SEPA
PEER REVIEW DRAFT -DO NOT QUOTE OR CITE
United States EPA DocumentW 740-R1-5001
Environmental Protection Agency February 2016
Office of Chemical Safety and
Pollution Prevention
TSCA Work Plan Chemical Risk Assessment
PEER REVIEW DRAFT
1-Bromopropane:
(n-Propyl Bromide)
Spray Adhesives, Dry Cleaning, and Degreasing Uses
CASRN: 106-94-5
February 2016
NOTICE: This information is distributed solely for the purpose of pre-dissemination peer review
under applicable information quality guidelines. It has not been formally disseminated by EPA.
It does not represent and should not be construed to represent any Agency determination or
policy. It is being circulated for review of its technical accuracy and science policy implications.
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TABLE OF CONTENTS
TABLE OF CONTENTS 2
AUTHORS / CONTRIBUTORS / ACKNOWLEDGEMENTS / REVIEWERS 15
ABBREVIATIONS 17
EXECUTIVE SUMMARY 22
1 BACKGROUND AND SCOPE 27
1.1 INTRODUCTION 27
1.2 USES AND PRODUCTION VOLUME 28
1.3 ASSESSMENT AND REGULATORY HISTORY 29
1.4 SCOPE OFTHE ASSESSMENT 30
1.5 PROBLEM FORMULATION 32
1.5.1 Physical and Chemical Properties 32
1.5.2 Environmental Fate 33
1.5.3 Persistence and Bioconcentration 34
1.5.4 Conceptual Model 35
1.5.4.1 Exposure Pathways 36
1.5.4.2 Health Effects and Human Receptors 37
1.5.5 Analysis Plan 37
2 HUMAN EXPOSURE ASSESSMENT 38
2.1 OCCUPATIONAL EXPOSURES 38
2.1.1 Approach and Methodology 38
2.1.2 Spray Adhesives 40
2.1.2.1 Process and Worker Activity Descriptions 40
2.1.2.2 Estimate of Number of Workers Potentially Exposed 41
2.1.2.3 Assessment of Inhalation Exposure Based on Monitoring Data 42
2.1.2.4 Estimate of Inhalation Exposure Based on Modeling 44
2.1.3 Dry Cleaning 44
2.1.3.1 Process and Worker Activity Descriptions 44
2.1.3.2 Estimate of Number of Workers Potentially Exposed 45
2.1.3.3 Assessment of Inhalation Exposure Based on Monitoring Data 46
2.1.3.4 Assessment of Inhalation Exposure Based on Modeling 47
2.1.4 Spot Cleaning at Dry Cleaners 52
2.1.4.1 Process and Worker Activity Descriptions 52
2.1.4.2 Estimate of Number of Workers Potentially Exposed 52
2.1.4.3 Assessment of Inhalation Exposure Based on Monitoring Data 52
2.1.4.4 Assessment of Inhalation Exposure Based on Modeling 53
2.1.5 Vapor Degreasing 55
2.1.5.1 Process and Worker Activity Descriptions 55
2.1.5.2 Estimate of the Number of Workers Potentially Exposed 57
2.1.5.3 Assessment of Inhalation Exposure Based on Monitoring Data 57
2.1.5.4 Assessment of Inhalation Exposure Based on Modeling 59
2.1.6 Cold Cleaning Degreasing 63
2.1.6.1 Process and Worker Activity Descriptions 63
2.1.6.2 Estimate of the Number of Workers Potentially Exposed 65
2.1.6.3 Assessment of Inhalation Exposure Based on Monitoring Data 65
2.1.6.4 Assessment of Inhalation Exposure Based on Exposure Modeling 66
2.1.7 Aerosol Degreasing 68
2.1.7.1 Process and Worker Activity Descriptions 68
2.1.7.2 Estimate of the Number of Workers Potentially Exposed 69
2.1.7.3 Assessment of Inhalation Exposure Based on Monitoring Data 69
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2.1.7.4 Assessment of Inhalation Exposure Based on Modeling 70
2.2 CONSUMER EXPOSURES 72
2.2.1 Approach and Methodology 72
2.2.1.1 Exposure Routes 73
2.2.1.2 Overview of the E-FAST-2/CEM Model 74
2.2.1.3 Consumer Model Scenario and Input Parameters for Indoor Exposure to Specific 1-BP Uses 75
2.2.1.4 Consumer Model Results 78
2.2.1.5 Sensitivity of Model Parameters 80
3 HUMAN HEALTH HAZARD ASSESSMENT 82
3.1 TOXICOKINETICS 83
3.1.1 Biomarkers of Exposure 86
3.1.2 Possible Mode of Action for 1-BP Toxicity 87
3.1.3 PBPK Models 90
3.2 HAZARD SUMMARY AND HAZARD IDENTIFICATION 90
3.2.1 Non-Cancer Hazard Identification 90
3.2.1.1 Toxicity Following Acute Exposure 90
3.2.1.2 Liver Toxicity 90
3.2.1.3 Kidney Toxicity 91
3.2.1.4 Immunotoxicity 91
3.2.1.5 Reproductive Toxicity 92
3.2.1.6 Developmental Toxicity 92
3.2.1.7 Neurotoxicity 92
3.2.2 Cancer Hazard Identification 94
3.2.2.1 Genetic Toxicity 94
3.2.2.2 Carcinogenicity 94
3.3 WEIGHT OF EVIDENCE/MULTIPLE LINES OF EVIDENCE SUPPORTING CRITICAL EFFECTS 96
3.3.1 Weight-of-Evidence for Reproductive and Developmental Toxicity 96
3.3.2 Weight-of-Evidence for Neurotoxicity 97
3.3.3 Weight-of-Evidence for Cancer 99
3.3.4 Summary of Hazard Studies Used to Evaluate Acute and Chronic Exposures 99
3.4 DOSE-RESPONSE ASSESSMENT 99
3.4.1 Non-Cancer Dose-Response Assessment 100
3.4.2 Carcinogenic Dose-Response Assessment Ill
3.5 SUMMARY OF HEALTH HAZARD 113
4 HUMAN HEALTH RISK CHARACTERIZATION 116
4.1 RISK ESTIMATION APPROACH 116
4.2 RISK ESTIMATION FOR ACUTE, NON-CANCER INHALATION EXPOSURES 121
4.3 RISK ESTIMATION FOR CHRONIC, NON-CANCER AND CANCER INHALATION EXPOSURES 126
4.3.1 Non-Cancer Risks for Chronic Occupational Exposure Scenarios 126
4.3.2 Cancer Risks for Occupational Scenarios 134
4.4 ASSUMPTIONS AND KEY SOURCES OF UNCERTAINTY 140
4.4.1 Uncertainties and Limitations of the Occupational Exposure Assessment 141
4.4.1.1 Variability 141
4.4.1.2 Uncertainties and Limitations 141
4.4.1.2.1 Number of Workers 141
4.4.1.2.2 Analysis of Exposure Monitoring Data 142
4.4.1.2.3 Near-Field / Far-Field Model Framework 143
4.4.1.2.4 Vapor Degreasing and Cold Cleaning Model 144
4.4.1.2.5 Aerosol Degreasing Model 146
4.4.1.2.6 Dry Cleaning Model 146
4.4.1.2.7 Spot Cleaning Model 147
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4.4.2 Uncertainties of the Consumer Exposure Assessment 148
4.4.2.1 Consumer Use Information 148
4.4.2.2 Model Assumptions and Input Parameters 148
4.4.2.3 Conversion of Acute Dose Rates to Air Concentrations 149
4.4.3 Uncertainties in the Hazard and Dose-Response Assessments 149
4.4.3.1 Uncertainties and Assumptions in the Non-Cancer Hazard/Dose-Response Assessments 149
4.4.3.2 Uncertainties and Assumptions in the Cancer Hazard/Dose-Response Assessments 151
4.4.4 Uncertainties in the Risk Assessment 152
4.5 RISK ASSESSMENT CONCLUSIONS 154
5 REFERENCES 159
Appendix A MARKET INFORMATION 181
A-l PRODUCTION VOLUME 181
A-2 MANUFACTURERS 181
A-3 DEGREASERS 182
A-4 SPRAY ADHESIVES 183
A-5 AEROSOL SOLVENTS 184
A-6 DRY CLEANING 184
A-7 SPOT CLEANERS 185
A-8 CONSUMER USES 185
Appendix B CHEMICAL DATA REPORTING RULE DATA FOR 1-BP 187
Appendix C STATE REGULATIONS OF 1-BP 188
Appendix D ENVIRONMENTAL EFFECTS SUMMARY 190
D-l ACUTE TOXICITY TO AQUATIC ORGANISMS 191
D-2 CHRONIC TOXICITY TO AQUATIC ORGANISMS 191
D-3 TOXICITY TO SEDIMENT AND SOIL DWELLING ORGANISMS 191
D-4 TOXICITY TO WILDLIFE 191
D-5 SUMMARY OF ENVIRONMENTAL HAZARD ASSESSMENT 191
Appendix E ENVIRONMENTAL FATE 192
E-l FATE IN AIR 192
E-2 FATE IN WATER 192
E-3 FATE IN SEDIMENT AND SOIL 193
Appendix F APPROACH FOR ESTIMATING NUMBER OF WORKERS 194
F-l ESTIMATES FOR NUMBER OF WORKERS USING SPRAY ADHESIVES 200
F-2 ESTIMATES FOR NUMBER OF WORKERS AT DRY CLEANERS 201
F-3 ESTIMATES FOR NUMBER OF WORKERS IN VAPOR DECREASING 203
F-4 ESTIMATES FOR NUMBER OF WORKERS POTENTIALLY USING AEROSOL DECREASING 206
Appendix G APPROACH USED TO COLLECT MONITORING DATA AND INFORMATION ON
MODEL PARAMETERS 208
Appendix H EQUATIONS FOR CALCULATING ACUTE AND CHRONIC EXPOSURES FOR NON-
CANCER AND CANCER 210
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Appendix I EXAMPLE OF MONITORING DATA ANALYSIS FOR SPRAY ADHESIVE USE 212
Appendix J OCCUPATIONAL EXPOSURE MODELING (NEAR-FIELD/FAR-FIELD) APPROACH 216
Appendix K OCCUPATIONAL EXPOSURE MODELING PARAMETERS 229
Appendix L CONSUMER EXPOSURE ASSESSMENT 242
L-l DEFAULT PARAMETERS USED IN CEM FOR EMISSION AND HOUSEHOLD CHARACTERISTICS 242
L-2 AIR EXCHANGE RATE 242
L-3 OVERSPRAY FRACTION 244
L-4 EMISSION RATE 244
L-5 ROOM AND HOUSE VOLUME AND MOVEMENT WITHIN THE HOME 246
L-6 INHALATION RATE AND BODY WEIGHT 246
L-7 CONSUMER BEHAVIOR PATTERNS 247
L-8 USE DATA FOR CONTACT CEMENT, SUPER GLUES OR SPRAY ADHESIVES 249
L-9 USE DATA FOR SPOT REMOVERS 250
L-10 USE DATA FOR ENGINE DEGREASERS 250
L-ll USE DATA FOR BRAKE QUIETERS/CLEANERS 250
L-12 USE DATA FOR SPECIALIZED ELECTRONIC CLEANERS 251
L-13 CONVERTING E-FASTADRsTO AIR CONCENTRATIONS 251
L-14 SENSITIVITY OF MODEL PARAMETERS 254
Appendix M STUDY QUALITY AND SELECTION CONSIDERATIONS 261
Appendix N TOXICOKINETICS 263
N-l ABSORPTION 263
N-2 DISTRIBUTION 264
N-3 METABOLISM 264
N-4 ELIMINATION 269
Appendix O ANIMAL AND HUMAN TOXICITY STUDIES CONSIDERED FOR USE IN RISK
ASSESSMENT 271
O-l REPRODUCTIVE TOXICITY 271
O-2 NEUROTOXICITY 272
O-3 HUMAN CASE REPORTS 275
O-4 HUMAN EPIDEMIOLOGY STUDIES 277
O-5 CARCINOGENICITYANDMUTAGENICITY 317
O-5-1 Skin Tumors 317
O-5-2 Large Intestine Tumors 317
O-5-3 Lung Tumors 318
O-5-4 Pancreatic Tumors 318
O-5-5 Malignant Mesothelioma 318
O-5-6 Genotoxicity 319
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O-5-7 Metabolism, Structure-Activity Relationships and Mechanism/Mode of Action 321
Appendix P BENCHMARK DOSE ANALYSIS 323
P-l BENCHMARK DOSE MODELING OF NON-CANCER EFFECTS FOR ACUTE EXPOSURES 323
P-2 BENCHMARK DOSE MODELING OF NON-CANCER EFFECTS FOR CHRONIC EXPOSURES 328
P-2-1 Increased Incidence ofVacuolization ofCentrilobular Hepatocytes in Males 328
P-2-2 Increased Incidence ofVacuolization ofCentrilobular Hepatocytes in Males 330
P-2-3 Increased Incidence ofVacuolization ofCentrilobular Hepatocytes in Females 333
P-2-4 Increased Incidence of Renal Pelvic Mineralization in Males 335
P-2-5 Increased Incidence of Renal Pelvic Mineralization in Females 338
P-2-6 Decreased Seminal Vesicle Weight 340
P-2-6-1 Decreased Relative Seminal Vesicle Weight 341
P-2-6-2 Decreased Absolute Seminal Vesicle Weight 343
P-2-7 Decreased Percent Normal Sperm Morphology 346
P-2-8 Decreased Percent Motile Sperm 349
P-2-9 Decreased Left Cauda Epididymis Weight 351
P-2-10 Decreased Right Cauda Epididymis Weight 354
P-2-11 Increased Estrus Cycle Length 357
P-2-12 Decreased Antral Follical Count 358
P-2-13 Decreased Male and Female Fertility Index 359
P-2-14 Decreased Implantations Sites 362
P-2-15 Decreased Pup Body Weight 365
P-2-15-1 Decreased Body Weight in Fl Male Pups at PND 28 365
P-2-15-2 Decreased Body Weight in F2 Female Pups at PND 14 368
P-2-15-3 Decreased Body Weight in F2 Female Pups at PND 21 371
P-2-15-4 Decreased Body Weight in F2 Male Pups at PND 14 373
P-2-15-5 Decreased Body Weight in F2 Male Pups at PND 21 376
P-2-16 Decreased Brain Weight 378
P-2-16-1 Decreased Brain Weight in F0 Females 378
P-2-16-2 Decreased Brain Weight in F0 Males 381
P-2-16-3 Decreased Brain Weight in FI Females as Adults 384
P-2-16-4 Decreased Brain Weight in FI Males as Adults 387
P-2-16-5 Decreased Brain Weight in F2 Females at PND 21 388
P-2-16-6 Decreased Brain Weight in F2 Males at PND 21 391
P-2-17 Decreased Hang Time 394
P-3 BENCHMARK DOSE MODELING OF TUMORS 397
P-3-1 Lung Tumors in Female Mice 398
P-3-2 Large Intestine Adenomas in Female Rats 400
P-3-3 Keratoacanthoma and Squamous Cell Carcinomas in Male Rats 402
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LIST OF TABLES
Table 1-1 Physical and Chemical Properties of 1-BP 33
Table 1-2 Environmental Fate Characteristics of 1-BP 34
Table 2-1 Estimated Number of Workers Potentially Exposed to 1-BP in Spray Adhesive Use in Foam Cushion
Manufacturing 42
Table 2-2 Summary of 1-BP Inhalation Exposures (AC, ADC and LADC) for Spray Adhesive Use Based on Monitoring
Data 44
Table 2-3 Estimated Number of Workers Potentially Exposed to 1-BP in Dry Cleaning Shops 46
Table 2-4 Summary of 1-BP Inhalation Exposures (AC, ADC and LADC) at Dry Cleaning Facilities Based on Monitoring
Data 47
Table 2-5 Statistical Summary of 1-BP Dry Cleaning Exposures for Workers and Occupational Non-users based on
Modeling 51
Table 2-6 Summary of Inhalation Exposure Data for Spot Cleaning 53
Table 2-7 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Use of Spot Cleaning at Dry
Cleaners Based on Modeling 54
Table 2-8 Estimated Number of Workers Potentially Exposed to 1-BP in Degreasing Uses 57
Table 2-9 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Vapor Degreasing Based on
Monitoring Data 59
Table 2-10 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Vapor Degreasing Based on
Modeling 63
Table 2-11 Summary of Inhalation Exposure Monitoring Data for Cold Cleaning 66
Table 2-12 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Cold Cleaning Based on Modeling
68
Table 2-13 Estimated Number of Workers Potentially Exposed to 1-BP in Aerosol Degreasing 69
Table 2-14 Summary of Inhalation Exposure Monitoring Data for Aerosol Degreasing 70
Table 2-15 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Aerosol Degreasing Based on
Modeling 72
Table 2-16 Consumer Use Products Containing 1-BP 73
Table 2-17 Consumer Model Scenarios and Populations of Interest 75
Table 2-18 Product Use Input Parameters for CEM Modeling 76
Table 2-19 Estimated31-BP Air Concentrations (Time Averaged Over 1 Day) Based on Residential Indoor Use of Spray
Adhesives or Aerosol Removers 79
Table 3-1 List of Inhalation Endpoints Suitable for the Non-Cancer Dose-Response Analysis of 1-BP 104
Table 3-2 Model-Average BMC and BMCL Estimates of 1-BP Exposure Associated with a 0.1% Added Risk of Tumors in
Rodents Ill
Table 3-3 BMC and BMCL Estimates of 1-BP Exposures Associated with a 0.1% Added Risk of Tumors in Humans
Exposed 40 hours/week (8 hours/day, 5 days/week) (ppm) 112
Table 3-4 Lowest HECs for Non-Cancer Effects for 1-BP 114
Table 4-1 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Occpational Risks
Following Acute Exposures to 1-BP Used In Spray Adhesives, Dry Cleaning, and Degreasing 117
Table 4-2 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Consumer Risks Following
Acute Exposures to 1-BP Use In Aerosol Spray Adhesives, Aerosol Spot Removers, and Aerosol Cleaners and
Degreasers 118
Table 4-3 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Occupational Risks
Following Chronic Exposures to 1-BP Used In Spray Adhesives, Dry Cleaning, and Degreasing 119
Table 4-4 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Spray
Adhesives Based on Monitoring Data 122
Table 4-5 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Dry
Cleaning Based on Monitoring Data 122
Table 4-6 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Dry
Cleaning Based on Modeling 122
Table 4-7 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Spot
Cleaning at Dry Cleaners Based on Monitoring Data 123
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Table 4-8 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Spot
Cleaning at Dry Cleaners Based on Modeling 123
Table 4-9 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Vapor
Degreasing Based on Monitoring Data 123
Table 4-10 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Vapor
Degreasing Based on Modeling 123
Table 4-11 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Cold
Cleaning Based on Monitoring Data 124
Table 4-12 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Cold
Cleaning Based on Modeling 124
Table 4-13 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Aerosol
Degreasing Based on Monitoring Data 124
Table 4-14 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP in Aerosol
Degreasing Based on Modeling 124
Table 4-15 Non-Cancer Risk Estimates for Acute Inhalation Exposure Following Consumer Uses of 1-BP 125
Table 4-16 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Spray
Adhesives Based on Monitoring Data 129
Table 4-17 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Dry
Cleaning Machines Based on Monitoring Data 129
Table 4-18 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Dry
Cleaning Machines Based on Modeling 130
Table 4-19 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Spot
Cleaning at Dry Cleaners Based on Monitoring Data 130
Table 4-20 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Spot
Cleaning at Dry Cleaners Based on Modeling 131
Table 4-21 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Vapor
Degreasing Based on Monitoring Data 131
Table 4-22 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Vapor
Degreasing Based on Modeling 132
Table 4-23 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Cold
Cleaning Based on Monitoring Data 132
Table 4-24 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Cold
Cleaning Based on Modeling 133
Table 4-25 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Aerosol
Degreasing Based on Monitoring Data 133
Table 4-26 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of 1-BP in Aerosol
Degreasing Based on Modeling 134
LIST OF APPENDIX TABLES
Table_ApxA-l Production Volume Data from 1986 to 2012 (Ibs) 181
Table_Apx A-2 CDR Manufacturers and Importers of 1-BP in 2011 182
Table_ApxA-3 1-BP Consumer Use Products 186
Table_Apx B-l National Chemical Information for 1-BP from 2012 CDR 187
Table_Apx B-2 Summary of Industrial 1-BP Uses from 2012 CDR 187
Table_Apx B-3 Commercial/Consumer Use Category Summary of 1-BP 187
Table_ApxC-l State 1-BP Regulations 188
Table_Apx D-l Ecological Hazard Characterization of 1-Bromopropane 190
Table_Apx F-l NAICS Codes for Degreasing, Dry Cleaning, and Spray Adhesive Uses 194
Table_Apx F-2 SOC Codes with 1-BP Exposure at Dry Cleaning Facilities 197
Table_Apx F-3 Sample Granularity Calculation 199
Table_Apx F-4 Estimated 1-BP Employment under NAICS 812320 199
Table_Apx F-5 NAICS Codes for Spray Adhesive Uses in Foam Cushion Manufacturing 201
Table_Apx F-6 SOC Codes for Worker Exposure in the Spray Adhesive Sector 201
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Table_Apx F-7 Estimated Number of Workers Potentially Exposed to 1-BP in Spray Adhesive Use in Foam Cushion
Manufacturing 201
Table_Apx F-8 NAICS Code for Dry Cleaning 202
Table_Apx F-9 SOC Codes for Worker Exposure in Dry Cleaning 202
Table_Apx F-10 Estimated Number of Workers Potentially Exposed to 1-BP in Dry Cleaning Shops 203
Table_Apx F-ll NAICS Codes for All Degreasing Types 203
Table_Apx F-12 SOC Codes for Worker Exposure in the Degreasing Sector 205
Table_Apx F-13 Estimated Number of Workers Potentially Exposed to 1-BP in Degreasing Uses 206
Table_Apx F-14 NAICS Codes for Aerosol Degreasing 206
Table_Apx F-15 Estimated Number of Workers Potentially Exposed to 1-BP in Aerosol Degreasing 207
Table_Apx G-l Data Quality Criteria and Acceptance Specifications for 1-BP Literature Review for Monitoring Data
and Information on Model Parameters 208
Table_Apx H-l Parameter Values for Calculating Exposure Estimates 211
Table_Apx 1-1 Categorization of Employees as Sprayers, Non-Sprayers, or Occupational Non-Users 212
Table_Apx 1-2 Categorization of Exposure Data into Pre-EC and Post-EC Scenarios 213
Table_Apx 1-3 Personal Breathing Zone Monitoring Data for Sprayers, Initial NIOSH Assessment (Pre-EC Scenario). 214
Table_Apx 1-4 Personal Breathing Zone Monitoring Data for Sprayers, Follow-up NIOSH Assessment (Post-EC
Scenario) 215
Table_Apx 1-5 Summary of Inhalation Exposure Monitoring Data for Spray Adhesives 215
Table_Apx K-l Summary of Environmental Parameters for Degreasing Facilities 230
Table_Apx K-2 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Vapor
Degreasing 231
Table_Apx K-3 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Aerosol
Degreasing 233
Table_Apx K-4 Summary of Environmental Parameters at Dry Cleaning Facilities 234
Table_Apx K-5 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for 1-BP,
Unloading Dry Cleaning Machines (Multi-Zone Model) 236
Table_Apx K-6 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Finishing
(Multi-Zone Model) 238
Table_Apx K-7 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Spot
Cleaning (Multi-Zone Model) 240
Table_Apx K-8 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Spot
Cleaning (Stand-Alone Model) 241
Table_Apx L-l Summary of Parameters Used for Estimation of Indoor Air Concentrations of 1-BP 243
Table_Apx L-2 Comparison of Westat Survey Data and Simulation Values for 1-BP 247
Table_Apx L-3 Estimated Acute Dose Rates from Consumer Use 252
Table_Apx L-4 Estimated Acute Air Concentrations from Consumer Use (rounded to one significant figure) 254
Table_Apx L-5 Plausible range for input parameters for Tier 1 analysis 255
Table_Apx L-6 Tier 1 Sensitivity Rankings for Acute Dose Rate 257
Table_Apx L-7 Tier 1 Sensitivity Rankings for Acute Air Concentration 258
Table_Apx L-8 Range of Input Parameters for Tier 2 Analysis 259
Table_Apx L-9 Tier 2 Sensitivity Results for ADR 259
Table_Apx L-10 Tier 2 Sensitivity Results for Acute Air Concentration 260
Table_Apx M-l Study Quality Considerations 262
Table_Apx O-l Case Reports on 1-BP 276
Table_Apx O-2 Summary of the Epidemiological and Toxicological Database for 1-BP 281
Table_Apx O-3 Tumors induced by 1-BP in Rats and Mice 319
Table_Apx O-4 Genotoxicity of 1-BP In Vitro 321
Table_Apx P-l Litter Size Data Selected for Dose-Response Modeling for 1-BP 323
Table_Apx P-2 Summary of BMD Modeling Results for Reduced Litter Size in Fo Generation Exposed to 1-BP by
Inhalation; BMRs of 1 Standard Deviation, and 5% and 1% Relative Deviation From Control Mean 324
Table_Apx P-3 BMD Modeling Results for Reduced Litter Size in Fo Generation Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study with Variances Fixed at Smallest, Pooled and Highest Values 327
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Table_Apx P-4 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response Modeling for 1-BP
328
Table_Apx P-5 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Male Fo Rats Following
Inhalation Exposure to 1-BP in a Two-Generation Study 329
Table_Apx P-6 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response Modeling for 1-BP
331
Table_Apx P-7 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Male Rats Following
Inhalation Exposure to 1-BP 331
Table_Apx P-8 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response Modeling for 1-BP
333
Table_Apx P-9 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Female Fo Rats Following
Inhalation Exposure to 1-BP in a Two-Generation Study 334
Table_Apx P-10 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for 1-BP 336
Table_Apx P-ll BMD Modeling Results for Renal Pelvic Mineralization in Male Fo Rats Following Inhalation Exposure
to 1-BP in a Two-Generation Study 336
Table_Apx P-12 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for 1-BP 338
Table_Apx P-13 BMD Modeling Results for Renal Pelvic Mineralization in Female Fo Rats Following Inhalation
Exposure to 1-BP in a Two-Generation Study 339
Table_Apx P-14 Relative Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP 341
Table_Apx P-15 Summary of BMD Modeling Results for Relative Seminal Vesicle Weight in Rats Exposed to 1-BP by
Inhalation 341
Table_Apx P-16 Absolute Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP 344
Table_Apx P-17 Summary of BMD Modeling Results for Seminal Vesicle Absolute Weight in Rats Exposed to 1-BP by
Inhalation 344
Table_Apx P-18 Sperm Morphology Data Selected for Dose-Response Modeling for 1-BP 346
Table_Apx P-19 Summary of BMD Modeling Results for Sperm Morphology in the Fo Generation Exposed to 1-BP by
Inhalation 347
Table_Apx P-20 Sperm Motility Data Selected for Dose-Response Modeling for 1-BP 349
Table_Apx P-21 BMD Modeling Results for Sperm Motility Fo Male Rats Following Inhalation Exposure to 1-BP 350
Table_Apx P-22 BMD Modeling Results for Sperm Motility Fo Male Rats Following Inhalation Exposure to 1-BP with
the Highest Dose Dropped 351
Table_Apx P-23 Left Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling for 1-BP 351
Table_Apx P-24 BMD Modeling Results for Left Cauda Epididymis Absolute Weight Fo Male Rats Following Inhalation
Exposure to 1-BP 352
Table_Apx P-25 Right Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling for 1-BP 355
Table_Apx P-26 BMD Modeling Results for Right Cauda Epididymis Absolute Weight Fo Male Rats Following Inhalation
Exposure to 1-BP 355
Table_Apx P-27 Estrus Cycle Length Data Selected for Dose-Response Modeling for 1-BP 357
Table_Apx P-28 BMD Modeling Results for Estrus Cycle Length Fo Female Rats Following Inhalation Exposure to 1-BP
358
Table_Apx P-29 Antral Follicle Count Data Selected for Dose-Response Modeling for 1-BP 359
Table_Apx P-30 BMD Modeling Results for Antral Follical Count in Female Rats Following Inhalation Exposure to 1-BP
359
Table_Apx P-31 Fertility Index Data Selected for Dose-Response Modeling for 1-BP 360
Table_Apx P-32 BMD Modeling Results for Fertility Index of Fo Rats Following Inhalation Exposure of Parental Rats to
1-BP in a Two-Generation Study 360
Table_Apx P-33 Implantations Site Data Selected for Dose-Response Modeling for 1-BP 362
Table_Apx P-34 BMD Modeling Results for Implantations Sites in Fo Rats Following Inhalation Exposure of Parental
Rats to 1-BP in a Two-Generation Study 363
Table_Apx P-35 Pup Body Weight Data in Fi Males at PND 28 for Dose-Response Modeling 365
Table_Apx P-36 BMD Modeling Results for Body Weight of Fi Male Rat Pups on PND 28 Following Inhalation Exposure
of Parental Rats to 1-BP in a Two-Generation Study 366
Table_Apx P-37 Pup Body Weight Data in Fz Females at PND 14 from Selected for Dose-Response Modeling 369
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Table_Apx P-38 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study 369
Table_Apx P-39 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study with Variances Fixed at Smallest, Pooled and Highest
Values 370
Table_Apx P-40 Pup Body Weight Data in F2 Females at PND 21 from Selected for Dose-Response Modeling 371
Table_Apx P-41 BMD Modeling Results for Body Weight of F2 Females on PND 21 Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study 371
Table_Apx P-42 Pup Body Weight Data in F2 Males at PND 14 from Selected for Dose-Response Modeling 373
Table_Apx P-43 BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 14 Following Inhalation Exposure
of Parental Rats to 1-BP in a Two-Generation Study 374
Table_Apx P-44 Pup Body Weight Data in F2 Males at PND 21 376
Table_Apx P-45 BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 21 Following Inhalation Exposure
of Parental Rats to 1-BP in a Two-Generation Study 376
Table_Apx P-46 Brain Weight Data in Fo Females for Dose-Response Modeling 378
Table_Apx P-47 BMD Modeling Results for Brain Weight of Fo Females Following Inhalation Exposure to 1-BP 379
Table_Apx P-48 Brain Weight Data in Fo Males for Dose-Response Modeling 381
Table_Apx P-49 BMD Modeling Results for Brain Weight of Fo Males Following Inhalation Exposure to 1-BP 382
Table_Apx P-50 BMD Modeling Results for Brain Weight of Fo Male Rats Following Inhalation Exposure to 1-BP in a
Two-Generation Study with Variances Fixed at Smallest, Pooled and Highest Values 383
Table_Apx P-51 Brain Weight Data in Fi Females as Adults from Selected for Dose-Response Modeling 384
Table_Apx P-52 BMD Modeling Results for Brain Weight of Fi Female Rats as Adults Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study 385
Table_Apx P-53 Brain Weight Data in Fi Males as Adults from Selected for Dose-Response Modeling 387
Table_Apx P-54 BMD Modeling Results for Brain Weight of Fi Male Rats as Adults Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study 388
Table_Apx P-55 Brain Weight Data in F2 Females at PND 21 from Selected for Dose-Response Modeling 388
Table_Apx P-56 BMD Modeling Results for Brain Weight of F2 Female Rats at PND 21 Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study 389
Table_Apx P-57 Brain Weight Data in F2 Males at PND 21 for Dose-Response Modeling 391
Table_Apx P-58 BMD Modeling Results for Brain Weight of F2 Male Rats as Adults Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study 392
Table_Apx P-59 Hang Time from a Suspended Bar Data for Dose-Response Modeling for 1-BP 394
Table_Apx P-60 Summary of BMD Modeling Results for Hang Time from a Suspended Bar; BMR = 1 std. dev. change
from control mean 395
Table_Apx P-61 Incidence of Lung Tumors in Female Mice 398
Table_Apx P-62 Summary of BMD Modeling Results for Lung Tumors in Female Mice 398
Table_Apx P-63 Incidence of Large Intestine Adenomas in Female Rats 400
Table_Apx P-64 Summary of BMD Modeling Results for Large Intestine Adenomas in Female Rats 400
Table_Apx P-65 Incidence of Keratoacanthoma and Squamous Cell Carcinomas in Male Rats 402
Table_Apx P-66 Summary of BMD Modeling Results for Keratoacanthoma and Squamous Cell Carcinomas in Male
Rats 402
LIST OF FIGURES
Figure 1-1 Chemical Structure of 1-Bromopropane 33
Figure 1-2 Schematic of Human and Environmental Exposure Pathways for 1-BP 35
Figure 2-1 Overview of Use of Spray Adhesive in the Furniture Industry 41
Figure 2-2 Overview of Dry Cleaning 45
Figure 2-3 Illustration of the Multi-Zone Model 49
Figure 2-4 Overview of Use of Spot Cleaning at Dry Cleaners 52
Figure 2-5 Schematic of the Near-Field/Far-Field Model for Spot Cleaning 53
Figure 2-6 Use of Vapor Degreasing in a Variety of Industries 55
Figure 2-7 Open-Top Batch Vapor Degreaser (U.S. EPA, 2006a) 56
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Figure 2-8 Schematic of the Near-Field/Far-Field Model for Vapor Degreasing 60
Figure 2-9 Typical Batch-Loaded, Maintenance Cold Cleaner (U.S. EPA, 1981) 64
Figure 2-10 Illustration for Use of Cold Cleaner in a Variety of Industries 65
Figure 2-11 The Near-Field/Far-field Model for Cold Cleaning Scenario 67
Figure 2-12 Overview of Aerosol degreasing 68
Figure 2-13 Schematic of the Near-Field/Far-Field Model for Aerosol degreasing 71
Figure 3-1 Hazard Identification and Dose-Response Process 83
Figure 3-2 Metabolism of 1-Bromopropane in Male F-344 Rats and B6C3F1 Mice Following Inhalation Exposure or Tail
Vein Injection* 85
Figure 3-3 Proposed Intermediary Metabolism for 1-BP 89
Figure 4-1 Cancer Risk Estimates for Occupational Use of 1-BP in Spray Adhesives Based on Monitoring Data 135
Figure 4-2 Cancer Risk Estimates for Occupational Use of 1-BP in Dry Cleaning Based on Monitoring Data 136
Figure 4-3 Cancer Risk Estimates for Occupational Use of 1-BP in Dry Cleaning Based on Modeling 136
Figure 4-4 Cancer Risk Estimates for Occupational Uses of 1-BP in Spot Cleaning at Dry Cleaners Based on Monitoring
Data 136
Figure 4-5 Cancer Risk Estimates for Occupational Uses of 1-BP in Spot Cleaning at Dry Cleaners Based on Modeling
136
Figure 4-6 Cancer Risk Estimates for Occupational Use of 1-BP in Vapor Degreasing Based on Monitoring Data 137
Figure 4-7 Cancer Risk Estimates for Occupational Use of 1-BP in Vapor Degreasing Based on Modeling 137
Figure 4-8 Cancer Risk Estimates for Occupational Use of 1-BP in Cold Cleaning Based on Monitoring Data 137
Figure 4-9 Cancer Risk Estimates for Occupational Use of 1-BP in Cold Cleaning Based on Modeling 137
Figure 4-10 Cancer Risk Estimates for Occupational Uses of 1-BP in Aerosol Degreasing Based on Monitoring Data 138
Figure 4-11 Cancer Risk Estimates for Occupational Uses of 1-BP in Aerosol Degreasing Based on Modeling 138
LIST OF APPENDIX FIGURES
Figure_Apx L-l Screen Capture of Summary of Recommended Values for Residential Building Parameters from the
Exposure Factors Handbook (2011) 244
Figure_Apx L-2 Screen Capture of E-FAST Equations for Estimation of Emission Rate 245
Figure_Apx N-l Formation of N-Acetyl-S-Propylcysteine from 1-Bromopropane Via Conjugation with Reduced
Glutathione (GSH) 266
Figure_Apx N-2 Mercapturic Acid Metabolites with a Sulfoxide Group or a Hydroxyl or Carbonyl Group on the Propyl
Residue Identified in Urine Samples of 1-Bromopropane-Exposed Workers 266
Figure_Apx P-l Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M2) Model with Modeled
Variance for Reduced Litter Size in Fo Generation Exposed to 1-BP by Inhalation; BMR = 5% Relative Deviation from
Control Mean 325
Figure_Apx P-2 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (LogLogistic) for
Vacuolization of Centrilobular Hepatocytes in Male Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added Risk.
329
Figure_Apx P-3 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Multistage 3°) for
Vacuolization of Centrilobular Hepatocytes in Male Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added Risk.
332
Figure_Apx P-4 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (LogLogistic) for
Vacuolization of Centrilobular Hepatocytes in Female Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added
Risk 334
Figure_Apx P-5 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Multistage 3°) for Renal
Pelvic Mineralization in Male Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added Risk 337
Figure_Apx P-6 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Probit) for Renal Pelvic
Mineralization in Female Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added Risk 339
Figure_Apx P-7 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model with Constant
Variance for Relative Seminal Vesicle Weight; BMR = 1 Standard Deviation Change from Control Mean 342
Figure_Apx P-8 Plot of Mean Response by Dose in ppm with Fitted Curve for Hill Model with Constant Variance for
Seminal Vesicle Absolute Weight; BMR = 1 Standard Deviation Change from Control Mean 345
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Figure_Apx P-9 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M2) Model with Constant
Variance for Sperm Morphology in Fo Rats Exposed to 1-BP by Inhalation; BMR = 1 Standard Deviation Change from
Control Mean 348
Figure_Apx P-10 Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model with Constant
Variance for Left Cauda Epididymis Absolute Weight; BMR = 1 Standard Deviation Change from Control Mean 353
Figure_Apx P-ll Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model with Constant
Variance for Right Cauda Epididymis Absolute Weight; BMR = 1 Standard Deviation Change from Control Mean.... 356
Figure_Apx P-12 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (LogLogistic) for Fertility
Index in Rats Exposed to 1-BP Via Inhalation in ppm BMR 10% Extra Risk 361
Figure_Apx P-13 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for Implantation
Sites in Rats Exposed to 1-BP Via Inhalation in ppm BMR 1 Standard Deviation 363
Figure_Apx P-14 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Exponential (M2)) for Pup
Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR 5% Relative Deviation 367
Figure_Apx P-15 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Polynomial 2°) for Pup
Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative Deviation 372
Figure_Apx P-16 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Polynomial 2°) for Pup
Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative Deviation 374
Figure_Apx P-17 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for Pup Body
Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative Deviation 377
Figure_Apx P-18 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for Brain Weight in
Fo Female Rats Exposed to 1-BP Via Inhalation in ppm BMR = 1 Standard Deviation 379
Figure_Apx P-19 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Exponential (M2)) for
Brain Weight in Fi Female Rats as Adults Exposed to 1-BP Via Inhalation in ppm BMR = 1% Relative Deviation 386
Figure_Apx P-20 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Exponential (M2)) for
Brain Weight in F2 Female Exposed to 1-BP Via Inhalation in ppm BMR = 1% Relative Deviation 390
Figure_Apx P-21 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Power) for Brain Weight
in Rats Exposed to 1-BP Via Inhalation in ppm BMR= 1% Relative Deviation 392
Figure_Apx P-22 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model with Modeled
Variance for Hang Time from a Suspended Bar; BMR = 1 Standard Deviation Change from Control Mean 396
LIST OF EQUATIONS
Equation 2-1 Equation for Calculating Vapor Degreasing Vapor Generation Rate 61
Equation 4-1 Equation to Calculate Non-Cancer Risks Following Acute or Chronic Exposures Using Margin of
Exposures 119
Equation 4-2 Equation to Calculate Added Cancer Risks 121
LIST OF APPENDIX EQUATIONS
Equation_ApxH-lADCandLADC 210
Equation_Apx J-l Near-Field Mass Balance for Vapor Degreasing, Cold Cleaning and Spot Cleaning 216
Equation_Apx J-2 Far-Field Mass Balance for Vapor Degreasing, Cold Cleaning and Spot Cleaning 217
Equation_Apx J-3 Instantaneous Near-Field Concentration as a Function of Time 217
Equation_Apx J-4 Instantaneous Far-Field Concentration as a Function of Time 217
Equation_Apx J-5 Regrouping of Parameters into Parameter ki 217
Equation_Apx J-6 Regrouping of Parameters into Parameter kz 217
Equation_Apx J-7 Regrouping of Parameters into Parameter ks 217
Equation_Apx J-8 Regrouping of Parameters into Parameter k4 217
Equation_Apx J-9 Regrouping of Parameters into Parameter ks 218
Equation_Apx J-10 Eigenvalue Ai 218
Equation_Apx J-ll Eigenvalue Az 218
Equation_ApxJ-12 Near-field Hourly TWA Concentration 218
Equation_ApxJ-13 Far-field Hourly TWA Concentration 218
Equation_Apx J-14 Free Surface Area 219
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Equation_ApxJ-15 Near-Field Ventilation Rate 219
Equation_ApxJ-16 Far-Field Ventilation Rate 219
Equation_Apx J-17 Near-Field Mass Balance for Aerosol Degreasing 219
Equation_Apx J-18 Far-Field Mass Balance for Aerosol Degreasing 219
Equation_Apx J-19 Instantaneous Near-Field Concentration as a Function of Time 220
Equation_Apx J-20 Instantaneous Far-Field Concentration as a Function of Time 220
Equation_Apx J-21 Regrouping of Parameters into Parameter ki 220
Equation_Apx J-22 Regrouping of Parameters into Parameter kz 220
Equation_Apx J-23 Regrouping of Parameters into Parameter ks 221
Equation_Apx J-24 Regrouping of Parameters into Parameter k4 221
Equation_Apx J-25 Near-field Concentration at the Moment of Aerosol Degreaser Application for each of the Seven
Applications 221
Equation_Apx J-26 Far-field Concentration at the Moment of Aerosol Degreaser Application for each of the Seven
Applications 221
Equation_Apx J-27 Eigenvalue Ai 221
Equation_Apx J-28 Eigenvalue \2 221
Equation_ApxJ-29 Near-Field Concentration, 1-hr TWA 221
Equation_ApxJ-30 Far-Field Concentration, 1-hr TWA 221
Equation_ApxJ-31 Near-Field Concentration, 8-hr TWA 222
Equation_Apx J-32 Far-Field Concentration, 8-hr TWA 222
Equation_Apx J-33 Near-Field Mass Balance for Spot Cleaning (Multi-Zone) 222
Equation_Apx J-34 Near-Field Mass Balance for Finishing (Multi-Zone) 222
Equation_Apx J-35 Near-Field Mass Balance for Dry Cleaning Machine (Multi-Zone) 222
Equation_Apx J-36 Far-Field Mass Balance for Dry Cleaning Facility (Multi-Zone) 222
Equation_Apx J-37 Free Surface Area for Spot Cleaning 223
Equation_Apx J-38 Free Surface Area for Finishing 223
Equation_Apx J-39 Free Surface Area for Dry Cleaning Machine 223
Equation_Apx J-40 Near-Field Ventilation Rate for Spot Cleaning 224
Equation_Apx J-41 Near-Field Ventilation Rate for Finishing 224
Equation_Apx J-42 Near-Field Ventilation Rate for Dry Cleaning Machine 224
Equation_Apx J-43 Far-Field Ventilation Rate for Dry Cleaning Facility 224
Equation_Apx J-44 Differential Equation for Spot Cleaning Near-Field Concentration 224
Equation_Apx J-45 Differential Equation for Finishing Near-Field Concentration 224
Equation_Apx J-46 Differential Equation for Dry Cleaning Machine Near-Field Concentration 224
Equation_Apx J-47 Differential Equation for Far-Field Concentration at Dry Cleaning Facility 224
Equation_Apx J-48 Alternative Representation for the Spot Cleaning Near-Field Concentration Differential Equation
225
Equation_Apx J-49 Alternative Representation for the Finishing Near-Field Concentration Differential Equation 225
Equation_Apx J-50 Alternative Representation for the Dry Cleaning Machine Near-Field Concentration Differential
Equation 225
Equation_Apx J-51 Alternative Representation for the Far-Field Concentration Differential Equation 225
Equation_Apx J-52 Redefinition of Time Derivative as Function of Independent and Dependent Variables (yi') 226
Equation_Apx J-53 Redefinition of Time Derivative as Function of Independent and Dependent Variables (yz') 226
Equation_Apx J-54 Redefinition of Time Derivative as Function of Independent and Dependent Variables (ys') 226
Equation_Apx J-55 Redefinition of Time Derivative as Function of Independent and Dependent Variables (y4') 226
Equation_Apx J-56 RK4 Beginning-of-lnterval Slope 226
Equation_ApxJ-57 RK4 First-Midpoint Slope 226
Equation_Apx J-58 RK4 Second-Midpoint Slope 226
Equation_ApxJ-59 RK4 End-of-lnterval Slope 226
Equation_Apx J-60 RK4 Calculation of the Dependent Variable, y, at the Next Time Step 227
Equation_Apx J-61 Acute Concentration for Dry Cleaning Model (Multi-Zone) 228
Equation_ApxJ-62 ADC and LADCfor Dry Cleaning Model (Multi-Zone) 228
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AUTHORS / CONTRIBUTORS / ACKNOWLEDGEMENTS /
REVIEWERS
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). The 2015 Work Plan Risk Assessment for 1-Brompropane (also called n-propyl bromide or
1-BP) was prepared based on existing data. Mention of trade names does not constitute
endorsement by EPA.
EPA Assessment Team
Co-Leads:
Katherine Anitole, OPPT/Risk Assessment Division (RAD)
Sharon Oxendine, OPPT/RAD
Team Members:
Chris Brinkerhoff, OPPT/RAD
Judith Brown, OPPT/Chemistry, Economics, & Sustainable Strategies Division (CESSD)
Anjali Lamba, OPPT/RAD
Greg Macek, OPPT/RAD
Paul Matthai, OPPT/CESSD
Albert Monroe, OPPT/CESSD
Ginger Moser, Office of Research and Development (ORD), National Health and
Environmental Effects Research Laboratory (NHEERL)
Alie Muneer, OPPT/RAD
Tina Ndoh, Office of Air and Radiation (OAR)/Office of Air Quality Planning and Standards
(OAQPS)
Andrea Pfahles-Hutchens, OPPT/RAD
Justin Roberts, OPPT/CESSD
Nina Simeonova, OPPT/RAD, National Older Worker Senior Career Center (NOWCC)
Christina Thompson, OPPT/CCD
Eva Wong, OPPT/RAD
Yin-Tak Woo, OPPT/RAD
Management Leads:
Stan Barone Jr., OPPT/RAD
Nhan Nguyen, OPPT/RAD
Contributors
Fred Arnold, OPPT/RAD (Retired)
Rehan Choudhary, OPPT/RAD (Resigned)
Masashi Horie, OPPT/Chemical Control Division (CCD) (Former Visiting Scientist)
Ruth Hummel, OPPT/RAD (Resigned)
James Kwon, OPPT/RAD (Reassigned)
David Lai, OPPT/RAD (Retired)
Andy Mamantov, OPPT/RAD (Retired)
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Scott Wesselkamper, Office of Research and Development (ORD)/National Center for
Environmental Assessment (NCEA)
Acknowledgements
Portions of this document were prepared for EPA/OPPT by Abt Associates, the Eastern Research
Group (ERG), Inc., SRC, and Versar.
Special thanks to ATSDR for permission to use Section 3.4 Toxicokinetics - 3.4.1 through 3.4.4
from the January 2015 draft Toxicological Profile of 1-BP for Appendix J.
Special thanks to Margaret Sheppard for consultation on previous SNAP assessment.
Special thanks to NIOSH for consultation on NIOSH assessment and inspection activities and
model averaging approach for cancer dose response.
Special thanks to OSHA staff who provided consultation and informal comment during development of
this document.
EPA Internal Peer Reviewers
Ines Pagan, OAR
John Schaefer, OAR
Margaret Sheppard, OAR
George Woodall, ORD
Sharon Cooperstein, OP
Ann Johnson, OP
Brenda Foos, OCHP
Greg Miller, OCHP
Federal Peer Reviewers
Christine Whittaker-NIOSH
James Bennett-NIOSH
Scott Dotson-NIOSH
Henry Abadin-ATSDR
Nicollete Roney-ATSDR
Please visit the EPA/OPPT's Work Plan Chemicals web page for additional information on the 1-
BP's peer review process (http://www.epa.gov/assessing-and-managing-chemicals-under-
tsca/assessments-tsca-work-plan-chemicals) and the public docket (Docket: EPA-HQ-OPPT-2015-
0084).
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ABBREVIATIONS
AC
ACH
ADC
ADR
ADRpot
AEGL
AER
ACGIH
Apx
AT
Atm
ATSDR
BAF
BCF
BL
BMCL
BMD
BMDL
BMR
BLS
BOD
BOP
BW
C
Cair
°C
CFF
CFFTWA
CNF
CNFiwA
Cp pot
CASRN
CBI
CCD
CCRIS
CDR
CEM
CESSD
Cl
cm
cm3
Acute concentration
Air changes per hour
Average daily concentration
Acute dose rate
Potential acute dose rate
Acute exposure guideline level
Air exchange rate
American Conference of Government Industrial Hygienists
Appendix
Averaging time
Atmosphere
Agency for Toxic Substances and Disease Registry
Bioaccumulation factor
Bioconcentration factor
Baseline
Benchmark concentration, lower confidence limit(s)
Benchmark dose
Benchmark dose, lower confidence limit(s)
Benchmark response level
Bureau of Labor Statistics
Biochemical oxygen demand
3-bromo-l-hydroxypropanone
Body weight
Contaminant concentration
Air concentration
Degree Celsius
Average far field concentration
Time weighted average far field concentration
Average near field concentration
Time weighted average near field concentration
Modeled peak concentration
Chemical Abstracts Service Registry Number
Confidential business information
Chemical Control Division
Chemical Carcinogenesis Research Information System
Chemical Data Reporting
Consumer exposure module
Chemistry, Economics, and Sustainable Strategies Division
Confidence interval
Centimeter(s)
Cubic meter(s)
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CNS Central nervous system
C02 Carbon dioxide
CYP Cytochrome P450
DEv Duration of an event
DIY Do-it-yourself
DNA Deoxyribonucleic acid
EC Engineering controls
ECA Enforceable consent agreement
ED Exposure duration
EF Exposure frequency
E-FAST2 Exposure and Fate Assessment Screening Tool version 2
EFH Exposure Factors Handbook
EMIC Environmental Mutagens Information Center
EPA Environmental Protection Agency
ERG Eastern Research Group, Inc.
