 1707
NTP. (1988). Toxicology and carcinogenesis studies of trichloroethylene (CAS No. 79-01-6) in four
strains of rats (ACI, August, Marshall, Osborne-Mendel) (gavage studies). Research Triangle
Park, NC: U.S. Department of Health and Human Services, Public Health Service, National
Institutes of Health, http://ntp.niehs.nih.gov/ntp/htdocs/ sZtr273.pdf
NTP. (2015). Handbook for conducting a literature-based health assessment using OHAT approach for
systematic review and evidence integration. U.S. Dept. of Health and Human Services, National
Toxicology Program, https://ntp.niehs.nih.eov/ntp/ohat/pubs/handbookian2015 508.pdf
O'Neit. MI; Heckelman. PE; Koch. CB. (2006). The Merck index: An encyclopedia of chemicals, drugs,
and biologicals (14th ed.). Whitehouse Station, NJ: Merck & Co.
O'Neit. MI; Smith. A: Heckelman. PE. (2001). Trichloroethylene. In Merck Index. Whitehouse Station,
NJ: Merck & Co., Inc.
Opdaro. II. (1989). Intra and interindividual variability in the kinetics of a poorly and highly
metabolising solvent. Br J Ind Med 46: 83 1 -845. http://dx.dou S/oem.46.12.831
Page 453 of 748
-------
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
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Ou i .h 1 Heesematro. LM; Smythe. J; Milln < -ill eson. GS; Keil. DE. (2006).
Developmental immunotoxicity of trichloroethylene (TCE): Studies in B6C3F1 mice. J Environ
Sci Health A Tox Hazard Subst Environ Eng 41: 249-271.
http://dx.doi.org/10.1080/109345205( 89
Pennsylvania State University. (2016). Horse Stable Ventilation, http s: //exten si on. p su. edu/h orse-stable-
ventilation
Perss Irikson. M. (1999). Some risk factors for non-Hodgkin's lymphoma. Int J Occup Med
Environ Health 12: 135-142.
Pescb L t uterting. J; Ranft. II; Klimpel. A; Oetschtagel L c.chill. W. (2000). Occupational risk factors
for renal cell carcinoma: Agent-specific results from a case-control study in Germany. Int J
Epidemiol 29: 1014-1024. http://dx.doi.org/10.1093/iie/
Poet. TS: Corley. RA; Thrall. 1 1* <1 »\\ < \ < jnoio. H; Weitz. KK; Hui. X; Maibach. HI; Wester.
RC. (2000). Assessment of the percutaneous absorption of trichloroethylene in rats and humans
using MS/MS real-time breath analysis and physiologically based pharmacokinetic modeling.
Toxicol Sci 56: 61-72. http://dx.doi.org/10.1093/toxsci/56.L61
Poole. C; Greenland. S. (1999). Random-effects meta-analyses are not always conservative. Am J
Epidemiol 150: 469-475. http://dx.doi.org/10.1093/oxfordiournals.aie jO 100 ;
Purdue. MP; Bakke. B; Stewart. P; De Roos. henk. M; Lynch. CF; Bernstein. L; Morton. LM;
Cerhan. JR; Sever son. RK; Cozen. W; Davis. S; Rothman. N; Hartge. P; Colt. IS. (2011). A
case-control study of occupational exposure to trichloroethylene and non-Hodgkin lymphoma.
Environ Health Perspect 119: 232-238. http://dx.doi.org/10.1289/elm 1002106
Purdue. MP; Stewai i I u^sen. MC; Colt. 1 ocke. SI; Hein. Ml; Water U\ ^aubaixl HI.
IX. r t Luterbusch t v t 1 liow. WH; Rothman. N; Hofmann. IN. (2016).
Occupational exposure to chlorinated solvents and kidney cancer: a case-control study. Occup
Environ Med 74: 268-274. http://dx.doi.org/10.1136/oemec 849
Raaschou-Nielsen. O; Hansen. J; Mclaughlin. IK; Kolstad. H; Christensen. JM; Tare sen. JH.
(2003). Cancer risk among workers at Danish companies using trichloroethylene: A cohort study.
Am J Epidemiol 158: 1182-1192. http://dx.doi.org/10.1093/aie/kwg282
Radican. L; Blair. A; Stewart. P; Wartent (2008). Mortality of aircraft maintenance workers
exposed to trichloroethylene and other hydrocarbons and chemicals: Extended follow-up. J
Occup Environ Med 50: 1306-1319. http://dx.doi.org/10.1097/JQM.01
Reif. DM; Sypa. M; Lock. EF; Wright I \ WtL<
-------
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Roonev. A.A; Boyle Wolfe. MS; Bucher. JR; Thayer. KA. (2014). Systematic review and evidence
integration for literature-based environmental health science assessments. Environ Health
Perspect 122: 711-718. http://dx.doi.ofe/' Vehp. 1307972
Ron B. \ ihwamathaii k 1} Kortc. F. (1982). Vergleichende Untersuchung der Anwendbarkeit
verschiedener Tests zur Uberpriifung der Abbaubarkeit von Umweltchemikalien. Chemosphere
11: 531-538. http://dx.doi.org/lO 1016/0045-6535182)90186-2
Ruckart. PZ; Bove. FJ; Maslia. M. (2013). Evaluation of exposure to contaminated drinking water and
specific birth defects and childhood cancers at Marine Corps Base Camp Lejeune, North
Carolina: a case—control study. Environ Health 12: 104. http://dx.doi.ore/ )X~
Ruckart. PZ; Bove. FJ; Maslia. M. (2014). Evaluation of contaminated drinking water and preterm birth,
small for gestational age, and birth weight at Marine Corps Base Camp Lejeune, North Carolina:
a cross-sectional study. Environ Health 13: 99. http://dx.doi.ore/10.1186/1 I ^59X-13-99
Rufer. ES; Hack>n I'\ I ientk ^ i .J 1 3dy. Ml; Loueh. J; Smith. SM. (2010). Altered
cardiac function and ventricular septal defect in avian embryos exposed to low-dose
trichloroethylene. Toxicol Sci 113: 444-452. http://dx.doi.ore/10.1093/toxsci/kfp269
Ruiiten. MW; Verberk. MM; Salle. HI. (1991). Nerve function in workers with long term exposure to
trichloroethene. Br J Ind Med 48: 87-92.
Saillenfait. AM; Langonne. I; Sab ate. IP. (1995). Developmental toxicity of trichloroethylene,
tetrachloroethylene and four of their metabolites in rat whole embryo culture. Arch Toxicol 70:
71-82. hi!tlvdoi.ore 10 100 KTO2733666
Sanchez-Fortun. S; San s. C.anta-Maria. A; Ros. JM; De Vicente. ML; Encinas. MT; Vina^ <
Barahona. MV. (1997). Acute sensitivity of three age classes of Artemia salina larvae to seven
chlorinated solvents. Bull Environ Contam Toxicol 59: 445-451.
http://dx.doi.ore/10.1007/s001289900498
Sanders. VM; Tucfcn Vs \\ H h t auffmaim. BM; Hallett. P; Carchman. RA; Borzelleca. JF;
Munson. AE. (1982). Humoral and cell-mediated immune status in mice exposed to
trichloroethylene in the drinking water. Toxicol Appl Pharmacol 62: 358-368.
http://dx.doi.ore 10 101 00 II 008X(82>>01 S ¦
Sato. A; Nakaiima. T; Fuji war; lurayama. N. (1977). A pharmacokinetic model to study the
excretion of trichloroethylene and its metabolites after an inhalation exposure. Br J Ind Med 34:
56-63.
Sauer. TC. (1981). Volatile organic compounds in open ocean and coastal surface waters. Organic
Geochemi stry 3: 91-101.
&i\ (Vnmett. DM 1 Inllrud. SN; Kinney. PL; Speneler. JD (2004). Differences in source emission
rates of volatile organic compounds in inner-city residences of New York City and Los Angeles.
J Expo Anal Environ Epidemiol 14: S95-109. http://dx.doi.ore< 8/si.iea.7500364
Schel (1987) Interactions of halogenated hydrocarbon mixtures in the embryo of the Japanese
medaka (Oryzias latipes). (Doctoral Dissertation). Rutgers University, New Brunswick, NJ.
Schmidt. KR; Tiehm. A. (2008). Natural attenuation of chloroethenes: identification of sequential
reductive/oxidative biodegradation by microcosm studies. Water Sci Technol 58: 1137-1145.
http://dx.doi.ore/10.2166/wst.2008.729
Schwn \ (1975). The effect of maternally inhaled trichloroethylene,
perchloroethylene, methyl chloroform, and methylene chloride on embryonal and fetal
development in mice and rats. Toxicol Appl Pharmacol 32: 84-96.
http://dx.doi.ore 10 101 00 11 008X(75)90197-0
Selerade. MK; Gilmour. MI. (2010). Suppression of pulmonary host defenses and enhanced
susceptibility to respiratory bacterial infection in mice following inhalation exposure to
Page 455 of 748
-------
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
trichloroethylene and chloroform. J Immunotoxicol 7: 350-356.
http://dx.doi.Org/10.3109/l •» i \ ;0l0 ->.01^
Selroin < H ; bi t P\ l .il< Swell. PT; Taylor. MR. (2008). Trichloroethylene and trichloroacetic acid
regulate calcium signaling pathways in murine embryonal carcinoma cells pi9. Cardiovasc
Toxicol 8: 47-56. http://dx.doi.ors -9014-2
Sexton. K; Mongin. SI; Adgatt achandran. G; Stock. TH; Morandi. MT. (2007).
Estimating volatile organic compound concentrations in selected microenvironments using time-
activity and personal exposure data. J Toxicol Environ Health A 70: 465-476.
http://dx.doi.org/10.1080/15287390600870858
Shelton. KL; Nicholson. KL. (2014). Pharmacological classification of the abuse-related discriminative
stimulus effects of trichloroethylene vapor. 3: 235839. http://dx.doi.org/10.4303/idar/235839
Siemiatvcki. J. . (1991). Risk factors for cancer in the workplace. In J Siemiatycki (Ed.). Boca Raton,
FL: CRC Press.
Silver. SR; Pinkerton. LE; Fleming. DA; Jon' ,uo. L; Bertke. SI. (2014). Retrospective
Cohort Study of a Microelectronics and Business Machine Facility. Am J Ind Med 57: 412-424.
http://dx.doi.org/10.1002/aiim.22288
Singh. HB; Sala tiles. RE. (1983). Selected man-made halogenated chemicals in the air and
oceanic environment. J Geophys Res 88: 3675-3683.
Smith \n Qharath. A; Mallai > I i Oh O Smith. K; Sutton. J A; Vukmanich. J; McCartv. LS; Ozburn.
GW. (1991). The acute and chronic toxicity of ten chlorinated organic compounds to the
American flagfish (Jordanella floridae). Arch Environ Contam Toxicol 20: 94-102.
http://dx.doi.org 10 100 nOtO 5334
Smith. EP; Lipkovich. I; Ye. K. (2002). Weight-of-Evidence (WOE): Quantitative Estimation of
Probability of Impairment for Individual and Multiple Lines of Evidence. Hum Ecol Risk Assess
8: 1585-1596.
Smith. MK; Randall. JL; Read. EI; Stober. JA. (1989). Teratogenic activity of trichloroacetic acid in the
rat. Teratology 40: 445-451. http://dx.doi.org/10.1002/tera. 1420400506
Smith. MK; Randall. JL; Read. EI; Stobe (1992). Developmental toxicity of dichloroacetate in the
rat. Teratology 46: 217-223. http://dx.doi.org/10.1002/tera. 1420460305
Stuckhardi ,11 . Poppe. SM. (1984). Fresh visceral examination of rat and rabbit fetuses used in
teratogenicity testing. Teratog Carcinog Mutagen 4: 181-188.
http://dx.doi.ors sm. 1770040203
Su. FC; Mukherjee. B; Batterman. S. (2013). Determinants of personal, indoor and outdoor VOC
concentrations: An analysis of the RIOPA data. Environ Res 126: 192-203.
http: //dx. doi. or g/10.1016/i. envres. 8.005
Swartz. MP; Cat \ 1 ban. W; Symanski. E; Mitchell 1< < Kmysh. HE; Langlois. PH.; Lupp * 1
(2015). Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple
pollutants and spina bifida. Environ Health 14: 16. http://dx.doi.Org/10.l 186/1 I 0< ° \ I I I
Taylor. PH.; Lagory. KE; Zacc ao * vohl. RJ; Lam > ^ 1 * (1985). Effect of trichloroethylene on the
exploratory and locomotor activity of rats exposed during development. Sci Total Environ 47:
415-420. http://dx.doi.org/ 5/0048-9697(85)90345-6
Tielemans. E; Schneide e. H; Tischer. M; Warren. N; Kromhout. H; Van Tongeren. M; Van
Hem men srrie. JW. (2008). Conceptual model for assessment of inhalation exposure:
Defining modifying factors. Ann Occup Hyg 52: 577-586.
http://dx.doi.org/10.1093/annhyg/men059
Tobaias. M; Verdugc \ Polo. AM; Rodriguez. II; Mohedano. \ I (2016). Assessment of toxicity and
biodegradability on activated sludge of priority and emerging pollutants. Environ Technol 37:
713-721. http://dx.doi.org/lO lQSQ/09-"" '« '>0 .01 ¦>. 10 92M
Page 456 of 748
-------
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Tsai. KP; Chen. CY. (2007). An algal toxicity database of organic toxicants derived by a closed-system
technique. Environ Toxicol Chem 26: 1931-1939. http://dx.doi.org/10.1S97/0 I _R.l
(2014). Employee Tenure News Release.
http://www.bls.eov/news.release/archives/tenure 091SI m
(2016). May 2016 Occupational Employment and Wage Estimates: National Industry-
Specific Estimates [Website], http://www.bls.eov/oes/tables.htm
sus Bureau. (2013). Census 2012 Detailed Industry Code List [Database],
https://www.census.eov/topics/emplovment/industrv-occupation/euidance/code-lists.html
sus Bureau. (2015). Statistics of U.S. Businesses (SUSB).
https://www.census.eov/data/tables/2015/econ/susb/2015-susb-annual.html
sus Bureau. (2019). Survey of Income and Program Participation data [Website],
https://www.census.gov/programs-survevs/sipp/data/datasets/20Q8-panel/wave-l.html
(1977). Environmental monitoring near industrial sites methylchloroform [EPA Report],
(EPA-560/5-77-025). Washington, DC.
(1987). Household solvent products: A national usage survey. (EPA-OTS 560/5-87-005).
Washington, DC: Office of Toxic Substances, Office of Pesticides and Toxic Substances.
https://ntrl.ntis.gov/NTRL/dashboard/searchResults. xhtml?searchQuery=PBS H
(1988). Recommendations for and documentation of biological values for use in risk
assessment [EPA Report] (pp. 1-395). (EPA/600/6-87/008). Cincinnati, OH: U.S. Environmental
Protection Agency, Office of Research and Development, Office of Health and Environmental
Assessment, http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=34855
(1991). Guidelines for developmental toxicity risk assessment (pp. 1-71). (EPA/600/FR-
91/001). Washington, DC: U.S. Environmental Protection Agency, Risk Assessment Forum.
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=23162
(1992). Guidelines for exposure assessment. Federal Register 57(104):22888-22938 [EPA
Report], (EPA/600/Z-92/001). Washington, DC.
http: //cfpub. epa. gov/n cea/cfm/recordi splay, cfiro ?der 3
(1994a). Guidelines for Statistical Analysis of Occupational Exposure Data: Final. United
States Environmental Protection Agency :: U.S. EPA.
(1994b). Methods for derivation of inhalation reference concentrations and application of
inhalation dosimetry [EPA Report], (EPA/600/8-90/066F). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Research and Development, Office of Health and
Environmental Assessment, Environmental Criteria and Assessment Office.
https://cfpub. epa.gov/ncea/risk/recordisplav. cfm?deid=?l 993«S 329&CFTOKEN=2
300b3 1 7
1 c. i i1 \ (1996). Guidelines for reproductive toxicity risk assessment (pp. 1-143). (EPA/630/R-
96/009). Washington, DC: U.S. Environmental Protection Agency, Risk Assessment Forum.
https://www.epa.gov/sites/production/files/201 I I I documents/guidelines ren*' 'xicitv.pdf
U.S. EPA. (1998). Guidelines for ecological risk assessment [EPA Report], (EPA/630/R-95/002F).
Washington, DC: U.S. Environmental Protection Agency, Risk Assessment Forum.
https://www.epa.gov/risk/guidelines-ecological-risk-assessment
U.S. EPA. (2001). Sources, emission and exposure for trichloroethylene (TCE) and related chemicals
[EPA Report], In Govt Reports Announcements & Index (pp. 138). (EPA/600/R-00/099).
Washington, DC. https://cfpub.epa.gov/ncea/risk/recordisplay.cfiro?deid=21006
U.S. EPA. (2002). A review of the reference dose and reference concentration processes (pp. 1-192).
(EPA/630/P-02/002F). Washington, DC: U.S. Environmental Protection Agency, Risk
Assessment Forum, http://www.epa.gov/osa/review-reference-dose-and-reference-concentration-
processes
Page 457 of 748
-------
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
U.S. EPA. (2005). Guidelines for carcinogen risk assessment [EPA Report] (pp. 1-166). (EPA/630/P-
03/001F). Washington, DC: U.S. Environmental Protection Agency, Risk Assessment Forum.
http://www2.epa.eov/osa/eiiidelines-carcinoeen-risk-assessment
U.S. EPA. (2006). Approaches for the application of physiologically based pharmacokinetic (PBPK)
models and supporting data in risk assessment (Final Report) [EPA Report] (pp. 1-123).
(EPA/600/R-05/043F). Washington, DC: U.S. Environmental Protection Agency, Office of
Research and Development, National Center for Environmental Assessment.
http: //cfpub. epa. gov/n cea/ cfm/recordi splay, cfm ?der >68
U.S. EPA. (2007). Exposure and fate assessment screening tool (E-FAST): Version 2.0, documentation
manual [EPA Report], (EPA Contract No. EP-W-04-035). Springfield, VA.
http://www.epa.eov/opptintr/exposiire/pubs/efast2man.pdf
U.S. EPA. (201 lb). Appendices for the Toxicological review of trichloroethylene (CAS No. 79-01-6) in
support of summary information on the Integrated Risk Information System (IRIS) [EPA
Report], (EPA/635/R-09/01 IF). Washington, DC.
https://nepis.epa. gov/Exe/ZyPURL.cgi?Dockev=P 100CB6V.txt
U.S. EPA. (201 lc). Exposure factors handbook: 201 1 edition (final) [EPA Report], (EPA/600/R-
090/052F). Washington, DC: U.S. Environmental Protection Agency, Office of Research and
Development, National Center for Environmental Assessment.
http://cfpub.epa. gov/ncea/cfm/recordisplav.cfm?deid=236252
U.S. EPA. (201 Id). Recommended use of body weight 3/4 as the default method in derivation of the
oral reference dose (pp. 1-50). (EPA/100/R11/0001). Washington, DC: U.S. Environmental
Protection Agency, Risk Assessment Forum, Office of the Science Advisor.
https://www.epa.gov/risk/recommended-use-bodv-weight-34-default-method-derivation-oral-
reference-dose
U.S. EPA. (201 le). Toxicological review of trichloroethylene (CASRN 79-01-6) in support of summary
information on the Integrated Risk Information System (IRIS) [EPA Report], (EPA/635/R-
09/01 IF). Washington, DC.
https://cfpub.epa.gov/ncea/iris/iris documents/documents/toxreviews/0106tr.pdf
U.S. EPA. (2012a). Benchmark dose technical guidance. (EPA/100/R-12/001). Washington, DC: U.S.
Environmental Protection Agency, Risk Assessment Forum.
https://www.epa.gov/risk/benchmark-dose-technical-guidance
U.S. EPA. (2012b). Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.1 1 [Computer
Program], Washington, DC. Retrieved from https://www.epa.gov/tsca-screening-tools/epi-
suitetm-estimation-proeram-interface
U.S. EPA. (2012c). Sustainable futures P2 framework manual [EPA Report], (EPA-748-B12-001).
Washington DC. http://www.epa.gov/sustainable-futures/sustainable-futures-p2-framework-
roanual
U.S. EPA. (2013a). Final peer review comments for the OPPT trichloroethylene (TCE) draft risk
assessment [Website], https://www.epa.eov/sites/production/files/!
06/documents/tce consolidated peer review comments septemb ;
U.S. EPA. (2013b). Interpretive assistance document for assessment of discrete organic chemicals.
Sustainable futures summary assessment [EPA Report], Washington, DC.
http://www.epa.eov/sites/production/files/2015-05/documents/05-iad discretes iune^O I ' pdf
U.S. EPA. (2014a). Framework for human health risk assessment to inform decision making. Final
[EPA Report], (EPA/100/R-14/001). Washington, DC: U.S. Environmental Protection, Risk
Assessment Forum, https://www.epa.gov/risk/framework-human-health-risk-assessment-inform-
decisi on-making
Page 458 of 748
-------
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
U.S. EPA. (2014b). TSCA work plan chemical risk assessment. Trichloroethylene: Degreasing, spot
cleaning and arts & crafts uses. (740-R1-4002). Washington, DC: Environmental Protection
Agency, Office of Chemical Safety and Pollution Prevention.
http://www2.epa. eov/sites/production/files/2015 -
09/documents/tee opptworkplanchemra final 0
U.S. EPA. (2014c). Exposure and Fate Assessment Screening Tool Version 2014 (E-FAST 2014).
Washington, DC: Office of Pollution Prevention and Toxics. https://www.epa.gov/tsca-
screening-tools/e-fast-exposure-and-fate-assessment-screening-tool-version^
U.S. EPA. (2015a). EDSP: Weight of Evidence Analysis of Potential Interaction with the Estrogen,
Androgen or Thyroid Pathways. Chemical: Glyphosate. Office of Pesticide Programs.
https://www.epa.eov/endocrine-dismption/endocrine-disruptor-screenine-proeram4ier-l-
screening-determinations-and
U.S. EPA. (2015b). Update of human health ambient water quality criteria: Trichloroethylene (TCE) 79-
01-6. (EPA 820-R-15-066). Washington D.C.: Office of Water, Office of Science and
Technology. https://www.regulations.gov/document?D=EPA-HQ-Q\A
U.S. EPA. (2016b). Instructions for reporting 2016 TSCA chemical data reporting. Washington, DC:
Office of Pollution Prevention and Toxics, https://www.epa.gov/chemical-data-
reportine/instmctions-reportine-2016~tsca-chemical~data~reportine
U.S. EPA. (2016c). Non-confidential 2016 Chemical Data Reporting (CDR) Database [Website],
http ://www. epa. gov/cdr/
I v •! P \ (2016d). Public database 2016 chemical data reporting (May 2017 release). Washington, DC:
US Environmental Protection Agency, Office of Pollution Prevention and Toxics. Retrieved
from https://www.epa.eov/chemical-data-reportine
U.S. EPA. (2016e). Supplemental exposure and risk reduction technical report in support of risk
management options for trichloroethylene (TCE) use in consumer aerosol degreasing.
Washington D.C.: Office of Chemical Safety and Pollution Prevention.
https://www.reeiilations.eov/documeiii * KPA-H.Q~QJT 1 _0l 01 3-0023
U.S. EPA. (2016f). Supplemental occupational exposure and risk reduction technical report in support of
risk management options for trichloroethylene (TCE) use in aerosol degreasing. (RIN 2070-
AK03). Washington D.C.: Office of Chemical Safety and Pollution Prevention.
https://www.reeiilations.eov/documeiii * KPA-H.Q~QJT 1 _0l 01 3-0021
U.S. EPA. (2016g). Supplemental occupational exposure and risk reduction technical report in support
of risk management options for trichloroethylene (TCE) use in spot cleaning. (RIN 2070-AK03).
Washington D.C.: Office of Chemical Safety and Pollution Prevention.
https://www.reeiilations.eov/documeiii * KPA-H.Q~QJT 1 _0l 01 3-0024
U.S. EPA. (2016h). Supplemental occupational exposure and risk reduction technical report in support
of risk management options for trichloroethylene (TCE) use in vapor degreasing. (RIN 2070-
AK11). Washington D.C.: Office of Chemical Safety and Pollution Prevention.
https://www.reeiilations.eov/documeiii * < 'PA-H.Q-01 * I
U.S. EPA. (2016i). Weight of evidence in ecological assessment [EPA Report], (EPA 100R16001).
Washington, DC: Office of the Science Advisor.
https://cfpub.epa.gov/si/si public i-voix! report.cfm?dirEntryLl ' ' ^'
U.S. EPA. (2017a). Chemical test rule data: Trichloroethylene. Washington, DC. Retrieved from
http://i ava.epa. gov/chemview
(2017b). Consumer Exposure Model (CEM) version 2.0: User guide. U.S. Environmental
Protection Agency, Office of Pollution Prevention and Toxics.
https://www.epa.eov/sites/prodiiction/files/2i 'documents/cem 2.0 user guide.pdf
Page 459 of 748
-------
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
U.S. EPA. (2017c). 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.reeiilations.eov/documeiii * KPA~H.Q~QJT 1 .01 0 '< 0003
U.S. EPA. (2017d). Scope of the risk evaluation for trichloroethylene. CASRN: 79-01-6 [EPA
Report], (EPA-740-R1-7004). https://www.epa.eov/sites/production/files/
06/docum ents/tce scot If
U.S. EPA. (2017e). Strategy for conducting literature searches for trichloroethylene (TCE):
Supplemental document to the TSCA Scope Document. CASRN: 79-01-6 [EPA Report],
https://www.epa.gov/sites/production/files/2Q17-
06/docum ents/tce lit search strategy 05 if
U.S. EPA. (2017f). Toxics Release Inventory (TRI), reporting year 2015. Retrieved from
https://www.epa.gov/toxics-release-inventorv-tri-program/tri-data-and-tools
(2017h). 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, https://www.epa.gov/sites/production/files/^
05/documents/instructions for reporting 2016 tsca cdi I 'i>nn .0 \ |;&m c=true&n ode=pt4 0 '<1 II K\.rgn=div5
U.S. EPA. (2019d). Biennial Review of 40 CFR Part 503 As Required Under the Clean Water Act
Section 405(d)(2)(C). https://www.epa.gov/sites/production/files/2019-06/documents/2016-
2017-biosolids-biennial-review.pdf
USGS. (2003). A national survey of methyl tert-butyl ether and other volatile organic compounds in
drinking-water sources: Results of the random survey. Reston, VA: U.S. Department of the
Interior, U.S. Geological Survey, https://pubs.er.usgs.gov/publication/wri024079
Page 460 of 748
-------
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LISGS. (2006). Water-quality conditions of Chester Creek, Anchorage, Alaska, 1998-2001. Reston, VA:
U.S. Department of the Interior, U.S. Geological Survey.
https://piibs.er.uses.eov/piiblication/sir20065229
Van Raaii. MTM; Janssen. PAH; Piersma. AH. (2003). The relevance of developmental toxicity
endpoints for acute limits settings (pp. 1-88). (RIVM Report 601900004). Nederlands:
Nederlands National Institute for Public Health and the Environment.
http://www2.epa.eov/sites/prodiiction/files/2014-04/documents/mte35b.pdf
Vidal. M; Basseres. A; Narbon (2001). Potential biomarkers of trichloroethylene and toluene
exposure in Corbicula fluminea. Environ Toxicol Pharmacol 9: 87-97.
http://dx.doi. ore/10.1016/S13 82-6689C00)00068-5
Vlaanderen. J ; Straif. K; Pukkala. E; Kauppinen. T; Kyyronen. P; Martin sen. J; Kiaerheim. K;
Trveevadott Hansen. J; Sparen. P. an Weiderpass. E. (2013). Occupational exposure to
trichloroethylene and perchloroethylene and the risk of lymphoma, liver, and kidney cancer in
four Nordic countries. Occup Environ Med 70: 393-401. http://dx.doi.ore 10 I l'< i-emc! JO I J
101188
Voeel. TM; McCartv. PL. (1985). Biotransformation of tetrachloroethylene to trichloroethylene,
dichloroethylene, vinyl chloride, and carbon dioxide under methanogenic conditions. Appl
Environ Microbiol 49: 1080-1083.
von Grot trlimann. C; Scherineer. M; Hungerbuhler. K (2006). Assessing occupational exposure
to perchloroethylene in dry cleaning. J Occup Environ Hyg 3: 606-619.
http://dx.doi.ore/ i 0. i 080/15459620600912173
Von Grote. J; Hurlimati Scherineer. 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.c 3/si.iea.7500288
Walk „ (1987). The total exposure assessment methodology (TEAM) study: Summary and
analysis: Volume I [EPA Report], (EPA/600/6-87/002a). Washington, DC: U.S. Environmental
Protection Agency; Office of Acid Deposition, Environmental Monitoring, and Quality
Assurance.
Wane. R: Zhang "S . 1 an. Q; Holft > > 1 k; Leaderer. jfam. SH; Bo*> ic P. Dosemeci. M; Rothman.
N: Z1 (2009). Occupational exposure to solvents and risk of non-
Hodgkin lymphoma in Connecticut women. Am J Epidemiol 169: 176-185.
http://dx.doi.ore/10.1093/aie/kwn300
1 • ^ v ijolrrim 1 * 1 i Vlacmahon. K; Ktiemp^l < umwalde. R:
Schulte. P. (2016). Current Intelligence Bulletin 68: NIOSH Chemical Carcinogen Policy.
Whittaker, C; Rice, F; Mckernan, L; Dankovic, D; Lentz, T; Macmahon, K; Kuempel, E;
Zumwalde, R; Schulte, P.
Whittaker. SG: Johansc (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.ore/piiblications/publications detail. aspx?DocID=Oh73%2fQile9Q%3
Page 461 of 748
-------
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
U (1985). Environmental health criteria: Trichloroethylene. Geneva, Switzerland.
Wikoff. D; IJrba Harvey. S; Haws. LC. (2018). Role of Risk of Bias in Systematic Review for
Chemical Risk Assessment: A Case Study in Understanding the Relationship Between
Congenital Heart Defects and Exposures to Trichloroethylene. Int J Toxicol 37: 125-143.
http://dx.doi.orE
Williams. FE; Sickelbaugh. TJ; Hassoun. E. (2006). Modulation by ellagic acid of DCA-induced
developmental toxicity in the zebrafish (Danio rerio). J Biochem Mol Toxicol 20: 183-190.
http://dx.doi.ore/10.1002/ibt.20135
Witroci i\\ . %>en&n « ill 's '• s. JS. (2014). Assessment of the genotoxicity of trichloroethylene
in the in vivo micronucleus assay by inhalation exposure. Mutagenesis 29: 209-214.
http://dx.doi.org/10.1093/mutage/geu006
Wirbiskv. SE; Damavanti. N: Mahapatra. CT; Seoulveda. MS; Irudav; ;man. XL. (2016).
Mitochondrial Dysfunction, Disruption of F-Actin Polymerization, and Transcriptomic
Alterations in Zebrafish Larvae Exposed to Trichloroethylene. Chem Res Toxicol 29: 169-179.
http://dx.doi.org/10J02 l/acs.chemrestox.5b00402
Woolhiser. MR; Krieger. SM; Thomav < i totchkiss .< \ (2006). Trichloroethylene (TCE):
Immunotoxicity potential in CD rats following a 4-week vapor inhalation exposure. (031020).
Midland, MI: Dow Chemical Company.
Wright. JM; Evans. A; Kaufman. J A; Rivera-Nunez. Z; Narotsky. MG. (2017). Disinfection by-product
exposures and the risk of specific cardiac birth defects. Environ Health Perspect 125: 269-277.
http://dx.doi.org/10.1289/E Hi 0 ».
Xu. H; Tanphaichitr. N; Forkert. PG; Anupriwan eerachatvanukul. W; Vincent. R; Leader. A;
Wade. MG. (2004). Exposure to trichloroethylene and its metabolites causes impairment of
sperm fertilizing ability in mice. Toxicol Sci 82: 590-597.
http://dx.doi.org/10.1093/toxsci/kfh277
Yauck. JS; Mallov. lair. K; Simpson. PM; Mccarver. DG. (2004). Proximity of residence to
trichloroethylene-emitting sites and increased risk of offspring congenital heart defects among
older women. Birth Defects Res A Clin Mol Teratol 70: 808-814.
http://dx.doi.org/10.1002/bdra.20060
Yoshioka. N i *se. Y; Sato. T. (1986). Correlation of the five test methods to assess chemical toxicity
and relation to physical properties. Ecotoxicol Environ Saf 12: 15-21.
Zeise. L; Wilson. R; Crouch. EA. (1987). Dose-response relationships for carcinogens: A review
[Review], Environ Health Perspect 73: 259-306.
Zhang. J. (2015). A review of spontaneous closure of ventricular septal defects. 28: 5 16-520.
Zhans- 1 <'assis- Moi.t J1 . \ cimeulen. R; Ge. \ 1 urry. JD; H.u. W; Shen. M; Qiu. C; Ji. Z;
Reiss :hale. CM; Lii: to. W; Purd nith. MT; Hua
X; Rothman. N; Lai (2013). Alterations in serum immunoglobulin levels in workers
occupationally exposed to trichloroethylene. Carcinogenesis.
http://dx.doi.org/10.1093/carcin/bgs403
Zhao. Y; Krishnadasan. A; Kennedy. N; Morgenstei (2005). Estimated effects of solvents
and mineral oils on cancer incidence and mortality in a cohort of aerospace workers. Am J Ind
Med 48: 249-258. http://dx.doi.org/10.1002/aiim.20216
Page 462 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1 APPENDICES
2
3 V|)|K'iHii\ \ ui (.1 i \iom iiisioin
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 (h„ t ^ 1
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 (
"i - 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
( , 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)
031 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
Page 463 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 ( 0816. August
16, 2011).