EU European Union
EvapTime Evaporation time
FF Far field
FQ Frequency of product use
FSA Free surface area
ft Foot/feet
ft2 Square foot/feet
ft3 Cubic foot/feet
g Gram(s)
g/cm3 Grams per cubic centimeters
g/L Grams per liter
G Average generation rate
GM Geometric mean
GSD Geometric standard deviation
GD Gestational day
GENE-TOX Genetic Toxicology Data Bank
GSH Glutathione (reduced)
HNF Near field height
HAPs Hazardous air pollutants
HCV Human cancer value
HEC Human equivalent concentration
HHE Human Health Evaluation
hr Hour(s)
HSDB Hazardous Substances Data Bank
HSIA Halogenated Solvents Industry Alliance
IA Indoor air
IARC International Agency for Research on Cancer
IMIS Integrated Management Information System
InhR Inhalation rate
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IRIS
IUR
k
Kow
kg
Koc
L
Ib
LNF
LADC
LADD
LEV
LT
LOAEL
MA
m
m2
m3
MCCEM
u.g/m3
mg
mg/kg-bw
mg/L
mg/m3
mg/mL
min
MITI
Mlbs
mm Hg
MOE
MOEacute
MOEchronic
MOU
MW
NAICS
NAPL
NAS
NCEA
NCI
NEI
NESHAP
NF
NF/FF
NHANES
Integrated Risk Information System
Inhalation unit risk
Emission rate
Octanol: water partition coefficient
Kilogram(s)
Soil organic carbon-water partitioning coefficient
Liter(s)
Pound(s)
Near field length
Lifetime average daily concentration
Lifetime average daily dose
Local exhaust ventilation
Lifetime
Lowest-observed-adverse-effect level
Model-averaging
Meter(s)
Square meter(s)
Cubic meter(s)
Multi-Chamber Concentration and Exposure Model
Microgram(s) per cubic meter
Milligram(s)
Milligram(s) per kilogram body weight
Milligram(s) per liter
Milligram(s) per cubic meter
Milligram(s) per milliliter
Minute(s)
Ministry of International Trade and Industry
Million of pounds
Millimeters of mercury
Margin of exposure
Margin of exposure for acute exposures
Margin of exposure for chronic exposures
Memorandum of understanding
Molecular weight
North American Industry Classification System
Nonaqueous phase liquid
National Academies of Science
National Center for Environmental Assessment
National Cancer Institute
National Emissions Inventory
National Emissions Standards for Hazardous Air Pollutants
Near field
Near field/far field
National Health and Nutrition Examination Survey
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NICNAS National Industrial Chemicals Notification and Assessment Scheme
NIH National Institutes of Health
NIOSH National Institute for Occupational Safety and Health
NIST National Institute of Standards and Technology
nm Nanometer(s)
NOAEL No-observed-adverse-effect level
NOES National Occupational Exposure Survey
NOHSC National Occupational Health and Safety Commission
NJDEP New Jersey Department of Environmental Protection
NPS Nonpoint source
NTP National Toxicology Program
OAR Office of Air and Radiation
OCSPP Office of Chemical Safety and Pollution Prevention
OECD Organization for Economic Co-operation and Development
OPPT Office of Pollution Prevention and Toxics
OR Odds ratio
OSHA Occupational Safety and Health Administration
OSWER Office of Solid Waste and Emergency Response
OW Office of Water
oz Ounce(s)
PERC Perchloroethylene
PEL Permissible exposure limit
PA Personal air
PBZ Personal breathing zone
PID Photoionization detector
PND Postnatal day
POD Point of departure
ppb Parts per billion
ppm Parts per million
PS Point Source
PVC Polyvinyl chloride
OFF Far field ventilation rate
QNF Near field ventilation rate
QA Quality assurance
QC Quality control
RAD Risk Assessment Division
RCRA Resource Conservation and Recovery Act
REACH Registration Evaluation Authorization and Restriction
RfC Reference concentration
RfD Reference dose
RR Rate ratio
RTECS Registry of Toxic Effects of Chemical Substances
s Second(s)
SAB Science Advisory Board
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SARA Superfund Amendments and Reauthorization Act
SCG Scientific Consulting Group, Inc.
SD Standard deviation
SDS Safety data sheet(s)
SNAP Significant New Alternative Policy for ozone depleting substances
SVHC Substance of Very High Concern
t Time
TCA Trichloroacetic acid
TCE Trichloroethylene
TOXLINE Toxicology Literature Online
TRI Toxics Release Inventory
TSCA Toxic Substances Control Act
TSCATS Toxic Substance Control Act Test Submission database
TWA Time-weighted average
UF Uncertainty factor
UFs Subchronic to chronic uncertainty factor
UFA Interspecies uncertainty factor
UFn Intraspecies uncertainty factor
UFL LOAEL to NOAEL uncertainty factor
UFo Database uncertainty factor
US EPA United States Environmental Protection Agency
VFF Far field volume
VNF Indoor wind speed
VNF Near field volume
VOC Volatile organic compound
VP Vapor pressure
WWTP Waste Water Treatment Plant
WNF Near field width
WY Working years
Yr (s) Year(s)
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EXECUTIVE SUMMARY
As a part of EPA's comprehensive approach to enhance the Agency's existing chemicals
management, in March 2012, EPA identified a work plan of chemicals for further assessment
under the Toxic Substances Control Act (TSCA).1 The Agency is performing risk assessments for
chemicals in the work plan. If an assessment identifies unacceptable risks to humans or the
environment, EPA will pursue risk management. EPA/OPPT assessed 1-Bromopropane (1-BP), also
referred to as n-propyl bromide (TSCA inventory name), as part of this work plan.
1-BP is a solvent that exhibits high volatility, low flammability, and no explosivity. It has low
persistence and low bioaccumulation potential in the environment. 1-BP is produced or imported
in the US in large quantities and is a high production volume chemical (over 15 million Ib in 2011).
It has a variety of TSCA uses including numerous solvent applications in degreasing, spray
adhesives, and dry cleaning. In the past, 1-BP was used as a solvent for fats, waxes, or resins and
as an intermediate in pharmaceutical, insecticide, quaternary ammonium compound, flavor, and
fragrance synthesis (NTP, 2013).
Focus of this Risk Assessment
EPA/OPPT identified 1-BP for further evaluation in the TSCA work plan based on high hazard
concerns due to its toxicity profile, and high exposure concerns due to its use in consumer
products. During scoping and problem formulation, EPA/OPPT considered all known TSCA uses for
1-BP and focused on those which involve products with high 1-BP content, and those which are
emissive, exhibiting high potential for worker and/or consumer exposure. Occupational uses of
concern identified for 1-BP include its use in spray adhesives, dry cleaning (including spot
cleaning), and degreasing (vapor, cold cleaning, and aerosol). Consumer uses of concern
identified for 1-BP include those that involve aerosol spray adhesives, aerosol spot removers, and
aerosol cleaning and degreasing products - many of which were identified to contain 60-100%
1-BP.
Based on the physical-chemical properties and use scenarios described in this assessment,
EPA/OPPT expects inhalation to be the primary exposure route of concern for 1-BP. Because of
limited toxicological data and the lack of toxicokinetic information needed to develop
physiologically-based pharmacokinetic models for route-to-route extrapolations, EPA/OPPT did
not evaluate dermal exposures.
EPA/OPPT reviewed the evidence for 1-BP toxicity and selected liver toxicity, kidney toxicity,
reproductive/developmental toxicity, neurotoxicity, and cancer as the most robust, sensitive and
consistent adverse human health effects for risk characterization. EPA/OPPT applied benchmark
dose (BMD) modeling and when modeling results were adequate, generated points of departure
(PODs) for the selected endpoints.
1 http://www.epa.gov/assessing-and-managing-chemicals-under-tsca/assessments-tsca-work-plan-chemicals
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EPA/OPPT did not include a quantitative evaluation of environmental effects in this risk
assessment because 1-BP exhibits a low hazard potential for ecological receptors and a low
persistence and bioaccumulation potential if released into aquatic or terrestrial environments.
Risk Assessment Approach
EPA/OPPT evaluated acute and chronic inhalation exposures to workers and occupational non-
users in association with 1-BP use in spray adhesives, dry cleaning (including use in spot cleaning),
and degreasing (vapor, cold cleaning, and aerosol). EPA/OPPT also evaluated acute exposures to
consumers in association with 1-BP use in aerosol spray adhesives, aerosol spot removers, and
aerosol cleaners and degreasers. Acute exposures were defined as those occurring within a single
day; whereas chronic exposures were defined as exposures comprising 10% or more of a lifetime
(U.S. EPA, 2011). Repeated exposures (e.g., five consecutive days or more) are anticipated during
chronic exposure.
For the occupational exposure assessment, EPA/OPPT used monitoring data from literature
sources where available, and a modeling approach to estimate potential inhalation exposures. For
the consumer exposure assessment, EPA/OPPT relied on models incorporating information on
generalized consumer use patterns, and the physical-chemical properties of 1-BP to estimate
potential inhalation exposures.
The evaluation of non-cancer risks associated with acute exposures was based on developmental
toxicity (WIL Research, 2001), which is representative of a sensitive subpopulation (i.e., adult
women of child-bearing age and their offspring). EPA/OPPT consulted EPA's Guidelines for
Developmental Toxicity Risk Assessment when making the decision to use a developmental
endpoint (i.e., decreased live litter size) as the basis of the dose-response analysis for acute
exposures. Other non-cancer endpoints from acute toxicity studies were not used to derive a POD
for acute exposures because the doses that caused other types of acute toxicity or lethality were
higher than those that negatively impacted development.
Although developmental studies typically involve multiple exposures, they are considered
relevant for evaluating single exposures because some developmental effects (e.g., fetal
resorptions and mortality), may result from a single exposure during a critical period of
development (Davis etal., 2009; Van Raaijetal., 2003; U.S. EPA, 1991). This is consistent with
EPA's Guidelines for Reproductive Toxicity Risk Assessment which state that repeated exposure is
not a necessary prerequisite for the manifestation of developmental toxicity. Consequently,
EPA/OPPT concluded that developmental endpoints are applicable when assessing acute
exposures, where it is assumed that the risk of their occurrence depends on the timing and
magnitude of exposure. This is based on the presumption and EPA's policy that a single exposure
during a critical window of development may produce adverse developmental effects (U.S. EPA,
1996. 1991).
The risk assessment for chronic exposures was based on a range of adverse outcomes with
neurotoxicity (Honma et al., 2003), determined to be the most sensitive human health domain for
chronic non-cancer effects. Non-cancer and cancer risk estimates for chronic exposures were only
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derived for occupational scenarios since the consumer exposure scenarios were not considered to
be chronic in nature.
1-BP is carcinogenic in laboratory animals. The weight-of-evidence analysis for the cancer
endpoint is sufficient to support a probable mutagenic mode of action for 1-BP carcinogenesis.
EPA/OPPT derived an inhalation unit risk (IUR) of 3 x 10"3 per ppm (7 x 10"7per u.g/m3) based on
lung tumors in female mice. The IUR adapted from the definition in U.S. EPA (2011) is the
estimated upper bound added lifetime cancer risk resulting from occupational exposure scenarios
(i.e., 8 hours per day, 5 days per week) to an airborne agent at 1 u.g/m3. For chronic scenarios,
cancer risk estimates were calculated by multiplying the inhalation unit risk value derived from
cancer bioassay data (NTP, 2011) by occupational scenario-specific exposure estimates.
Risks for adverse developmental effects following acute inhalation exposure and adverse
neurological effects following chronic inhalation exposure were identified for the 1-BP uses
considered under the scope of this assessment. Cancer risks associated with chronic worker
inhalation exposure in adults were also identified. EPA/OPPT did not use the IUR to estimate
added cancer risks for acute exposures because the relationship between cancer induction in
humans and a single short-term exposure to 1-BP has not been firmly established in the scientific
literature.
Uncertainties of this Risk Assessment
There are a number of uncertainties associated with the monitoring and modeling approaches
used to assess 1-BP exposures. For example, the sites used to collect exposure monitoring data
were not selected randomly, and the data reported therein may not be representative of all
exposure scenarios. Further, of necessity, exposure modeling approaches employed knowledge-
based assumptions that may not apply to all use scenarios. Because site-specific differences in
use practices and engineering controls exist, but are largely unknown, this represents another
source of variability that EPA/OPPT could not quantify in the assessment. Consumer exposures
were estimated based on modeling approaches due to the lack of monitoring information that
could be used to assess consumer products. In addition, the inability to include dermal exposures
results in potential underestimation of overall exposure and risk.
Human Populations Considered in this Assessment
EPA/OPPT assessed risks for acute and chronic exposure scenarios in workers and occupational
non-users for 1-BP use as a spray adhesive, during dry cleaning (including spot cleaning), and
during degreasing operations (vapor, cold cleaning, and aerosol). EPA/OPPT assumed that
workers (those directly handling 1-BP at the facility) and occupational non-users (workers at the
facility not directly involved with the 1-BP use; for example, cashiers at a dry cleaner) would be
individuals of both sexes (> 16 and older, including pregnant workers) based upon occupational
work permits, although exposures to younger workers in occupational settings cannot be ruled
out. An objective of the monitored and modeled inhalation data was to provide separate
exposure level estimates for workers and occupational non-users.
EPA/OPPT also examined risks for acute exposure scenarios for consumer uses. EPA/OPPT
assumed that consumers would be individuals (> 16 and older; both sexes including women of
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childbearing age) that intermittently use 1-BP in aerosol spray adhesives, aerosol spot cleaners,
and aerosol degreasers/cleaners, although exposures to younger non-users may be possible in
residential settings. Non-users may be individuals of any age group (e.g., children, adults, and
elderly) who are nearby during product application.
Main Conclusions of this Risk Assessment
Most acute exposure scenarios for occupational and consumer uses presented risks based on
concerns for adverse developmental effects that may occur as a result of a single exposure to 1-BP
during a critical window of susceptibility. Particularly, inhalation risks were identified for all
occupational and consumer acute exposure scenarios, with only a few MOE values above the
benchmark MOE of 100 (acceptable risk range). These included the 50th percentile estimates for
dry cleaning (modeling post-EC worker and pre-EC occupational non-user), vapor degreasing
(monitoring post-EC occupational non-user), and cold cleaning (modeling post-EC occupational
non-user); and for the 95th percentile estimates for vapor degreasing (monitoring and modeling
post-EC occupational non-user) and cold cleaning (modeling post-EC occupational non-user).
There is a concern for a range of adverse human health effects due to chronic inhalation
exposures resulting from 1-BP use in spray adhesive, dry cleaning, and degreasing applications.
Cancer and neurological effects represent the greatest human health concern for chronic
exposure, with the highest risks expected for the spray adhesive occupational exposure scenario.
In general, risks were observed across all uses in workers and occupational non-users. High-end
(95th percentile/pre-EC) exposures (considered to represent exposure levels at the baseline
exposure condition) showed risks to workers and occupational non-users for all health effects and
all use scenarios evaluated. Risks for adverse neurological and developmental effects were
apparent regardless of the type of 1-BP exposure (50th percentile/central tendency or 95th
percentile/high-end) pre-EC for all the uses evaluated. Occupational non-users showed risks for
adverse neurological and developmental effects with high-end exposures (95th percentile)
regardless of the availability of engineering controls for most use scenarios.
Cancer risks were determined as added lifetime cancer risks, meaning the probability that an
individual will develop cancer as a result of occupational exposure over a normal lifetime of
70 years. Added lifetime cancer risk estimates from 1-BP exposure were compared to
benchmark cancer risk levels of 10"6,10"5 and 10"4 (i.e., 1 in 10,000, 1 in 100,000 and 1 in
1,000,000). All of the spray adhesive exposure scenarios evaluated using monitoring data
exceeded the benchmark cancer risk levels by multiple orders of magnitude and were near or
above the cancer risk of 10"2 (1 in 100). This analysis showed higher estimated cancer
incidences for occupational exposures associated with commercial use of 1-BP in spray adhesives,
vapor degreasing, cold cleaning, dry cleaning and aerosol degreasing in descending order. A
greater cancer risk was observed with the spray adhesive and degreasing (vapor, cold cleaning)
occupational exposure scenarios, with the highest risks resulting from direct use of 1-BP
containing spray adhesive and degreasing formulations in the absence of engineering controls
(e.g., local exhaust ventilation) in the workplace.
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EPA/OPPT estimated the population size for workers and occupational non-users at risk as:
• Spray Adhesives: 1,503 to 11,952
• Dry Cleaning and/or Spot Cleaning at Dry Cleaning: 1,088
• Vapor Degreasing: 4,712 to 23,558
• Aerosol Degreasing: 2,466 to 12,329
At this time, there is not sufficient information to develop estimates of the number of workers
and occupational non-users potentially exposed to 1-BP during cold-cleaning; however, the use of
1-BP in this sector is expected to be minimal.
Also, at this time, there is not sufficient information to develop estimates of the populations for
consumers and non-users exposed to 1-BP during the use of aerosol spray adhesives, aerosol spot
removers, and aerosol cleaners and degreasers.
In summary, the risk assessment showed the following risk findings:
There Are Non-Cancer Risks Identified for Consumers as a Result of Acute Exposure to 1-BP from
Use in Spray Adhesives, Spot Removers, and Degreasers.
A concern for adverse developmental effects was identified for all acute consumer exposure
scenarios (i.e., MOEs were below the benchmark MOE of 100), with 1-BP use in aerosol spray
cleaners and degreasers showing the greatest risk. Risks for most acute consumer scenarios
were 1-2 orders of magnitude below the benchmark MOE.
There Are Non-Cancer Risks Identified for Workers as a Result of Acute Exposure to 1-BP from
Occupational Use in Spray Adhesives, Dry Cleaning, and Degreasing Operations.
A concern for non-cancer risks (including risks to workers and occupational non-users) was
identified for all but three acute occupational exposure scenarios (i.e., MOEs were below the
benchmark MOE of 100), with 1-BP use in spray adhesives showing the greatest risk. Risks for most
acute occupational scenarios were 1-2 orders of magnitude below the benchmark MOE.
There are Non-Cancer Risks Identified for Workers as a Result of Chronic Exposure to 1-BP from
Occupational Use as a Spray Adhesive, Dry Cleaning (including as a spot cleaner), and
Degreasing Operations (vapor, cold cleaning, and aerosol)
A concern for non-cancer risks (including risks to workers and occupational non-users) was identified
for all chronic occupational exposure scenarios evaluated based on a range of adverse human
health effects. In general, higher risks were indicated for adverse neurological effects in
association with 1-BP use in spray adhesives.
All chronic occupational exposure scenarios presented risks for adverse neurological or
developmental effects in the absence of engineering controls (pre-EC).
In many instances, occupational non-users with chronic high-end exposures (95th percentile)
showed risks for adverse neurological effects regardless of the availability of engineering controls.
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Risks for non-cancer effects following chronic occupational exposure (without engineering
controls) were 2-3 orders of magnitude below the benchmark MOE.
There are Added Cancer Risks Identified for Workers as a Result of Chronic Exposure to 1-BP
from Occupational Use as a Spray Adhesive, Dry Cleaning (including as a spot cleaner), and
Degreasing Operations (vapor, cold cleaning, and aerosol)
Added cancer risks were identified for workers and occupational non-users who may be exposed
as a result of 1-BP use in spray adhesive, dry cleaning (including spot cleaning), and degreasing
operations (vapor, cold cleaning, and aerosol).
Cancer risk estimates exceeded 1 in 1,000 (exceeding all of the cancer risk benchmarks) for all
occupational use scenarios evaluated (workers and occupational non-users) based on
monitoring and modeling estimates (regardless of the use of engineering controls), with
relatively few exceptions. 1-BP use in spray adhesives presented the greatest cancer risk
concern.
1 BACKGROUND AND SCOPE
1.1 INTRODUCTION
As a part of EPA's comprehensive approach to enhance the Agency's existing chemicals
management, in March 2012 EPA/OPPT identified a work plan of chemicals for further
assessment under the Toxic Substances Control Act (TSCA)2. EPA/OPPT is assessing chemicals in
this work plan and if an assessment identifies unacceptable risks to humans or the environment,
EPA/OPPT will pursue risk reduction options. After gathering input from stakeholders, EPA/OPPT
developed criteria used for identifying chemicals for further assessment3. The criteria focused on
chemicals that meet one or more of the following: (1) potential concern to children's health (for
example, because of reproductive or developmental effects); (2) neurotoxic effects; (3)
persistent, bioaccumulative and toxic (PBT); (3) probable or known carcinogen; (4) use in
children's products; or (5) detected in biomonitoring programs. Using this methodology,
EPA/OPPT developed a TSCA Work Plan of chemicals as candidates for risk assessment in the next
several years. In the prioritization process, 1-Bromopropane or n-propyl bromide (1-BP; Chemical
Abstracts Service Registry Number [CASRN] 106-94-5) was identified for assessment based on
high human health hazard and exposure concerns based on its use profile and physical chemical
properties.
The target audience for this risk assessment is primarily EPA/OPPT risk managers; however, it may
also be of interest to the broader risk assessment community as well as US stakeholders that are
interested in issues related to 1-BP, especially when used in spray adhesive, dry cleaning
(including spot cleaning) or degreasing (vapor, cold cleaning and aerosol) uses. The information
2 http://www.epa.gov/oppt/existingchemicals/
3 http://www2.epa.gov/sites/production/files/2014-03/documents/work plan methods document web final.pdf
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presented in the risk assessment may be of assistance to other Federal, State and Local agencies
as well as members of the general public who are interested in the risks associated with 1-BP use.
The initial step in EPA/OPPT's risk assessment development process includes scoping and problem
formulation, which is distinct from the prioritization criteria used to add a chemical to the work
plan. During scoping and problem formulation EPA/OPPT reviews currently available data and
information, including but not limited to, assessments conducted by others (e.g., authorities in
other countries), published or readily available reports and published scientific literature. During
scoping and problem formulation, a robust review may result in refinement - either addition/
expansion or removal/contraction - of specific hazard or exposure concerns previously identified
in the work plan methodology.
1.2 USES AND PRODUCTION VOLUME
According to data collected in EPA's 2012 Chemical Data Reporting (CDR) Rule, 15.4 million
pounds of 1-BP were produced or imported in the US in 2011 (U.S. EPA, 2012c). Albemarle
Corporation, Dow Chemical Company, ICL, Special Materials Company, and one company claiming
CBI status currently manufacture or import 1-BP in the US (Appendix A), (Appendix B).
1-BP is a high production volume chemical used in numerous solvent applications including spray
adhesive, dry cleaning, and degreasing uses (vapor, cold cleaning, and aerosol). In the past, 1-BP
was used as a solvent for fats, waxes, or resins and as an intermediate in pharmaceutical,
insecticide, quaternary ammonium compound, flavor, and fragrance synthesis (NTP, 2013). See
Appendix B for more details.
The largest use of 1-BP (six to eight million pounds per year) is as a vapor degreaser for cleaning
optics, electronics, plastics, and metals (NCDOL. 2013: NTP. 2013: U.S. EPA. 2007c). Industry
estimates indicate 500 to 2,500 businesses currently use 1-BP for vapor degreasing, a process by
which soiled components are cleaned using vaporized solvents. 1-BP is also used in cold cleaning,
which is similar to vapor degreasing, except that cold cleaning does not require the solvent to be
heated to its boiling point in order to clean a given component. Vapor degreasing and cold
cleaning scenarios may include a range of open-top or closed systems,
conveyorized/enclosed/inline systems, spray wands, containers, and wipes.
The second largest use of 1-BP (five to seven million pounds per year) is as an adhesive, primarily
for foam cushion manufacturing and (less often) for laminates (NTP, 2013; HSIA, 2012; U.S. EPA,
2007c). EPA estimates 100 to 280 foam manufacturers (one-third of all such manufacturers) use
1-BP as an adhesive. Use of 1-BP as an industrial adhesive is expected to decline overtime due to
health and safety concerns.
1-BP is occasionally used in dry cleaning, both in machine cleaning and as a component of spot
cleaners used to remove stains before and after machine cleaning. EPA has included dry cleaning
in this risk assessment because 1-BP is a drop-in replacement for regulated chlorinated solvents
currently used in dry cleaning (TURI, 2012). Perchloroethylene (perc) remains the solvent of
choice for textiles (dry cleaning), but its market share is decreasing as dry cleaners continue to
transition to alternative solvents. 1-BP can be used as a substitute for the dominant solvent used
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in dry cleaning machines (perc) and the heavily-used solvent (trichloroethylene) used in spot
cleaners.
EPA found a variety of consumer products containing 1-BP based on their current safety data
sheets (SDS; see Table 2-). These products include aerosol spray solvents used in spray adhesives
(5 products), spot removers (4 products), degreasers and cleaners (11 products), coin cleaning (1
product), paintable mold release (1 product), automotive refrigerant flush (1 product), and
lubricants (1 product). Although EPA does not believe 1-BP is the solvent predominantly used in
these specific product markets, this could not be confirmed with sales data.
1.3 ASSESSMENT AND REGULATORY HISTORY
Under the Clean Air Act (CAA), EPA evaluated 1-BP as a substitute for ozone-depleting substances
(ODS) through the Significant New Alternatives Policy (SNAP) program. In the 2003 Notice of
Proposed Rulemaking, EPA proposed to allow use of 1-BP as a carrier solvent in adhesives; as an
aerosol solvent; and as a solvent in cleaning equipment for metals, electronics, and precision
cleaning, subject to a limit of no more than 0.05% isopropyl bromide (2-bromopropane) by
weight (U.S. EPA, 2003). In 2007, EPA issued a final rule where EPA determined that 1-BP is an
acceptable substitute for ozone-depleting substances (i.e., methyl chloroform and
chlorofluorocarbon (CFC)-113); for metals cleaning, electronics cleaning and precision cleaning
(U.S. EPA, 2007c). At the same time, EPA proposed a new rule to list 1-BP as an acceptable
substitute in the coatings end use (subject to use restrictions) and to list 1-BP as an unacceptable
substitute in adhesives or aerosol solvents (U.S. EPA, 2007b). EPA has not finalized this proposal
to date.
1-BP is regulated as a volatile organic compound under Clean Air Act regulations (see 40 CFR
51.100(s)) addressing the development of State Implementation Plans to attain and maintain the
National Ambient Air Quality Standards. In 2015, EPA announced the receipt of a complete
petition requesting that EPA add 1-BP to the list of hazardous air pollutants (HAP) under section
112(b)(l) of the CAA (U.S. EPA. 2015b). EPA proposed to add 1-BP to the Toxics Release Inventory
(TRI) subject to reporting under section 313 of the Emergency Planning and Community Right-to-
Know Act (EPCRA) and section 6607 of the Pollution Prevention Act (PPA) (U.S. EPA. 2015a). Both
of these actions are still pending.
In July 2013, the Occupational Safety and Health Administration (OSHA) and the National Institute
for Occupational Safety and Health (NIOSH) issued a hazard alert to urge employers that use 1-BP
to take appropriate steps to protect workers from exposure (OSHA, 2013). OSHA has not issued a
Permissible Exposure Limit (PEL) for 1-BP. The American Conference of Governmental Industrial
Hygienists (ACGIH) has adopted a Threshold Limit Value (TLV) of 0.1 ppm as an 8-hour TWA
(ACGIH. 2014).
The National Toxicology Program (NTP) evaluated the toxicity of 1-BP in a technical report (NTP,
2011), and identified 1-BP as 'reasonably anticipated to be a human carcinogen' in the thirteenth
report on carcinogens in 2014 (NTP, 2014).
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In 2004, California's Office of Environmental Health Hazard Assessment listed 1-BP for
developmental and male and female reproductive toxicity under Proposition 65 (OEHHA, 2004).
California has proposed a 5 ppm (25 mg/m3) time-weighted average PEL along with a skin
notation for 1-BP (CDIR, 2009b). A number of other U.S. states have taken action to address 1-BP
hazard and risk concerns. This information is available in Appendix C.
In 2006, under the Canadian Environmental Protection Act (CEPA) (Health Canada, 2006), 1-BP
was prioritized and categorized as an additional substance for consideration due to its
developmental and reproductive toxicity. The Notice with respect to certain inanimate
substances (chemicals) on the Domestic Substances List was published in the Canada Gazette in
October 2009 to collect information on its manufacture and import (Environment Canada, 2009).
As a result of this notice, 1-BP was reported as a chemical manufactured and/or imported in
Canada during 2008 (Environment Canada, 2013).
In August 2012, the European Chemicals Agency (ECHA), at the request of the European
Commission (EC), presented a proposal on the identification of 1-BP as a Substance of Very High
Concern (SVHC) under Registration, Evaluation, Authorization, and Restriction (REACH) due to its
reproductive toxicity (ECHA, 2012a, c, d_). In September 2012, a dossier was circulated to Member
States and was made available for comment on the ECHA website (ECHA, 2012b). The dossier,
which classified 1-BP as toxic for reproduction category IB, was referred to the Member State
Committee and was adopted in November 2012 (ECHA, 2012b). At the same time, the Member
State Committee agreed on the identification of 1-BP as a SVHC (ECHA, 2012a). In December
2012, 1-BP was listed on the Candidate list as a SVHC (ECHA. 2012a).
Due to its reproductive toxicity, 1-BP is registered as a Class I Designated Chemical Substance
subject to reporting requirements under the Pollutant Release and Transfer Register Law in Japan
(METI, 2009). It was listed both as a Hazardous Air Pollutant under the Japanese Air Pollution
Control Law in 2009 (NITE, 2014b), and "General Chemical Substance", with lower risks expected
for human health and the environment, through the 2013 screening assessment (NITE, 2014a).
The Organisation for Economic Co-operation and Development (OECD) lists 1-BP as a High
Production Volume (HPV) chemical (OECD. 2015).
1.4 SCOPE OF THE ASSESSMENT
Most of 1-BP (approximately 47%) is used as a vapor degreaser to clean optics, electronics,
plastics, and metals. Roughly 30-45% of the total production volume is used in spray adhesives.
Use in the dry cleaning sector is less clearly defined. Perchloroethylene remains the solvent of
choice for textiles dry cleaning but its market share is decreasing as dry cleaners continue to
transition to alternative solvents; 1-BP is also used as a solvent in aerosol spot cleaners, spray
adhesive and degreasing products.
Reports of adverse neurologic effects in 1-BP exposed workers and carcinogenic, developmental,
and reproductive effects following 1-BP exposure in rodents (NTP, 2013) prompted the Agency to
evaluate risks associated with its occupational and consumer uses.
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The following occupational and consumer uses were selected due to their high exposure
potential:
1. Occupational use in spray adhesives (workers and occupational non-users)
2. Occupational use in dry cleaning machines (workers and occupational non-users)
3. Occupational use in spot cleaning during dry cleaning (workers and occupational non-
users)
4. Occupational use in vapor degreasing (workers and occupational non-users). Vapor
Degreasing was assessed as a broad category of use. At this point, EPA/OPPT has not
developed assessments by the specific type of degreasing such as open-top, closed and in-
line.
5. Occupational use in cold cleaning degreasing (workers and occupational non-users)
6. Occupational use in aerosol degreasing (workers and occupational non-users)
7. Consumer use in aerosol spray adhesives (consumer users and non-users),
8. Consumer use in aerosol spot removers (consumer users and non-users), and
9. Consumer use in aerosol cleaners and degreasers (including engine degreasing, brake
cleaning and electronics cleaning scenarios for consumer users and non-users)
Readily available information on the physicochemical properties of 1-BP support inhalation as the
primary route of exposure, and information regarding its toxicity supports human health
concerns. Risk estimates based on cancer and non-cancer endpoints were developed for the
identified occupational (acute and chronic) and consumer (acute) use scenarios. Dermal
exposures are possible; however, limited toxicological data are available for this route of
exposure, and no toxicokinetic information is available to develop physiologically-based
pharmacokinetic models for route-to-route extrapolations. Therefore, dermal exposure
estimates, and route to route extrapolations are not included in this assessment. Upon
consideration of physical chemical properties, environmental fate, persistence and
bioconcentration factors derived for 1-BP, it was determined that a quantitative assessment of
environmental risks would not be included under the scope of this risk assessment.
In summary, the 1-BP assessment addresses the following questions:
1. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP in spray adhesives?
2. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP in dry cleaning machines?
3. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP for spot cleaning during dry
cleaning?
4. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP in vapor degreasing?
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5. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP in cold cleaning degreasing?
6. Do risks of concern exist (i.e., acute and chronic non-cancer and cancer) for workers and
occupational non-users during occupational use of 1-BP in aerosol degreasing?
7. Do risks of concern exist (i.e., acute) for consumer users and non-users where 1-BP is used
in consumer products (i.e., aerosol spray adhesives, aerosol spot removers and aerosol
cleaners and degreasers)?
1.5 PROBLEM FORMULATION
During problem formulation, EPA/OPPT identified the exposure pathways, receptors and health
endpoints that would be included in this risk assessment. To make this determination, physical
chemical properties and environmental fate were evaluated within the context of selected
scenarios: occupational uses (spray adhesive; dry cleaning, (includes spot cleaning); degreasing
(includes vapor, cold, and aerosol cleaning) and consumer uses (spray adhesives, spot removers
and aerosol degreasers/cleaners).
During problem formulation, it was determined that a quantitative assessment of environmental
risks associated with 1-BP releases would not be included in this assessment. EPA/OPPT reviewed
and summarized available published studies on ecotoxicity (U.S. EPA, 2012d, 1999) to understand
the potential effects of 1-BP releases on ecological receptors, including toxicity to fish,
invertebrates, plants and birds. Based on this review, EPA/OPPT concluded that the acute hazard
of 1-BP to aquatic organisms is low based on available data. The hazard of 1-BP is expected to be
low for chronic aquatic organisms, sediment, and terrestrial organisms based on physical and
chemical properties of 1-BP and that data were not available to assess risk to sediment dwelling
or terrestrial organisms. Thus, environmental risks were not evaluated further in this assessment.
Appendix D contains a summary of the aquatic toxicity studies considered during the initial
environmental hazard evaluation for 1-BP.
1.5.1 Physical and Chemical Properties
1-BP is a colorless liquid with a sweet hydrocarbon odor. It is a brominated hydrocarbon that is
slightly soluble in water. 1-BP is a volatile organic compound (VOC) that exhibits high volatility, a
low boiling point, low flammability and no explosivity. Figure 1-1 presents the chemical structure
and Table 1-1 summarizes the physical chemical properties of 1-BP.
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Figure 1-1 Chemical Structure of 1-Bromopropane
Table 1-1 Physical and Chemical Properties of 1-BP
Molecular formula
Molecular weight
Physical form
Melting point
Boiling point
Density
Vapor pressure
Vapor Density
Log Kow ^—
Water solubility
Flash point
C3H7Br
122.99
Colorless liquid; sweet hydrocarbon
odor
-110 °C
71 °C at 760 mmHg
1.353 g/cm3 at 20 °C
146.26 mmHg (19.5 kPa) at 20 °C
4.25 (Patty. 1963)
2.10 (Hansch. 1995)
2.450 s/L at 20 °C (Yalkowskv et al..
2010)
22 °C
Source: The Merck Index (2013)
1.5.2
Environmental Fate
This section summarizes current knowledge of the transport, persistence, bioconcentration and
bioaccumulation of 1-BP in the environment, including biological and abiotic reactions and
environmental distribution. Fate characteristics are summarized in Table 1-2.
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Table 1-2 Environmental Fate Characteristics of 1-BP
Property
CASRN
Photodegradation half-life
Hydrolysis half-life
Biodegradation
Bioaccumulation
Log Koc
Fugacity (Level III Model)
Air (%)
Water (%)
Soil (%)
Sediment (%)
Value
106-94-5
9 to 12 days (estimated, l.SxlO6 hydroxyl radicals per
a 12-hour day)
cm3 for
26 days at pH 7 and 25°C
70% after 28 days (readily biodegradable, OECD 301C)
19.2% after 28 days (not readily biodegradable, OECD
(See Appendix E for study details)
301D)
BAF = 12 (estimated)
1.6 (estimated) V ^
44.1
45.7
10.1
<0.1
1-BP is a volatile liquid with high vapor pressure, moderate water solubility, and high mobility in
soil. It is expected to exhibit low adsorption to soils and thus can migrate rapidly through soil to
groundwater. 1-BP is slowly degraded by sunlight and reactants when released to the atmosphere
(half-life 9-12 days). Based on this estimated half-life in air, long range transport via the
atmosphere is possible (see Appendix E). Volatilization and microbial degradation influence the
fate of 1-BP when released to water, sediment, or soil. Biotic and abiotic degradation rates
ranging from days to months have been reported.
The manufacturing, processing, and use of 1-BP can result in releases to air, water, sediment, and
soil. However, since 1-BP does not currently have Toxics Release Inventory reporting data, and is
not listed as a Hazardous Air Pollutant (HAP), data on the environmental releases of 1-BP to air
(fugitive source air releases via ambient air monitoring data), landfills or surface water are not
available.
1.5.3
Persistence and Bioconcentration
Biotic and abiotic degradation studies have not shown this substance to be persistent (overall
environmental half-life of less than two months). No measured bioconcentration studies for 1-BP
are available. An estimated bioaccumulation factor of 12 suggests that bioconcentration and
bioaccumulation in aquatic organisms are low (bioconcentration/bioaccumulation factor of less
than 1000).
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1.5.4
Conceptual Model
ACTIVITIES/USES
PATHWAYS
EXPOSURE
ROUTES
RECEPTORS
EFFECTS
Manufacturing - _• »•
*-* X r..l *.
Occupational:
Spray Adhesives
Dry Cleaning
Machine Cleaning I
Degreasing
Vapor uegreasing
Cold Cleaning
Degreasing
Aerosol Deereasine
N 1 ~ *• Oral
lndoor M Dermal
vapor/Mist inhalation
Emissions
Consumer:
Aerosol Spray Adhesives
Aerosol Spot Removers
Aerosol Spray
Degreasers/Cleaners
Risks Associated
with Chronic Exposures
Liver
Kidney
Reproductive
Developmental
Neurotoxicity
Carcinogenicity
Indoor
Vapor/Mist
Emissions
Risks Associated
with Acute Exposures
• Reproductive/
Developmental
Legend
Solid lines: Pathway can be quantified
Dashed lines: Pathway not quantifiable
Shaded boxes/ovals: Elements included in risk
assessment; exposure and toxicity can be
quantified
Figure 1-2 Schematic of Human and Environmental Exposure Pathways for 1-BP
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1.5.4.1 Exposure Pathways
The conceptual model above (Figure 1-2) illustrates the 1-BP uses and pathways that may result
in exposure (e.g., occupational, consumer, general population, or environmental). Shaded areas
indicate the exposure pathways included in this risk assessment; unshaded areas are not included
in this assessment. EPA/OPPT considered all TSCA uses and focused on uses of products that have
high 1-BP content and which present high potential for exposures to workers and consumers.
Occupational exposure assessment: Risks to workers and occupational non-users in association
with 1-BP use in spray adhesives, dry cleaning (including use as a spot cleaner), and degreasing
(vapor, cold, and aerosol cleaning) based on acute and chronic inhalation exposures.
Consumer exposure assessment: Risks to consumers and non-users from use of 1-BP-based
aerosol spray adhesives, aerosol spot removers, and aerosol cleaners and degreasers (including
engine degreasing, brake cleaning, and electronics cleaning), based on acute inhalation
exposures.
Pathways Excluded from the Risk Assessment
EPA/OPPT excluded the following exposure pathways from this assessment as indicated (via
dashed lines) in the conceptual model:
General population exposure: The manufacturing, processing, and use of 1-BP can result in
releases to air, water, sediment, and soil; however, general population exposures that may result
from environmental releases of 1-BP were excluded from this assessment because no reliable
exposure data for calculating general population risks are available. As 1-BP is not on the Toxics
Release Inventory (TRI) database or the National Emissions Inventory (NEI), quantitative data on
the environmental releases of 1-BP to air (fugitive or point source air releases via ambient
measured/monitoring data), landfills, or surface water are not available. EPA/OPPT is aware of a
petition by the New York State Department of Environmental Conservation (NYSDEC) and the
Halogenated Solvent Industry Alliance (HSIA) to list 1-BP (n-propyl bromide) as a hazardous air
pollutant (HAP) under Section 122 of the Clean Air Act. EPA/OPPT is coordinating with the Office
of Air and Radiation, Office of Air Quality Planning and Standards to review information
submitted to the docket regarding the potential human health impacts on communities within
the vicinity of facilities that emit 1-BP.
Occupational and consumer population exposure by the oral and dermal route: Based on the
physical-chemical properties of 1-BP (e.g., volatility) and the emissive nature of uses identified for
this assessment, EPA/OPPT expects inhalation to be the predominant route of consumer/
occupational exposure to 1-BP. Data from an in-vitro dermal penetration study (Frasch et al.,
2011) indicate that 1-BP has the potential for substantial dermal penetration depending on the
type and duration of exposure. Un-occluded (e.g., splash) exposures may not lead to significant
systemic uptake, whereas submersion or occluded exposures may contribute to greater dermal
uptake (Frasch, 2014). Despite the potential for uptake, dermal uptake is likely to be orders of
magnitude lower than uptake by inhalation because 1-BP will evaporate quickly if it comes in
contact with the skin.
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Ecological Receptors Exposure: Because 1-BP exhibits a low ecological hazard profile and low
persistence and bioaccumulation potential if released into aquatic or terrestrial environments, a
quantitative assessment of environmental risks was not included in this assessment. Appendix D
contains a summary of the aquatic toxicity studies considered during the evaluation of the
ecological hazard potential for 1-BP.
1.5.4.2 Health Effects and Human Receptors
EPA/OPPT reviewed available toxicological data, including the published and unpublished
literature and assessments completed by other organizations (NTP, 2013, 2011; U.S. EPA, 2007b,
c) to support hazard characterization. Based on this review, EPA/OPPT narrowed the hazard
assessment to a suite of effects (e.g., liver toxicity, kidney toxicity, reproductive/developmental
toxicity, neurotoxicity, and cancer) that are sensitive, robust, and biologically relevant to humans.
1-BP was initially prioritized for work plan assessment based on high concern for reproductive
toxicity; however, EPA/OPPT's more detailed dose-response analysis revealed that use of the
developmental toxicity endpoints for point of departure derivation would be protective of
reproductive effects and those that may adversely impact the most sensitive subpopulations,
including women of child bearing age and the developing fetus.
1.5.5 Analysis Plan
For each of the exposure pathways included in the assessment (Figure 1-2), EPA/OPPT quantified
occupational exposures based on a combination of monitoring data and modeled exposure
concentrations. Inhalation exposures were assessed for both workers and occupational non-
users. EPA/OPPT estimated consumer exposure based on consumer behavioral patterns and
modeled exposure concentrations.
For hazard characterization and dose-response analysis, EPA/OPPT reviewed available data and
selected studies that, taken as a whole, demonstrated the most robust, sensitive and consistent
effects for use in the risk assessment. EPA/OPPT used benchmark dose (BMD) modeling where
practicable and when model results were adequate, they were used to generate the point of
departure (POD) for acute and chronic exposure scenarios. EPA/OPPT quantified risk based on the
Margin of Exposure (MOE), which is the ratio between the exposure (50th and 95th percentiles)
and the POD. The endpoint specific MOEs were compared to endpoint specific benchmark MOEs
to determine if the relevant exposure scenarios exhibit unacceptable risks. EPA/OPPT calculated
acute or chronic MOEs (MOEaCute or MOEchronic) separately based on the appropriate POD and
estimated exposure. MOEs below the benchmark MOEs are considered to be indicative of
unacceptable risks.
For chronic occupational scenarios considering cancer risk estimation, scenario-specific exposure
estimates were multiplied by the cancer slope factor derived from the dose-response of bioassay
data to obtain the cancer inhalation unit risk value.
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2 HUMAN EXPOSURE ASSESSMENT
2.1 OCCUPATIONAL EXPOSURES
Workplace exposures have been assessed for the following occupational uses of 1-BP in:
1. Spray Adhesives
2. Dry Cleaning - dry cleaning facility has converted their PERC dry cleaning machine system
to 1-BP and also uses 1-BP in spot cleaning
3. Spot Cleaning at Dry Cleaners - dry cleaning facility uses 1-BP based spot cleaner
formulations but has not converted their dry cleaning machine system to 1-BP
4. Vapor Degreasing - vapor degreasing was assessed as a broad category of use of 1-BP. At
this point, EPA/OPPT has not yet developed more specific assessments by the type of
vapor degreasing operation such as open-top, closed and in-line.
5. Cold Cleaning Degreasing
6. Aerosol Degreasing
^
2.1.1 Approach and Methodology
The objectives of the occupational exposure assessment for each of the uses in the scope were
to:
1. Describe the process and worker activities with a potential for inhalation exposure.
2. Estimate the number of workers potentially exposed.
3. Assess inhalation exposure based on monitoring data. This involved:
a. Conducting a literature search to obtain available monitoring data.
b. Where possible, breaking down the data into exposures for workers and
occupational non-user categories and pre- or post-engineering controls (EC). The
pre-EC is considered the baseline exposure condition. The post-EC could be
measures such as local exhaust ventilation or equipment substitution which reduce
the 1-BP exposure concentrations in the workplace. Workers are those directly
involved in handling the 1-BP, for example, sprayers for the 1-BP spray adhesive
use, and occupational non-users are workers at the facility who are not directly
involved, for example, cashiers and clerks, but still have a potential for exposure to
1-BP. Pre-EC estimates are considered to represent exposure levels at the baseline
exposure condition with Post-EC representing exposure levels after improvements
in engineering controls or equipment substitution were made.
c. Calculating central tendency (50th) and high-end (95th) percentile exposures in ppm
as 8-hr Time-Weighted Averages (TWAs).
4. Assess inhalation exposure based on modeling for all uses except spray adhesives. For
some of the 1-BP uses, monitoring data was limited. Modeling allowed for assessment of
exposures to workers and non-occupational users for both pre- and post-engineering
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control conditions. Modeling also allows for use of different values for key parameters to
see the effect they have on the exposure and risk estimates. The modeling approaches for
this assessment included:
a. Using a near-field/far-field modeling approach to estimate 1-BP air concentrations
in the workplace. The near-field concentrations are assumed to represent potential
exposures to workers and the far-field concentrations represent potential
exposure to occupational non-users.
b. Dry cleaning modeling as a special case using a multi-zone modeling approach
which considered emissions from three separate sources within the dry cleaning
facility: spot cleaning; loading and unloading the dry cleaning machines; and
finishing steps. The other modeling scenarios considered 1-BP being emitted from
one source.
c. Conducting a targeted literature search to identify chemical and industry specific
information to calculate the 1-BP vapor generation rates. The majority of model
parameters were assumed to be the same across all use scenarios.
d. Using a Monte Carlo simulation to capture variability in the model input
parameters. The number of iterations was selected as 1 million.
e. Presenting central tendency (50th) percentile and high-end (95th) percentile
modeling results in ppm as 8-hr TWAs.
f. Presenting a second set of 50th and 95th percentile estimates with an additional
assumption of engineering control effectiveness (90% or 98%) to assess inhalation
exposures pre- and post- engineering controls (EC). These control effectiveness are
"what-if" values where engineering control (e.g. local exhaust ventilation) is
effectively implemented (90%) or when equipment is substituted (98%) to reduce
exposure.
5. Convert monitoring and modeling exposures estimates in ppm (as 8-hr TWAs) to the
values to be used in the risk assessment. The 8-hr TWAs were used as the estimates of
Acute Concentration (AC). These values (50th and 95th percentile ACs) were also converted
to estimates of Average Daily Concentration (ADC) and Lifetime Average Daily
Concentration (LADC). The AC, ADC and LADC values were then used in the risk
assessment to evaluate acute and chronic health risks as further described in Section 4 of
this document.
In assessing exposure using monitoring data, EPA/OPPT analyzed and used 8-hour TWA personal
breathing zone (PBZ) data obtained from published literature. Short-term and partial-shift
exposure monitoring data that cannot be translated into 8-hr TWA values and area samples are
not used for the exposure assessment because they are not representative of 1-BP exposure
throughout the work day. Several sources describe the data as "full-shift TWA" but do not specify
the duration of the shift. In these cases, EPA/OPPT assumed the work shift lasted eight hours and
that the data are equivalent to 8-hr TWA values.
The assessments of each of the identified uses are presented below in Sections 2.1.2 through
2.1.7. The following appendices provide further details and examples.
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Appendix F - Approach for Estimating Number of Workers
Appendix G - Approach Used to Collect Monitoring Data and Information on Model Parameters
Appendix H - Equations for Calculating Acute and Chronic (Non-Cancer and Cancer) Exposures
Appendix I - Example of Monitoring Data Analysis for the Spray Adhesive Use
Appendix J - Occupational Exposure Modeling (Near-field/Far-field) Approach
Appendix K - Occupational Exposure Modeling Parameters
2.1.2 Spray Adhesives
2.1.2.1 Process and Worker Activity Descriptions
1-BP is used in spray adhesives for foam cushion manufacturing (e.g., the furniture industry).
Figure 2-1 illustrates a typical process of using spray adhesives for foam cushion manufacturing.
During foam cushion manufacturing, spray guns are used to spray-apply an adhesive onto flexible
foam surfaces. Adhesive spraying typically occurs either on an open top workbench with side
panels that may have some local ventilation, or in an open workspace with general room
ventilation. After the adhesive is applied, workers assemble the cushions by hand-pressing
together pieces of cut flexible foam (NIOSH, 2003, 2002b).
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Spray adhesive
Align and compress foam
pieces to form bond
Finished furniture products
Figure 2-1 Overview of Use of Spray Adhesive in the Furniture Industry
2.1.2.2 Estimate of Number of Workers Potentially Exposed
EPA/OPPT estimated the number of workers potentially exposed to 1-BP in spray adhesives using
Bureau of Labor Statistics' Occupational Employment Statistics (OES) data (2015) and U.S. Census'
Statistics of US Businesses (SUSB) (2012). The method for estimating number of workers is
detailed in Appendix F. The worker estimates were derived using industry- and occupation-
specific employment data from these sources. The industry sectors and occupations that
EPA/OPPT determined to be relevant to spray adhesive use are presented in Appendix F.
The number of businesses in this use sector of 1-BP is estimated to be between 100 and 280 (U.S.
EPA, 2007b). Based on a total of 2,386 establishments in the industry sectors shown in Appendix
F, the 1-BP market penetration is 4.2 percent to 11.7 percent. Alternatively, an article published
in The New York Times estimated that one third (33 percent) of the foam cushion industry
switched from 1,1,1-trichloroethane (TCA) to 1-BP based adhesives when 1-BP was introduced in
the 1990s (NY Times, as cited in (U.S. EPA, 2013c)). Table 2-1 presents the estimated number of
workers and occupational non-users using the low-end market penetration of 4.2 percent and the
high-end market penetration of 33 percent. The total number of potentially exposed workers and
occupational non-users ranges from 1,503 to 11,952. Note the high-end estimate is based
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information on past 1-BP market, and may not be representative of the current foam cushion
industry. It is possible that some companies have switched to a different chemical due to reports
of worker health issues. The New York Times article also stated that two large chemical
manufacturers have since stopped selling 1-BP (NY Times, as cited in (U.S. EPA, 2013c)).
Table 2-1 Estimated Number of Workers Potentially Exposed to 1-BP in Spray Adhesive Use in Foam
Cushion Manufacturing
Exposed
Workers
Exposed
Occupational
Non-Users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
Non-Users per
Site
Low-end
551
952
1,503
100
6
10
High-end
4,384
7,568
11,952
795
6
10
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
2.1.2.3 Assessment of Inhalation Exposure Based on Monitoring Data
1-BP exposure monitoring data were identified in several literature studies, including journal
articles, NIOSH Human Health Evaluations (HHEs), and OSHA Integrated Management Information
System (IMIS). NIOSH HHEs are conducted at the request of employees, employers, or union
officials and help inform on potential hazards present at the workplace. OSHA IMIS data are
workplace monitoring data from OSHA inspections. These inspections can be random or targeted,
or can be the result of a worker complaint.
Among these sources, three NIOSH studies provide the most comprehensive information on
worker exposure to 1-BP from spray adhesives in foam cushion manufacturing. Two of the three
HHEs also compare exposure pre- and post-engineering controls (EC). A summary of these HHEs
follows:
• From March 1998 to April 2001, NIOSH investigated a facility in Mooresville, North
Carolina to assess 1-BP exposures during manufacturing of foam seat cushions (NIOSH,
2002a). The company had four departments: Saw, Assembly, Sew, and Covers. Workers in
Assembly and Covers departments worked directly with the adhesive; however, workers
in all four departments were exposed. The spray adhesive used at this facility contained
between 60 and 80 percent 1-BP. NIOSH conducted an initial exposure assessment in
1998, and observed that the ventilation exhaust filters were clogged with adhesive. In
2001, NIOSH conducted a follow-up exposure assessment after the facility made
improvements to its ventilation system.
• From November 2000 to August 2001, NIOSH investigated workplace exposures to 1-BP
during manufacturing of foam seat cushions at another cushion company in North
Carolina (NIOSH, 2002b). This facility uses a spray adhesive containing 55 percent 1-BP.
NIOSH conducted an initial exposure assessment in 2000, and recommended that the
facility reduce worker exposure by enclosing the spray stations to create "spray booths".
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Subsequently, in 2001, NIOSH conducted a follow-up assessment after spray station
enclosures were installed.
• From April 1999 to May 2001, NIOSH investigated another cushion company in North
Carolina (NIOSH, 2003). In this study, NIOSH conducted two separate exposure
assessments. In the initial assessment, NIOSH measured 1-BP inhalation exposures to
workers in and near the adhesive spray operation areas. In the second assessment, NIOSH
measured additional 1-BP inhalation exposures at the facility. There were no changes to
the facility's ventilation system (i.e. engineering controls) between the first and second
assessment.
Table 2-2 summarizes the 1-BP exposures in pre-EC and post-EC scenarios for each worker job
category. EPA/OPPT defined three job categories for 1-BP spray adhesive use:
• Sprayers: Workers who perform manual spraying of 1-BP adhesive as a regular part of his
or her job;
• Non-sprayers: Workers who are not "sprayers", but either handle the 1-BP adhesive or
spend the majority of their shift working in an area where spraying occurs. For example,
the NIOSH (2002a) study indicated spraying occurs in the Assembly and Covers
departments. EPA/OPPT assumes workers in these departments who do not perform
spraying still work in the vicinity of spraying operations and may be regularly exposed to
1-BP; and
• Occupational non-users: Workers who do not regularly perform work in an area of the
facility where spraying occurs. For example, EPA/OPPT assumes workers in the Saw and
Sew departments of the 2002 NIOSH study (NIOSH, 2002a) are "occupational non-users".
Pre-EC exposure scenarios suggest that all workers at foam cushion manufacturing facilities that
use 1-BP spray adhesives have substantial exposure to 1-BP. Sprayers have the highest levels of
exposure because they work directly with the 1-BP adhesive. However, non-sprayers and
occupational non-users may be exposed. Exposure levels for occupational non-users vary widely
depending on their specific work activity pattern, individual facility configuration, and proximity
to the 1-BP adhesive. For example, workers in the Saw and Sew departments in the NIOSH
(2002a) study classified as "occupational non-users" are exposed at levels above 100 ppm 8-hr
TWA. The high exposure levels are caused by their proximity to spraying operations in other
departments, even though no adhesive is used in the Saw and Sew departments (NIOSH, 2002a).
Post-EC exposure scenarios suggest that engineering controls, if well designed, maintained, and
operated, can reduce worker exposures by an order of magnitude. However, engineering controls
alone do not reduce exposures for sprayers and non-sprayers to levels below 0.1 ppm, the time-
weighted average threshold limit value (TLV) recommended by the American Conference of
Governmental Industrial Hygienists (ACGIH).
Additional 1-BP worker exposure monitoring data have been identified in other literature studies
such as Hanley et al. (2009; 2006), Ichihara et al. (2002), and Majersik et al. (2007). However,
these studies are not used in EPA/OPPT's analysis because they either do not provide individual
data points or lack specific information on worker job descriptions to adequately categorize the
exposure results.
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Table 2-2 Summary of 1-BP Inhalation Exposures (AC, ADC and LADC) for Spray Adhesive Use Based on
Monitoring Data
Category
Acute and Chronic, N<
(8-Hour TW
ACi-Bp, s-hr TWA anc
95th Percentile
>n-Cancer Exposures
As in ppm)
ADCl-BP, 8-hr TWA
50th Percentile
Chronic, Cancer E
LADCi-up
95th Percentile
xposures (ppm)
8-hr TWA
50th Percentile
Data Points
Sprayers
PreEC
Post ECa
253
41.9
131
17.8
145
23.9
75.1
10.2
85
49
Non-sprayersb
PreEC
Post ECa
211
28.8
127
18.0
120
16.5
72.7
10.3
31
9
Occupational non-users0
PreEC
Post ECa
129
5.48
3.00
2.00
73.5
3.13
1.71
1.14
39
17
Note: AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. Equations
and parameters for calculation of the AC, ADC, and LADC are described in Appendix H;
Sources: (OSHA. 2013: NIOSH. 2003. 2002a. b)
a EC = Engineering Controls. Pre-EC = Initial NIOSH visit; Post EC = Follow-up NIOSH visit engineering controls implemented:
Enclosing spray tables to create "spray booths" and/or improve ventilation.
b Non-Sprayer refers to those employees who are not sprayers, but either handle the adhesive or spend the majority of their shift
working in an area where spraying occurs.
c Occupational non-user refers to those employees who do not regularly work in a department/area where spraying occurs (e.g.,
employees in Saw and Sew departments).
2.1.2.4 Estimate of Inhalation Exposure Based on Modeling
A near-field/far-field modeling approach was not developed for the use of 1-BP as spray adhesive.
EPA/OPPT determined the monitoring was adequate and of acceptable quality.
2.1.3
Dry Cleaning
2.1.3.1 Process and Worker Activity Descriptions
1-BP is a solvent used in dry cleaning machines. 1-BP formulations such as DrySolv® are often
marketed as "drop-in" replacements for perchloroethylene (PERC), which indicates they can be
used in third generation or higher PERC equipment (TURI, 2012). Third generation equipment,
introduced in the late 1970s and early 1980s, are non-vented, dry-to-dry machines with
refrigerated condensers. These machines are essentially closed systems, and are only open to the
atmosphere when the machine door is opened. In third generation machines, heated drying air is
recirculated back to the drying drum through a vapor recovery system (CDC, 1997).
Fourth generation dry cleaning equipment are essentially third-generation machines with added
secondary vapor control. These machines "rely on both a refrigerated condenser and carbon
adsorbent to reduce the PERC concentration at the cylinder outlet below 300 ppm at the end of
the dry cycle", and are more effective at recovering solvent vapors. Fifth generation equipment
have the same features as fourth generation machines, but also have a monitor inside the
machine drum and an interlocking system to ensure that the concentration is below
approximately 300 ppm before the loading door can be opened (CDC, 1997).
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Dry cleaners who opt to use 1-BP can either convert existing PERC machines or purchase a new
dry cleaning machine specifically designed for 1-BP. To convert existing PERC machines to use
1-BP, machine settings and components must be changed to prevent machine overheating and
solvent leaks (Blando et al., 2010). 1-BP is known to damage rubber gaskets and seals. It can also
degrade cast aluminum, which is sometimes used on equipment doors and other dry cleaning
machine components. In addition, 1-BP is not compatible with polyurethane and silicone (TURI,
2012).
Figure 2-2 provides an overview of the dry cleaning process. Worker exposure monitoring studies
for 1-BP at dry cleaning facilities suggest workers are exposed when 1) adding makeup solvent,
typically by manually dumping it through the front hatch, 2) opening the machine door during the
wash cycle, and 3) removing loads from the machines (Blando et al., 2010).
Engineering controls such as local exhaust ventilation (LEV) located at or near the machine door
can reduce worker exposure during machine loading, machine unloading, and maintenance
activities (NCDOL, 2013). However, there are currently no regulatory requirements for installing
such controls to reduce 1-BP emissions and associated worker exposures at dry cleaning facilities.
Receiving Garments
Pre-Spotting
Dry Cleaning
Finishing
Figure 2-2 Overview of Dry Cleaning
2.1.3.2 Estimate of Number of Workers Potentially Exposed
EPA/OPPT estimated the number of workers and occupational non-users potentially exposed to
1-BP at dry cleaners using Bureau of Labor Statistics' OES data (2015) and the U.S. Census' SUSB
(2012). The method for estimating number of workers is detailed in Appendix F. These estimates
were derived using industry- and occupation-specific employment data from the BLS and U.S.
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Census. The industry sectors and occupations that EPA/OPPT determined to be relevant to dry
cleaning use are presented in Appendix F.
There are 22,359 dry cleaning establishments in the United States under NAICS 812320 (U.S.
Census Bureau, 2012). Among these establishments, only a small subset use 1-BP as a dry
cleaning solvent. In 2009, the Drycleaning and Laundry Institute (DLI) estimated only about 50 dry
cleaning systems used DrySolv® (U.S. EPA, 2013c). A more recent survey conducted by
AmericanDrycleaner.com in 2012 indicated that 1.1% of respondents used DrySolv, but did not
specify the number of respondents participating in the survey (Beggs, 2012, as cited in (U.S. EPA,
2013c). EPA/OPPT conservatively assumed a 1-BP market penetration of 1.1 percent. Using this
factor, EPA/OPPT estimated that approximately 246 dry cleaning establishments and 1,088
workers and occupational non-users are exposed to 1-BP (Table 2-3).
Table 2-3 Estimated Number of Workers Potentially Exposed to 1-BP in Dry Cleaning Shops
Exposed
Workers
821
Exposed
Occupational
non-users
267
Total Exposed
1,088
Estimated
Number of
Establishments
246
Workers per Site
3
Occupational
non-users per Site
1
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
2.1.3.3 Assessment of Inhalation Exposure Based on Monitoring Data
Table 2-4 presents an analysis of the 8-hr TWA Personal Breathing Zone (PBZ) monitoring data
from literature. The data were obtained from two literature studies of dry cleaning shops in New
Jersey. The studies noted significant variability in 1-BP exposure among different dry cleaning
shops, different job titles, and in some cases on different days when the exposure monitoring was
conducted. The exposure data were also impacted by the willingness of individual shops to
participate in exposure monitoring. Note the study (NIOSH, 2010) contains additional partial-shift
exposure data that are not summarized here. For those data, an 8-hr TWA value was not obtained
because owners of the shop requested that NIOSH remove the sampling equipment once they
had finished running the dry cleaning machines (NIOSH, 2010).
The facilities studied had general building ventilation, ceiling-mounted or wall-mounted fans, but
lacked controls specifically designed to reduce exposure to the dry cleaning solvent. Therefore,
EPA/OPPT did not identify any monitoring data to be representative of a post-EC scenario.
EPA/OPPT defined workers as dry cleaning machine operators. For workers, the 95th and 50th
percentile exposures are 50.2 and 29.4 ppm 8-hr TWA, respectively. The exposure level is
impacted by the number of loads cleaned, the number of solvent cooking cycles used, and
whether any "make-up" solvent was added in that particular shop and on that particular day
when the monitoring was conducted (Blando et al., 2010). These activities can result in a larger
release of solvent vapors into the work environment, contributing to higher worker exposure to
1-BP. The studies also noted that work load and work practices varied greatly among the shops
(NIOSH. 2010). Further, NIOSH (NIOSH. 2010) noted that the highest 1-BP concentration in air was
found when a facility with a converted PERC machine cooked the solvent, a practice that "had
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been performed widely for PERC but is no longer recommended by the manufacturers for 1-BP
operation" (NIOSH. 2010).
EPA/OPPT defined occupational non-users as employees who work in the dry cleaning shops but
do not operate the machine. For occupational non-users, the 95th and 50th percentile exposures
are 20.6 and 12.1 ppm 8-hr TWA, respectively. The data suggest that 1-BP exposure for cashiers,
clerks, and other employees at the shop can still be significant.
Table 2-4 Summary of 1-BP Inhalation Exposures (AC, ADC and LADC) at Dry Cleaning Facilities Based on
Monitoring Data
Category
Acute and Chronic, Non-Cancer Exposures
(8-Hour TWAs in ppm)
ACl-BP, 8-hrTWA and ADCl-BP, 8-hrTWA
95th Percentile
50th Percentile
Chronic, Cancer Exposures (ppm)
LADCl-BP, 8-hr TWA
95th
Percentile
50th Percentile
Data
Points
Workers a
Pre ECC
50.2
29.4
28.7
16.8
11
Occupational non-users b
Pre ECC
20.6 ^^
12.1
11.8
6.89
5
Sources: (Blando et al.. 2010: NIOSH. 2010).
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. Equations and
parameters for calculation of the AC, ADC, and LADC are described in Appendix H
a Worker refers to dry cleaning machine operators.
b Occupational non-user refers to cashiers and clerks.
a Pre-EC = Pre-Engineering Controls. All data assumed to be representative of a Pre-EC scenario
2.1.3.4 Assessment of Inhalation Exposure Based on Modeling
Because there are multiple activities with potential 1-BP exposure at a dry cleaner, a multi-zone
modeling approach is used to account for 1-BP vapor generation from multiple sources. Figure
2-3 illustrates this multi-zone approach, which considers the following three worker activities:
• Spot cleaning of stains on both dirty and clean garments: On receiving a garment, dry
cleaners inspect for stains or spots they can remove as much of as possible before
cleaning the garment in a dry cleaning machine. Spot cleaning may also occur after dry
cleaning if the stains or spots were not adequately removed. Spot cleaning occurs on a
spotting board and can involve the use of a spotting agent containing various solvents,
such as 1-BP. Workers are exposed to 1-BP when applying it via squeeze bottles, hand-
held spray bottles, or even from spray guns connected to pressurized tanks. Once applied,
the worker may come into further contact with the 1-BP if using a brush, spatula,
pressurized air or steam, or their fingers to scrape or flush away the stain (Young, 2012;
NIOSH, 1997). For modeling, EPA/OPPT assumed the near-field is a rectangular volume
covering the body of a worker.
• Unloading garments from dry cleaning machines: At the end of each dry cleaning cycle,
dry cleaning workers manually open the machine door to retrieve cleaned garments.
During this activity, workers are exposed to 1-BP vapors remaining in the dry cleaning
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machine cylinder. For modeling, EPA/OPPT assumed that the near-field consists of a
hemispherical area surrounding the machine door, and that the entire cylinder volume of
air containing 1-BP exchanges with the workplace air, resulting in a "spike" in 1-BP
concentration in the near-field, CD, during each unloading event. This concentration is
directly proportional to the amount of residual 1-BP in the cylinder when the door is
opened. The near-field concentration then decays with time until the next unloading
event occurs.
• Finishing and pressing: The cleaned garments taken out of the cylinder after each dry
clean cycle contain residual solvents and are not completely dried (von Grote et al., 2003).
The residual solvents are continuously emitted into the workplace during pressing and
finishing, where workers manually place the cleaned garments on the pressing machine to
be steamed and ironed. EPA/OPPT assumed any residual solvent is entirely evaporated
during pressing, resulting in an increase in the near-field 1-BP concentration during this
activity. Workers are exposed to 1-BP vapors while standing in vicinity of the press
machine. Because this activity is typically performed while standing, EPA/OPPT assumed
the near-field to be a rectangular volume covering the upper body of the worker.
As the figure shows, 1-BP vapor is generated in each of the three near-fields, resulting in worker
exposures at concentrations Cs, CD, and Cp. The volume of each zone is denoted by Vs, VD, and Vp.
The ventilation rate for the near-field zone (Qs, QD, OF) determines how quickly 1-BP dissipates
into the far-field (i.e., the facility space surrounding the near-fields), resulting in occupational
non-user exposures to 1-BP at a concentration CFF. VFF denotes the volume of the far-field space
into which the 1-BP dissipates out of the near-field. The ventilation rate for the surroundings,
denoted by OFF, determines how quickly 1-BP dissipates out of the surrounding space and into the
outside air.
quick
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Far-field (background)
CFF
QFF
Figure 2-3 Illustration of the Multi-Zone Model
The dry cleaning industry is characterized by a large number of small businesses, many are family-
owned and operated. In addition, many dry cleaning facilities are open longer than eight hours
per day. As such, EPA/OPPT assumed small dry cleaners operate up to 12 hours a day and up to 6
days a week. In addition, EPA/OPPT assumed each facility has a single converted third generation
or fourth generation machine in modeling 1-BP exposure. This assumption is based on a 2000
HSIA survey that very few PERC machines were fifth generation at the time (ERG, 2005). It should
also be noted that all three New Jersey dry cleaners evaluated in the Blando et al. (2010) study
used converted third generation machines.
Appendix J summarizes the modeling equations. Appendix K summarizes the environmental
parameters for the multi-zone model. The far-field volume, air exchange rate, and near-field
indoor wind speed are identical to those used in the 1-BP Spot Cleaning Model (see Section
2.1.4.4). These values were selected using engineering judgment and literature data that
EPA/OPPT believed to be representative of a typical dry cleaner.
EPA/OPPT assessed three types of workers within the modeled dry cleaning facility: 1) a worker
who performs spot cleaning; 2) a worker who unloads the dry cleaning machine and finishes and
presses the garments; and 3) an occupational non-user. Each worker type is described in further
detail below. EPA/OPPT assumed each worker activity is performed over two eight-hours shifts.
The two shifts cover the full 12-hour operating day with a four-hour overlap in the middle of the
day when both shifts are present at the facility.
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EPA/OPPT assumed spot cleaning occurs for eight hours (see Section 2.1.4.4) in the middle of the
12-hour work day (from hour 2 through hour 10). The first-shift worker spot cleans garments
from hour 2 through hour 8, while the second-shift worker spot cleans garments from hour 8 to
hour 10. The first-shift worker is exposed at the far-field concentration for two hours, and then at
the spot cleaning near-field concentration for six hours. The second-shift worker is exposed at the
far-field concentration for four hours, at the spot cleaning near-field concentration for two hours,
and then again at the far-field concentration for two hours. Spot cleaning can occur throughout
the day for both dry cleaned loads and for laundered loads, because many dry cleaning facilities
also perform laundering.
During each shift, EPA/OPPT assumed a separate worker unloads the dry cleaning machine, and
finishes and presses the garments. After each load, EPA/OPPT assumed this worker spends five
minutes unloading the machine, during which he or she is exposed at the machine near-field
concentration. After unloading, the worker spends five minutes in the finishing near-field to
prepare the garments. Then, the worker spends another 20 minutes finishing and pressing the
cleaned garments. During this 20-minute period of finishing and pressing, the residual 1-BP
solvent is off-gassed into the finishing near-field. The amount of residual 1-BP solvent is
estimated using measured data presented in (von Grote et al., 2003) for a non-vented, dry-to-dry
machine (i.e., 3rd generation). These unloading and finishing activities are assumed to occur at
regular intervals throughout the twelve-hour day. The frequency of unloading and finishing
depends on the number of loads dry cleaned each day, which varies from one to 14, where 14
was the maximum number of loads observed in the (NIOSH, 2010) and (2010) studies. When this
worker is not unloading the dry cleaning machine or finishing and pressing garments, the worker
is exposed at the far-field concentration. During the 4-hr overlap period, EPA/OPPT assumed the
first-shift worker performs the work activity if a given load can be completed prior to the end of
the first shift (i.e. hour 8). EPA/OPPT defined a load as being "completed" if it is completely
unloaded, finished, and pressed. If a load cannot be completed by the end of the first shift, it is
assigned to the second-shift worker.
EPA/OPPT assumed one occupational non-user is present during the first shift, and another is
present during the second shift, such that each occupational non-user is exposed at the far-field
concentration for eight hours a day. The occupational non-user could be the cashier, tailor, or
launderer, who works at the facility but does not perform dry cleaning activities.
Table 2-5 presents the Monte Carlo results with the Latin hypercube sampling method and 5,000
iterations. For each iteration, the average exposure for each work category is calculated across
the two shifts. Statistics of the average-shift exposures (95th and 50th percentiles) are then
calculated at the end of the simulation after all iterations have completed. For the dry cleaning
worker who performs unloading and finishing, the average shift 95th and 50th percentile
exposures are 60.7 ppm and 7.35 ppm 8-hr TWA, respectively (Table 2-5). For spot cleaning
worker, the average shift 95th and 50th percentile exposures are 6.93 ppm and 1.83 ppm 8-hr
TWA, respectively. For occupational non-users, the average shift 95th and 50th percentile
exposures are 4.84 ppm and 0.931 ppm 8-hr TWA. The model values cover a wider distribution of
exposure levels when compared to the monitoring data. This is likely due to the wide range of
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model input parameter values covering a higher number of possible exposure scenarios.
However, the modeled occupational non-user exposures are lower than actual monitoring results
presented in Section 2.1.3.3. The model assumes the occupational non-user spends their time
entirely in the far-field. In reality, it is possible that these employees will occasionally perform
activities in the near-field, thereby having a higher level of exposure.
The AC, ADC, and LADC calculations are included in Appendix H. These calculations are integrated
into the Monte Carlo simulation, such that the exposure frequency matches the model input
values for each iteration. The exposure frequency varies from 250 to 312 days per year.
Note there are additional activities with potential 1-BP exposure at dry cleaners that are not
included in this multi-zone model. For example, workers could be exposed to 1-BP emitted due to
equipment leaks, when re-filling 1-BP solvent into dry cleaning machines, when interrupting a dry
cleaning cycle, or when performing maintenance activities (e.g., cleaning lint and button traps,
raking out the still, changing solvent filter, and handling solvent waste) (OSHA, 2005). However,
there is a lack of information on these activities in the literature, and the frequency of these
activities is not well understood. The likelihood of equipment leaks is dependent on whether the
PERC machines are properly converted and maintained. The frequency of solvent re-filling
depends on a specific dry cleaner's workload and solvent consumption rate, which is also affected
by the presence of leaks. Based on observations reported by (NIOSH, 2010) and (Blando et al.,
2010), solvent charging is not performed every day. EPA/OPPT was unable to develop a modeling
approach for these exposure activities due to the lack of available information.
Table 2-5 Statistical Summary of 1-BP Dry Cleaning Exposures for Workers and Occupational Non-users
based on Modeling
Category
Acute and Chronic, t
(Average Shift 8-h
ACi-BP, 12-hrTWA 3T
95th Percentile
Jon-Cancer Exposures
Hour TWAs in ppm)
id ADCl-BP, 12-hr TWA
50th Percentile
Chronic, Cancer
LADCi-u
95th Percentile
Exposures (ppm)
', 12-hr TWA
50th Percentile
Workers: Machine Unloading and Finishing (Near-Field)
PreEC
Post EC
60.7
6.07
7.35
^P 0.735
34.7
3.47
4.20
0.420
Workers: Spot Cleaning (Near-Field)
PreEC
Post EC
6.93
0.693
1.83
0.183
3.96
0.396
1.04
0.104
Occupational non-users (Far-Field)
PreEC
Post EC
4.84
0.484
0.931
0.0931
2.76
0.276
0.532
0.0532
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. Equations and
parameters for calculation of the AC, ADC, and LADC are described in Appendix H.
Pre-EC: refers to modeling where no reduction due to engineering controls was assumed.
Post-EC: refers to modeling where engineering controls with an assumed 90% efficiency were implemented.
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2.1.4 Spot Cleaning at Dry Cleaners
2.1.4.1 Process and Worker Activity Descriptions
On receiving a garment, dry cleaners inspect for stains or spots they can remove as much of as
possible before cleaning the garment in a dry cleaning machine. As Figure 2-4 shows, spot
cleaning occurs on a spotting board and can involve the use of a spotting agent containing various
solvents, such as 1-BP. The spotting agent can be applied from squeeze bottles, hand-held spray
bottles, or even from spray guns connected to pressurized tanks. Once applied, the dry cleaner
may come into further contact with the 1-BP if using a brush, spatula, pressurized air or steam, or
their fingers to scrape or flush away the stain (Young, 2012; NIOSH, 1997).
Figure 2-4 Overview of Use of Spot Cleaning at Dry Cleaners
EPA/OPPT assesses a separate spot cleaning at dry cleaners scenario to account for dry cleaners
that may use 1-BP-based spot cleaner formulations but not convert their PERC dry cleaning
machine system to 1-BP. Therefore, this scenario represents dry cleaners where spot cleaning is
the only source of 1-BP exposure.
2.1.4.2 Estimate of Number of Workers Potentially Exposed
See Section 2.1.3.2 for the estimated number of workers and occupational non-users at dry
cleaning shops. Workers at these shops often perform multiple activities; as such, a single worker
who spot treats the garments using 1-BP may also load and unload the dry cleaning machines.
2.1.4.3 Assessment of Inhalation Exposure Based on Monitoring Data
Table 2-6 presents 8-hr TWA PBZ monitoring data from OSHA IMIS for a David's Bridal, Inc.
facility. The facility is a bridal store (not a dry cleaners) where alterations, steaming, pressing and
spot cleaning are performed. The facility used approximately one gallon of Albatross per month, a
formulation containing 40 to 70 percent 1-BP. Workers spray-applied the solvent formulation to
stained portions of the dresses via a 16-oz handheld Arrow Textile spray gun. The workers
operated approximately 8 to 10 feet apart from each other and did not wear any personal
protective equipment. Each worker may clean up to 8 dresses per day. The study did not mention
the use of any engineering controls at the facility to mitigate worker exposure.
Actual exposure for the two workers were 1.8 and 1.2 ppm 8-hr TWA. EPA/OPPT presented the
data as a range because only two data points are available from this source. It should be noted
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that these exposure levels may not be representative of spot cleaning exposure at dry cleaning
facilities, where a larger work load is likely handled.
Table 2-6 Summary of Inhalation Exposure Data for Spot Cleaning
Category
Acute and Chronic, Non-Cancer Exposures
(8-Hour TWAs in ppm)
ACi-BP, 8-hr TWA an
High-end
d ADCl-BP, 8-hr TWA
Low-end
Chronic, Cancer Exposures (ppm)
LADd.B
High-end
>, 8-hr TWA
Low-end
Data
Points
Workers
Pre ECa
1.80
1.20
1.03
0.686
2
Source: (OSHA, 2013).
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. Equations and
parameters for calculation of the AC, ADC, and LADC are described in Appendix H.
a Pre-EC = Pre-Engineering Controls. Data assumed to be representative of a Pre-EC scenario
2.1.4.4 Assessment of Inhalation Exposure Based on Modeling
A more detailed description of the modeling approach is provided in Appendix J. Figure 2-5
illustrates the near-field/far-field modeling approach that EPA/OPPT 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 OFF, determines how quickly the chemical dissipates out of the surrounding space and
into the outdoor air.
Far-Field
Figure 2-5 Schematic of the Near-Field/Far-Field Model for Spot Cleaning
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It should be noted that although 1-BP has been marketed for use as a spot cleaner, the
prevalence of this use is not known at this time.
To determine the 1-BP use rate, EPA/OPPT conducted a targeted literature search to identify
information on the typical amount of spotting agents used at dry cleaners. The Massachusetts
Department of Environmental Protection (MADEP) provided a comparative analysis of several dry
cleaner case studies using various PERC alternatives. This document estimates a dry cleaner using
1-BP spends $60 per month on spotting agents. This particular facility dry cleans 100 pieces of
garments per day. MADEP noted that the facility size can vary greatly among individual dry
cleaners (MassDEP, 2013). Blando et al. (2009) estimated that 1-BP solvent products cost $45 per
gallon. Based on this information, EPA/OPPT calculated a spot cleaner use rate of 1.33 gallons per
month, or 16 gallons per year. The Safety Data Sheet for DrySolv, a common 1-BP formulation,
indicates the product contains greater than 87 percent 1-BP by weight (EnviroTech International,
2013). The model input parameters are documented in Appendix K.
EPA/OPPT performed Monte Carlo simulations, applying one million iterations and the Latin
hypercube sampling method. Table 2-7 presents a statistical summary of the exposure modeling
results for the pre-EC and post-EC scenarios. For pre-EC, the 50th percentile near-field exposure is
2.57 ppm 8-hr TWA, with a 95th percentile of 9.44 ppm 8-hr TWA. These results are generally
comparable to the monitoring data. With engineering controls, model exposure is reduced to
0.257 and 0.944 ppm 8-hr TWA, respectively. Engineering control (e.g., LEV) is assumed to be 90
percent effective as a "what-if" engineering assumption.
For occupational non-users (far-field), model exposure has a 50th percentile value of 0.888 ppm
and a 95th percentile value of 3.79 ppm 8-hr TWA. With engineering controls, the exposure is
reduced to 0.0888 and 0.379 ppm 8-hr TWA, respectively.
Estimates of Acute Concentration (AC), Average Daily Concentrations (ADC) and Lifetime Average
Daily Concentration (LADC) for use in assessing risk were made using the approach and equations
described in Appendix H.
Table 2-7 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Use of Spot Cleaning
at Dry Cleaners Based on Modeling
Category
Acute and Chronic, Non
TWAs
ACl-BP, 8-hr TWA a
95th Percentile
-Cancer Exposures (8-Hour
in ppm)
Td ADCi-BP, 8-hr TWA
50th Percentile
Chronic, Cancer
LADCi.
95th Percentile
Exposures (ppm)
3P, 8-hr TWA
50th Percentile
Workers (Near-Field)
PreEC
Post EC
9.44
0.944
2.57
0.257
5.39
0.539
1.47
0.147
Occupational non-users (Far-Field)
PreEC
Post EC
3.79
0.379
0.888
0.0888
2.16
0.216
0.507
0.0507
Note: AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. Equations
and parameters for calculation of the AC, ADC, and LADC are described in Appendix H; EC - Engineering controls
Pre-EC: refers to modeling where no reduction due to engineering controls was assumed
Post-EC: refers to modeling where engineering controls with an assumed 90% efficiency were implemented
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2.1.5
Vapor Degreasing
2.1.5.1 Process and Worker Activity Descriptions
1-BP is a potential replacement for chlorinated solvents in vapor degreasing. Vapor degreasing is
used to remove dirt, grease, and surface contaminants in a variety of metal cleaning industries.
The suitability of 1-BP for use in vapor degreasing is due to its high purity, compatibility with
many metals, low corrosivity, and suitability for use in most modern vapor degreasing equipment.
Figure 2-6 is an illustration of vapor degreasing operations, which can occur in a variety of
industries.
V»t- a Dl
Fabrication
Shops
Metal
Plating
Shops
Electronics
Assembly
Shops
I \
EUrtropliting
•••-•"•
Repair
Shops
Figure 2-6 Use of Vapor Degreasing in a Variety of Industries
There are several types of vapor degreasing equipment, including batch degreaser, in-line
degreaser, and airless, vacuum degreaser. The batch degreaser, traditionally an open-top unit, is
a tank with condensing coils at the top (see Figure 2-7). Heating elements at the bottom of the
degreaser heat the liquid solvent to above its boiling point. Solvent vapor rises to the height of
chilled condensing coils on the inside walls of the unit, producing a hot vapor zone below the
coils. The condensing coils cool the vapor, causing it to condense and return to the bottom of the
degreaser (U.S. EPA. 2006a).
To clean dirty parts, the substrates are lowered into the vapor zone. The hot vapor condenses
onto the substrate, which is cooler in temperature, and the condensation dissolves the grease
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and carries it off the substrate surface as it drains into the solvent reservoir below. The process
continues until the substrate temperature reaches that of the vapor, at which point the cleaned
and dried substrate is lifted out of the vapor zone. The degreaser can also contain one or more
immersion tanks below the vapor zone for additional cleaning and rinsing. 1-BP emissions and
worker exposures from batch, open-top degreasers can occur from solvent dragout or vapor
displacement when the substrates are raised out of or lowered into the equipment, respectively
(Kanegsberg and Kanegsberg, 2011). Worker exposure is also possible while charging new solvent
or disposing spent solvent.
Work lte» aid frocedive Orate
Source: Kl'A.
Figure 2-7 Open-Top Batch Vapor Degreaser (U.S. EPA, 2006a)
An in-line type degreaser consists of a material handling system that automatically conveys the
workload in and out of the degreaser. In-line degreasers are used where there is a high volume of
workload, typically custom designed for large scale industrial operations. These units utilize the
same general cleaning techniques as batch units, but have different emission points due to
differences in equipment configuration. In-line degreasers are semi-enclosed above the
solvent/air interface, with the only openings at substrate entry and exit ports. Therefore,
emissions are substantially lower than those from equal sized batch, open-top vapor degreasers.
However, most in-line degreasers are larger than batch, open-top vapor degreasers. Some in-line
degreasers are equipped with an exhaust system that pumps air from inside the cleaning machine
to an outside vent (U.S. EPA, 2006a).
In airless degreaser systems, air is removed from an enclosed degreaser using a vacuum pump.
The hot solvent vapor contacts the substrate via spraying action, condenses on the cooler
substrate, and is removed by vacuum. The spraying and vacuum removal steps are then repeated
until the substrate is cleaned. Because the system is under a vacuum, solvent can boil at
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temperatures below their normal boiling points. These types of degreasers have very low solvent
emissions; users of these systems have reported using the equipment for over five years without
solvent changeout (Kanegsberg and Kanegsberg, 2011).
2.1.5.2 Estimate of the Number of Workers Potentially Exposed
EPA/OPPT estimated the number of workers potentially exposed to 1-BP in vapor degreasing
using Bureau of Labor Statistics' OES data (2015) and (2012) U.S. Census SUBS. The method for
estimating number of workers is detailed in Appendix F. The worker estimates were derived using
industry- and occupation-specific employment data from these sources. The industry sectors and
occupations that EPA/OPPT determined to be relevant to degreasing uses are presented below.
EPA/OPPT was unable to determine which industry sectors and occupations perform specific
degreasing types (e.g., vapor degreasing versus cold cleaning). It is possible that establishments
under the same NAICS code perform a combination of vapor degreasing and cold cleaning.
There are 109,966 establishments among the industry sectors (see Appendix F). The number of
businesses that use 1-BP for vapor degreasing is estimated at 500 to 2,500 businesses (U.S. EPA,
2007b). This translates to a 1-BP market penetration of 0.5 percent to 2.3 percent.
Table 2-8 presents the estimated number of workers and occupational non-users based on
industry- and occupational-specific employment data. The low-end estimates correspond to a 0.5
percent market penetration, while the high-end estimates correspond to a 2.3 percent market
penetration. The total number of potentially exposed workers and occupational non-users range
from 4,712 to 23,558.
Table 2-8 Estimated Number of Workers Potentially Exposed to 1-BP in Degreasing Uses
Exposed
Workers
Exposed
Occupational
non-users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
non-users per
Site
^^^f ~"^^J Low-end
3,245
1,466
4,712
500
6
3
High-end
16,226
7,332
23,558
2,500
6
3
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
2.1.5.3 Assessment of Inhalation Exposure Based on Monitoring Data
Table 2-9 summarizes the 1-BP exposure data for pre-EC and post-EC vapor degreasing scenarios.
EPA/OPPT obtained exposure monitoring data from several sources, including journal articles
(e.g., (Hanleyetal.. 2010)). NIOSH HHEs, OSHA IMIS database, and data submitted to the EPA
SNAP program. 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. OSHA IMIS data are workplace monitoring data from OSHA inspections; EPA SNAP
program data are collected as part of the EPA/OPPT's effort to identify substitutes for ozone-
depleting substances.
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Data from these sources cover exposure at a variety of industries that conduct vapor degreasing,
including telecommunication device manufacturing, aerospace parts manufacturing, electronics
parts manufacturing, helicopter transmission manufacturing, hydraulic power control component
manufacturing, metal product fabrication, optical prism and assembly, and printed circuit board
manufacturing. It should be noted that sources that only contain a statistical summary of worker
exposure monitoring, but exclude the detailed monitoring results, are not included in EPA/OPPT's
analysis below.
Most of the gathered data were for batch open-top vapor degreasers with the only exception
being data obtained from the EPA SNAP program, which did not specify the type of degreaser
used. The EPA SNAP program data were included in the data analysis despite the uncertainty in
the degreaser type. Additionally, the OSHA IMIS data from Da-Tech indicated that spray cleaning
occurred while parts were inside the degreasers (OSHA, 2013). Such activities could further
increase worker exposure.
To analyze the exposure monitoring data, EPA/OPPT categorized these data into pre-EC and post-
EC scenarios. EPA/OPPT identified the data to be representative of a "pre-EC" scenario if they
were gathered before implementation of engineering controls designed to reduce worker
exposure to 1-BP. EPA/OPPT identified data to be "post-EC" if they were gathered after the
implementation of engineering controls designed to reduce worker exposure to 1-BP at the
facility. These controls can include local exhaust ventilation, dedicated ventilated degreasing
room, or controls to the degreasing equipment such as larger and improved chillers.
EPA/OPPT defined a vapor degreasing "worker" as an employee who operates or performs
maintenance tasks on the degreaser, such as draining, cleaning, and charging the degreaser bath
tank. EPA/OPPT defined "occupational non-user" as an employee who does not regularly handle
1-BP or operate the degreaser but performs work in the area around the degreaser. The data
sources do not describe their work activities in detail, and the exact proximity of these
occupational non-users to the degreaser is unknown.
Pre-EC exposure data for vapor degreasing shows that workers handling the solvent and
operating the degreasers are exposed to significant levels of 1-BP, with 95th and 50th percentile
exposures of 47.7 and 8.20 ppm, respectively. In post-EC scenarios, worker exposures are
reduced to 8.40 and 1.50 ppm at the 95th and 50th percentile levels, respectively, suggesting that
good engineering controls can significantly reduce worker exposure to 1-BP during vapor
degreasing. For occupational non-users, both pre-EC and post-EC inhalation levels of 1-BP are
below 5 ppm.
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Table 2-9 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Vapor Degreasing
Based on Monitoring Data
Category
Acute and Chronic, Non-Cancer
Exposures (8-Hour TWAs in ppm)
ACl-BP, 8-hr TWA and ADCl-BP, 8-hrTWA
95th Percentile
50th Percentile
Chronic, Cancer Exposures
(ppm)
LADCl-BP, 8-hr TWA
95th
Percentile
50th
Percentile
Data
Points
Worker
PreEC
Post EC
47.7
8.40
8.20
1.50
27.3
4.80
4.69
0.857
167
26
Occupational non-users3
PreEC
Post EC
4.90
0.0200
0.440
0.0200
2.80
0.0114
0.251
0.0114
7
13
Source: (OSHA, 2013; U.S. EPA, 2006b). Note the (NIOSH, 2001) study only contains post-EC data.
a Occupational non-users refers to those employees who do not regularly handle the solvent or operate the degreaser but work in
the degreaser area.
Note: the occupational non-users, post EC had the same exposure concentration 0.02 ppm at the 50th and 95th percentiles because
in the 13 data points the reported exposure concentration had a very small range with multiple data points at 0.02 ppm.
Equations and parameters for calculation of the AC, ADC, and LADC are described in Appendix H.
2.1.5.4 Assessment of Inhalation Exposure Based on Modeling
A more detailed description of the modeling approach is provided in Appendix J. Figure 2-8
illustrates the near-field / far-field model that can be applied to vapor degreasing (Keil et al.,
2009). As the figure shows, volatile 1-BP vapors evaporate into the near-field, resulting in worker
exposures at a concentration CNF. The concentration is directly proportional to the evaporation
rate of 1-BP, G, into the near-field, whose volume is denoted by VNF. The ventilation rate for the
near-field zone (QNF) determines how quickly 1-BP dissipates into the far-field, resulting in
occupational non-user exposures to 1-BP at a concentration CFF. VFF denotes the volume of the
far-field space into which the 1-BP dissipates out of the near-field. The ventilation rate for the
surroundings, denoted by OFF, determines how quickly 1-BP dissipates out of the surrounding
space and into the outside air. Appendix J outlines the equations uses for this model.
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Far-Field
<]
Near-Field
Figure 2-8 Schematic of the Near-Field/Far-Field Model for Vapor Degreasing
Appendix K presents the model parameters, parameter distributions, and assumptions for the
1-BP vapor degreasing model. To estimate the 1-BP vapor generation rate, the model references
an emission factor developed by the California Air Resources Board (CARB) for the California
Solvent Cleaning Emissions Inventories (CARB, 2011). CARB surveyed facilities that conduct
solvent cleaning operations, and gathered site-specific information for 213 facilities. CARB
estimated a 1-BP emission factor averaging 10.43 Ib/employee-yr, with a standard deviation of
17.24 Ib/employee-yr, where the basis is the total number of employees at a facility. The majority
of 1-BP emissions were attributed to the vapor degreasing category.
It should be noted that the "vapor degreasing" category in CARB's study includes the batch-
loaded vapor degreaser, aerosol surface preparation process, and aerosol cleaning process. It is
not known what percentage, if any, of the 1-BP emission factor is derived from aerosol
applications. This modeling approach assumes the 1-BP emission factor is entirely attributed to
vapor degreasing applications. The emission factor is expected to represent emissions from
batch-loaded degreasers used in California at the time of study. It is not known whether these are
specifically open-top batch degreasers, although open-top is expected to be the most common
design. The CARB survey data did not include emissions for conveyorized vapor degreasers.
The CARB emission factor is then combined with U.S. employment data for vapor degreasing
industry sectors from the Economic Census4. The 1-BP RA identified 78 NAICS industry codes that
are applicable to vapor degreasing. For these industry codes, the Census data set indicates a
minimum industry average of 8 employees per site, with a 50th percentile and 90th percentile of
4 For the purpose of modeling, EPA/OPPT used data from the 2007 Economic Census for the vapor degreasing NAICS
codes as identified in the TCE RA (U.S. EPA, 2014c). The 2012 Economic Census did not have employment data
(average number of employees per establishment) for all vapor degreasing NAICS codes of interest.
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25 and 61 employees per site, respectively. A lognormal distribution is applied to the Census data
set to model the distribution of the industry-average number of employees per site for the NAICS
codes applicable to vapor degreasing.
These nationwide Census employment data are comparable to the 2008 California employment
data cited in CARB's study. According to the CARB study, approximately 90 percent of solvent
cleaning facilities in California had less than 50 employees (whereas the national Census data
estimate 90 percent of facilities have less than or equal to 61 employees). It is important to note
that the Census data report an average number of employees per site for each NAICS code. The
number of employees for each individual site within each NAICS code is not reported. Therefore,
the distribution EPA/OPPT calculated represents a population of average facility size for each
NAICS code, and not the population of individual facility sizes over all NAICS codes.
The vapor generation rate, G (kg/unit-hr), is calculated in-situ within the model, as follows:
Equation 2-1 Equation for Calculating Vapor Degreasing Vapor Generation Rate
G = EF x EMP / (2.20462 x OH x OD x U)
Where EF = emission factor (Ib/employee-yr)
EMP = Number of employees (employee/site)
OH = Operating hours per day (hr/day)
OD = Operating days per year (day/yr)
U = Number of degreasing units (unit/site)
2.20462 = Unit conversion from Ib to kg (Ib/kg)
EPA/OPPT performed a Monte Carlo simulation with one million iterations and the Latin
Hypercube sampling method in @Risk5 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).
The modeled 8-hr TWA results and the values in Table_Apx H-l are used to calculate 8-hr acute
exposure, ADC, and LADC (also see Appendix H).
Table 2-10 presents a statistical summary of the exposure modeling results. Estimates of Acute
Concentration (AC), Average Daily Concentrations (ADC) and Lifetime Average Daily
Concentration (LADC) for use in assessing risk were made using the approach and equations
described in Appendix H.
5 A risk analysis software tool (Microsoft Excel add-in) using Monte Carlo simulation
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These exposure estimates represent modeled exposures for the workers and occupational non-
users. For workers, the 50th percentile exposure is 1.76 ppm 8-hr TWA, with a 95th percentile of
25.61 ppm 8-hr TWA. Compared to literature studies:
• Hanley et al. (2010) reported a geometric mean of 2.63 ppm 8-hr TWA exposure with a
range of 0.078 to 21.4 ppm 8-hr TWA among 44 samples;
• NIOSH (2001) reported a range of 0.01 to 0.63 ppm 8-hr TWA among 20 samples;
• A 2003 EPA analysis suggested that 87 percent of the samples were less than 25 ppm 8-hr
TWA among 500 samples at vapor degreasing facilities (U.S. EPA, 2003).
The modeled mean near-field exposure is found to be generally comparable to the exposures
reported in literature.
For occupational non-users, the modeled far-field exposure has a 50th percentile value of
0.671 ppm and a 95th percentile of 9.38 ppm 8-hr TWA. These modeled far-field results are
somewhat higher than reported literature values. (Hanley et al., 2010) reported workers away
from the degreasers are exposed at concentrations of 0.077 to 1.69 ppm 8-hr TWA, with a
geometric mean of 0.308 ppm 8-hr TWA.
The post-EC scenarios reference Wadden et al. (1989) and NEWMOA (2001). The model assumes
engineering controls can be 90 percent effective; this value is based on a LEV system for an open-
top vapor degreaser (lateral exhaust hoods installed on two sides of the tank) (Wadden et al.,
1989). This assumption is likely an overestimate because the study covered only reductions in
degreaser machine emissions due to LEV and did not address other sources of emissions such as
dragout, fresh and waste solvent storage and handling. Furthermore, a caveat in the study is that
most LEV likely do not achieve ACGIH design exhaust flow rates, indicating that the emission
reductions in many units may not be optimized. Actual exposure reductions from added
engineering controls can be highly variable and can only be verified by monitoring studies.
The model assumes 98 percent exposure reduction can be achieved using equipment
substitution. This value is based on the NEWMOA study, which states air emissions can be
reduced by 98 percent or more when a closed-loop degreaser is used instead of an open-top
vapor degreaser (NEWMOA, 2001).
The modeled post-EC scenarios suggest that 1-BP exposure during vapor degreasing could be
effectively reduced using either equipment substitution or improved ventilation.
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Table 2-10 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Vapor Degreasing
Based on Modeling
Category
Acute and Chronic, Non
TWAs
ACi-BP, 8-hr TWA 3
95th Percentile
-Cancer Exposures (8-Hour
in ppm)
•\d ADCl-BP, 8-hr TWA
50th Percentile
Chronic, Cancer
LADCi-u
95th Percentile
Exposures (ppm)
P, 8-hr TWA
50th Percentile
Workers (Near-Field)
PreEC
Post EC 90%
Post EC 98%
25.6
2.56
0.512
1.76
0.176
0.0352
14.6
1.46
0.293
1.01
0.101
0.0202
Occupational non-users (Far-Field)
PreEC
Post EC 90%
Post EC 98%
9.38
0.938
0.188
0.671
0.0671
0.00134
5.36
0.536
0.0107
0.383
0.0383
0.00767
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. See Appendix H.
Pre-EC: refers to modeling where no reduction due to engineering controls was assumed
Post-EC: refers to modeling where engineering controls with 90% efficiency were implemented or equipment substitution with
98% efficiency
2.1.6
Cold Cleaning Degreasing
2.1.6.1 Process and Worker Activity Descriptions
Cold cleaners are non-boiling solvent degreasing units. Cold cleaning operations include spraying,
brushing, flushing, and immersion. Figure 2-9 shows the design of a typical batch-loaded,
maintenance cold cleaner, where dirty parts are cleaned manually by spraying and then soaking
in the tank. After cleaning, the parts are either suspended over the tank to drain or are placed on
an external rack that routes the drained solvent back into the cleaner. Batch manufacturing cold
cleaners could vary widely, but have two basic equipment designs: the simple spray sink and the
dip tank. The dip tank design typically provides better cleaning through immersion, and often
involves an immersion tank equipped with agitation (U.S. EPA, 1981). Emissions from batch cold
cleaning machines typically result from (1) evaporation of the solvent from the solvent-to-air
interface, (2) "carry out" of excess solvent on cleaned parts, and (3) evaporative losses of the
solvent during filling and draining of the machine (U.S. EPA, 2006a).
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Cold Cleaner
Figure 2-9 Typical Batch-Loaded, Maintenance Cold Cleaner (U.S. EPA, 1981)
Emissions from cold in-line (conveyorized) cleaning machines result from the same mechanisms,
but with emission points only at the parts' entry and exit ports (U.S. EPA, 2006a).
The general worker activities for cold cleaning include placing the parts that require cleaning into
a vessel. The vessel is usually something that will hold the parts but not the liquid solvent (i.e., a
wire basket). The vessel is then lowered into the machine, where the parts could be sprayed, and
then completely immersed in the solvent. After a short time, the vessel is removed from the
solvent and allowed to drip/air dry. Depending on the industry and/or company, these operations
may be performed manually (i.e., by hand) or mechanically. Sometimes parts require more
extensive cleaning; in these cases, additional operations are performed including directly spraying
solvent on the part, agitation of the solvent or parts, wipe cleaning and brushing (NIOSH, 2001;
U.S. EPA. 1997b).
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Fabrication
Shops
Metal
Plating
Shops
I J
"i »
Electronics
Assembly
Shops
Repair
Shops
<5&
Figure 2-10 Illustration for Use of Cold Cleaner in a Variety of Industries
2.1.6.2 Estimate of the Number of Workers Potentially Exposed
There is no information to determine the number of workers and occupational non-users
potentially exposed 1-BP during cold cleaning. The use of 1-BP is this sector is expected to be
minimal. It is possible that some of the degreasing facilities presented in Section 2.1.5.2 also use
1-BP as a cold cleaning solvent.
2.1.6.3 Assessment of Inhalation Exposure Based on Monitoring Data
Table 2-11 presents OSHA IMIS data for two facilities: McFadden Lighting and Danville Metal
Stamping. The first facility manufactures decorative, architectural, and church lighting, and uses
1-BP to clean parts in an immersion process in an area with general ventilation. The second
facility manufactures parts for the aerospace industry, and uses 1-BP in a degreasing tank
equipped with a spray nozzle. The degreasing operation is conducted in an area with local
exhaust ventilation. The degreasing equipment and process activity in the two studies appear to
refer to cold cleaning; however, the equipment is not described in detail in the OSHA IMIS data.
The 95th and 50th percentile exposures for workers are 46.9 and 13.7 ppm 8-hr TWA, respectively.
For occupational non-users, the exposure value is based on a single data point for a person
described as "CSHO" (i.e. Chemical Safety and Health Officer), which is an official from OSHA or a
state plan occupational safety and health program. The exposure for this individual measured
2.60 ppm 8-hr TWA. This data point represents a what-if inhalation exposure level for
occupational non-users; the representativeness of this data point is unknown. It should be further
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noted that IMIS data are obtained from OSHA inspections, and not intended to be representative
of average worker exposure.
Table 2-11 Summary of Inhalation Exposure Monitoring Data for Cold Cleaning
Category
Acute and Chronic, Non-Cancer
Exposures (8-Hour TW As in ppm)
ACl-BP, 8-hrTWA and ADCl-BP, 8-hr TWA
95th Percentile
50th Percentile
Chronic, Cancer Exposures (ppm)
LADCl-BP, 8-hr TWA
95th Percentile
50th Percentile
Data
Points
Workers
PreEC
Category
46.9
13.7
What-if
26.8
7.83
What-if
10
Data
Points
Occupational non-users
PreEC
2.60
1.49
1
Source: (OSHA, 2013).
What-if: Represents a what-if inhalation exposure level for occupational non-user based on a single data point.
2.1.6.4 Assessment of Inhalation Exposure Based on Exposure Modeling
A more detailed description of the modeling approach is provided in Appendix J. The EPA AP-42,
Compilations of Air Pollution Emission Factors contains emission factors and process information
developed and compiled from source test data, material balance studies, and engineering
estimates (U.S. EPA, 1981). Chapter 4.6 provides generic, non-methane VOC emission factors for
several solvent cleaning operations, including cold cleaning and vapor degreasing. These emission
factors suggest that cold cleaning emissions range from 3.2 to 57.1 percent of the emissions from
a traditional open-top vapor degreaser (U.S. EPA, 1981). To model exposures during 1-BP cold
cleaning, an exposure reduction factor, RF, with uniform distribution from 0.032 to 0.571 is
applied to the vapor degreasing model.
Figure 2-11 presents the model approach for cold cleaning. Except for the exposure reduction
factor, the model approach and input parameters for cold cleaning are identical to those
previously presented for vapor degreasing. EPA/OPPT performed a Monte Carlo simulation with
one million iterations and the Latin Hypercube sampling method in @Risk to estimate 8-hr TWA
near-field and far-field exposures. EPA/OPPT then used these model exposure estimates to
calculate acute exposure, ADC, and LADC. Note the cold cleaning model approach and the
underlying data used (i.e. EPA AP-42) do not differentiate between a spray versus immersion cold
cleaner.