TSCA - Section 8(b)
EPA must compile, keep current and
publish a list (the TSCA Inventory) of
each chemical substance manufactured,
processed or imported in the United
States.
TCE was on the initial TSCA
Inventory and was therefore not
subject to EPA's new chemicals
review process (60 FR 16309,
March 29, 1995).
TSCA - Section 8(e)
Manufacturers (including imports),
processors and distributors must
immediately notify EPA if they obtain
information that supports the conclusion
that a chemical substance or mixture
presents a substantial risk of injury to
health or the environment.
28 substantial risk notifications
received for TCE (U.S. EPA,
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 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
prevention activities (under section 6607
TCE is a listed substance subject
to reporting requirements under
40 CFR 372.65 effective as of
January 1, 1987.
Page 464 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
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) - Section 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)
Section 112(d) states that the EPA must
establish a National Emission Standards
for Hazardous Air Pollutants (NESHAP)
for each category or subcategory of
major sources and area sources of HAPs
(listed pursuant to Section 112(c)). The
standards must require the maximum
degree of emission reduction that EPA
determines to be achievable by each
particular source category. Different
criteria for maximum achievable control
technology (MACT) apply for new and
existing sources. Less stringent
standards, known as generally available
EPA has promulgated a number of
NES regulating industrial
source categories that emit
trichloroethylene and other HAP.
These include, for example, the
NESHAP for Halogenated Solvent
Cleaning (59 FR 61801; December
2, 1994), among others.
Page 465 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
control technology (GACT) standards,
are allowed at the Administrator's
discretion for area sources.
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.
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 to
the CWA. The statute specifies a list of
families of toxic pollutants also listed in
40 CFR 401.15. The "priority pollutants"
specified by those families are listed in
40 CFR part 423, Appendix A. These are
pollutants for which best available
technology effluent limitations must be
established on either a national basis
through rules, or on a case-by-case best
professional judgement basis in National
Pollutant Discharge Elimination System
(NPDES) permits.
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
EPA issued drinking water
standards for TCE pursuant to
section 1412 of the SDWA. EPA
promulgated the NPDWR for TCE
Page 466 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
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
in 1987 with a MCLG of zero an
enforceable MCL of 0.005 mg/L
(52 FR 25690, July 8, 1987).
RCRA - Section 3001
Directs EPA to develop and promulgate
criteria for 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)
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
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 467 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
knowledge of a release of a hazardous
substance above the reportable quantity
threshold.
Other Federal Regulations
OSHA
Requires employers to provide their
workers with a place of employment free
from recognized hazards to safety and
health, such as exposure to toxic
chemicals, excessive noise levels,
mechanical dangers, heat or cold stress or
unsanitary conditions.
In 1971, OSHA issued
occupational safety and health
standards for TCE that included a
Permissible Exposure Limit (PEL)
of 100 ppm TWA, exposure
monitoring, control measures and
respiratory protection (29 CFR
1910.1000).
While OSHA has established a
PEL for TCE, OSHA has
recognized that many of its
permissible exposure limits (PELs)
are outdated and inadequate for
ensuring protection of worker
health. Most 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. For
TCE, OSHA recommends the use
of the NIOSH 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.
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
Page 468 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Statutes/Regulations
Description of Authority/Regulation
Description of Regulation
of decaffeinated coffee and spice
oleoresins (21 CFR 173.290).
7
8
9 A.2 State Laws and Regulations
10
11 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.
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).
12
Page 469 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
13 A.3 International Laws and Regulations
14
15 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 470 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
-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).
16
17
18
Page 471 of 748
-------
19
20
21
22
23
a
26
27
28
29
30
31
32
33
34
35
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix B LIST OF SUPPLEMENTAL DOCUMENTS
List of supplemental documents (see Docket: I 1: \-Uo-Ul']' \ - JO l^-O^OO for access to all flies):
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 472 of 748
-------
67
68
69
70
71
72
73
74
75
76
77
78
79
I?
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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: 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
q. Risk Evaluation for Trichloroethylene, Supplemental Information File: Environmental
Releases and Occupational Exposure Assessment
r. Risk Evaluation for Trichloroethylene, Supplemental Information File: Risk Calculator for
Occupational Exposures
Consumer and Environmental Exposures
5. Risk Evaluation for Trichloroethylene, Supplemental Information File: Aquatic Exposure
Modeling Outputs from E-FAST
t. Risk Evaluation for Trichloroethylene, Supplemental Information File: Consumer Exposure
Assessment Model Input Parameters
u. Risk Evaluation for Trichloroethylene, Supplemental Information File: Exposure Modeling
Results and Risk Estimates for Consumer Inhalation Exposures
v. Risk Evaluation for Trichloroethylene, Supplemental Information File: Exposure Modeling
Results and Risk Estimates for Consumer Dermal Exposures
Human Health
w. Risk Evaluation for Trichloroethylene, Supplemental Information File: Data Table for
Congenital Heart Defects Weight of Evidence Analysis
x. Risk Evaluation for Trichloroethylene, Supplemental Information File: Personal
Communication to OPPT. Raw Data Values from Selgrade and Gilmour, 2010
y. Risk Evaluation for Trichloroethylene, Supplemental Information File: PBPK Model and
ReadMe (zipped)
Additional Information
z. Risk Evaluation for Trichloroethylene, Supplemental Information File:
Memorandum NIOSH BLS Respirator Usage in Private Sector Firms
Page 473 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
116 Appendix € ENVIRONMENTAL EXPOSURES
117
118
119
120
121
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
OES: Manufacturing
3
0
Axiall Corporation,
Westlake, LA
NPDES: LA0007129
350
1.266
0.00156
0.0051
788
0
Surface
NPDES
Surface
52,000
0
Water
LA0007129
water
3
0
20
22.150
0.0273
0.0897
788
0
52,000
0
3
37
Olin Blue Cube,
Freeport, TX
NPDES: Not available
Off-site
Organic
Chemicals
Manuf.
350
0.069
0.26
2.42
788
0
Waste-
Surface
52,000
0
water
water
3
11
Treatment
20
1.200
4.51
42.14
788
0
52,000
0
3
17
Off-site
Organic
Chemicals
Manuf.
350
0.015
0.0564
0.53
788
0
Waste-
Surface
52,000
0
water
water
3
5
Solvents & Chemicals,
Pearland, TX
NPDES: Not available
Treatment
20
0.265
1.01
9.48
788
0
52,000
0
3
40
Organic
Chemicals
Manuf.
350
0.015
0.30
2.77
788
0
Surface
Surface
52,000
0
Water
water
3
12
20
0.265
5.34
49.91
788
0
52,000
0
Page 474 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
0
Surrogate
NPDES
KS0043036
350
0.015
0.02
0.07
788
0
Surface
Surface
52,000
0
Water
water
3
0
Occidental Chemical Corp
Wichita,
Wichita, KS
20
0.265
0.27
1.33
788
0
52,000
0
17
NPDES: KS0096903 and
Organic ChemMFG SIC
3
Off-site
Organic
Chemicals
Manuf.
350
0.015
0.0564
0.53
788
0
Waste-
Surface
52,000
0
water
water
3
5
Treatment
20
0.265
1.01
9.48
788
0
52,000
0
OES: Processing as a Reactant
3
5
Off-site
Organic
Chemicals
Manufacture
350
0.005
0.0188
0.18
788
0
Waste-
Surface
52,000
0
water
water
3
2
Treatment
20
0.089
0.33
3.13
788
0
440 unknown sites8
52,000
0
NPDES: Not applicable
3
23
Organic
Chemicals
Manufacture
350
0.005
0.0989
0.92
788
0
Surface
Surface
52,000
0
Water
water
3
7
20
0.089
1.76
16.45
788
0
52,000
0
0.00073
7
3
0
Arkema Inc.
Calvert City, KY
NPDES: KY0003603
350
0.017
0.000197
788
0
Surface
NPDES
Surface
52,000
0
Water
KY0003603
water
3
0
20
0.295
0.00342
0.128
788
0
52,000
0
350
0.0128
0.0000158
3
0
Page 475 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
Honeywell International -
Geismar Complex,
Geismar, LA
NPDES: LA0006181
0.00005
788
0
Surface
Water
NPDES
LA0006181
Surface
water
18
52,000
0
0.00090
7
3
0
20
0.224
0.000276
788
0
52,000
0
3
350
Praxair Technology Center,
Tonawanda, NY
NPDES: NY0000281
350
0.00169
n/a
169.00
788
0
Surface
NPDES
Still body
52,000
0
Water
NY0000281
3
20
20
0.030
n/a
3000.00
788
20
52,000
0
OES: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
3
0
Texas Instruments, Inc.,
Attleboro, MA
NPDES: MA0001791
260
0.005
0.00502
0.0188
788
0
Surface
NPDES
Surface
52,000
0
Water
MA0001791
water
3
0
20
0.067
0.0673
0.25
788
0
52,000
0
3
0
Accellent Inc/Collegeville
Microcoax, Collegeville, PA
NPDES: PA0042617
260
0.002
0.00711
0.0425
788
0
Surface
NPDES
Surface
52,000
0
Water
PA0042617
water
3
0
20
0.029
0.10
0.62
788
0
52,000
0
3
0
Ametek Inc. U.S. Gauge Div.,
Sellersville, PA
NPDES: PA0056014
Surrogate
NPDES
PA0020460
260
0.001
0.0113
0.0619
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.011
0.12
0.68
788
0
52,000
0
Atk-Allegany Ballistics Lab
Surface
NPDES
Surface
260
0.0005
0.000669
0.00311
3
0
(Nirop),
Water
WV0020371
water
788
0
Page 476 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
Keyser, WV
52,000
0
NPDES: WV0020371
3
0
20
0.0061
0.00803
0.0373
788
0
52,000
0
Handy & Harman Tube
Co/East Norriton, Norristown,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
PA
NPDES: PA0011436
Water
exceed the most sensitive COC using the most conservative input assumptions.
3
260
US Nasa Michoud Assembly
Facility,
Surrogate
NPDES
LA0003280
260
1.96
n/a
765.63
788
0
Surface
Still body
52,000
0
New Orleans, LA
Water
3
20
NPDES: LA0052256
20
25.44
n/a
9937.50
788
20
52,000
0
3
117
GM Components Holdings
LLC,
260
0.13
3.14
10.97
788
0
Surface
NPDES
Surface
52,000
0
Lockport, NY
Water
NY0000558
water
3
20
NPDES: NY0000558
20
1.71
41.38
144.47
788
0
52,000
0
3
27
Akebono Elizabethtown Plant,
Elizabethtown, KY
NPDES: KY0089672
Surrogate
NPDES
KY0022039
260
0.07
1.15
4.87
788
0
Surface
Surface
52,000
0
Water
water
3
16
20
0.897
14.77
62.38
788
0
52,000
0
3
0
Delphi Harrison Thermal
Systems,
260
0.04
0.0175
0.0752
788
0
Surface
NPDES
Surface
52,000
0
Dayton, OH
Water
OH0009431
water
3
0
NPDES: OH0009431
20
0.465
0.20
0.87
788
0
52,000
0
Chemours Company Fc LLC,
260
0.03
0.000631
0.00301
3
0
Page 477 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
Washington, WV
788
0
NPDES: WV0001279
Surface
NPDES
Surface
52,000
0
0
Water
WV0001279
water
J
20
0.334
0.00703
0.0335
788
0
52,000
0
3
38
Equistar Chemicals Lp,
La Porte, TX
NPDES: TXO119792
Primary Metal
Forming
Manuf.
260
0.02
0.46
2.22
788
1
Surface
Surface
52,000
0
Water
water
3
12
20
0.218
5.06
24.44
788
1
52,000
0
3
0
GE Aviation,
Lynn, MA
NPDES: MA0003905
260
0.01
n/a
0.0425
788
0
Surface
NPDES
Still water
52,000
0
Water
MA0003905
3
0
20
0.128
n/a
0.54
788
0
52,000
0
3
28
Certa Vandalia LLC,
Vandalia, OH
NPDES: OHO 122751
Primary Metal
Forming
Manuf.
260
0.01
0.23
1.11
788
0
Surface
Surface
52,000
0
Water
water
3
9
20
0.107
2.46
11.89
788
1
52,000
0
3
0
GM Components Holdings
LLC Kokomo Ops,
260
0.01
0.0387
0.20
788
0
Surface
NPDES
Surface
52,000
0
Kokomo, IN
Water
IN0001830
water
3
0
NPDES: IN0001830
20
0.086
0.33
1.73
788
0
52,000
0
Amphenol Corp-Aerospace
Surface
Water
NPDES
NY0003824
Surface
water
3
0
Operations,
260
0.01
0.00882
0.0486
788
0
Sidney, NY
52,000
0
Page 478 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
NPDES: NY0003824
3
0
20
0.082
0.0723
0.40
788
0
52,000
0
0.00040
0
3
3
Emerson Power Trans Corp,
Maysville, KY
NPDES: KY0100196
Surrogate
NPDES
KY0020257
260
0.01
0.000076
788
3
Surface
Surface
52,000
3
Water
water
3
0
20
0.081
0.000995
0.00522
788
0
52,000
0
3
0
Olean Advanced Products,
Olean, NY
NPDES: NY0073547
Surrogate
NPDES
NY0027162
260
0.01
0.00462
0.0188
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.068
0.0314
0.13
788
0
52,000
0
3
24
Hollingsworth Saco Lowell,
Easley, SC
NPDES: SC0046396
Primary Metal
Forming
Manuf.
260
0.00469
0.11
0.52
788
0
Surface
Surface
52,000
0
Water
water
3
6
20
0.061
1.40
6.78
788
1
52,000
0
3
1
Trelleborg YSH Incorporated
Sandusky Plant,
260
0.00360
0.21
1.76
788
0
Surface
NPDES
Surface
52,000
0
Sandusky, MI
Water
MI0028142
water
3
4
NPDES: MI0028142
20
0.047
2.69
23.04
788
0
52,000
0
3
2
Timken Us Corp Honea Path,
Surface
Water
Surrogate
Surface
water
260
0.00355
0.20
1.06
788
0
Honea Path, SC
NPDES
52,000
0
NPDES: SC0047520
SC0000698
20
0.0462
2.63
13.77
3
5
788
0
Page 479 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
Johnson Controls
260
0.00228
0.0068
0.0548
788
0
Incorporated,
Surface
NPDES
Surface
52,000
0
Wichita, KS
Water
KS0000850
water
3
0
NPDES: KS0000850
20
0.0296
0.0898
0.72
788
0
52,000
0
National Railroad Passenger
Corporation (Amtrak)
Wilmington Maintenance
3
21
Primary Metal
Forming
Manuf.
260
0.00203
0.0467
0.230
788
0
Surface
Surface
52,000
0
Facility,
Water
water
3
3
Wilmington, DE
20
0.026
0.60
2.89
788
0
NPDES: DE0050962
52,000
0
3
0
Electrolux Home Products
260
0.00201
0.00644
0.0171
788
0
(Formerly Frigidaire),
Surface
NPDES
Surface
52,000
0
Greenville, MI
Water
MI0002135
water
3
0
NPDES: MI0002135
20
0.026
0.0834
0.22
788
0
52,000
0
3
0
Rex Heat Treat Lansdale Inc,
Lansdale, PA
NPDES: PA0052965
Surrogate
NPDES
PA0026182
260
0.00194
0.00896
0.0523
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.025
0.12
0.67
788
0
52,000
0
3
0
Carrier Corporation,
Syracuse, NY
NPDES: NY0001163
260
0.00177
n/a
0.220
788
0
Surface
NPDES
Still water
52,000
0
Water
NY0001163
3
0
20
0.023
n/a
2.84
788
0
52,000
0
Cascade Corp (0812100207),
260
0.00117
0.0269
0.130
3
18
Page 480 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
Springfield, OH
788
0
NPDES: OH0085715
Surface
Water
Primary Metal
Surface
water
52,000
0
Forming
3
3
Manuf.
20
0.015
0.35
1.67
788
0
52,000
0
0.00075
3
3
0
USAF-Wurtsmith Afb,
Oscoda, MI
NPDES: MI0042285
Surrogate
NPDES
MI0028282
260
0.00115
0.000320
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.015
0.00417
0.00983
788
0
52,000
0
3
0
AAR Mobility Systems,
Cadillac, MI
NPDES: MI0002640
Surrogate
NPDES
MI0020257
260
0.00112
0.00413
0.00916
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.014
0.0517
0.11
788
0
52,000
0
3
0
Eaton Mdh Company Inc,
Kearney, NE
NPDES: NE0114405
Surrogate
NPDES
NE0052647
260
0.00107
n/a
0.130
788
0
Surface
Still water
52,000
0
Water
3
0
20
0.014
n/a
1.69
788
0
52,000
0
3
0
Lake Region Medical,
Trappe, PA
NPDES: PA0042617
260
0.000500
0.00178
0.0106
788
0
Surface
NPDES
Surface
52,000
0
Water
PA0042617
water
3
0
20
0.007
0.0249
0.15
788
0
52,000
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
52,000
0
Page 481 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
3
0
20
0.0125
0.19
0.83
788
0
52,000
0
3
17
Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244
Primary Metal
Forming
Manuf.
260
0.000897
0.0206
0.0997
788
0
Surface
Surface
52,000
0
Water
water
3
2
20
0.012
0.28
1.33
788
0
52,000
0
3
0
GE (Greenville) Gas Turbines
LLC,
260
0.000806
0.0378
0.0821
788
0
Surface
NPDES
Surface
52,000
0
Greenville, SC
Water
SC0003484
water
3
0
NPDES: SC0003484
20
0.010
0.47
1.02
788
0
52,000
0
3
16
Parker Hannifin Corporation,
Waverly, OH
NPDES: OH0104132
Primary Metal
Forming
Manuf.
260
0.000747
0.0172
0.0830
788
0
Surface
Surface
52,000
0
Water
water
3
2
20
0.010
0.23
1.11
788
0
52,000
0
3
0
Mahle Engine Components
Usa Inc,
260
0.000742
0.00808
0.0336
788
0
Surface
NPDES
Surface
52,000
0
Muskegon, MI
Water
MI0004057
water
3
0
NPDES: MI0004057
20
0.010
0.11
0.45
788
0
52,000
0
General Electric Company -
Waynesboro,
Waynesboro, VA
3
0
Surface
Water
NPDES
VA0002402
Surface
water
260
0.000733
0.00241
0.00705
788
0
52,000
0
0
NPDES: VA0002402
20
0.010
0.0329
0.0962
3
788
0
Page 482 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
Globe Engineering Co Inc,
Wichita, KS
NPDES: KS0086703
Surrogate
NPDES
KS0043036
260
0.00173
0.00175
0.00853
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.023
0.0232
0.110
788
0
52,000
0
3
0
Gayston Corp,
Dayton, OH
NPDES: OHO 127043
Surrogate
NPDES
OH0024881
260
0.000643
0.000281
0.00121
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.008
0.0035
0.0150
788
0
52,000
0
0.00022
1
3
0
Styrolution America LLC,
Channahon, IL
NPDES: IL0001619
260
0.000637
0.0000845
788
0
Surface
NPDES
Surface
52,000
0
Water
IL0001619
water
3
0
20
0.008
0.00106
0.00278
788
0
52,000
0
0.00079
9
3
0
Remington Arms Co Inc,
Ilion, NY
NPDES: NY0005282
260
0.000612
0.000291
788
0
Surface
NPDES
Surface
52,000
0
Water
NY0005282
water
3
0
20
0.008
0.00380
0.0104
788
0
52,000
0
0.00008
22
3
0
United Technologies
Corporation, Pratt And
Whitney Division,
East Hartford, CT
NPDES: CT0001376
260
0.000480
0.0000218
788
0
Surface
NPDES
Surface
52,000
0
Water
CT0001376
water
3
0
20
0.006
0.000273
0.00103
788
0
52,000
0
260
0.000470
0.000629
0.00292
3
0
Page 483 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
Atk-Allegany Ballistics Lab
(Nirop),
Keyser, WV
NPDES: WV0020371
788
0
Surface
Water
NPDES
WV0020371
Surface
water
52,000
0
3
0
20
0.006
0.00803
0.0373
788
0
52,000
0
3
0
Sperry & Rice Manufacturing
Co LLC,
260
0.000328
0.00117
0.00569
788
0
Surface
NPDES
Surface
52,000
0
Brookville, IN
Water
IN0001473
water
3
0
NPDES: IN0001473
20
0.004
0.0143
0.0694
788
0
52,000
0
3
0
Owt Industries,
Pickens, SC
NPDES: SC0026492
260
0.000314
0.000820
0.00213
788
0
Surface
NPDES
Surface
52,000
0
Water
SC0026492
water
3
0
20
0.004
0.0104
0.0272
788
0
52,000
0
3
0
Boler Company,
Hillsdale, MI
NPDES: MI0053651
Surrogate
NPDES
MI0022136
260
0.000269
0.00461
0.0204
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.003
0.0514
0.23
788
0
52,000
0
0.00091
1
3
0
Mccannalnc.,
Carpentersville, IL
NPDES: IL0071340
Surrogate
NPDES
IL0027944
260
0.000268
0.000260
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.003
0.00291
0.0102
788
0
52,000
0
Cutler Hammer,
Surface
Water
Surrogate
Surface
water
3
0
Horseheads, NY
NPDES
260
0.000238
0.00352
0.0153
788
0
NPDES: NY0246174
NY0004081
52,000
0
Page 484 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
0
20
0.003
0.0443
0.19
788
0
52,000
0
3
5
US Air Force Offutt Afb Ne,
Offutt A F B, NE
NPDES: NE0121789
Primary Metal
Forming
Manuf.
260
0.000159
0.00366
0.0177
788
0
Surface
Surface
52,000
0
Water
water
3
2
20
0.002
0.0460
0.22
788
0
52,000
0
0.00074
1
3
0
Troxel Company,
Moscow, TN
NPDES: TN0000451
260
0.000134
0.000254
788
0
Surface
NPDES
Surface
52,000
0
Water
TN0000451
water
3
0
20
0.002
0.00379
0.0111
788
0
52,000
0
3
3
Austin Tube Prod,
Baldwin, MI
NPDES: MI0054224
Primary Metal
Forming
Manuf.
260
0.000114
0.00262
0.0127
788
0
Surface
Surface
52,000
0
Water
water
3
1
20
0.001
0.023
0.11
788
0
52,000
0
3
0
LS Starrett Precision Tools,
Athol, MA
NPDES: MA0001350
260
0.000102
0.000339
0.00153
788
0
Surface
NPDES
Surface
52,000
0
Water
MA0001350
water
3
0
20
0.001
0.00333
0.015
788
0
52,000
0
3
2
Avx Corp,
Surface
Water
Primary Metal
Surface
water
260
0.0000883
0.00203
0.00981
788
0
Raleigh, NC
Forming
52,000
0
NPDES: NC0089494
Manuf.
20
0.001
0.023
0.11
3
1
788
0
Page 485 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
52,000
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),
Scottsboro, AL
NPDES: AL0069701
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.
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
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 486 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
NPDES: WV0003204
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
Boise State University,
Boise, ID
NPDES: IDG911006
Surrogate
NPDES
ID0023981
300
0.00008
0.000205
0.00388
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.001
0.00256
0.0485
788
0
52,000
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.
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
Hubbard-Hall Inc,
Waterbury, CT
NPDES: Unknown
Off-site
Receiving
Facility:
250
1.108
5.33
27.18
788
0
Waste-
Surface
52,000
0
water
Recycle Inc.;
water
3
20
Treatment
POTW (Ind.)
20
13.85
66.45
339.11
788
1
52,000
0
3
2
Oiltanking Houston Inc,
Houston, TX
NPDES: TX0091855
Surrogate
NPDES
TX0065943
250
0.003
0.32
6.52
788
0
Surface
Surface
52,000
0
Water
water
3
4
20
0.041
4.36
89.13
788
0
52,000
0
Page 487 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
0.00002
23
3
0
St. Gabriel Terminal,
Saint Gabriel, LA
NPDES: LA0005487
250
0.00550
0.00000677
788
0
Surface
NPDES
Surface
52,000
0
Water
LA0005487
water
0.00027
9
3
0
20
0.069
0.0000850
788
0
52,000
0
0.00001
89
3
0
Vopak Terminal Westwego
Inc,
Surrogate
NPDES
LA0042064
250
0.00468
0.00000576
788
0
Surface
Surface
52,000
0
Westwego, LA
Water
water
0.00023
5
3
0
NPDES: LAO 124583
20
0.058
0.0000714
788
0
52,000
0
Research Solutions Group Inc,
Pelham, AL
NPDES: AL0074276
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.
Carlisle Engineered Products
Inc, Middlefield, OH
NPDES: OH0052370
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.
OES: Process Solvent Recycling and Worker Handling of Wastes
3
250
Clean Water Of New York
Surrogate
NPDES
NJ0000019
250
0.004
n/a
11.76
788
0
Inc,
Surface
Still body
52,000
0
Staten Island, NY
Water
3
20
NPDES: NY0200484
20
0.047
n/a
138.24
788
0
52,000
0
Reserve Environmental
Services,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
Ashtabula, OH
Water
exceed the most sensitive COC using the most conservative input assumptions.
NPDES: OH0098540
VeoliaEs Technical Solutions
LLC,
Middlesex, NJ
Off-site
Waste-
water
Receiving
Facility:
Middlesex
3
0
250
24.1
n/a
2.85
788
0
Still body
52,000
0
20
NPDES: NJ0020141
Treatment
Cnty UA;
20
301.78
n/a
35.72
3
788
0
Page 488 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
NPDES
52,000
0
NJ0020141
3
110
Clean Harbors Deer Park
Off-site
250
0.35
1.68
8.57
788
0
LLC,
Waste-
POTW (Ind.)
Surface
52,000
0
La Porte, TX
water
water
3
19
NPDES: TX0005941
Treatment
20
4.36
20.92
106.75
788
0
52,000
0
3
6
Clean Harbors El Dorado
Off-site
250
0.04
0.19
0.98
788
0
LLC,
Waste-
POTW (Ind.)
Surface
52,000
0
El Dorado, AR
water
water
3
11
NPDES: AR0037800
Treatment
20
0.455
2.21
11.26
788
0
52,000
0
OES: Adhesives, Sealants, Paints, and Coatings
Able Electropolishing Co Inc,
Chicago, IL
Adhesives and
Surface
water
3
8
POTW
Sealants
250
0.298
0.86
7.28
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
250
0.00033
0.00252
0.00716
788
0
Garlock Sealing Technologies,
Palmyra, NY
NPDES: NY0000078
Surface
NPDES
Surface
52,000
0
Water
NY0000078
water
3
0
20
0.00407
0.0312
0.0889
788
0
52,000
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.,
East Camden, AR
3
0
Surface
Adhesives and
Sealants
Manuf.
Surface
250
0.013
0.20
1.67
788
0
NPDES: AR0051071,
Water
water
52,000
0
ARR00A521, ARR00A520
20
0.160
2.42
20.57
3
3
Page 489 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Best One Tire & Service8,
Water
Adhesives and
Surface
water
3
3
Nashville, TN
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Bridgestone Aircraft Tire
Water
Adhesives and
Surface
water
3
3
(Usa), Inc.8,
Mayodan, NC
NPDES: Not available
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
Clayton Homes Inc8,
Oxford, NC
Surface
Water
Adhesives and
Surface
water
250
0.013
0.20
1.67
788
0
Sealants
52,000
0
NPDES: Not available
Manuf.
20
0.160
2.42
20.57
3
3
788
0
Page 490 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Cmh Manufacturing, Inc.
Dba Schult Homes - Plant
Surface
52,000
0
Water
Adhesives and
Surface
water
3
3
958s,
Sealants
20
0.160
2.42
20.57
788
0
Richfield, NC
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
2
250
0.013
0.31
1.10
788
0
Surface
NPDES
52,000
0
Water
NY0000558
3
11
Delphi Thermal Systems8,
Surface
water
20
0.160
3.87
13.50
788
0
Lockport, NY
NPDES: NY0000558
52,000
0
No info on
3
0
receiving
facility;
Adhesives and
Sealants
Manuf.
788
0
POTW
250
0.013
0.0374
0.32
52,000
0
Green Bay Packaging Inc -
Coon Rapids8,
3
0
Surface
Adhesives and
Sealants
Manuf.
Surface
250
0.013
0.20
1.67
788
0
Coon Rapids, MN
NPDES: Not available
Water
water
52,000
0
20
0.160
2.42
20.57
3
3
Page 491 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Mastercraft Boat Company8,
Vonore, TN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
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
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
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
52,000
0
20
0.160
2.42
20.57
3
3
788
0
Page 492 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.08
0.18
788
0
Surface
NPDES
52,000
0
Water
IL0000230
3
7
Olin Corp8,
Surface
water
20
0.160
1.03
2.26
788
0
East Alton, IL
NPDES: IL0000230
52,000
0
No info on
3
0
receiving
facility;
Adhesives and
Sealants
Manuf.
788
0
POTW
250
0.013
0.0374
0.32
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Parker Hannifin Corp -
Water
Adhesives and
Surface
water
3
3
Paraflex Division8,
Manitowoc, WI
NPDES: Not available
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
Parrish Tire Company8,
Yadkinville, NC
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
250
0.013
0.20
1.67
788
0
Water
water
52,000
0
20
0.160
2.42
20.57
3
3
Page 493 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Republic Doors And Frames8,
Mckenzie, TN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Ro-Lab Rubber
Company Inc.8,
Tracy, CA
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Royale Comfort Seating, Inc.8
-Plant No. 1,
Taylorsville, NC
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
Page 494 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Snider Tire, Inc.8,
Statesville, NC
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Snyder Paper Corporation8,
Hickory, NC
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Stellana Us8,
Lake Geneva, WI
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
52,000
0
Water
water
3
3
20
0.160
2.42
20.57
788
0
52,000
0
Page 495 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Thomas Built Buses -
Courtesy Road8,
High Point, NC
NPDES: Not available
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Unicel Corp8,
Escondido, CA
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Acme Finishing Co Lie8,
Elk Grove Village, IL
Surface
Adhesives and
Surface
water
52,000
0
Water
Sealants
3
3
NPDES: Not available
Manuf.
20
0.160
2.42
20.57
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
Page 496 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
Aerojet Rocketdyne, Inc.8,
Rancho Cordova, CA
NPDES: CA0004111
Surface
Water
NPDES
CA0004111
Surface
water
250
0.013
0.000295
0.00081
8
3
0
788
0
52,000
0
20
0.160
0.00363
0.0101
3
0
788
0
52,000
0
POTW
No info on
receiving
facility;
Adhesives and
Sealants
Manuf.
250
0.013
0.0374000
0.32000
0
3
0
788
0
52,000
0
Allegheny Cnty Airport Auth/
Pgh Intl Airport8, Coroapolis
Pittsburgh, PA
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Amphenol Corp -
Aerospace Operations8,
Sidney, NY
NPDES: NY0003824
Surface
Water
NPDES
NY0003824
Surface
water
250
0.013
0.0115
0.0631
3
0
788
0
52,000
0
20
0.160
0.14
0.78
3
0
788
0
52,000
0
Page 497 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
No info on
3
0
receiving
facility;
Adhesives and
Sealants
Manuf.
788
0
POTW
250
0.013
0.03740
0.3200
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Aprotech Powertrain8,
Asheville, NC
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Coating & Converting Tech
Corp /
Adhesive Coatings8,
Surface
52,000
0
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
Philadelphia, PA
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
Corpus Christi Army Depot8,
Corpus Christi, TX
Surface
Water
Adhesives and
Surface
water
250
0.013
0.20
1.67
788
0
Sealants
52,000
0
NPDES: Not available
Manuf.
20
0.160
2.42
20.57
3
3
788
0
Page 498 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Electronic Data Systems
Camp Pendleton8, Camp
Pendleton, CA
NPDES: Not available
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Florida Production
Water
Adhesives and
Surface
water
3
3
Engineering, Inc.8,
Ormond Beach, FL
NPDES: Not available
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Goodrich Corporation8,
Jacksonville, FL
NPDES: Not available
Surface
Adhesives and
Sealants
Manuf.
Surface
52,000
0
Water
water
3
3
20
0.160
2.42
20.57
788
0
52,000
0
Page 499 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Kasai North America Inc8,
Water
Adhesives and
Surface
water
3
3
Madison Plant, Madison, MS
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Kirtland Air Force Base8,
Water
Adhesives and
Surface
water
3
3
Albuquerque, NM
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Marvin Windows & Doors8,
Surface
Adhesives and
Surface
water
52,000
0
Warroad, MN
Water
Sealants
3
3
NPDES: Not available
Manuf.
20
0.160
2.42
20.57
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
Page 500 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
Mcneilus Truck &
Manufacturing Inc8,
Dodge Center, MN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Metal Finishing Co.8 -
Wichita (S Mclean Blvd),
Wichita, KS
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
52,000
0
Murakami Manufacturing Usa
Inc8, Campbellsville, KY
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
0.20
1.67
3
0
788
0
52,000
0
20
0.160
2.42
20.57
3
3
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
Page 501 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Peterbilt Motors Denton
Water
Adhesives and
Surface
water
3
3
Facility8,
Denton, TX
NPDES: Not available
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Portsmouth Naval Shipyard8,
Kittery, ME
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
R.D. Henry & Co.8,
Wichita, KS
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
Page 502 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
250
250
0.013
n/a
10.83
788
0
Surface
NPDES
52,000
0
Water
RI0000281
3
20
Raytheon Company8,
20
0.160
n/a
133.33
788
0
Portsmouth, RI
NPDES: RI0000281
Still body
52,000
0
No info on
3
0
receiving
facility;
Adhesives and
Sealants
Manuf.
788
0
POTW
250
0.013
0.03740
0.32
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Rehau Inc8,
Water
Adhesives and
Surface
water
3
3
Cullman, AL
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Rotochopper Inc8,
Saint Martin, MN
NPDES: Not available
Water
Adhesives and
Sealants
Manuf.
Surface
3
3
water
20
0.160
2.42
20.57
788
0
52,000
0
POTW
250
0.013
0.0374
0.32
3
0
788
0
Page 503 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Rubber Applications8,
Mulberry, FL
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Sapa Precision Tubing
Rockledge, Lie8,
Rockledge, FL
NPDES: Not available
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Thomas & Betts8,
Water
Adhesives and
Surface
water
3
3
Albuquerque, NM
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
Page 504 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Thomas Built Buses - Fairfield
Water
Adhesives and
Surface
water
3
3
Road8,
High Point, NC
NPDES: Not available
Sealants
20
0.160
2.42
20.57
788
0
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Timco,
Dba Haeco Americas
Surface
52,000
0
Water
Adhesives and
Surface
water
3
3
Airframe Services8,
Sealants
20
0.160
2.42
20.57
788
0
Greensboro, NC
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Trelleborg Coated Systems
Us, Inc8 -
Grace Advanced Materials,
Surface
52,000
0
Water
Adhesives and
Surface
water
3
3
Sealants
20
0.160
2.42
20.57
788
0
Rutherfordton, NC
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
250
0.013
0.20
1.67
3
0
Page 505 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
788
0
Surface
Water
52,000
0
U.S. Coast Guard Yard -
Curtis Bay8,
3
3
Adhesives and
Sealants
Manuf.
Surface
20
0.160
2.42
20.57
788
0
Curtis Bay, MD
NPDES: Not available
water
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
3
0
250
0.013
0.20
1.67
788
0
Surface
52,000
0
Viracon Inc8,
Water
Adhesives and
Surface
water
3
3
Owatonna, MN
Sealants
20
0.160
2.42
20.57
788
0
NPDES: Not available
Manuf.
52,000
0
3
0
POTW
250
0.013
0.0374
0.32
788
0
52,000
0
OES: Industrial Processing Aid
3
0
Occidental Chemical Corp
Niagara Plant,
300
0.019
n/a
0.14
788
0
Surface
NPDES
Still body
52,000
0
Niagara Falls, NY
Water
NY0003336
3
0
NPDES: NY0003336
20
0.292
n/a
2.200
788
0
52,000
0
0.00041
9
3
0
Stepan Co Millsdale Road,
Surface
Water
NPDES
IL0002453
Surface
water
300
0.001
0.00016
788
0
Elwood, IL
52,000
0
NPDES: IL0002453
20
0.008
0.00128
0.00335
3
0
788
0
Page 506 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
140
Entek International LLC,
Lebanon, OR
NPDES: N/A
Off-site
No info on
300
0.38
1.82
9.30
788
0
Waste-
receiving
Surface
52,000
0
water
facility;
water
3
20
Treatment
POTW (Ind.)
20
5.65
27.11
138.34
788
0
52,000
0
3
0
National Electrical Carbon
Products
Dba Morgan Adv Materials,
Fostoria, OH
NPDES: OH0052744
Off-site
Receiving
Facility: City
of Fostoria;
NPDES
OH0052744
300
0.008
0.0336
0.15
788
0
Waste-
Surface
52,000
0
water
water
3
1
Treatment
20
0.115
0.50
2.32
788
0
52,000
0
3
0
PPG Industries Inc Barberton,
Barberton, OH
NPDES: OH0024007
Off-site
Receiving
Facility: City
of Barberton;
NPDES
OH0024007
300
0.005
0.00478
0.0141
788
0
Waste-
Surface
52,000
0
water
water
3
0
Treatment
20
0.070
0.067
0.20
788
0
52,000
0
3
0
Daramic LLC,
Corydon, IN
NPDES: IN0020893
300
0.008
0.00572
0.0206
788
0
Surface
NPDES
Surface
52,000
0
Water
IN0020893
water
3
0
20
0.114
0.0816
0.29
788
0
52,000
0
OES: Commercial Printing and Copying
3
0
Printing And Pub Sys Div,
Weatherford, OK
Surface
Water
Surface
water
250
0.00020
0.000662
0.00292
788
0
Printing
52,000
0
NPDES: OK0041785
3
0
20
0.00250
0.00827
0.0365
788
0
Page 507 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
OES: Other Industrial Uses
3
35
Eli Lilly And Company-
Lilly Tech Ctr,
250
1.553
1.63
9.03
788
0
Surface
NPDES
Surface
52,000
0
Indianapolis, IN
Water
IN0003310
water
3
17
NPDES: IN0003310
20
19.410
20.47
113.09
788
0
52,000
0
3
1
Oxy Vinyls LP - Deer Park
Pvc,
250
0.148
0.13
0.49
788
0
Surface
NPDES
Surface
52,000
0
Deer Park, TX
Water
TX0007412
water
3
9
NPDES: TX0007412
20
1.854
1.58
5.98
788
0
52,000
0
3
22
Washington Penn Plastics,
Frankfort, KY
NPDES: KY0097497
Surrogate
NPDES
KY0028410
250
0.032
1.25
7.53
788
0
Surface
Surface
52,000
0
Water
water
3
13
20
0.399
15.62
94.12
788
0
52,000
0
3
0
Natrium Plant,
New Martinsville, WV
NPDES: WV0004359
250
0.022
0.000566
0.00262
788
0
Surface
NPDES
Surface
52,000
0
Water
WV0004359
water
3
0
20
0.274
0.00695
0.0322
788
0
52,000
0
3
0
Leroy Quarry,
Leroy, NY
NPDES: NY0247189
Surrogate
NPDES
NY0030546
250
0.019
0.16
0.71
788
0
Surface
Surface
52,000
0
Water
water
3
3
20
0.242
2.05
8.91
788
0
52,000
0
Page 508 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
0
George C Marshall Space
Flight Center,
Surrogate
NPDES
AL0025585
250
0.010
0.0738
0.20
788
0
Surface
Surface
52,000
0
Huntsville, AL
Water
water
3
8
NPDES: AL0000221
20
0.128
0.96
2.63
788
0
52,000
0
3
30
Whelan Energy Center Power
Plant,
250
0.009
0.67
2.92
788
0
Surface
NPDES
Surface
52,000
0
Hastings, NE
Water
NE0113506
water
3
13
NPDES: NEO113506
20
0.118
8.95
38.96
788
0
52,000
0
0.00010
3
3
0
Army Cold Regions Research
& Engineering Lab,
Surrogate
NPDES
NHO100099
250
0.0002
0.0000266
788
0
Surface
Surface
52,000
0
Hanover, NH
Water
water
3
0
NPDES: NH0001619
20
0.0029
0.000398
0.00154
788
0
52,000
0
0.00034
0
3
0
Corning - Canton Plant,
Canton, NY
NPDES: NY0085006
Surrogate
NPDES
NY0034762
250
0.0002
0.000101
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.0028
0.00152
0.00510
788
0
52,000
0
3
53'
Ames Rubber Corp Plant #1,
Hamburg Boro, NJ
NPDES: NJ0000141
Surrogate
NPDES
NJ00001411
250
0.00011
0.00258
0.0149
788
50'
Surface
Surface
52,000
50'
Water
water
3
6
20
0.00133
0.0304
0.18
788
4
52,000
4
Gorham,
Surface
POTW (Ind.)
Surface
250
0.0001
0.00253
0.0129
3
0
Providence, RI
Water
water
788
0
Page 509 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
NPDES: RIG85E004
52,000
0
3
0
20
0.0012
0.0253
0.13
788
0
52,000
0
3
3
Solvay - Houston Plant,
Houston, TX
NPDES: TX0007072
350
0.024
0.22
4.44
788
0
Surface
NPDES
Surface
52,000
0
Water
TX0007072
water
3
5
20
0.414
3.72
75.93
788
0
52,000
0
0.00068
Q
3
0
Akzo Nobel Surface
350
0.000329
0.000300
788
0
Chemistry LLC,
Surface
NPDES
Surface
52,000
0
Morris, IL
Water
IL0026069
water
3
0
NPDES: IL0026069
20
0.006
0.00546
0.0125
788
0
52,000
0
0.00009
41
3
0
Solutia Nitro Site,
Nitro, WV
NPDES: WV0116181
Surrogate
NPDES
WV0023229
350
0.000318
0.0000214
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.006
0.000401
0.00176
788
0
52,000
0
3
0
Amphenol Corporation -
Columbia,
Organic
Chemicals
Manufacture
350
0.000202
0.00395
0.037
788
0
Surface
Surface
52,000
0
Columbia, SC
Water
water
3
1
NPDES: SC0046264
20
0.004
0.0791
0.74
788
0
52,000
0
Keeshan and Bost Chemical
3
350
Co., Inc.,
Surface
NPDES
Still body
350
0.000095
n/a
9.50
788
0
Manvel, TX
Water
TX0072168
52,000
0
NPDES: TX0072168
20
0.002
n/a
200.00
3
20
Page 510 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(ppb)
7Q10
SWC6
(ppb)
COC
(ppb)
Days of
Exceedance7
(days/yr)
788
0
52,000
0
Chemtura North and South
Plants,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
Morgantown WV
NPDES: WV0004740
Water
exceed the most sensitive COC using the most conservative input assumptions.
Indoraina Ventures Olefins,
LLC,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
Sulphur, LA
NPDES: LA0069850
Water
exceed the most sensitive COC using the most conservative input assumptions.
Emerson Power Transmission,
Ithaca, NY
NPDES: NY0002933
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.
William E. Warne Power
Plant,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
Los Angeles County, CA
NPDES: CA0059188
Water
exceed the most sensitive COC using the most conservative input assumptions.
Raytheon Aircraft Co(Was
Beech Aircraft), Boulder, CO
NPDES: COG315176
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.
OES: Other Commercial Uses
3
0
Corning Hospital,
Corning, NY
NPDES: NY0246701
Surrogate
NPDES
NY0025721
250
0.013
0.00597
0.0271
788
0
Surface
Surface
52,000
0
Water
water
3
0
20
0.159
0.0735
0.33
788
0
52,000
0
3
0
Water Street Commercial
Surrogate
NPDES
OH0009521
250
0.003
0.00131
0.00564
788
0
Bldg,
Surface
Surface
52,000
0
Dayton OH
Water
water
3
0
NPDES: OHO 141496
20
0.035
0.0153
0.0658
788
0
52,000
0
250
0.00040
0.0196
0.0881
3
213J
Page 511 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
788
213J
Union Station North Wing
Surface
Water
Surrogate
Surface
water
52,000
213J
Office Building, Denver, CO
NPDES
3
18
NPDES: COG315293
C00020095J
20
0.00499
0.24
1.10
788
17
52,000
17
3
213J
Confluence Park Apartments,
Denver, CO
NPDES: COG315339
Surrogate
NPDES
C00020095J
250
0.00028
0.0137
0.0617
788
213J
Surface
Surface
52,000
213J
Water
water
3
17
20
0.00354
0.17
0.77
788
17
52,000
17
3
250
Park Place Mixed Use
Surrogate
NPDES
MD0052868
250
0.00027
n/a
9.00
788
0
Development,
Surface
Still body
52,000
0
Annapolis, MD
Water
n/a
3
20
NPDES: MD0068861
20
0.00334
110.00
788
0
52,000
0
Tree Top Inc Wenatchee
Plant,
Surface
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to
Wenatchee, WA
Water
exceed the most sensitive COC using the most conservative input assumptions.
NPDES: WA0051527
Wynkoop Denver LLCP St,
Denver, CO
NPDES: COG603115
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.
Greer Family Lie,
South Burlington, VT
NPDES: VT0001376
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.
John Marshall III Site,
Mclean, VA
NPDES: VA0090093
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.
OES: N/A (WWTP)
New Rochelle STP,
Surface
Water
NPDES
NY0026697
3
0
New Rochelle, NY
Still body
365
0.043
n/a
0.70
788
0
NPDES: NY0026697
52,000
0
Page 512 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
coc
(PPb)
Days of
Exceedance7
(days/yr)
3
20
20
0.786
n/a
12.79
788
0
52,000
0
3
0
Everett Water Pollution
365
0.016
0.13
0.17
788
0
Control Facility,
Surface
NPDES
Surface
52,000
0
Everett, WA
Water
WA0024490
water
3
7
NPDES: WA0024490
20
0.299
2.37
3.11
788
0
52,000
0
3
2
Sullivan WWTP,
Sullivan, MO
NPDES: MOO 104736
365
0.010
0.16
0.61
788
0
Surface
NPDES
Surface
52,000
0
Water
MOO 104736
water
3
7
20
0.176
2.81
10.97
788
0
52,000
0
3
0
Sunnyside STP,
Sunny side, WA
NPDES: WA0020991
365
0.005
0.00146
0.00673
788
0
Surface
NPDES
Surface
52,000
0
Water
WA0020991
water
3
0
20
0.083
0.0242
0.110
788
0
52,000
0
3
0
Port Of Sunnyside Industrial
WWTF,
365
0.002
0.0505
0.26
788
0
Surface
POTW (Ind.)
Surface
52,000
0
Sunnyside, WA
Water
water
3
5
NPDES: WA0052426
20
0.035
0.88
4.51
788
0
52,000
0
3
0
U.S. Air Force Shaw AFB SC,
Surface
Water
Surface
water
365
0.002
0.0505
0.26
788
0
Shaw AFB, SC
POTW (Ind.)
52,000
0
NPDES: SC0024970
20
0.032
0.81
4.12
3
4
788
0
Page 513 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
52,000
0
3
0
Gnf-A Wilmington-Castle
Hayne WWTP,
365
0.0004
0.000304
0.00194
788
0
Surface
NPDES
Surface
52,000
0
Wilmington, NC
Water
NC0001228
water
3
0
NPDES: NC0001228
20
0.0067
0.00533
0.0340
788
0
52,000
0
3
0
Cameron Trading Post
WWTP,
365
0.0003
0.00758
0.0387
788
0
Surface
POTW (Ind.)
Surface
52,000
0
Cameron, AZ
Water
water
3
0
NPDES: NN0021610
20
0.0047
0.13
0.64
788
0
52,000
0
0.00001
27
3
0
Coal Grove WWTP,
Coal Grove, OH
NPDES: OH0104558
365
0.0002
0.00000250
788
0
Surface
NPDES
Surface
52,000
0
Water
OH0029432
water
3
0
20
0.0031
0.0000375
0.00019
788
0
52,000
0
1 Release media are either direct (release from facility directly to surface water) or indirect (transfer of 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.
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 water" 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 draft 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.
Page 514 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID of
Active Releaser Facility
Release
Media1
Modeled
Facility or
Industry
Sector in
EFAST2
EFAST
Waterbody
Type3
Days of
Release4
Release5
(kg/day)
Harmonic
Mean SWC
(PPb)
7Q10
SWC6
(PPb)
COC
(PPb)
Days of
Exceedance7
(days/yr)
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.
122
123
124
125
Page 515 of 748
-------
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix D CONSUMER EXPOSURES
D.1 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 ( )).
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.
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 ( >).
D.l.l 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: Vol Building (building
volume); AER_Zone2 (air exchange rate in Zone 2); AER Zonel (air exchange rate in Zone 1);
Vol Zonel (room of use, or Zone 1 volume). Inhalation exposures from liquid consumer product
formulations were modeled using El and the two most sensitive variables identified in this analysis were
varied to estimate a range of exposures.
Page 516 of 748
-------
159
160
161
162
163
164
165
166
167
168
169
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
1.2
FigureApx 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.
-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 13 CADD Negative ~ CADD Positive
Page 517 of 748
-------
170
171
172
173
174
175
176
111
178
179
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
E3 Elasticity for ADR and CADD
WF
VP*
VP
Vol_Zonel
Vol_Building
Q_zl2
MW*
MW
M_Chronic
MAciite
Du rationChron ic
DurationAcute
CSATA
Ae ros o l_Fra ct i o n
AER_Zorie2
AER_Zoriel
-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
FigureApx D-2. Elasticities (> 0.05) for Parameters Applied in E3
For acute exposures generated from emission model P_DER2b, the chemical properties that inform
absorption rate, or absorption rate estimates, have the greatest elasticities (see Figure Apx D-3). Dermal
exposures from consumer product formulations were modeled using P DER2B with a measured Kp
(permeability coefficient). Therefore, LogKow (octanol/water partition coefficient) and MW (molecular
weight) were not used to estimate skin penetration.
Page 518 of 748
-------
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LogKow*
-2.0 -1.0 0.0 1.0 2.0 3.0
Elasticity (% change in dose/% change in variable)
¦ ADR Megative ~ ADR Positive ~ CADD Negative ~ CADD Positive
FigureApx D-3. Elasticities (> 0.05) for Parameters Applied in P_DER2b
D.1.2 Categorical Variables
For categorical variables there were multiple parameters that affected other model inputs. For example,
varying the room type changed the ventilation rates, volume size and the amount of time per day that a
person spent in the room. Thus, each modeling result was calculated as the percent difference from the
base run. For continuous variables, each modeling result was calculated as elasticity.
Among the categorical variables, the most sensitive parameters included receptor type (adult vs. child),
room of use (Zone 1) selection, and application of the near-field bubble within Zone 1. However, these
types of variables were held constant within a given product modeling scenario and were applied using
consistent assumptions across all modeling scenarios.
D.2 Monitoring Data
D.2.1 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.
Page 519 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
209 Data extracted for residential indoor air samples from studies conducted outside of North America, as
210 well as studies conducted in schools and commercial establishments in the US and other countries, are
211 provided in [Data Extraction Tables for Environmental Monitoring Data. Docket: EPA-HQ-OPPT-
212 2019-05001
213
214 TableApx D-l. TCE Residential Indoor Air Concentrations (jig/m3) in the United States and
215 Canada
Study Info
Site Description
LOQ
Min.
Mean
Median
Max.
Variance
Data Eval.
Score
(Chin et aL 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
US, 2004-2005 (n=83; DF =
Boston, MA; Interior
room of residences
0.04
ND
0.6
0.2
2.2 (95th)
1.7 (SD)
High
0.93)
(Dodson et aL 2(>08)a
US, 2004-2005 (n=52; DF =
Boston, MA; Basement
of residences
0.04
ND
0.4
0.1
1.4 (95th)
1.1 (SD)
High
0.75)
(Dodson et aL 2(>08)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 2(>08)a
US, 2004-2005 (n=16; DF =
Boston, MA; Garage of
residences
0.04
ND
3.3
0.1
42 (95th)
10 (SD)
High
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
(Adeate 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
(Sax et al.. 2004)
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
Page 520 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Study Info
Site Description
LOQ
Min.
Mean
Median
Max.
Variance
Data Eval.
Score
neighborhood, sampled
in the summer
rsu 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
(Clayton 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)
CA, 1987 (n=6; DF = 0.83)
Homes (n=6), main
floor
ND
1.6
5
Medium
(Chan et al.. 1990)
CA, 1986 (n=12; DF = 0.42)
Homes (n=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.
216
217 D.2.2 Personal breathing Zone Monitoring Data
218 Concentrations of TCE (TCE) in the personal breathing zones of residents in the United States collected
219 from seven studies identified during Systematic Review are summarized in Table Apx D-2. Overall, the
220 measured concentration dataset contains approximately 2,750 samples that were collected between 1981
221 and 2001, and represents time spent in various microenvironments (i.e., home, school, work, transit)
222 during the monitoring period. Only the 3-hr samples from Heavner et al. (1995) represent time inside the
223 home only. Concentrations ranged from non-detect (limits varied) to 327.3 |ig/m3. The highest
224 concentration was observed in samples collected in 2000 as part of the NHANES 1999-2000 study (Jia
225 et al.. 2008b). The study states that the top ten highest concentrations exceeded 300 (J,g/m3, which they
226 suggest may indicate exposure from immediate contact with solvents. The 95th percentile concentration
227 in this study is 7.4 [j,g/m3. All other studies showed maximum concentrations less than 10 |ig/m3.
228 Median concentrations ranged from ND to 1.05 (J,g/m3; and average concentrations ranged from 0.66 to
229 13 |ig/m3.
230
231 Data extracted for residential/general personal breathing zones studies conducted outside of North
232 America, as well as studies conducted in schools and commercial establishments in the US and other
Page 521 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
233 countries, is provided in [Data Extraction Tables for Environmental Monitoring Data. Docket: EPA-
234 HO-OPPT-2019-0500],
235
236 TableApx D-2. Personal Breathing Zone Concentrations (jig/m3) for TCE in the United States
237 (General/Residential)
Data
Study Info
Type
Site Description
LOD
Min.
Mean
Median
Max
Variance
Eval.
Score
(Suetal.. 2013V
48-hr
Elizabeth, NJ; Houston
US, 1999-2001
(n=544; DF = 0.23)
TX; and Los Angeles, CA;
Adults (n=309) and
children (n=l 18) from 310
non-smoking households.
--
ND
1.44
0.22
2.37
(95th)
10.74
(SD)
Medium
(Jia 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
95th1
(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
(Clavtonet al.. 1999)°
US, 1995-1997
(n=386; DF = 0.394)
6-day
IL, IN, OH, MI, MN, WI
(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/ml 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.
238
Page 522 of 748
-------
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 2019). 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 algae SSD, algae hazard data were curated to prioritize study quality and to assure comparability
between toxicity values (e.g., comparing ECsos to EC50s). The dataset included both saltwater and
freshwater species, because the only saltwater species value was within the range of values reported for
freshwater species. With this dataset, the Toolbox was used to apply a variety of algorithms to fit and
visualize SSDs with different distributions. FigureApx E-l shows the Toolbox interface after each
distribution and fitting method was fit to the data. A hazardous concentration for 5% of species (HC05) is
calculated for each.
Figure Apx E-l. SSD Toolbox interface and list of HCoss for each distribution and fitting method
using TCE's algae hazard data (Etterson, 2019)
¦A SSD Toolbox
File Plot
~
X
C:\U5er5\KKoehrn\DDCuments\RAD\TCE\SSD_TCE_algae_files\Algae_revised.xl5x
Fit Distribution
Fitting Method:
metro poiis-h a stings
Distribution:
burr
Scaling parameters
1.15
Mineau scaling
Target weight: ioo
Goodness of Fit:
Iteration 1000
Units:
mg/L
Status:
Ready
Results:
Distribution
Method
HC 05
1
normal
|
70.7294
0.9930
2
normal
MO
65.4897
1
3
normal
GR
4S.7427
0.9950
4
normal
MH
50.1369
0.0985
5
logistic
ML
61.3796
0.9S20
6
logistic
MO
66.6519
0.9800
7
logistic
GR
43.9740
0.9141
8
logistic
MH
40.85-68
0.1075
9
triangular
ML
86.6386
1
10
triangular
MO
63.S997
1
11
triangular
GR
51.9060
1
12
triangular
MH
57.8295
0.1375
13
g umbel
ML
81.3972
1
14
gumbel
MO
86.4467
1
15
gumbel
GR
67.3150
0.9990
16
gumbel
MH
65.1299
0.1639
17
burr
MH
121.5295
0.0770
Page 523 of 748
-------
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The SSD Toolbox's output contained several methods for choosing an appropriate distribution and
fitting method, including goodness-of-fit and standard error among others. However, choosing the
distribution with the best fit was challenging with a small dataset (e.g., hazard data for 9 algae species).
P values for goodness-of-fit were all above 0.05, showing no evidence for lack of fit, and providing no
help in discriminating among distributions (Figure Apx E-l). Standard error was lowest across fitting
methods for the Gumbel and Burr distributions (TableApx E-l). Because the ability for these measures
to distinguish between distributions was limited, visual inspection of the distributions was used. For
example, visual inspection showed Burr was not a good fit (Figure Apx E-2).
Table Apx E-l. Standard Error for all dsitributions and fitting methods using TCE's algae
hazard data (Etterson, 2019)
Normal Distribution
Logistic Distribution
Triangular Distribution
Gumbel Distribution
Bun-
Distribution
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
MH
Standard Error
for HCos
35.7
33.9
26.1
27.9
36.4
33.7
26.1
29.2
34.0
33.4
26.5
28.9
26.6
28.6
26.2
23.9
20.9
Figure Apx E-2. All distributions and fitting methods in the SSD Toolbox for TCE's algae hazard
data (Etterson, 2019)
1
0.9
0.8
0.7
1 0.6
.Q
£
CL
o) 0.5
>
To
| 0.4
Z>
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
triangular distributions
normal distributions
logistic distributions Synechococcus elongaJf+pTs'
gumbel distributions 'fy /
burr distribution /// /
~ Desmodesmus subspicattmWw/ i
Synechococcus leopolien&is •/
M • (Mhioreiia kessieri
® CMUydominas rainhartdtii
• Wmmocystm aeruginosa
- o^BBmonem&costatum
~ ^rjflUUKaphido cm lis subcapitata
i
Page 524 of 748
-------
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Using standard error and visual inspection, the distributions with the best fit for the most sensitive algae
species included triangular and Gumbel. The triangular distribution with graphical methods fitting was
the most protective, and was used as a line of evidence for assessing algae in this assessment
(Figure_Apx E-3). The resulting SSD calculated an HCos of 52 mg/L or 52,000 |ig/L.
Figure Apx E-3. TCE algae data fit with triangular distribution fit with graphical methods
(Etterson. 2019)
Toxicity Value (Log 10[EC50]) mg/L
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 LC50S 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. With this dataset, the Toolbox was used to apply a variety of
algorithms to fit and visualize SSDs with different distributions. Figure Apx E-4 shows the Toolbox
interface after each distribution and fitting method was fit to the data. An HCos is calculated for each.
Page 525 of 748
-------
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx E-4. SSD Toolbox interface showing HCoss and P values for each distribution and
fitting method using TCE's acute hazard data (Etterson, 2019)
A SSD Toolbox
File Plot
~
X
C:\U sers\KKo eh rn\Do cu ments\RADYTCE\SSD_TCE_a lg a e_f iles\AII_species.xlsx
Fit Distribution
Fitting Method:
metro poiis-h a stings
Distribution
burr
Scaling parameters
Mineau scaling
Target weight: 10Q
1.15
Goodness of Fit:
Iteration 1000
Units
mg/L
Status:
Ready
Results:
Distribution
Method
HC.05
P
1
normal
ML
7.1130
0.8182
2
normal
HO
6.3275
0.7173
3
normal
GR
4.1033
0.4266
4
normal
MH
4.1173
0.390S
5
logistic
ML
6.9555
0.5215
6
logistic
HO
6.4792
0.4785
7
logistic
GR
3.5349
0.1319
8
logistic
MH
3.8530
0.3991
9
triangular
ML
7.2234
1
10
triangular
MO
6.1216
0.9SS0
11
triangular
GR
4.4SE6
0.8122
12
triangular
MH
3.9220
0.445S
13
gumbel
ML
11.9649
0.6783
14
gumbel
MO
9.1953
0.4106
15
gumbel
GR
6.3906
0.1379
16
gumbel
MH
8.7641
0.35B6
17
burr
MH
26.6552
0.9037
Again 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, rBurnham and Anderson. 20021). P values for goodness-of-fit were all above 0.05,
showing no evidence for lack of fit, and providing no help in discriminating among distributions
(Figure Apx E-4). Standard error was mixed across fitting methods for some distributions but generally
the lowest for the burr distribution (Table Apx E-2). Figure Apx E-5 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. For example,
visual inspection showed Burr was not a good fit (Figure Apx E-6).
Page 526 of 748
-------
309
310
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx E-2. Standard Error for all distributions and fitting methods using TCE's acute
Normal Distribution
Logistic Distribution
Triangular Distribution
Gumbel Distribution
Bun-
Distribution
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
ML
MO
GR
MH
MH
Standard Error
for HC05
5.8
5.2
3.7
3.7
4.8
5.9
3.4
3.8
6.9
5.0
3.9
4.1
4.1
4.6
3.6
3.9
2.9
Figure Apx E-5. AICc for the four distribution options in the SSD Toolbox for TCE's acute
lazard data (Etterson, 2019)
5 AIC
Percentile of interest:
Model-averaged HCp:
Model-averaged SE of HCp:
9.8989
4.1585
CV of HCp: 0.42011
AICc Table
1
_2_
4
Distribution
AICc
delta AICc Weight
HCp
SE HCp
gumbel
logistic
triangular
normal
84.9297
87.3190
87.9152
88.0905
0
2.8892
2.9865
3.1608
0.5769
0.1747
0.1296
0.1188
11.9649
6.9555
7.2234
7.1130
3.3637
3.7861
2.0728
3.9783
Page 527 of 748
-------
317
318
319
320
321 EPA used a model average of the Gumhel, logistic, triangular, and normal distributions, because it was
322 not clear which distribution had the best fit after considering standard error, AICc, and visual inspection.
323 The model-averaged HCos from all four distributions was 9.9 mg/L or 9,900 |ig/L, and the SSDs showed
324 aquatic invertebrates were the most sensitive species (FigureApx E-7).
325
1aphnia
Mystdopas bahia
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Figure Apx E-6. All distributions and fitting methods in the SSD Toolbox for TCE's acute hazard
data (Etterson, 2019)
TO
X!
E
CL
Q)
>
_ro
=s
E
zs
O
0.6
¦i 0.4
normal distributions
¦ logistic distributions Xenopus laevisj
¦triangular distributions
gumbel distributions . , , , _ „ „
^ Cypnnodon vaneqatus (sheepshead) •
¦ burr distribution
Lepomis macrochirus (bluegill)
Pimephales promelas (fathead minnow
Manilla ftoridae (flagfish)
fdaphnja dubia
1 1.5 2 2.5
Toxicity Value (Log 10[EC50])
Page 528 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
326 Figtire Apx E-7. TCE's acute hazard data fit with the normal, logistic, triangular, and Gunibel
327 distributions fit with maximum likelihood in the SSD Toolbox (Etterson, 2019)
328
329
330
Toxicity Value (Log 10[EC50])
Page 529 of 748
-------
331
332
333
334
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
E.2 Environmental Risk Quotients (RQs) for Facilities Releasing TCE to Surface Water as Modeled
in E-FAST
Table Apx E-3. Environmental
RQs by Facility (with RQs > 1 in bold)
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
20
22.15
0.0897
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
0.81
0.00
20
1.2
42.14
0.01
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
0.18
0.00
20
0.265
9.48
0.00
0.01
3.16
0.00
Surface
Water
Organic
Chemicals
Manuf.
Surface
water
350
0.015
2.77
0.00
0.00
0.92
0.00
20
0.265
49.91
0.02
0.06
16.64
0.00
Occidental Chemical Corp
Wichita,
Wichita, KS
NPDES: KS0096903 and
Organic ChemMFG SIC
Surface
Water
Surrogate
NPDES
KS0043036
Surface
water
350
0.015
0.07
0.00
0.00
0.02
0.00
20
0.265
1.33
0.00
0.00
0.44
0.00
Off-site
Waste-
water
Treatment
Organic
Chemicals
Manuf.
Surface
water
350
0.015
0.53
0.00
0.00
0.18
0.00
20
0.265
9.48
0.00
0.01
3.16
0.00
OES: Processing as a Reactant
Page 530 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.06
0.00
20
0.089
3.13
0.00
0.00
1.04
0.00
Surface
Water
Organic
Chemicals
Manufacture
Surface
water
350
0.005
0.92
0.00
0.00
0.31
0.00
20
0.089
16.45
0.01
0.02
5.48
0.00
Arkerna 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
20
0.295
0.128
0.00
0.00
0.04
0.00
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
20
0.224
0.000907
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
0.05
0.21
56.33
0.00
20
0.03
3000
0.94
3.81
1000.00
0.06
US DOE Paducah Site,
Kevil, KY
NPDES: KY0102083
Surface
Water
Annual releases estimated to be <0.02 kg/year were not modeled, as they were determined to be unlikely to exceed the most
sensitive COC using the most conservative input assumptions.
GNF-A Wilmington-Castle
Hayne,
Wilmington NC
NPDES: NC0001228
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: Repackaging
Page 531 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
9.06
0.00
20
13.85
339.11
0.11
0.43
113.04
0.01
Oiltanking Houston Inc.