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Far-Field
Near-Field
Figure 2-11 The Near-Field/Far-field Model for Cold Cleaning Scenario
Table 2-12 presents a statistical summary of the exposure modeling results. Estimates of Acute
Concentration (AC), Average Daily Concentrations (ADC) and Lifetime Average Daily
Concentration (LADC) for use in assessing risk were made using the approach and equations
described in Appendix H. For workers, the pre-EC exposures are 0.442 ppm 8-hr TWA at the 50th
percentile and 7.82 ppm 8-hr TWA at the 95th percentile. These exposure levels are substantially
lower than actual monitoring data. This may be because the model assumes the cold cleaner only
operates two hours per day, which could underestimate exposure if the equipment is operated
for a longer duration. For occupational non-users, the pre-EC exposures are 0.168 ppm at the 50th
percentile and 2.88 ppm 8-hr TWA at the 95th percentile. With engineering controls, these
exposures are further reduced, with some being reduced to levels below the ACGIH TLV of
0.1 ppm. We assume the engineering control effectiveness would be similar to that of a vapor
degreaser.
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Table 2-12 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Cold Cleaning Based
on Modeling
Category
Acute and Chronic, Non-Cancer Exposures (8-Hour
TWAs in ppm)
ACl-BP, 8-hr TWA and ADCl-BP, 8-hr TWA
95th Percentile 50th Percentile
Chronic, Cancer Exposures (ppm)
LADCl-BP, 8-hr TWA
95th Percentile 50th Percentile
Workers (Near-Field)
PreEC
Post EC 90%
Post EC 98%
7.82
0.782
0.156
0.442
0.0442
0.00884
4.47
0.447
0.0894
0.253
0.0253
0.00505
Occupational non-users (Far-Field)
PreEC
Post EC 90%
Post EC 98%
2.88
0.288
0.0575
0.168
0.0168
0.00336
1.65
0.165
0.0329
0.0962
0.00962
0.00192
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration.
Pre-EC: refers to modeling where no reduction due to engineering controls was assumed.
Post-EC: refers to modeling where engineering controls with a 90% efficiency implemented or equipment substitution with 98%
efficiency.
2.1.7
Aerosol Degreasing
2.1.7.1 Process and Worker Activity Descriptions
Aerosol degreasing is a process that uses an aerosolized solvent spray, typically applied from a
pressurized can, to remove residual contaminants from fabricated parts. The aerosol droplets
bead up on the fabricated part and then drip off, carrying away any contaminants and leaving
behind a clean surface.
Figure 2-12 illustrates the typical process of using aerosol degreasing to clean components in
commercial settings. One example of a commercial setting with aerosol degreasing operations is
repair shops, where service items are cleaned to remove any contaminants that would otherwise
compromise the service item's operation. Internal components may be cleaned in place or
removed from the service item, cleaned, and then re-installed once dry (U.S. EPA, 2014a).
o
• -
4 • 4 O ^
4
i •
:•€>:
Figure 2-12 Overview of Aerosol degreasing
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2.1.7.2 Estimate of the Number of Workers Potentially Exposed
NAICS industry sectors relevant to aerosol degreasing and BLS occupation codes where workers
are potentially exposed to degreasing solvents are detailed in Appendix F. EPA/OPPT assumed the
types of occupation with potential solvent exposure are similar between vapor degreasing and
aerosol degreasing.
There are 222,940 establishments among the industry sectors presented in Table 2-13. The
EPA/OPPT market report on 1-BP estimated that "1,000 to 5,000 businesses used 1-BP-based
aerosol solvents in 2002 (U.S. EPA. 2007b). as cited in (U.S. EPA. 2013c)". This translates to a
market penetration of approximately 0.4 percent to 2.2 percent. Based on these estimates,
approximately 2,466 to 12,329 workers and occupational non-users are potentially exposed to
1-BP as an aerosol degreasing solvent. It is unclear whether the number of establishments using
1-BP-based aerosol solvents has increased since 2002.
Table 2-13 Estimated Number of Workers Potentially Exposed to 1-BP in Aerosol Degreasing
Exposed
Workers
Exposed
Occupational
non-users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
non-users per
Site
Low-end ^^^
2,227
238
2,466
1,000
2
0.2
High-end
11,137
1,192
12,329
5,000
2
0.2
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. The number of workers per site is rounded to
the nearest integer. The number of occupational non-users per site is shown as 0.2, as it rounds down to zero.
2.1.7.3 Assessment of Inhalation Exposure Based on Monitoring Data
Table 2-14 summarizes 8-hr TWA PBZ monitoring data for aerosol degreasing obtained from
(Stewart, 1998) and (Tech Spray, 2003). The Stewart (1998) study measured 1-BP worker PBZ
during an aerosol spray can application on a test substrate consisting of a small electric motor;
the scenario was intended to simulate workers performing typical repair and maintenance work.
The (Tech Spray, 2003) study measured worker exposure in a test scenario that simulated
cleaning of printed circuit boards for the repair of computers and electrical systems. Among the
two test studies, the 95th and 50th percentile worker exposures were 31.6 and 16.1 ppm,
respectively.
The Tech Spray study tested an exposure scenario where the aerosol degreasing occurred inside a
non-vented booth. Subsequently, the company tested the same scenario in a vented booth. With
a non-vented booth, worker exposure ranged from 13 to 32 ppm 8-hr TWA. With the vented
booth, worker exposure was reduced to 5.50 ppm 8-hr TWA based on a single data point. The
data suggest the significance of ventilation and its impact on worker exposure. The single data
point for worker exposure with a vented booth represents a "what-if" exposure level for a post-
EC scenario. The representativeness of this exposure level is unknown.
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Table 2-14 Summary of Inhalation Exposure Monitoring Data for Aerosol Degreasing
Category
Acute and Chronic, Non-Cancer Exposures
(8-Hour TWAs in ppm)
ACi-BP, 8-hr TWA an
95th Percentile
d ADCl-BP, 8-hr TWA
50th Percentile
Chronic, Cancer Exposures (ppm)
LADd.B
95th Percentile
Workers a
PreEC
Category
Post EC
31.6
16.1
What- if
5.50
18.0
>, 8-hr TWA
50th Percentile
Data
Points
9.17
What-if
3.14
7
Data
Points
1
Source: Stewart (1998); Tech Spray (2003), as cited in (U.S. EPA, 2006b).
What-if: Represents a what-if inhalation exposure level based on a single data point.
a Worker includes operators, technicians, mechanics, and maintenance supervisor.
In addition to the data summarized above, the Tech Spray study included a test scenario that
measured short-term worker exposure that simulated an automotive repair shop. In this test,
1-BP was sprayed continuously over a 15-minute period. In reality, workers are only expected to
spray 1-BP for a few minutes at a time; as such, the test was intended to simulate a "worst-case"
scenario with heavy 1-BP usage. The 15-min short term exposure for operators ranged from 190
to 1,100 ppm. Further, the 15-minute short term exposure for a worker in an adjacent room
measured 11 ppm ((Tech Spray, 2003), as cited in (U.S. EPA, 2006b)). The presence of 1-BP in the
adjacent room suggests the infiltration of contaminated air into other work areas.
2.1.7.4 Assessment of Inhalation Exposure Based on Modeling
A more detailed description of the modeling approach is provided in Appendix J. Figure 2-13
illustrates the near-field/far-field for the aerosol degreasing scenario. As the figure shows, 1-BP 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 1-BP dissipates into the far-field (i.e., the facility space surrounding the near-field),
resulting in occupational non-user exposures to 1-BP at a concentration CFF. VFF denotes the
volume of the far-field space into which the 1-BP dissipates out of the near-field. The ventilation
rate for the surroundings, denoted by OFF, determines how quickly 1-BP dissipates out of the
surrounding space and into the outside air.
In this scenario, 1-BP vapors 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. For the purpose of modeling, it is assumed
that a worker applies the aerosol degreaser once per hour with seven applications in an eight-
hour work day. EPA/OPPT assumes a worker does not use the aerosol degreaser during the first
hour of the day. EPA/OPPT assumes an application rate of 26.7 g degreaser/m2 and a
characteristic throughput of 7.2 m2/day, based on data for oven cleaning (Golsteijn et al., 2014). It
is uncertain whether this use rate is representative of a typical aerosol degreasing facility. In
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addition, EPA/OPPT assumed the facility operates 260 days per year; this value is based on EPA's
Generic Scenario for Use of Vapor Degreasers (developed by ERG (2001), which estimates
degreasers of all sizes operate 260 days per year. EPA/OPPT assumed aerosol degreasers operate
at the same frequency. Model parameters and assumptions for aerosol degreasing are presented
in Appendix K.
Far-Field
Near-Field
Volatile Source
t
Figure 2-13 Schematic of the Near-Field/Far-Field Model for Aerosol degreasing
EPA/OPPT performed a Monte Carlo simulation with one million iterations and the Latin
hypercube sampling method to model near-field and far-field exposure concentrations in the
aerosol degreasing pre-EC scenario. Table 2-15 presents a statistical summary of the exposure
modeling results. Estimates of Acute Concentration (AC), Average Daily Concentrations (ADC) and
Lifetime Average Daily Concentration (LADC) for use in assessing risk were made using the
approach and equations described in Appendix H.
For workers, the pre-EC exposures are 2.20 ppm 8-hr TWA at the 50th percentile, and 6.81 ppm
8-hr TWA at the 95th percentile. The model exposure levels are substantially lower than
monitoring data. For occupational non-users, the model pre-EC exposures are 1.02 ppm at the
50th percentile and 3.42 ppm 8-hr TWA at the 95th percentile.
For the post-EC scenario, engineering control of local exhaust ventilation (LEV) is assumed to be
90 percent effective. Although worker and occupational non-user exposures are reduced by 90
percent, exposure level at the 95th and 50th percentile are still be above the ACGIH TLV of 0.1
ppm.
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Table 2-15 Statistical Summary of 1-BP 8-hr TWA Exposures (AC, ADC and LADC) for Aerosol Degreasing
Based on Modeling
Category
Acute and Chronic,
(8-Hour T\
ACi-BP, 8-hr TWA 3
95th Percentile
Mon-Cancer Exposures
A/As in ppm)
Td ADCi-BP, 8-hr TWA
50th Percentile
Chronic, Cane
LADC
95th Percentile
er Exposures (ppm)
•1-BP, 8-hr TWA
50th Percentile
Workers (Near-Field)
PreEC
Post EC 90%
6.81
0.681
2.20
0.220
3.89
0.389
1.26
0.126
Occupational non-users (Far-Field)
PreEC
Post EC 90%
3.42
0.342
1.02
0.102
1.95
0.195
0.583
0.0583
AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. See Appendix H.
Pre-EC: refers to modeling where no reduction due to engineering controls was assumed
Post-EC: refers to modeling where engineering controls with a 90% efficiency were implemented
2.2
CONSUMER EXPOSURES
Consumer exposures have been assessed for the use of 1-BP in consumer products:
1. Aerosol spray adhesives (including spray adhesives and spray accelerant)
2. Aerosol spot removers
3. Aerosol cleaners and degreasers (including engine degreasing, brake cleaning, and
electronics cleaning scenarios)
2.2.1
Approach and Methodology
EPA/OPPT selected consumer products containing 1-BP used as aerosol spray adhesives, aerosol
spot removers, and aerosol cleaning and degreasing products for further risk evaluation. The
decision to focus the assessment on these specific consumer products took into consideration (1)
consumer use patterns, (2) information reported in Safety Data Sheets (SDS), and (3) potential
risk to consumers.
EPA/OPPT searched the National Institutes of Health (NIH) Household Products Database, various
government and trade association sources for products containing 1-BP, company websites for
SDSs, Kirk-Othmer Encyclopedia of Chemical Technology, and the internet in general. The NIH
Household Products Database and Kirk-Othmer Encyclopedia of Chemical Technology contained
no relevant information on consumer products containing 1-BP. Through the other afore-
mentioned search means, EPA/OPPT identified several products which contain 1-BP and are
available to consumers (Table 2-16). There may be other consumer products containing 1-BP but
not all SDSs display a complete list of chemical ingredients such that some products may contain
1-BP but cannot be confirmed by EPA/OPPT. The availability of products, listed in Table 2-16,
ranging from 1 to 100 weight percent 1-BP raised sufficient concern to include these uses in this
risk assessment. Additional uses and products (coin cleaning, refrigerant flush, and lubricant)
were not further evaluated as reliable information regarding use practices such as mass of
product used, room of use, method of use, and frequency of use were not readily available. While
exposures from use of these products were not quantified, this does not imply that EPA/OPPT
believes the exposure to be insignificant.
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Table 2-16 Consumer Use Products Containing 1-BP
Use
Aerosol
Spray
Adhesive
Aerosol
Spot
Remover
Aerosol
Cleaner
or
Degreaser
Company
Maple Leaf Sales II
ITW TACC
Choice Brand
Adhesives
Blair Rubber Company
Satellite City a
Albatross USA
EnviroTech
Pettyjohn's Solutions
The Sherwin-Williams
Company b
ITW Pro Brands
ITW Pro Brands
ZEP, Inc
ACL, Inc
CRC Industries, Inc
CRC Industries, Inc
MRO Solutions
Osborn
ITW Chemtronics b
ITW Chemtronics b
Sprayon
Product
K-Grip 503
STA'-PUTSP4H Canister
Adhesive
751G
Endurabond™ Normac900R-
NPB
NCF Accelerator
Everblum Gold Cleaning Fluid
DrySolv Spray Testing &
Spotter
Homerun Cleaning Fluid
SPRAYON LIQUI-SOL® Food
Grade ULTRA-FORCE™ Safety
Solvent & Degreaser
LPS Instant Super Degreaser
LPS NoFlash Nu
Power Solv 5000
Precision Rinse NS
Super Degreaser/Cleaner
Cable Clean RD
525 Contact Cleaner
76334 High Tech Electronic
Cleaner
Electro-Wash NR
Kontact Restorer
EL 2846 Non-Chlorinated Flash
Free Electronic Solvent
%1-BP
(wt%)
35-60
35-60
40-60
60-85
98-99
20-30
>93
>96
100
60-70
60-70
60-100
65-75
90-100
1-3
47-84
50
65-75
65-75
96
Source
(Maple Leaf Sales II Inc.,
2013)
(ITW Inc., 2014)
(Choice Brand Adhesives,
2010)
(Blair Rubber Co., 2011)
(Satellite City Instant Glues,
2015)
(Albatross USA Inc., 2015)
(EnviroTech International,
2013)
(Pettyjohn's Solutions, 2012)
(Sherwin Williams, 2014)
(ITW Pro Brands, 2015)
(ITW Pro Brands, 2014)
(ZEP, 2015)
(ACL Inc., 2014)
(CRC Industries Inc., 2014)
(CRC Industries Inc., 2015)
(MRO Solutions, 2015)
(Osborn, 2015)
(ITW Chemtronics, 2008)
(ITW Chemtronics, 2012)
(Sprayon Products, 2014)
Notes:
a Technically, the NCF Accelerator is added to another spray adhesive to make it dry more quickly.
b Not currently made by the manufacturer, but available on the secondary market.
In the absence of available emissions and monitoring data for use of consumer products
containing 1-bromopropane (1-BP), a modeling approach was utilized to assess consumer
exposure. Aerosol spray adhesive, spot remover, and cleaner and degreaser (brake cleaning,
engine degreasing and electronics cleaning) scenarios were selected for exposure modeling.
2.2.1.1 Exposure Routes
Readily available information on the toxicity profile and physicochemical properties of 1-BP
support inhalation as the primary route of exposure for human health concerns. Dermal
exposures are possible; however, limited toxicological data are available for this route of
exposure, and no toxicokinetic information is available to develop physiologically-based
pharmacokinetic models or route-to-route extrapolations. Therefore, this assessment does not
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evaluate aggregate exposures and may underestimate total exposures resulting from the uses of
1-BP due to this assumption.
Based on anticipated use patterns of aerosol spray adhesives, aerosol spot removers, and aerosol
spray cleaners and degreasers by consumers and non-users in residential settings, acute
exposures via the inhalation route were the primary scenarios of interest. EPA/OPPT assumed
that consumer users would generally be male or female adults (>16 and older, including women
of childbearing age), although exposures to adolescents or younger individuals may be possible.
Acute inhalation exposure to 1-BP for both user and non-user were quantified using modeling
approaches as monitoring data was not readily available to estimate air concentrations.
2.2.1.2 Overview of the E-FAST-2/CEM Model
The Exposure and Fate Assessment Screening Tool Version 2 (E-FAST2) Consumer Exposure
Module (CEM) was selected for the consumer exposure modeling as the most appropriate model
to use due to the lack of available emissions and monitoring data for the 1-BP uses under
consideration. Moreover, EPA/OPPT did not have the input parameter data required to run more
complex indoor air models for the consumer products under the scope of this assessment. CEM
uses high-end input parameters/assumptions to generate conservative, upper-bound inhalation
exposure estimates for aerosol spray products. The advantages of CEM are the following:
1. CEM model has been peer-reviewed.
2. CEM accommodates the inputs available for the products containing 1-BP in the indoor
air model.
3. CEM uses the same calculation engine to compute indoor air concentrations from a
source as the Multi-Chamber Concentration and Exposure Model (MCCEM), but does
not require measured emission values (e.g. chamber studies).
The model used a two-zone representation of a house to calculate the potential acute dose rate
(mg/kg-bw/day) of 1-BP for users and non-users. Zone 1 represents the area where the consumer
is using the product, whereas Zone 2 represents the remainder of the house. Zone 2 was used for
modeling passive exposure to non-users in the home (bystanders), such as children, adults,
women of child bearing age, and the elderly.
The general steps of the calculation engine within the CEM model included:
1. Introduction of the chemical (i.e., 1-BP) into the room of use (Zone 1),
2. Transfer of the chemical to the rest of the house (Zone 2) due to exchange of air
between the different rooms,
3. Exchange of the house air with outdoor air and,
4. Summation of the exposure doses as the modeled occupant moves about the house
The chemical of concern (i.e., 1-BP) enters the room air through two pathways: (1) overspray of
the product and (2) evaporation from a thin film. One percent (1%) of the product was assumed
to become instantly aerosolized (i.e. product overspray) and was available for inhalation.
The CEM model uses data from the evaporation of a chemical film to calculate the rate of the
mass evaporating from the application surface covered during product use (DTIC DLA, 1981). The
model assumes air exchanges from the room of use (Zone 1) and the rest of the house (Zone 2)
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according to interzonal flow. The model also allows air exchange from the house (Zone 1 & 2)
with the outdoor air.
EPA/OPPT used the default activity pattern in CEM based on the occupant being present in the
home for most of the day. As the occupants moved around the house in the model, their
exposure to the calculated air concentrations were summed to estimate a potential 24-hr dose.
The potential inhalation acute dose rates (ADRpot) are computed iteratively by calculating the
peak concentrations for each simulated 10-second interval and then summing the doses over
24 hrs. These calculations take into consideration the chemical emission rate over time, the
volume of the house and the zone of use, the air exchange rate and interzonal airflow rate, the
exposed individual's locations, body weights and inhalation rates during and after the product
use. The reader is referred to EPA's E-FAST2 website (http://www.epa.gov/tsca-screening-
tools/e-fast-exposure-and-fate-assessment-screening-tool-version-2014) and Appendix L to
obtain additional information about the model, including the model documentation and
algorithms used.
2.2.1.3 Consumer Model Scenario and Input Parameters for Indoor
Exposure to Specific 1-BP Uses
Table 2-17 describes the acute inhalation indoor scenarios and populations of interest that
EPA/OPPT evaluated in the consumer exposure assessment. As indicated in Section 2.2.1.1,
EPA/OPPT believes that inhalation is the main exposure pathway. Exposure via ingestion from the
use of these consumer products appears to be unlikely based on the intended method of use (i.e.,
spray application).
Table 2-17 Consumer Model Scenarios and Populations of Interest
Acute Inhalation Indoor
Scenario
Aerosol Spray Adhesive Use
Aerosol Spot Remover Use
Aerosol Spray Cleaner and
Degreaser Use (engine
degreasing, brake cleaning,
electronics cleaning)
Population of Interest
Consumer User
Adult Consumers >16 yrs old
Adult Consumers >16 yrs old
Adult Consumers >16 yrs old
Non-User
Individuals of all ages
Individuals of all ages
Individuals of all ages
To estimate exposures to these products, numerous input parameters are required to generate a
single exposure estimate. These parameters include the characteristics of the house, the behavior
of the consumer and the emission rate of the chemical into the room of use. In the absence of
measured values for many of the needed inputs, the E-FAST2/CEM modeling for 1-BP used a
combination of upper (90th) percentile, mean, and median input parameters and assumptions in
the calculation of potential exposure for consumer users and non-users. This approach produced
high-end (90th percentile) and central tendency (50th percentile) acute inhalation estimates that
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are hypothetical. The general input parameters and assumptions are summarized in Table 2-18
and the input values specific to each use scenario are summarized and explained more fully in
Appendix L.
Table 2-18 Product Use Input Parameters for CEM Modeling
Modeling Parameter
Molecular weight
(g/mol)
Vapor pressure
(torr)
Frequency of use, acute
(events/day)
Air exchange rate - air
exchanges per hour
(ACH)
Overspray fraction
Emission rate constant
(hours'1)
Exposure duration, acute
(days)
Whole house volume
(m3)
All Consumer Use
Scenarios
123
146.2
1
0.45
0.01
183.09
1
492
Source and Description of Parameter Selection
The Merck Index (2013); as shown in Table 1-1.
The Merck Index (2013); as shown in Table 1-1.
Assumed to occur no more than once per day for acute
exposures.
Recommended 50th percentile value of residential air
exchange rate for all regions within the United States
Koontz (1995), based on EPA (2011).
Selection based on professional judgment (Patrick
Kennedy, 1990 as cited in E-FAST). It should be noted
that the CEM model is insensitive to this parameter.
Estimated using Chinn's algorithm (DTIC DLA, 1981),
based on E-FAST model documentation. This algorithm
utilizes molecular weight and vapor pressure to
estimate emission rates.
General (hypothetical) assumptions used for CEM
modeling in absence of consumer product data for 1-
Bromopropane.
Volume of house where product is applied. Mean value
recommended for use as a central tendency for all
single family homes, including mobile homes and
multifamily units. This US EPA recommended value was
taken from Exposure Factors Handbook (EFH) (2C LI)
Consumer behavior pattern parameters in CEM include the mass of product used, the duration of
use and the frequency of use. Although the default values in CEM for these consumer behavior
parameters are set to high end values, they were not used in this risk assessment. The other
parameters (e.g. house volume) in CEM are set to mean or median values obtained from the
literature. A combination of high end and mean or median values was utilized to produce high
end acute inhalation exposure estimates, whereas a combination of mean and median values was
used to produce central tendency acute inhalation exposure estimates.
To determine the appropriateness of the consumer behavior pattern parameters chosen in this
risk assessment, EPA/OPPT examined the consumer categories available in the Westat (1987)
survey. The authors of the Westat (1987) survey contacted thousands of Americans to gather
information on consumer behavior patterns related to product categories that may contain
halogenated solvents. The Westat (1987) survey data aligned reasonably well with the description
of the products that were used in this consumer exposure assessment. The data informed the
values that EPA/OPPT used for the mass of product used, and the time spent in the room of use
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when considering all surveyed individuals who identified as users of spray adhesives, spot
removers, engine cleaners, brake cleaners or electronics cleaners.
The input parameter for house volume was taken from the Exposure Factors Handbook (2011).
The room volume for aerosol spray adhesives and aerosol spot removers was calculated as a
proxy utility room measuring 9 ft x 10 ft, with 8 ft ceilings (U.S. EPA, 2014c). The area of use most
frequently cited for aerosol degreasers and cleaners (used as engine degreasers and brake
cleaners) was the outdoors. However, CEM does not have a module for outdoor use, therefore,
the modeling for these use scenarios designated the room of use (zone 1) as the garage. While
this presents a more conservative estimate, it should be noted that users surveyed in the Westat
(1987) report also reported use in the garage. The E-FAST model does not include a garage
volume in its default room parameters, thus the median garage volume from a 2007 indoor air
quality study (Batterman et al., 2007) of 15 homes in Michigan was used as a reasonable proxy
value. The room of use most frequently cited in the (1987) Westat survey for electronics cleaning
was the living room; therefore a room volume of 48 m3 (U.S. EPA, 2011) was used to estimate
exposure from this use.
The user's body weight and inhalation rate were set to either the mean or the median values
from the Exposure Factors Handbook (2011) for the simulations used in this assessment.
The air exchange rate in the room of use does not take into consideration open windows or the
use of an exhaust fan. While it is possible that some users may employ these exposure reduction
techniques inside their homes, the goal of the consumer exposure assessment was to provide an
acute exposure estimate for ventilation conditions representing average household air exchange
rates. Moreover, residential users would not necessarily have the type of indoor exposure
reduction tools/equipment (e.g., gloves, exhaust ventilation) that workers are likely to have in
occupational settings. Consumers may not necessarily be as aware of potential chemical hazards
as workers and would not have a standard operating procedure in place to assure that they use
exposure reduction techniques each time they use a product.
In this assessment it was assumed that there was no pre-existing concentration of 1-BP in the
home before product use began. The outdoor air was also assumed to be free of 1-BP, meaning
that the air exchange rate described the intake of air with no pre-existing 1-BP contamination.
The products were assumed to be sprayed on varying surfaces, where a thin film of the product
was assumed to build up, evaporate, and contribute to the air concentration of the chemical in
the room. We relied on modeled emission rates because data from chamber studies were not
available. To generate emission rates, E-FAST2/CEM used empirical data from studies assessing
the emission rates of pure solvents (DTIC DLA, 1981). E-FAST2/CEM used the Chinn study as
surrogate data to calculate the rate of evaporation of 1-BP from the surface to the air in the
home.
These solvent studies supported the use of an exponentially decaying emission rate for 1-BP from
the application surface based on vapor pressure and molecular weight (DTIC DLA, 1981), the
equations using the Chinn method are in Appendix L. The spot remover application should be well
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modeled by the Chirm study since the spot remover product was over 90% 1-BP. On the other
hand, the spray adhesive product may have more components, and the interaction of these
chemicals could alter the evaporation rate of 1-BP. This introduces uncertainty into the
assessment, however EPA/OPPT could not find a better data set available to model the emission
rates. Within the current exposure assessment, the 24-hr exposure was not strongly dependent
on the emission rate due to the amount of time the product user spends in the room of use (see
Appendix L for details).
2.2.1.4 Consumer Model Results
The 'Aerosol Paint' default scenario within the Consumer Exposure Module (CEM) of the E-FAST
model was chosen for conducting the modeling runs. This selection was the closest match to the
spray adhesive scenario among the default CEM exposure scenarios. The common modeling
inputs required to run CEM for all consumer scenarios evaluated in this assessment are provided
in Table 2-18. Table 2-18 also has a brief explanation of the source of each parameter and the
justification for the parameter selection. Other scenario-specific input parameters are provided in
Appendix L. The body weight and inhalation rates for adults (age group 21 to 78) and other age
groups are provided in the appendices.
CEM calculated air concentrations over the course of the simulation for the room of use and the
rest of the house (Zone 1 and Zone 2). These concentrations were converted to acute dose rates
(ADRs) using the body weight and respiration rate for each age group. The varying weight and
respiration rates of the different age groups resulted in different doses; younger age groups had a
higher ratio of inhalation rate to body mass creating a larger dose for a given air concentration of
a chemical. However, the same air concentrations were used to generate the doses for each age
group within the model's calculation engine. The standard output files for CEM did not include
the air concentrations for the different parts of the house, only the doses were included.
Table 2-19 presents the results of the conversion from potential acute dose rates (mg/kg-bw/day)
to indoor air concentrations (ppm) for the user and bystander, with both central tendency (50th
percentile) and high end (90th percentile) estimated exposures for the consumer use scenarios.
Calculations detailing the conversion from acute dose rates to air concentrations are provided in a
supplemental Excel spreadsheet file.6 The indoor air concentrations shown in Table 2-19 could be
applied to users of different age groups. Although adults are generally the users of these
products, EPA/OPPT cannot rule out scenarios where teenagers or younger children may be users
or be in the same room with the user during use of the product.
6 See attached document titled "Consumer Exposure Calculations.xlsx".
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Table 2-19 Estimated31-BP Air Concentrations (Time Averaged Over 1 Day) Based on Residential Indoor
Use of Spray Adhesives or Aerosol Removers
Consumer Use Scenario
Aerosol Spray Adhesive
Aerosol Spot Remover
Aerosol Spray Cleaners and Degreasers
Engine Degreasing Use
Brake Cleaning Use
Electronics Cleaning Use
Air Concentration3 (ppm)
Central Tendency13
(50th percentile)
Userd
0.5
2
16
5 A
0.5
Non-User6
0.1
0.7
6
2
0.2
High End0
(90th percentile)
Userd
6
23
54
22
7
Non-User6
2
6
20
8
3
Notes:
a See Appendix K for details about the model inputs and the method used to convert acute dose rates (ADRs)
to air concentrations of 1-BP.
b Central tendency estimate based on using 50th percentile values for use patterns from Westat Survey (1987).
See Appendix L for additional details.
c High end estimate based on using 90th percentile values for use patterns from Westat Survey, (1987). See
Appendix L for additional details.
d Air concentrations for the user categories can be extended to different age groups, however, EPA/OPPT
believes the users of these products to be adults.
e All age categories (<1 yrs; 1-2 yrs; 3-5 yrs; 6-10 yrs; 11-15 yrs; 16-20yrs; and >21 yrs)
Detailed CEM modeling results are provided in Appendix L
CEM has certain restrictions on the age that is assumed for simulated users, which in turn sets
limits for the dose rates generated for different age groups. However, these restrictions should
not be interpreted as suggesting that younger users would not be exposed. EPA/OPPT believes
that the users of these products are generally adults, but teenagers or younger children may be
users or may be in the same room with the user. 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 2-19 could be extended to all users.
The model output reports the peak concentration of 1-BP, however this air concentration was not
used in the risk assessment. The peak concentration was the highest concentration among all of
the 10-second time intervals that CEM simulated within a 24-hr period. The peak concentration
may only exist in the room of use for a short duration and was not considered a good indicator of
what the concentration of 1-BP would be for longer time periods. Thus, we did not use the peak
concentration in the risk assessment as it was not representative of a 24-hr exposure.
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Lastly, a chronic consumer exposure assessment was not performed because the frequency of
product used was considered to be too low to create chronic exposure concerns. Although CEM
model results given in the supplemental information included chronic exposure estimates, they
were not used in this assessment.
2.2.1.5 Sensitivity of Model Parameters
In order to explore the dependencies of chemical concentrations in air on modeled parameters, a
sensitivity analysis was performed based on the nominal range sensitivity analysis method (Frey
and Patil, 2002). Using this approach, a 'baseline scenario' is first defined which is a modeling
scenario that consists of central tendency values. For this sensitivity analysis, we considered the
spray adhesive scenario for adults as the baseline scenario. This baseline scenario was based on a
consumer using a spray adhesive product containing 85% 1-BP in a residential setting. After
identifying the base case, the next step is to systematically vary the input parameters one at a
time and capture the subsequent model responses. For this sensitivity analysis, we chose the ADR
and acute air concentrations as the representative model outputs to observe model responses.
Methodology
The sensitivity analysis was carried out in a two-tiered approach. The Tier 1 model runs were
conducted in order to identify the key input parameters that the model was most sensitive to.
After having identified the key input parameters, the Tier 2 runs were focused on a more detailed
analysis of the model responses to these key input parameters. Thus, the Tier 2 runs could be
considered to be a more 'refined' approach to measuring model sensitivity to key inputs. Model
responses were analyzed by calculating the "index of sensitivity" for each model scenario. The
"index of sensitivity" can be defined as the percent change in magnitude of the model output
with respect to the baseline scenario output. Nine CEM input parameters were selected for the
sensitivity testing and the remaining were treated as static parameters.
Tier 1 analysis
For the Tier 1 analysis, a plausible range of values was established for each input parameter. This
range consisted of a low, medium (baseline scenario), and high value. These plausible values and
the justification for the parameter selection for each input parameter are provided in Appendix K,
Table_Apx L-5.
The plausible inputs for each parameter were varied one at a time and the model responses (i.e.,
changes in the ADR and acute concentration values) were noted. The results were first ranked by
their output differences using the maximum response value minus the minimum response value
of the plausible range and then by their index of sensitivity. The "index of sensitivity" was
calculated by dividing the percent change in ADR by the percent change of the input values for
each parameter. The rankings from both were averaged for an overall rank for each parameter
tested. This exercise was repeated for the acute air concentration results.
The resulting ADRs (mg/kg-bw) and acute air concentrations (ppm) along with the rankings for
each of the tested parameters are provided in Appendix K, Table_Apx L-6 and Table_Apx L-7.
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The Tier 1 analysis indicated that the four most sensitive parameters affecting the ADR and the
acute air concentration were as follows:
Acute Dose Rate
1. mass of product used per use;
2. whole house volume;
3. air exchange rate; and
4. body weight.
Acute air concentration
1. mass of product used per use;
2. whole house volume;
3. air exchange rate; and
4. consumer product weight fraction.
The parameter most influential in determining the acute dose rate and acute air concentration is
the mass of product applied per use. The emission rate is directly dependent upon the chemical
properties such as vapor pressure and because 1-BP is quite volatile, the mass of product used
subsequently strongly influences the air concentration and dose rate. Because the modeled
scenario follows the user over a 24 hour period limiting the period of use to 0.5 hrs in the utility
room, the whole house volumes (the remaining 23.5 hours) plays a larger factor in influencing the
final acute dose rate and acute air concentration. As shown in Appendix L Table_Apx L-6 and
Table_Apx L-7, the air exchange rate and product weight fraction can influence the contaminant
concentration but do not play as large a role in the final outcome. The above-mentioned 5 input
parameters were chosen for the Tier 2 analysis.
Tier 2 Analysis
For the Tier 2 analysis, all the parameters were adjusted by equal increments from the base
value. All of the baseline input values were adjusted by -10% and +10% to calculate sensitivity
near the baseline value and by -50% and +50% to calculate sensitivity for values farther removed
from the baseline value. The baseline scenario was the same baseline scenario that was used for
the Tier 1 analysis with the exception of the consumer product weight fraction. Due to a
limitation with this value (since the baseline consumer weight fraction was 85% and we could not
increase that by 50% as the model would only consider weight fractions that were less than
100%) the consumer product weight fraction was lowered from 85% to 50% for the baseline
scenario. The inputs for the Tier 2 analysis are provided in Appendix K, Table_Apx L-8.
Similar to the protocol followed in the Tier 1 analysis, the input parameters were varied one at a
time and the model responses (ADR and acute concentration) were recorded. There were a total
of four variable runs for each parameter. The sensitivity was calculated near the base value (-10%
and +10%) and farther removed from the base value (-50% and +50%) for each of the tested
parameters. Appendix K Table_Apx L-9 provides the calculated sensitivities for the parameters
affecting the ADR and Table_Apx L-10 provides the calculated sensitivities for the parameter
affecting the acute air concentration.
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Results of the Tier 2 analysis indicate that the CEM model is most sensitive to changes in body
weight when using the ADR as the model output since the air concentrations are consistent
depending on whether you are the user or bystander. When the acute concentration is used as
the model output, the CEM model is most sensitive to the mass of product used. It should be
noted that the sensitivity analysis was conducted using some hypothetical values that were based
solely on mathematical interpolation. Although some of these values might not correspond to
actual product uses based on aerosol spray adhesive, spot remover, or degreasing/cleaning
scenarios, they lend themselves in the overall understanding of the model sensitivity.
3 HUMAN HEALTH HAZARD ASSESSMENT
Figure 3-1 depicts the process EPA/OPPT used to review and select studies used in the
1-BP risk assessment. EPA/OPPT reviewed EPA assessments (U.S. EPA, 2007b), the primary peer
reviewed literature and secondary sources (NTP, 2013; NTP-CERHR, 2003) identified through
August 2015 to identify key endpoints (Section 3.2), meaning those that are relevant, sensitive
and found in multiple studies. (EPA/OPPT notes that an EPA Integrated Risk Information System
(IRIS) toxicological review is not currently available for 1-BP). Once key endpoints were identified,
EPA/OPPT collected all publicly available data to refine the hazard characterization and conduct
dose-response analysis and benchmark dose modeling.
A comprehensive summary table which includes all endpoints considered for this assessment can
be found in Appendix 0. Additional information on data quality criteria used for selection of key
studies is provided in Appendix M. All endpoints were evaluated for consistency, sensitivity and
human relevance. Based on this review, EPA/OPPT narrowed the focus of the 1-BP hazard
characterization to liver toxicity, kidney toxicity, reproductive/developmental toxicity,
neurotoxicity, and cancer (brief summaries are presented for each hazard endpoint in Section
3.2). In addition, a summary of key studies and endpoints carried forward in the risk assessment
can be found in Table 3-1, including the no-observed- or lowest-observed-adverse-effect levels
(NOAEL and LOAEL) for health endpoints by target organ/system, the corresponding benchmark
dose lower confidence limits (BMDLs), when available, and the corresponding human equivalent
concentrations (HECs), and uncertainty factors (UFs).
These key studies provided the dose-response information necessary for selection of points of
departure (PODs)7. 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 on the dose for an estimated
incidence, or a change in response level from a dose-response model (e.g., benchmark dose or
BMD), a NOAEL value, or a lowest-observed-adverse-effect level (LOAEL) for an observed
incidence, or a change in the level (i.e., intensity) of a given response. PODs were adjusted as
appropriate to conform to the specific exposure scenarios evaluated.
7 A point of departure (POD) is a dose-response point that marks the beginning of a low-dose extrapolation. This
point can be the lower bound on dose for an estimated incidence or a change in response level from a dose-response
model (BMD), or a NOAEL or LOAEL for an observed incidence, or change in level of response (U.S. EPA, 2002).
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The dose-response assessment used for selection of PODs for cancer and non-cancer endpoints
and the benchmark dose analysis used for use in the risk characterization are found in Section
3.4. Development of the 1-BP hazard and dose-response assessments considered principles set
forth in various risk assessment guidance, and guidelines issued by the National Research Council
and the U.S. EPA. Limited toxicological data are available by the oral and dermal routes. Because
physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models that would facilitate
route-to-route extrapolation have not been identified for 1-BP, only studies conducted via the
inhalation route of exposure were evaluated in this assessment. There are no relevant kinetic or
metabolic information for 1-BP that would facilitate development of dosimetric comparisons. In
accordance with EPA guidance, the exposure concentrations used in animal studies were adjusted
according to the ratio of the blood:air partition coefficients, where a default ratio of 1 is applied
when the partition coefficient for rats is greater than that of humans (U.S. EPA, 2002, 1994). For
HEC calculations, these exposure concentrations were further adjusted from the exposure
durations used in animal studies to durations deemed relevant for human exposure scenarios
(e.g., 8-hours/day and 5 days/week for occupational exposures).
EPA/OPPT consulted EPA's Guidelines for Developmental Toxicity Risk Assessment when making
the decision to use developmental toxicity studies to evaluate risks that may be associated with
acute exposure to 1-BP during occupational or consumer use of spray adhesive, dry cleaning or
degreasing products that contain 1-BP. This decision is based on EPA policy, and assumes that a
single exposure during a critical window of fetal development may produce adverse
developmental effects (U.S. EPA. 1991).
3.1
Figure 3-1 Hazard Identification and Dose-Response Process
Toxicokinetics
Studies in humans and laboratory animals show that 1-BP may be absorbed following oral,
inhalation or dermal exposure; however, dermal and inhalation pathways are expected to be
more relevant for occupational exposures (Frasch et al., 2011; Hanley et al., 2009; NIOSH, 2007;
Garner et al., 2006; Jones and Walsh, 1979). The extent of absorption via the inhalation route
depends on the rate of transfer from pulmonary capillaries to blood (i.e., blood/air partition
coefficient), and the rate of metabolism in various tissue compartments.
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The blood:air partition coefficients calculated for 1-BP in rats (11.7) and humans (7.08) indicate
that it is readily absorbed (Meulenberg and Vii'verberg, 2000). Upon uptake, 1-BP distribution via
the systemic circulation follows the individual tissue/blood partition coefficients for respective
tissue compartments. The fat:blood partition coefficient (calculated as the ratio of fat:air and
blood:air partition coefficients) for 1-BP in rats (20) and humans (18) suggests that it may
partition to fat (Meulenberg and Vii'verberg, 2000). Higher partitioning to muscle, liver and fat has
been predicted for 1-BP in female versus male rats (ECHA, 2012d).
Metabolism studies in rats and mice have shown that 1-BP can directly conjugate with
glutathione forming N-acetyl-S-propyl cysteine, or be oxidized via cytochrome P450 enzymes
(primarily CYP2E1) to reactive metabolites that can be further oxidized and/or conjugated with
glutathione (Jones and Walsh, 1979; Barnsleyet al., 1966) (Figure 3-2). Gluthathione conjugates
formed via the glutathione-S-transferase catalyzed pathway are eventually excreted as
mercapturic acid derivatives in urine. Although both pathways remain operative, the CYP2E1
pathway generally predominates at lower exposure concentrations (Garner et al., 2006).
Further evidence for the specific contribution of CYP2E1 to 1-BP metabolism is provided by
studies with Cyp2el"/" knockout mice (Garner et al., 2007) which show the elimination half-life in
these animals to be more than twice that seen in wild type mice (3.2 vs. 1.3 hours, respectively)
following 1-BP inhalation exposure. The ratio of glutathione conjugation to 2-hydroxylation
reactions increased 5-fold in Cyp2el~/~ versus wild-type mice. Earlier work from this laboratory
has shown that administration of 1-aminobenzotriazole (a general suicide inhibitor of P450)
caused nearly complete elimination of 1-BP oxidative metabolism, and a compensatory shift
toward GSH conjugation in rats (Garner et al., 2006).
1-BP is rapidly eliminated from the body primarily via exhalation, with lesser amounts excreted in
urine and feces (Garner and Yu, 2014; Garner et al., 2006; Ishidao et al., 2002). In gas uptake
studies with male and female rats, the elimination half-times calculated for 1-BP decreased with
increasing air concentrations (Garner and Yu, 2014). Terminal elimination half-times in male and
female mice following 1-BP inhalation exposure at < 800 ppm ranged from 0.5 to 2 hrs (Garner
and Yu, 2014; Garner etal., 2006). (Garner et al., 2006) investigated the metabolism of 1-BP in
male F344 rats and B6C3F1 mice following inhalation or tail vein injection and determined that
the proportion of 1-BP metabolized via CYP2E1 oxidation versus glutathione conjugation was
inversely proportional to dose in rats, but independent of dose in mice.
Occupational exposure studies have consistently identified significant correlations between 1-BP
concentrations in ambient air and the levels of 1-BP or its metabolites in urine (Ichihara et al.,
2004b; Kawai et al., 2001). N-acetyl-S-(n-propyl)-L-cysteine (AcPrCys), produced via direct
glutathione conjugation of 1-BP, was the primary urinary metabolite detected in exposed workers
(Hanley etal..2010. 2009: NIOSH. 2007: Valentine et al.. 2007: Hanley et al.. 2006).
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1-Bromopropane
Glue
1-Bromo-2-hydroxypropane-O-glucuronide
••CO,
COOH O
A
A/-Acetyl-S-propylcysteine
N CH3
W-Acetyl-S-(2-oxopropyl)cysteme
A
A/-Acetyl-S-(2-hydroxypropyl)cysteine
O COOH O
II
,S
i_i
A.
A/-Acetyl-3-(propylsulfinyl)alanine
[A/-acetyl-S-propylcysteine-S-oxide]
CYP
or
FMO
H3C
O COOH
II
.s
u
A,
N CH3
H
A/-Acetyl-3-[(2-oxopropyl)sulfinyl]alanine
OH
COOH O
OH
COOH O
N
H
A/-Acetyl-3-[(2-propenol)sulfinyl]alanine
A/-Acetyl-3-[(2-hydroxypropyl)sulfinyl]alanine
[A/-acetyl-S-(2-hydroxypropyl)cysteine-S-oxide]
Figure 3-2 Metabolism of 1-Bromopropane in Male F-344 Rats and B6C3F1 Mice Following Inhalation
Exposure or Tail Vein Injection*
*Structures in brackets are proposed intermediates and were not isolated in urine.
CYP = cytochrome P450 monooxygenase; FMO = flavin-containing monooxygenease; GSH = glutathione
Sources: Adapted from (NTP, 2013; Garner et al., 2007; Garner etal., 2006)
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3.1.1 Bio markers of Exposure
Several human and laboratory animal studies have investigated the utility of urinary biomarkers
of 1-BP exposure (Mathias et al., 2012; Hanleyetal., 2009; Valentine et al., 2007; Hanleyet al.,
2006; B'Hymer and Cheever, 2005; Ichihara et al., 2004a; Kawaietal., 2001). Bromide ion and N-
acetyl-S-(n-Propyl)-L-Cysteine (AcPrCys) have shown the most promise at occupationally-relevant
exposure concentrations.
1-BP is metabolized rapidly, via glutathione conjugation and cytochrome P-450 mediated
oxidation, producing many metabolites which are subsequently excreted in urine. Glutathione
conjugation leads to bromide ion release and formation of mercapturic acid derivatives. Bromide
ion levels have been used as an internal biomarker of 1-BP exposure. They are slowly excreted
from the body; the elimination half-life of bromide ions in blood generally ranges from 10.5 to
14 days (Mathias et al., 2012; Hanleyetal., 2006). N-acetyl-S-(n-propyl)-L-cysteine (AcPrCys) is
the primary urinary metabolite found in 1-BP exposed workers (see below); it also is considered
to be a valid biomarker for 1-BP exposure (Mathias et al., 2012; Valentine et al., 2007).
Both Kawai (2001) and Ichihara (2004a) have shown a correlation between urinary 1-BP levels
and 1-BP occupational exposure; however, the degree of correlation varied between studies.
Kawai et al. (2001) reported a correlation coefficient of 0.9 for 1-BP concentrations in air and
urine; the highest 1-BP concentration in air was 27.8 ppm (geometric mean = 1.42 ppm). Ichihara
et al. (2004a) also reported a statistically significant correlation between 1-BP air concentrations
and urinary levels measured on the same day (r2 = 0.39; p < 0.05). NIOSH has suggested that
urinary 1-BP levels may be a more suitable biomarker than urinary bromide concentrations;
however, to ensure accuracy, samples must be tested immediately after collection using gas
chromatography-mass spectrometry, which may be unfeasible or cost prohibitive (NIOSH, 2003).
Both urine and serum bromide ion levels have been used as biomarkers of 1-BP exposure in
workers. Toraason et al. (2006) found a high correlation (p < 0.0001) between 1-BP exposure and
bromide ion concentrations in serum (r2= 0.7 to 0.8), and urine (r2= 0.6 to 0.9) when evaluating
personal breathing zone samples from approximately 50 workers. Workplace exposures ranged
from 0.2 to 270 ppm (TWA), and the correlation coefficient for 1-BP air levels and urinary
bromide levels was 0.5. Using gas chromatography with electron capture detection to evaluate
samples taken from Japanese workers (n=33) following 1-BP exposure during an 8-hour shift of
cleaning and painting, (Kawai et al., 2001) reported a good correlation (r2= 0.5) between bromide
levels in urine and 1-BP levels in air; however, control subjects exhibited high background levels
of urinary bromide, which were subsequently linked to dietary exposure (Zhang et al., 2001).
Hanley et al. (2006) measured urinary bromide levels to investigate the influence of non-
occupational bromine exposure in 30 workers who used adhesives to make polyurethane foam
seat cushions. Personal breathing zone samples indicated a geometric mean exposure of 92 ppm
(range = 45-200 ppm) for sprayers and 11 ppm for workers in other parts of the plant. The
composite (48-hour) urinary bromide concentrations for sprayers (n=12) ranged from 119 to
250 mg/g creatinine and for non-sprayers (n=17) ranged from 5.5 to 149 mg/g creatinine. The
composite bromide concentration in unexposed control subjects (n=7) ranged from 2.6 to
5.9 mg/g creatinine. Daily bromide excretion was approximately four times greater for sprayers
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than non-sprayers. Based on these results, urinary bromide concentration appears to be a useful
index of 1-BP exposure.
Given the confounding factors identified (Kawai et al., 2001), a search for biomarkers of 1-BP
exposure that are not influenced by dietary (or other non-occupational exposures) was initiated.
(Mathiasetal.. 2012: Valentine et al.. 2007) and Hanley et al. (2009) demonstrated that the
mercapturic acid derivative, AcPrCys, could be used as a urinary biomarker of 1-BP exposure.
Both the availability of sensitive methods with an acceptable limit of detection (LOD) for this
metabolite, and its demonstrated persistence in urine suggest that it may serve as a reliable
biomarker of exposure. In addition, 1-BP volatility and rapid elimination in exhaled breath
suggests that the measurement of mercapturic acid derivatives in urine may be preferable to 1-
BP measurements. Valentine et al. (2007) sampled blood and urine from women in a 1-BP
production facility in China (Ichihara et al., 2004b). A significant increase in AcPrCys adducts on
human globin was demonstrated using LC/MS/MS to evaluate samples taken from 26 1-BP
exposed workers and 32 non-exposed controls. Worker exposures ranged from 0.34 ppm to
49.2 ppm, and urinary AcPrCys levels analyzed using GC/MS, increased with increasing 1-BP
exposure (n=47). Hanley et al. (2009) used the same group of workers who applied spray
adhesives to foam cushions as described above, to determine the utility of AcPrCys as a
biomarker for 1-BP exposure. Higher levels of urinary AcPrCys were observed in sprayers than
non-sprayers (geometric mean was approximately four times higher in sprayers). AcPrCys and
bromide levels were highly correlated (p < 0.0001) in the same urine samples, and both showed
statistically significant Spearman's correlation coefficients based on 1-BP TWA exposure
concentrations. Mathias et al. (2012) evaluated the same cohort of workers, reporting the results
of Hanley et al. (2009) and 3-bromopropionic acid (3-BPA), which was evaluated for its potential
to induce mutagenic effects and tumor formation in toxicological studies. When urine samples
were analyzed for 3-BPA, it was not detected in 50 samples (LOD = 0.01 u.g/mL). The results of
these analyses support the use of AcPrCys as a reliable biomarker for 1-BP occupational
exposures.
3.1.2 Possible Mode of Action for 1-BP Toxicity
Various chemicals known to produce neuropathies in humans can be classified as hard or soft
electrophiles according to the Hard and Soft Acid Base theory (Pearson, 1990). Based on this
classification scheme, 1-BP is expected to induce adduct formation in vivo.
The primary metabolic pathways identified for 1-BP involve cytochrome P450 mediated oxidation
(CYP2E1) and glutathione conjugation reactions which can produce numerous reactive
intermediates (see Figure 3-3). Over 20 metabolites have been identified in rodent studies,
including the four metabolites detected in urine samples taken from workers exposed to 1-BP
(Hanley et al., 2009). The mode of action for 1-BP toxicity likely relates to the ability of these
metabolites to react with critical cysteine, histidine and lysine amino acid residues which may
ultimately impact the structural and functional integrity of the cell (Lopachin et al., 2009).
Various reactive metabolites (e.g., glycidol, a-bromohydrin, bromoacetone) and potential targets
for cellular binding interactions (e.g., DNA, mitochondria) have been identified for 1-BP (NTP,
2013). Some 1-BP metabolites may exhibit alkylating activity. For example, further metabolism of
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bromoacetone in a manner analogous to acetone (Casazza et al., 1984), would result in formation
of 1-hydroxy-l-bromoacetone, which yields pyruvate and C02, or 3-bromo-l-hydroxypropanone
(BOP). BOP has been shown to inhibit sperm energetics and motility via its conversion to
bromolactaldehyde and bromopyruvaldehyde, ultimately yielding 3-bromopyruvate (Garner et
al.. 2007: Porter and Jones. 1995).
3-Bromopyruvate (3-BP) has been shown to produce many untoward effects, incuding lowered
cell viability via production of reactive oxygen species (Qin et al., 2010) mitochondrial
depolarization (Macchioni et al., 2011) and activation of mitochondrial apoptosis (Ko et al., 2004).