Houston. TX
NPDES: TX0091855
Surface
Water
Surrogate
NPDES
TX0065943
Surface
water
250
0.003
6.52
0.00
0.01
2.17
0.00
20
0.041
89.13
0.03
0.11
29.71
0.00
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
20
0.069
0.000279
0.00
0.00
0.00
0.00
Vopak Terminal Westwego
Inc,
Westwego, LA
NPDES: LAO 124583
Surface
Water
Surrogate
NPDES
LA0042064
Surface
water
250
0.00468
0.0000189
0.00
0.00
0.00
0.00
20
0.058
0.000235
0.00
0.00
0.00
0.00
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: OTVD (Includes releases for Closed-Loop Degreasing, Conveyorized Degreasing, Web Degreasing, and Metalworking Fluids)
Texas Instruments, Inc.,
260
0.005
0.0188
0.00
0.00
0.01
0.00
Page 532 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility"
Release
Media b
Modeled
Facility or
Industry
Sector in
EFAST c
EFAST
Waterbody
Typell
Days of
Release1
Release
(kg/day)f
7Q10
swe
(ppb) b
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Attleboro, MA
NPDES: MA0001791
Surface
Water
NPDES
MA0001791
Surface
water
20
0.067
0.25
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
0.01
0.00
20
0.029
0.62
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
0.02
0.00
20
0.011
0.68
0.00
0.00
0.23
0.00
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
20
0.0061
0.0373
0.00
0.00
0.01
0.00
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.
US Nasa Michoud
Assembly Facility,
New Orleans, LA
NPDES: LA0052256
Surface
Water
Surrogate
NPDES
LA0003280
Still body
260
1.96
765.63
0.24
0.97
255.21
0.01
20
25.44
9937.5
3.11
12.61
3312.50
0.19
GM Components Holdings
LLC,
Surface
Water
NPDES
NY0000558
Surface
water
260
0.13
10.97
0.00
0.01
3.66
0.00
Page 533 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Lockport, NY
NPDES: NY0000558
20
1.71
144.47
0.05
0.18
48.16
0.00
Akebono Elizabethtown
Plant,
Elizabethtown, KY
NPDES: KY0089672
Surface
Water
Surrogate
NPDES
KY0022039
Surface
water
260
0.07
4.87
0.00
0.01
1.62
0.00
20
0.897
62.38
0.02
0.08
20.79
0.00
Delphi Harrison Thermal
Systems,
Dayton, OH
NPDES: OH0009431
Surface
Water
NPDES
OH0009431
Surface
water
260
0.04
0.0752
0.00
0.00
0.03
0.00
20
0.465
0.87
0.00
0.00
0.29
0.00
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
20
0.334
0.0335
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
0.74
0.00
20
0.218
24.44
0.01
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
0.01
0.00
20
0.128
0.54
0.00
0.00
0.18
0.00
Certa Vandalia LLC,
Vandalia, OH
NPDES: OH0122751
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.01
1.11
0.00
0.00
0.37
0.00
20
0.107
11.89
0.00
0.02
3.96
0.00
Page 534 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
PPb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.07
0.00
20
0.086
1.73
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
0.02
0.00
20
0.082
0.4
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
20
0.081
0.00522
0.00
0.00
0.00
0.00
Olean Advanced Products,
Olean NY
NPDES: NY0073547
Surface
Water
Surrogate
NPDES
NY0027162
Surface
water
260
0.01
0.0188
0.00
0.00
0.01
0.00
20
0.068
0.13
0.00
0.00
0.04
0.00
Hollingsworth Saco
Lowell,
Easley, SC
NPDES: SC0046396
Surface
Water
Primary Metal
Fonning
Manuf.
Surface
water
260
0.00469
0.52
0.00
0.00
0.17
0.00
20
0.061
6.78
0.00
0.01
2.26
0.00
Trelleborg YSH
Incorporated Sandusky
Plant,
Sandusky, MI
NPDES: MI0028142
Surface
Water
NPDES
MI0028142
Surface
water
260
0.0036
1.76
0.00
0.00
0.59
0.00
20
0.047
23.04
0.01
0.03
7.68
0.00
Page 535 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.35
0.00
20
0.0462
13.77
0.00
0.02
4.59
0.00
Johnson Controls
Incorporated,
Wichita, KS
NPDES: KS0000850
Surface
Water
NPDES
KS0000850
Surface
water
260
0.00228
0.0548
0.00
0.00
0.02
0.00
20
0.0296
0.72
0.00
0.00
0.24
0.00
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
0.08
0.00
20
0.026
2.89
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
0.01
0.00
20
0.026
0.22
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
0.02
0.00
20
0.025
0.67
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
0.07
0.00
20
0.023
2.84
0.00
0.00
0.95
0.00
Page 536 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Cascade Corp
(0812100207),
Springfield, OH
NPDES: OH0085715
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.00117
0.13
0.00
0.00
0.04
0.00
20
0.015
1.67
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
20
0.015
0.00983
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
20
0.014
0.11
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
0.04
0.00
20
0.014
1.69
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
20
0.007
0.15
0.00
0.00
0.05
0.00
Motor Components LLC,
Elmira, NY
NPDES: NY0004081
Surface
Water
NPDES
NY0004081
Surface
water
260
0.00096
0.0618
0.00
0.00
0.02
0.00
20
0.0125
0.83
0.00
0.00
0.28
0.00
Salem Tube Mfg,
Greenville, PA
NPDES: PA0221244
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000897
0.0997
0.00
0.00
0.03
0.00
20
0.012
1.33
0.00
0.00
0.44
0.00
Page 537 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility"
Release
Media b
Modeled
Facility or
Industry
Sector in
EFAST c
EFAST
Waterbody
Typell
Days of
Release1
Release
(kg/day)f
7Q10
swe
(ppb) b
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
GE (Greenville) Gas
Turbines LLC,
Greenville, SC
NPDES: SC0003484
Surface
Water
NPDES
SC0003484
Surface
water
260
0.000806
0.0821
0.00
0.00
0.03
0.00
20
0.01
1.02
0.00
0.00
0.34
0.00
Parker Hannifin
Corporation,
Waverly, OH
NPDES: OH0104132
Surface
Water
Primary Metal
Forming
Manuf.
Surface
water
260
0.000747
0.083
0.00
0.00
0.03
0.00
20
0.01
1.11
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
0.01
0.00
20
0.01
0.45
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
20
0.01
0.0962
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
20
0.023
0.11
0.00
0.00
0.04
0.00
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
20
0.008
0.015
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
20
0.008
0.00278
0.00
0.00
0.00
0.00
Page 538 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
PPb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Remington Anns 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
20
0.008
0.0104
0.00
0.00
0.00
0.00
United Teclinologies
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
20
0.006
0.00103
0.00
0.00
0.00
0.00
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
20
0.006
0.0373
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
20
0.004
0.0694
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
20
0.004
0.0272
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
0.01
0.00
20
0.003
0.23
0.00
0.00
0.08
0.00
Page 539 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb) s
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
20
0.003
0.0102
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
0.01
0.00
20
0.003
0.19
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
0.01
0.00
20
0.002
0.22
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
20
0.002
0.0111
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
20
0.001
0.11
0.00
0.00
0.04
0.00
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
20
0.001
0.015
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
20
0.001
0.11
0.00
0.00
0.04
0.00
Page 540 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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),
Scottsboro, AL
NPDES: AL0069701
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 541 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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.
Hitaclii 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: Adhesives, Sealants, Paints, and Coatings
Page 542 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
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
20
0.00407
0.0889
0.00
0.00
0.03
0.00
Ls Starrett Co,
Athol, MA
NPDES: MAR05B615
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.
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Clayton Homes Inc,
Oxford, NC
Surface
Water
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
Page 543 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility"
Release
Media b
Modeled
Facility or
Industry
Sector in
EFAST c
EFAST
Waterbody
Typell
Days of
Release1
Release
(kg/day)f
7Q10
swe
(ppb) b
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
NPDES: Not available
POTW
Adhesives and
Sealants
Manuf.
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.37
0.00
20
0.16
13.5
0.00
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
0.11
0.00
Green Bay Packaging Inc -
Coon Rapids,
Coon Rapids, MN
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 544 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.06
0.00
20
0.16
2.26
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
0.11
0.00
Parker Hannifin Corp -
Paraflex Division
Manitowoc, WI
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
Manuf.
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Ro-Lab Rubber Company
Inc.,
Surface
Water
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
Page 545 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Tracy, CA
NPDES: Not available
POTW
Adhesives and
Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Acme Finishing Co Lie,
Elk Grove Village, IL
Surface
Water
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
Page 546 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
NPDES: Not available
POTW
Adhesives and
Sealants
Manuf.
250
0.013
0.32
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
20
0.16
0.0101
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
0.11
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.02
0.00
20
0.16
0.78
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
0.11
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 547 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Corpus Christi Army
Depot,
Corpus Christi, TX
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Florida Production
Engineering, Inc.,
Onnond Beach, FL
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Kirtland Air Force Base,
250
0.013
1.67
0.00
0.00
0.56
0.00
Page 548 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb) s
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Albuquerque, NM
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 549 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility"
Release
Media b
Modeled
Facility or
Industry
Sector in
EFAST c
EFAST
Waterbody
Typell
Days of
Release1
Release
(kg/day)f
7Q10
swe
(ppb) b
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.00
0.01
3.61
0.00
20
0.16
133.33
0.04
0.17
44.44
0.00
POTW
No info on
receiving
facility;
Adhesives and
Sealants
Manuf.
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 550 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Thomas & Betts,
Albuquerque, NM
NPDES: Not available
Surface
Water
Adhesives and
Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
OES: Other Industrial Uses
Page 551 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
3.01
0.00
20
19.41
113.09
0.04
0.14
37.70
0.00
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
0.16
0.00
20
1.854
5.98
0.00
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
2.51
0.00
20
0.399
94.12
0.03
0.12
31.37
0.00
Solvay - Houston Plant,
Houston TX
NPDES: TX0007072
Surface
Water
NPDES
TX0007072
Surface
water
350
0.024
4.44
0.00
0.01
1.48
0.00
20
0.414
75.93
0.02
0.10
25.31
0.00
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
20
0.274
0.0322
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
0.24
0.00
20
0.242
8.91
0.00
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
0.07
0.00
20
0.128
2.63
0.00
0.00
0.88
0.00
Page 552 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb) s
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Whelan Energy Center
Power Plant,
Hastings, NE
NPDES:NE0113506
Surface
Water
NPDES
NE0113506
Surface
water
250
0.009
2.92
0.00
0.00
0.97
0.00
20
0.118
38.96
0.01
0.05
12.99
0.00
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
20
0.006
0.0125
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
20
0.006
0.00176
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
0.01
0.00
20
0.004
0.74
0.00
0.00
0.25
0.00
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
20
0.0029
0.00154
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
20
0.0028
0.0051
0.00
0.00
0.00
0.00
Page 553 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Keeshan and Bost
Chemical Co., Inc.,
Manvel, TX
NPDES: TX0072168
Surface
Water
NPDES
TX0072168
Still body
350
0.000095
9.5
0.00
0.01
3.17
0.00
20
0.002
200
0.06
0.25
66.67
0.00
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
20
0.00133
0.18
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
20
0.0012
0.13
0.00
0.00
0.04
0.00
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,
Sulphur, LA
NPDES: LA0069850
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.
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,
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 554 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Los Angeles County, CA
NPDES: CA0059188
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: 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
20
0.001
0.0485
0.00
0.00
0.02
0.00
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.
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: Industrial Processing Aid
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
0.05
0.00
20
0.292
2.2
0.00
0.00
0.73
0.00
Stepan Co Millsdale Road,
Elwood, IL
Surface
Water
NPDES
IL0002453
Surface
water
300
0.001
0.000419
0.00
0.00
0.00
0.00
Page 555 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb) s
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
NPDES: IL0002453
20
0.008
0.00335
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
3.10
0.00
20
5.65
138.34
0.04
0.18
46.11
0.00
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
0.05
0.00
20
0.115
2.32
0.00
0.00
0.77
0.00
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
20
0.07
0.2
0.00
0.00
0.07
0.00
Darainic LLC,
Corydon, IN
NPDES: IN0020893
Surface
Water
NPDES
IN0020893
Surface
water
300
0.008
0.0206
0.00
0.00
0.01
0.00
20
0.114
0.29
0.00
0.00
0.10
0.00
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
20
0.0025
0.0365
0.00
0.00
0.01
0.00
OES: Other Commercial Uses
Corning Hospital,
250
0.013
0.0271
0.00
0.00
0.01
0.00
Page 556 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
Corning, NY
NPDES: NY0246701
Surface
Water
Surrogate
NPDES
NY0025721
Surface
water
20
0.159
0.33
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
20
0.035
0.0658
0.00
0.00
0.02
0.00
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
0.03
0.00
20
0.00499
1.1
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
0.02
0.00
20
0.00354
0.77
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
3.00
0.00
20
0.00334
110
0.03
0.14
36.67
0.00
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.
Page 557 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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: 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
0.00
0.01
3.92
0.00
20
0.047
138.24
0.04
0.18
46.08
0.00
Reserve Environmental
Services,
Ashtabula, OH
NPDES: OH0098540
Surface
Water
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
0.95
0.00
20
301.78
35.72
0.01
0.05
11.91
0.00
Clean Harbors Deer Park
LLC,
Off-site
Waste-
POTW (Ind.)
Surface
water
250
0.35
8.57
0.00
0.01
2.86
0.00
Page 558 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
La Porte, TX
NPDES: TX0005941
water
Treatment
20
4.36
106.75
0.03
0.14
35.58
0.00
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
0.33
0.00
20
0.455
11.26
0.00
0.01
3.75
0.00
OES: Wastewater 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
0.23
0.00
20
0.786
12.79
0.00
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
0.06
0.00
20
0.299
3.11
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
0.20
0.00
20
0.176
10.97
0.00
0.01
3.66
0.00
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
20
0.083
0.11
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
0.09
0.00
20
0.035
4.51
0.00
0.01
1.50
0.00
Page 559 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location, and ID
of Active Releaser
Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute
RQs
(using
COC of
3,200
ppb)
Chronic
RQs (using
fish COC
of 788
ppb)
Algae RQs
(using COC of
3 ppb)
Algae RQs
(using COC of
52,000 ppb)
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
0.09
0.00
20
0.032
4.12
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
20
0.0067
0.034
0.00
0.00
0.01
0.00
Cameron Trading Post
WWTP,
Cameron, AZ
NPDES: NN0021610
Surface
Water
POTW (Ind.)
Surface
water
365
0.0003
0.0387
0.00
0.00
0.01
0.00
20
0.0047
0.64
0.00
0.00
0.21
0.00
Coal Grove WWTP,
Coal Grove, OH
NPDES: OH0104558
Surface
Water
NPDES
OH0029432
Surface
water
365
0.0002
0.0000127
0.00
0.00
0.00
0.00
20
0.0031
0.00019
0.00
0.00
0.00
0.00
335
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFAST c
EFAST
Waterbody
Type d '
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb) s
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
2.43
0.00
Page 560 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
250
0.00033
0.00716
0.00
0.00
0.00
0.00
Garlock Sealing
Technologies,
Surface
NPDES
Surface
Palmyra, NY,
NPDES: NY0000078
Water
NY0000078
water
20
0.00407
0.0889
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,
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Inc.,
Water
20
0.16
20.57
0.01
0.03
6.86
0.00
East Camden AR
NPDES:
AR0051071,
ARR00A521,
ARR00A520
Adhesives
and Sealants
Surface
water
POTW
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Best One Tire &
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Service,
Water
Adhesives
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Nashville, TN
NPDES: Not
available
POTW
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Bridgestone Aircraft
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Tire (Usa), Inc.,
Water
Adhesives
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Mayodan, NC
NPDES: Not
available
POTW
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Clayton Homes Inc,
Oxford, NC
Surface
Adhesives
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
Water
and Sealants
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 561 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
NPDES: Not
available
Cmh Manufacturing,
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Inc.
Water
20
0.16
20.57
0.01
0.03
6.86
0.00
Dba Schult Homes -
Plant 958,
Richfield, NC
NPDES: Not
POTW
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Surface
NPDES
250
0.013
1.1
0.00
0.00
0.37
0.00
Delphi Thermal
Systems,
Lockport, NY
NPDES: NY0000558
Water
NY0000558
20
0.16
13.5
0.00
0.02
4.50
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Green Bay Packaging
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Inc - Coon Rapids,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Coon Rapids, MN
NPDES: Not
available
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Mastercraft Boat
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Company,
Water
Adhesives
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Vonore, TN
NPDES: Not
available
POTW
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Michelin Aircraft
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Tire Company,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Norwood, NC
NPDES: Not
available
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
M-Tek, Inc,
Surface
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Manchester, TN
Water
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Page 562 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Seelor in
I I AS 1 '
F.I-AST
W;i(erl>od\
Tj pe
DjIJS ol
Release'
Release
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Acute HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Algae UQs
(using COC"
of 3 |)|)l>)
Algae UQs
(using COC
ol'52.000
pph)
NPDES: Not
available
POTW
Adhesives
and Sealants
Manuf.
250
0.013
0.32
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
0.06
0.00
20
0.16
2.26
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
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Royale Comfort
Seating, Inc. - Plant
No. 1,
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 563 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Taylorsville, NC
NPDES: Not
available
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 564 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Aerojet Rocketdyne,
Inc.,
Rancho Cordova, CA
NPDES: CA0004111
Surface
Water
NPDES
CA0004111
Surface
water
250
0.013
0.00081
8
0.00
0.00
0.00
0.00
20
0.16
0.0101
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
0.11
0.00
Allegheny Cnty
Airport Autli/
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.02
0.00
20
0.16
0.78
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
0.11
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
250
0.013
1.67
0.00
0.00
0.56
0.00
Page 565 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Seelor in
I I AS 1 '
F.I-AST
W;i(erl>od\
Tj pe
DjIJS ol
Release'
Release
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Acute HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Algae UQs
(using COC"
of 3 |)|)l>)
Algae UQs
(using ('()(
ol'52.000
pph)
Corpus CMsti Army
Depot,
Surface
Water
Adhesives
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Corpus CMsti, TX
NPDES: Not
POTW
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Electronic Data
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Systems
Water
20
0.16
20.57
0.01
0.03
6.86
0.00
Camp Pendleton,
Camp Pendleton, CA
Adhesives
and Sealants
Surface
water
NPDES: Not
POTW
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Florida Production
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Engineering, Inc.,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Ormond Beach, FL
NPDES: Not
available
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Goodrich
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Corporation,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Jacksonville, FL
Surface
NPDES: Not
available
POTW
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Kasai North America
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Inc,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Madison Plant,
Surface
Madison, MS
NPDES: Not
POTW
water
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Kirtland Air Force
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Base,
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Albuquerque, NM
NPDES: Not
available
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
250
0.013
1.67
0.00
0.00
0.56
0.00
Page 566 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Marvin Windows &
Doors,
Surface
Water
Adhesives
and Sealants
20
0.16
20.57
0.01
0.03
6.86
0.00
Warroad, MN
NPDES: Not
available
Surface
water
POTW
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Mcneilus Truck &
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Manufacturing Inc.
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Dodge Center, MN
NPDES: Not
available
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Metal Finishing Co. -
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Wichita (S Mclean
Water
Adhesives
and Sealants
Manuf.
20
0.16
20.57
0.01
0.03
6.86
0.00
Blvd),
Wichita, KS
NPDES: Not
POTW
Surface
water
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Murakami
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Manufacturing Usa
Water
Adhesives
and Sealants
20
0.16
20.57
0.01
0.03
6.86
0.00
Inc, Campbellsville,
KY
NPDES: Not
Surface
water
POTW
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
available
Peterbilt Motors
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Denton Facility,
Water
20
0.16
20.57
0.01
0.03
6.86
0.00
Denton, TX
Adhesives
and Sealants
Manuf.
NPDES: Not
Surface
available
POTW
water
250
0.013
0.32
0.00
0.00
0.11
0.00
Portsmouth Naval
Surface
250
0.013
1.67
0.00
0.00
0.56
0.00
Shipyard,
Water
Adhesives
Surface
water
20
0.16
20.57
0.01
0.03
6.86
0.00
Kittery, ME
NPDES: Not
available
POTW
and Sealants
Manuf.
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 567 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.00
0.01
3.61
0.00
20
0.16
133.33
0.04
0.17
44.44
0.00
POTW
No info on
receiving
facility;
Adhesives
and Sealants
Manuf.
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Sapa Precision
Tubing Rockledge,
Lie,
Rockledge, FL
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Page 568 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
NPDES: Not
available
Thomas & Betts,
Albuquerque, NM
NPDES: Not
available
Surface
Water
Adhesives
and Sealants
Manuf.
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
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
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
POTW
250
0.013
0.32
0.00
0.00
0.11
0.00
Viracon Inc,
Owatonna, MN
Surface
Water
Surface
water
250
0.013
1.67
0.00
0.00
0.56
0.00
20
0.16
20.57
0.01
0.03
6.86
0.00
Page 569 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
NPDES: Not
available
POTW
Adhesives
and Sealants
Manuf.
250
0.013
0.32
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
20
0.0025
0.0365
0.00
0.00
0.01
0.00
OES: Industrial Processing Aid
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
0.05
0.00
20
0.292
2.2
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
0.00041
9
0.00
0.00
0.00
0.00
20
0.008
0.00335
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
3.10
0.00
20
5.65
138.34
0.04
0.18
46.11
0.00
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
0.05
0.00
20
0.115
2.32
0.00
0.00
0.77
0.00
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
20
0.07
0.2
0.00
0.00
0.07
0.00
Darainic LLC,
Corydon, IN
NPDES: IN0020893
Surface
Water
NPDES
IN0020893
Surface
water
300
0.008
0.0206
0.00
0.00
0.01
0.00
20
0.114
0.29
0.00
0.00
0.10
0.00
Page 570 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
20
22.15
0.0897
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
0.81
0.00
20
1.2
42.14
0.01
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
0.18
0.00
20
0.265
9.48
0.00
0.01
3.16
0.00
Surface
Water
Organic
Chemicals
Manuf.
Surface
water
350
0.015
2.77
0.00
0.00
0.92
0.00
20
0.265
49.91
0.02
0.06
16.64
0.00
Occidental Chemical
Corp Wichita,
Wichita, KS
NPDES: KS0096903
and Organic Chem
MFG SIC
Surface
Water
Surrogate
NPDES
KS0043036
Surface
water
350
0.015
0.07
0.00
0.00
0.02
0.00
20
0.265
1.33
0.00
0.00
0.44
0.00
Off-site
Waste-
water
Treatment
Organic
Chemicals
Manuf.
Surface
water
350
0.015
0.53
0.00
0.00
0.18
0.00
20
0.265
9.48
0.00
0.01
3.16
0.00
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
0.23
0.00
20
0.786
12.79
0.00
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
0.06
0.00
20
0.299
3.11
0.00
0.00
1.04
0.00
Sullivan WWTP,
365
0.01
0.61
0.00
0.00
0.20
0.00
Page 571 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Sullivan, MO
NPDES:
MOO 104736
Surface
Water
NPDES
M00104736
Surface
water
20
0.176
10.97
0.00
0.01
3.66
0.00
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
20
0.083
0.11
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
0.09
0.00
20
0.035
4.51
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
0.09
0.00
20
0.032
4.12
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
20
0.0067
0.034
0.00
0.00
0.01
0.00
Cameron Trading
Post WWTP,
Cameron AZ
NPDES: NN0021610
Surface
Water
POTW (Ind.)
Surface
water
365
0.0003
0.0387
0.00
0.00
0.01
0.00
20
0.0047
0.64
0.00
0.00
0.21
0.00
Coal Grove WWTP,
Coal Grove, OH
NPDES: OH0104558
Surface
Water
NPDES
OH0029432
Surface
water
365
0.0002
0.00001
27
0.00
0.00
0.00
0.00
20
0.0031
0.00019
0.00
0.00
0.00
0.00
OES: Other Commercial Uses
Corning Hospital,
250 0.013
0.0271
0.00
0.00
0.01
0.00
Page 572 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Sector in
I I AS 1 '
F.I-AST
W;i(crhod\
Tj pc
DjIJS ol
Kclc.isc'
Kclc.isc
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Aculc HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
\l»;io UQs
(iisinii COC"
ol'3 |)|)h)
Al»iie UQs
(usiiiii ('()(
ol'52.000
pph)
Corning, NY
NPDES: NY0246701
Surface
Water
Surrogate
NPDES
NY0025721
Surface
water
20
0.159
0.33
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
20
0.035
0.0658
0.00
0.00
0.02
0.00
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
0.03
0.00
20
0.00499
1.1
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
0.02
0.00
20
0.00354
0.77
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
3.00
0.00
20
0.00334
110
0.03
0.14
36.67
0.00
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 573 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
3.01
0.00
20
19.41
113.09
0.04
0.14
37.70
0.00
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
0.16
0.00
20
1.854
5.98
0.00
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
2.51
0.00
20
0.399
94.12
0.03
0.12
31.37
0.00
Solvay - Houston
Plant,
Houston TX
NPDES: TX0007072
Surface
Water
NPDES
TX0007072
Surface
water
350
0.024
4.44
0.00
0.01
1.48
0.00
20
0.414
75.93
0.02
0.10
25.31
0.00
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
20
0.274
0.0322
0.00
0.00
0.01
0.00
Leroy Quarry,
250
0.019
0.71
0.00
0.00
0.24
0.00
Page 574 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Leroy, NY
NPDES: NY0247189
Surface
Water
Surrogate
NPDES
Surface
water
20
0.242
8.91
0.00
0.01
2.97
0.00
NY0030546
George C Marshall
Surrogate
NPDES
AL0025585
250
0.01
0.2
0.00
0.00
0.07
0.00
Space Flight Center,
Huntsville, AL
NPDES: AL0000221
Surface
Water
Surface
water
20
0.128
2.63
0.00
0.00
0.88
0.00
Whelan Energy
250
0.009
2.92
0.00
0.00
0.97
0.00
Center Power Plant,
Surface
NPDES
Surface
Hastings, NE
Water
NE0113506
water
20
0.118
38.96
0.01
0.05
12.99
0.00
NPDES: NE0113506
Anny Cold Regions
Research &
Surface
Water
Surrogate
Surface
water
250
0.0002
0.00010
3
0.00
0.00
0.00
0.00
Engineering Lab,
Hanover, NH
NPDES
NHO100099
20
0.0029
0.00154
0.00
0.00
0.00
0.00
NPDES: NH0001619
Corning - Canton
Surrogate
NPDES
NY0034762
250
0.0002
0.00034
0.00
0.00
0.00
0.00
Plant,
Surface
Surface
Canton, NY
Water
water
20
0.0028
0.0051
0.00
0.00
0.00
0.00
NPDES: NY0085006
Ames Rubber Corp
250
0.00011
0.0149
0.00
0.00
0.00
0.00
Plant #1,
Hamburg Boro, NJ
NPDES: NJG000141
Surface
Water
Surrogate
NPDES
NJ0000141
Surface
water
20
0.00133
0.18
0.00
0.00
0.06
0.00
Gorham
250
0.0001
0.0129
0.00
0.00
0.00
0.00
Providence, RI
Surface
POTW (Ind.)
Surface
NPDES: RIG85E004
Water
water
20
0.0012
0.13
0.00
0.00
0.04
0.00
Akzo Nobel Surface
Chemistry LLC,
350
0.000329
0.00068
8
0.00
0.00
0.00
0.00
Morris, IL
Surface
NPDES
Surface
NPDES: IL0026069
Water
IL0026069
water
20
0.006
0.0125
0.00
0.00
0.00
0.00
Solutia Nitro Site,
Surface
Surface
350
0.000318
0.00009
0.00
0.00
0.00
0.00
Nitro, WV
Water
water
41
Page 575 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
NPDES:
WV0116181
Surrogate
NPDES
WV0023229
20
0.006
0.00176
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
0.01
0.00
20
0.004
0.74
0.00
0.00
0.25
0.00
Keeshan and Bost
Chemical Co., Inc.,
Manvel, TX
NPDES: TX0072168
Surface
Water
NPDES
TX0072168
Still body
350
0.000095
9.5
0.00
0.01
3.17
0.00
20
0.002
200
0.06
0.25
66.67
0.00
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).
Emerson Power
Transmission
Ithaca, NY
NPDES: NY0002933
Surface
Water
Not assessed (below the min risk level).
William E. Warne
Power Plant,
Los Angeles County,
CA
NPDES: CA0059188
Surface
Water
Not assessed (below the min risk level).
Raytheon Aircraft
Co(Was Beech
Aircraft), Boulder,
CO
Surface
Water
Not assessed (below the min risk level).
Page 576 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
0.01
0.00
20
0.067
0.25
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
0.01
0.00
20
0.029
0.62
0.00
0.00
0.21
0.00
Ainetek 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
0.02
0.00
20
0.011
0.68
0.00
0.00
0.23
0.00
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
20
0.0061
0.0373
0.00
0.00
0.01
0.00
Handy & Hannan
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
0.24
0.97
255.21
0.01
20
25.44
9937.5
3.11
12.61
3312.50
0.19
260
0.13
10.97
0.00
0.01
3.66
0.00
Page 577 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Seelor in
I I AS 1 '
r.i- \s i
W;i(erl>od\
Tj pe
DjIJS ol
Release'
Release
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Acute HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Algae UQs
(using COC"
of 3 |)|)h)
Algae UQs
(usiiiii ('()(
ol'52.000
pph)
GM Components
Holdings LLC,
Lockport, NY
NPDES: NY0000558
Surface
Water
NPDES
NY0000558
Surface
water
20
1.71
144.47
0.05
0.18
48.16
0.00
Akebono
Elizabethtown Plant,
Elizabethtown, KY
NPDES: KY0089672
Surface
Water
Surrogate
NPDES
KY0022039
Surface
water
260
0.07
4.87
0.00
0.01
1.62
0.00
20
0.897
62.38
0.02
0.08
20.79
0.00
Delphi Harrison
Thermal Systems,
Dayton, OH
NPDES: OH0009431
Surface
Water
NPDES
OH0009431
Surface
water
260
0.04
0.0752
0.00
0.00
0.03
0.00
20
0.465
0.87
0.00
0.00
0.29
0.00
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
20
0.334
0.0335
0.00
0.00
0.01
0.00
Equistar Chemicals
LP,
La Porte, TX
NPDES: TXO119792
Surface
Water
Primary
Metal
Forming
Manuf.
Surface
water
260
0.02
2.22
0.00
0.00
0.74
0.00
20
0.218
24.44
0.01
0.03
8.15
0.00
GE Aviation,
Lynn, MA
NPDES:
MA0003905
Surface
Water
NPDES
MA0003905
Still
water
260
0.01
0.0425
0.00
0.00
0.01
0.00
20
0.128
0.54
0.00
0.00
0.18
0.00
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
0.37
0.00
20
0.107
11.89
0.00
0.02
3.96
0.00
GM Components
Holdings LLC
Kokomo Ops,
Surface
Water
NPDES
IN0001830
Surface
water
260
0.01
0.2
0.00
0.00
0.07
0.00
20
0.086
1.73
0.00
0.00
0.58
0.00
Page 578 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Kokomo, IN
NPDES: IN0001830
Amphenol Corp-
Aerospace
Operations,
Sidney, NY
NPDES: NY0003824
Surface
Water
NPDES
NY0003824
Surface
water
260
0.01
0.0486
0.00
0.00
0.02
0.00
20
0.082
0.4
0.00
0.00
0.13
0.00
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
20
0.081
0.00522
0.00
0.00
0.00
0.00
Olean Advanced
Products,
Olean. NY
NPDES: NY0073547
Surface
Water
Surrogate
NPDES
NY0027162
Surface
water
260
0.01
0.0188
0.00
0.00
0.01
0.00
20
0.068
0.13
0.00
0.00
0.04
0.00
Hollingsworth Saco
Lowell,
Easley, SC
NPDES: SC0046396
Surface
Water
Primary
Metal
Forming
Manuf.
Surface
water
260
0.00469
0.52
0.00
0.00
0.17
0.00
20
0.061
6.78
0.00
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
0.59
0.00
20
0.047
23.04
0.01
0.03
7.68
0.00
TiinkenUs Corp
Honea Path,
Honea Path. SC
NPDES: SC0047520
Surface
Water
Surrogate
NPDES
SC0000698
Surface
water
260
0.00355
1.06
0.00
0.00
0.35
0.00
20
0.0462
13.77
0.00
0.02
4.59
0.00
Johnson Controls
Incorporated,
Wichita, KS
NPDES: KS0000850
Surface
Water
NPDES
KS0000850
Surface
water
260
0.00228
0.0548
0.00
0.00
0.02
0.00
20
0.0296
0.72
0.00
0.00
0.24
0.00
Page 579 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
0.08
0.00
20
0.026
2.89
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
0.01
0.00
20
0.026
0.22
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
0.02
0.00
20
0.025
0.67
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
0.07
0.00
20
0.023
2.84
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
0.04
0.00
20
0.015
1.67
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
0.00075
3
0.00
0.00
0.00
0.00
20
0.015
0.00983
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
20
0.014
0.11
0.00
0.00
0.04
0.00
Page 580 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Seelor in
I I AS 1 '
r.i- \s i
W;i(erl>od\
Tj pe
DjIJS ol
Release'
Release
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Acute HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Algae UQs
(using COC"
of 3 |)|)h)
Algae UQs
(using COC
ol'52.000
pph)
Eaton Mdh Company
Inc,
Kearney, NE
NPDES: NE0114405
Surface
Water
Surrogate
NPDES
NE0052647
Still
water
260
0.00107
0.13
0.00
0.00
0.04
0.00
20
0.014
1.69
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
20
0.007
0.15
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
0.02
0.00
20
0.0125
0.83
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
0.03
0.00
20
0.012
1.33
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
0.03
0.00
20
0.01
1.02
0.00
0.00
0.34
0.00
Parker Hannifin
Corporation,
Waverly, OH
NPDES: OH0104132
Surface
Water
Primary
Metal
Forming
Manuf.
Surface
water
260
0.000747
0.083
0.00
0.00
0.03
0.00
20
0.01
1.11
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
0.01
0.00
20
0.01
0.45
0.00
0.00
0.15
0.00
General Electric
Company -
Waynesboro,
Waynesboro, VA
Surface
Water
NPDES
VA0002402
Surface
water
260
0.000733
0.00705
0.00
0.00
0.00
0.00
20
0.01
0.0962
0.00
0.00
0.03
0.00
Page 581 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Sector in
I I AS 1 '
r.i- \s i
W;i(crhod\
Tj pc
DjIJS ol
Kclc.isc'
Kclc.isc
(kii/d;i>)1
¦'ym
s\\<
(|)|)b)"
Aculc HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Algae UQs
(using COC"
of 3 |)|)h)
Al»iie UQs
(usiiiii ('()(
ol'52.000
pph)
NPDES: VA0002402
Globe Engineering
260
0.00173
0.00853
0.00
0.00
0.00
0.00
Co Inc,
Wichita, KS
NPDES: KS0086703
Surface
Water
Surrogate
NPDES
KS0043036
Surface
water
20
0.023
0.11
0.00
0.00
0.04
0.00
Gayston Corp,
Surface
Water
Surrogate
Surface
water
260
0.000643
0.00121
0.00
0.00
0.00
0.00
Dayton, OH
NPDES: OHO 127043
NPDES
OH0024881
20
0.008
0.015
0.00
0.00
0.01
0.00
Styrolution America
LLC,
Surface
NPDES
Surface
260
0.000637
0.00022
1
0.00
0.00
0.00
0.00
Channahon, IL
NPDES: IL0001619
Water
IL0001619
water
20
0.008
0.00278
0.00
0.00
0.00
0.00
Remington Arms Co
Inc,
260
0.000612
0.00079
0.00
0.00
0.00
0.00
Surface
NPDES
Surface
Ilion, NY
NPDES: NY0005282
Water
NY0005282
water
20
0.008
0.0104
0.00
0.00
0.00
0.00
United Technologies
Corporation, Pratt
260
0.00048
0.00008
22
0.00
0.00
0.00
0.00
And Whitney
Surface
NPDES
Surface
Division,
East Hartford, CT
Water
CT0001376
water
20
0.006
0.00103
0.00
0.00
0.00
0.00
NPDES: CT0001376
Atk-Allegany
260
0.00047
0.00292
0.00
0.00
0.00
0.00
Ballistics Lab
(Nirop),
Keyser, WV
NPDES:
Surface
Water
NPDES
WV0020371
Surface
water
20
0.006
0.0373
0.00
0.00
0.01
0.00
WV0020371
Sperry & Rice
260
0.000328
0.00569
0.00
0.00
0.00
0.00
Manufacturing Co
LLC,
Brookville, IN
Surface
NPDES
Surface
Water
IN0001473
water
20
0.004
0.0694
0.00
0.00
0.02
0.00
NPDES: IN0001473
Page 582 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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
20
0.004
0.0272
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
0.01
0.00
20
0.003
0.23
0.00
0.00
0.08
0.00
Mccanna Inc.,
Carpentersville, IL
NPDES: IL0071340
Surface
Water
Surrogate
NPDES
IL0027944
Surface
water
260
0.000268
0.00091
1
0.00
0.00
0.00
0.00
20
0.003
0.0102
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
0.01
0.00
20
0.003
0.19
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
0.01
0.00
20
0.002
0.22
0.00
0.00
0.07
0.00
Troxel Company,
Moscow, TN
NPDES: TN0000451
Surface
Water
NPDES
TN0000451
Surface
water
260
0.000134
0.00074
1
0.00
0.00
0.00
0.00
20
0.002
0.0111
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
20
0.001
0.11
0.00
0.00
0.04
0.00
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
20
0.001
0.015
0.00
0.00
0.01
0.00
Page 583 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. I.oi'iilion.
iiml II) ol' \cli\c
Kck'.iscr l-';icilil> ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Sector in
I I AS 1 '
F.I-AST
W;i(crhod\
Tj pc
DjIJS ol
Kclc.isc'
Kclc.isc
(kii/d;i>)1
¦'ym
s\\<
(|)|)h)"
Aculc HQs
(using
COC of
3.200 |)|)h)
('limine
UQs (iisinvi
I'isli ( (K ill'
¦'SS |)|)h)
Al»;ie UQs
(iisinu COC"
ol'3 |)|)h)
Al»iie UQs
(iisinu ('()(
ol'52.000
pph)
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
20
0.001
0.11
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).
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).
Page 584 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
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).
Hitaclii Electronic
Devices (Usa), Inc.,
Greenville, SC
NPDES: SC0048411
Surface
Water
Not assessed (below the min risk level).
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
0.00
0.01
3.92
0.00
20
0.047
138.24
0.04
0.18
46.08
0.00
Reserve
Environmental
Services,
Ashtabula, OH
NPDES: OH0098540
Surface
Water
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;
Still body
250
24.1
2.85
0.00
0.00
0.95
0.00
20
301.78
35.72
0.01
0.05
11.91
0.00
Page 585 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
NPDES
NJ0020141
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
2.86
0.00
20
4.36
106.75
0.03
0.14
35.58
0.00
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
0.33
0.00
20
0.455
11.26
0.00
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
0.06
0.00
20
0.089
3.13
0.00
0.00
1.04
0.00
Surface
Water
Organic
Chemicals
Manufacture
Surface
water
350
0.005
0.92
0.00
0.00
0.31
0.00
20
0.089
16.45
0.01
0.02
5.48
0.00
Arkeina Inc.
Calvert City, KY
NPDES: KY0003603
Surface
Water
NPDES
KY0003603
Surface
water
350
0.017
0.00073
7
0.00
0.00
0.00
0.00
20
0.295
0.128
0.00
0.00
0.04
0.00
US DOE Paducah
Site,
Kevil, KY
NPDES: KY0102083
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
0.01
1.48
0.00
20
0.414
75.93
0.02
0.10
25.31
0.00
Page 586 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Honeywell
International -
Geismar Complex,
Geismar, LA
NPDES: LA0006181
Surface
Water
NPDES
LA0006181
Surface
water
350
0.0128
0.00005
18
0.00
0.00
0.00
0.00
20
0.224
0.00090
7
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
0.05
0.21
56.33
0.00
20
0.03
3000
0.94
3.81
1000.00
0.06
US DOE Paducah
Site,
Kevil, KY
NPDES: KY0102083
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).
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
9.06
0.00
20
13.85
339.11
0.11
0.43
113.04
0.01
250
0.003
6.52
0.00
0.01
2.17
0.00
Page 587 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Name, Location,
and ID of Active
Releaser Facility a
Release
Media b
Modeled
Facility or
Industry
Sector in
EFASTc
EFAST
Waterbody
Type d
Days of
Releasee
Release
(kg/day)f
7Q10
SWC
(ppb)g
Acute RQs
(using
COC of
3,200 ppb)
Chronic
RQs (using
fish COC of
788 ppb)
Algae RQs
(using COC
of 3 ppb)
Algae RQs
(using COC
of 52,000
ppb)
Oiltanking Houston
Inc.
Houston. TX
NPDES: TX0091855
Surface
Water
Surrogate
NPDES
TX0065943
Surface
water
20
0.041
89.13
0.03
0.11
29.71
0.00
St. Gabriel Terminal.
Saint Gabriel, LA
NPDES: LA0005487
Surface
Water
NPDES
LA0005487
Surface
water
250
0.0055
0.00002
23
0.00
0.00
0.00
0.00
20
0.069
0.00027
9
0.00
0.00
0.00
0.00
Vopak Terminal
Westwego Inc.
Westwego, LA
NPDES: LAO 124583
Surface
Water
Surrogate
NPDES
LA0042064
Surface
water
250
0.00468
0.00001
89
0.00
0.00
0.00
0.00
20
0.058
0.00023
5
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
20
0.001
0.0485
0.00
0.00
0.02
0.00
Page 588 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
NiiiiK*. l.oc;ilion.
iiml II) ol' \cli\c
Kck'siscr l";icilil\ ;|
Kok'iiso
Mediah
Modeled
l';icili(\ oi'
Indusln
Seelor in
I I AS 1 '
F.I-AST
\\;Mcrliod\
Tj po
l);i\s of
Kck'iiso'
Rck.ise
(kg/d;i>)1
"'QUI
SWC
(|)|)b)"
Aculi* HQs
(using
COC of
3.200 |)|)h)
Chronic
UQs (iisillvi
lisli ( (K ill'
¦'XX |)|)h)
Algsie UQs
(using COC
of 3 |)|)b)
Algsie UQs
(using COC
ol' 52.000
ppli)
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, as well as direct releases from WWTPs.
c. If a valid NPDES of the direct or indirect releaser was not available in EFAST, the release was modeled using either a surrogate representative facility in EFAST
(based on location) or a 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.
336
Page 589 of 748
-------
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix F BENCHMARK DOSE ANALYSIS FOE (Si
)
F.l BMDS Wizard Output Report - Mortality
The benchmark dose (BMD) modeling of dichotomous data was conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version 1.11). All 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 %2 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.
F.l.l BMDS Summary of Mortality - BMR 10%
Table Apx F-l. Summary of BMD Modeling Results for Mortality from Introduced Infection in
Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra
Risk
Model"
Goodness of fit
BMDioPct
(ppm)
BMDLioPct
(ppm)
Basis for model selection
/>-value
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 3°°
Multistage 4od
0.177
344.14
39.9
27.9
Page 590 of 748
-------
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Multistage 5oe
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 Hie 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
FigureApx F-l. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
Model for Mortality from Introduced Infection in Mice Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra Risk
Probit Model. (Version: 3.4; Date: 5/21/2017)
The form of the probability function is: P[response] = Background + (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 591 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Variable
Estimate
Default Initial
Parameter Values
background
0.0281182
0.0338983
intercept
-5.1238E+00
-5.2930E+00
slope
1
1
387
388 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
389
390 AIC: = 338.719
391
392 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
393
394 ChiA2 = 3.78 d.f=5 P-value = 0.5818
395
396
Page 592 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
397 F.1.2 BMDS Summary of Mortality - BMR: 5%
398 Table Apx F-2. Summary of BMD Modeling Results for Mortality from Introduced Infection in
399 Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra
400 Risk
Model"
Goodness of fit
BMDspct
(ppm)
BMDLspct
(ppm)
Basis for model selection
/>-value
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 4oc
Multistage 5od
Multistage 6°e
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). Hie 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
<
J 0.3
BMDL BMD
401 dose
402 Figure Apx F-2. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
403 Model for Mortality from Introduced Infection in Mice Following Inhalation
404 Exposure to TCE (Selgrade and Gilmour 2010); BMR = 5% Extra Risk
405 Probit Model. (Version: 3.4; Date: 5/21/2017)
Page 593 of 748
-------
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The form of the probability function is: P[response] = Background + (1-Background) *
CumNorm(Intercept+Slope*Log(Dose)),where CumNormQ 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 594 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
427 F.1.3 BMDS Summary of Mortality - BMR: 1%
428 Table Apx F-3. Summary of BMD Modeling Results for Mortality from Introduced Infection in
429 Mice Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 1% Extra
430 Risk
Model"
Goodness of fit
BMDiPct
(ppm)
BMDLiPct
(ppm)
Basis for model selection
/>-value
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 5oe
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.
431
Page 595 of 748
-------
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
LogProbit Model, with BMR of 1% Extra Risk for the BMD and 0.95 Lower Confidence Limit for the BMDL
FigureApx F-3. Plot of Incidence by Dose (ppm) with Fitted Curve for Log-Probit
Model for Mortality from Introduced Infection in Mice Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR =1% Extra Risk
Probit Model. (Version: 3.4; Date: 5/21/2017)
The form of the probability function is: P[response] = Background + (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 596 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
455 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
456
457 ChiA2 = 3.78 d.f=5 P-value = 0.5818
458
459
Page 597 of 748
-------
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
F.2 BMDS Wizard Output Report - Number of Mice Infected
The benchmark dose (BMD) modeling of dichotomous data was conducted with the EPA's BMD
software (BMDS (version 2.7) via BMDS Wizard (version 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 %2 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.
F.2.1 BMDS Summary of Infected at 72 hours - BMR - 10%
Table Apx F-4. Summary of BMD Modeling Results for Number of Mice Infected at 72 Hours
after Infection Following Inhalation Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10%
Extra Risk
Model"
Goodness of fit
BMDioPct
(ppm)
BMDLioPct
(ppm)
Basis for model selection
/>-value
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
(5 gg
Weibull
0.189
23.606
±43
4-r2£
Multistage 2°
0.202
23.480
432
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 598 of 748
-------
488
489
490
491
492
493
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Probit Modei. with BMR of 10% Extra Risk for the BMD and 0-95 Lower Confidence Limit for the BMDL
TableApx F-5. Plot of Incidence by Dose (ppm) with Fitted Curve for Probit Model
for Number of Mice Infected at 72 Hours after Infection Following Inhalation
Exposure to TCE (Selgrade and Gilmour 2010); BMR = 10% Extra Risk
494
Page 599 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix G WEIGHT OF EVIDENCE FOR CONGENITAL HEART
DEFECTS
G.J_ EPA Review of the Charles River ( ) Study
G.l.l 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 with the stated purpose of replicating the findings of
(Dawson etai. 1993) and (Johnson et al. 2003). which observed increased cardiac malformations in the
fetuses of pregnant female Sprague Dawley rats administered TCE in drinking water.
The study 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 G-l, which is adapted from Text Table 4 in the study.
Table Apx G-l. Experimental Design
(Iroup
TiViilmcnl
Tsir»ei
(onccnlnilion
Route of
Ailminisli'iilion
Nil in ho r ol'
l-'cmsilcs (l)itms)
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 G-2 below, which is adapted from
Text Table 14 in the study, with a few small edits. The Charles River study described the statistical
estimate used as "summation per group (%)", which appears to be the sum of viable fetuses affected per
litter (%) / number of litters per group". EPA determined that while this method is appropriate, the
description is unclear and would be better described as "Mean % Affected / Litter per Group". EPA
therefore replaced the descriptor "% per litter" with the above descriptor. EPA also identified that the
Page 600 of 748
-------
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 G-2. Summary of C
Observed Interventricular E
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%
Size of Opening
(Number of
Fetuses)
2mm (1)
-------
549
550
551
552
553
554
555
556
557
558
559
560
561
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1.5 ppm
9/181
(5.0%)
5/13
(38.5%)
(Johnson et
aL 2003)
5/321
(1.5%)
3/24
(12.5%)
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
500 ppm
N/A
N/A
N/A
13/330
(3.9%)
8/24
(33.3%)
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
1000 (Charles River) or
1100 (Johnson) ppm
11/105
(10.5%)
6/9
(66.7%)
(Johnson et
aL 2003)
12/342
(3.5%)
6/24
(25.0%)
(Charles River
Laboratories. 2019).
Table 15
(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 Table Apx G-4, with the incidence of membranous VSDs displayed in parentheses.
Table Apx G-4. Incidence of VSDs in Johnson and Charles River studies.
Johnson 2003
Charles River 2019
Dose
% fetuses
affected
(mem. only)
Source
%
fetuses
affected
Source/Notes
0 ppm
0.66%
(0.33%)
(Johnson et al.. 2003).
Table 2
2.5%
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
2.5 ppb
0%
(Johnson et al. 2003).
Table 2
N/A
N/A
0.25 ppm
0%
(Johnson et al. 2003).
Table 2
1.4%
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
1.5 ppm
2.21%
(1.66%)
(Johnson et al. 2003).
Table 2
1.5%
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
500 ppm
N/A
N/A
3.9%
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
1000 (Charles River) or
1100 (Johnson) ppm
3.81%
(2.86%)
(Johnson et al. 2003).
Table 2
3.5%
(Charles River
Laboratories. 2019).
Table 15
(soft tissue), p. 86
Page 602 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
562 G.l.2.2 Differences in Types of Malformations Observed
563 The majority of cardiac malformations observed in the Johnson study were not VSDs (see Table 2 in
564 (Johnson et al.. 2003). while the Charles River study only identified VSDs in controls and TCE-treated
565 offspring. Of note, two major categories of heart malformations identified in the Johnson study that are
566 absent from even the positive control group of the Charles River study are atrial septal defects and valve
567 defects. The Charles River study methodology appeared to be focused primarily on identification of VSDs
568 over other heart defects, which may explain the observed positive bias toward detection of VSDs in
569 vehicle control and low-dose fetuses as compared to both the Johnson study and historical control data.
570 Table Apx G-5 compares the heart defects observed across all in vivo oral studies. Fisher at al. (20011 a
571 gavage study that also did not find a statistically significant association of TCE exposure with congenital
572 cardiac defects, is also included for comparison. Of note, the (Fisher et al.. 2001) study utilized the same
573 dissection and evaluation methodology as the (Johnson et al.. 2003) studies. There is substantial overlap in
574 the many type of defects identified in the three studies, while only membranous VSDs were observed in
575 TCE-treated animals in (Charles River Laboratories. 2019) (great blood vessel variation was identified in a
576 few TCE-treated pups but was considered incidental by the study authors). When comparing the results
577 from (Fisher et al.. 2001) and (Charles River Laboratories. 2019). EPA acknowledges that differences in
578 dosing method, vehicle volume, and other variables may also contribute to any observed differences.
579
58 0 Table Apx G-5. Heart and Cardiovascular Defects Observed in Oral TCE studies
Cardiac Malformations Observed Across Select Oral TCE and Retinoic Acid (RA) Developmental Toxicity Studies in Rats
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
Page 603 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Cardiac Malformations Observed Across Select Oral TCE and Retinoic Acid (RA) Developmental Toxicity Studies in Rats
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
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.
581
582 EPA's conclusion that the Charles River study insufficiently sensitive to non-VSD defects was supported
583 by the limited variety of malformations observed in the RA positive control based on a compiled literature
584 search:
585 1. EPA searched HERO and PubMed for studies investigating heart defects and malformations that
586 occur during prenatal exposure to all-trans retinoic acid (RA). Of the 37 studies reviewed, 12
587 studies were excluded from analysis because they were abstracts, book chapters, reviews, or
588 studies that did not expose animals to all-trans RA. Thus, EPA reviewed 25 studies and
589 compared the results of these studies to those reported by the Charles River and Johnson studies.
590 2. In all species examined, a total of 35 heart defects were associated with prenatal exposure to RA
591 in the identified literature.
592 3. The Charles River study reported 10 types of heart defects in animals exposed to RA.
593 4. Heart defects associated with TCE exposure partially overlap defects associated with RA
594 exposure. The Johnson study identified 10 types of cardiac defects in TCE-exposed fetuses.
595 Charles River only identified one defect (membranous VSDs) associated with TCE exposure
596 (major blood vessel variation was observed in 1-2 TCE-treated fetuses, but this effect was not
597 considered treatment-related).
598 5. All 35 defects associated with RA exposure were observed in rodents in the literature review. If
599 we limit the analysis to studies examining only rats, 31 of the total 35 defects were observed.
600 Only 6 of the 35 defects were noted in chickens, and 2 of the 35 were noted in zebrafish.
601 Therefore, the differences between defects captured in the Charles River study and the general
602 literature cannot be explained simply by inclusion of additional experimental species in the
603 general literature.
604
605 EPA therefore concludes that Charles River did not capture the entirety of cardiac defects that were
606 expected upon exposure to RA.
Page 604 of 748
-------
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
EPA searched HERO using the following keywords:
• Retinoic Acid
• Retinoic Acid + Cardiac
EPA also searched PubMed using the following keywords:
• retinoic acid (RA)-induced cardiac defects
• retinoic acid AND (cardiac defects OR cardiac malformations OR heart defects OR heart
malformations OR cardiac teratogenesis OR aorta OR ventricle OR endocardial cushion OR
pulmonary valve OR mitral valve OR aortic valve OR ventricular septum OR atrial septum OR
tricuspid valve OR aneurysm).
TableApx G-6 presents all of the cardiac defects found in the literature search.
TableApx G-7 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.
Table Apx G-6. Cardiac Defects O
jserved in Literature
Number of
( iinliiic IkTed
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
Page 605 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
628
629 Table Apx G-7. 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
VSDs2
a/
a/
a/
V (12)
C. H. M.
R
Atrium, Ventricle and Valve
Defects
Atrial Septal Defect
a/
V(5)
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
V(1)
R
Atrium, Ventricle and Valve
Defects
Tricuspid defects
a/
V(1)
H
Atrium, Ventricle and Valve
Defects
Aortic valve defects
a/3
V(1)
R
Atrium, Ventricle and Valve
Defects
Mitral valve defects
a/
Atrium, Ventricle and Valve
Defects
Right ventricular hypertrophy
V (2)
R
Atrium, Ventricle and Valve
Defects
Left ventriclular hypertrophy
V(1)
R
Atrium, Ventricle and Valve
Defects
Right atrial hypertrophy
V (2)
R
Atrium, Ventricle and Valve
Defects
Left atrial hypertrophy
V(1)
R
Atrium, Ventricle and Valve
Defects
Small ventricle
a/
Atrium, Ventricle and Valve
Defects
Complete Atrioventricular
Canal defect (CAVC)
a/
V(1)
R
Symmetry
Situs Inversus
a/
V(2)
C.R
Symmetry
Dextrocardia
V (5)
M.R
Symmetry
d-Transposition of the great
arteries
a/
a/(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)
a/
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
Page 606 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
Right aortic arch defects
(RAA)
a/
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
a/
V(1)
M
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
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
V
Other vessel defects
Right subclavian artery absent
V(1)
R
Other vessel defects
Pulmonary artery hypoplasia
a/
Other vessel defects
Coronary artery/sinus defects
V
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 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]).
630 G.l.2.3 Methodology Differences
631 There are likely several contributing factors explaining why the Charles River study failed to identify
632 atrial or valve defects. In the Johnson study, the materials and methods section described examination of
633 the internal structure of the heart for all fetuses. The dissection methodology allows detailed
634 examination of the atrial septum. In contrast, the Charles River study states that the fetal evaluation
635 methods were conducted according to Stuckhardt and Poppe (1984). which does not include
Page 607 of 748
-------
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
examination of atrial septal defects. Therefore, the methodology used by the Charles River study was
likely to miss this important category of cardiac malformations. As shown in
Table Apx G-7, five other studies were identified in the literature that observed atrial septal defects
following RA exposure, while none were observed in the Charles River study.
The Stuckhardt and Poppe method (1984) does includes visualization of the valves (the tricuspid, mitral,
aortic, and pulmonary valves) but the methods as described in the Johnson study and supporting
information are more likely to reveal valvular defects as compared to the Stuckhardt and Poppe
methodology. The Stuckhardt and Poppe method specifies that two cuts are made in the fresh fetal heart.
This allows visualization of the tricuspid valve, between the right atrium and right ventricle, the three
cusps of the semilunar valve of the pulmonary artery, and the interventricular septum. In comparison,
the Johnson study clearly specified that the fetal hearts were to be examined in situ for external defects
and then excised, preserved with glutaraldehyde, and dissected. The examination of the internal structure
of the heart for all fetuses specifically included removing tissue to expose the pulmonary, aortic,
tricuspid, and mitral valves. The location of the coronary ostium was noted, each valve was probed for
patency, and the formation of each valve leaflet was examined.
EPA believes that there is a certain amount of tissue elasticity in fresh fetal hearts that can obscure the
detection of valvular defects during fetal morphological evaluation. Because the Johnson study evaluated
the internal structure of the fetal hearts post-fixation, examination of the valvular structures would have
been facilitated. Additionally, valve defects may be overlooked during examination unless the technician
is directly focusing on evaluating the cardiac valves in all fetuses (not just those, for example, in which
external cardiac morphological differences, such as a collapsed ventricle, might suggest a potential valve
problem). No indication is given in the Charles River report whether a directed effort was made to
identify valvular abnormalities.
Other identified differences and uncertainties in the methodology between the two studies may or may
not have contributed to the differences in results. These factors could potentially make either the Johnson
or the Charles River data more precise. These include the following:
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
Page 608 of 748
-------
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
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
5. Source and strain of rats. The rats used in all the studies conducted as part of the TCE research
program at the University of Arizona that included (Dawson et al. 1993) and (Johnson et al.
2003) were Harlan Sprague-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 (Makris et al.. 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.
G.l.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
-------
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
of defect size. More significantly, the study concluded that muscular VSDs are much more likely to
close spontaneously than membranous VSDs (which were the only VSD type associated with TCE
exposure in the Charles River study). The incidence in humans of spontaneous closure in cited studies
examining only muscular VSDs ranges from 22% to 84%, while for studies examining only
membranous or perimembranous VSDs the incidence ranges from only 4% to 47%. Additionally, the
morphological characterization of closure of the membranous VSD seems to most commonly involve
the use of a leaflet of the tricuspid valve, which would be expected to impact the functional ability of
that heart valve. Therefore, even if a membranous VSD is able to spontaneously close, there are likely
functional impacts of that closer, resulting in an adverse health effect.
Overall, it is impossible to speculate whether the specific VSDs identified in these studies 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.
G.2 WOE Analysis for Congenital Cardiac Defects
G.2.1 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. 2005).
b. Gough. 2007. Weight of Evidence: A Framework for the Appraisal of the Quality and
Relevance of Evidence. Research Papers in Education 22(2): 213-228 (Gough. 2007).
c. Rhomberg et al. 2013. A survey of frameworks for best practices in weight-of-evidence
analyses. CritRev Toxicol 43(9): 753-784 (Rhomberg et al.. 2013).
d. Rooney et al. 2014. Systematic Review and Evidence Integration for Literature-Based
Environmental Health Science Assessments. Env Health Perspect 122 (7): 711-718
(Rooney et al. 2014).
e. NTP. 2015. Handbook for Conducting a Literature-Based Health Assessment Using
OHAT Approach for Systematic Review and Evidence Integration ( ).
f. EPA. 2016. Weight of Evidence in Ecological Assessment. Risk Assessment Forum.
EPA/100/R16/001 (U.S. EPA. 20161).
g. EPA. 2015. EDSP: Weight of Evidence Analysis of Potential Interaction with the
Estrogen, Androgen or Thyroid Pathways. Chemical: Glyphosate. Office of Pesticide
Programs (\ ^ \ :01 \t).
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. ).
Page 610 of 748
-------
lie
111
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 2016).
o. Bridges and Solomon. 2016. Quantitative weight-of-evidence analysis of the persistence,
bioaccumulation, toxicity, and potential for long-range transport of the cyclic volatile
methyl siloxanes. J Toxicol Environ Health Part B 19(8): 345-379 (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).
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).
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 ( Q16i). 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 ( 2016t) 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
Page 611 of 748
-------
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
859
860
861
862
863
864
865
866
867
868
869
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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-2019-0500], as follows:
a. The pieces of evidence are studies (or distinct experiments within studies). They are
organized into lines of evidence based on study type: epidemiological, in vivo animal),
and mechanistic. Within each line of evidence, pieces of evidence are further organized
into subsets based on route of exposure (oral, inhalation, other) and test material (TCE or
metabolite) for toxicological studies or vertebrate class of tissue, embryo or animal
studied (mammalian, avian, fish) for mechanistic studies. WOE determinations are made
in succession, first for subsets of a line of evidence, then for the full lines of evidence,
and then for the overall database, each building on the assessments that came before.
b. Each piece of evidence (study) was graded in 3 areas: reliability (quality),
outcome/strength, and relevance. The rationale for each grade was recorded.
i. Reliability is defined in ( 0161) as inherent properties that make
evidence convincing. For our implementation, because each piece of evidence is
a study, this refers primarily to aspects of study design, execution, and
transparency.
1. Possible scores for reliability were 0, +, ++, or +++ for unusable, low,
medium and high.
2. In contrast to the study quality evaluations performed in Distiller, which
included >20 specific quality criteria for each study, here each study was
given only a single overall grade. We considered the same issues, but we
did not formally go through and assign grades on each one individually.
Instead, focus was on key attributes. Noteworthy deficiencies were
recorded and grades were assigned based on the number and nature of the
specific deficiencies identified.
ii. Outcome/strength is defined in ( ji) 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 ( i) 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
Page 612 of 748
-------
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
904
905
906
907
908
909
910
911
912
913
914
915
916
917
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. 20.1.60. 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.
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-OPPT-2019-0500.]. Of
note, studies that scored Unacceptable in data quality evaluation were not considered in the WOE
analysis. Their evaluation is included for reference, but their scores had no impact on the overall grades
Page 613 of 748
-------
918
919
920
921
922
923
924
925
926
927
928
929
930
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
for each line of evidence or subset. Unacceptable studies are indicated by red text in the below tables
and the supplemental data table. Studies that were not rated (NR) because EPA determined that they
were not pertinent are indicated by blue text in the supplemental data table, however they are not
included in the tables below.
G.2.2 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
mechanistic studies. For this analysis, the three lines of evidence will be considered both individually
and collectively.
Table Apx G-8 shows the weight-of-evidence for the various epidemiology studies that were considered
in this review. Ruckart et al. ( ) 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. Q ) 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 A JO I I; h >t and 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 G-8. Weight-of-Evidence Table for Epidemiology Studies
Kvidenee Area
Reliability
Strength
Relevance
Overall Grade
TCE
(Lagakos et al.. 1986)
+
0/-
++
0/-
(Bove. 1996; Bove et al..
1995)
+
0
++
0
(Yauck et al., 2004)
+
0/+
++
0/+
(Forand et al., 2012)
+
++
++
+
Page 614 of 748
-------
956
957
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
987
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Evidence Area
Reliability
Strength
Relevance
Overall Grade
(Gilboa et al.. 2012)
+
-
++
-
(Brender et al.. 2014)
+
+
++
+
(Goldbere et al.. 1990)
0
+
++
0
METABOLITES (TCA, DCA)
(Wrieht et al.. 2017)
++
+
+
+
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. Hie WOE scores are
provided for reference but were not incorporated into the overall score for the line of evidence.
Table Apx G-9 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 G.l). It therefore
also scored (0/-) for Strength. The overall summary for the TCE oral studies was characterized as
ambiguous to weakly positive (0/+) due to conflicting study results, with a lean toward positive based on
the ambiguity of the negative studies.
Six oral experiments using TCE metabolites (TCA or DCA) were rated as lower relevance (+), because
a metabolite was administered (not TCE) and the relevance of these effects to humans likely dependent
upon individual toxicokinetic variability and 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
Page 615 of 748
-------
988
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
1020
1021
1022
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 DC A, but the single dose level used in these studies was too low to rule out effects at higher
doses based on results of the other studies. The overall summary score for the oral metabolite studies
was (+).
Three inhalation studies using TCE were considered relevant (natural exposure route) and reliable.
Reliability ratings were reduced for studies with a single exposure group and poor reporting (+,
(Schwetz et al.. 1975)) in addition to small group sizes and high negative control responses with a lack
of dose-responsiveness (0/+, (Dorfmueller et al.. 1979)). These studies were also reduced in relevancy
score (+) because they were general teratology studies and the focus on cardiac effects was unclear. Two
studies scored an Unacceptable in data quality and a 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 G-9. Weight-of-Evidence Table 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)
+
01-
++
01-
(Charles River
Laboratories. 2019)
++
01-
++
01-
Integrated Area Scores
+/++
01+
++
Summary Score (TCE)
01+
Page 616 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Evidence Area
Reliability
Strength
Relevance
Overall Grade
METABOLITES (TCA, DCA)
(Smith etal.. 1989)
+++
++
+
+
(Smith etal.. 1992)
+++
++
+
+
(Johnson et al.. 1998)
0/+
+
+
0/+
(Fisher et al.. 2001)
++
-
+
-
(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/+
01-
+
01-
(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/+
+
+
01+
Integrated Area Scores
(in vivo - all routes)
+/++
0/+
+/++
Summary Score (in
vivo - all routes)
0
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.
1023
1024 Mechanistic studies that inform the weight-of-evidence for developmental heart defects include
1025 evaluations of cardiac structure and function in chick and rodent embryos and mode-of-action or key
1026 event data focused on processes and pathways that contribute to the observed valvulo-septal defects
1027 (e.g., altered calcium flux, inhibition of stem cell differentiation and endothelial cell proliferation) as
1028 well as altered expression of oxidative metabolism enzymes. A mechanistic study from Palbykin et al.
1029 (2011) was graded as not relevant and was dropped from the analysis because it merely examined
Page 617 of 748
-------
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
molecular mechanisms underlying the results observed in (Caldwell et ai. 2008) without contributing
any additional WOE to the endpoint. The remaining mechanistic studies in mammalian cells/tissues,
chick embryos and zebrafish embryos were generally rated as lower relevance in comparison to human
studies and in vivo animal studies using a natural route of administration except for studies on ex vivo
whole rat embryos or in vivo data from rodents or humans, which were assigned a relevance score of
(+/++). All other studies were rated as (+) relevance.
Mechanistic studies in mammalian systems included an occupational worker study (Green et ai. 2004).
in vivo rat studies (Collier et ai. 2003; Dow and Green. 2000). studies using rat and mouse whole
embryo cultures (Hunter et ai. 1996; Saillenfait et ai. 1995) and in vitro studies using cell lines (Jiang et
ai. 2015; Caldwell et ai. 2008; Selmin et ai. 200S; Ou et ai. 2003). Ou et ai (2003) and Jiang et ai
(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 ai. 1996). suppression of endothelial cell proliferation in cell culture ( et ai. 2003). and
inhibition of cardiac differentiation from embryonic stem cells (Jiang et ai. 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 ai.