It is a strong alkylating agent, and a known inhibitor of numerous enzymes, including glutamate
decarboxylase (Fonda, 1976), glutamate dehydrogenase (Baker and Rabin, 1969), the
mitochondrial pyruvate transporter (Thomas and Halestrap, 1981) and the pyruvate
dehydrogenase complex (Apfel et al., 1984; Lowe and Perham, 1984). 3-BP induced alkylation and
inhibition of glyceraldehyde-3-phosphate dehydrogenase can impair energy production via
glycolysis (Da Silva et al., 2009; Ganapathy-Kanniappan et al., 2009) and induce apoptosis or
necrosis as a result of ATP depletion due to impaired mitochondrial function (Kim et al., 2008).
The precise mechanism of action of 1-BP toxicity is not clearly understood, but likely relates to
structural or functional modification of key signaling proteins as a result of cellular binding
interactions induced by 1-BP or its metabolites. More research is needed to identify specific
molecular targets and precursor events (e.g., organ-specific DNA adduct formation, oxidative
stress responses) that precede toxicity. Since 1-BP can induce adverse effects in multiple organs
acting directly as an alkylating agent, or indirectly via formation of reactive metabolites, different
mechanisms may be operative in different target organs. At least four possible mechanisms (e.g.,
genotoxicity, oxidative stress, immunosuppression, and cell proliferation) have been proposed
(NTP. 2013).
Several pathological conditions (e.g., alcoholism, diabetes), as well as chronic drug administration
can induce CYP2E1 activity, and numerous cellular targets exist for 1-BP metabolites generated
via CYP2E1 mediated oxidative metabolism. Interindividual variability in the expression and
functional capacity of CYP2E1 has been observed (Neafsey et al., 2009) and genetic
polymorphisms in CYP2E1 expression have been linked to altered disease susceptibility (Trafalis et
al., 2010). Though inconsistencies exist in the available data, it is suggested that chronic exposure
to CYP2E1 inducers such as ethanol and other solvents, as well as Pharmaceuticals such as
isoniazid, may increase the probability of developing malignancy, especially for carriers of certain
CYP2E1 alleles (Trafalis et al.. 2010).
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Br
CH3
1-Bromopropane
P450
HO
1 -bromo-2-hydroxypropane
[O]
Br
HO
HO-
a-Bromohydrin
[O]
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Glue
CO,
H3C
Br
1-Bromo-2-hydroxypropane-0-glucuronide
Br
P450
[O]
Br
[O]
Bromoacetone
Br
HO
HO
[O]
[O]
Br
Bromolactaldehyde
HO
[O]
[O]
OH
Pyruvate
Pyruvaldehyde
Br
3-Bromo-1 -hydroxypropanone
[O]
Br
Bromopyruvaldehyde
[O]
Br
[O]
Bromopyruvate
OH
*(GSH Conjugates not listed)
OH
Bromolactate
Figure 3-3 Proposed Intermediary Metabolism for 1-BP
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(Garner et al., 2007; Garner et al., 2006)
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3.1.3 PBPK Models
A PBPK model for 1-BP in rats was developed by (Garner et al., 2015). The model simulates 1-BP
exposures via inhalation wherein distribution of 1-BP to tissues is assumed to be flow-limited.
Metabolism of 1-BP was simulated with Michaelis-Menten kinetics for oxidative metabolism by
cytochrome P450 and first order kinetics for GSH conjugation; parameters were fit to the time
course data of chamber concentrations for 1-BP used in rat inhalation studies. Additional
metabolic parameters were fit to time course data of chamber concentrations of 1-BP for rat
inhalation studies when female rats were pretreated with either the cytochrome P450 inhibitor 1-
aminobenzotriazole (ABT) or the GSH synthesis inhibitor D,L-buthionine (S,R)-sulfoximine (BSD).
These results show the relative contributions of oxidative metabolism via cytochrome P450 and
conjugation with GSH in female rats. Confidence in the PBPK model predictions for 1-BP
concentrations in blood and tissues are limited by the lack of comparison of model predictions
with measured data. The PBPK model was further extended to simulate human exposures by
scaling the physiological parameters to humans, assuming the partition coefficients are the same
in rats and humans and scaling metabolic parameters by BW3/4. Cross species and route to route
extrapolations with the Garner et al. (2015) model are precluded by the lack of data to inform a
model of a species other than rat and a route other than inhalation.
3.2 Hazard Summary and Hazard Identification
This section summarizes the available cancer and non-cancer hazard information for 1-BP. A
comprehensive summary table which includes all endpoints considered for this assessment is
located in Appendix 0. EPA/OPPT reviewed the available data and narrowed the focus of this
assessment to six adverse health effect domains: (1) liver toxicity, (2) kidney toxicity, (3)
reproductive toxicity, (4) developmental toxicity, (5) neurotoxicity, and (6) carcinogenicity. For
non-cancer endpoints, emphasis was placed on acute/short term inhalation, and repeated-dose
inhalation studies identified as most appropriate for hazard characterization and dose-response
analysis.
3.2.1 Non-Cancer Hazard Identification
3.2.1.1 Toxicity Following Acute Exposure
In animals, deaths from acute inhalation exposure to 1-BP occurred only at high exposure
concentrations. LCso values in rats ranged from 7,000 to 14,374 ppm for 4-hour inhalation
exposure (Elf Atochem, 1997; (Kim et al., 1999a). Deaths were associated with an acute
inflammatory response and alveolar edema (Elf Atochem S.A., 1997). Similarly, for oral
exposure, the LDso was >2,000 mg/kg (Elf Atochem S.A., 1993a). No information on 1-BP toxicity
following acute exposure in humans was located.
3.2.1.2 Liver Toxicity
Data from animal studies suggest the liver is a target for 1-BP. Reported effects include liver
histopathology (e.g., hepatocellular vacuolation, swelling, degeneration and necrosis), increased
liver weight, and clinical chemistry changes indicative of hepatotoxicity (Wang et al., 2012; NTP,
2011: Liu etal.. 2009; Lee et al.. 2007: Yamada et al.. 2003: WIL Research. 2001: Kimetal.. 1999a:
Kim et al., 1999b; ClinTrials, 1997a, b). Hepatic endpoints selected for dose-response analysis
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include datasets for histopathology (e.g., hepatocellular vacuolation) from subchronic duration
inhalation studies in rats (WIL Research, 2001; ClinTrials, 1997a, b).
Hepatotoxicity was not directly evaluated in any of the human studies identified in the literature;
however, one study evaluated liver function indirectly in a cohort of 86 Chinese workers exposed
to 1-BP (median exposure levels up to 22.6 ppm) for an average of approximately 40 months (Li et
al., 2010b) and no statistically significant clinical chemistry changes indicative of liver damage
were observed.
3.2.1.3 Kidney Toxicity
Laboratory animal studies have provided evidence of renal toxicity following 1-BP exposure.
Reported kidney effects include increased organ weight, histopathology (pelvic mineralization,
tubular casts) and associated clinical chemistry changes (e.g., increased blood urea nitrogen)
(NTP. 2011: Yamadaetal.. 2003; WIL Research. 2001: Kim et al.. 1999a; ClinTrials. 1997a. b).
Renal endpoints selected for dose-response analysis were for increased incidence of pelvic
mineralization in male and female rats from a subchronic duration inhalation study by (Yamada et
al.. 2003: WIL Research. 2001).
No studies that directly evaluated 1-BP induced renal effects in humans were identified in the
published literature; however, no significant clinical chemistry changes indicative of kidney
damage were observed in a cohort of 86 Chinese workers exposed to 1-BP (median exposure
levels up to 22.58 ppm) for an average of approximately 40 months (Li et al., 2010b) or in 45
workers exposed to a geometric mean concentration of 81.2 ppm for an average of 29 months
(NIOSH. 2003).
3.2.1.4 Immunotoxicity
There is limited evidence for immune effects of 1-BP in animal studies. Two independent studies
of immune function showed that 1-BP can suppress immune responses in rodents (Anderson et
al., 2013; Lee et al., 2007). (Anderson et al., 2010) reported a decreased IgM plaque-forming
response to immunization with sheep red blood cells (sRBC ) in splenocytes harvested from
female rats and mice following subchronic inhalation exposure to 1-BP (NOAEL = 500 ppm in rats;
LOAEL [no NOAEL identified] = 125 ppm in mice). Associated effects in both species included
decreases in T cells and increases in natural killer cells in the spleen; other effects reported in
mice include reduced splenic cellularity and decreased absolute spleen weight. (Lee et al., 2007)
also reported a decreased antibody response to sRBC and reduced splenic cellularity in female
mice after a single oral dose of 1-BP (LOAEL [no NOAEL identified] = 200 mg/kg). Investigation of
immune endpoints in other studies (limited to organ weights and histopathology of spleen,
thymus, and other lymphoreticular tissues) showed no effects at concentrations as high as
1000 ppm in rats and 500 ppm in mice following subchronic inhalation exposure, and 500 ppm in
rats and 250 ppm in mice following chronic inhalation exposure (NTP, 2011; Yamada et al., 2003;
WIL Research, 2001; Ichihara etal., 2000a; Kimetal., 1999b; ClinTrials, 1997a, b). No information
regarding 1-BP immunotoxicity in humans was located.
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3.2.1.5 Reproductive Toxicity
Animal studies suggest that the reproductive system is a target of concern for 1-BP exposure. A
two-generation reproduction study in rats reported adverse effects on male and female
reproductive parameters (WIL Research, 2001). The majority of these effects exhibited a dose-
response beginning at 250 ppm, with statistical significance observed at 500 ppm. Further details
on each of these endpoints can be found in Appendix 0.
Significant increases in the number of 'former' or 'unaccounted' implantation sites (i.e., the
difference between the total number of implantation sites counted and the number of pups born)
were reported by (WIL Research, 2001). EPA/OPPT considers this finding to be indicative of post-
implantation loss (pre-implantation loss could not be determined because of a lack of data on the
number of primordial follicles at 100, 250 and 500 ppm). Fofemales experienced a 48% reduction
in fertility at 500 ppm and complete infertility at 750 ppm. Other effects reported in this study
include dose-related decreases in mating indices, increased estrous cycle length, and a significant
trend of increasing numbers of Fo females with evidence of mating without delivery (a Cochran
Armitage trend test conducted by EPA calculated a p-value <0.0001).
Statistically significant changes in reproductive endpoints in Fo males include decreased absolute
prostate and epididymal weights at exposures > 250 and 500 ppm respectively, as well as
decreased sperm motility, and decreased mating (500 ppm) and fertility indices (750 ppm) (WIL
Research, 2001). The findings described above are supported by similar reports of reproductive
toxicity from independent laboratory studies with rats and mice, including spermatogenic effects
(decreased sperm count, altered sperm morphology and decreased sperm motility) and organ
weight changes in males (decreased epididymis, prostate and seminal vesicle weights) as well as
estrous cycle alterations and decreased numbers of antral follicles in females (NTP, 2011; Qin et
al.. 2010: Liuetal.. 2009: Yuetal.. 2008: Banuetal.. 2007: Yamada et al.. 2003: WIL Research.
2001;lchiharaetal.. 2000b).
3.2.1.6 Developmental Toxicity
The developmental effects of 1-BP exposure have been evaluated on the basis of standard
prenatal developmental toxicity studies, and a two-generation reproductive toxicity study in rats
exposed via the inhalation route. Evidence for 1-BP induced developmental toxicity include dose
related adverse effects on live litter size (WIL Research, 2001), postnatal survival (Furuhashi et al.,
2006), pup body weight, brain weight and skeletal development (Huntingdon Life Sciences, 1999),
(Huntingdon Life Sciences, 2001); (WIL Research, 2001). Further information on these endpoints
can be found in Appendix 0. No data were located on the developmental effects of 1-BP exposure
in humans.
3.2.1.7 Neurotoxicity
Data from studies in humans and animals demonstrate that the nervous system is a sensitive
target of 1-BP exposure. Both the central and peripheral nervous systems are affected. In animal
inhalation studies, the degree or severity of neurotoxicity produced by 1-BP depends on the
concentration as well as duration of exposure, with lower concentrations being effective at longer
exposures. Most inhalation studies using concentrations of >1000 ppm reported ataxia
progressing to severely altered gait, hindlimb weakness to loss of hindlimb control, convulsions,
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and death (e.g., (Banuetal., 2007; Ishidao et al., 2002; Yu etal., 2001; Fueta etal., 2000; Ichihara
etal., 2000a; Ohnishi et al., 1999; ClinTrials, 1997a, b). Concentrations of 400-1000 ppm produced
neuropathological changes including peripheral nerve degeneration, myelin sheath abnormalities,
and spinal cord axonal swelling (Wang et al., 2002; Yu et al., 2001; Ichihara et al., 2000a). Brain
pathology has also been reported in several studies, including white and gray matter
vacuolization, degeneration of Purkinje cells in the cerebellum and decreased noradrenergic but
not serotonergic axonal density in frontal cortex and amygdala at exposures >400 ppm
(Mohideen etal..2013: Mohideen et al.. 2011: Ohnishi et al.. 1999: ClinTrials. 1997a. b).
Decreased brain weight has been reported in adult and developmental studies (Subramanian et
al.. 2012: Wang et al.. 2003: WIL Research. 2001: Ichihara etal.. 2000a; Kim etal.. 1999a;
ClinTrials, 1997b). In a two-generation study (WIL Research, 2001), the NOAEL for decreased brain
weight in Pi-generation males was 100 ppm (BMD modeling did not produce an acceptable fit);
this value is brought forward for risk assessment representing neuropathological changes.
Physiological, behavioral, and biochemical measures have been used to characterize and develop
dose-response data for neurological effects. Motor nerve conduction velocity and latency
measured in the rat tail nerve were altered at concentrations > 800 ppm with progressive
changes from 4 to 12 weeks of exposure (Yu et al., 2001; Ichihara et al., 2000a). In the brain,
electrophysiological changes in hippocampal slices were seen at concentrations of 400 ppm and
above (Fueta etal.. 2002a: Fueta etal.. 2002b: Fueta et al.. 2000): Fueta, 2004,1717472; Fueta,
2007, 1519111; Ueno, 2007, 1717460}. Behavioral tests such as hindlimb grip strength, landing
foot splay, traction (hang) time, gait assessment, motor activity, and water maze performance
provide dose-response data and tend to be more sensitive than neuropathology or physiological
changes, with effects at concentrations as low as 50-200 ppm (Banu et al., 2007; Honma et al.,
2003; Ichihara et al., 2000a). Exposures to concentrations > 50 ppm produce changes in
neurotransmitters, biomarkers, and proteome expressions suggesting alterations in the function
and maintenance of neural and astrocytic cell populations (Huang et al., 2015; Mohideen et al.,
2013; Zhang etal., 2013; Huang etal., 2012; Subramanian et al., 2012; Huang etal., 2011;
Mohideen et al.. 2009: Sudaetal.. 2008: Yoshida et al.. 2007: Wang etal.. 2003: Wang etal..
2002). Although less extensively tested, oral or subcutaneous dosing of 1-BP resulted in similar
findings as for inhalation exposure, with effects at >200 mg/kg-day (Guo et al., 2015; Zhong et al.,
2013; Wang et al., 2012; Zhao et al., 1999). Neurological endpoints selected for dose-response
analysis were datasets for decreased time hanging from a suspended bar (traction time) in rats in
a 3 -week inhalation study (Honma et al., 2003) and decreased hind limb grip strength in rats in a
12 -week inhalation study (Ichihara et al., 2000a). These functional measures are relevant to
peripheral neurotoxicity reported in human studies.
Human studies (case-control studies, industrial surveys, and case reports) corroborate that the
nervous system is a sensitive target of 1-BP exposure in humans. Clinical signs of neurotoxicity
(including headache, dizziness, weakness, numbness in lower extremities, ataxia, paresthesias,
and changes in mood) and motor and sensory impairments were noted in the case reports of
workers occupationally exposed to 1-BP for 2 weeks to 3 years at estimated concentrations
exceeding averages of 100 ppm (Samukawa et al., 2012; Majersik et al., 2007; Raymond and Ford,
2007; Ichihara et al., 2002; Sclar, 1999), and in industrial surveys with average exposures greater
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than 81 ppm (ranging from 2 weeks to 9 years) (NIOSH, 2003, 2002a, JD). Cross-sectional studies of
Chinese workers reported increased distal latency and decreased sural nerve conduction velocity
in workers, although they were not statistically significant. Statistically significant decreased
vibration sense in toes was observed across all exposure groups (0.07-106.4 ppm) compared to
controls (Lietal., 2010a; Lietal., 2010b; Ichihara et al., 2004b). However, there were many
methodological limitations in these studies, which are discussed in depth in Appendix 0.
3.2.2 Cancer Hazard Identificatio n
3.2.2.1 Genetic Toxicity
There is some evidence for mutagenicity and DNA damage associated with exposure to 1-BP in
vitro, but the results are not conclusive as to whether and to what extent such effects may occur
in mammals in vivo. 1-BP was mutagenic with or without metabolic activation in an assay for
reverse mutation in Salmonella typhimurium conducted under closed conditions to control for
loss of test material due to volatilization (Barber et al., 1981). Other tests for mutagenicity in
bacteria were negative, but may not have been conducted in closed systems (e.g, (NTP, 2011; Kim
et al., 1998). In mammalian cells tested in vitro, increased mutation frequency was observed in
mouse lymphoma cells exposed to 1-BP with or without activation (Elf Atochem S.A., 1996a), and
DNA damage was significantly increased in human leukocytes following in vitro exposure to 1-BP
(Toraason et al., 2006). Tests conducted in vivo, however, were mostly negative, including assays
for dominant lethal mutations and micronuclei induction in rats and mice (Kim et al., 1998); (Elf
Atochem S.A.. 1995): (NTP. 2011: Yu et al.. 2008: Saito-Suzuki et al.. 1982). An evaluation of the
leukocytes of workers exposed to 1-BP showed no definitive evidence of DNA damage (i.e.,
damage was not significantly higher in workers exposed to the highest levels of 1-BP [sprayers]
compared to those exposed to the lowest levels of 1-BP [non-sprayers]) (Toraason et al., 2006).
Positive results have been observed in several genotoxicity tests using known or postulated
metabolites of 1-BP (including glycidol, propylene oxide, a-bromohydrin, 3-bromo-l-propanol,
and l-bromo-2-propanol) (NTP. 2014: IARC. 2000. 1994).
3.2.2.2 Carcinogenicity
Evidence from chronic cancer bioassays in rats and mice suggests that 1-BP may pose a
carcinogenic hazard to humans. Significant increases in the incidences of skin tumors
(keratoacanthoma/squamous cell carcinomas) in male F344 rats, rare large intestine adenomas in
female F344 rats, and alveolar/bronchiolar adenomas or carcinomas (combined) in female
B6C3F1 mice were observed following exposure to 1-BP via inhalation for 2 years (NTP, 2011).
NTP concluded these data showed some evidence for carcinogenicity in male rats, clear evidence
for carcinogenicity in female rats, no evidence for carcinogenicity in male mice, and clear
evidence for carcinogenicity in female mice. No other animal data, and no human data, were
located on the carcinogenicity of 1-BP.
1-BP has been shown to be a multi-target carcinogen in rats and mice. The exact mechanism/
mode of action of 1-BP carcinogenesis is not clearly understood. There are, however, an
abundance of data that may provide a basis for weight of evidence consideration.
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a. Ames test: Mixed results in the Ames test were reported in a number of 1-BP studies. Some of
these studies were probably complicated by the high volatility of 1-BP and lack of use of
closed systems and therefore should be invalidated. Among studies done in desiccator or
closed systems, both positive and negative results have been reported.
b. Genotoxicity tests of mammalian cells: 1-BP caused mutations in cultured mammalian cells
with or without metabolic activation and DNA damage in cultured human cells without
metabolic activation. There was also limited evidence of DNA damage in leukocytes in 1-BP-
exposed workers. Two in vivo micronucleus assays in bone marrow and circulating
erythrocytes were negative; however, it should be mentioned that in vitro micronucleus
assays have recently been suggested to be prone to yielding false negatives (e.g., (Benigni et
al.. 2012).
c. Metabolic activation to mutagenic intermediates: Rodent metabolic studies have indicated
that 1-BP can be activated by CYP2E1 to at least five mutagenic intermediates (NTP, 2014;
IARC, 2000, 1994), including two clearly mutagenic and carcinogenic chemicals, glycidol and
propylene oxide, which are listed in NTP Report on Carcinogens as reasonably anticipated to
be human carcinogens by the NTP (NTP, 2013). Glycidol has been shown to induce tumors in
intestines, one of the carcinogenic targets of 1-BP. There is evidence that humans have
CYP2E1 activity in lung and similar metabolic pathways for 1-BP as rodents.
d. Evidence for multi-species and multiplicity of cancer targets of 1-BP exists: In general,
chemical carcinogens that induce cancer in more than one animal species and in multiple
targets tend to act via mutagenic mechanism/mode of action. 1-BP has been shown to induce
a variety of tumors in both rats and mice.
e. Structure-Activity Relationship (SAR) consideration: SAR has been routinely used as one of the
criteria for consideration in EPA's Guidelines for Carcinogen Risk Assessment. From the SAR
point of view, 1-BP is a low M.W. alkyl bromide that is generally known to be a good alkylating
agent. In fact, 1-BP has been shown to bind to DNA in vitro. Bromoethane and
1-bromobutane, two of the closest analogs of 1-BP, were both reported to give positive
results in the Ames test when tested in closed systems.
f. Other possible mechanism of action: Besides mutagenicity/genotoxicity, at least three other
possible mechanisms - oxidative stress, immunosuppression, and cell proliferation —have
been suggested by the NTP (NTP, 2013). These mechanisms can act synergistically to
complete the multi-stage process of carcinogenesis. Although more research (e.g., organ-
specific in vivo DNA adduct studies) is needed to ascertain mutagenicity as the key molecular
event, there is no evidence that the other three mechanisms may play a more important role
than mutagenicity.
Following EPA's Guidelines for Carcinogen Risk Assessment, overall, the totality of the available
data/information and the weight of evidence support a justifiable basis to conclude a probable
mutagenic mode of action for 1-BP carcinogenesis. 1-BP may be considered to be "Likely to be
Carcinogenic in Human". Given the lack of information to inform a specific dose-response curve a
linear extrapolation from the point of departure is recommended as the default risk assessment
model per EPA's Guidelines for Carcinogen Risk Assessment.
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3.3 Weight of Evidence/Multiple Lines of Evidence Supporting
Critical Effects
3.3.1 Weight-of-Evidence for Reproductive and Developmental Toxicity
Reproductive and developmental toxicity were identified as critical targets for 1-BP exposure
based on a constellation of effects reported in a number of studies, including a two-generation
reproduction study by (WIL Research, 2001), which showed adverse effects on male and female
reproductive parameters, as well as the developing fetus. Some of these endpoints were
considered sufficient to include as PODs (see Section 3.4), while others were used as qualitative
supportive evidence. Additional details on the results of this study can be found in Appendix 0.
Quantitative and qualitative evidence of 1-BP induced reproductive toxicity in Fo males include
decreases in sperm motility, changes in normal sperm morphology, decreases in mating and
fertility indices (WIL Research, 2001), and decreases in epididymal, prostate, and seminal vesicle
weights following 1-BP inhalation exposure (NTP, 2011; WIL Research, 2001; Ichihara etal.,
2000b). Evidence of reproductive toxicity in Fo females include decreased numbers of corpora
lutea, antral follicles, and implantation sites (NTP, 2011; Yamada etal., 2003; WIL Research,
2001). Other reported reproductive effects include increased estrous cycle length, and a
significant trend of increasing numbers of Fo females with evidence of mating without delivery
(WIL Research, 2001). Reported impairments in male and female reproductive function resulted
in a 48% reduction in fertility at 500 ppm and complete infertility at 750 ppm in Fo mating pairs
(WIL Research, 2001). Although the adverse reproductive effects of 1-BP exposure have not been
directly evaluated in humans, the results from laboratory animal studies suggest that it may
impair reproductive function.
Evidence supporting fetal development as a sensitive target of 1-BP exposure is provided by a
number of laboratory animal studies. The current database consists of developmental toxicity
studies that show severe effects resulting from prenatal exposures during gestation and postnatal
exposure studies showing adverse developmental effects that manifest at various stages of
development, and span multiple generations (WIL Research, 2001). Overall, the general
consistency of findings indicative of impaired development reported in multiple studies from
independent laboratories is taken as evidence of a causative association between 1-BP exposure
and developmental toxicity. Reported adverse developmental effects following 1-BP exposure
include dose-related decreases in live litter size (WIL Research, 2001), postnatal survival
(Furuhashi et al., 2006), and pup body weight, brain weight and skeletal development
(Huntingdon Life Sciences, 1999), (Huntingdon Life Sciences, 2001); (WIL Research, 2001). (WIL
Research, 2001) also reported decreases in the number of implantation sites, and increases in
'unaccounted' implants for corresponding ovulatory events, reported as the difference between
the total number of implantation sites counted and the number of pups born. EPA/OPPT
interpreted this finding as an indication of post-implantation loss (pre-implantation loss could not
be determined due to insufficient data on the number of primordial follicles). Additional
qualitative evidence of impaired development following 1-BP exposure is provided by results
from dominant lethal assays with 1-BP which show increased implantation loss in rats (only at
week 8) subjected to five days of oral 1-BP exposure at 400 mg/kg (Saito-Suzuki et al., 1982) and
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in mice (only at week 5) gavaged at 600 mg/kg for ten days prior to mating (Yu et al., 2008). The
findings described above are supported by consistent reports of 1-BP induced adverse
developmental effects from independent laboratory studies with rats and mice. No corresponding
epidemiological studies have been identified; however, the concordance of results obtained from
laboratory animal studies suggests that 1-BP may adversely affect human development.
3.3.2 Weight-of-Evidence for Neurotoxicity
Neurotoxicity has been identified as a critical effect for 1-BP based on over 15 years of behavioral,
neuropathological, neurochemical, and neurophysiological studies in rodents as well as cross-
sectional studies and case reports in humans (Section 3.2.1.7 and Appendices 0-1, 0-3, and 0-4).
Overall, there is considerable support for the finding of peripheral neurotoxicity, and consistency
in reports of impaired peripheral nerve function (sensory and motor) and adverse neuromuscular
impacts. The effects are progressive in terms of exposure duration and concentration, and range
from subtle changes in nervous system function and neurochemistry progressing to physiological
manifestations of neuron damage to structural evidence of neuronal pathology.
This spectrum of adverse manifestations of peripheral neurotoxicity is reproducible across almost
all of the experimental studies, with a few notable exceptions. In addition, symptoms in humans,
such as peripheral weakness, numbness, ataxia, and paraparesis, are concordant with the signs
seen in many rodent studies. At high concentrations (>1000 ppm), toxicological reports in rodents
include observations such as hindlimb weakness, ataxia, altered gait, and other signs typical of
peripheral neuropathy (Mohideen et al., 2013; Zhang et al., 2013; Banu et al., 2007; Honma et al.,
2003: Fuetaetal.. 2002a: Fuetaetal.. 2002b: Ishidao et al.. 2002: Yuetal.. 2001: Fueta etal..
2000: Ichihara et al.. 2000a: Kimetal.. 1999a: Ohnishi et al.. 1999: ClinTrials. 1997a. b). However,
in a chronic bioassay (NTP, 2011) these signs were reported at 2000 ppm but not 1000 ppm;
differences in timing and specificity of observations as well as training and blinding of personnel
to dose assignment could account for the relative insensitivity of those specific outcomes. A
number of papers that did not report any information at all about the general appearance and
health of the animals were mostly mechanistic studies focused only on ex vivo endpoints (Huang
etal.. 2015: Huang et al.. 2012: Huang etal.. 2011: Mohideen et al.. 2011: Mohideen et al.. 2009
Subramanian. 2012. 1533580: Sudaetal.. 2008: Fueta et al.. 2007: Ueno et al.. 2007: Yoshida et
al., 2007; Fueta etal., 2004; Wang etal., 2003; Wang etal., 2002). In human reports, severe
neurological effects in workers occurred at relatively high exposures (>100 ppm) over a period of
time of exposure ranging from weeks to months (Samukawa et al., 2012; CDC, 2008; Majersik et
al., 2007; Raymond and Ford, 2007; Ichihara etal., 2002; Sclar, 1999).
There is generally agreement of 1-BP's neurotoxic effects across studies using measures of
peripheral nerve integrity evaluated by electrophysiological and behavioral tests. Nerve
conduction velocity and distal latency in motor neurons are decreased in animals (Yu et al., 2001;
Ichihara et al., 2000a; Zhao et al., 1999) [subcutaneous exposure]). These experimental findings
corroborate the studies of factory workers that describe decreased nerve conduction and/or
peripheral sensory impairment (Li et al., 2010a; Li et al., 2010b; Ichihara et al., 2004a). The
epidemiological studies are, however, somewhat limited by poorly defined exposures as well as
concerns about the sensitivity and implementation of the test methods used to assess motor and
sensory deficits. Using an objective measure of grip strength in rats, decreased function that
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worsens with continued exposure has been reported in several laboratories (Wang et al., 2012;
Banu etal., 2007; Ichihara etal., 2000a), [oral exposure]) except one (ClinTrials, 1997a).
A number of animal studies report histopathology of the nervous system (brain, spinal cord,
and/or peripheral nerves) at concentrations as low as 400 ppm (Mohideen et al., 2013;
Subramanian et al., 2012; Mohideen et al., 2011; Wang etal., 2002; Yu etal., 2001; Ichihara etal.,
2000a; Ohnishi et al., 1999; ClinTrials, 1997b), but not in other studies that used even at higher
concentrations (NTP. 2011: Fueta et al.. 2004: Sohnetal.. 2002: WIL Research. 2001: Kim etal..
1999a). There are a few conflicting reports from the same laboratory (ClinTrials, 1997a, b), [4 wk
vs 13 wk studies]; (Sohnetal., 2002; Kim etal., 1999a). Such differences may be attributable to a
number of experimental factors, including tissue preparation, fixation, staining, and sampling,
measurement methodology, and training and blinding of personnel to dose group assignment.
Additional experimental animal studies report changes in brain weight, which is considered
indicative of neurotoxicity even in cases where other histopathological changes are not evident
(U.S. EPA, 1998); however, several studies do describe corresponding neuropathology (Wang et
al., 2002; WIL Research, 2001; Kim etal., 1999a). Decreased brain weight was reported with
subacute to subchronic exposures in adult rats (Subramanian et al., 2012; Wang et al., 2003;
Ichihara etal., 2000a; Kim etal., 1999a; ClinTrials, 1997b), as well as decreased brain weight in
offspring from a multi-generational study with lifetime exposures (WIL Research, 2001). Only two
studies have measured brain weight and reported no effects: 1) (Wang et al., 2002), in which
exposure was only 7 days and may not have been a sufficient exposure duration and/or
concentration, and 2) the 13-wk study of (ClinTrials, 1997a), even though the same laboratory
reported decreased brain weight at the same concentration with only 4 weeks of exposure
(ClinTrials did not provide explanations for this contradictory finding).
Several studies report alterations in central nervous system neuronal communication,
neurotransmitter levels, proteins, and oxidative stress markers, all of which are markers of
neurotoxicity (U.S. EPA, 1998). It is notable that database consistency is partially a function of
multiple studies from a few laboratories (Huang et al., 2015; Mohideen et al., 2013; Zhang et al.,
2013; Huang etal., 2012; Subramanian et al., 2012; Huang etal., 2011; Mohideen et al., 2011;
Mohideen et al.. 2009: Sudaetal.. 2008: Fueta etal.. 2007: Ueno et al.. 2007: Fueta et al.. 2004:
Wang etal., 2003; Fueta etal., 2002a; Fueta etal., 2002b; Fueta etal., 2000). Other studies have
reported cognitive deficits following 1-BP inhalation exposure (Guo et al., 2015; Zhong et al.,
2013: Honma etal.. 2003).
Overall, the sheer number of experimental studies, supported by the epidemiological studies,
reporting clinical and neurohistological signs provide strong evidence for peripheral
neuropathology. Where quantifiable endpoints that are sensitive to relatively low exposures have
been measured across laboratories, there is generally good consistency in outcomes, with only a
few notable exceptions. There is also agreement in findings of central nervous system dysfunction
in laboratory rodents, but there are no corresponding studies in humans with which to compare.
Thus, the strength and concordance of these lines of evidence provide good confidence in
conclusions of adverse neurological findings with 1-BP.
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3.3.3 Weight-of-Evidence for Cancer
Evidence from chronic cancer bioassays in rats and mice suggests that 1-BP may pose a
carcinogenic hazard to humans. Significant increases in the incidences of skin tumors
(keratoacanthoma/squamous cell carcinomas) in male F344 rats, rare large intestine adenomas in
female F344 rats, and alveolar/bronchiolar adenomas or carcinomas (combined) in female
B6C3F1 mice were observed following exposure to 1-BP via inhalation for 2 years (NTP, 2011).
The exact mechanism/mode of action of 1-BP carcinogenesis is not clearly understood. There are,
however, an abundance of data that may provide a basis for weight of evidence considerations;
these include in vitro tests, similarity in metabolism across species, SAR and other potential
mechanisms of action. Although the results from Ames and other genotoxicity tests for 1-BP have
been mixed, two positive mammalian cell test results provide some evidence of genotoxicity/DNA
damage. Rodent metabolic studies have indicated that 1-BP can be activated by CYP2E1 to at
least five mutagenic metabolites/intermediates, including two that are clearly mutagenic and
carcinogenic. Since humans have CYP2E1 activity in the lung and exhibit similar metabolic
pathways for 1-BP as rodents, the evidence from multiple species (rats and mice) for multiple
cancer types following 1-BP exposure supports a carcinogenic hazard to humans. From the SAR
point of view, 1-BP is a low molecular weight alkyl bromide that is generally known to be a good
alkylating agent and two of its closest analogs (bromoethane and 1-bromobutane) both have
provided positive Ames test results in closed systems. Other possible mechanisms of action -
oxidative stress, immunosuppression, and cell proliferation—can act synergistically to complete
the multi-stage process of carcinogenesis. Per EPA Guidelines for Carcinogen Risk Assessment,
overall, the totality of the available data/information and the weight of evidence support a
justifiable basis to conclude a probable mutagenic mode of action for 1-BP carcinogenesis. 1-BP
may be considered to be "Likely to be Carcinogenic in Humans". Linear extrapolation from the
POD is recommended as the default risk assessment model.
3.3.4 Summary of Hazard Studies Used to Evaluate Acute and Chro nic
Exposures
EPA/OPPT considered adverse effects for 1-BP across organ systems and a comprehensive
summary table is in Appendix 0 (Table_Apx 0-2). The full list of effects was screened to those
that are relevant, sensitive and found in multiple studies which include the following types of
effects: hepatotoxicity, renal toxicity, immunotoxicity, developmental/reproductive toxicity,
neurotoxicity, and cancer as described above. In general, adverse effects were observed in all of
these systems in rats exposed to 1-BP by inhalation in the range of 100 - 1000 ppm (LOAELs).
From these effects EPA/OPPT selected endpoints for both non-cancer and cancer that were
amenable to quantitative analysis for dose-response assessment as discussed in more detail
below in Section 3.4. In the following sections, EPA identifies the appropriate toxicological studies
to be used for acute and chronic exposure scenarios.
3.4 Dose-Response Assessment
EPA/OPPT evaluated data from studies described above (Section 3.2) to characterize the dose-
response relationships of 1-BP 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. EPA/OPPT
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defines a POD as the dose-response point that marks the beginning of a low-dose extrapolation.
This point can be the lower bound on 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.
3.4.1 Non-Cancer Dose-Response Assessment
The non-cancer dose-response analysis in this assessment commenced with the review and
selection of high quality toxicity studies that reported both adverse non-cancer health effects and
quantitative dose-response data (Table_Apx 0-2). As a result, the non-cancer dose-response
assessment was organized into five health effect domains: (1) liver; (2) kidney; (3) reproductive;
(4) developmental and (5) nervous system. Inhalation PODs were identified in earlier steps. HEC
values were calculated for the inhalation PODs identified within each health effect domain.
Endpoint and study-specific UFs were selected based on EPA guidance (U.S. EPA, 2002) and used as
the benchmark MOEs for risk calculations. These UFs were applied to the PODs to account for (1)
variation in susceptibility among the human population (i.e., inter-individual or intraspecies
variability); (2) uncertainty in extrapolating animal data to humans (i.e., interspecies uncertainty);
(3) uncertainty in extrapolating from data obtained in a study with less-than-lifetime exposure
(i.e., extrapolating from subchronic to chronic exposure); and (4) uncertainty in extrapolating from
a LOAEL rather than from a NOAEL, with default values of 10 applied for each (U.S. EPA, 2002).
Table 3-1 summarizes the hazard studies and health endpoints by target organ/system that
EPA/OPPT considered suitable for risk evaluation of the exposure scenarios identified in this work
plan risk assessment. Key studies in Table 3-1 are briefly described in the Human Health Hazard
Summary, Section 3.2. Table 3-4 lists the lowest HECs by study type and duration (i.e., acute vs.
chronic).
Benchmark dose (BMD) modeling was applied to these endpoints in a manner consistent with EPA
Benchmark Dose Technical Guidance. When the models were adequate, the model results were
used as PODs. For studies in which BMD modeling did not achieve an adequate fit to the data, the
NOAEL or LOAEL value was used for the POD. Details regarding BMD modeling can be found in
Appendix P. The PODs were converted from air concentrations in laboratory animals to HECs by
accounting for the duration of exposure and applying an interspecies dose adjustment factor
(DAF). The DAF was based on the ratio of the blood:gas partition coefficient for 1-BP, as
recommended for a systemically acting gas (U.S. EPA, 1994). For 1 BP, the blood:air partition
coefficient for rats is greater than that for humans, so a default ratio of 1 was applied (U.S. EPA,
1994). The HECs were adjusted from the respective study conditions to exposures of 8 hours per
day for occupational exposure scenarios (acute and chronic) and to exposures of 24 hours per day
for consumer exposure scenarios. For chronic exposure effects, air concentrations were adjusted
to 5 days of exposure per week to reflect a 40 hour work week. HECs were rounded to two
significant figures.
BMRs were selected for each endpoint. In cases where biologically relevant BMRs were not
available the BMR was 10% for dichotomous endpoints and 1 standard deviation for continuous
endpoints consistent with EPA Benchmark Dose Technical Guidance. A BMR of 10% was used for
liver and kidney effects. A BMR of 1 standard deviation was used for reproductive effects. Lower
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BMRs were used for developmental endpoints with 5% for pup body weight and 1% for brain
weight to account for the increased severity of these endpoints (U.S. EPA, 1991). A BMR of 1
standard deviation was used for functional nervous system effects. When BMD modeling was
successful, the PODs were the BMDLs determined for each endpoint. The PODs for endpoints
selected following dose-response analysis were calculated either by benchmark dose (BMD)
modeling (when the model fit was adequate) or a NOAEL/LOAEL approach based on the endpoint
evaluated (see Section 3.4.1 and Table 3-1 for all of the PODs).
Given the different exposure scenarios considered (both acute and chronic for spray adhesives,
dry cleaning, and degreasing activities for occupational exposure scenarios; and only acute for spot
cleaners for consumer exposure scenarios), 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 and humans, risks for developmental effects that may result from a single
exposure were evaluated for acute (short-term) exposures, whereas risks for other adverse effects
(e.g., toxicity to liver, kidney, reproduction, development and nervous system) were evaluated for
repeated (chronic) exposures to 1-BP. Although developmental studies typically involve multiple
exposures, they are considered relevant for evaluating single exposures because some
developmental effects (e.g., fetal resorptions and mortality), may result from a single exposure
during a critical period of development (Davis etal., 2009; Van Raaij et al., 2003; U.S. EPA, 1991).
This is consistent with EPA's Guidelines for Reproductive Toxicity Risk Assessment which state
that repeated exposure is not a necessary prerequisite for the manifestation of developmental
toxicity. Consequently, EPA/OPPT concluded that developmental endpoints are applicable when
assessing acute exposures, where it is assumed that the risk of their occurrence depends on the
timing and magnitude of exposure. This is based on the presumption and EPA's policy that a
single exposure during a critical window of development may produce adverse developmental
effects (U.S. EPA, 1996, 1991). The rationale for using the range of toxic effects for chronic
scenarios is based on the fact that relatively low dose and short term/sub-chronic exposures can
result in long-term adverse consequences.
PODs for Acute Exposure
Acute exposure was defined for occupational settings as exposure over the course of a single work
shift 8 hours and for consumers as a single day. Developmental toxicity (i.e. reduced number of
live pups per litter) was the endpoint selected as most relevant for calculating risks associated
with acute occupational or consumer exposure (WIL Research, 2001). The acute scenario covers
exposures incurred during a single day, with varying time intervals assumed for worker (an 8 hour
work shift), and consumer (a 24 hour day) exposure scenarios. Usually, the daily dose is not
adjusted for duration of exposure because appropriate pharmacokinetic data are not available. In
cases where such data are available, adjustments may be made to provide an estimate of equal
average concentration at the site of action for the human exposure scenario of concern.
However, the short half-life for 1-BP suggests there will not be increasing body burden over
multiple exposure days, therefore, no duration adjustment is needed. Further support for using
this endpoint for acute (short-term) exposures is the fact that the constellation of both male and
female reproductive effects (in the Fo males and females) collectively contributing to the
decreases in live litter size, all occurred within a short window of exposure between ovulation and
implantation. In addition, decreased live litter size occurred at relatively low exposures, suggesting
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that this was a sensitive and relevant endpoint, suitable for use in the risk assessment. A BMR of
5% was used to address the severity of this endpoint (U.S. EPA, 2012a). The POD for the decreased
live litter size was a BMDL of 31 ppm.
PODsfor Chronic Exposure
Chronic exposure was defined for occupational settings as exposure reflecting a 40-hour work
week. Non-cancer endpoints selected as most relevant for calculating risks associated with
chronic (repeated) occupational exposures to 1-BP included toxicity to the liver, kidney,
reproductive system, developmental effects, and the nervous system.
Table 3-1 summarizes the hazard studies and health endpoints by target organ/system that
EPA/OPPT considered suitable for the risk evaluation of chronic exposure scenarios in the work
plan risk assessment for 1-BP. Key studies in Table 3-1 are briefly described in the Human Health
Hazard Summary, Section 3.2, along with other toxicity and epidemiological studies. BMD
modeling was performed for these endpoints in a manner consistent with EPA Benchmark Dose
Technical Guidance. BMRs were selected for each endpoint.
Hepatic endpoints selected for dose-response analysis include datasets for histopathology (e.g.,
hepatocellular vacuolation) from subchronic inhalation studies in rats (ClinTrials, 1997a, b) and
(WIL Research, 2001). Benchmark dose modeling determined BMDL values of 143, 226 and
322 ppm for the three datasets modeled from these studies.
Renal endpoints selected for dose-response analysis include an increased incidence of pelvic
mineralization in male and female rats from a subchronic inhalation study (WIL Research, 2001).
Benchmark dose modeling determined BMDL values of 428 and 135 ppm, respectively, for these
datasets.
Decreased epididymal weight, decreased prostate weight, decreased seminal vesicle weight,
altered sperm morphology and decreased sperm motility were the male reproductive endpoints
selected for dose-response analysis (WIL Research, 2001; Ichihara et al., 2000b). Increased
estrous cycle length and decreased antral follicle count were the female reproductive endpoints
selected for dose-response analysis (Yamada etal., 2003; WIL Research, 2001). The PODs for
endpoints selected following dose-response analysis were calculated either by benchmark dose
(BMD) modeling (when the model fit was adequate) or a NOAEL/LOAEL approach based on the
reproductive endpoint evaluated (see Section 3.4 and Table 3-1 for all of the PODs). The PODs
were 38, 227, 250, 313 and 338 ppm for decreased relative seminal vesicle weight (use of
absolute seminal vesicle weight produced the same BMDL), decreased percent normal sperm,
decreased percent motile sperm, and absolute left and right cauda epididymal weights
respectively, in males. The PODs were 200 and 250 ppm for decreased antral follicle count and
increased estrous cycle length respectively, in females.
Decreased live litter size (i.e. reduced number of live pups per litter) was the endpoint selected as
most relevant for calculating risks associated with developmental toxicity following chronic,
exposures (WIL Research, 2001). Decreased live litter size may result from single as well as
repeated exposures at a developmentally critical period (Davis et al., 2009; Van Raaij et al., 2003;
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U.S. EPA, 1991) and therefore was considered a relevant endpoint for chronic as well as acute
exposures. In addition, decreased live litter size occurred at relatively low exposures, suggesting
that this was a sensitive and relevant endpoint, suitable for use in the risk assessment. A BMR of
5% was used to address the severity of this endpoint (U.S. EPA, 2012a). The POD for the decreased
live litter size was a BMDL of 43 ppm.
Neurological endpoints selected for dose-response analysis for chronic, repeated exposures were
datasets for decreased time hanging from a suspended bar (traction time) in rats in a 3-week
inhalation study (Honma et al., 2003) and decreased hind limb grip strength in rats in a 12-week
inhalation study (Ichihara et al., 2000a). These functional measures are relevant to peripheral
neurotoxicity reported in human studies. Benchmark dose modeling determined BMDL values of
18 and 214, respectively, for these datasets.
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Table 3-1 List of Inhalation Endpoints Suitable for the Non-Cancer Dose-Response Analysis of 1-BP
Target Organ/
System
Liver
Liver
Liver
Species, sex
(#animals/dose)
Rat (male)
(n=25/group)
Rat (male)
(n=15/group)
Rat (female)
(n=25/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 750
100 to 600
100 to 750
4
Duration2
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
6 hours/day, 5
days/week for 13
weeks
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
GD 20; from PND
5 until WGsnins of
offspring (~PND
^J 21) for Fo ^J
POD Type
(ppm)3
BMDLio-
143.5
BMDLio-
226.1
BMDLio-
322.1
Effect
Increased
incidence of
vacuolization of
centrilobular
hepatocytes (Fo)
Increased
incidence of
cytoplasmic
vacuolization
Increased
incidence of
vacuolization of
centrilobular
hepatocytes (Fo)
HEC(ppm)4
150
170
340
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research,
2001)
(ClinTrials,
1997a)
(WIL Research,
2001)
Kidney
Rat (female)
(n=25/group)
100 to 750
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
GD 20; from PND
5 until weaning of
offspring (~PND
21) for Fo
BMDLio=
135.0
Increased
incidence of
pelvic
mineralization
(Fo)
140
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
(WIL Research,
2QQ1)
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Target Organ/
System
Kidney
Species, sex
(#animals/dose)
Rat (male)
(n=25/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 750
Duration2
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
POD Type
(ppm)3
BMDLio-
428.3
Effect
Increased
incidence of
pelvic
mineralization
(Fo)
HEC(ppm)4
450
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research,
2001)
Reproductive
System
Reproductive
System
Reproductive
System
Rat (male)
(n=8-9/group)
Rat (female)
(n=22-25/group)
Rat (male)
(n=15-25/group)
200 to 800
100 to 500
4
100 to 750
8 hours/day, 7
days/week for 12
weeks
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
BMDLisD=
38
BMDLisD=
188
NOAEL*=
250
Decreased
absolute/relative
seminal vesicle
weight
Decreased
number of
implantation
sites
Decreased
percent motile
sperm (Fo)
53
200
260
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
(Ichihara et al..
2000b)
(WIL Research,
2001)
(WIL Research,
2001)
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Target Organ/
System
R6productiv6
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
Species, sex
(#animals/dose)
Rat (female)
(n=22-25/group)
Rat (female)
(n=10/group)
Rat (male)
(n=25/group)
Rat (male)
(n=24-25/group)
Rat (male)
Range of Doses
or
Concentrations1
(ppm)
100 to 750
200 to 800
100 to 750
100 to 750
100 to 750
Duration2
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
GD 20; from PND
5 until weaning of
offspring (~PND
21) for Fo
8 hours/day 7
days/week for 7
or 12 weeks
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
6 hours/day
(> 70 days),
throughout
mating, and until
sacrifice for Fo
POD Type
(ppm)3
NOAEL*-
250
LOAEL*-
200
BMDLiso"
313
BMDLisD=
327
BMDLiso"
338
Effect
Increase in
estrous cycle
length (Fo)
Decreased
number of antral
follicles (Fo)
Decreased left
cauda epididymis
absolute weight
(Fo)
Decreased
percent normal
sperm
morphology (Fo)
Decreased right
absolute weight
(Fo)
HEC(ppm)4
260
280
330
340
350
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFc-1-
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFc-1-
UFA=10;
UFH=10;
UFL=10;
Total
UF=1000
UFc-1-
UFA=10;
UFH=10;
UFi_-r
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFc-1-
UFa-10-
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research
2001)
(Yamada 6t al
2QQ3)
(WIL Research
2001)
(WIL Research,
2QQ1)
(WIL Research
2001)
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Target Organ/
System
Reproductive
System
Species, sex
(#animals/dose)
Rat
(n=25/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 750
Duration2
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
sacrifice for Fo
POD Type
(ppm)3
BMDLio-
356
Effect
Decreased Male
and Female
Fertility Index
(Fo)
HEC(ppm)4
370
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA- 10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research,
2001)
Developmental
Effects
Developmental
Effects
Developmental
Effects
Rat
(n=25/group)
Rat
(female)
(n=15-22/group)
Rat
(female)
(n=25/group)
100 to 500
100 to 500
100 to 500
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
GD20fortheFi
litters
6 hours/day
during pre-mating
(> 70 days),
throughout
mating, and until
GD 20; from PND
5 until weaning of
offspring (~PND
21)
6 hours/day
during gestation
plus > 21 weeks
after PND21
BMDLs-
41
BMDLi-
50
BMDLi-
82
Decreased live
litter size (Fi) at
PNDO
Decreased brain
weight in Fz
females at PND
21
Decreased brain
weight in adult Fi
females
Acute6:
31
Chronic6:
43
53
86
UFs=l;
UFA- 10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA- 10;
UFH=10;
UFL=1;
Total UF-100
UFs=l;
UFA- 10;
UFH=10;
UFL=1;
Total UF=100
(WIL Research.
2001)
(WIL Research.
2001)
(WIL Research.
2001)
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Target Organ/
System
D6V6lopm6ntal
Effects
Developmental
Effects
Devsl op msntal
Effects
Developmental
Effects
Devsl op msntal
Effects
Species, sex
(#animals/dose)
Rat
(n=15-22/group)
Rat
(male)
(n=24-25/group)
Rat
(male)
(n=15-22/group)
Rat
(male)
(n=10-24/group)
Rat
(female)
(n=15-22/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 500
100 to 500
100 to 500
100 to 500
100 to 500
Duration2
6 hours/day
(> 70 days),
throughout
mating, and until
GD 20; from PND
5 until weaning of
offspring (~PND
21)
6 hours/day
during gestation
plus> 21 weeks
after PND21
6 hours/day
during gestation
until GD20and
from PND 5 until
weaning (~PND
21) for F2
6 hours/day
during gestation
until GD20and
from PND 5 until
weaning (~PND
21) for Fi
6 hours/day
during gestation
until GD20and
from PND 5 until
weaning (~PND
21) for F2
POD Type
(ppm)3
BMDLi-
98
LOAEL* -
100
BMDLs-
116
BMDL5=
123
NOAEL* -
250
Effect
D6cr6as6d brain
males at PND 21
Decreased brain
weight in adult Fi
males
Decreased pup
body weights
on PND 21
(p2 males)
Decreased pup
body weights
on PND 28
(Fi males)
Decreased pup
body weights
on PND 14
(Fz females)
HEC(ppm)4
100
110
120
130
260
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFc-1-
UFa-10-
UFH=10;
UFL=1;
Total UF=100
UFc-1-
UFA=10;
UFH=10;
UFL=10;
Total
UF=1000
UFc-1-
UFA=10;
UFH=10;
Dpi.-!1
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFc-1-
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research
2001)
(WIL Research
2001)
(WIL Research
2001)
(WIL Research,
2QQ1)
(WIL Research
2001)
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Target Organ/
System
Developmental
Effects
Developmental
Effects
Species, sex
(#animals/dose)
Rat
(male)
(n=15-22/group)
Rat
(female)
(n=15-22/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 500
100 to 500
Duration2
6 hours/day
during gestation
until GD20and
from PND5 until
weaning (~PND
21) for F2
6 hours/day
during gestation
until GD20and
from PND5 until
weaning (~PND
21) for F2
POD Type
(ppm)3
BMDLs-
288
BMDLs-
303
Effect
Decreased pup
body weights
on PND 14
(Fz males)
Decreased pup
body weights
on PND 21
(Fz females)
HEC(ppm)4
300
320
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research,
2001)
(WIL Research,
2001)
Nervous System
Nervous System
Nervous System
Rat
(male)
(n=5/group)
Rat
(male)
(n=25/group)
Rat
(male)
(n=8-9/group)
10 to 1000
4
100 to 750
200 to 800
8 hours/day, 7
days/week for 3
weeks
6 hours/day
during pre-mating,
throughout
mating, and until
GD 20 (> 16
weeks)
8 hours/day, 7
days/week for 12
weeks
BMDLiso-
18.2
NOAEL* =
100
BMDLisD=
213.8
Decreased time
hanging from a
suspended bar
(traction time)
Decreased brain
weight in Fo
males
Decreased hind
limb grip
strength
25
110
300
UFs=10;
UFA=10;
UFH=10;
UFL=1;
Total
UF=1000
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
(Honma et al..