2015; Caldwell et ai. 2008; Selmin et ai. 2008; Collier et ai. 2003) and in vivo folate deficiency (Green
et ai. 2004; Dow and Green. 2000) (which has been associated with congenital heart defects in humans
(Mao et ai. 2017)). Saillenfait et ai (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 ai.
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 ai. 2010; Loeber et ai. 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
Page 618 of 748
-------
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 DCA) to effect cardiac development in zebrafish. The study by Wirbisky et al. (2016) was
the most comprehensive study of the three (rated as +++ for strength), identifying multiple dose-
responsive cardiovascular effects as well as associated gene changes. The other two studies received a
(++) for strength because of observed severe changes in heart rate but at concentrations associated with
other toxicities (Hassoun et al.. 2005) or because only a single, elevated dose was used (Williams et al..
2006). The overall summary score for zebrafish embryo studies was (+). The overall summary score for
mechanistic studies across all species and study designs was (++) due to consistent positive outcomes
observed in all study types. The WOE from mechanistic studies therefore provides stronger positive
evidence of an association between TCE exposure and congenital cardiac defects.
Table Apx G-10. Weight-of-Evidence Table 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)
++
+
+
+
(Ouetal.. 2003)
+++
++
+
+
(Jians et al.. 2015)
+++
++
+
+
(Dow and Green. 2000)
0/+
+
+/++
0/+
(Green et al.. 2004)
++
+
+/++
+
METABOLITES (TCA, DCA, Trichloroethanol, Chloral)
(Saillenfait et al.. 1995)
++
+/++
(Collieretal.. 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
Page 619 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Evidence Area
Reliability
Strength
Relevance
Overall Grade
(Loeber et al.. 1988)
+/++
++
+
+
(Bover et al.. 2000)
++
+
+
+
(Mishima et al.. 2006)
++
+
+
+
(Drake et al.. 2006a)
++
+
+
+
(Drake et al.. 2006b)
++
0
+
0
(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
1095
1096 In summary, the database contains a large and diverse set of studies pertinent to assessing congenital
1097 heart defects from TCE exposure (overall relevance was rated as ++). Well-designed, conducted and
1098 reported studies were located for all categories, although the epidemiology studies were limited to
1099 ecological or case-control study designs with high potential for misclassification of exposure and the
1100 many of the in vivo animal studies contained at least one major limitation (overall reliability rating of
1101 +/++). The integrated strength area score was (+), indicating a suggestive positive association of TCE
1102 with congenital cardiac defects. The epidemiology studies as a group provide suggestive evidence for an
1103 effect of TCE on cardiac defects in humans (summary score of +). Oral in vivo studies provided
Page 620 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1104 ambiguous to weakly positive (0/+) results for TCE itself, but positive results for its TCA and DC A
1105 metabolites (+), while inhalation studies contributed negative evidence (-). Mechanistic studies provided
1106 solid, consistent supporting information for effects of TCE and metabolites on cardiac development and
1107 precursor effects (summary score of ++). Overall, the database is both reliable and relevant and
1108 provides positive overall evidence that TCE may produce cardiac defects in humans (based on positive
1109 evidence from epidemiology studies, mixed evidence from animal toxicity studies, and stronger positive
1110 evidence from mechanistic studies).
1111
1112 Table Apx G-ll. 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.
1113
1114
Page 621 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1115 Appendix H META-ANALYSIS FOR CANCER
1116 H.1 Study Screening and Selection
1117 All epidemiologic studies included in the U.S. EPA 2011 IRIS assessment of TCE (Appendix C, (
1118 ) were considered to be informative and carried forward for meta-analysis. Informative
1119 epidemiologic studies of non-Hodgkin lymphoma (NHL), kidney cancer or liver cancer and exposure to
1120 TCE published since the 2011 IRIS assessment were identified through a systematic literature search.
1121 Studies examining only other cancer types were excluded from consideration.
1122 H.l.l Data Quality and Inclusion/Exclusion Criteria Screening
1123 Relevant studies were evaluated for data quality and were additionally screened through
1124 inclusion/exclusion criteria developed based on the criteria established in the 2011 IRIS assessment
1125 (Appendix C, (U.S. EPA. 201 lb)), as described in TableApx H-l. Results of this criteria screening are
1126 presented in
1127 Table Apx H-2.
1128
1129 Table Apx H-l. Meta-Analysis Inclusion/Exclusion Criteria for Considering Cancer Studies
1130 Identified in EPA's Literature Search
Inclusion ( l ileriit
Kxclusion C i ilci iit
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.
Page 622 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1131
1132 TableApx H-2. Screening Results of Cancer Studies Identified in EPA's Literature Search Based
1133 on Inclusion/Exclusion Criteria
Studies recommended for inclusion in quantitative meta-analvsis:
Studies
Primary reason(s)
(Bove et al.. 2014a)
(Bove et al.. 2014b)
(Buhaeen et al.. 2016)
(Christensen et al.. 2013)
(Cocco et al.. 2013)
(Hansen et al.. 2013)
(Limvorth et al.. 2011)
(Purdue et al.. 2016)
(Silver et al.. 2014)
(Vlaanderen et al.. 2013)
Analytical study designs of cohort or case-control; evaluation
of incidence or mortality; adequate selection in cohort studies
of exposure and control groups and of cases and controls in
case-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
Studies NOT recommended for inclusion in Quantitative meta-analysis:
Studies
Primary reason(s)
(Alanee et al.. 2015)
Weakness with respect to analytical study design (i.e.,
geograpliic-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).
(Bassis 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.
(Bahretal.. 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 umnentioned even
in the discussion indicate inexperience and poor quality
control).
1134 H.1.2 Screening results
1135 Data quality and inclusion/exclusion criteria screening identified ten studies suitable for use in meta-
1136 analysis. Of these, there were nine new studies with suitable informative data on the association of
1137 exposure to TCE and NHL (Bove et al.. 2014a; Bove et al.. 2014b; Christensen et al.. 2013; Cocco et al..
1138 2013; Hansen et al.. 2013; Lipworth et al.. 2011; Purdue et al.. 2016; Silver et al.. 2014; Vlaanderen et
1139 al.. 2013). eight new studies with informative data for kidney cancer (Bove et al.. 2014a; Buhagen et al..
1140 2016; Christensen et al.. 2013; Hansen et al.. 2013; Lipworth et al.. 2011; Purdue et al.. 2016; Silver et
1141 al.. 2014; Vlaanderen et al.. 2013). and six new studies with informative data for liver cancer (Bove et
1142 al.. 2014a; Christensen et al.. 2013; Hansen et al.. 2013; Lipworth et al.. 2011; Silver et al.. 2014;
1143 Vlaanderen et al.. 2013). All of these studies scored Acceptable for data quality except (Bahr et al..
1144 2011). which was excluded for scoring Unacceptable. Every study scored at least a Medium except for
1145 (Buhagen et al.. 2016). which scored a Low but was recommended for inclusion by inclusion/exclusion
1146 criteria. The respective data quality scores were considered in sensitivity analyses of the meta-analyses
1147 results (see Appendix H.2.2.2).
1148
1149 All studies from the 2011 IRIS meta-analysis were Acceptable in data quality and scored at least a
1150 Medium. Therefore, data from the ten new studies that passed the criteria screening were extracted along
Page 623 of 748
-------
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
with results from previous studies identified in the 2011 IRIS assessment (U.S. EPA. 201 le). When
more than one report was available for a single study population, only the most recent publication or the
publication reporting the most informative data for TCE was selected for inclusion in the meta-analysis
(see TableApx H-3). This resulted in a smaller set of data included in the meta-analysis as compared to
the total list of studies.
H.1.3 Pooled Cohorts
Two of the new papers pooled data from earlier studies included in the 2011 IRIS meta-analysis.
(Hansen et al.. 2013) pooled and updated three Nordic national cohort studies of workers biologically
monitored for exposure to TCE (Anttila et al.. 1995; Axelson et al.. 1994; Hansen et al.. 2001).
Similarly, (Cocco et al.. 2013) pooled 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 (Christensen 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
biomonitored workers (Hansen et al.. 2013). 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 H-3.
Table Apx H-3. Cancer Studies Covering the Same Cohort as Previous Studies from either the
2011 IRIS Assessment or EPA Literature Search
Study reviewed
Other assessed studies with participants from the same cohort
2011 IRIS Assessment
(Anttila et al.. 1995) (Finland onlv)
Included in (Hansen et al.. 2013)
(Axelson et al.. 1994) (Sweden onlv)
Included in (Hansen et al.. 2013)
(Boice et al.. 1999)
Undated in (Limvorth et al.. 2011)
(Boice et al.. 2006)
(Zhao et al.. 2005) (partial)
(Briinine et al.. 2003)
None
(Charbotel 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)
(Raaschou-Nielsen et al.. 2003) (partial): Included in (Hansen et al.. 2013)
(Hardell et al.. 1994)
None
(Miliei et al.. 2006)
Included in (Cocco et al.. 2013)
(Moore et al.. 2010)
None
Page 624 of 748
-------
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Study reviewed
Other assessed studies with participants from the same cohort
(Morean et al.. 1998)
None
(Nordstrom et al.. 1998)
None
(Persson and Fredrikson. 1999)
None
(Peschet 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)
(Wans 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
(Buhaeen et al.. 2016)
None
(Cocco et al.. 2013)
(Cocco et al.. 2010); (Miliei 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)
(Limvorth et al.. 2011)
(Boice et al.. 1999)
(Purdue et al.. 2016)
None
(Silver et al.. 2014)
None
(Vlaanderen et al.. 2013)
None
H.2 Meta-Analysis Methods and Results
H.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 perchloroethylene or 2) comparisons of
groups with the highest and lowest level of exposure to perchloroethylene, in that order. For NHL,
estimates of association for the most highly exposed group were also abstracted, when they were
reasonably available. For each comparison, the most fully adjusted risk estimate was selected.
Page 625 of 748
-------
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (I v < < \ IVI I were abstracted
from tables in Appendix C of that assessment. The measures of association, confidence limits and
estimates of SE listed in those tables were utilized for consistency with the previous assessment.
For newer studies not included in the IRIS assessment, log-relative risks and their standard errors were
estimated from the extracted data; the data for the newer studies are provided in tables in Section H.2.3.
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
(Biggins et al. 2003) and visual inspection of the plots. If no important heterogeneity was indicated, the
fixed-effects meta-estimate was taken as the measure of overall association. Fixed effects models are
preferred for this purpose, as they are generally unbiased (Poole and Greenland! I ^ V). 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 H.2.4.
Page 626 of 748
-------
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
H.2.2 Results
H.2.2.1 Initial Meta-Analyses
Non-Hodgkin lymphoma
In the fixed-effects model for NHL (Figure Apx H-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 H-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
H-3 and Figure Apx H-4). Extracted RR estimates and confidence intervals from each NHL study are
presented in TableApx H-7, TableApx H-8, and TableApx H-9.
Figure Apx H-l. Fixed-effects model, overall association of NHL and exposure to TCE.
Study
ID
RR (95% CI)
%
Weight
Bove 2014a
Bove 2014b
Hansen 2013
Lipworth 2011
Silver 2014
Vlaanderen 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)
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 627 of 748
-------
1264
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-2. Random-effects model, overall association of NHL and exposure to TCE.
1265
1266
1267
Study
ID
Bove 2014a
Bove 2014b
Hansen 2013
Lipworth 2011
Silver 2014
Vlaanderen 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)
NOTE: Weights are from random effects analysis
1 T"
.1 .2
RR (95% CI)
%
Weight
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 H-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
1268
1269
Page 628 of 748
-------
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-4. Random-effects model, association of NHL and high exposure to TCE.
Sludy
10
Weight
Hansen 2013
Vlaanderen 2013
Chrtstenseo 2013
COCCO2013
Mwgan 1998 -
ftaaschou-f^elsen 2003
Radican 2000
Zhao 2005
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 (0.29, 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 (0.71,2.78)
11.48
1.30 (0.52, 3.24)
7.89
3.30(1.00,10.01)
5.68
2.20 (0.90, 5.39)
8.11
1.33 (0.98, 1.00)
100.00
Kidney Cancer
For kidney cancer, the fixed effects model (Figure Apx H-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 H-6), the meta-RR was 1.22 (95% CI 1.07-1.38). Extracted RR estimates and confidence
intervals from each kidney cancer study are presented in TableApx H-10 and TableApx H-l 1.
Page 629 of 748
-------
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx 11-5. Fixed-effects model, overall association of kidney cancer and
exposure to TCE.
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-Nlelsen 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 H-6. Random-effects model, overall association of kidney cancer and
exposure to TCE.
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
Page 630 of 748
-------
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 H-7 and Figure_Apx
H-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 H-12 and
TableApx H-13.
Figure Apx H-7. Fixed-effects model, overall association of liver cancer and
exposure to TCE.
Weight
Bove 2014a
Hansen 2013
Llpworth 2011
Sliver 2014
Vlaandefen 2013
Chrisiensen 2013
Bo*cc 2006
Greenland 1994 —
Morgan 1998
Radican 2008
Raaschou-Nialsen 2003
Overall (l-squared = 36.5%, p = 0.107)
o
0.66 (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 631 of 748
-------
1305
1306
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-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
Boco 2006
Greenland 1994
Morgan 1998
Radican 2008
Raaschou-Nielsan 2003
Overall (l-squared = 36.5%, p = 0.107)
NOTE; Weights are from random effects analysis
$
RR {95% CI)
%
Weight
0.86 (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(0.98,1.43)
100.00
1307
1308
Page 632 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1309 H.2.2.2 Sensitivity analyses
1310 Removal of Vlaanderen et al. (2013)
1311 In analyses of influential observations, the study of (Vlaanderen et al.. 2013) strongly influenced the
1312 meta-RRs for all three cancers (TableApx H-4, TableApx H-5, and TableApx H-6). No other single
1313 study had an appreciable impact on the overall association. Further meta-analyses were conducted to
1314 characterize the sensitivity of the results to the influence of that study.
1315
Table Apx H-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 et al. 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
Hardelletal. 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 H-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 et al. 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
Briining et al. 2003
1.05
1.00
1.11
Page 633 of 748
-------
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Table Apx H-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 H-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 et al. 2014
1.08
0.99
1.19
Vlaanderen et al. 2013
1.34
1.13
1.59
Christensen et al. 2013
1.08
0.99
1.18
Boice et al. 2006
1.08
0.99
1.18
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 ai. 2013) from the analysis of overall exposure to TCE
(Figure_Apx H-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 H-10), no heterogeneity remained when the (Vlaanderen et ai. 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 ai. 2013) from the model for kidney cancer (Figure Apx H-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 H-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 ai. 2013) study and achieved statistical significance.
Page 634 of 748
-------
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-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
Greenland 1994
Morgan 1996
Raaschou-Nielsen 2003
Radlcan 2006
Zhao 2005
HarcJell 1994
Nordstrom 1993
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 H-10. Fixed-effects model, association of NHL and high exposure to TCE, study of
Vlaanderen et al. (2013) omitted.
Hansen 2013
Ctirisl
Cocco 2013
Morgan 1990
Raaschou-Welsan 2003
Radican 2006
Zhao 2005
Purdue 2011
Wang 2009
Overall (l-squarsd = 0,0%, p = 0.5J
0.68 (0.25,1.76)
1.00 {0.29. 3.44)
2,30 {0.71. 6.67)
0.61 {0.10. 6.47)
1.60 {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. &.»)
1.S»{1.19. 1,97)
Page 635 of 748
-------
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx II-11. 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 H-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 636 of 748
-------
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 H-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 H-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 H-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 H-13. Fixed-effects model, overall association of NHL and
exposure to TCE stratified by study quality score.
Study
ID
Medium/Low
Bove 2014a
Bove 2014b
Silver 2014
Vlaanderen 2013
Christensen 2013
Greenland 1994 —
Morgan 1998
Raaschou-Nielsen 2003
Radican 2008
Hardell 1994
Nordstrom 1998
Persson 1999
Purdue_2011
Wang 2009
Subtotal (l-squared = 40.0%, p = 0.061)
High
Hansen 2013
Lipworth 2011
Cocco 2013
Zhao 2005
Subtotal (l-squared = 0.0%, p = 0.784)
Heterogeneity between groups: p = 0.027
Overall (l-squared = 38.5%, p = 0.049)
RR (95% CI)
%
Weight
1.15(0.56,
0.33(0.14,
0.87 (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(0.81,
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)
1.21 (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
1,02(0.97,1.09) 100,00
~~r~
10
Page 637 of 748
-------
1383
1384
1385
1386
1387
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-14. Fixed-effects model, overall association of kidney cancer and
exposure to TCE stratified by study quality score.
1388
1389
1390
Study
ID
Medium/Low
Bove 2014a
Buhagen 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
Lip worth 2011 —
Zhao 2005
Charbotel 2006
Subtotal (l-squared = 0.0%, p = 0.453)
Heterogeneity between groups: p = 0.614
Overall (l-squared = 41.1%, p = 0.036)
<>
RR (95% CI)
%
Weight
1.52(0.64, 3.60)
1.70 (0.94, 3.06)
1.24(0.87, 1.76)
1.00 (0.94, 1.06)
0.90 (0.36, 2.21)
0.80(0.41, 1.56)
0.99 (0.30, 3.29)
1.14(0.51,2.58)
1.18(0.47, 2.95)
2.47 (1.36, 4.49)
1.30 (0.89, 1.89)
2.05(1.13, 3.73)
1.24 (1.03, 1.49)
1.20 (0.96, 1.50)
1.06(1.00, 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
—r~
10
Figure Apx H-15. Fixed-effects model, overall association of liver cancer and
Page 638 of 748
-------
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
exposure to TCE stratified by study quality score.
Sludy
ID
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
Sublolal (l-squared = 36.0%, p = 0.209)
Heterogeneity between groups: p = 0.009
Overall (l-squared = 36.5%, p = 0.107)
O
RR (95% CI)
0.86 (0.37, 2.00)
0.99 (0.50,1.97)
1.00 (0.90,1.11)
1.10(0.13. 0.50)
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)
Weight
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
1.08(0.99,1.18) 100.00
~I
10
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 H-16, a-c) were consistent with modest publication bias,
with a possible tendency toward omission of moderate-sized studies with weak or null associations.
With the ("Vlaanderen et al.. 2013) study omitted, however, the plots became more symmetrical,
consistent with an absence of publication bias among the remaining studies (Figure Apx H-16, d-f).
Page 639 of 748
-------
1410
1411
1412
1413
1414
1415
1416
a.
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx H-16. Funnel plots for publication bias.
All studies: a. NHL; b. kidney cancer; c. liver cancer;
Omitting Vlaanderen et al. (2013): d. NHL; e. kidney cancer; f. liver cancer.
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
1417
1418
1419
1420
e.
Funnel plot with pseudo 95% confidence limits
/ \
/ \
' i N
/ • \
/ \
/ • \
/ ' \
/ / •
0
LnEst
Page 640 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1421 H.2.3 Selected RR estimates and confidence intervals by study and cancer type
1422 Table Apx H-7. Selected RR estimates for NHL associated with TCE exposure (overall effect) from cohort studies published after
1423 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 641 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Study
RR
95%
LCL
95%
UCL
RR
type
In
RR
SE
(In
RR)
Alternate RR
estimates (95% CI)
Comments
Vlaanderen
et al (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)
1424
1425 TableApx H-8. Selected RR estimates for NHL associated with TCE exposure (overall effect) from case-control studies
1426 published after U.S. EPA (2011) ^
Study
RR
95%
LCL
95%
UCL
In RR
SE
(In RR)
Alternate RR
estimates (95% CI)
Comments
Christensen
et al. (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, gender,
and contributing study (50 cases, 38 controls).
1427
1428
1429 Table Apx H-9. Selected RR estimates for NHL associated with TCE exposure (effect in the highest exposure group) studies
1430 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 642 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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,
gender, and study.
1431
1432 TableApx H-10. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from cohort studies
1433 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 643 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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)
paycode; 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)
men and women;
cumulative exposure (n=1372 cases); occupationally
(2128436)
cumulative exposure for
high exposure groups
only (n=251 cases)
unexposed individuals were used as the reference
group; unlagged exposure (up to 20 years of lag time
had a negligible impact on HR)
1434
1435 TableApx H-ll. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from case-control studies
1436 published after U.S. EPA (2011)
Study
RR
95%
LCL
95%
UCL
In
RR
SE (In
RR)
Alternate RR estimate
(95% CI)
Comments
Christense
n 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 644 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
1437
1438 TableApx H-12. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from cohort studies
1439 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
andpaycode; 10-year lag time
Page 645 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Study
RR
95%
LCL
95%
UCL
RR
type
In RR
SE (In
RR)
Alternate RR
estimates (95% CI)
Comments
Vlaandere
n 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)
1440
1441
1442
TableApx H-13. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from case-control studies
published after U.S.
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
1443
Page 646 of 748
-------
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
H.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 647 of 748
-------
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix I APPROACH FOR ESTIMATING WATER
RELEASES FROM MANUFACTURING SITES
USING EFFLlJENT 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 ( ). 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 ( ;). Direct dischargers are facilities that discharge
effluent directly to surface waters and indirect dischargers are facilities that discharge effluent to
publicly-owned treatment works (POTW). The OCPSF limits for TCE in each of the Subparts
are provided in Table Apx 1-1.
Table Apx 1-1. Summary of OCPSF Effluent Guidelines for Trichloroethylene
0( PSI- Subpart
.Maximum
for Any
One Day
(u«/L)
.Maximum
for Any
Monthly
Average
(tig/1.)
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 648 of 748
-------
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
BAT = Best Available Technology Economically Achievable; NSPS = New Source Performance Standards; PSES =
Pretreatment Standards for Existing Sources; PSNS = Pretreatment Standards for New Sources.
Source: ( )
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 1-2. 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 1-1
DLxPWx PV
DR ~ 1,000,000,000 x OD
Equation 1-2
DLxPW x PV
AD =
1,000,000,000
Table Apx 1-2. 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 Days0
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 ) (ESIG1
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 649 of 748
-------
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (ESIG). 2012). 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 1-3 provides estimated daily production volume
and wastewater flow for each facility that EPA used the EG to assess water releases.
Table Apx 1-3. Summary of Facility Trichloroethylene Production Volumes and
Wastewater Flow Rates
Site
Annual Production
Volume
(kg/site-yr)
Annual
Operating Days
(days/yr)
Daily
Production
Volume
(kg/site-day)
Daily
Wastewater
Flow
(L/site-day)
Solvents &
Chemicals,
Pearland, TXa
20,382,094
350
58,234
582,345
Occidental
Chemical Corp.
Wichata, KSa
20,382,094
350
58,234
582,345
a The 2015 annual production volumes in the 2016 CDR for these sites was either claimed as CBI or withheld. EPA
estimate the production volume by 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.
b Annual production volume for this site is based on the 2015 production volume reported in the 2016 CDR and
converting from pounds to kilograms.
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):23
69^x10-^x 20,382,094^ hn
L kq yr kg
Max DR = 2 _^L_ = o.04- M
1,000,000,000^ x 350
kg yr
day
23 This estimated production volume is equal to the estimated production volume assessed for all manufacturing sites.
Page 650 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1559
26^r- X IQj— X 20,382,094— hn
L kq yr _ kg
1560 Average DR = -j = 0.015
1,000,000,000^ x 350 day
kg yr
1561
26^X10-^X 20,382,094^ hn
L kq yr kg
1562 AR= 777J — =5.3 —
i,ooo,ooo,ooo^| yr
1563
Page 651 of 748
-------
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix J 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 [Environmental Releases and Occupational
Exposure Assessment. Docket: EPA-HO-OPPT-2019-0500\. The final values will have two
significant figures since they are based on values from modeling.
J.l Example High-End AC, ADC, and LADC
Calculate AChe:
Calculate ADChe:
CHE x ED
ACHE =
AT,
acute
2.6 ppm x 8 hr/day
AChe = 2A~hrJday = °"87 Ppm
CHE x ED xEFx EWY
ADC HE = — —
Ht AT
hv days
2.6 ppm x 8^— x 250—x 40 years
ADC in = j— = 0. 59 ppm
0 ^ _ days „.hours\
(40 years x 365 ^ x 24
Calculate LADChe:
CHE x ED xEFx EWY
ladche =
AT
LADC
hv days
2.6 ppm x 8^— x 250 —^ x 40 years
LADChe = 7 j— z = 0.30 ppm
0 ^ _ days „.hours\
(78 years x 365^ x 24-^)
Page 652 of 748
-------
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
J.2 Example Central Tendency A EC, ADC, and LADC
Calculate ACct:
Cct x ED
ACct =
AT
ri1 acute
0.03 ppm x 8 hr/day
AC"= 24 hr I day = °**PP
-------
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix K VAPOR DECREASING AND COLD CLEANING
NEAR-FIELD/FAR-FIELD INHALATION EXPOSURE
MODELS M'I'KOU'll \M) l>\K\ME IEKS
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 654 of 748
-------
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
K.1 Model Design Equations
FigureApx K-l through FigureApx K-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
Figure Apx K-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 655 of 748
-------
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
FigureApx K-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
•¦¦1
_W Q
N
Figure Apx K-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 656 of 748
-------
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Near-Field Mass Balance
Equation K-l
Far-Field Mass Balance
Vnf Jt — CffQnf ~ CNFQNF + G
dt
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 ("Micas. 2009):
Equation K-3
Equation K-4
Where:
Equation K-5
CNF = G(k1 + k2eXlt - k3eX2t)
CFF = G (^— + k4eXlt -
fci =
Equation K-6
Equation K-7
Equation K-8
Equation K-9
kn =
ko =
(<1nf +'<2 J Qff
QnfQff + ^-2^nf(.Qnf + Qff)
QnfQff^nf^i ~ ^2)
QnfQff + A.1Vnf(.Qnf + Qff)
QnfQff^nf(^i ~ ^2)
, MiVnf + Qnf\ ,
4 = ( q7f > 2
_ /A2Vnf + Qnf\ j
5 V qnf ) 3
Page 657 of 748
-------
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Equation K-10
= 0.5
( Qnf^ff + Vnf(Qnf + Qff)
V ^NF^FF ,
+
/ Qnf^ff + Vnf(Qnf + Qff)\
\ Vnf^ff J
^ (QnfQff\
V VpjpVpp /
Equation K-ll
^ _ q 5 _ / Qnf^ff + Vnf(Qnf + Qff)
VNFVFF
/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 M.2) while the denominator is
fixed to an average time span, t avg, of eight hours (since EPA is interested in calculating 8-hr TWA
exposures). Mathematically, the numerator and 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
j^2 CNFdt j^2 G{kx + k2eXlt — k3eX2t)dt
CnF TWA = 77 = I =
f aV9 dt tavg
J\J
r(i <- i MAlt2 MA2t2\ r (l + i MAltl k2eX^\
G ^,t2 + a_j _ G + -2^
tavg
Equation K-13
;t'2 Crrdt J,'' '¦ + kte^' - k5e^') dt
~ ~ ^ "
n(t2 , k4eXlt2 kseX2t2\ _ / t± k4eXltl k5eX2tl\
G(g^ + ~ IT)-G{
-------
1753
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Equation K-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-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 = VppAE R
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.
K.2 Model Parameters
TableApx K-l through TableApx K-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 659 of 748
-------
1780
1781
1782
TableApx K
Inhalation Ex
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1. Summary of Parameter Values and Distributions Used in the Open-Top Vapor Degreasing Near-Field/Far-Field
posure 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 K.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section K.2.2
Near-field
indoor wind
speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section K.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 K.2.4
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
tl
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
8
—
2
8
—
—
See Section K.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section K.2.6
Vapor
generation
rate
G
mg/hr
2.34E+07
Average
4.54E+02
4.67E+07
—
Discrete
See Section K.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 660 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx K-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 K.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section K.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section K.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section K.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
tl
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
24
—
24
8
—
Constant
Value
See Section K.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section K.2.6
Vapor
generation
rate
G
mg/hr
1.6E+07
Average
3.63E+05
3.29E+07
—
Discrete
See Section K.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 661 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx K-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 K.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section K.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section K.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section K.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
tl
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
8
—
8
8
—
Constant
Value
See Section K.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section K.2.6
Vapor
generation
rate
G
mg/hr
—
—
1.12E+05
1.12E+05
—
Discrete
See Section K.2.7; Single Data
Point
Operating
hours per
day
OH
hr/day
24
—
—
—
—
Constant
See Section M.2.8
Page 662 of 748
-------
1787
1788
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx K-4. Summary of Parameter Values and Distributions Used in the Cold Cleaning Near-Field/Far-Field Inhalation
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 K.2.1
Air
exchange
rate
AER
hr"1
2
Mode
2
20
3.5
Triangular
See Section K.2.2
Near-field
indoor
wind speed
VNF
ft/hr
1,181
50th
percentile
154
23,882
—
—
See Section K.2.3
cm/s
10
50th
percentile
1.3
202.2
—
—
Near-field
length
Lnf
ft
10
—
—
—
—
Constant
Value
See Section K.2.4
Near-field
width
Wnf
ft
10
—
—
—
—
Constant
Value
Near-field
height
Hnf
ft
6
—
—
—
—
Constant
Value
Starting
time
tl
hr
0
—
—
—
—
Constant
Value
Constant.
Exposure
Duration
t2
hr
—
—
3
8
—
Discrete
See Section K.2.5
Averaging
Time
tavg
hr
8
—
—
—
—
Constant
Value
See Section K.2.6
Vapor
generation
rate
G
mg/hr
5.14E+05
Average
6.28E+02
1.02E+06
—
Discrete
See Section K.2.7
lb/hr
1.13
Average
0.001
2.26
—
Discrete
Operating
hours per
day
OH
hr/day
—
—
—
—
—
—
See Section M.2.8
1789
Page 663 of 748
-------
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
K.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 (Von Grote et al. 2003) that indicated volumes at German metal
degreasing facilities can vary from 300 to several thousand cubic meters. They noted that smaller
volumes are more typical and assumed 400 and 600 m3 (14,126 and 21,189 ft3) in their exposure models
(Von Grote et al.. 2003). These are the highest and lowest values EPA identified in the literature;
therefore, EPA assumes a triangular distribution bound from 300 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).
K.2.2 Air Exchange Rate
EPA used the same air exchange rate distribution for each of the models discussed. The air exchange
rate is based on data from (Hellwee et al.. 2009) and information received from a peer reviewer during
the development of the 2014 TSCA Work Plan Chemical Risk Assessment Trichloroethylene:
Degreasing, Spot Cleaning and Arts & Crafts Uses (I v « « \ _^13a). (Hellwee et al.. 2009) reported
that average air exchange rates for occupational settings using mechanical ventilation systems vary from
3 to 20 hr"1. The risk assessment peer reviewer comments indicated that values around 2 to 5 hr"1 are
likely ( ), 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 ( i) and a maximum of 20 hr"1 per (Hellwee et al..
2009).
K.2.3 Near-Field Indoor Air 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 Mayna 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 and Mavn; )8a) (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 and Mayng >8a) (1998)) to prevent the model from sampling
values that approach infinity or are otherwise unrealistically large.
(Baldwin and Mavnard. 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 664 of 748
-------
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
K.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.
K.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).
K.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.
K.2.7 Vapor Generation Rate
For the vapor generation rate from each machine type (OTVD, conveyorized and cold), EPA used a
discrete distribution based on the annual unit emission rates reported in the (U.S. EPA. 2018a). No web
degreasers were reported in the 2014 NEI, therefore, (U.S. EPA. 2011a) data was used for web
degreasers. Annual unit emission rates were converted to hourly unit emission rates by dividing the
annual reported emissions by the reported annual operating hours (see Appendix E.2.8). Reported annual
emissions in NEI without accompanying reported annual operating hours were not included in the
analysis. Emission rates reported as zero were also excluded as it is unclear if this is before or after
vapor controls used by the site and if the vapor controls used would control emissions into the work area
(thus reducing exposure) or only control emissions to the environment (which would not affect worker
exposures). TableApx K-5 summarizes the data available in the 2014 NEI.
TableApx K-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
Conveyorized 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 K-6 through Table Apx K-9 summarize the distribution of hourly unit emissions for each
machine type calculated from the annual emission in the 2014 NEI.
Page 665 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1873 TableApx K-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
Page 666 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Count
of
Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
1
1.59
0.0132
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
1874
1875
TableApx K-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
1876
Page 667 of 748
-------
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx K-8. Distribution of Trichloroethylene Web Degreasing Unit Emissions
Count
of Units
Unit
Emissions
(lb/unit-hr)
Fractional
Probability
—
0.247
1.00
TableApx K-9. Distribution of Trichloroethylene Cold (
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
leaning Unit Emissions
K.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 K-10 through Table Apx
K-13 summarize the distribution of operating hours per day for each machine type.
Table Apx K-10. Distribution of Trichloroethylene Open-Top Vapor Degreasing Operating Hours
Count of
Occurrences
Operating
Hours
(hr/day)
Fractional
Probability
—
24
0.4048
—
16
0.0952
—
8
0.2381
—
6
0.0476
—
4
0.0714
—
2
0.1429
Table Apx K-ll. Distribu
ion of Trichloroethylene Conveyorized Degreasing Operating Hours
Count of
Occurrences
Operating
Hours
(hr/day)
Fractional
Probability
—
24
1.0000
Page 668 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1892 TableApx K-12. Distribu
ion of Trichloroethylene Web Degreasing Operating Hours
Count of
Occurrences
Operating
Hours
(hr/day)
Fractional
Probability
—
24
1.0000
1893
1894
Table Apx K-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
1895
1896
Page 669 of 748
-------
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix L 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
CNlcas. 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 j ob;
• Number of degreaser applications per brake job;
• Time duration of brake j ob;
• Operating hours per week; and
• Number of j obs 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.
L.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
Page 670 of 748
-------
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 L-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 L-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 L.2.5 and L.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
Page 671 of 748
-------
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (CARB. 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 ( ,000).
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
near-field volume;
far-field volume;
near-field ventilation rate;
far-field ventilation rate;
average near-field concentration;
average far-field concentration; and
Page 672 of 748
-------
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
t = elapsed time.
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
Cnf t _li — C^l t eXlt ^2 t
iyir>Lm,n+1 v z>Lm,n J
Equation L-4
CpF t ^=(^3t eXlt-k4t eX2t)
rr>Lm,n+1 v 3,im,n ^>Lm,n J
Where:
Equation L-5
Equation L-6
/Cl £
1>Lm,n
2, tm,n
Qnf (CFF,o(.tm,n) CWF 0(tmn)^ A2VNFCNF,o(tm,n)
VnfVi ~ ^2)
Qnf — CfF,0 {tm,rS) + ^l^NF^NF.oiSm.n)
Vnf&i ~ ^2)
Equation L-7
(.Qnf + ^-1^Nf)(QnF {^FF.oiSm.n} CNF,o{im,n)) ^I^NF^NF.oiSm.n})
3}tm,n
Qnf^nf (^1 — ^2)
Equation L-8
4
-------
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Equation L-9
= 0.5
(QnfVff + Vnf(.Qnf + Qff)\ /QnfVff + ^nf(Qnf + Qff)\ _ . /QnfQff\
\ ^NF^FF ) J V ^NF^FF ) ^ ^NF^FF '
Equation L-10
^ _ q 5 _ / Qnf^ff + Vnf(Qnf + Qff)
VirpVi
NFV FF
(Qnf^ff + Vnf(Qnf + Qff) \ _ . /QnfQff\
Vnf^ff / V VNpVpp)
Equation L-ll
CNF,o{pm,n) — j—f1,000——+ CWF(tmn_1) , n > 0 for all m where brake job occurs
I 'vp .Q '
0, m = 0
Equation L-12
Equation L-13
r 0, m = 0
FF,o\tm,n) — {CFF(trriin^1), for all n where m > 0
^ ^ \ /k k N
I j — I | 2.trn,n-l rX-,U
. g/Llcl -| " g'
^1 ^2
NF, 5-min TWA, tm rl
^2
Equation L-14
kz.tm.n-l CX,U j k4,tm,n-l _ (^}tm,n-l £A1t1 | ^4tm,n-1
Ai A2 / \ Ai A2
CfF, 5-min TWA, tm „ . .
I? — ll
After calculating all near-field/far-field 5-minute TWA exposures (i.e., C«f, 5-min TWA,tmn and
Cpp 5.min TWA tmn) for each five-minute period of the work day, EPA calculated the near-field/far-field
8-hour TWA concentration and 1-hour TWA concentrations following the equations below:
Equation L-15
C,
Sm=0 2j71=o[^NF,5-i
NF, 8-hr TWA ~
min TWA,tm rl
x 0.0833 hr\
8 hr
Equation L-16
C,
Hm=oHn=o[^FF,
5-min TWA,tn
x 0.0833 hr]
NF, 8-hr TWA ~
8 hr
Page 674 of 748
-------
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
2127
2128
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Equation L-17
r _ I!r! = o[C/VF,5-min TWA,tm,n X 0-0833 hr\
CNF, 1-hr TWA = ~\\xr
Equation L-18
r _ hn=o[CFF,5-mmTWA,tmn X 0.0833 hr\
CFF, 1-hr TWA = Yhr
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 Figure Apx L-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 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 = R
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.
L.2 Model Parameters
Table Apx L-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 675 of 748
-------
2129
2130
2131
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx L-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
Value
Basis
Variable Model Parameter Values
Lower
Bound
Upper
Bound
Mode
Distributio
n Type
Comments
Far-field volume
Vff
in'
206
70,679
3,769
Triangular
Distribution based on data
collected by CARB (CARB.
2000).
Air exchange
rate
AER
lir1
20
3.5
Triangular
(Demou et al.. 2009) identifies
typical AERs of 1 lir1 and 3 to 20
lir1 for occupational settings
without and with mechanical
ventilation systems, respectively.
(Hellweg et al.. 2009) identifies
average AERs for occupational
settings utilizing mechanical
ventilation systems to be between
3 and 20 lir1. (Golsteiinet 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 likely (U.S. EPA. 2013a). in
agreement with (Golsteiinet al..
2014). 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').
Near-field indoor
wind speed
Mir
23,882
Lognonnal
Vnf
cm/s
202.2
Lognonnal
Lognormal distribution fit to
commercial-type workplace data
from (Baldwin and Mavnard.
1998a).
Near-field radius
R.
m
1.5
Constant
Value
Constant.
Starting time for
each application
period
lir
Constant
Value
Constant.
Page 676 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 Preliminaiy
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
Lognormal 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.
2132
Page 677 of 748
-------
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
L.2.1 Far-Field Volume
The far-field volume is based on information obtained from ( ) from site visits of 137
automotive maintenance and repair shops in California. (GARB. 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 (GARB. 2000)).
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.
L.2.2 Air Exchange Rate
The air exchange rate (AER) is based on data from (Demou et al. 2009). (Hellwee et al. 2009).
(Golsteiin et al. 2014). and information received from a peer reviewer during the development of the
2014 TSCA Work Plan Chemical Risk Assessment Trichloroethylene: Degreasing, Spot Cleaning and
Arts & Crafts Uses ( .). (Demon 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,
(Hellwee 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
(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 1 hr"1, per (Demon et al.. 2009) and a maximum of 20 hr"1 per (Demon et
al.. 2009) and (Hellwee et al.. 2009)).
L.2.3 Near-Field Indoor Air 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 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 Maym )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,
Page 678 of 748
-------
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
the lognormal distribution is truncated at a maximum allowed value of 202.2 cm/s (largest surveyed
mean air speed observed in (Baldwin and Mavm ?8a) to prevent the model from sampling values
that approach infinity or are otherwise unrealistically large.
(Baldwin and Mavnard. 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.
L.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 L-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 g
L.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 ( 30). 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.
L.2.6 Averaging Time
EPA was interested in estimating 8-hr TWAs for use in risk calculations; therefore, a constant averaging
time of eight hours was used.
L.2.7 Trichloroethylene Weight Fraction
EPA reviewed the Preliminary Information on Manufacturing, Processing, Distribution, Use, and
Disposal: Trichloroethylene report ( ) 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 ( '00); 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
Page 679 of 748
-------
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
type. Table Apx L-2 provides a summary of the reported TCE content reported in the safety data sheets
identified in (U.S. EPA. 2017c). the number of occurrences of each product type, and the fractional
probability of each product type.
Table Apx L-2. Summary of Trichloroethylene-Based Aerosol Degreaser Formulations
Name of Aerosol
Degreaser Product
Identified in fU 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
L.2.8 Volume of Degreaser Used per Brake Job
(CARB. 2000) assumed that brake jobs require 14.4 oz of aerosol product. EPA did not identify other
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).
L.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.
Page 680 of 748
-------
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
L.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).
L.2.11 Operating Hours per Week
(( 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).
L.2.12 Number of Brake Jobs per Work Shift
(C 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^M£_x8
site-year shift
r„weeks ...
52 x OHpW
yr r
Number of brake jobs per work shift (j obs/site-shift); and
Operating hours per week (hr/week).
Page 681 of 748
-------
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
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix M SPOT CLEANING NEAR-FIELD/FAR-FIELD
INHALATION EXPOSURE MODEL APPROACH AND
PA.RAMETERS
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 ( 309), 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.
M.l_ Model Design Equations
Figure Apx M-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-
Page 682 of 748
-------
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
Figure Apx M-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
Vnf ^ = CffQnf ~ CNFQNF + G
Far-Field Mass Balance
Equation M-2
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.
Page 683 of 748
-------
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Both of the previous equations can be solved for the time-varying concentrations in the near-field and
far-field as follows ( 09):
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
= 0.5
CNF = G(k1 + k2eXlt - k3e^2t)
CFF = G (— 1- k4eXlt - k$eX2t
\Qff
)
fci =
k7 =
ko =
(qnf + e J Qff
QnfQff + ^-2^nf(.Qnf + Qff)
QnfQff^nf(^i ~ ^2)
QnfQff + A.1Vnf(.Qnf + Qff)
QnfQffVnf(.^i ~ ^2)
, MiVnf + Qnf\ ,
_ /A2Vnf + Qnf\ j
5 V ) 3
/ QnfQff + Vnf(Qnf + Qff)
V ^/vf^ff ,
+
/QnfQff + Vnf(Qnf + Qff)\ _ . (QnfQff\
V ^/VF^FF / ^ ^/VF^FF /
Equation M-ll
Az = 0.5
( QnfQff + ^nf(Qnf + Qff)\
V ^/VF^FF /
/ QnfQff + ^nf(Qnf + Qff)\
' \ ^/VF^FF /
^ /QnfQff\
V K/vf^Vf '
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
Page 684 of 748
-------
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
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
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
j^2 CNFdt j^2 G{kx + k2eXlt — k3eX2t)dt
Cnftwa = t = =
' f*avg dt tavg
/ k2ex& _ k2ex^\ _ ( k2ex^ _ k3ex^\
" ykit2 ~i~ Ai Az J Cr^jti+ Ai J
tavg
Equation M-13
J",*2 Cppdt £ a (^- + k,e^c - k5e^') dt
n ( t2 , k4eXlt2 kseX2t2\ n ( t1 , k4eXltl kseX2tl\
b[QFF+ A2 ) b[QFF+ Ax h. )
^avg
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-:
Page 685 of 748
-------
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Equation M-16
Qff = VffAER
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.
M.2 Model Parameters
Table Apx M-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 686 of 748
-------
2431
2432
2433
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx M-l. Summary of Parameter Values and Distributions Used in the Spot Cleaning Near-Field/Far-Field Inhalation
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
range reported in (U.S. EPA, 2013a).
Near-field
cm/s
—
—
0
2022
—
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 687 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
—
—
—
—
—
(IRTA, 2007) used estimates of the
amount of TCE-based spot cleaner
sold in California and the number of
textile cleaning facilities in California
to calculate a use rate value.
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 688 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
2434
Page 689 of 748
-------
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
M.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 M-2
TableApx M-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, a.2 = 108.22, min = 500 ft2, max
= 20,000 ft2
M.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.
M.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).
M.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.
Page 690 of 748
-------
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
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
2509
2510
2511
2512
2513
2514
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 Mavm >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 and Mavn 98a) 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.
M.2.5 Averaging Time
EPA is interested in estimating 8-hr TWAs for use in risk calculations; therefore, a constant averaging
time of eight hours was used.
M.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. 2007) 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.
M.2.7 Vapor Generation Rate
EPA set the vapor generation rate for spot cleaning (G) equal to the use rate of TCE with appropriate
unit conversions. EPA multiplied the spotting agent use rate by the weight fraction of TCE (which
ranges from 0.1 to 1) and assumed all TCE applied to the garment evaporates. EPA used a density of
1.46 g/cm3 (U.S. EPA. 2018d). To calculate an 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.
M.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
Page 691 of 748
-------
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
cleaner with a spotting labor of 2.46 hr/day and the large dry cleaner with a spotting labor of 5 hr/day.
EPA models a uniform distribution of spotting labor varying from 2 to 5 hr/day.
M.2.9 Operating Days
EPA modeled the operating days per year using a triangular distribution from 250 to 312 days per year
with a mode of 300 days per year.24 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.
M.2.10 Fractional Number of Operating Days that a Worker Works
To account for lower exposure frequencies and part-time workers, EPA defines a fractional days of
exposure as a 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).
24 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.
Page 692 of 748
-------
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2558
2559
2560
2561
2564
2565
2566
2567
2568
2569
2570
2571
2572
m
2575
2576
2579
2582
2585
2586
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix N BENCHMARK DOSE MODELING UPDATE FOR
NESTED FETAL DATA FROM ( _
BMD modeling of the nested fetal data for cardiac defects from (Johnson et al. 2003) was done to verify
the BMD modeling results reported in Appendix F.4.2.1 of the EPA 2011 IRIS Toxicological Review
for TCE Appendices (U.S. EPA. 2 ).
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 (U.S.
EPA. 201 le) 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 (\ v « « \ JO I I e) 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 (U.S.
) for the nested models are incorrect, apparently due to a problem with the software
used at that time, suggesting that the models did not have adequate fit to the data. The exercise
reported in Section F.4.2.1.2 of ( ) was performed to show that the p-values were
much higher than indicated in the raw modeling results and that model fit was acceptable.
Calculation of p-values for the nested models in the current version of BMDS follows a
bootstrap methodology similar to that described in Section F.4.2.1.2. of the IRIS
assessment. Because the original p-values in presented in (\ v « « \ JO I I 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 N-l).
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).
7) Model fits (AICs) and BMD/BMDL values are identical (within rounding error) between the
updated modeling results and those reported in ( )
Page 693 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx N-l. Results for Best-Fitting Model in Comparison to Results
Reported in IRIS (U.S. EPA. 201 le) (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
0.71114
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
243.816
NR
0.00202535
.000648179
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
2587
2588
Page 694 of 748
-------
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
m
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix O 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 ( 2012a). the benchmark dose
(BMD) and 95% lower confidence limit on the BMD (BMDL) were estimated using a benchmark
response (BMR) to represent a minimal, biologically significant level of change, when possible. The
BMR is represented by a specified percentage change, or relative deviation (RD), for continuous data.
The BMR for dichotomous data is 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 was used to estimate the BMD and BMDL, and to facilitate a consistent basis of
comparison across endpoints, studies, and assessments. Endpoint-specific BMRs are described further
below. Where modeling was feasible, the estimated BMDLs were used as points of departure (PODs).
Further details, including the modeling output and graphical results for the model selected for each
endpoint, can be found in the 2011 EPA IRIS Assessment ( ) and Appendix G (for
(Selerade and Gilmour. 2010)). A comparison of results from updated BMDL modeling runs with
results from ( ) for (Johnson et ai. 2003) are provided in Appendix N. Where dose-
response modeling was not feasible, NOAELs or LOAELs were also identified and are summarized.
O.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 (
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)25 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
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).
25
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 695 of 748
-------
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
m
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2663
2664
2665
2666
2667
2668
2669
2670
m
2673
2674
2675
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
0.2 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 ( )), 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):26
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
26 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 696 of 748
-------
2676
2677
2678
2679
2680
2681
2682
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 697 of 748
-------
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Appendix P OCCUPATIONAL INHALATION EXPOSURE AND
WATER RELEASE ASSESSMENT
P.l Manufacturing
P.l.l Exposure Assessment
EPA assessed inhalation exposures during manufacturing using identified inhalation exposure
monitoring data. TableApx P-l summarizes 8-hr TWA samples obtained from data submitted by the
Halogenated Solvents Industry Alliance (HS1A) via public comment for one company (Halosenated
Solvents Industry Alliance. ) listed as "Company B". HS1A 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 was
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 16 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 P-l. Summary of Worker Inhalation Exposure Monitoring Data from TCE
Manufacturing
SiTiuirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Nil m ho
i- ol"
Diilii
Points
CoiiIkIciht
killing of Air
Concent nilion
D;il:i
High-End
2.59
0.86
0.59
0.30
16
High
Central
Tendency
0.38
0.13
0.09
0.03
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
Source: (Halogenated Solvents Industry Alliance. 20.1.8 5176415")
P.1.2 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 (OE ). 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;
Page 698 of 748
-------
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
and water removed from the TCE product in drying columns (Most 1989). Additional water releases
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) (U.S. EPA. 2019g). 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.
EPA, 2019»). 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 P-2.
Table Apx P-2. Summary of OC
>SF Effluent Limitations for Trichloroethylene
OCPSF Subpart
Maximum
for Any One
Day
(^g/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)
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. 2019a)
Page 699 of 748
-------
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (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.27
TableApx P-3 summarizes water releases from the manufacturing process for sites reporting to TRI and
TableApx P-4 summarizes water releases from sites not reporting to TRI. The estimated total annual
release across all sites is 60.5 - 453.6 kg/yr discharged to surface water or POTWs.
Table Apx P-3. Reported Water Releases of Trichloroethylene from Manufacturing Sites
Reporting to 2016 TRI
Site
Annual
Release3
(kg/site-yr)
Annual
Release
Days
(days/yr)
Average
Daily
Release3
(kg/site-day)
NPDES Code
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
Axiall Corporation dba
Eagle US 2 LLC,
Westlake, LAb
49.9-443°
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).
27 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 700 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2797
2798 TableApx P-4. Estimated Water Releases of Trichloroethylene from Manufacturing Sites Not
2799 Reporting to 2016 TRI
Site
Annual
Operating
Days
(days/yr)
Daily
Production
Volume3
(kg/site-
day)
Daily
Wastewater
Flowb
(L/site-day)
Maximum
Daily
Release0
(kg/site-
day)
Average
Daily
Released
(kg/site-
day)
Average
Annual
Release6
(kg/site-
yr)
NPDES
Code
Release
Media
Solvents &
Chemicals,
Pearland,
TX
350
58,234
582,345
0.04
0.02
5.3
Not
available
Surface
Water
or
POTW
Occidental
Chemical
Corp.
Wichita,
KS
350
58,234
582,345
0.04
0.02
5.3
Not
available
Surface
Water
or
POTW
2800 POTW = Publicly-Owned Treatment Works
2801 a Daily production volume calculated using the annual production volume and dividing by the annual operating days per year
2802 (300 days/yr).
2803 b The estimated wastewater flow rate is calculated assuming 10 m3 of wastewater is produced per metric ton of TCE
2804 produced (equivalent to 10 L wastewater/kg of TCE) based on the SpERC for the manufacture of a substance (ES1G. 2012).
2805 0 The maximum daily release is calculated using the maximum daily concentration from the OCPSF EG, 26 |ig/L. and
2806 multiplying by the daily wastewater flow.
2807 d The average daily release is calculated using the maximum monthly average concentration from the OCPSF EG, 69 |ig/L.
2808 and multiplying by the daily wastewater flow.
2809 e The average annual release is calculated as the maximum monthly average concentration multiplied by the daily wastewater
2810 production and 350 operating days/year.
2811
2812 P.2 Processing as a Reactant
2813 P.2.1 Exposure Assessment
2814 EPA did not identify inhalation exposure monitoring data related processing TCE as a reactant.
2815 Therefore, EPA used monitoring data from the manufacture of TCE as surrogate. EPA believes the
2816 handling and TCE concentrations for both conditions of use to be similar. However, EPA is unsure of
2817 the representativeness of these surrogate data toward actual exposures to TCE at all sites covered by this
2818 condition of use.
2819
2820 EPA considered the assessment approach, the quality of the data, and uncertainties in assessment results
2821 to determine a level of confidence for the 8-hr TWA inhalation air concentrations. The primary strengths
2822 include the assessment approach, which is the use of surrogate monitoring data, in the middle of the
2823 inhalation approach hierarchy. These monitoring data include 16 data points from 1 source, and the data
2824 quality ratings from systematic review for these data were medium. The primary limitations of these
2825 data include the uncertainty of the representativeness of these surrogate data toward the true distribution
2826 of inhalation concentrations for the industries and sites covered by this scenario. Based on these
2827 strengths and limitations of the inhalation air concentration data, the overall confidence for these 8-hr
2828 TWA data in this scenario is medium to low.
2829
Page 701 of 748
-------
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The surrogate data was obtained from (HSIA) via public comment (Halogenated Solvents Industry
Alliance. 2018 51764151 presented in Table_Apx P-5 below. No data was found to estimate ONU
exposures during use of TCE as a reactant. EPA estimates that ONU exposures are lower than worker
exposures, since ONUs do not typically directly handle the chemical.
TableApx P-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)
Numbe
r of
Data
Points
Confidence
Rating of
Associated Air
Concentration
Data
High-End
2.59
0.86
0.59
0.30
16
Medium
Central
Tendency
0.38
0.13
0.09
0.03
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
P.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 P-6 summarizes the low and high end water release estimates.
Table Apx P-6. Water Release Estimates for Sites Using TCE as a I
teactant
Number of Sites
Annual
Release
(kg/site-yr)
Annual
Release Days
(days/yr)
Daily
Release
(kg/site-day)
NPDES
Code
Release Media
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.
Page 702 of 748
-------
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
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.3 Formulation of Aerosol and Non-Aerosol Products
P.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,
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 P-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
was 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 P-7. Summary of Worker Inhalation Exposure Monitoring Data for Unloading TCE
Scenario
8-hr TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of
Data Points
Confidence
Rating of Air
Concentration
Data
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.
P.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.
Page 703 of 748
-------
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.4 Repackaging
P.4.1 Exposure Assessment
EPA identified inhalation exposure monitoring data related unloading/loading TCE into/from bulk
transport containers. TableApx P-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
(DOW Deutschland. 2014b). It should be noted that this study indicates that the filling system uses a
"largely automated process" (DOW Deutschland. 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 was 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 P-8. Summary of Worker Inhalation Exposure Monitoring Data for
Unloading/Loading TCE from Bulk Containers
Scenario
8-hr TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of
Data Points
Confidence
Rating of Air
Concentration
Data
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.
P.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
Page 704 of 748
-------
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 TableApx
P-9.
Table Apx P-9. Reported Water Releases of Trichloroethylene from Sites Repackaging TCE
Site Identity
Annual
Release
(kg/site-
yr)a
Annual Release
Days (days/yr)
Daily Release
(kg/site-day)a
NPDES
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
release rate and assuming 250 days of operation per year.
Sources: (U.S. EPA. 2016a) and "(U.S. EPA. 2017c)'
, daily releases are back-calculated from the annual
P.5 Batch Open Top Vapor Degreasing
P.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 P-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 al.. 19881 (Ruhe et al.. 1981). (Barsan.
1991). (Ruhe. 1982). (Rosensteel and Lucas. 1975). (Seitz and Driscoll. 1989). (Gorman et al.. 1984).
(Gilles et al.. 1977). (Vandervort and Polakoff. 1973). 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
Page 705 of 748
-------
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
TableApx P-10. Summary of Worker Inhalation Exposure Monitoring Data for Batch Open-Top
Vapor Degreasing
Scenario
8-hr
TWA
(PPm)
AC
(PPm)
ADC
(ppm)
LADC
(ppm)
Number
of Data
Points
Confidence Rating
of Air
Concentration
Data
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 P-l illustrates the near-field/far-field model that can be applied to open-top vapor
degreasing (ATHA. 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,
Page 706 of 748
-------
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
denoted by Qff, determines how quickly TCE dissipates out of the surrounding space and into the
outside air.
Far-Field
FigureApx P-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 P-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 707 of 748
-------
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Percentile
8-hr TWA
(PPm)
ACa
(PPm)
ADC
(PPm)
LADC
(PPm)
Confidence Rating of Air
Concentration Data
Workers (Near-field)
High-End
388
129.3
88.5
35.3
N/A - Modeled Data
Central
Tendency
34.8
79.0
8.0
3.0
Occupational non-users (Far-Field)
High-End
237
79.0
54.0
21.1
N/A - Modeled Data
Central
Tendency
18.1
6.0
4.1
1.5
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
a Acute exposures calculated as a 24-hr TWA.
P.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; Kanegsberg and
Kanegsberg. 2011; 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 P-12.
TableApx P-12. Reported Water Releases of Trichloroethylene from Sites Using TCE in Open-
Top Vapor Degreasing
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)
NPDES
Code
Release Media
US Nasa Michoud Assembly
Facility, New Orleans, LA
509
260
1.96
LA0052256
Surface Water
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
Page 708 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)
NPDES
Code
Release Media
Delphi Harrison Thermal
Systems, Dayton. OH
9.3
260
0.04
OH0009431
Surface Water
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
Page 709 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)
NPDES
Code
Release Media
Ametek Inc. U.S. Gauge Div.,
Sellersville, PA
0.227
260
8.72E-04
PA0056014
Surface Water
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
OHO 127043
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,
Offutt 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 & Hannan 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 Teclisystems Operations
LLC, Elkton MD
3.02E-03
260
1.16E-05
MD0000078
Surface Water
Page 710 of 748
-------
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)
NPDES
Code
Release Media
Daikin Applied America, Inc.
(Formally Mcquay
International), Scottsboro, AL
2.15E-03
260
8.26E-06
AL0069701
Surface Water
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 (U.S. 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);28
• Iron and Steel Manufacturing Point Source Category Subpart J (U.S. EPA. 2019e);
• Metal Finishing Point Source Category Subpart A (U.S. EPA. 2019f);29
• Coil Coating Point Source Category Subpart D (U.S. EPA. 2019b);
• Aluminum Forming Point Source Category Subparts A, B, C, D, E, and F (U.S. EPA. 2019a);
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
28 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).
29 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 711 of 748
-------
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
P.6 Batch Closed-Loop Vapor Degreasing
P.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 P-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 P-13. Summary of Worker Inhalation Exposure Monitoring Data for Batch Closed-
^oop Vapor Degreasing
Scenario
8-hr
TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of Data
Points
Confidence
Rating of Air
Concentration
Data
High-End
1.4
0.5
0.3
0.2
19
High
Central
Tendency
0.5
0.2
0.1
0.04
AC = Acute Concentration. ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
P.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. 2014; Kanegsberg and Kanegsberg. 2011; NIOSH. 2002a. b, c, d). Similar to
Page 712 of 748
-------
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
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.
P.7 Conveyorized Vapor Degreasing
P.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 P-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). (Kinnes. 1998).
Table Apx P-14. Summary of Worker Inhalation Exposure Monitoring Data for Conveyorized
Vapor Degreasing
Scenario
8-hr
TWA
(ppm)
AC
(ppm)
ADC
(ppm)
LADC
(ppm)
Number of
Data Points
Confidence Rating of Air
Concentration Data
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
Page 713 of 748
-------
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
FigureApx P-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
Figure Apx P-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).
Page 714 of 748
-------
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx P-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
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.
Table Apx P-15. Summary of Exposure Modeling Results for TCE Degreasing in Conveyorized
Degreasers
Scenario
8-hr TWA
(ppm)
ACa
(ppm)
ADC
(ppm)
LADC
(ppm)
Data Quality Rating
of Associated Air
Concentration Data
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.
P.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 P.5.2 for OTVDs.
Page 715 of 748
-------
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.8 Web Vapor Degreasing
P.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.
FigureApx P-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
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
Figure Apx P-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
Page 716 of 748
-------
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
3282
3283
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 P-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
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.
Table Apx P-16. Summary of Exposure Mode ing Results for TCE Degreasing in Web Degreasers
Scenario
8-hr TWA
(ppm)
ACa
(ppm)
ADC
(ppm)
LADC
(ppm)
Confidence Rating
of Air
Concentration
Data
Workers (Near-field)
High-End
14.1
4.7
3.2
1.4
N/A - Modeled Data
Central
Tendency
5.9
2.0
1.4
0.5
Occupational non-users (Far-Field)
High-End
9.6
3.2
2.2
0.9
N/A - Modeled Data
Central
Tendency
3.1
1.0
0.7
0.3
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
a Acute exposures calculated as a 24-hr TWA.
P.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. 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.
Page 717 of 748
-------
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
P.9 Cold Cleaning
P.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.
FigureApx P-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
-> Q,
NF
Figure Apx P-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.
Page 718 of 748
-------
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
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 P-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 P-17. Summary of Exposure Modeling Results for Use of Trichloroethylene in Cold
Cleaning
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
ConrillciHT
killing of Air
C'oiHTiilmlion
D;itii
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.
Page 719 of 748
-------
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.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 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 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.
P. 10 Aerosol Applications: Spray Degreasing/Cleaning, Automotive
Brake and Parts Cleaners, Penetrating Lubricants, and Mold
Releases
P.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 P-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.
Page 720 of 748
-------
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (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. 20001 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
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.
FigureApx P-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. Table_Apx P-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.
Page 721 of 748
-------
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx P-18. Summary of Worker and Occupational Non-User Inhalation Exposure Modeling
Results for Aerosol Degreasing
Scenario
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.AIK
(ppm)
Confidence Killing
ol Air
C'onccnlriilion Dsitsi
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.
P.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
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.
P.ll Metalworking Fluids
P.ll.l Exposure Assessment
EPA identified inhalation exposure monitoring data from OSHA facility inspections (OJ ) 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 (
2( ). 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.
Table Apx P-19 summarizes the 8-hr TWA monitoring data for the use of TCE in MWFs. No data was
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.
Page 722 of 748
-------
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx P-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)
Number
of Data
Points
Confidence Rating of
Air Concentration
Data
High-End
75.4
25.1
17.2
8.8
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
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 Table Apx P-20 and are based on a NIOSH study of 79 small metalworking
facilities (OECD. 2011b). The concentrations for these estimates are for the solvent-extractable portion
and do not include water contributions (OECD. 2011b). The "typical" mist concentration is the
geometric mean of the data and the "high-end" is the 90th percentile of the data (OECD. 2011b).
Table Apx P-20. ESD Exposure Estimates for Metalworking Fluids Based on Monitoring Data
Type of Metalworking Fluid
Typical Mist Concentration
(mg/m3)a
High-End Mist Concentration
(mg/m3)b
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. 2011b)
Page 723 of 748
-------
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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. TableApx P-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 P-21. Summary of Exposure Results for Use of TCE in Metalworking Fluids Based on
ESD Estimates
Scenario
8-hr TWA
(PPm)a
ADC
(ppm)
LADC
(ppm)
Data Quality
Rating of
Associated Air
Concentration Data
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.
a The TCE exposure concentrations are calculated by multiplying the straight oil mist concentrations in Table Apx P-20 by
98% (the concentration of TCE in the metalworking fluid) and converting to ppm.
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.
P.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 (OECD. 2011b). Facilities
typically treat wastewater onsite due to stringent discharge limits to POTWs (OECD. 2011b). Control
technologies used in onsite wastewater treatment in the MP&M industry include ultrafiltration, oil/water
separation, and chemical precipitation (OECD. 2011b). Facilities that do not treat wastewater onsite
contract waste haulers to collect wastewater for off-site treatment (OECD. 2011b).
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.
P.12 Adhesives, Sealants, Paints, and Coatings
P.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.
Page 724 of 748
-------
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
TableApx P-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). The HHE data also
provided two data points where the worker job description was "foreman." 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 P-22. Summary of Worker Inhalation Exposure Monitoring Data for
Adhesives/Paints/Coatings
Scensirio
8-hr TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.ADC
(ppm)
Number
ol' Dili:t
Points
Con riclciHT
killing of Air
ConceiKriilion
Dsilsi
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.
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
Page 725 of 748
-------
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
commercial coatings, as EPA believes the activities and exposures will be similar between industrial and
commercial sites covered by this condition of use.
P.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, 2011a).
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, 2011a). A summary of the water releases can be found in TableApx
P-23.
Table Apx P-23. Reported Water Releases of Trichloroethylene from Sites Using TCE in
Adhesives, Sealants, Paints and Coatings
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)a
NPDES
Code
Release 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
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
Page 726 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)a
NPDES
Code
Release Media
Mastcrcraft 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
Royale 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
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
Page 727 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)a
NPDES
Code
Release Media
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 Cliristi Army Depot,
Corpus Cliristi, 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
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
Page 728 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Silo l(k'ii(i(>
Amiiiiil
Kck'iisc
(kii/si(e-\ n
Amiiiiil
Kok'.iso
l);i\ s
(il;i\s/\ n
l);iil\
Ki'loiiso
(k*i/si(e-
d;i\) 1
SIMMS
Cock'
Kcloiiso Modiii
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
3574 POTW = Publicly Owned Treatment Works
3575 Releases of 4.4 kg/site-yr for NEI sites estimated from total releases fromTRI andDMR sites and divided by the 3 sites
3 576 reporting water releases and the 14 sites reporting zero water releases in TRI).
3 577 a Daily releases are back-calculated from the annual release rate and assuming 250 days of operation per year.
3578 Sources: (U.S. EPA. 2018a. 2017c. 2016a)
3579
Page 729 of 748
-------
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
P.13
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Other Industrial Uses
P.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 P. 1.1 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 16 data points from 1 source, and the data
quality ratings from systematic review for these data were medium. The primary limitations of these
data include the uncertainty of the 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 P-24 summarizes the 8-hr TWA from monitoring data from TCE manufacturing. The data
were obtained from obtained from data submitted by the Halogenated Solvents Industry Alliance
(HSIA) via public comment for one company (Halogenated Solvents Industry Alliance. ).
No data was 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 P-24 Summary of Occupational Exposure Surrogate Monitoring Data for Unloading
TCE During Other Industrial Uses
Scciiiirio
8-hr
TWA
(ppm)
AC
(ppm)
ADC
(ppm)
I.ADC
(ppm)
Nil ill her of D;it;i
Points
Confidence killing of Air
Concenlnilion D;il;i
High-End
2.6
0.9
0.6
0.3
16
Medium
Central
Tendency
0.4
0.1
0.1
0.03
AC = Acute Concentration; ADC = Average Daily Concentration; and LADC = Lifetime Average Daily Concentration.