2QQ3)
(WIL Research,
2001)
(WIL Research,
2QQ1)
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Target Organ/
System
Nervous System
Species, sex
(#animals/dose)
Rat
(female)
(n=25/group)
Range of Doses
or
Concentrations1
(ppm)
100 to 750
Duration2
6 hours/day
during pre-mating,
throughout
mating, and until
GD 20 (> 16
weeks)
POD Type
(ppm)3
BMDLiso-
509
Effect
Decreased brain
weight in Fo
females
HEC(ppm)4
530
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL Research,
2001)
Control concentrations are not included in the table.
2 Acute exposures defined as those occurring within a single day. Chronic exposures defined as 10% or more of a lifetime (U.S. EPA, 2011).
3POD type can be NOAEL, LOAEL, or BMDL For BMDLs, the subscript indicates the associated BMR. The BMRs are a percentage relative deviation (e.g., 10% relative
deviation BMDLio) or 1 standard deviation change (BMDLiso) from the mean for continuous data.
4HECs are adjusted from the study conditions by the equation HECEXRESP = POD x duration adjustment x DAF where the DAF is the ratio of blood:gas partition
coefficients (animahhuman). For 1-BP, the blood:air partition coefficient for rats is greater than that for humans, so a default ratio of 1 is applied (U.S. EPA, 1994).
The baseline used for the duration adjustment was an 8 hours/day exposure for occupational exposure scenarios and 24 hours/day exposure for consumer
exposure scenarios. For acute exposure the duration adjustment was (hours per day exposed 4- 8) and for chronic exposure (occupational scenarios) was (hours per
day exposed -f 8) x (days per week exposed -f 5) to reflect a 40-hour work week. All of the endpoints used the chronic exposure duration adjustment except for the
decreased live litter size (Fi) at PND 0 as described above in Section 3.4.1. HECs are rounded to two significant digits.
5UFs = subchronicto chronic UF (default value = 10); UFA = interspecies UF (default value of 10); UFH = intraspecies UF (default value = 10); UFL= LOAEL to NOAEL UF
(default value = 10) (U.S. EPA, 2002).
6The HEC for decreased live litter size was adjusted for acute and chronic occupational exposures as described in footnote 4.
BMD modeling did not adequately fit the variance in the data so the NOAEL or LOAEL is presented.
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Carcinogenic Dose-Response Assessment
No data were located on the carcinogenicity of 1-BP in humans. In animals, the carcinogenicity of
1-BP was evaluated in well-designed studies conducted in rodents (NTP, 2011). Male and female
rats and mice were exposed to 1-BP via inhalation 6 hours/day, 5 days/week for 2 years. Cancer
findings included significant increases in the incidences of: 1) skin tumors
(keratoacanthoma/squamous cell carcinomas) in male F344 rats, 2) rare large intestine adenomas
in female F344 rats, and 3) alveolar/bronchiolar adenomas and carcinomas (combined) in female
B6C3F1 mice.
Dose-Response Modeling
Dose-response modeling of the (NTP, 2011) cancer data was performed by EPA. A brief summary
of the methodology is presented here and more details are available in Appendix P-3. Benchmark
dose modeling was performed for all three statistically significantly increased tumor types from
the NTP study (i.e., skin tumors in male rats, intestinal tumors in female rats and lung tumors in
female mice). All dichotomous models in the BMD software (BMDS Version 2.6) were fit to the
incidence data for each of the three tumor types. The benchmark response level (BMR) used was
0.1% added risk (corresponding to a l-in-1,000 working lifetime added risk of cancer). Because
extrapolation to a 0.1% response level is sensitive to model selection, a model-averaging (MA)
technique (Wheeler and Bailer, 2007) was used. This technique uses statistics (bootstrapping
technique) to weigh, based on fit, the models providing acceptable fit to the experimental dataset
(as evidenced by a chi-square goodness-of-fit value > 0.10). Model-averaging software was
restricted to avoid supralinear models, which exhibit properties at the low dose that are not
considered biologically plausible. The resulting model-average benchmark concentrations (MA
BMCs) associated with 0.1% added risk and their 95% lower confidence limits (MA BMCLs) are
shown in Table 3-2 for each of the three cancer datasets.
Table 3-2 Model-Average BMC and BMCL Estimates of 1-BP Exposure Associated with a 0.1% Added Risk
of Tumors in Rodents
Species; Tumor Type
Male F344 rats; keratoacanthoma/squamous cell carcinoma (combined)
Female F344 rats; large intestine adenoma
Female B6C3F1 mice; alveolar/bronchiolar adenoma or carcinoma
(combined)
MA
BMC
(ppm)
3.73
13.5
0.85
MA
BMCL
(ppm)
2.25
4.85
0.64
Extrapolation to Humans
The BMC and BMCL values shown in Table 3-2 represent the concentrations estimated by the
model to generate the target response in rodents exposed 6 hours/day for 5 days/week. These
data were extrapolated to humans based on occupational exposure to 1-BP during a 40-hour
work week (8 hours/day, 5 days/week) using the following methodology:
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1. Conversion of MA BMC/BMCLs (ppm) to benchmark dose values (BMD/BMDL in mg/kg-
day) by adjusting for the experimental exposure duration 6 hours/day8;
2. Conversion of BMD/BMDLs in rodents to human equivalent BMD/BMDLs on the basis of
the mg/kg-day dose scaled by body weight to the 0.75 power9; and
3. Adjustment of the human equivalent BMD/BMDLs (mg/kg-day) to BMC/BMCLs (ppm) that
reflect exposure for an 8-hour work day10.
The human equivalent BMC and BMCL (BMCHEc and BMCLHEc) estimates based on a BMR of 0.1%
added risk are shown in Table 3-3.
Table 3-3 BMC and BMCL Estimates of 1-BP Exposures Associated with a 0.1% Added Risk of Tumors in
Humans Exposed 40 hours/week (8 hours/day, 5 days/week) (ppm)
Species; Tumor Type
Male F344 rats; keratoacanthoma/squamous cell carcinoma (combined)
Female F344 rats; large intestine adenoma
Female B6C3F1 mice; alveolar/bronchiolar adenoma or carcinoma
(combined) ^^
BMCHEC
1.75
6.17
0.39
BMCLHEc
1.05
2.22
0.30
Derivation of Inhalation Unit Risk
As shown in Table 3-3, the data for lung tumors (based on the combined incidence of
alveolar/bronchiolar adenoma or carcinoma) in female mice generated the lowest
BMChEc/BMCLHEc values; these values are considered protective for the other tumor types. The
BMCLHEc (0.30 ppm) represents the 95% lower confidence limit estimate of the occupational
exposure concentration expected to produce a l-in-1,000 lifetime added risk of lung cancer. This
value was selected as the POD for the inhalation unit risk (IUR) value because it reflects the
statistical variability of the data and is more health-protective than the central estimate (BMChEc).
Although data suggest a probable genotoxic mode of action (MOA), the exact MOA of 1-BP-
induced tumorigenesis is not known. In the absence of more definitive knowledge regarding the
MOA of 1-BP, the inhalation unit risk was calculated using the default linear approach, as follows:
IUR = BMR T- BMCL
= 0.001-^0.30 ppm
= 3 x 10"3 per ppm (7 x 10"7per u,g/m3)
8BMD/BMDL (mg/kg-day) = BMC/BMCL (ppm) x (6 hours/24 hours) x (5.031 mg/m3 per ppm) x default inhalation rate
(m3/day) x default body weight (kg); where the default inhalation rate and body weight values are 0.36 m3/day and
0.380 kg for male F344 rats, 0.24 m3/day and 0.229 kg for female F344 rats, and 0.06 m3/day and 0.0353 kg for
female B6C3F1 mice in chronic studies (U.S. EPA, 1988).
9Human equivalent BMD/BMDL (mg/kg-day) = BMC/BMCL (mg/kg-day) x (default body weight in rats or mice
[kg]/default body weight in humans [kg]) °25; where default body weight values are 0.380 kg for male F344 rats,
0.229 kg for female F344 rats, 0.0353 kg for female B6C3F1 mice, and 70 kg for humans (U.S. EPA, 1988; ICRP, 1975).
10BMC/BMCL (ppm) = (1 ppm per 5.031 mg/m3) x (default body weight in humans [kg]/default minute volume for
human occupational exposure based on an 8-hour shift [m3/day]); where default body weight and minute volume
values are 70 kg and 9.6 m3/day (U.S. EPA, 1994).
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The IUR was used in the EPA/OPPT risk assessment to estimate added cancer risks for the
inhalation occupational exposures scenarios. There is high confidence in the IUR because it was
based on good quality animal data. Moreover, current weight of evidence suggests that 1-BP
operates through at least four possible mechanisms in different target organs - genotoxicity,
oxidative stress, immunosuppression, and cell proliferation to complete a multi-stage process of
carcinogenesis.
EPA/OPPT did not use the IUR to calculate the theoretical cancer risk associated with a single
(acute) exposure to spray adhesive, degreasing, and dry cleaning activities containing 1-BP.
Published methodology for extrapolating cancer risks from chronic to short-term exposures to
mutagenic carcinogens caveat that extrapolation of lifetime theoretical added cancer risks to
single exposures has great uncertainties (NRC, 2001).
As NRC (2001) explains, "There are no adopted state or federal regulatory methodologies for
deriving short-term exposure standards for workplace or ambient air based on carcinogenic risk,
because nearly all carcinogenicity studies in animals and retrospective epidemiologic studies
have entailed high-dose, long-term exposures. As a result, there is uncertainty regarding the
extrapolation from continuous lifetime studies in animals to the case ofonce-in-a-lifetime
human exposures. This is particularly problematical, because the specific biologic mechanisms at
the molecular, cellular, and tissue levels leading to cancer are often exceedingly diverse,
complex, or not known. It is also possible that the mechanisms of injury of brief, high-dose
exposures will often differ from those following long-term exposures. To date, U.S. federal
regulatory agencies have not established regulatory standards based on, or applicable to, less
than lifetime exposures to carcinogenic substances."
Thus, the EPA/OPPT work plan risk assessment for 1-BP does not estimate added cancer risks
for acute exposures because the relationship between a single short-term exposure to 1-BP and
the induction of cancer in humans has not been established in the current scientific literature.
3.5 Summary of Health Hazard
Table 3-1 summarizes the hazard studies, health endpoints (PODs) by target organ/system, HEC
and UFs that are relevant for the risk evaluation of acute and chronic exposure scenarios. Table
3-4 lists the lowest HECs by study type and duration category (acute vs. chronic). Appendix 0
contains a comprehensive summary table of adverse effects.
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Table 3-4 Lowest HECs for Non-Cancer Effects for 1-BP
Exposure
Duration
for Risk
Analysis
HRONIC
UPATIONAL
0 o
O
0
Target Organ/
System
Liver
Kidney
Reproductive
System
Developmental
Effects
Nervous System
Species
Rat
(male)
(n=25/
group)
Rat
(female)
(n=25/
group)
Rat
(male)
(n=8-9)/
group
Rat
(n=25/
group)
Rat
(male)
(n=5/
group)
Route of
Exposure
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Range of
Doses or
Concentra-
tions1
(ppm)
100 to 750
100 to 750
200 to 800
100 to 500
10 to 1000
Duration2
6 hours/day during
pre-mating (> 70
days), throughout
mating, and until
sacrifice
6 hours/day during
pre-mating (> 70
days), throughout
mating, and until GD
20; from PND5 until
weaning of offspring
(-PND21)
8 hours/day, 7
days/week for 12
weeks
6 hours/day during
pre-mating (> 70
days), throughout
mating, and until GD
20 for the Fi litters
8 hours/day, 7
days/week for 3
weeks
POD Type
(ppm)3
BMDLio =
143.5
BMDLio =
135.0
BMDLisD=
38
BMDL5=41
BMDLiso =
18.2
Effect
Increased
incidence of
vacuolization
of
centrilobular
hepatocytes
(Fo)
Increased
incidence of
pelvic
mineralization
(Fo)
Decreased
absolute/
relative
seminal
vesicle weight
Decreased live
litter size (Fi)
atPNDO
Decreased
time hanging
from a
suspended bar
(traction time)
HEC
(ppm)4
150
140
53
43
25
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=10;
UFA=10;
UFH=10;
UFL=1;
Total
(Jf =1,000
Reference
(WIL
Research,
2001)
(WIL
Research,
2001)
(WIL
Research,
2001)
(WIL
Research,
2001)
(Honma et
al., 2003)
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Exposure
Duration
for Risk
Analysis
_i
Z
ill O
1- r
ACU
OCCUPAl
III el
h- £
3 3
gz
^ 0
o
Target Organ/
System
Developmental
Effects
Developmental
Effects
Species
Rat
(male)
(n=24-
25/
group)
Rat
(male)
(n=24-
25/
group)
Route of
Exposure
Inhalation
Inhalation
Range of
Doses or
Concentra-
tions1
(ppm)
100 to 500
100 to 500
Duration2
6 hours/day during
pre-mating (> 70
days), throughout
mating, and until
sacrifice in males; or
until GD 20 and from
PND 5 until weaning
of offspring (~PND
21) in females
6 hours/day during
pre-mating (> 70
days), throughout
mating, and until
sacrifice in males; or
until GD 20 and from
PND 5 until weaning
of offspring (~PND
21) in females
POD Type
(ppm)3
BMDLs
= 41
BMDLs
= 41
Effect
Decreased live
litter size (Fi)
Decreased live
litter size (Fi)
HEC
(ppm)4
31
10
Uncertainty
Factors (UFs)
for
Benchmark
MOE5
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
UFs=l;
UFA=10;
UFH=10;
UFL=1;
Total UF=100
Reference
(WIL
Research,
2001)
(WIL
Research,
2001)
Control concentrations are not included in the table.
2 Acute exposures defined as those occurring within a single day. Chronic exposures defined as 10% or more of a lifetime (U.S. EPA, 2011).
3POD type can be NOAEL, LOAEL, or BMDL For BMDLs, the subscript indicates the associated BMR. The BMRs are a percentage relative deviation (e.g. 10% relative
deviation BMDLio) or 1 standard deviation change (BMDLiso) from the mean for continuous data.
4 HECs are adjusted from the study conditions by the equation HECEXRESP = POD x duration adjustment x DAF. The DAF is the ratio of blood:gas partition coefficients
(animahhuman). For 1-BP, the blood:air partition coefficient for rats is greater than that for humans, so a default ratio of 1 is applied (U.S. EPA, 1994). For acute exposure
the duration adjustment was (hours per day exposed -f 8 or 24) and for chronic exposure the duration adjustment was (hours per day exposed -f 8) x (days per week
exposed 4- 5) to reflect a 40-hour work week. The effects all used the chronic exposure duration adjustment except for the decreased live litter size (Fi) at PND 0 as
described above in Section 3.4.1. The differences in the HECs between the occupational and consumer exposures are due to the baseline used for the duration adjustment
of acute occupational and consumer exposures; occupational exposures was 8 hours/day, and consumer exposures was 24 hours/day. HECs are rounded to two significant
digits.
5UFs = subchronicto chronic UF (default value = 10); UFA = interspecies UF (default value of 10); UFH = intraspecies UF (default value = 10); UFL= LOAEL to NOAEL UF
(default value = 10) (U.S. EPA, 2002).
BMD modeling did not adequately fit the variance in the data so the LOAEL is presented
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4 HUMAN HEALTH RISK CHARACTERIZATION
l-BP exposure is associated with a variety of cancer and non-cancer effects deemed relevant to
humans for risk estimations for the chronic scenarios and populations addressed in this risk
assessment. Based on a weight-of-evidence analysis of the available toxicity studies from rats and
humans, these effects include liver toxicity, kidney toxicity, reproductive toxicity, developmental
toxicity and neurotoxicity. The rationale for using the range of toxic effects for chronic exposures
is based on the fact that relatively low dose, short term/sub-chronic exposures can result in long-
term adverse consequences. The adverse developmentally toxic effects are also deemed
important for risk estimation for the acute exposure scenarios and populations addressed in this
risk assessment. The rationale for using l-BP associated developmental effects for evaluating risks
associated with acute exposures is based on the understanding that a relatively short critical
window of vulnerability exists in humans and in rodents and short half-life of the chemical and
reactive nature of the metabolites of l-BP with cellular components (e.g., DNA and proteins) in
multiple organ systems.
l-BP is carcinogenic in animals. The cancer risk assessment uses the EPA/OPPT derived IUR based
on lung tumors in female mice. The weight-of-evidence analysis for the cancer endpoint was
sufficient to support a probable mutagenic mode of action for l-BP carcinogenesis.
4.1 RISK ESTIMATION APPROACH
Table 4-1, Table 4-2, and Table 4-3 show the use scenarios, populations of interest and
toxicological endpoints used for acute and chronic exposures, respectively.
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Table 4-1 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Occpational
Risks Following Acute Exposures to 1-BP Used In Spray Adhesives, Dry Cleaning, and Degreasing
Populations And
Toxicological Approach
Occupational Use Scenarios of 1-BP at Commercial Facilities Including
Spray Adhesives, Dry Cleaning, and Degreasing
Population of Interest
and Exposure Scenario:
Users:
Adult pregnant1 worker (>16 years old) exposed to 1-BP for a single 8-hr exposure2'3.
Occupational Non-user:
Adult pregnant women 1 (>16 years old) exposed to 1-BP indirectly by being in the same
work area of building.
Health Effects of
Concern, Concentration
and Time Duration
Non-Cancer Health Effects: Decreased live litter size (Fi) (WIL Research, 2001)
1. Non-Cancer Hazard values or Point of Departures (PODs): 8-hr HEC: 31 ppm
Cancer Health Effects: Cancer risks following acute exposures were not estimated.
Relationship is not known between a single short-term exposure to 1-BP and the
induction of cancer in humans.
Uncertainty Factors (UF)
used in Non-Cancer
Margin of Exposure
(MOE) calculations
(UFS=1) x (UFA=10) x (UFH=10) x (UFL=1) = 100
Total UF=Benchmark MOE=100
Notes:
^he risk assessment for acute exposures focused on the most sensitive life stage in humans, which is women of childbearing age
and fetus (i.e., pregnant worker) due to concerns for developmental effects.
2 Exposure estimate was adjusted to an 8-hr exposure estimate in order to combine it with the 8-hr HECs.
3 It is assumed no substantial buildup of 1-BP in the body between exposure events due to 1-BP's short biological half-life (< 2 hours).
The risk assessment for acute exposures focused on developmental toxicity effects as the most sensitive health effect when
compared to other potential acute effects (i.e., neurotoxicity).
5 UFs=subchronic to chronic UF; UFA=interspecies UF; UFH=intraspecies UF; UFL=LOAEL to NOAEL UF
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Table 4-2 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Consumer
Risks Following Acute Exposures to 1-BP Use In Aerosol Spray Adhesives, Aerosol Spot Removers, and
Aerosol Cleaners and Degreasers
Population and Toxicological
Approach
CONSUMER USE SCENARIOS
Aerosol Spray
Adhesives Use
Aerosol Spot
Removers Use
Aerosol Spray Cleaning and Degreasing
Engine
Degreasers
Brake Cleaners
Use
Electronics
Cleaners Use
Population of Interest
Women of child bearing age1 consumers (>16 yrs old)
Exposure Scenario2:
Users, High End
A single 0.5-hr
exposure3.
A single 0.5-hr
exposure3.
A single 1.0-hr
exposure3.
A single 0.8-hr
exposure3.
A single 0.3-hr
exposure3.
Exposure Scenario2:
Users, Central Tendency
A single 0.07-hr
exposure3.
A single 0.08-hr
exposure3.
A single 0.25-hr
exposure3.
A single 0.25-hr
exposure3.
A single 0.03-hr
exposure3.
Population of Interest and
Exposure Scenario:
Non-User
Women of child bearing age non-users4 and individuals of multiple age groups that
are exposed to indirect 1-BP exposures by being in the rest of the house.
Health Effects of Concern,
Concentration and Time
Duration
Non-Cancer Health Effects: Decreased live litter size (Fi) (WIL Research,
2001)5
1. Non-Cancer Hazard values or Point of Departures (PODs): 24-hr HEC: 10 ppm
Cancer Health Effects: Cancer risks following acute exposures were not
estimated. Relationship is not known between a single short-term exposure to
1-BP and the induction of cancer in humans.
Uncertainty Factors (UF) used
in Non-Cancer Margin of
Exposure (MOE) calculations
(UFS=1) x (UFA= 10) x (UFH=10) x (UFL=1) = 100
Total UF=Benchmark MOE=100
Notes:
1 The risk assessment for acute exposures focused on the most sensitive life stage in humans, which is women of childbearing age
and fetus (i.e., pregnant user) due to concerns for developmental effects.
2 E-FAST/CEM provided the 24-hr acute exposure estimate and the HECs were adjusted to 24-hrs.
3 It is assumed no substantial buildup of 1-BP in the body between exposure events due to 1-BP's short biological half-life (<2 hours).
4 EPA/OPPT believes that the users of these products are generally adults, but teenagers and even children may be users or be in the
same room with the user.
5 The risk assessment for acute exposures focused on developmental toxicity effects as the most sensitive health effect when
compared to other potential acute effects (i.e., neurotoxicity).
6 UFs=subchronic to chronic UF; UFA=interspecies UF; UFH=intraspecies UF; UFL=LOAEL to NOAEL UF
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Table 4-3 Use Scenarios, Populations of Interest and Toxicological Endpoints for Assessing Occupational
Risks Following Chronic Exposures to 1-BP Used In Spray Adhesives, Dry Cleaning, and Degreasing
Populations and Toxicological
Approach
Occupational Use Scenarios of 1-BP at Commercial Facilities
Including Spray Adhesives, Dry Cleaning, and Degreasing
Population of Interest and
Exposure Scenario:
Users
Adult worker (>16 years old)1>2 exposed to 1-BP for the entire 8-hr workday for
260 days per year for 40 working years.
Population of Interest and
Exposure Scenario:
Occupational Non-users
Adult worker (>16 years old)1>2 repeatedly exposed to indirect 1-BP exposures by
being in the same work area of building.
Health Effects of Concern,
Concentration and Time
Duration
Non-Cancer
1. Non-cancer health effects: A range of possible chronic non-cancer
adverse effects in liver, kidney, nervous system, reproductive system
and developmental effects
2. Non-Cancer Hazard values or Point of Departures (PODs): The lowest
POD (i.e., 8-hr HEC expressed in ppm) within each health endpoint
domain. See Table 3-4.
Cancer
1. Cancer health effects: Possible cancer effects in the lung from chronic
exposure (NTP, 2011).
2. Cancer Inhalation Unit Risk (IUR): 3 x 10'3 per ppm
Uncertainty Factors (UF) Used
in Non-Cancer Margin of
Exposure (MOE) calculations
Study- and endpoint-specific UFs. See Table 3-4.
Notes:
1 Adult workers (>16 years old) include both healthy female and male workers.
2The risk assessment for chronic exposures for developmental effects focused on the most sensitive life stage in
humans, which are women of child-bearing age and fetus (i.e., pregnant worker). For other health effects (e.g., liver,
kidney, etc.), healthy female or male workers were assumed to be the population of interest.
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
Where:
MOE
acute or chronic ~
Non — cancer Hazard value (POD)
Human Exposure
MOE = Margin of exposure (unitless)
Hazard value (POD) = HEC (ppm)
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Human Exposure = Exposure estimate (in ppm) from occupational or consumer
exposure assessment. ADCs were used for non-cancer chronic
risks and acute concentrations were used for acute risks (see
sections 2.1.2 through 2.1.7).
EPA/OPPT used margin of exposures (MOEs)11 to estimate acute or chronic risks for non-cancer
based on the following:
1. the lowest HECs within each health effects domain reported in the literature;
2. the endpoint/study-specific UFs applied to the HECs per the EPA Guidance (U.S. EPA, 2002);
and
3. the exposure estimates calculated for 1-BP uses examined in this risk assessment (see
Section 2 Exposure Assessment).
MOEs allow for the presentation of a range of risk estimates. The occupational exposure
scenarios considered both acute and chronic exposures. All consumer uses considered only acute
exposure scenarios. 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 (toxicity to the liver, kidney,
nervous system, developmental effects, and the reproductive system) were evaluated for
repeated (chronic) exposures to 1-BP.
For occupational exposure calculations, the 8 hrTWA was used to calculate MOEs for risk
estimates for acute and chronic exposures.
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 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, the more unlikely it is
that a non-cancer adverse effect would occur.
Risk estimates were calculated for all of the studies per health effects domain that EPA/OPPT
considered suitable for the risk evaluation of acute and chronic exposure scenarios in the work plan
risk assessment for 1-BP.
Added cancer risks for repeated exposures to 1-BP were estimated using Equation 4-2.
Estimates of added 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 added individual lifetime cancer risk).
11 Margin of Exposure (MOE) = (Non-cancer hazard value, POD) 4- (Human Exposure). Equation 4-1. The benchmark
MOE is used to interpret the MOEs and consists of the total UF shown in Table 3-4. See Section 4.1 for an explanation
of the benchmark MOE.
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Equation 4-2 Equation to Calculate Added Cancer Risks
Risk = Human Exposure x IUR
Where:
Risk = Added cancer risk (unitless)
Human exposure = Exposure estimate (LADC in ppm) from occupational exposure assessment
IUR = Inhalation unit risk (3 x 10~3 per ppm)
4.2 RISK ESTIMATION FOR ACUTE, NON-CANCER INHALATION
EXPOSURES
Non-cancer risk estimates for acute inhalation exposures to 1-BP were derived for both
occupational scenarios and consumer uses. Cancer risk estimates for acute inhalation exposures to
1-BP were not derived for occupational or consumer uses because the published methodology for
extrapolating cancer risks from chronic to short-term exposures to mutagenic carcinogens caveat
that extrapolation of lifetime theoretical added cancer risks to single exposures has great
uncertainty (NRC, 2001).
The risk assessment for acute inhalation exposures used developmental toxicity data to evaluate
the risks associated following acute exposures with the TSCA use scenarios identified for 1-BP
under the scope of this assessment. As indicated previously, EPA's policy supports use of
developmental studies to evaluate the risks of acute exposures. This policy is based on the
presumption that a single exposure to a chemical during a critical window of development may
produce adverse developmental effects (U.S. EPA, 1991). Thus, EPA/OPPT based its acute risk
assessment on developmental toxicity (i.e., decreased live litter size), the lowest HEC identified
for an acute exposure duration (WIL Research, 2001), which is representative of a sensitive
subpopulation (i.e., adult women of child-bearing age and their offspring).
The risk assessment for acute exposures used the hazard value from the (WIL Research, 2001)
two-generation reproductive toxicity study to evaluate risks for each occupational and
consumer exposure scenario.
EPA/OPPT chose to focus on the high-end acute exposure estimates to calculate non-cancer risks
(MOEs) for the occupational (95th percentile) and consumer (90th percentile) populations. Non-
cancer acute MOE calculations for the 50th percentile (central tendency) exposure estimates are
provided in the supplemental Excel spreadsheet12. Non-cancer risk estimates for acute
occupational exposure scenarios are presented in Table 4-4 through Table 4-14 below. Risk
estimates were calculated for all of the occupational exposure scenarios described in Section 2.1.
Non-cancer risk estimates for acute consumer exposure scenarios are presented in Table 4-15.
Risks were identified for most of the acute occupational exposure scenarios (user and
occupational non-user alike) even with the use of engineering controls (post-EC), with few
exceptions. These exceptions include post-EC MOE values for the vapor degreasing (monitoring
12 See attached document titled "Supplemental File 1-BP Non-Cancer MOE Risk Estimates.xlsx".
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and modeling data for the occupational non-user, Table 4-9 and Table 4-10) and cold cleaning
(modeling data for both the worker and occupational non-user, Table 4-12) uses. Similar findings
were noted for the 50th percentile exposure estimates in most cases (see supplemental Excel
spreadsheet13). For the 90th percentile exposure estimates, risks were identified for all of the
acute inhalation consumer exposure scenarios (Table 4-15). For the 50th percentile exposure
estimates, risks were identified for all of the consumer exposure scenarios (user and non-user),
except for the aerosol spray adhesive non-user where the MOE was at the benchmark MOE of
100 (see supplemental Excel spreadsheet13). In all cases where risk was identified, the MOE values
were approximately 1 to 2 orders of magnitude below the benchmark MOE of 100.
Table 4-4 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Spray Adhesives Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER (SPRAYER) MOE1
PreEC
0.12
Post EC
0.74
WORKER (NON-SPRAYER)
MOE1
PreEC
0.15
Post EC
1.07
OCCUPATIONAL NON-USER
MOE1
PreEC
0.24
Post EC
5.66
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-5 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Dry Cleaning Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
0.62
OCCUPATIONAL NON-USER MOE1
PreEC
1.50
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Only monitoring data characterized as"Pre-EC" by this assessment was available for dry cleaning. See Section 2.1.3.
Table 4-6 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Dry Cleaning Based on Modeling
Health Effect, Endpoint and
Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKERS: MACHINE
UNLOADING AND
FINISHING (NEAR-
' FIELDJMOE1
PreEC
0.5
Post EC
5.1
WORKERS: SPOT CLEANING
(NEAR-FIELD) MOE1
PreEC
4.5
Post EC
45
OCCUPATIONAL NON-
USERS (FAR-FIELD)
PreEC
6.4
Post EC
64
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEsfHEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
13See attached document titled "Supplemental File 1-BP Non-Cancer MOE Risk Estimates.xlsx".
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Table 4-7 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Spot Cleaning at Dry Cleaners Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
17.5
Benchmark MOE
(= Total UF)
100
Notes: 'MOEsfHEC in ppm/exposure estimate in ppm) lowerthanthe Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-8 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Spot Cleaning at Dry Cleaners Based on Modeling
Health Effect, Endpoint and
Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
3.28
Post EC with 90%
Efficiency
32.8
OCCUPATIONAL NON-USER (FAR-FIELD) MOE
PreEC
8.2
Post EC with 90%
Efficiency
82
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEsfHEC in ppm/exposu re estimate in ppm) lowerthanthe Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-9 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of 1-BP
in Vapor Degreasing Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
0.65
Post EC
3.69
OCCUPATIONAL NON-USERS MOE1
PreEC
6.33
Post EC
1550
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposu re estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-10 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of
1-BP in Vapor Degreasing Based on Modeling
Health Effect, Endpoint and
Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
1.21
Post EC with
90% Efficiency
12.1
Post EC with
98% Efficiency
61
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE1
PreEC
3.30
Post EC with
90% Efficiency
33.0
Post EC with
98% Efficiency
165
Benchmark MOE
(= Total UF)
100
Notes: 'MOEsfHEC in ppm/exposu re estimate in ppm) lowerthanthe Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-11 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of
1-BP in Cold Cleaning Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute HEC
(ppm)
31
Acute Exposure 95th Percentile
Estimates
WORKER MOE1
PreEC
0.66
Acute 'What If Estimates
OCCUPATIONAL NON-USER MOE1
PreEC
11.92
Benchmark MOE
(= Total UF)
100
Notes: 'MOEsfHEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-12 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of
1-BP in Cold Cleaning Based on Modeling
Health Effect, Endpoint and
Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute
HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
4.0
Post EC with
90% Efficiency
40
Post EC with
98% Efficiency
198
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE1
PreEC
10.8
Post EC with
90% Efficiency
108
Post EC with
98% Efficiency
538
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposu re estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-13 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of
1-BP in Aerosol Degreasing Based on Monitoring Data
Health Effect, Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute HEC
(ppm)
31
Acute Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
0.98
Post EC
5.64
Benchmark MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposu re estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-14 Non-Cancer Risk Estimates for Acute Inhalation Exposures Following Occupational Use of
1-BP in Aerosol Degreasing Based on Modeling
Health Effect, Endpoint and
Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
Acute HEC
(ppm)
31
^L Acute Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
4.55
Post EC with 90%
Efficiency
45.5
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE1
PreEC
9.1
Post EC with 90%
Efficiency
91
Benchmark
MOE
(= Total UF)
100
Notes: 'MOEs (HEC in ppm/exposu re estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-15 Non-Cancer Risk Estimates for Acute Inhalation Exposure Following Consumer Uses of 1-BP
Health Effect Domain,
Endpoint and Study
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(Wl 101)
Acute HEC
(ppm)*
10
AEROSOL SPRAY ADHESIVE
MOE1
User2
1.7
Non-User3
5
AEROSOL SPOT REMOVER
MOE1
User2
0.435
Non-User3
1.7
AEROSOL SPRAY CLEANERS AND DEGREASERS
ENGINE DEGREASER MOE1
User2
0.185
Non-User3
0.5
BRAKE CLEANER MOE1
User2
0.454
Non-User3
1.25
ELECTRONICS CLEANER MOE1
User2
1.43
Non-User3
3.33
Benchmark MOE
(- Total UF)
100
Notes:
* The acute consumer HECs were adjusted for 24 hour exposure (see Table 3-1)
'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are denoted in bold.
2MOEs for the use categories can be extended to different age groups; however, EPA/OPPT believed the users of these products to be adults.
3AII age categories (< 1 yrs; 1-2 yrs; 3-5 yrs; 6-10 yrs; 11-15 yrs; 16-20 yrs; and > 21 yrs)
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4.3 RISK ESTIMATION FOR CHRONIC, NON-CANCER AND
CANCER INHALATION EXPOSURES
Non-cancer and cancer risk estimates for chronic exposures were only derived for occupational
scenarios since consumer exposures were not considered chronic in nature.
4.3.1 Non-Cancer Risks for Chronic Occupational Exposure Scenarios
EPA/OPPT estimated the non-cancer risks associated with chronic exposures following 1-BP
use in spray adhesive, dry cleaning, and degreasing applications in the workplace. Since 1-BP
exposure may be associated with a variety of non-cancer health effects, this assessment
estimated risks for liver toxicity, kidney toxicity, reproductive toxicity, developmental toxicity
and neurotoxicity following chronic inhalation exposures. EPA/OPPT used the HEC specific to
each health effect domain for calculating risk estimates (MOEs). Non-cancer risk estimates for
chronic exposures for each occupational use scenario and the lowest HECs for each health
effect domain (shown in Table 3-4) are presented below (Table 4-16 through Table 4-26). Risk
estimates for a range of health effects were calculated (See excel spreadsheet provided in the
supplemental materials).
EPA/OPPT focused on the 95th percentile (high-end) chronic exposure estimates to calculate
non-cancer risks (MOEs) for occupational populations at risk. Non-cancer MOE calculations
for the 50th percentile (central tendency) exposure estimates are provided in a supplemental
Excel spreadsheet (See footnote 12). Monitoring data are presented for all occupational
exposure scenarios (i.e., spray adhesives, dry cleaning, spot cleaning, vapor degreasing, cold
cleaning and aerosol degreasing); modeling data are presented for all occupational exposure
scenarios except spray adhesives.
Spray Adhesives
Based on monitoring data for the 50th (central tendency) and 95th (high-end) percentile
exposure estimates, workers and occupational non-users (i.e., sprayers and non-sprayers) in
spray adhesive facilities showed risks for all of the health effects examined regardless of the
type of engineering controls used (Table 4-16).
Dry Cleaning and Spot Cleaning
Monitoring data for the 50th and 95th percentile exposure estimates from dry cleaning facilities
reporting 1-BP use in machines (Table 4-17), and workers using 1-BP formulations when spot
cleaning (Table 4-19) showed risks (to workers and occupational non-users) for all of the health
effects examined. The MOE for spot cleaning for liver and kidney toxicity in workers based on
monitoring data was very close to the benchmark MOE (84.75 and 79.10, respectively, vs. 100;
Table 4-19). Exposure data was only available for pre-EC scenarios.
Modeling data for dry cleaning facilities using 1-BP in machines (Table 4-18) and spot cleaning
(Table 4-20), showed risks for all health effects examined in workers and occupational non-
users (pre-EC). Risks for neurological and developmental effects in workers remained even after
engineering controls were applied (post-EC). For occupational non-users (post-EC) for dry
cleaning and spot cleaning, the MOE for developmental toxicity was very close to or slightly
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over the benchmark MOE (89 and 113, respectively, vs. 100). The 50th percentile exposure
estimates for dry cleaning available for pre-EC and post-EC scenarios showed risks for
neurological and developmental effects in workers, but only risks for neurological effects were
identified for occupational non-users. The 50th percentile exposure estimate for spot cleaning
(pre-EC) showed risks for neurological and developmental effects (for workers and occupational
non-users).
Vapor Degreasing
Monitoring data for workers using 1-BP for vapor degreasing showed risks for all health effects
examined regardless of the type of engineering controls applied (Table 4-21). Likewise,
occupational non-users in these facilities also showed risks for all five health effects in the
absence of engineering controls (pre-EC), but did not show risks when engineering controls
were applied. For the 50th percentile exposure estimates, risks were shown for neurological and
developmental effects regardless of the availability of engineering controls for the worker, but
not for the occupational non-user when engineering controls were applied.
When using modeling data for workers and occupational non-users using 1-BP for vapor
degreasing, risks were shown for all five health effects in the pre-EC scenarios (Table 4-22).
Likewise, risks were shown for workers and occupational non-users for neurological effects
regardless of the availability of engineering controls. When engineering controls were applied,
the MOE for developmental toxicity for workers was very close to the benchmark MOE (84 vs.
100). No risks were shown for developmental effects in occupational non-users when
engineering controls were applied. For the 50th percentile exposure estimates, workers showed
risks for all health effects (pre-EC); for the occupational non-user (pre-EC), risks for adverse
neurological and developmental effects were shown.
Cold Cleaning
Monitoring data for 1-BP in cold cleaning activities showed risks for each of the five health
effects examined in workers and occupational non-users (Table 4-23). Data was available only
for pre-EC scenarios. The 50th percentile exposure estimates also showed risks for all five health
effects.
When using modeling data, workers and occupational non-users showed risks for adverse
neurological effects regardless of the type of engineering controls applied (Table 4-24). No risks
were shown for developmental effects in either workers or occupational non-users when
engineering controls were applied. Neither workers nor occupational non-users showed risks
for the remaining health effects when engineering controls were applied. The 50th percentile
exposure estimates showed risks for adverse neurological and developmental effects in
workers and occupational non-users before engineering controls were applied (pre-EC).
Occupational non-users without engineering controls (pre-EC) showed risks for developmental
effects.
Aerosol Degreasing
Monitoring data for 1-BP use in aerosol degreasing activities showed risks for each of the five
adverse health effects in workers regardless of the type of engineering controls applied (Table
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4-25). Data was not available for occupational non-users. The (pre-EC) 50th percentile exposure
estimates for workers also showed risks for each of the five adverse health effects examined.
Modeling data for 1-BP use in aerosol degreasing activities showed risks for workers and
occupational non-users for each of the five health effects examined pre-EC (Table 4-26). Risk for
adverse neurological effects in workers and occupational non-users were shown regardless of
the availability of engineering controls. Risks were shown for developmental effects in workers
even after engineering controls were applied. No risks were shown for developmental effects in
occupational non-users when engineering controls were applied. For the (pre-EC) 50th
percentile exposure estimates, risks were shown for adverse neurological and developmental
effects in workers and occupational non-users.
Conclusions
Overall, risks were observed across all of the uses in workers and occupational non-users for
both monitoring and modeling data in most cases. High-end exposures (95th percentile) without
engineering controls (pre-EC) using monitoring and modeling data showed risks for workers and
occupational non-users for all five health effects in all the uses evaluated. Both monitoring data
and modeling exposure estimates showed risks for adverse effects on the nervous system and
development at the high-end (95th percentile) exposures for occupational non-users regardless
of the availability of engineering controls for most uses. Risks were reduced when engineering
controls were applied (post-EC) in only one use for adverse effects on the nervous system;
vapor degreasing (monitoring data for occupational non-users). Risks were reduced when
engineering controls were applied (post-EC) in only a few uses for adverse effects on
development. These included spot cleaning at dry cleaning (modeling data for occupational
non-user); vapor degreasing (monitoring data for occupational non-users; modeling data for
occupational non-users), cold cleaning (modeling data for workers and occupational non-users),
and aerosol degreasing (modeling data for occupational non-users). Furthermore, there are
risks for workers and occupational non-users for the central tendency exposures (50th
percentile) before engineering controls are applied (pre-EC) in all of the uses evaluated for
adverse effects on the nervous system and development.
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Table 4-16 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Spray Adhesives Based on Monitoring Data
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara et al., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER (SPRAYER) MOE1
PreEC
0.59
0.55
0.21
0.17
0.10
Post EC
3.58
3.34
1.26
1.03
0.60
WORKER (NON-SPRAYER)
MOE1
PreEC
0.71
0.66
0.25
0.20
0.12
Post EC
5.20
4.85
1.84
1.49
0.87
OCCUPATIONAL NON-USER
MOE1
PreEC
1.17
1.09
0.41
0.33
0.19
Post EC
27.37
25.55
9.67
7.85
4.56
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-17 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Dry Cleaning Machines Based on Monitoring Data
Health Effect, Endpoint and Study
LIVER
Increased hepatocellular vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara et al., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
2.99
2.79
1.06
0.86
0.50
OCCUPATIONAL NON-USER MOE1
PreEC
7.27
6.78
2.57
2.08
1.21
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-18 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Dry Cleaning Machines Based on Modeling
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara et al., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Acute
HEC
(ppm)
150
140
53
43
25
Acute Exposure 95th Percentile Estimates
WORKERS: MACHINE
UNLOADING AND
FINISHING (NEAR-
FIELDJMOE1
PreEC
2.5
2.3
0.9
0.7
0.4
Post EC
25
23
9
7
4
WORKERS: SPOT CLEANING
(NEAR-FIELD) MOE1
PreEC
21.7
20.2
7.7
6.2
3.6
Post EC
217
202
77
62
36
OCCUPATIONAL NON-
USERS (FAR-FIELD)
PreEC
31.0
28.9
11.0
8.9
5.2
Post EC
310
289
110
89
52
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-19 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Spot Cleaning at Dry Cleaners Based on Monitoring Data
Health Effect, Endpoint and Study
LIVER
Increased hepatocellular vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara etal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER MOE1
k Pre EC
84.75
79.10
29.94
24.29
14.12
Benchmark MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-20 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Spot Cleaning at Dry Cleaners Based on Modeling
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichiharaetal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
16
15
6
5
3
Post EC with 90%
Efficiency
159
148
56
46
26
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE
PreEC
40
37
14
11
7
Post EC with 90%
Efficiency
396
369
140
113
66
Benchmark
MOE
(- Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-21 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Vapor Degreasing Based on Monitoring Data
Health Effect, Endpoint and Study
LIVER
Increased hepatocellular vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichiharaetal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
3.1
2.9
1.1
0.9
0.5
Post EC
17.9
16.7
6.3
5.1
3.0
OCCUPATIONAL NON-USERS MOE1
PreEC
30.6
28.6
10.8
8.8
5.1
Post EC
7500
7000
2650
2150
1250
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-22 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Vapor Degreasing Based on Modeling
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichiharaetal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaet al., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
5.9
5.5
2.1
1.70
1.0
Post EC with
98% Efficiency
294
275
104
84
49
Post EC with
90% Efficiency
59
55
21
17
10
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE1
PreEC
16
15
6
5.0
3.0
Post EC with
98% Efficiency
798
745
282
229
133
Post EC with
90% Efficiency
160
149
57
46
27
Benchmark
MOE
(- Total UF)
100
100
100
100
1,000
"Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-23 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Cold Cleaning Based on Monitoring Data
Health Effect, Endpoint and Study
LIVER
Increased hepatocellular vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara et al., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honma et al., 2003)
Chronic
HEC (ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER MOE1
PreEC
3.20
2.99
1.13
0.92
0.53
OCCUPATIONAL NON-USER MOE1
PreEC
57.69
53.85
20.38
16.54
9.62
Benchmark MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-24 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Cold Cleaning Based on Modeling
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichiharaetal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honmaetal., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th Percentile Estimates
WORKER (NEAR-FIELD) MOE1
PreEC
19
18
7
5.0
3.0
Post EC with
98% Efficiency
962
897
340
276
160
Post EC with
90% Efficiency
192
179
68
55
32
OCCUPATIONAL NON-USER (FAR-FIELD)
MOE1
PreEC
52
49
18
15.0
9
Post EC with
98% Efficiency
2604
2431
920
747
434
Post EC with
90% Efficiency
521
487
184
149
87
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
Table 4-25 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Aerosol Degreasing Based on Monitoring Data
Health Effect, Endpoint and Study
LIVER
Increased hepatocellular vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichihara et al., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honma et al., 2003)
Chronic
HEC (ppm)
150
140
53
43
25
Chronic Exposure 95th percentile Estimate
WORKER MOE1
PreEC
4.75
4.44
1.68
1.36
0.79
Post EC
27.27
25.45
9.64
7.82
4.55
Benchmark MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lowerthan the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
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Table 4-26 Non-Cancer Risk Estimates for Chronic Inhalation Exposures Following Occupational Use of
1-BP in Aerosol Degreasing Based on Modeling
Health Effect, Endpoint and
Study
LIVER
Increased hepatocellular
vacuolization
(WIL Research, 2001)
KIDNEY
Increased pelvic mineralization
(WIL Research, 2001)
REPRODUCTIVE SYSTEM
Decreased seminal vesicle weight
(Ichiharaetal., 2000b)
DEVELOPMENTAL EFFECTS
Decreased live litter size (Fi)
(WIL Research, 2001)
NERVOUS SYSTEM
Decreased traction time
(Honma et al., 2003)
Chronic
HEC
(ppm)
150
140
53
43
25
Chronic Exposure 95th percentile Estimate
WORKER (NEAR-FIELD) MOE1
PreEC
22
21
8.0
6.0
4.0
Post EC with 90%
Efficiency
220
206
78
63
37
OCCUPATIONAL NON-USER (FAR-
FIELD) MOE1
PreEC
44
41
15
13
7
Post EC with 90%
Efficiency
439
409
155
126
73
Benchmark
MOE
(= Total UF)
100
100
100
100
1,000
Notes: 'MOEs (HEC in ppm/exposure estimate in ppm) lower than the Benchmark MOE (Total UF) indicate potential health risks and are
denoted in bold.
4.3.2
Cancer Risks for Occupational Scenarios
EPA/OPPT estimated the added cancer risks associated with chronic exposures following 1-BP
use in spray adhesive, dry cleaning, and degreasing applications in the workplace. The added
cancer risk estimation for 1-BP consisted of multiplying the occupational scenario-specific
estimates (i.e., LADC) for both workers and occupational non-users by EPA's inhalation unit
risk (IUR) to estimate the added cancer risk. Added cancer risks were expressed as number of
cancer cases per million. Figure 4-1 through Figure 4-11 present the incremental individual
lifetime cancer risks for the 95th percentile for exposures to 1-BP occurring during the
occupational use of spray adhesives, vapor degreasing, dry cleaning, cold cleaning, and aerosol
degreasing activities. Occupational exposure estimates for the 50th percentile/central
tendency, as well as the entire suite of calculations of cancer risks (including estimates with
the 90% engineering control effectiveness) are provided in the supplemental Excel
spreadsheet14.
It was assumed that the exposure frequency (i.e., the amount of days per year for workers or
occupational non-users exposed to 1-BP) was 260 days per year and the occupational
exposure duration was 40 years over a 70-year lifespan. It is recognized that these exposure
assumptions are likely yielding conservative cancer risk estimates, but EPA/OPPT does not
have additional information for further refinement.
14See attached document titled "Supplemental File 1-BP Cancer Risk Estimates.xlsx".
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EPA typically uses a benchmark cancer risk level between IxlO"4 and IxlO"6 for determining the
acceptability of the cancer risk in a population. Since the benchmark cancer risk level will be
determined during risk management, the occupational estimates for added cancer risk were
compared to the benchmark levels of 10~4,10~5, and 10~6 incremental or added individual
lifetime risk. The benchmark levels were:
1.
2.
3.
1-6.
1x10" : the probability of 1 chance in 1 million of an individual developing cancer
IxlO"5: the probability of 1 chance in 100,000 of an individual developing cancer,
which is equivalent to 10 cancer cases in 1 million
IxlO"4: the probability of 1 chance in 10,000 of an individual developing cancer,
which is equivalent to 100 cancer cases in 1 million
All three benchmark cancer risk estimates of IxlO"4, IxlO"5 and IxlO"6 (and beyond) were
exceeded for all of the uses in workers and occupational non-users for both monitoring and
modeling data regardless of the type of engineering controls (pre- and post-EC) with only a
few exceptions and only after engineering controls were applied (post-EC). These included
vapor degreasing (monitoring data for occupational non-users only exceeded IxlO"5, Figure
4-6) and cold cleaning (modeling data for occupational non-users only exceeded IxlO"5, Figure
4-9). Based on monitoring data, spray adhesives showed the greatest cancer risk, followed by
dry cleaning, cold cleaning, vapor degreasing, aerosol degreasing, and spot cleaning at dry
cleaners. In most cases, benchmark cancer risk estimates were similar between monitoring
and modeling within each use.
Figure 4-1 Cancer Risk Estimates for Occupational Use of 1-BP in Spray Adhesives Based on Monitoring
Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
WORKER
(SPRAYER)
WORKER
(NON-SPRAYER)
OCCUPATIONAL
NON-USER
0.1
10 1000
excess risk per million
100000
Post-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
WORKER
(SPRAYER)
WORKER
(NON-SPRAYER)
OCCUPATIONAL
NON-USER
0.1
10 1000
excess risk per million
100000
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Figure 4-2 Cancer Risk Estimates for Occupational Use of 1-BP in Dry Cleaning Based on Monitoring
Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
0.1 10 1000
excess risk per million
100000
Figure 4-3 Cancer Risk Estimates for Occupational Use of 1-BP in Dry Cleaning Based on Modeling
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers: Machine
Unloading and
Finishing
Workers: Spot
Cleaning
Occupational non-
users
0.1
10 1000
excess risk per million
100000
Post-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers: Machine
Unloading and
Finishing
Workers: Spot
Cleaning
Occupational non-
users
0.1
10 1000
excess risk per million
100000
Figure 4-4 Cancer Risk Estimates for Occupational Uses of 1-BP in Spot Cleaning at Dry Cleaners Based
on Monitoring Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
0,1
10 1000
excess risk per miltion
100000
Figure 4-5 Cancer Risk Estimates for Occupational Uses of 1-BP in Spot Cleaning at Dry Cleaners Based
on Modeling
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
non-users
0.1
10 1000
excess risk per million
100000
Post-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
0.1
10 1000
excess risk per million
100000
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Figure 4-6 Cancer Risk Estimates for Occupational Use of 1-BP in Vapor Degreasing Based on
Monitoring Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
Non-Users
Post-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
Non-Users
0.1 10 1000 100000
excess risk per million
0.1 10 1000 100000
excess risk per million
Figure 4-7 Cancer Risk Estimates for Occupational Use of 1-BP in Vapor Degreasing Based on Modeling
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
0.1 10 1000 100000
excess risk per million
Post-EC 98% 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
non-users
0.1 10 1000 100000
excess risk per million
Figure 4-8 Cancer Risk Estimates for Occupational Use of 1-BP in Cold Cleaning Based on Monitoring
Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
Non-Users
0.1
10 1000
excess risk per million
100000
Figure 4-9 Cancer Risk Estimates for Occupational Use of 1-BP in Cold Cleaning Based on Modeling
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
0.1 10 1000
excess risk per million
100000
Post-EC 98% 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
non-users
0.1 10 1000
excess risk per million
100000
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Figure 4-10 Cancer Risk Estimates for Occupational Uses of 1-BP in Aerosol Degreasing Based on
Monitoring Data
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
Non-Users
0.1 10 1000
excess risk per million
100000
Figure 4-11 Cancer Risk Estimates for Occupational Uses of 1-BP in Aerosol Degreasing Based on
Modeling
Pre-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Post-EC 95th Percentile Exposure Estimates
excess risk
1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0
Workers
Occupational
non-users
0.1
10 1000
excess risk per million
100000
0.1 10 1000
excess risk per million
100000
Added cancer risks calculated for workers and occupational non-users exposed at the 95th
percentile exceeded all identified cancer benchmarks (i.e., IxlO"4 (1 in 10,000), IxlO"5 (1 in
100,000) and IxlO"6 (1 in 1,000,000)) in most of the use scenarios evaluated under the scope of
this assessment. In most cases a 1,000-fold exceedance of the 1 in 1,000,000 cancer risk
benchmark was observed (this corresponds to a cancer risk greater than IxlO"3 (or a probability
of 1 in 1,000 that an exposed individual will develop cancer). It is important to note however,
that this value reflects the added lifetime cancer risk estimated for high end (i.e., 95th
percentile) exposures that occur over the assumed duration of an occupational life (i.e.
8 hours/day, 260 days/yr for 40 years of a 70 year lifespan). Although most occupational
exposure concentrations at the 50th percentile are about an order of magnitude lower than
those at the 95th percentile (Shown in section 2), the associated risk estimates exceeded the 1
in 10,000 cancer benchmark (calculations of cancer risks at the 50th percentile are shown in the
supplemental excel file). The range of added cancer risks calculated for workers in each use
category are described below. Risk estimates are based on occupational exposure values
derived from monitoring and modeling data (with and without engineering controls).
Spray Adhesives Use in Foam Cushion Manufacturing:
For the occupational use of 1-BP in spray adhesives, the range of added cancer risks in workers
(sprayers and non-sprayers) exposed at the 95th percentile was 5xlO"2 to 4X10"1 (Figure 4-1). The
estimated number of workers potentially exposed in spray adhesive use ranged from 551 to
4,384 (Table 2-1). If the number of workers is roughly in the middle of the estimated range, i.e.,
about 2,000, and of those workers, 5 percent (100 workers) are exposed at the 95th percentile
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exposure concentration or higher for the assumed occupational duration (i.e. 8 hours/day,
260 days/yr for 40 years of a 70 year lifespan), then 5 to 40 workers may have an increased
cancer incidence.
For the occupational use of 1-BP in spray adhesives, the 50th percentile estimated exposure
concentrations in workers (sprayers and non-sprayers) were roughly 2-fold lower than the 95th
percentile (Table 2-2). The range of added cancer risks in workers exposed at the 50th percentile
was 3xlO"2 to 2X10"1. The added cancer risks are lower in workers exposed at the 50th percentile
however more workers are exposed at the 50th percentile concentration. If the estimated
number of workers is about 2,000 (as described above), and of those workers, half are assumed
to be exposed at the 50th percentile exposure concentration or higher for the assumed
occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a 70 year lifespan), then 30
to 200 workers may have an increased cancer incidence.
Degreasing Use (Vapor, Cold Cleaning and Aerosol):
The range of added cancer risks in workers with 1-BP exposure at the 95th percentile for vapor
degreasing was 8xlO~4 to 8xlO~2 (Figure 4-6 and Figure 4-7), for cold cleaning was 3xlO~4 to
SxlO"2 (Figure 4-8 and Figure 4-9) and for aerosol degreasing was IxlO"3 to IxlO"2 (Figure 4-10
and Figure 4-11). The estimated number of workers potentially exposed in vapor degreasing
ranged from 3,245 to 16,226 (Table 2-8), the number of workers potentially exposed in cold
cleaning were not estimated and in aerosol degreasing ranged from 2,227 to 11,137 (Table
2-13). If the number of workers is roughly in the middle of the estimated ranges, i.e., about
10,000 for vapor degreasing and 6,000 for aerosol degreasing, of those workers, 5 percent (500
and 300 workers respectively) are exposed at the 95th percentile exposure concentration or
higher for the assumed occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a
70 year lifespan), then <1 to 40 workers for vapor degreasing and <1 to 3 workers for aerosol
degreasing may have an increased cancer incidence.
For the occupational use of 1-BP in degreasing, the 50th percentile estimated exposure
concentrations in workers were roughly one order of magnitude lower than the 95th percentile
(Table 2-9 through Table 2-12, Table 2-14, and Table 2-15). The range of added cancer risks in
workers with 1-BP exposure at the 50th percentile for vapor degreasing workers was 6xlO"5 to
IxlO"2, for cold cleaning was and for aerosol degreasing were 4xlO"4 to 2xlO"2. The added
cancer risks are lower in workers exposed at the 50th percentile however more workers are
exposed at the 50th percentile concentration. If the numbers of workers potentially exposed are
about 10,000 for vapor degreasing and 6,000 (as described above), and of those workers, if half
are exposed at the 50th percentile exposure concentration or higher for the assumed
occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a 70 year lifespan), then < 1
to 50 workers for vapor degreasing and cold cleaning and 1 to 60 workers for aerosol
degreasing may have an increased cancer incidence.
Dry Cleaning and Spot Cleaning Uses:
For the occupational use of 1-BP in dry cleaning and spot cleaning, the range of added cancer
risks in workers with 1-BP exposure at the 95th percentile was IxlO"3 to IxlO"1. The estimated
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number of workers potentially exposed in dry cleaning shops is 821 (Table 2-3). If 5 percent
(41 workers) are exposed at the 95th percentile exposure concentration or higher for the
assumed occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a 70 year lifespan),
then <1 to 4 workers may have an increased cancer incidence.
For the occupational use of 1-BP in dry cleaning and spot cleaning, the 50th percentile estimated
exposure concentrations in workers were roughly 10-fold lower than the 95th percentile (Table
2-4 through Table 2-7). The range of added cancer risks in workers exposed at the 50th
percentile was 3xlO"4 to 5xlO"2. The added cancer risks are lower in workers exposed at the 50th
percentile however more workers are exposed at the 50th percentile concentration. If the
number of workers potentially exposed in dry cleaning shops is 821 (Table 2-3), and of those
workers if half are exposed at the 50th percentile exposure concentration or higher for the
assumed occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a 70 year lifespan),
then <1 to 21 workers may have an increased cancer incidence.
Overall, there are significant increased risks to developing cancer in workers if they are exposed
to 1-BP for the assumed occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a
70 year lifespan) at the concentrations estimated for the spray adhesive, dry cleaning and
degreasing uses. While not included in the calculations above occupational non-users also have
significant increased risks to developing cancer if they are exposed to 1-BP for the assumed
occupational duration (i.e. 8 hours/day, 260 days/yr for 40 years of a 70 year lifespan) at the
concentrations estimated as shown by the added cancer risks in Figure 4-1 through Figure 4-11.
The cancer risk calculations are based on assumptions and have uncertainties such as the
exposure frequency of 260 days/year and 40 years of exposure over a 70-year lifespan which
may produce conservative cancer risk estimates. The assumptions and uncertainties are further
explained in the following section.
4.4 ASSUMPTIONS AND KEY SOURCES OF UNCERTAINTY
The characterization of variability and uncertainty is fundamental to any risk assessment.
Variability refers to "the true heterogeneity or diversity in characteristics among members of a
population (i.e., inter-individual variability) or for one individual over time (intra-individual
variability)" (U.S. EPA, 2001). The risk assessment was designed to reflect critical sources of
variability to the extent allowed by available methods and data and given the resources and
time available.
On the other hand, uncertainty is "the lack of knowledge about specific variables, parameters,
models, or other factors" (U.S. EPA, 2001) and can be described qualitatively or quantitatively.
Uncertainties in the risk assessment can raise or lower the confidence of the risk estimates. In
this assessment, the uncertainty analysis also included a discussion of data gaps/limitations.
The next sections describe the uncertainties and data gaps in the exposure, hazard/dose-
response and risk characterization.
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4.4.1 Uncertainties and Limitations of the Occupational Exposure
Assessment
4.4.1.1 Variability
In the 1-BP exposure assessment, EPA/OPPT addressed variability by applying a Monte Carlo
simulation to the vapor degreasing, cold cleaning, aerosol degreasing, and spot cleaning
scenarios. The Monte Carlo method is a stochastic technique for propagating variability through
a model.
EPA/OPPT addressed variability in the exposure models by identifying key model parameters to
apply a statistical distribution that mathematically defines the parameter's variability.
EPA/OPPT defined statistical distributions for parameters using documented statistical
variations where available.
4.4.1.2 Uncertainties and Limitations ^
Uncertainty is "the lack of knowledge about specific variables, parameters, models, or other
factors" and can be described qualitatively or quantitatively (U.S. EPA, 2001). The following
sections discuss uncertainties in each of the assessed 1-BP use scenarios.
4.4.1.2.1 Number of Workers
There are a number of uncertainties surrounding the estimated number of workers potentially
exposed to 1-BP, as outlined below. Most are unlikely to result in a systematic underestimate or
overestimate, but could result in an inaccurate estimate. The exception is for our inability to
estimate the percentage of workers in the degreasing application group using 1-BP rather than
other solvents, which results in an overestimate of exposed workers.
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 solvents for the
assessed applications. EPA/OPPT addressed this issue by refining the OES estimates using total
employment data from the U.S. Census' SUSB (2012) (see Appendix F). 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 solvent exposure differs from the overall distribution of workers in
each NAICS, then this approach will result in inaccuracy, but would be unlikely to systematically
either overestimate or underestimate the count of exposed workers.
Second, EPA/OPPT's judgments about which industries (represented by NAICS codes) and
occupations (represented by SOC codes) are associated with degreasing, dry cleaning, and the
use of spray adhesives are based on EPA/OPPT's understanding of how solvents are used in
each industry. Designations of which industries and occupations have potential exposures is
nevertheless subjective, and some industries/occupations with few exposures might
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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.
Finally, the accuracy of estimates of the percentage of workers using 1-BP instead of other
chemicals could fail to capture either the market penetration or any changes in market
penetration over time. The estimates for dry cleaning and spray adhesive applications are
based on the EPA market reports (U.S. EPA, 2013b, c), but these single point estimates might
not fully or accurately capture 1-BP use.
For degreasing, EPA/OPPT referenced EPA's Work Plan Chemical Assessment for TCE to
determine the NAICS industry sectors where solvent degreasing may occur. However, it should
be noted that degreasing is not an industry-specific activity. Many of these industries do not
perform degreasing as a primary part of their business; some facilities within the degreasing
NAICS codes may not perform degreasing at all. Therefore, using a broad range of NAICS codes
likely overestimate the number of workers and occupational non-users. Additionally, there is a
lack of data on the prevalence of 1-BP use in solvent degreasing. Therefore, EPA/OPPT
presented the total number of workers in the industry/occupation combinations using any
solvents rather than just 1-BP. This likely results in an overestimate of the number of exposed
workers (see Appendix F).
4.4.1.2.2 Analysis of Exposure Monitoring Data
This report uses existing worker exposure monitoring data to assess exposure to 1-BP during
spray adhesive, vapor degreasing, aerosol degreasing, cold cleaning, dry cleaning, and spot
cleaning applications. To analyze the exposure data, EPA/OPPT categorized each PBZ data point
as either "worker" or "occupational non-user". In addition, EPA/OPPT categorized the data into
"pre-EC" and "post-EC" scenarios. The categorizations are based on descriptions of worker job
activity and engineering control as provided in literature and EPA's judgment. Some data
sources, such as the OSHA IMIS, lack details on the worker activity and presence of ventilation.
Where information is not available, EPA/OPPT assumed no specific engineering controls are
implemented and categorized the data as a "pre-EC" scenario.
The analysis combines exposure data from multiple sources. The aggregated data show a
distribution of exposure levels at multiple facilities. It should be noted that the environmental
conditions and engineering controls likely differ from facility to facility. The representativeness
of the exposure levels and the engineering controls used at these facilities has not been
evaluated. For each 1-BP use, the facilities included in the pre-EC and post-EC scenario may also
differ. Therefore, the aggregated exposure data should not be used to calculate the engineering
control effectiveness; rather, any such calculation should be done on the facility-level. The post-
EC exposure levels presented in this report represent a snapshot of possible exposure levels
when engineering controls are implemented.
Exposures for occupational non-users can vary substantially. Most data sources do not
sufficiently describe the proximity of these employees to the 1-BP exposure source. As such,
exposure levels for the "occupational non-user" category will have high variability depending
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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, NIOSH HHEs for the spray adhesive
use were conducted to address concerns regarding adverse human health effects reported
following 1-BP exposure with spray adhesive use in furniture manufacturing. Two HHEs were
requested by the North Carolina Department of Labor; one was conducted in response to a
confidential request submitted by the facility's employees. OSHA IMIS data are obtained from
OSHA inspections, which also may be the result of worker complaints, and may provide
exposure results that are generally more conservative than the industry average.
There are limited exposure monitoring data in literature for cold cleaning and for spot-cleaning.
Where there are few data points available, it is unlikely the results will be representative of
worker exposure across the industry. Additionally, there is uncertainty as to whether the
exposure monitoring data presented for cold cleaning are specific to a "cold cleaner" of
interest, or whether they are associated with other types of degreasing equipment.
The 95th and 50th percentile exposure concentrations were calculated using available data. The
95th percentile exposure concentration is intended to represent a high-end exposure level,
while the 50th percentile exposure concentration represents typical exposure level. The
underlying distribution of the data, and the representativeness of the available data, are not
known.
EPA/OPPT calculated ADC and LADC values assuming a high-end exposure duration of 260 days
per year over 40 years. This assumes the workers and occupational non-users 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 1-BP,
and that actual ADC and LADC values become lower than the estimates presented.
4.4.1.2.3 Near-Field / Far-Field Model Framework
Because the near-field / far-field approach applies to all of the workplaces modeled, the
following describe uncertainties and simplifying assumptions generally associated with this
modeling approach:
• There is some degree of uncertainty associated with each model input parameter. In
general, the model inputs were determined based on review of available 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 each well mixed, such that each of
these zones can be approximated by a single, average concentration.
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• All of the emissions from the facility are assumed to enter the near-field zone. This
assumption will overestimate exposures and risks in facilities where some of the
emissions do not enter the airspaces relevant to the worker exposure modeling.
• The exposure models are actually modeling airborne concentrations. Exposures are
calculated by assuming the workers spend the entire activity time in each of their
respective exposure zones (i.e., the worker in the near field and the occupational non-
user in the far field). Since vapor and cold degreasing 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 worker activity duration may result in an
overexposure.
• For certain 1-BP applications (e.g. vapor degreasing and spot cleaning), 1-BP vapor is
assumed to be emitted continuously while the equipment operates, with a constant
vapor generation rate. It is possible that actual vapor generation will 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 1-BP application (e.g.
vapor degreasing, cold cleaning, dry cleaning, etc). While monitoring studies were used
to determine appropriate model input values during model development, it should be
noted that the models have not been regressed or fitted with monitoring data.
Therefore, the model results do not represent specific facilities being monitored.
• The models represent a baseline scenario that do not have LEV. EPA/OPPT does not
have adequate data to construct LEV systems into the exposure models. Additionally,
there is no data on the fraction of U.S. facilities that use LEV. "What-if" values on
engineering control effectiveness are applied to the model baseline to provide post-EC
scenarios. These values were obtained by reviewing statements made in published
literature regarding potential emission or exposure reductions after implementation of
engineering control or equipment substitution.
Each subsequent section below discuss uncertainties associated with the individual model.
4.4.1.2.4 Vapor Degreasing and Cold Cleaning Model
The 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:
• The indoor air speed is based on Baldwin and Maynard (1998) measurements at a
variety of workplaces (e.g. industrial facilities, office, schools, etc.). The range of indoor
wind speed at degreasing facilities may be more narrow than the range of values
measured by Baldwin and Maynard.
• To estimate vapor generation rate for vapor degreasing, EPA/OPPT references a 1-BP
emission factor developed by CARB for the California Solvent Cleaning Emissions
Inventories (CARB, 2011). The emission factor is an average emission for the "vapor
degreasing" category for the California facilities surveyed by CARB. The category
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includes batch-loaded vapor degreaser, aerosol surface preparation process, and
aerosol cleaning process. For the purpose of modeling, EPA/OPPT assumes the 1-BP
emission factor is entirely attributed to vapor degreasing applications. The
representativeness of the emission factor for vapor degreasing emissions in other
geographic locations within the U.S. is uncertain.
The CARB emission factor covers batch degreasing units. However, CARB does not
further specify whether these are open-top vapor degreasers, enclosed, or other types
of batch degreasers. EPA/OPPT assumes the emission factor is representative of open-
top vapor degreaser, as it is the most common design for batch units. In addition,
EPA/OPPT assumes that the surveyed facilities likely switched to using 1-BP, an
alternative, non-HAP solvent, as a way of complying with Federal and State regulations
for HAP halogenated solvents (i.e., chemical substitution, rather than equipment
changes).
The CARB emission factor, in the unit of pound per employee-year, was developed for
the purpose of estimating annual emissions. These types of emission factor typically
reflect the amount of solvent lost / emitted, some of which may not be relevant to
worker exposure. For example, 1-BP emitted and captured through a stack may not
result in worker exposure. Therefore, assuming all of the 1-BP is emitted into the
workplace air may result in overestimating of exposure. In addition, the use of an annual
emission factor does not capture time variability of emissions. The approach assumes a
constant emission rate over a set number of operating hours, while actual emissions and
worker exposures will vary as a function of time and worker activity.
EPA/OPPT combines the CARB emission factor with nationwide Economic Census
employment data across 78 NAICS industry sector codes. It should be noted that vapor
degreasing is not an industry-specific operation. Only a subset of facilities within the 78
selected industry sectors are expected to operate vapor degreasers. Therefore, the
industry-average employment data may not be representative of the actual number of
employees at vapor degreasing facilities.
To estimate worker exposure during cold cleaning, EPA/OPPT applied an emission
reduction factor to the vapor degreasing model by comparing the AP-42 emission
factors for the two applications. The AP-42 emission factors are dated. Furthermore, the
cold cleaning model results have not been validated with actual monitoring data.
Both models assume the equipment operates two hours per day. The value was derived
from the 2001 CEB Generic Scenario for the Use of Vapor Degreasers (ERG, 2001). Actual
worker exposure will increase as the hours of equipment operation increases.
The exposure models assume that exposures are zero outside of the degreasing hours
per day. However, even if a worker were to completely remove the source of 1-BP
emissions at the conclusion of a task, residual 1-BP would remain in the air and decay to
zero as the ventilation replaces the contaminated air with clean air. EPA/OPPT assumes
the 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 the residual airborne concentrations. Note that this assumption does not
apply to aerosol degreasing, where the task continues for seven hours of the eight-hour
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work day. EPA/OPPT only assumes there are no exposures during the first hour as the
workers prepare for the aerosol degreasing task.
• The model assumes an exposure reduction of 90 percent with engineering control and
98 percent with equipment substitution based on two studies. In reality, engineering
controls and their effectiveness are site-specific, and the representativeness of these
studies is not known.
4.4.1.2.5 Aerosol Degreasing Model
The aerosol degreasing assessment also uses a near-field/far-field approach to model worker
exposure. Uncertainties and limitations with the near-field/far-field model have been described
previously. Additional uncertainties associated with the aerosol degreasing scenario are
presented below:
• The model references indoor air speed measurements from the Baldwin and Maynard
(1998) study, which covers a variety of workplaces (e.g. industrial facilities, office,
schools, etc.). The variability in wind speed contributes to a wide range of exposure
levels; actual wind speed at aerosol degreasing facilities may be less variable than the
data set presented in Baldwin and Maynard.
• The model assumes the worker applies the aerosol degreaser once per hour with seven
applications in an eight-hour work day. In reality, the application frequency will vary
depending on the workload at each facility.
• The model assumes an application amount of 27.5 grams of degreaser per application.
This value is based on a 2014 literature study for general degreasing applications (oven
cleaning); it is uncertain whether this value is representative of a typical aerosol
degreasing facility. EPA/OPPT assumes the amount per application is not chemical-
specific. The actual application amount will depend on the specific work practice and
surface area to be cleaned.
• Information on engineering control effectiveness was not found for this workplace
setting. The post-EC scenario references Wadden et al. (1989), which estimates 90
percent LEV effectiveness for an open-top vapor degreaser. The applicability of this
value to the aerosol degreasing model has not been demonstrated.
4.4.1.2.6 Dry Cleaning Model
The multi-zone dry cleaning model also uses a near-field/far-field approach. Uncertainties and
limitations with the near-field/far-field model have been described previously. Additional
uncertainties associated with the dry cleaning scenario are presented below:
• The model references indoor air speed measurements from the Baldwin and Maynard
(1998) study, which covers a variety of workplaces (e.g. industrial facilities, office,
schools, etc.). The variability in wind speed contributes to a wide range of exposure
levels; actual wind speed at dry cleaning facilities may be less variable than the data set
presented in Baldwin and Maynard.
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• The model assumes each facility only has one dry cleaning machine, cleaning one to
fourteen loads of garments per day. While the dry cleaning facilities in Blando et al.
(2010) and NIOSH (2010) appear to only have one machine, the representativeness of
these two studies is not known. Larger facilities are likely to have more machines, which
could result in additional 1-BP exposures.
• The model conservatively uses a hemispherical volume based on the dry cleaning
machine door diameter as the near-field for machine unloading. The small near-field
volume results in a large spike in concentration when the machine door is opened,
where any residual 1-BP solvent is assumed to be instantaneously released into the
near-field. In reality, the residual solvent will likely be released continuously over a
period of time. In addition, the worker may move around while unloading the garments,
such that the worker's breathing zone will not always be next to the machine door
throughout the duration of this activity. Therefore, these assumptions may result in an
overestimate of worker exposure during machine unloading.
• Many of the model input parameters were obtained from (von Grote et al., 2003), which
is a German study. Aspects of the U.S. dry cleaning facilities may differ from German
facilities. However, it is not known whether the use of German data will under-or over-
estimate exposure.
• Information on engineering control effectiveness was not found for this workplace
setting. The post-EC scenario references Wadden et al. (1989), which estimates 90
percent LEV effectiveness for an open-top vapor degreaser. This value may not be
conservative, as it is uncertain whether engineering control at dry cleaning facilities
could achieve 90 percent exposure reduction.
• EPA/OPPT assumed dry cleaning shops operate twelve hours a day, and individual
employees work eight-hour shifts. The model exposures are therefore calculated as 8-hr
TWA. In some cases, owners of small dry cleaning shops may be present at the shop
longer than a typical eight-hour shift, and could have a longer exposure duration.
Therefore, the use of 8-hr TWA values is not expected to present a "worst-case" or
conservative exposure estimate.
4.4.1.2.7 Spot Cleaning Model
The spot cleaning assessment also uses a near-field/far-field approach to model worker
exposure. Uncertainties and limitations with the near-field/far-field model have been described
previously. Additional uncertainties associated with the spot cleaning scenario are presented
below:
• The model references indoor air speed measurements from the Baldwin and Maynard
(1998) study, which covers a variety of workplaces (e.g. industrial facilities, office,
schools, etc.). The variability in wind speed contributes to a wide range of exposure
levels; actual wind speed at dry cleaning facilities may be less variable than the data set
presented in Baldwin and Maynard.
• The model estimates a use rate of 16 gallons per year spot cleaner. This value was
derived using a MADEP case study for one specific dry cleaner in Massachusetts,
handling 100 pieces of garments per day. MADEP noted that the size of each dry cleaner
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can vary substantially. As such, the spot cleaner use rate will also vary by the individual
facility work load.
Information on engineering control effectiveness was not found for this workplace setting. The
post-EC scenario references Wadden et al. (1989), which estimates 90 percent LEV
effectiveness for an open-top vapor degreaser. This value may not be conservative, as it is
uncertain whether engineering control at dry cleaning facilities could achieve 90 percent
exposure reduction.
4.4.2 Uncertainties of the Consumer Exposure Assessment
Due to the absence of indoor air monitoring data from consumer use of 1-BP, the EPA used
modeling based on experimental data, survey information and a number of assumptions to
estimate indoor air concentrations resulting from the use of consumer spray adhesives, aerosol
spot removers and aerosol cleaners and degreasers. Use of a modeling approach to estimate
indoor air concentration has a number of limitations, as detailed below.
4.4.2.1 Consumer Use Information
Although EPA/OPPT found some information about 1-BP products intended for consumer use,
there is some general uncertainty regarding the nature and extent of the consumer use of 1-BP
for the products within the scope of this assessment. The model input for the use profile was
derived from an older products survey (Westat, 1987), thereby introducing uncertainty as to
the relevance for current consumer settings where spray adhesives, spot removers or aerosol
cleaners and degreasers containing 1-BP may be used. EPA/OPPT considers the assumptions
used for the model exposure scenarios to be reasonable, but recognizes that these assumptions
may not reflect actual current usage patterns or use conditions in consumer settings.
Consequently, the limited data and variable results associated with different exposure
scenarios, when used to extrapolate to consumer inhalation risk characterization, have
associated uncertainty.
4.4.2.2 Model Assumptions and Input Parameters
There is a high degree of confidence in the consumer product weight fractions identified for the
consumer products evaluated in this assessment. Also, there is a medium to high degree of
confidence in certain modeling inputs to the CEM model, including vapor pressure, molecular
weight, room volumes, whole house volume, air exchange rate, body weight, and inhalation
rate. There are no chamber data available for the products modeled in the exposure
assessment, thus CEM was used to calculate the mass of 1-BP entering the room of use by
relying on data from a paper that studied the emission rates of solvents from a surface (DTIC
DLA, 1981). The consumer uses described in this assessment with higher weight fraction 1-BP
result in only 1-BP being on the surface so these uses fit well into the Chinn data set, however if
the product has a significant fraction other components this may affect the evaporation rate of
1-BP. This introduces uncertainty and a further discussion of this issue is provided in Appendix
L.
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4.4.2.3 Conversion of Acute Dose Rates to Air Concentrations
Because the E-FAST2/CEM model outputs for exposure to the user and bystander scenarios are
reported in mg/kg-bw/day, it was necessary to convert these values to air concentrations (ppm)
in order to perform the non-cancer assessment. This conversion introduces some uncertainty,
and therefore may over- or under-estimate exposures.
4.4.3 Uncertainties in the Hazard and Dose-Response Assessments
4.4.3.1 Uncertainties and Assumptions in the Non-Cancer Hazard/Dose-
Response Assessments
EPA/OPPT's risk assessment relied on the hazard values (i.e., HECs) derived in this evaluation.
These hazard values were used to estimate acute and chronic risks to various health effects
following 1-BP exposure related to specific 1-BP uses.
There are several uncertainties inherent to the data and the assumptions used to support the
derivation of the acute and chronic non-cancer PODs for different health effects domains.
Below is a summary of the major uncertainties affecting the non-cancer hazard/dose response
approach used for this assessment. However, the key endpoints identified in this assessement
(liver toxicity, kidney toxicity, reproductive/developmental toxicity, neurotoxicity, and cancer)
showed a strength of evidence among the studies in the database for consistency, sensitivity,
and relevance.
The uncertainties in hazard and dose response assessment are predicated on assumptions of
relevancy of cancer and non-cancer findings in rodents being relevant to humans.
Decreased live litter size was selected as an endpoint to evaluate risks associated with acute
exposures to 1-BP. Although the developmental toxicity studies included repeated exposures,
EPA/OPPT considered evidence that a single exposure to a toxic substance can result in adverse
developmental effects, described by (Van Raaij et al., 2003), as relevant to 1-BP.
Although there is evidence of biological effects in both the fetus and neonate, there are
uncertainties in extrapolating doses for these lifestages. It is not known if 1-BP or its
metabolites are transferred to the pups via lactation. It is possible that the doses reaching the
fetus and the neonate are similar and that these lifestages are equally sensitive; however, it is
also possible that one lifestage is more sensitive than the other or that internal doses are
different. Additional data would be needed to refine dose estimates for the fetus and pups and
to determine if there are specific windows of sensitivity.
Neurotoxicity produced by 1-BP are based on rodent and human literature, with considerable
similarities in both qualitative and quantitative outcomes. In the human and rodent literature,
the most consistent responses are symptoms of frank neurotoxicity occurring at high exposures,
with effects that are progressive at repeated exposures to low concentrations. In humans, the
reports of effects in factory workers with lower exposures are limited by questions regarding
exposure characterization as well as measurement techniques, sensitivity, and analysis: for
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these reasons the data are not sufficiently robust for quantitative exposure-response function.
On the other hand, the findings of decreased peripheral nerve function are supported by
parallel measures in several rodent studies.
Protection of Different Lifestages and Subpopulations: EPA also is interested in the impact of
1-BP on other lifestages and subpopulations. Consideration of other lifestages, such as male
and non-pregnant female workers in the occupational environment, children in the home
environment would require using an alternative POD based on systemic toxicity, instead of
using the POD based on developmental toxicity. Other endpoints associated with systemic
toxicity generally had higher human equivalent concentrations than those associated with
developmental toxicity. Therefore EPA assumed that margins of exposure for pregnant women
would also be protective of other lifestages.
While it is anticipated that there may be differential 1-BP metabolism based on lifestage;
currently there are no data available, therefore the impact of this cannot be quantified.
Similarly, while it is known that there may be genetic differences that influence CYP2E1
metabolic capacity, there may also be other metabolizing enzymes that are functional and
impact vulnerability. There is insufficient data to quantify these differences for risk assessment
purposes.
Heterogeneity among humans is an uncertainty associated with extrapolating the derived PODs
to a diverse human population. One component of human variability is toxicokinetic, such as
variations in CYP2E1 and glutathione transferase activity in humans (Arakawa et al., 2012;
Trafalis et al., 2010) which are involved in 1-BP metabolism in humans. EPA did not have the
chemical specific information on susceptible human populations, or the distribution of
susceptibility in the general population to decrease or increase the default intraspecies UFn for
toxicodynamic variability of 3. As such, EPA used an intraspecies UFn of 10 for the risk
assessment.
Uncertainties in the acute and chronic hazard values stem from the following sources:
Non-cancer hazard values (e.g., NOAELs, LOAELs, BMD): PODs were identified from the
animal studies that were suitable for dose-response analysis. The process of identifying PODs
for various health effects domains involved the evaluation of the strengths and limitations of
the data and the weight of evidence for a particular health effects domain before supporting
an association between 1-BP exposure and various human health effects. The selected PODs
values (e.g., NOAEL, LOAEL or BMD) depend on the current available data and could change as
additional studies are published.
Also, when selecting a BMD as a POD, the selection of the benchmark dose response (BMR)
(e.g., 1%, 5% or 10% level) directly affects the calculation of the BMD. There are uncertainties
related to the BMRs since their selection depends on scientific judgments on the statistical
and biological characteristics of the dataset and how the BMDs will be finally used.
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In addition, there are uncertainties about the appropriate dose-response model used to
generate the BMDs. However, these uncertainties should be minimal if the chosen model fits
well the observable range of the data, as discussed in EPA Benchmark Dose Technical
Guidance.
1. Duration adjustment to continuous exposure: Most of the PODs used to derive HECs
came from studies that did not expose animals or humans to 1-BP on a continuous basis.
These PODs were then mathematically adjusted to reflect equivalent continuous
exposures (daily doses) over the study exposure period under the assumption that the
effects are related to concentration x time (C x t), independent of the daily (or weekly)
exposure regimen (U.S. EPA, 2011). 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 (U.S. EPA, 2011). A single exposure to 1-BP at a critical
window of fetal development may produce adverse developmental effects (U.S. EPA,
2011). This was assumed to be a health protective approach and no duration
adjustment was performed for adverse developmental outcomes.
2. Extrapolation of repeated dose developmental effects to acute scenarios: There are
uncertainties related to whether developmental effects observed in developmental
toxicity studies may result from a single exposure to 1-BP. In this assessment, the acute
risk assessment used the hazard value for decreases in litter size from the (WIL Research,
2001) two-generation reproductive toxicity study. However, EPA policy is based on the
presumption that a single exposure to a chemical during a critical window of development
may be sufficient to produce adverse developmental toxicity.
4.4.3.2 Uncertainties and Assumptions in the Cancer Hazard/Dose-
Response Assessments
For cancer hazard assessment, the major uncertainty is whether the mechanism/mode of
action of 1-BP carcinogenesis should be considered mutagenic/genotoxic or nongenotoxic. The
uncertainty arose mainly because of the equivocal results of the Ames tests complicated by the
high volatility of 1-BP. Despite focusing solely on tests using desiccators or closed systems, the
equivocality remains as both positive and negative data were reported. To circumvent the
problem, EPA/OPPT used the weight of evidence approach using related test data: (a)
Genotoxicity tests of mammalian cells: 1-BP caused mutations in cultured mammalian cells with
or without metabolic activation and DNA damage in cultured human cells without metabolic
activation. There was also limited evidence of DNA damage in leukocytes in 1-BP-exposed
workers, (b) Metabolic activation to mutagenic intermediates: Rodent metabolic studies have
indicated that 1-BP can be activated by CYP2E1 to at least five mutagenic intermediates,
including two clearly mutagenic and carcinogenic chemicals, glycidol and propylene oxide.
Glycidol has been shown to induce tumors in intestines, one of the carcinogenic targets of 1-BP.
There is evidence that humans have CYP2E1 in lung and similar metabolic pathways for 1-BP as
rodents (c) Multiplicity of cancer targets of 1-BP: In general, chemical carcinogens that induce
cancer in more than one animal species and in multiple targets tend to act via mutagenic
mechanism/mode of action. 1-BP has been shown to induce a variety of tumors in rats and
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mice, (d) Structure-Activity Relationship (SAR) consideration: 1-BP is a low M.W. alkyl bromide
that is generally known to be a good alkylating agent. In fact, 1-BP has been shown to bind to
DNA in vitro. Bromoethane and 1-bromobutane, two of the closest analogs of 1-BP, were both
reported to give positive results in the Ames test when tested in closed systems, (e) Other
possible mechanism of action: Besides genotoxicity, at least three other possible mechanisms -
oxidative stress, immunosuppression, and cell proliferation—have been suggested by the NTP
(2013). These mechanisms can act synergistically to complete the multi-stage process of
carcinogenesis. While there is residual uncertainty in the mechanism/mode of action for 1-BP
carcinogenesis, overall, the totality of the available data/information support a justifiable basis
to support a probable mutagenic mode of action for 1-BP carcinogenesis.
While a mutagenic mode of action of action may be assumed to be operative at least in part for
the carcinogenicity of 1-BP the default linear extrapolation method for dose-response is used.
For the cancer dose-response assessment uncertainties exist arising from the animal to human
extrapolation in the derivation of the IUR. A source of uncertainty is the cancer model used to
estimate the POD for the IUR derivation. The POD was based on a model averaging approach to
fit the bioassay data for lung tumors. Although the model average fit the data alternate model
selections can also fit the data. A sensitivity analysis comparing reasonable alternate model
choices found similar PODs therefore the impact of selecting between alternative models
results in similar lURs.
4.4.4 Uncertainties in the Risk Assessment
The non-cancer acute or chronic risks were expressed in terms of MOEs. MOEs are obtained
by comparing the hazard values (i.e., HEC) for various 1-BP-related health effects with the
exposure concentrations for the specific use scenarios. Given that the MOE is the ratio of the
hazard value divided by the exposure, the confidence in the MOEs is directly dependent on
the uncertainties in the hazard/dose-response and exposure assessments that supported the
hazard and exposure estimates used in the MOE calculations.
Overall uncertainties in the exposure estimates used in the MOE calculations include
uncertainties in the exposure monitoring and modeling. In the occupational exposure
monitoring data for workers the sites used to collect 1-BP were not selected randomly;
therefore, the reported data may not be representative of all occupational exposure scenarios.
The exposure modeling approaches used for both occupational and consumer scenarios
employed knowledge-based assumptions that may not apply to all occupational- and consumer-
use scenarios.
The benchmark MOE used to evaluate risks for each use scenario represents the product of
of all UFs used for each non-cancer POD. These UFs accounted for various uncertainties
including:
1. Animal-to-human extrapolation (UFA): The UFA accounts for the uncertainties in
extrapolating from rodents to humans. In the absence of data, the default UFA of 10 is
adopted which breaks down to a factor of 3 for toxicokinetic variability and a factor of 3
for pharmacodynamic variability. There is no PBPK model for 1-BP to account for the
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interspecies extrapolation using rodent pharmacokinetic data in order to estimate
internal doses for a particular dose metric.
2. Inter-individual variation (UFh): The UFn accounts for the variation in sensitivity within
the human population. In the absence of data, the default UFn of 10 is adopted which
breaks down to a factor of 3 for toxicokinetic variability and a factor of 3 for
pharmacodynamic variability. Since there is no PBPK model for 1-BP to reduce the
human toxicokinetic/toxicodynamic variability, the total UFn of 10 was retained.
Qualitative evidence exists from mechanistic information and population evidence for
burden of disease that metabolic disorders including diabetes, nutritional deficits and
smoking will predispose some of the population to greater risk for adverse developmental
exacerbated by concurrent 1-BP exposure.
3. Extrapolation from subchronic to chronic (UFs): The UFs accounts for the uncertainty in
extrapolating from a subchronic to a chronic POD. Typically, a UFs of 10 is used to
extrapolate a POD from a less-than-chronic study to a chronic exposure. The same is true
for a developmental toxicity study because the developmental period is recognized as a
susceptible life stage where exposure during certain time windows is more relevant to the
induction of developmental effects than lifetime exposure (U.S. EPA, 1991). Thus, a UFs of
10 was retained for all of the HECs discussed in the OPPT's risk assessment.
4. LOAEL-to-NOAEL extrapolation (UFi_): The UFi accounts for the uncertainty in
extrapolating from a LOAELto a NOAEL. Avalue of 10 is the standard default UFi value,
although lower values (e.g., 3) can be used if the effect is considered minimally adverse
at the LOAEL or is an early marker for an adverse effect (U.S. EPA, 2002). Typically, UFL
ranging from 3 to 30 (i.e., 3,10, or 30) are used in the HECs. For one of the reproductive
PODs (Yamada et al., 2003), a UFi_ value of 10 was used based on a minimally adverse
effect, which resulted in a total UF of 1000.
The human populations considered in this risk assessment include individuals of both sexes (> 16
and older, including pregnant females) for occupational and consumer settings. Although
exposures to younger non-users may be possible, the margins of exposure calculated for
women of childbearing age are expected to be protective of this sensitive subpopulation.
Currently there is insufficient data regarding specific genetic and/or lifestage differences that
could impact 1-BP metabolism and toxicity for further refinement of the risk assessment.
The chronic risks for the occupational scenarios assumed that the non-cancer human health
effects are constant for a working lifetime based on the exposure assumptions used in the
occupational exposure assessment. However, the risks could be under- or over-estimated
depending on the variations to the exposure profile of the workers and occupational non-users
using 1-BP-containing adhesives, dry cleaning and spot cleaners, vapor degreasing, cold
cleaning, and aerosol degreasers.
Confidence in the PBPK model predictions for 1-BP concentrations in blood and tissues are
limited by the lack of comparison of model predictions with measured data. The PBPK model
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was further extended to simulate human exposures by scaling the physiological parameters to
humans, assuming the partition coefficients are the same in rats and humans and scaling
metabolic parameters by BW3/4. Cross species and route to route extrapolations with the
Garner et al. (2015) model are precluded by the lack of data to inform a model of a species
other than rat and a route other than inhalation.
The impact of dermal exposures on human health risks was not assessed in this assessment for
the consumer and occupational scenarios. Dermal exposure was not quantifiable and could
not be aggregated with inhalation exposures. Although dermal exposures are possible,
physical-chemical properties (e.g., volatility) in combination with data indicating dermal
uptake to be orders of magnitude lower than uptake by inhalation, limited toxicological data
for this route of exposure, and no available toxicokinetic information to develop
physiologically-based pharmacokinetic models or route-to-route extrapolations, all lessen the
concern for the dermal route of exposure. Exclusion of an exposure assessment of dermal and
aggregate exposures would be expected to underestimate the overall risks of the selected 1-BP
uses. However, this would only be an issue of concern in those exposure scenarios that
resulted in a "no-risk" finding, especially those that reported MOEs close to the benchmark
MOE, but still above the benchmark.
As discussed previously, the estimates for added cancer risk were based on the assumption of
linearity in the relationship between 1-BP exposure and probability of cancer. Uncertainties
are introduced in the cancer risks when there is limited information justifying the linear
cancer dose-response model when compared to other available models. In the case of 1-BP,
the cancer IUR was based on reliable data supporting a mutagenic mode of action for at least
1-BP-induced lung tumors (NTP, 2011).
4.5 RISK ASSESSMENT CONCLUSIONS
This risk assessment focused on the occupational uses of 1-BP-containing spray adhesives, dry
cleaning, and degreasing activities; and consumer uses of aerosol spray adhesives and spot
removers, and aerosol degreasers/cleaners. The population of interest consisted of workers
and consumers with direct (users) or indirect (occupational non-users) exposure to 1-BP. Only
the inhalation route of exposure was considered in this risk assessment. The occupational and
consumer exposures were generated for all of these 1-BP scenarios to derive non-cancer and
cancer risks.
MOEs were used to evaluate non-cancer risks for both acute and chronic exposures using the
hazard values identified in this assessment. Hazard values based on the developmental
toxicity endpoint (WIL Research, 2001) were used to estimate non-cancer risks for acute
exposures in the occupational and consumer scenarios. Non-cancer risks for chronic
occupational exposure scenarios were evaluated based on hazard values reported following
long-term exposure to 1-BP (i.e., liver toxicity, kidney toxicity, reproductive toxicity,
developmental toxicity, and neurotoxicity). Note that minimal variability (i.e., < 3-fold) exists
among the acute and chronic non-cancer hazard values (i.e., HEC) used in this assessment.
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Most of the acute exposure scenarios for occupational and consumer uses presented risks based
on concerns for adverse developmental effects that may occur as a result of a single exposure to
1-BP during a critical window of susceptibility. Particularly, inhalation risks were identified for all
occupational and consumer acute exposure scenarios, with only a few MOE values above the
benchmark MOE of 100. These included the 50th percentile estimates for dry cleaning
(modeling post-EC worker and pre-EC occupational non-user), vapor degreasing (monitoring
post-EC occupational non-user), and cold cleaning (modeling post-EC occupational non-user);
and for the 95th percentile estimates for vapor degreasing (monitoring and modeling post-EC
occupational non-user) and cold cleaning (modeling post-EC occupational non-user).
There is a concern for a range of adverse human health effects other than cancer that may
appear after chronic exposures to 1-BP during the occupational use of 1-BP-containing spray
adhesives, dry cleaning, and degreasing activities. The greatest concern is for nervous system
effects, followed by developmental effects (i.e., decreased live litter size), and then reproductive
toxicity, kidney toxicity, and liver toxicity, with an overall higher risk for the spray adhesive
exposure scenarios. In general, risks were observed across all of the uses in workers and
occupational non-users. High-end (95th percentile) exposures pre-EC had risks for workers and
occupational non-users for all health effects in all the uses evaluated. Furthermore, there are
risks for adverse effects on the nervous system and development regardless of the type of 1-BP
exposure (50th percentile/central tendency or 95th percentile/high-end) pre-EC in all the uses
evaluated. Occupational non-users had risks for adverse effects on the nervous system and
development at high-end (95th percentile/high-end) exposures regardless of the availability of
engineering controls for most uses.
Cancer risks were presented as added lifetime risks, meaning the the probability that an
individual will develop cancer as a result of occupational exposure over a normal lifetime of
70 years. Added lifetime cancer risk estimates from 1-BP exposure were compared to
benchmark cancer risk levels ranging from 10"6 to 10"4. All of the spray adhesive exposure
scenarios using monitoring data exceeded the benchmark cancer risks of 10"6,10"5 and 10"4
and in many cases exceeded the benchmark cancer risks by 2-3 orders of magnitude. This
analysis resulted in higher modeled incidences of cancer in the commercial use of spray
adhesives, vapor degreasing and cold cleaning, dry cleaning and aerosol degreasing in
descending order. Thus, the greatest potential for added cancer risk came from the
occupational exposures to commercial adhesive and vapor cold cleaning degreaser uses.
Furthermore, higher added cancer risk estimates resulted from direct use of the adhesive and
degreaser when there was a lack of local exhaust ventilation at the workplace.
Main Conclusions of this Risk Assessment
Most acute exposure scenarios for occupational and consumer uses presented risks based on
concerns for adverse developmental effects that may occur as a result of a single exposure to
1-BP during a critical window of susceptibility. Particularly, inhalation risks were identified for all
occupational and consumer acute exposure scenarios, with only a few MOE values above the
benchmark MOE of 100 (acceptable risk range). These included the 50th percentile estimates for
dry cleaning (modeling post-EC worker and pre-EC occupational non-user), vapor degreasing
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(monitoring post-EC occupational non-user), and cold cleaning (modeling post-EC occupational
non-user); and for the 95th percentile estimates for vapor degreasing (monitoring and modeling
post-EC occupational non-user) and cold cleaning (modeling post-EC occupational non-user).
There is a concern for a range of adverse human health effects due to chronic inhalation
exposures resulting from 1-BP use in spray adhesive, dry cleaning, and degreasing applications.
Cancer and neurological effects represent the greatest human health concern for chronic
exposure, with the highest risks expected for the spray adhesive occupational exposure
scenario. In general, risks were observed across all uses in workers and occupational non-users.
High-end (95th percentile/pre-EC) exposures (considered to represent exposure levels at the
baseline exposure condition) showed risks to workers and occupational non-users for all health
effects and all use scenarios evaluated. Risks for adverse neurological and developmental
effects were apparent regardless of the type of 1-BP exposure (50th percentile/central tendency
or 95th percentile/high-end) pre-EC for all the uses evaluated. Occupational non-users showed
risks for adverse neurological and developmental effects with high-end exposures (95th
percentile) regardless of the availability of engineering controls for most use scenarios.
Cancer risks were determined as added lifetime cancer risks, meaning the probability that an
individual will develop cancer as a result of occupational exposure over a normal lifetime of
70 years. Added lifetime cancer risk estimates from 1-BP exposure were compared to
benchmark cancer risk levels of 10~6,10~5 and 10~4 (i.e., 1 in 10,000, 1 in 100,000 and 1 in
1,000,000). All of the spray adhesive exposure scenarios evaluated using monitoring data
exceeded the benchmark cancer risk levels by multiple orders of magnitude and were near or
above the cancer risk of 10"2 (1 in 100). This analysis showed higher estimated cancer
incidences for occupational exposures associated with commercial use of 1-BP in spray
adhesives, vapor degreasing, cold cleaning, dry cleaning and aerosol degreasing in descending
order. A greater cancer risk was observed with the spray adhesive and degreasing (vapor, cold
cleaning) occupational exposure scenarios, with the highest risks resulting from direct use of
1-BP containing spray adhesive and degreasing formulations in the absence of engineering
controls (e.g., local exhaust ventilation) in the workplace.
EPA/OPPT estimated the population size for workers and occupational non-users at risk as:
• Spray Adhesives: 1,503 to 11,952
• Dry Cleaning and Spot Cleaning at Dry Cleaning: 1,088
• Vapor Degreasing: 4,712 to 23,558
• Aerosol Degreasing: 2,466 to 12,329
At this time, there is not sufficient information to develop estimates of the number of workers
and occupational non-users potentially exposed to 1-BP during cold-cleaning; however, the use
of 1-BP in this sector is expected to be minimal.
Also, at this time, there is not sufficient information to develop estimates of the populations for
consumers and non-users exposed to 1-BP during the use of aerosol spray adhesives, aerosol
spot removers, and aerosol cleaners and degreasers.
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In summary, the risk assessment showed the following risk findings:
There Are Non-Cancer Risks Identified for Consumers as a Result of Acute Exposure to 1-BP
from Use in Spray Adhesives, Spot Removers, and Degreasers.
A concern for adverse developmental effects was identified for all acute consumer exposure
scenarios (i.e., MOEs were below the benchmark MOE of 100), with 1-BP use in aerosol
spray cleaners and degreasers showing the greatest risk. Risks for most acute consumer
scenarios were 1-2 orders of magnitude below the benchmark MOE.
There Are Non-Cancer Risks Identified for Workers as a Result of Acute Exposure to 1-BP from
Occupational Use in Spray Adhesives, Dry Cleaning, and Degreasing Operations.
A concern for non-cancer risks (including risks to workers and occupational non-users) was
identified for all but three acute occupational exposure scenarios (i.e., MOEs were below the
benchmark MOE of 100), with 1-BP use in spray adhesives showing the greatest risk. Risks for most
acute occupational scenarios were 1-2 orders of magnitude below the benchmark MOE.
There are Non-Cancer Risks Identified for Workers as a Result of Chronic Exposure to 1-BP
from Occupational Use as a Spray Adhesive, Dry Cleaning (including as a spot cleaner), and
Degreasing Operations (vapor, cold cleaning, and aerosol)
A concern for non-cancer risks (including risks to workers and occupational non-users) was
identified for all chronic occupational exposure scenarios evaluated based on a range of adverse
human health effects. In general, higher risks were indicated for adverse neurological effects in
association with 1-BP use in spray adhesives.
All chronic occupational exposure scenarios presented risks for adverse neurological or
developmental effects in the absence of engineering controls (pre-EC).
In many instances, occupational non-users with chronic high-end exposures (95th percentile)
showed risks for adverse neurological effects regardless of the availability of engineering
controls.
Risks for non-cancer effects following chronic occupational exposure (without engineering
controls) were 2-3 orders of magnitude below the benchmark MOE.
There are Added Cancer Risks Identified for Workers as a Result of Chronic Exposure to 1-BP
from Occupational Use as a Spray Adhesive, Dry Cleaning (including as a spot cleaner), and
Degreasing Operations (vapor, cold cleaning, and aerosol)
Added cancer risks were identified for workers and occupational non-users who may be exposed
as a result of 1-BP use in spray adhesive, dry cleaning (including spot cleaning), and degreasing
operations (vapor, cold cleaning, and aerosol).
Cancer risk estimates exceeded 1 in 1,000 (exceeding all of the cancer risk benchmarks) for
all occupational use scenarios evaluated (workers and occupational non-users) based on
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monitoring and modeling estimates (regardless of the use of engineering controls), with
relatively few exceptions. 1-BP use in spray adhesives presented the greatest cancer risk
concern.
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Appendix A MARKET INFORMATION
l-BP is a high production volume chemical (over 15 million Ib in 2011) used in numerous
solvent applications including non-aerosol solvent cleaning, spray adhesives, and dry cleaning.
In the past, l-BP was used as a solvent for fats, waxes, or resins and as an intermediate in
pharmaceutical, insecticide, quaternary ammonium compound, flavor, and fragrance synthesis
(NTP. 2013).