P.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 (
2019V
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 730 of 748
-------
3620
3621
3622
3623
3624
3625
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
To estimate the daily release, EPA assumed a default of 250 days/yr of operation and averaged the
annual release over the operating days. Table Apx P-25 summarizes the water releases from the 2016
TRI and DMR for sites with non-zero discharges.
Table Apx P-25. Reported Water Releases of Trichloroethylene from Other Industrial Uses
Site Identity
Annual
Release
(kg/site-yr)
Annual
Release
Days
(days/yr)a
Daily
Release
(kg/site-
day)a
NPDES
Code
Release
Media
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
3626 a Annual release amounts are based on the site reported values. Therefore, daily releases are calculated from the annual
3 627 release rate and assuming 250 days of operation per year.
3628 Sources: (U.S. EPA. 2017c. 2016a)
3629
Page 731 of 748
-------
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.14 Spot Cleaning, Wipe Cleaning and Carpet Cleaning
P.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 P-26 summarizes the 8-hr TWA monitoring data and acute TWAs from the monitoring data
for the use of TCE in in spot cleaning. No data was found to estimate ONU exposures during spot
cleaning. The data were obtained from NIOSH a Health Hazard Evaluation report (HHE) (Burton and
Monesterskev. 1996). as well as a NIOSH Report on Control of Health and Safety Hazards on
Commercial Drycleaners 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
Monesterskev. 1996). 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 P-26. Summary of Worker Inhalation Exposure Monitoring Data for Spot Cleaning
Using TCE
Scenario
8-hr TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of 8-
hr TWA Data
Points
Confidence
Rating of Air
Concentration
Data
High-End
2.8
1.0
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 732 of 748
-------
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 (IRTA. 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 P-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
Figure Apx P-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 P-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 733 of 748
-------
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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 P-27. Summary of Exposure Modeling Results for Spot Cleaning Using TCE
Scenario
8-hr TWA
(ppm)
AC (24-hr)
(ppm)
A IK
(ppm)
I.AIK
(ppm)
Qiiiililv Killing of
Associated Air Concent nil ion
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.
P.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 P-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 P-28. Reported Water Releases of Trichloroethylene from Sites Using TCE Spot
Cleaning
Silc
Annual
Release11
(kg/silc-vcar)
Annual
Release
Days
(davs/vr)
Dailv Uclcasc
(kg/sitc-dav)11
Media of Uclcasc
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
Page 734 of 748
-------
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Site
Annual
Release3
(kg/site-year)
Annual
Release
Days
(days/yr)
Daily Release
(kg/site-day)a
Media of Release
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)
P. 15 Industrial Processing Aid
P.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 P-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. 2014). The data was 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 P-29. Summary of Exposure Monitoring Data for Use as a Processing Aid
Scenario
12-hr
TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of 12-
hr Data Points
Confidence
Rating of Air
Concentration
Data
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
Page 735 of 748
-------
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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.
P.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
P-30.
Table Apx P-30. Reported Water Releases of Trichloroethylene from Industrial Processing Aid
Sites Using TCE
Site Identity
Annual
Release
(kg/site-yr)a
Annual
Release
Days
(days/yr)
Daily Release
(kg/site-day)a
NPDES
Code
Release
Media
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,
Barbcrton. 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: (U.S. EPA. 2017c. 2016a)
Page 736 of 748
-------
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.16 Commercial Printing and Copying
P.16.1 Exposure Assessment
EPA identified inhalation exposure monitoring data from a NIOSH a Health Hazard Evaluation report
(HHE) (Finely and Page. 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. TableApx P-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. 2005).
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
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.
Table Apx P-31. Summary of Worker Inhalation Exposure Monitoring Data for High Speed
Printing Presses
Scenario
8-hr TWA
(PPm)
AC
(PPm)
ADC
(PPm)
LADC
(PPm)
Number of
Data Points
Confidence Rating of
Air Concentration
Data
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.
P.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. 2010). 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 P-32.
Table Apx P-32. Reported Water Releases of Trichloroethylene from Commercial Printing and
C°Pying
Annual
Release
(kg/site-yr)a
Annual
Site Identity
Release
Days
(days/yr)
Daily Release
(kg/site-day)a
NPDES
Code
Release
Media
Page 737 of 748
-------
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Printing and Pub Sys Div, Weatherford,
OK
0.05
250
2.0E-4
OK0041785
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.
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.
P. 17 Other Commercial Uses
P.17.1 Exposure Assessment
EPA did not identify any inhalation exposure monitoring data related to TCE use in other commercial
uses. See Section P. 14.1 for the assessment of worker exposure during spot cleaning activities. EPA
assumes the exposure sources, routes, and exposure levels are similar to those for spot cleaners.
P.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 P-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 P-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 P-33. Reported Water Releases of Trichloroethylene from Other Commercial Uses in
the 2016 DMR
Site Identity
Annual
Release
(kg/site-
yr)
Annual
Release
Days
(days/yr)
Daily
Release
(kg/site-
day)
NPDES
Code
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
OHO 141496
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
Page 738 of 748
-------
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
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
a 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)
P.18 Process Solvent Recycling and Worker Handling of Wastes
P.18.1 Exposure Assessment
EPA did not identify any inhalation exposure monitoring data related to waste handling/recycling. See
Section P.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.
P.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 P-34 summarizes the water releases
from the 2016 DMR and 2016 TRI for sites with non-zero discharges.
Table Apx P-34. Estimated Water Releases of Trichloroethylene from Disposal/Recycling of TCE
Site Identity
Annual
Release
(kg/site-
yr)a
Annual Release
Days (days/yr)
Daily Release
(kg/site-day)a
NPDES
Code
Release Media
Veolia Es Technical
Solutions LLC,
Middlesex, NJ
6035
250
24.1
Not
available
POTW WWT (0.02%)
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 Enviromnental
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: (U.S. EPA 2017c) and "(U.S. EPA 2016a)'
Page 739 of 748
-------
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
P.19 Appendix P References
All Li (American Industrial Hygiene Association). (2009). Mathematical models for estimating
occupational exposure to chemicals. In CB Keil; CE Simmons; TR Anthony (Eds.), (2nd ed.).
Fairfax, VA: AIHA Press.
Arkema Inc. (2018). Ark em a Inc. comments to inform EPAs rulemaking on problem formulations for
the risk evaluations to be conducted under the Toxic Substances Control Act, and general
guiding principles to apply systematic review in TSCA risk evaluations. (EPA-HQ-OPPT-2016-
0737-0109). Washington, D.C. https://www.regulations.gov/document?D=EPA-HQ-OPPT-
ATSDR (Agency for Toxic Substances and Disease Registry). (2014). Toxicological profile for
tetrachloroethylene (Draft for public comment). Atlanta, GA: US Department of Health and
Human Services, Public Health Service.
http://www.atsdr.cdc. eov/ToxProfiles/tp.asp?id=265&tid=48
Bakke. B; Stewart. P; Waters. M. (2007). Uses of and exposure to trichloroethylene in U.S. industry: A
systematic literature review [Review], J Occup Environ Hyg 4: 375-390.
http://dx.doi.org/10.1080/15459620701301763
Baldwin. PE; Mavn (1998). A survey of wind speed in indoor workplaces. Ann Occup Hyg 42:
303-313. http://dx.doi.( 5/80003-4878(98)00031-3
Barsan. ME. (1991). Health hazard evaluation report no. HETA 90-344-2159, AAV. Cash Valve
Manufacturing Corporation, Decatur, Illinois. (HETA 90-344-2159). Cincinnati, OH: National
Institute for Occupational Safety and Health.
Burton. NC: Monesterskev. J. (1996). Health hazard evaluation report no. HETA 96-0135-2612, Eagle
Knitting Mills, Inc., Shawano, Wisconsin. (HETA 96-0135-2612). Cincinnati, OH: National
Institute for Occupational Safety and Health.
C (California Air Resources Board). (2000). Initial statement of reasons for the proposed airborne
toxic control measure for emissions of chlorinated toxic air contaminants from automotive
maintenance and repair activities.
C (2006). California Dry Cleaning Industry Technical Assessment Report. Stationary Source
Division, Emissions Assessment Branch.
https://www.arb.ca.eov/toxics/drYclean/finaldrycleantechreport.pdf
Cherrie. JW; Semple. S: Brouwer. D. (2004). Gloves and Dermal Exposure to Chemicals: Proposals for
Evaluating Workplace Effectiveness. Ann Occup Hyg 48: 607-615.
http://dx.doi.ore/10.1093/annhve/meh060
Chrostek ine. M. S. (1981). Health hazard evaluation report no. HHE 30-153-881, Palmer
Industrial Coatings Incorp., Williamsport, Pennsylvania. (HHE 30-153-881). Cincinnati, OH:
National Institute for Occupational Safety and Health.
Crandall. MS; Albrecht. WN. (1989). Health Hazard Evaluation Report No. HETA-86-380-1957, York
International Corporation, Madisonville, Kentucky (pp. 86-380). (NIOSH/00189611). Crandall,
MS; Albrecht, WN.
Dancik. Y; Bietiardi. PL; Bieliardi-Oi. M. ei. (2015). What happens in the skin? Integrating skin
permeation kinetics into studies of developmental and reproductive toxicity following topical
exposure. Reprod Toxicol 58: 252-281. http://dx.doi.on 10 101 i.reprotox.201 ^ 10 001
Daniels. \\ i Urn1 P; Kramkowski. R; Aim ague i 1} (1988). Health Hazard Evaluation Report No.
HETA-86-121-1923, Modern Plating Corporation, Freeport, Illinois (pp. 86-121).
(NIOSH/00184446). Daniels, WJ; Orris, P; Kramkowski, R; Almaguer, D.
Demou. E; Hettwee. S; Wilson. MP; Hammond. SK; Mckone. TE. (2009). Evaluating indoor exposure
modeling alternatives for LCA: A case study in the vehicle repair industry. Environ Sci Technol
43: 5804-5810. http://dx.doi.ore/10.1021/es8
Page 740 of 748
-------
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Dow Chemical (Dow Chemical Company). (2014). Product safety assessment: Trichloroethylene.
DOW Deutschland. (2014a). Chemical safety report: Use of trichloroethylene in industrial parts cleaning
by vapour degreasing in closed systems where specific requirements (system of use-parameters)
exist. Ispra, Italy: European Commission Joint Research Centre, Institute for Health and
Consumer Protection, European Chemicals Bureau.
http://ec.europa.eu/DocsRoom/documents/14369/attachments/l/translations/en/renditions/native
DOW Deutschland. (2014b). Chemical safety report: Use of trichloroethylene in packaging. Ispra, Italy:
European Commission Joint Research Centre, Institute for Health and Consumer Protection,
European Chemicals Bureau.
http://ec.europa.eu/DocsRoom/documents/14371/attachments/l/translations/en/renditions/native
Dufkee. J. (2014). Cleaning with solvents: Methods and machinery. In Cleaning with solvents: Methods
and machinery. Oxford, UK: Elsevier Inc.
https://www.sciencedirect.com/book/978Q3232252Q5/cleaning~with~solvents~methods~and~
machinery
EC (European Commission). (2014). Exposure scenario: Use: Trichloroethylene as an extraction solvent
for removal of process oil and formation of the porous structure in polyethylene based separators
used in lead-acid batteries. Ispra, Italy: European Commission Joint Research Centre, Institute
for Health and Consumer Protection, European Chemicals Bureau.
http://ec.europa.eu/DocsRoom/documents/12344/attachments/l/translations/en/renditions/native
ECB (European Chemicals Bureau). (2004). European Union risk assessment report: Trichloroethylene
(pp. 1-348). (EUR 21057 EN). European Commission.
https://echa.eiiropa.eii/documents/10162/83f0c99f-f687-4cdf-a64b~514fle26fdc0
Elkin. LM. (1969). Process Economics Program, Chlorinated Solvents, Report No. 48. In Kirk-Othmer
Encyclopedia of Chemical Technology. Menlo Park, CA: Stanford Research Institute.
ENTEK International Limited. (2014). Analysis of alternatives: Use of trichloroethylene as an extraction
solvent for removal of process oil and formation of the porous structure in polyethylene based
separators used in lead-acid batteries. Helsinki, Finland: European Chemicals Agency.
https://echa.eiiropa.eii/documents/10162/9a728963~e57f-48de~b977-7d05462c43e9
ESIG. (2012). SPERC fact sheet: Manufacture of substance - industrial (solvent-borne). Brussels,
Belgium: European Solvents Industry Group (ESIG). https://www.esig.org/reach-
ges/environment/
FH. F. (2012). Dermal Absorption of Finite doses of Volatile Compounds. J Pharm Sci 101: 2616-2619.
http://dx.doi.org/10.1080/15287394
Finely. M; Page. E. (2005). Health hazard evaluation report no. HETA 2003-0203-2952, Wallace
Computer Services, Clinton, Illinois. (HETA 2003-0203-2952). Cincinnati, OH: National
Institute for Occupational Safety and Health.
Frasch. HF; Bunge. AL. (2015). The transient dermal exposure II: post-exposure absorption and
evaporation of volatile compounds. J Pharm Sci 104: 1499-1507.
http://dx.doi.org/10.1002/jps.24334
Frasch. HF; Dotson. GS: Barbero. AM. (201 1). In vitro human epidermal penetration of 1 -
bromopropane. J Toxicol Environ Health A 74: 1249-1260.
http://dx.doi.org/i0J080/15287394.201 I ^5666
Gitto! Vx Phillips. AM; Pembertori J \ (2001). Potential exposure of hands inside protective glovesa
summary of data from non-agricultural pesticide surveys. Ann Occup Hyg 45: 55-60.
http://dx.doi.orE >0003-4878(00)00013-2
Gilbei* I* er. M; Lyman. W; Magil. G; Walker. P; Wallace. D; Wechslei \ \ i (1982). An
exposure and risk assessment for tetrachloroethylene. (EPA-440/4-85-015). Washington, DC:
Page 741 of 748
-------
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
U.S. Environmental Protection Agency, Office of Water Regulations and Standards.
http://nepis.epa.gov/Exe/ZvPURL.cgi?Dockey=2000LLQH.txt
Gille; riania. TL; Ilka. R. (1977). Health hazard evaluation report no. HHE 77-12-418, Airtex
Products, Fairfield, Illinois. (HHE 77-12-418). Cincinnati, OH: National Institute for
Occupational Safety and Health.
Gille? lilbin. E. (1976). Health hazard evaluation report no. HHE 76-61-337, TRW Incorporated,
Philadelphia, Pennsylvania. (HHE 76-61-337). Cincinnati, OH: National Institute for
Occupational Safety and Health.
GmbH. WC. (1940). German Patent 901,774. In Kirk-Othmer Encyclopedia of Chemical Technology.
Wacker Chemie GmbH.
Golsteiin. L; Huizei tuck. M; van Zelm. R; Huiibregts. MA. (2014). Including exposure variability
in the life cycle impact assessment of indoor chemical emissions: the case of metal degreasing.
Environ Int 71: 36-45. http://dx.doi.org/10.1016/i .envint.2014.06.003
Gorman. R; Rinsky. R; Stein. G; Anderson. K. (1984). Health hazard evaluation report no. HETA 82-
075-1545, Pratt & Whitney Aircraft, West Palm Beach, Florida. (HETA 82-075-1545).
Cincinnati, OH: National Institute for Occupational Safety and Health.
Halogenated Solvents Industry Alliance. I. (2017). RE: Docket no. EPA-HQ-2016-0737. (EPA-HQ-
OPPT-2016-0737-0027). Washington, D C. https://www.regulations.gov/document?D=
HQ-QPPT-201 0 ¦« 00.
Halogenated Solvents Industry Alliance. I. (2018). Re: Docket no. EPA-HQ-OPPT-2016-0737. (EPA-
HQ-OPPT-2016-0737-0103). Washington, D.C.
https://www.regiilations.gov/dociimeiii * r,PA-HQ-Q]T 1 .01 0 '< 0l0'<
Hellweg. S: Demon. E: Bruzzi. R; Meiier. A: Rosenbaum. RK; Huiibregts. MA; Mckone. TE. (2009).
Integrating human indoor air pollutant exposure within Life Cycle Impact Assessment [Review],
Environ Sci Technol 43: 1670-1679. http://dx.doi.org/10.1021/es8
ICF Consulting. (2004). The U.S. solvent cleaning industry and the transition to non ozone depleting
substances. https://www.epa.gov/sites/production/files/2Q14-
11 /documents/epasolventmarketreport.pdf
IRTA. (Institute for Research and Technical Assistance). (2007). Spotting chemicals: Alternatives to
perchloroethylene and trichloroethylene in the textile cleaning industry. Prepared for: Cal/EPAs
Department of Toxic Substances Control and U.S. Environmental Protection Agency Region IX.
http://www.irta.us/report s/DTSC%20Spotting%2QChemical%20for%2QWeb.pdf
Kanegsbi legsberg. E. (201 1). Handbook for critical cleaning, cleaning agents and systems
(2nd ed.). Boca Raton, FL: CRC Press.
Kasti iller. MA. (2006). Kinetics of finite dose absorption through skin 2: Volatile
compounds. J Pharm Sci 95: 268-280. http://dx.doi.Org/l0.1002/ips.20497
Kinnes. GM. (1998). Health hazard evaluation report no. HETA 97-0214-2689, Dorm a Door Controls,
Inc., Reamstown Pennsylvania. (HETA 97-0214-2689). Cincinnati, OH: National Institute for
Occupational Safety and Health.
Klein. P; Kurz. J. (1994). [Reduction of Solvent Concentrations in Surroundings of Dry-Cleaning
Shops], Bonningheim, Germany: Hohenstein Physiological Institute on Clothing.
Lewis. FA. (1980). Health hazard evaluation report no. HHE 80-87-708, Harowe Servo Contorls Inc.,
West Chester, Pennsylvania. (HHE 80-87-708). Cincinnati, OH: National Institute for
Occupational Safety and Health.
Marquart. H; Franken. R; Goede. H; Fransman. W; Schinkel. J. (2017). Validation of the dermal
exposure model in ECETOC TRA. Annals of Work Exposures and Health 61: 854-871.
http://dx.doi.org/10.1093/annweh/wxx059
Page 742 of 748
-------
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
4061
4062
4063
4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Morf (2017). Comment submitted by Richard G. Morford, General Counsel, Enviro Tech
International, Inc. Available online at https://www.regulations.gov/document?D=EPA-HQ-
QPPT-2016-0741-0016
Morris. M; Wolf. K. (2005). Evaluation of New and Emerging Technologies for Textile Cleaning.
Institute for Research and Technical Assistance (IRTA).
http://wsppn.ore/pdf/irta/Emereine Technologic lie %20Clea.pdf
Most. €€. (1989). Locating and estimating air emissions from sources of perchloroethylene and
trichloroethylene. (EPA-450/2-89-013). Research Triangle Park, NC: U.S. EPA.
https://www3.epa.eov/ttn/chief/le/perc.pdf
NEWMOA (Northeast Waste Management Officials' Association). (2001). Pollution prevention
technology profile - Closed loop vapor degreasing. Boston, MA.
http://www.newmoa.ore/prevention/p2t.ech/ProfileVaporDeereasine.pdf
NIH. (2012). Hazardous Substances Data Bank (HSDB): Trichloroethylene. Bethesda, MD: U.S.
Department of Health & Human Services, National Institutes of Health, U.S. National Library of
Medicine, https://toxnet.nlm.nih.eov/newtoxnet/hsdb.htm
NIOSH (National Institute for Occupational Safety and Health). (1997). Control of health and safety
hazards in commercial drycleaners: chemical exposures, fire hazards, and ergonomic risk factors.
In Education and Information Division. (DHHS (NIOSH) Publication Number 97-150). Atlanta,
GA. http://www.cdc.eov/niosh/docs/97-150/
NIOSH (National Institute for Occupational Safety and Health). (2001). Evaluation of Solvent
Exposures from the Degreaser. Trilthic Inc., IN. In Hazard Evaluation Technical Assisstance
Branch. (HETA 2000-0233-2845). NIOSH Publishing Office: National Institute of Occupational
Safety and Health, http://www.cdc.eov/niosh/hhe/reports/pdfs/2000-0233-2845.pdf
NIOSH. (2002a). In-depth survey report: control of perchloroethylene (PCE) in vapor degreasing
operations, site #1. (EPHB 256-19b). Cincinnati, Ohio: National Institute for Occupational
Safety and Health (NIOSH).
NIOSH (National Institute for Occupational Safety and Health). (2002b). In-depth survey report:
Control of perchloroethylene (PCE) in vapor degreasing operations, site #2. (EPHB 256-16b).
CDC. https://www.cdc.gov/niosh/surveyreports/pdfs/256-16b.pdf
NIOSH. (2002c). In-depth survey report: control of perchloroethylene (PCE) in vapor degreasing
operations, site #4. (EPHB 256-18b). Cincinnati, Ohio: National Institute for Occupational
Safety and Health (NIOSH).
NIOSH (National Institute for Occupational Safety and Health). (2002d). In-depth survey report:
Control of perchloroethylene exposure (PCE) in vapor degreasing operations, site #3. (EPHB
256-17b). CDC. https://www.cdc.gov/niosh/surveyreports/pdfs/ECTB-256-17b.pdf
OECD (Organisation for Economic Co-operation and Development). (2004). Emission scenario
document on lubricants and lubricant additives. In OECD Series On Emission Scenario
Documents. (JT00174617). Paris, France.
http://www.olis.oecd. org/olis/2004doc.nsf/LinkTo/env-im-mono(2004)21
OECD (Organisation for Economic Co-operation and Development). (2009a). Emission scenario
document on adhesive formulation. (JT03263583). Paris, France.
OECD (Organisation for Economic Co-operation and Development). (2009b). Emission scenario
documents on coating industry (paints, lacquers and varnishes). (JT03267833). Paris, France.
OECD (Organisation for Economic Co-operation and Development). (2010). Scoping Document for
Emission Scenario Document on Manufacturing and Use of Printing Inks. OECD Environmental
Health and Safety Publications.
OECD (Organisation for Economic Co-operation and Development). (201 la). EMISSION SCENARIO
DOCUMENT ON RADIATION CURABLE COATING, INKS AND ADHESIVES. In Series
Page 743 of 748
-------
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
on Emission Scenario Documents No 27. Paris: OECD Environmental Health and Safety
Publications, http://www.oecd-
ilibrarv.org/docserver/download/5 df?expires=1497031939&id=id&accname=guest&c
hecksum=C794B2987D98 82280
OECD (Organisation for Economic Co-operation and Development). (201 lb). Emission scenario
document on the use of metal working fluids. In OECD Environmental health and safety
publications Series on emission scenario documents Emission scenario document on coating and
application via spray painting in the automotive refinishing industry Number 11. (JT03304938).
Organization for Economic Cooperation and Development.
OECD (Organisation for Economic Co-operation and Development). (2015). Emission scenario
document on use of adhesives. (Number 34). Paris, France.
http://www.oecd. org/officialdocuments/publicdisplavdocumentpdf/?cote=ENV/JM/MONO(2015
)4&docl an guage=en
OECD. (2017). Draft ESD on Vapor Degreasing - Internal EPA document. Organization for Economic
Co-operation and Development (OECD).
OECD. (2019). Emission scenario documents. Available online at http://www.oecd.org/env/ehs/risk-
as ses sm ent/em i s si on seen ari odocum ents.htm (accessed August 29, 2019).
Orris. liets. W (1981). Health Hazard Evaluation Report 80-201-816: Peterson/Puritan Company.
(HE 80-201-816). NIOSH. https://www.cdc.gov/niosh/hhe/reports/pdfs/80-201-
816.pdf?td= 10.26616/NIQ SHHHE8 0
OSHA (Occupational Safety & Health Administration). (2017). Chemical Exposure Health Data
(CEHD) provided by OSHA to EPA. U.S. Occupational Safety and Health Administration.
Products. lAfSDaM. (2012). A1SE SPERC fact sheet - wide dispersive use of cleaning and maintenance
products. International Association for Soaps Detergents and Maintenance Products.
https://www.aise.eu/our-activities/regulatorv-context/reach/environmental-exposure-
assessment.aspx
Rosenste :as. IB. (1975). Health hazard evaluation report no. HHE 74-28-212, Westinghouse
Air Brake Company, Wilmerding, Pennsyvlania. (HHE 74-28-212). Cincinnati, OH: National
Institute for Occupational Safety and Health.
Ruhe. R.L. (1982). Health hazard evaluation report no. HETA 82-040-1 19, Synthes Ltd. (USA),
Monument, Colorado. (HETA 82-040-119). Cincinnati, OH: National Institute for Occupational
Safety and Health.
Ruhe. R.L; Watanabe. A: Stein. G (1981). Health hazard evaluation report no. HHE 80-49-808, Superior
Tube Company, Collegeville, Pennsylvania. (HHE 80-49-808). Cincinnati, OH: National
Institute for Occupational Safety and Health, https://www.cdc.gov/niosh/hhe/reports/pdfs/80-49-
8Q8.pdf
& aica. 1. (2017). Memorandum: Trichoroethylene, Docket ID number EPA-HQ-OPPT-2016-
0737. (EPA-HQ-OPPT-2016-0737-0007). Washington, D.C.
https://www.regulations.gov/documei11 \-HQ-OP" I _0l 0 '< 0007
SCO (Scientific Consulting Group, Inc.). (2013). Final peer review comments for the OPPT
trichloroethylene (TCE) draft risk assessment. Available online at
https://www.epa.gov/sites/production/files/2Q17-
06/docum ents/tce consolidated peer review comments septemb ;
Seitz. T; Driscoll. R (1989). Health hazard evaluation report no. HETA 88-082-1971, Jostens
Incorporated, Princeton, Illinois. (HETA 88-082-1971). Cincinnati, OH: National Institute for
Occupational Safety and Health, https://www.cdc.gov/niosh/hhe/reports/pdfs/1988-0082-
:
Page 744 of 748
-------
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Smart. BE; Fernandez. RE. (2000). Fluorinated aliphatic compounds [Encyclopedia], In Kirk-Othmer
encyclopedia of chemical technology. Hoboken, NJ: John Wiley and Sons, Inc.
http://dx.doi.ore 10 100: 047123896) 0>-i:: I nrUQl IS.aOl
Snedecor. G; Hickman. JC: Mertens. J A. (2004). Chloroethylenes and chloroethanes.
Tomer. A; Kane. J. (2015). The great port mismatch. U.S. goods trade and international transportation.
The Global Cities Initiative. A joint project of Brookings and JPMorgon Chase.
https://www.brookines.edii/wp-content/iiploads/2015/06/brekssrvyecifreiehtnetworks.pdf
lis JUS (U.S. Bureau of Labor Statistics). (2014). Employee Tenure News Release. Available online
at http://www.bls.eov/news.release/archives/tem 314.htm
(U.S. Bureau of Labor Statistics). (2016). May 2016 Occupational Employment and Wage
Estimates: National Industry-Specific Estimates. Available online at
http://www.bls.eov/oes/tables.htm
sus Bureau. (2013). Census 2012 Detailed Industry Code List [Database], Retrieved from
https://www.census.gov/topics/emplovment/industrv-occupation/guidance/code-lists.html
sus Bureau. (2015). Statistics of U.S. Businesses (SUSB).
https://www.census.eov/data/tables/2015/econ/susb/2015-susb-annual.html
sus Bureau. (2019). Survey of Income and Program Participation: SIPP introduction and
history. Washington, DC. https://www.census.gov/programs-survevs/sipp/about/sipp-
introducti on -history .html
(U.S. Environmental Protection Agency). (1977). Control of volatile organic emissions from
solvent metal cleaning [EPA Report], (EPA-450/2-77-022). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Air and Waste Management, Office of Air Quality
Planning and Standards.
(U.S. Environmental Protection Agency). (1980). Chapter 4.7: Waste Solvent Reclamation. In
AP-42 Compilation of air pollutant emission factors (5th ed.). Research Triangle Park, NC:
Office of Air and Radiation, Office of Air Quality and Planning Standards.
https://www3.epa.eov/ttn/chief/ap42/ch04/index.html
(U.S. Environmental Protection Agency). (1981). AP-42. Compilation of air pollutant
emission factors. Chapter 4.6: Solvent degreasing. Washington, DC.
http://www3.epa.gov/ttn/chief/ap42/ch04/final/c4sQ6.pdf
(U.S. Environmental Protection Agency). (1985). Occupational exposure and environmental
release assessment of tetrachloroethylene. Office of Pesticides and Toxic Substances.
(U.S. Environmental Protection Agency). (1992). Guidelines for exposure assessment. Federal
Register 57(104):22888-22938 [EPA Report], In Guidelines for exposure assessment.
(EPA/600/Z-92/001). Washington, DC.
http: //cfpub. epa. gov/n cea/cfm/recordi splay, cfm ?der 3
(1994). Guidelines for Statistical Analysis of Occupational Exposure Data: Final. United
States Environmental Protection Agency :: U.S. EPA.
(U.S. Environmental Protection Agency). (1997). Solvent Cleaning. Volume III, Chapter 6.
pp. 6.2.1. Washington, DC. http://www3.epa.gov/ttnchiel/eiip/techreport/volume03/iii06fin.pdf
(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. 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.
Page 745 of 748
-------
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
(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.reeulations.eov/document?D=EPA-HQ »1 \k J00J 000')-0022
(U.S. Environmental Protection Agency). (2011). The 2011 National Emissions Inventory.
Retrieved from https://www.epa.eov/air-emissions4nventories/2011 -national-emissions-
inventory-nei-data
U.S. EPA (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/prodiiction/files/2015-05/dociiments/user euide.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).
Washington, DC: Environmental Protection Agency, Office of Chemical Safety and Pollution
Prevention, http://www2.epa.gov/sites/production/files/2015-
09/documents/tce opptworkplanchemra fin<
(U.S. Environmental Protection Agency). (2016a). EPA Discharge Monitoring Report Data.
Retrieved from https://cfpub.epa.eov/dmr/
(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-
reportine/instmctions-reportine-2016~tsca-chemical~data~reportine
(U.S. Environmental Protection Agency). (2017a). Chemical data reporting under the Toxic
Substances Control Act. Available online at https://www.epa.eov/chemical-data-reportine
(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.
http s: // www, reeul ati on s. gov/docum ei P A-HQ-OPPT-2016-0737-0003
(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.
https://www.epa.eov/sites/production/files/2016-
0 5/docum ents/in structi on s for reportine 2016 tsca cdi
(U.S. Environmental Protection Agency). (2018a). 2014 National Emissions Inventory
Report, https://www.epa.eov/air-emissions-inventories/ ati onal-emis 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.
https://www.epa.eov/sites/production/files/2018-
06/documents/final application of sr in tsc if
Page 746 of 748
-------
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
(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.eov/sites/production/files/2018-06/documents/tce problem formulation C
31.pdf
(U.S. Environmental Protection Agency). (2018d). TRI Reporting Forms and Instructions
(RFI) Guidance Document.
https://ofmpiib.epa.eov/apex/euideme ext/P?p=euideme 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.eov/cei-bin/text-
ic Ifl78f42f8141c0887e5f4&mc=true&node=pt40.32.467&ren=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-
Ifl78f42f8141c0887e5f4&mc=true&node=pt40.32.465&ren=div5
(U.S. Environmental Protection Agency). (2019c). Electrical and electronic components point
source category. (40 CFR Part 469). Washington, D.C. https://www.ecfr.eov/cei-bin/text-
ic Ifl78f42f8141c0887e5f4&mc=true&node=pt40.32.469&ren=div5
(U.S. Environmental Protection Agency). (2019d). Electroplating Point Source Category. (40
CFR Part 413). Washington, D.C. https://www.ecfr.eov/cei-bin/text-
"v5al9d4dd729dble53fb9c , > \ o ¦&mc=tme&node=pt40 '<1 i I 'A.rgn=div5
(U.S. Environmental Protection Agency). (2019e). Iron and steel manufacturing point source
category. (40 CFR Part 420). Washington, D.C. https://www.ecfr.eov/cei-bin/text-
ic!\ "v5al9d4dd729dble53fb9c , t s o '&mc=true&node=pt4^ l L^V..rgn=div5
(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-
Ifl78f42f8141c0887e5f4&mc=true&node=pt40.32.433&ren=div5
(U.S. Environmental Protection Agency). (2019g). Organic chemicals, plastics, and synthetic
fibers. (40 CFR Part 414). Washington, D.C. https://www.ecfr.eov/cei-bin/text-
ic!\ "v5al9d4dd729dble53fb9c i > s ^ '&mc=tme&node=pt40 '<1 i I K\.rgn=div5
(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; Scherineer. M; Huneerbilhler. K. (2006). Assessing occupational exposure
to perchloroethylene in dry cleaning. J Occup Environ Hyg 3: 606-619.
http://dx.doi.ore/ i 0. i 080/15459620600912173
Von Grote. J; Hurliman Scheringer. M; Huneerbilhler. 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.( 3/si.iea.7500288
Whittaker. SG; Johansc (2011). 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.ore/piiblications/publications detail. aspx?DocID=Oh73%2fQile9Q%3
d
Page 747 of 748
-------
PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
4266 Young. ML. (2012). Pre-spotting step toward better cleaning. Available online at
4267 https://arnericandrvcleaner.corn/art.icles/pre-spotting-step-toward-better-cleaning
4268
Page 748 of 748
------- |