A-l
Production Volume
There has been a tremendous change in production volume of l-BP from 1986 to 201215. The
reported production volume of l-BP has steadily increased since 1986 as seen in Table_Apx A-l.
1-BP's use may have recently increased in many industrial applications because the chemical is
used as an alternative to ozone-depleting substances and chlorinated solvents. l-BP was
reported as used as a solvent for cleaning or degreasing for the 2012 CDR (summarized in EPA
(2013b).
Table_Apx A-l Production Volume Data from 1986 to 2012 (Ibs)
Chemical
l-BP
1986
10K-<500K
1990
10K-<500K
1994
500K-<1M
1998
1M-<10M
2002
1M-<10M
2006
1M-<10M
2012
15,348,727
Import volumes for l-BP reported to the 2012 CDR were claimed confidential and are therefore
not publically available. Import data for the chemical from other sources indicate that
10.9 million pounds of brominated derivatives of acyclic hydrocarbons were imported into the
U.S. in 2007 which dropped to 10.3 million pounds in 2011 (NTP, 2013). Import data for l-BP
alone were not located, and therefore the category "brominated derivatives of acyclic
hydrocarbons" includes import volumes for chemicals other than l-BP.
A-2
Manufacturers
The most recently-collected EPA production information, the 2012 CDR data, indicates two
companies that manufacture and three that import l-BP in the United States (U.S. EPA, 2013b).
Table_Apx A-2 contains a list of U.S. l-BP manufacturers and importers. For the 2012 CDR cycle,
manufacturers (including importers) of substances on the TSCA inventory were required to
report information about those substances manufactured (including imported) in amounts of
25,000 Ib or more at a single site during calendar year 2011. Additional CDR information is
included in Appendix B. An industry estimate of the price of l-BP ranges from $40/gallon to
$64/gallon (TURI. 2012).
15 In CDR reporting periods prior to 2012, production volumes were reported in the public database in ranges
instead of a single value.
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Table_Apx A-2 CDR Manufacturers and Importers of 1-BP in 2011
Company
Albemarle Corporation
CBI
Dow Chemical Company
ICL
Special Materials Company
City
Magnolia
CBI
Midland
St. Louis
New York
State
AR
CBI
Ml
MO
NY
Manufacture
Yes
Yes
No
No
No
Import
No
No
Yes
Yes
Yes
Source: EPA (2013b)
The Hazardous Substances Data Bank also lists Diaz Chemical Corporation as a possible
manufacturer of the chemical (HSDB)
blends include:
• Petroferm;
• M.G. Chemicals;
• Albatross USA;
• Alpha Metals;
• Amity UK;
• Enviro Tech International;
• Poly Systems USA;
• Baker (NTP-CERHR. 2003).
Other companies that have or are marketing 1-BP solvent
A-3
Degreasers
1-BP is primarily used as a vapor degreaser for cleaning optics electronics, plastics, and metals
(NTP, 2013) and (NCDOL, 2013). The prevalence of its use is partly due to its high quality,
compatibility with many metals, low tendency to cause corrosion, and ability to be used in most
modern vapor degreasing equipment (ICF Consulting, 2004; UNEP, 2001). The vapor degreasing
sector is assumed to account for six to eight million pounds of 1-BP use per year (U.S. EPA,
2007c). The number of businesses in this use sector of 1-BP is estimated at 500 to 2,500
businesses (U.S. EPA, 2007c). Vapor degreasing products are estimated to contain between 80
and >95 percent 1-BP by weight.
The dominant solvents historically used for vapor degreasing are methyl chloroform, methylene
chloride, and CFC-113 (TURI, 1996). However, both methyl chloroform and CFC-113 were
phased-out in 1996 under the Montreal Protocol. Along with methylene chloride, other popular
solvents currently used in the vapor degreasing industry are trichloroethylene and
perchloroethylene. As part of EPA's Significant New Alternatives Policy (SNAP) Program, EPA
issued a final rule in 2007 determining 1-BP to be an acceptable substitute to methyl
chloroform and CFC-113 in the solvent cleaning sector in industrial equipment for metals
cleaning, electronics cleaning, or precision cleaning (U.S. EPA, 2013c). The Brominated Solvents
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Consortium (BSOC) estimated that 1-BP may take over a large portion of the methyl chloroform
market because of the similar performance characteristics and prices of the chemicals (UNEP,
2001).
In addition to vapor degreasing, 1-BP is used in cold cleaning which covers a wide variety of
machines or other cleaning processes. In vapor degreasing the solvent is heated to its boiling
point and then the component to be cleaned passes through the vapor. With cold cleaning,
even if the solvent is heated above room temperature, it never reaches the solvent's boiling
point. Components are dipped, sprayed, wiped or run through the solvent on an inline,
conveyor type machine. The current number of businesses using 1-BP in cold cleaning is
unknown.
A-4 Spray Adhesives
1-BP adhesives are primarily used in foam cushion manufacturing and, to a lesser degree, for
laminates (NTP, 2013) and (HSIA, 2010). Approximately one third of all foam cushion
manufacturers use 1-BP based glues (Urbina, 2013). While 1-BP is used in this industry, it is not
the primary chemical used in most spray adhesives due to cheaper solvents being able to fit the
same need. Some companies use 1-BP instead of cheaper alternatives because it is less
flammable, but larger companies in the foam cushion manufacturing industry will likely use a
less expensive, flammable solvent and add fire-proofing. The adhesives sector is assumed to
account for five to seven million pounds of 1-BP use per year (U.S. EPA, 2007b). The number of
businesses in this use sector of 1-BP is estimated to be between 100 and 280 (U.S. EPA, 2007c).
Spray adhesive products are estimated to contain between 35 and 85 percent 1-BP by weight.
Global demand volume for adhesives and sealants increased by 2.8 percent in 2012 and was
expected to grow at a rate of 3.5 to 4 percent through 2013 (FEICA, 2013). Note that these
estimates do not necessarily pertain to solely spray adhesives. Methyl chloroform had been the
dominant adhesive before being phased out by the Montreal Protocol in 1990 (Adams, 2008).
Alternatives to methyl chloroform include water-based adhesives and methylene chloride.
However, water-based adhesives perform poorly and methylene chloride is subject to strict
OSHA TWA exposure limits (Adams, 2008). 1-BP gained popularity as an alternative to both of
these options because it is non-flammable, fast-drying and works well in foam-fabricating
formulations (Adams, 2008).
In 2007, EPA proposed to list 1-BP as an unacceptable alternative to CFC-113 and methyl
chloroform for adhesive solvents (U.S. EPA, 2013c). Many in the industry have voluntarily
halted use of 1-BP, including Protonique, Great Lakes, and Atofina (Urbina, 2013). Current
production and use data for 1-BP spray adhesives could not be found, most likely due to effects
of EPA's proposed rule.
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A-5 Aerosol Solvents
l-BP aerosol solvents are often used to spot clean electrical or electronic equipment, aircraft
maintenance or synthetic fiber production (FR, 2007 as cited in (NTP, 2013). It may also be used
in asphalt production (OSHA, 2013). An estimated 1,000 to 5,000 businesses used 1-BP-based
aerosol solvents in 2002 (U.S. EPA, 2007c). Aerosol solvent products are estimated to contain
between 10 and 100 percent l-BP by weight.
The Consumer Specialty Products Association (CSPA) conducted a survey of 29 member
businesses that use 1-BP-based aerosol solvent products and estimated that 690,900 pounds of
aerosol solvents were sold by 8 companies per year (CSPA, 2007). CSPA acknowledged that
although this figure did not represent the entire market, it did capture a significant portion of
l-BP industrial aerosol products (CSPA, 2007). This figure was consistent with EPA estimates of
0.5-2 million pounds of l-BP aerosols sold per year (U.S. EPA, 2007b, c). The Halogenated
Solvents Industry Association estimated in 2010 that l-BP solvents in the U.S. were growing at a
rate of 15 to 20 percent per year (HSIA, 2010). Note that it is unclear whether this estimate
refers to just aerosol solvents, or all cleaning solvents (which would include vapor degreasing).
A-6 Dry Cleaning
One of the most commonly used l-BP products in dry cleaning is DrySolv®. DrySolv® is a
mixture of l-BP (>87 percent by weight) and nitromethane and 1,2-butylene oxide (<5 percent)
(EnviroTech International, 2013). The product is manufactured by Enviro Tech International,
Inc., a small company with an estimated 10-49 employees and annual sales of $5 to $9.9 million
(IdeVw, 2013; Thomasnet.com, 2013). DrySolv® evolved from EnSolv®, a l-BP degreasing and
cleaning solvent used in various industries including aerospace, precision engineering, medical
equipment, and electronics (Childers, 2008). Fabrisolv™ XL, manufactured by Poly Systems USA,
is another 1-BP-based dry cleaning solvent (Poly Systems USA, 2013). Poly Systems USA is also a
small business, with estimated annual revenue of $1.7 million (Manta Media, 2015).
At the end of 2007, the California Air Resources Board (CARB) passed the Airborne Toxic Control
Measure for Emissions of Perchloroethylene from Dry Cleaning Operations (Dry Cleaning ATCM)
into law. The Dry Cleaning ATCM requires all perchloroethylene dry cleaning facilities in
California with machines at co-residential facilities to be removed by January 1, 2023 (CARB,
2009). The law also requires perchloroethylene dry cleaning machines that are 15 years or older
to be removed by 2023. Literature provided to affected dry cleaning facilities by CARB listed
l-BP as one of the seven available perchloroethylene alternatives (CARB, 2009).
It is estimated that only a small fraction of the 36,000 dry cleaning establishments in the U.S.
(NIOSH, 2012) use l-BP solvents. According to figures from the Dry Cleaning and Laundry
Institute (DLI) cited in 2009, only about 50 dry cleaning systems in the U.S. are using DrySolv®
compared to 70 percent of the market that still uses perchloroethylene, 27 percent using
hydrocarbon, and 2 percent using GreenEarth (Vince, 2009). Findings from a survey conducted
about dry cleaning solvent systems in 2009 by AmerianDrycleaner.com revealed that 2.0
percent of respondents use DrySolv® (Murphy, 2009). Respondents reported using other
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solvents such as perchloroethylene (50.5 percent), high-flash point hydrocarbon (33.3 percent),
GreenEarth (11.1 percent), liquid C02 (2.0 percent), Solvair (1.0 percent), petroleum (14.1
percent), and GreenJet (2.0 percent) (Murphy, 2009). The survey also polled respondents on
which solvent system they plan to use in the next dry cleaning machine they purchase. Only 4.1
percent of respondents indicated that the next solvent system they plan to use is DrySolv®
compared to 27.6 percent for high-flash point hydrocarbons, 16.3 percent for
perchloroethylene , 14.3 percent for GreenEarth, 11.2 percent for Solvair, 5.1 percent for low-
flash petroleum, and 4.1 percent for liquid C02 (Murphy, 2009).
Overall, growth of the 1-BP market is forecasted to be small according to a five-year projection
by DLI (Vince, 2009). The Institute also predicted that use of perchloroethylene and liquid C02
systems will decrease and that there will be moderate growth in the use of hydrocarbon
systems, Solvair, and GreenEarth. Use of wet cleaning was forecasted to grow from 2009 to
2014 (Vince. 2009).
1-BP is considered a drop-in replacement for perchloroethylene in existing dry cleaning
machinery (TURI, 2012). Perchloroethylene has historically been the standard dry cleaning
solvent due to its effectiveness, ease of use, and relatively low cost (TURI, 2012). However, due
to human health and environmental concerns associated with perchloroethylene, many states
have taken action to manage perchloroethylene's use in dry cleaning (U.S. EPA, 2012b).
A-7 Spot Cleaners
1-BP is used in some spot cleaner formulations in commercial dry cleaning businesses.
Commercial dry cleaners may spot clean garments both before and after the items are run
through the dry cleaning machine. While 1-BP is in known formulations and currently used to a
certain degree within the dry cleaning industry, potential regulatory action on
perchloroethylene could increase the presence of 1-BP in this sector.
A-8 Consumer Uses
EPA/OPPT searched the NIH Household Products Database, various government and trade
association sources (including Halogenated Solvents Industry Association, Association of the
European Adhesive and Sealant Industry, and the National Toxicology Program reports) for
products containing 1-BP, company websites for SDSs, Kirk-Othmer Encyclopedia of Chemical
Technology, and general internet searches. The NIH Household Products Database and Kirk-
Othmer Encyclopedia of Chemical Technology contained no relevant information on consumer
products containing 1-BP. Through the other search means, EPA/OPPT identified a number of
products available to consumers which contain 1-BP. There may be other consumer products
containing 1-BP which are available to consumers since not all SDSs display a complete list of
chemical ingredients, therefore, some products may contain 1-BP but this cannot be confirmed
by EPA. However, the availability of the products found with percent 1-BP by weight ranging
from 1 to 100 raised sufficient concern within the Agency to include these uses in the Risk
Assessment.
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Table_Apx A-3 1-BP Consumer Use Products
Use
Aerosol
Spray
Adhesive
Aerosol
Spot
Remover
Aerosol
Spray
Cleaner or
Degreaser
Mold
Release
Coin
Cleaner
Refrigerant
Flush
Lubricant
Company
Maple Leaf Sales II
ITW TACC
Choice Brand
Adhesives
Blair Rubber
Company
Satellite City a
Albatross USA
EnviroTech
Pettyjohn's Solutions
The Sherwin-Williams
Company b
ITW Pro Brands
ITW Pro Brands
ZEP, Inc
ACL, Inc
CRC Industries, Inc
CRC Industries, Inc
MRO Solutions
Osborn
ITW Chemtronics b
ITW Chemtronics b
Sprayon
Flexbar Machine
Corporation
Amity International
Technical Chemical
Company
Slide Products, Inc
Slide Products, Inc
Product
K-Grip 503
STA'-PUTSP4H Canister
Adhesive
751G
Endurabond™ Normac900R-
NPB
NCF Accelerator
Everblum Gold Cleaning Fluid
DrySolv Spray Testing &
Spotter
Homerun Cleaning Fluid
SPRAYON LIQUI-SOL® Food
Grade ULTRA-FORCE™ Safety
Solvent & Degreaser
LPS Instant Super Degreaser
LPS NoFlash Nu
Power Solv 5000
Precision Rinse NS
Super Degreaser/Cleaner
Cable Clean RD
525 Contact Cleaner
76334 High Tech Electronic
Cleaner
Electro-Wash NR
Kontact Restorer
EL 2846 Non-Chlorinated Flash
Free Electronic Solvent
Epoxy Parfilm Paintable Mold
Release
Koinsolv
Johnsen's Premium AC Flush
Non-flammable
Cutting Oil -aerosol
Cutting Oil -bulk
%1-BP
(wt%)
35-60
35-60
40-60
60-85
98-99
20-30
>93
>96
100
60-70
60-70
60-100
65-75
90-100
1-3
47-84
50
65-75
65-75
96
35-65
>93
>90
10-20
11
Source
(Maple Leaf Sales II Inc.,
2013)
(ITW Inc., 2014)
(Choice Brand Adhesives,
2010)
(Blair Rubber Co., 2011)
(Satellite City Instant
Glues, 2015)
(Albatross USA Inc., 2015)
(EnviroTech International,
2013)
(Pettyjohn's Solutions,
2012)
(Sherwin Williams, 2014)
(ITW Pro Brands, 2015)
(ITW Pro Brands, 2014)
(ZEP, 2015)
(ACL Inc., 2014)
(CRC Industries Inc., 2014)
(CRC Industries Inc., 2015)
(MRO Solutions, 2015)
(Osborn, 2015),
(ITW Chemtronics, 2008)
(ITW Chemtronics, 2012)
(Sprayon Products, 2014)
(Flexbar Machine
Corporation, 2010)
(Amity International, 2006)
(TCC, 2014)
(Slide Products Inc., 2012b)
(Slide Products Inc., 2012a)
Notes:
a Technically, the NCF Accelerator is added to another spray adhesive to make it dry
more quickly.
b Not currently made by the manufacturer, but available on the secondary market.
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Appendix B
1-BP
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CHEMICAL DATA REPORTING RULE DATA FOR
EPA's 2012 Chemical Data Reporting (CDR) reported a 1-BP production volume of 15.4 million
pounds. Albemarle Corporation and a CBI company reported domestic manufacturing of 1-BP
(U.S. EPA, 2012c). Dow Chemical Company, Special Materials Company, and ICL reported
imports of 1-BP (U.S. EPA, 2012c). Data in Table_Apx B-l through Table_Apx B-3 were extracted
from the 2012 CDR records (U.S. EPA. 2012c).
Table_Apx B-l National Chemical Information for 1-BP from 2012 CDR
Production Volume (aggregate)
Maximum Concentration (at manufacture or import site)
Physical form(s)
Number of reasonably likely to be exposed industrial manufacturing,
processing, and use workers (aggregated)
Was industrial processing or use information reported?
Was commercial or consumer use information reported?
15.4 million pounds
>90%
Liquid
> 1,000
Yes
Yes
Table_Apx B-2 Summary of Industrial 1-BP Uses from 2012 CDR
Industrial Sector
(Based on NAICS)
Soap, Cleaning Compound,
and Toilet Preparation
Manufacturing
Soap, Cleaning Compound,
and Toilet Preparation
Manufacturing
Industrial Function
Solvents (for cleaning or
degreasing)
Solvents (for cleaning or
degreasing)
Type of Processing
Processing-repackaging
Processing-incorporation
Abbreviations: NAICS=North American Industry Classification System
Table_Apx B-3 Commercial/Consumer Use Category Summary of 1-BP
Commercial/ Consumer
Product Category
Cleaning and Furnishing Care
Products
Electrical and Electronic
Products
Intended for Commercial
and/or Consumer Uses or Both
Commercial
Commercial
Intended for Use in Children's
Products in Related Product
Category
No
No
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Appendix C STATE REGULATIONS OF 1-BP
Table_Apx C-l State 1-BP Regulations
State Regulation
Link or Reference
California
California
California's
Proposition 65
list for
developmental,
female and
male
reproductive
toxicity (Intent
to list for
cancer 7/10/15)
State of California Environmental Protection Agency Office
of Environmental Health Hazard Assessment (OEHHA) (
2015). OEHHA Proposition 65 Notice of Intent to List:
1-Bromopropane.
http://oehha.ca.gov/prop65/CRNR notices/admin listing/in
tent to list/NOIL0710151bromopropane.html
Proposed a
permissible
exposure limit
(PEL) at 5 ppm
as an 8-hr time-
weighted
average (TWA)
Division of Occupational Safety and Health, Department of
Industrial Relations, State of California (2009a). Permission
Exposure Limits for Chemical Contaminants.
http://www.dir.ca.gov/oshsb/airborne contaminants09.htm
California
Biomonitoring
California
Designated
Chemicals
State of California Environmental Protection Agency Office
of Environmental Health Hazard Assessment (CA EPA
OEHHA) ( 2015) Biomonitoring California Designated
Chemicals.
http://oehha.ca.gov/prop65/CRNR notices/admin listing/in
tent to list/NOIL0710151bromopropane.html
Massachusetts
Toxic or
hazardous
substances list
Massachusetts
Minnesota
Higher hazard
substances
New
Hampshire
Listed chemical
of high concern
(Development,
Reproduction)
Class III
Regulated Toxic
Air Pollutants
Massachusetts Executive Office of Energy and Environmental
Affairs (2013) Massachusetts 301 CMR 41.00: Toxic or
Hazardous Substances List.
http://www.mass.gov/eea/docs/eea/ota/tur-prog/clean-tdi-
301-cmr-41.pdf
Massachusetts Executive Office of Energy and Environmental
Affairs (2015) Designation of TURA Higher & Lower Hazard
Substances in Massachusetts, February 2015
http://www.mass.gov/eea/docs/eea/ota/programs/hhs-lhs-
fact-sheet-final-2015.pdf
Minnesota Department of Health (2013) Chemicals of High
Concern list, July 1, 2013.
http://www. health, state, mn.us/divs/eh/hazardous/topics/t
oxfreekids/chclist/mdhchc2013.pdf
New Hampshire Department of Environmental Service
(2013) Regulated Toxic Air Pollutants, New Hampshire Code
of Administrative Rules, CHAPTER Env-A 1400, Table-1450-1,
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New Jersey
Ambient Air
Level (AAL)
2,096 ug/m3
(24 hours), 499
M€/m3 (Annual)
Pennsylvania
Rhode Island
Right to Know
Hazardous
Substance List,
Special Health
Hazard
Substance List
(F3-
Flammable -
Third Degree)
Hazardous
Substance Lists
Toxic Air
Contaminants
Acceptable
Ambiente
Levels 5,000
ug/m3 (24
hour), 1,000
ug/m3 (annual)
adopted May 26, 2006
http://des.nh.gov/organization/divisions/air/pehb/ehs/atp/
documents/toxl istann.pdf
State of New Jersey Department Health (2010) Right To
Know Hazardous Subtances List
http://www.ni.gov/health/eoh/rtkweb/documents/hsl alph
a.pdf
Pennsylvania Department of Labor and Industry (1982)
Pennsylvania Worker and Community Right-to-Know Act
http://www. portal.state, pa. us/portal/server.pt?open=514&
obilD=552975&mode=2
State of Rhode Island Department of Environmental
Management (2008) Air Pollution Control Regulation No. 22
Air Toxics
http://www.dem.ri.gov/pubs/regs/regs/air/air22 08.pdf
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Appendix D ENVIRONMENTAL EFFECTS SUMMARY
The ecological hazard summary of 1-BP is based on available hazard data. In addition, an
updated literature survey was conducted to identify articles on ecological toxicity. The search
terms included freshwater and saltwater fish, aquatic invertebrates, and aquatic plants; pelagic
and benthic organisms; acute and chronic sediment toxicity in freshwater and saltwater and
terrestrial toxicity to soil organisms, birds, and mammals. The test species, test conditions,
toxicity endpoints, statistical significance, and strengths/limitations of the study were evaluated
for data quality.
Table_Apx D-l contains all data considered for the ecological hazard characterization of 1-BP.
1-BP has been tested for acute aquatic toxicity. With the exception of algae, no chronic aquatic
or terrestrial data were found. In order to characterize the effects of 1-BP to the environment, a
hazard rating was assigned based on EPA methodology for existing chemical classification (U.S.
EPA, 2013a). Included in this assessment are five acute aquatic toxicity studies which includes
both algae and micro-organism studies. There are no available sediment, soil, avian, chronic
fish, or chronic aquatic invertebrate toxicity studies found in literature for 1-BP.
The data show that there is a low acute ecotoxicity for fish, aquatic invertebrates, aquatic
plants and micro-organisms. Hazard to sediment and terrestrial organisms as well as chronic
aquatic toxicity is expected to be low since 1-BP does not bioaccumulate, does not persist in the
environment, highly volatile, and photodegrades quickly.
%
Table_Apx D-l Ecological Hazard Characterization of 1-Bromopropane
Test Species
Test
System
Duration
End-
point
Cone.
(mg/L)
Test Analysis
Effect
References
Fish
Rainbow trout
(Oncorhynchus mykiss)
Fathead minnow
(Pimphales promelas)
Freshwater,
semi-static
Freshwater,
flow-
through
96 hours
96 hours
ECso
NOEC
LCso
24.3
1.77
67.3
Measured
Measured
Not Reported
Mortality
(ECHA, 2015)
2008 study
Geiger (1988)
Aquatic Invertebrates
Water flea
(Daphnia magna)
Fresh
48 hours
ECso
NOEC
99.3
29.6
Measured
Immobility
(ECHA, 2015)
2008 study
Algae
Green algae
(Pseudokirchnerella
subcapitata)
Freshwater,
static
96 hours
ECso
ECso
NOEC
52.4
72.3
12.4
Biomass
Growth Rate
Biomass and
Growth Rate
(ECHA, 2015)
2008 study
Micro-organism
n/a
Freshwater,
static
5 min
ECso
270
Mortality
(ECHA, 2015)
2008 study
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D-l Acute Toxicity to Aquatic Organisms
Acute Toxicity to Fish
The 96-hour LCso value for 1-BP with rainbow trout was 24.3 mg/L. Conditions were in a sealed
environment to prevent the volatile test substance from escaping.
The 96-hr LCso for 1-BP with fathead minnow was 67.3 mg/L. Affected fish lost schooling
behavior and swam near the tank surface. They were hypoactive, underreactive to external
stimuli, had increased respiration, were darkly colored and lost equilibrium prior to death.
Acute Toxicity to Aquatic Invertebrates
The ECso and NOEC for aquatic invertebrate were 99.3 mg/L and 29.6 mg/L, respectively.
Toxicity to Aquatic Plants
There were no available acute or chronic toxicity studies that characterize the hazard of 1-BP to
aquatic plants.
Toxicity to Micro-organisms
The ECso and NOEC for micro-organisms toxicity study for a 5 minute time period was 270 mg/L
and 100 mg/L, respectively. Testing protocols require the test duration to be 3 hours and
rigorous aeration of the test vessels. The submitter reduced the test duration from 3 hours to
5 minutes due to the volatile nature of 1-BP. To minimize any 1-BP losses, the submitter kept
1-BP test preparations in suspension by stirring via magnetic stirrers and sealed all vessels with
film instead of vigorously aerating the test vessels.
D-2 Chronic Toxicity to Aquatic Organisms
With the exception of algae, no chronic aquatic toxicity data were found. The ECso for the algae
toxicity test was 52.4 mg/L (biomass) and 72.3 mg/L (growth rate). The NOEC for the algae
toxicity test was 12.4 mg/L. The LOEC was not defined in the study; thus, a chronic value (ChV)
was not calculated.
D-3 Toxicity to Sediment and Soil Dwelling Organisms
There were no available acute or chronic toxicity studies that characterize the hazard of 1-BP to
sediment- or soil-dwelling organisms.
D-4 Toxicity to Wildlife
There were no available acute or chronic toxicity studies that characterize the hazard of 1-BP to
wildlife.
D-5 Summary of Environmental Hazard Assessment
Table_Apx D-l provides a summary of the toxicity data available for 1-BP. The acute hazard of
1-BP to aquatic organisms is considered low based on available data. The hazard of 1-BP is
expected to be low for chronic aquatic organisms, sediment, and terrestrial organisms based on
physical and chemical properties of 1-BP.
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Appendix E ENVIRONMENTAL FATE
E-l Fate in Air
If released to the atmosphere, 1-BP is expected to exist solely in the vapor-phase based on its
vapor pressure. In the vapor phase, it is degraded by reaction with photochemically produced
hydroxyl radicals. The half-life of this reaction is approximately 9-12 days assuming a hydroxyl
radical concentration over a 12 hour day of l.SxlO6 hydroxyl radicals per cubic centimeter of air
(Version 4.10 EPISuite). Its atmospheric degradation and its photooxidation products were
investigated for their ozone depletion potential (Burkholder et al., 2002). It was shown that the
hydroxyl radical initiated degradation does not lead to long-lived bromine containing species
that can migrate to the stratosphere. The major photodegradation products were
bromoacetone, propanal and 3-bromopropanal. Bromoacetone was rapidly photolyzed
releasing bromine which was removed from the atmosphere by wet deposition. 1-BP does not
absorb light greater than 290 nm; therefore, degradation of this substance by direct photolysis
is not expected to be an important fate process. The bromoacetone and propanal constitute
about 90% of 1-BP that is degraded in the atmosphere, and they, as well as 3-bromopropanal,
are expected to be rapidly degraded. Apparently, the major atmospheric degradative fate of
1-BP is the rapid and irreversible release of Br atoms. Based on the 1-BP estimated half-life of
9-12 days in air, it is possible that it can undergo long range transport via the atmosphere.
E-2 Fate in Water
When released to water, 1-BP is not expected to adsorb to suspended solids and sediment in
the water column based upon its Koc value of about 40 (U.S. EPA, 2013a). The rate of
volatilization is expected to be rapid based on a Henry's Law constant of 7.3 x 10~3 atm-
m3/mole. 1-BP was reported to achieve 70% of its theoretical biochemical oxygen demand
(BOD) in the MITI (OECD 301C) test (Sakuratani et al., 2005) which is considered readily
biodegradable. However, an OECD 301D (closed bottle) test showed 19.2% degradation after
28 days which is not considered readily biodegradable (European Chemicals (ECHA) registry
substances data base). Bacterial strains isolated from organobromide-rich industrial
wastewater were shown to degrade it (HSDB). Arthrobacter HA1 debrominated 1-BP under
aerobic conditions yielding 1-propanol as a degradation product and Acinetobacter strain GJ70,
isolated from activated sludge was able to utilize it as a carbon source (HSDB). These results
suggest that 1-BP will undergo biodegradation in the environment under aerobic conditions.
Hydrolysis of 1-BP is expected based on studies of (Mabeyand Mill, 1978) and it cited in the
Hazardous Substances Databank (HSDB). A hydrolysis half-life of about 26 days was calculated
at pH 7 and 25 degrees Celsius from its first-order neutral rate constant of S.OlxlO"7 sec"1. The
expected hydrolysis product is propanol and the hydrodebromination product propene is also
possible. Photooxidation in water has not been reported to be an important environmental fate
process. 1-BP is not expected to be persistent in water.
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E-3 Fate in Sediment and Soil
l-BP is expected to have high mobility in soil based on an estimated log Koc of 1.6.
Volatilization is expected to be an important fate process given its relatively high Henry's law
constant of 7.3xlO~3 atm m3/mole and it is expected to volatilize from dry soil surfaces based
upon its high vapor pressure. Its biodegradation is considered to be moderate in sediment and
soil. l-BP is not persistent in sediment or soil.
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Appendix F
WORKERS
APPROACH FOR ESTIMATING NUMBER OF
This appendix summarizes the methods that EPA/OPPT used to estimate number of workers
who are potentially exposed to 1-BP during degreasing, dry cleaning and spot cleaning, and
spray adhesive use. The method consists of the following steps:
1. Identify the North American Industry Classification System (NAICS) codes for the
industry sectors associated with these uses.
2. Estimate total employment by industry/occupation combination using the Bureau of
Labor Statistics' Occupational Employment Statistics (OES) data (2015).
3. Refine the OES estimates where they are not sufficiently granular by using the U.S.
Census' Statistics of US Businesses (SUSB) (2012) data on total employment by 6-digit
NAICS.
4. Estimate the percentage of employees likely to be using 1-BP instead of other chemicals.
5. Combine the data generated in Steps 1 through 4 to produce an estimate of the number
of establishments and employees using 1-BP in each industry/occupation combination,
and sum these to arrive at a total estimate of the number of employees with exposure.
Step 1: Identify Affected NAICS Codes
As a first step, EPA/OPPT identified NAICS industry codes associated with the uses in the scope.
For vapor degreasing, EPA/OPPT referenced EPA'sTrichloroethylene (TCE) risk assessment, in
which EPA/OPPT has identified a list of all possible NAICS industry sectors that may have
degreasing operations (U.S. EPA, 2014c). It should be noted that degreasing encompasses a
large number of industry sectors, and not all facilities in the identified NAICS code will have a
degreasing operation. Additionally, EPA/OPPT identified NAICS codes for repair and
maintenance shops that are likely to perform aerosol degreasing.
For dry cleaning and spray adhesive uses, EPA/OPPT evaluated all NAICS codes and identified
those that are applicable to dry cleaning and foam cushion manufacturing. Table_Apx F-l lists
the proposed 6-digit NAICS codes for the uses. In addition, the table lists the corresponding BLS
NAICS code at the 4-digit or 5-digit level. Note BLS employment data for certain sectors are only
available at the 4-digit or 5-digit NAICS level (see Step 3 for refinement of BLS data).
Table_Apx F-l NAICS Codes for Degreasing, Dry Cleaning, and Spray Adhesive Uses
NAICS
337121
337125
337127
337214
812320
314999
321113
323111
325180
BLS NAICS
337120
337120
337120
337200
812300
314900
321100
323100
325100
Industry
Upholstered Household Furniture Manufacturing
Household Furniture (except Wood and Metal) Manufacturing
Institutional Furniture Manufacturing
Office Furniture (except Wood) Manufacturing
Drycleaning and Laundry Services (except Coin-Operated)
All Other Miscellaneous Textile Product Mills
Sawmills
Commercial Printing (except Screen and Books)
Other Basic Inorganic Chemical Manufacturing
Application
Foam Cushions
Foam Cushions
Foam Cushions
Foam Cushions
Dry Cleaning
Degreasing
Degreasing
Degreasing
Degreasing
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Table_Apx F-l NAICS Codes for Degreasing, Dry Cleaning, and Spray Adhesive Uses
NAICS
325998
326299
331110
331210
331410
331420
332111
332112
332119
332117
332215
332216
332311
332313
332431
332510
332618
332721
332722
332811
332812
332813
332912
332913
332919
332994
332996
332999
333132
333249
333318
333410
333415
333921
333994
333999
BIS NAICS
325900
326200
331100
331200
331400
331400
332100
332100
332100
332100
332200
332200
332300
332300
332400
332500
332600
332720
332720
332800
332800
332800
332900
332900
332900
332900
332900
332900
333100
333200
333300
333400
333400
333900
333900
333900
Industry
All Other Miscellaneous Chemical Product and Preparation
Manufacturing
All Other Rubber Product Manufacturing
Iron and Steel Mills and Ferroalloy Manufacturing
Iron and Steel Pipe and Tube Manufacturing from Purchased
Steel
Nonferrous Metal (except Aluminum) Smelting and Refining
Copper Rolling, Drawing, Extruding, and Alloying
Iron and Steel Forging
Nonferrous Forging
Metal Crown, Closure, and Other Metal Stamping (except
Automotive)
Powder Metallurgy Part Manufacturing
Metal Kitchen Cookware, Utensil, Cutlery, and Flatware
(except Precious) Manufacturing
Saw Blade and Handtool Manufacturing
Prefabricated Metal Building and Component Manufacturing
Plate Work Manufacturing
Metal Can Manufacturing
Hardware Manufacturing
Other Fabricated Wire Product Manufacturing
Precision Turned Product Manufacturing
Bolt, Nut, Screw, Rivet, and Washer Manufacturing
Metal Heat Treating
Metal Coating, Engraving (except Jewelry and Silverware), and
Allied Services to Manufacturers
Electroplating, Plating, Polishing, Anodizing, and Coloring
Fluid Power Valve and Hose Fitting Manufacturing
Plumbing Fixture Fitting and Trim Manufacturing
Other Metal Valve and Pipe Fitting Manufacturing
Small Arms, Ordnance, and Ordnance Accessories
Manufacturing
Fabricated Pipe and Pipe Fitting Manufacturing
All Other Miscellaneous Fabricated Metal Product
Manufacturing
Oil and Gas Field Machinery and Equipment Manufacturing
Other Industrial Machinery Manufacturing
Other Commercial and Service Industry Machinery
Manufacturing
Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing
Air-Conditioning and Warm Air Heating Equipment and
Commercial and Industrial Refrigeration Equipment
Manufacturing
Elevator and Moving Stairway Manufacturing
Industrial Process Furnace and Oven Manufacturing
All Other Miscellaneous General Purpose Machinery
Manufacturing
Application
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
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Table_Apx F-l NAICS Codes for Degreasing, Dry Cleaning, and Spray Adhesive Uses
NAICS
334220
334413
334416
334417
334419
334513
334515
335120
335121
335210
335310
335312
335313
335911
335921
335929
335999
336320
336340
336410
336411
336413
336414
336510
337125
337127
339114
339990
339992
339995
339999
488111
493110
811310
811111
811112
811113
811118
BIS NAICS
334200
334400
334400
334400
334400
334500
334500
335100
335100
335200
335300
335300
335300
335900
335900
335900
335900
336300
336300
336400
336400
336400
336400
336500
337120
337120
339100
339900
339900
339900
339900
488100
493100
811300
811110
811110
811110
811110
Industry
Radio and Television Broadcasting and Wireless
Communications Equipment Manufacturing
Semiconductor and Related Device Manufacturing
Capacitor, Resistor, Coil, Transformer, and Other Inductor
Manufacturing
Electronic Connector Manufacturing
Other Electronic Component Manufacturing
Instruments and Related Products Manufacturing for
Measuring, Displaying, and Controlling Industrial Process
Variables
Instrument Manufacturing for Measuring and Testing
Electricity and Electrical Signals
Lighting Fixture Manufacturing
Residential Electric Lighting Fixture Manufacturing
Small Electrical Appliance Manufacturing
Electrical Equipment Manufacturing
Motor and Generator Manufacturing
Switchgear and Switchboard Apparatus Manufacturing
Storage Battery Manufacturing
Fiber Optic Cable Manufacturing
Other Communication and Energy Wire Manufacturing
All Other Miscellaneous Electrical Equipment and Component
Manufacturing
Motor Vehicle Electrical and Electronic Equipment
Manufacturing
Motor Vehicle Brake System Manufacturing
Aerospace Product and Parts Manufacturing
Aircraft Manufacturing
Other Aircraft Parts and Auxiliary Equipment Manufacturing
Guided Missile and Space Vehicle Manufacturing
Railroad Rolling Stock Manufacturing
Household Furniture (except Wood and Metal) Manufacturing
Institutional Furniture Manufacturing
Dental Equipment and Supplies Manufacturing
All Other Miscellaneous Manufacturing
Musical Instrument Manufacturing
Burial Casket Manufacturing
All Other Miscellaneous Manufacturing
Air Traffic Control
General Warehousing and Storage
Commercial and Industrial Machinery and Equipment (except
Automotive and Electronic) Repair and Maintenance
General Automotive Repair
Automotive Exhaust System Repair
Automotive Transmission Repair
Other Automotive Mechanical and Electrical Repair and
Maintenance
Application
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
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Table_Apx F-l NAICS Codes for Degreasing, Dry Cleaning, and Spray Adhesive Uses
NAICS
811121
811122
811191
811192
811198
811211
811212
811213
811219
811310
811411
811490
451110
BIS NAICS
811120
811120
811190
811190
811190
811200
811200
811200
811200
811300
811400
811400
451110
Industry
Automotive Body, Paint, and Interior Repair and Maintenance
Automotive Glass Replacement Shops
Automotive Oil Change and Lubrication Shops
Car Washes
All Other Automotive Repair and Maintenance
Consumer Electronics Repair and Maintenance
Computer and Office Machine Repair and Maintenance
Communication Equipment Repair and Maintenance
Other Electronic and Precision Equipment Repair and
Maintenance
Commercial and Industrial Machinery and Equipment (except
Automotive and Electronic) Repair and Maintenance
Home and Garden Equipment Repair and Maintenance
Other Personal and Household Goods Repair and
Maintenance
Sporting Goods Stores
Application
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Aerosol degreasing
Step 2: Estimating Total Employment by Industry and Occupation
BLS's OES data (2015) 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/OPPT reviewed the occupation
description and identified those occupations (SOC codes) where workers will potentially come
in contact with 1-BP.
Table_Apx F-2 shows example SOC codes where workers and occupational non-users are likely
exposed to 1-BP at dry cleaning facilities. EPA/OPPT classified the SOC codes into "workers (W)"
(near-field exposure) and "occupational non-users (N)" (far-field exposure), where possible.
Table_Apx F-2 SOC Codes with 1-BP Exposure at Dry Cleaning Facilities
Application
Dry cleaning
SOC
41-2000
49-9040
49-9070
49-9090
51-6010
51-6020
51-6030
51-6040
51-6050
51-6090
^ "^ Occupation
Retail Sales Workers
Industrial Machinery Installation, Repair, and Maintenance
Workers
Maintenance and Repair Workers, General
Miscellaneous Installation, Maintenance, and Repair Workers
Laundry and Dry-Cleaning Workers
Pressers, Textile, Garment, and Related Materials
Sewing Machine Operators
Shoe and Leather Workers
Tailors, Dressmakers, and Sewers
Miscellaneous Textile, Apparel, and Furnishings Workers
Designation
N
W
W
W
W
W
N
N
N
N
After identifying relevant NAICS and SOC codes, EPA/OPPT used BLS data to determine total
employment by industry and by occupation based on the NAICS and SOC combinations. For
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example, there are 106,440 employees associated with 4-digit NAICS 812300 (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 a gross overestimate, because not all
workers employed in that industry sector will be exposed. However, note 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/OPPT's methodology was to further refine the employment estimates by
using total employment data in the U.S. Census' SUSB (2012). 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 1-BP exposure are included. For
instance, OES data are available for the 5-digit NAICS 81230 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.
Only NAICS 812320 is of interest, while the remaining 6-digit NAICS are unlikely to cover dry
cleaning facilities that use 1-BP. The Census data allow us to calculate employment in the
specific 6-digit NAICS of interest as a percentage of employment in the BLS 5-digit NAICS.
Table_Apx F-3 and Table_Apx F-4 provide example calculations. NAICS 812320 make up 48
percent of total employment under NAICS 81230. This percentage can be multiplied by the
occupation-specific employment estimates given in the BLS OES data to further refine our
estimates of the number of employees with potential 1-BP exposure.
For example, the number of workers under NAICS 812320 is calculated as:
206,250 (Employment in NAICS/SOC) x 48% (Granularity Adjustment Percentage) = 98,920
workers and occupational non-users under 6-digit NAICS 812320.
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Table_Apx F-3 Sample Granularity Calculation
NAICS
Industry
Total
Employment
Percent of Total
Employment
5-Digit Parent NAICS
81230
Drycleaning and Laundry Services
290,868
100%
6-Digit NAICS Relevant to 1-BP Use
812320
Drycleaning and Laundry Services (except Coin-
Operated)
139,504
48%
Source: U.S. Census Bureau (2012)
Table_Apx F-4 Estimated 1-BP Employment under NAICS 812320
NAICS
812300
812300
812300
812300
812300
812300
812300
812300
812300
812300
812300
Total
SOC
CODE
41-2000
49-9040
49-9070
49-9090
51-6010
51-6020
51-6030
51-6040
51-6050
51-6090
41-2000
SOC Description
Retail Sales Workers
Industrial Machinery Installation,
Repair, and Maintenance Workers
Maintenance and Repair Workers,
General
Miscellaneous Installation,
Maintenance, and Repair Workers
Laundry and Dry-Cleaning Workers
Pressers, Textile, Garment, and
Related Materials
Sewing Machine Operators
Shoe and Leather Workers
Tailors, Dressmakers, and Sewers
Employment by
SOCat4-digit
NAICS level
45,570
1,640
3,410
930
106,440
43,160
1,810
0
~ 3,160
Miscellaneous Textile, Apparel, and 1 130
Furnishings Workers
Retail Sales Workers
K 45,570
206,250
% of Total
Employment
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
48.0%
Estimated
Employment by
SOCat6-digit
NAICS level
21,856
787
1,635
446
51,050
20,700
868
0
1,516
62
21,856
98,920
Sources: U.S. Census Bureau (2012) and U.S. BLS (2015).
Step 4: Estimating the Percentage of Workers Using 1-BP instead of Other Chemicals
In the final step, EPA/OPPT accounted for the market share by applying a factor to the number
of workers determined in Step 3. This accounts for the fact that 1-BP is only one of many
chemicals used for the applications of interest. EPA/OPPT determined the "factor", or 1-BP
market penetration, using information provided in EPA's 1-BP market reports, Use and Market
Profiles (U.S. EPA, 2013c) and Use and Market Profile for 1-Bromopropane in Dry Cleaning (U.S.
EPA, 2013b). For dry cleaning, the market penetration is estimated to be 1.1 percent based on a
2012 survey conducted by AmericanDrycleaner.com.
Step 5: Final Worker Estimates
For the final estimates, EPA/OPPT calculated the number of workers and occupational non-
users in each industry/occupation combination potentially exposed to 1-BP, using the formula
below (note granularity adjustment is only applicable where SOC data are not available at the
6-digit NAICS level):
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Employment in NAICS/SOC (Step 2) x Granularity Adjustment Percentage (Step 3) x Percentage
of Workers Using 1-BP (Step 4) = Employees using 1-BP
For example, the estimated number of workers and occupational non-users under NAICS
812320 from Step 3, after granularity adjustment, is 98,920. Assuming a 1-BP market
penetration of 1.1 percent, the estimated number of workers and occupational non-users using
1-BP under this NAICS code is:
98,920 x 1.1% = 1,088 workers and occupational non-users using 1-BP under NAICS 812320
The number of establishments is calculated by multiplying the total establishments under
6-digit NAICS 812320 by the market penetration.
22,359 establishments under NAICS 812320 x 1.1% = 246 establishments using 1-BP
The number of workers and occupational non-users can then be divided by the number of
establishments to calculate the average number of workers and occupational non-users per
site.
F-l Estimates for Number of Workers Using Spray Adhesives
EPA/OPPT estimated the number of workers potentially exposed to 1-BP in spray adhesives
using Bureau of Labor Statistics' Occupational Employment Statistics (OES) data (2015) and U.S.
Census' Statistics of US Businesses (SUSB) (2012). The method for estimating number of
workers is detailed above in Appendix F. The worker estimates were derived using industry- and
occupation-specific employment data from these sources. The industry sectors and occupations
that EPA/OPPT determined to be relevant to spray adhesive use are presented below.
Table_Apx F-5 presents the NAICS industry sectors relevant to spray adhesive use, while
Table_Apx F-6 presents BLS occupation codes where workers are potentially exposed to 1-BP.
EPA/OPPT designated each occupation code as either "Worker (W)" or "Occupational non-user
(N)" to separately estimate the number of potentially exposed workers and occupational non-
users. There are no occupation codes described as adhesive "sprayers". EPA/OPPT assumed
SOC 51-9121 "Coating, Painting, and Spraying Machine Setters, Operators, and Tenders" and
SOC 51-9191 "Adhesive Bonding Machine Operators and Tenders" could involve manual or
automated spraying of 1-BP adhesives. EPA/OPPT also assumed that assemblers and fabricators
work in areas where the spraying occurs, and are directly exposed. EPA/OPPT assumed
production supervisors and other production workers are "occupational non-users".
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Table_Apx F-5 NAICS Codes for Spray Adhesive Uses in Foam Cushion Manufacturing
NAICS
337121
337125
337127
337214
BIS NAICS
337120
337120
337120
337200
Industry
Upholstered Household Furniture Manufacturing
Household Furniture (except Wood and Metal) Manufacturing
Institutional Furniture Manufacturing
Office Furniture (except Wood) Manufacturing
Table_Apx F-6 SOC Codes for Worker Exposure in the Spray Adhesive Sector
SOC
51-1000
51-2090
51-6000
51-9121
51-9191
51-9198
51-9199
Occupation
Supervisors of Production Workers
Miscellaneous Assemblers and Fabricators
Textile, Apparel, and Furnishings Workers
Coating, Painting, and Spraying Machine Setters, Operators, and Tenders
Adhesive Bonding Machine Operators and Tenders
Helpers-Production Workers
Production Workers, All Other
Exposure
Designation
N
W
N
W
W
N
N
Source: U.S. BLS (2015) W - worker, N - occupational non-user
The number of businesses in this use sector of 1-BP is estimated to be between 100 and 280
(U.S. EPA, 2007b). Based on a total of 2,386 establishments in the industry sectors shown in
Table_Apx F-5, the 1-BP market penetration is 4.2 percent to 11.7 percent. Alternatively, an
article published in The New York Times estimated that 33 percent of the foam cushion industry
uses 1-BP based adhesives (NY Times, as cited in (U.S. EPA, 2013c)). Table_Apx F-7 presents the
estimated number of workers and occupational non-users using the low-end market
penetration of 4.2 percent and the high-end market penetration of 33 percent. The total
number of potentially exposed workers and occupational non-users ranges from 1,503 to
11,952.
Table_Apx F-7 Estimated Number of Workers Potentially Exposed to 1-BP in Spray Adhesive Use in
Foam Cushion Manufacturing
Exposed
Workers
Exposed
Occupational
Non-Users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
Non-Users per
Site
Low-end
551
952
1,503
100
6
10
High-end
4,384
7,568
11,952
795
6
10
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
F-2 Estimates for Number of Workers at Dry Cleaners
EPA/OPPT estimated the number of workers and occupational non-users potentially exposed to
1-BP at dry cleaners using Bureau of Labor Statistics' OES data (2015) and the U.S. Census' SUSB
(2012). The method for estimating number of workers is detailed above in Appendix F. These
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estimates were derived using industry- and occupation-specific employment data from the BLS
and U.S. Census.
Table_Apx F-8 presents the NAICS industry sector relevant to dry cleaning, while Table_Apx F-9
presents BLS occupation codes where workers are potentially exposed to dry cleaning solvents.
EPA/OPPT designated each occupation code as either "Worker (W)" or "Occupational non-user
(N)" to separately estimate the number of potentially exposed workers and occupational non-
users. EPA/OPPT classified laundry and dry cleaning workers, pressers, and machine repairers
as "Workers" because they are likely to have direct exposure to the dry cleaning solvents.
EPA/OPPT classified retail sales workers (e.g., cashiers), sewers, tailors, and other textile
workers as "occupational non-users" because they perform work at the dry cleaning shop, but
do not directly handle dry cleaning solvents.
Table_Apx F-8 NAICS Code for Dry Cleaning
NAICS
812320
BLS NAICS
812300
Industry
Drycleaning and Laundry Services (except Coin-Operated)
Table_Apx F-9 SOC Codes for Worker Exposure in Dry Cleaning
SOC
41-2000
49-9040
49-9070
49-9090
51-6010
51-6020
51-6030
51-6040
51-6050
51-6090
Occupation
Retail Sales Workers
Industrial Machinery Installation, Repair, and Maintenance Workers
Maintenance and Repair Workers, General
Miscellaneous Installation, Maintenance, and Repair Workers
Laundry and Dry-Cleaning Workers
Pressers, Textile, Garment, and Related Materials
Sewing Machine Operators
Shoe and Leather Workers
Tailors, Dressmakers, and Sewers
Miscellaneous Textile, Apparel, and Furnishings Workers
Exposure
Designation
N
W
W
W
W
W
N
N
N
N
Source: U.S. BLS (2015) W - worker, N - occupational non-user
There are 22,359 dry cleaning establishments in the United States under NAICS 812320 (U.S.
Census Bureau, 2012). Among these establishments, only a small subset use 1-BP as a dry
cleaning solvent. In 2009, the Drycleaning and Laundry Institute (DLI) estimated only about 50
dry cleaning systems used DrySolv® (U.S. EPA, 2013b). A more recent survey conducted by
AmericanDrycleaner.com in 2012 indicated that 1.1% of respondents used DrySolv, but did not
specify the number of respondents participating in the survey (Beggs, 2012, as cited in (U.S.
EPA, 2013b)). EPA/OPPT conservatively assumed a 1-BP market penetration of 1.1 percent.
Using this factor, EPA/OPPT estimated that approximately 246 dry cleaning establishments and
1,088 workers and occupational non-users are exposed to 1-BP; see Table_Apx F-10.
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Table_Apx F-10 Estimated Number of Workers Potentially Exposed to 1-BP in Dry Cleaning Shops
Exposed
Workers
821
Exposed
Occupational
non-users
267
Total Exposed
1,088
Estimated
Number of
Establishments
246
Workers per Site
3
Occupational
non-users per
Site
1
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
F-3 Estimates for Number of Workers in Vapor Degreasing
EPA/OPPT estimated the number of workers potentially exposed to 1-BP in vapor degreasing
using Bureau of Labor Statistics' OES data (2015) and (2012) U.S. Census SUBS. The method for
estimating number of workers is detailed above in Appendix F. The worker estimates were
derived using industry- and occupation-specific employment data from these sources. The
industry sectors and occupations that EPA/OPPT determined to be relevant to degreasing uses
are presented below. EPA/OPPT was unable to determine which industry sectors and
occupations perform specific degreasing types (e.g., vapor degreasing versus cold cleaning). It is
possible that establishments under the same NAICS code perform a combination of vapor
degreasing and cold cleaning.
Table_Apx F-ll presents the NAICS industry sectors relevant to degreasing applications. These
NAICS codes were obtained from the list of degreasing NAICS codes in EPA's Work Plan
Chemical Assessment of Trichloroethylene (TCE) (U.S. EPA, 2014c). These codes cover a wide
range of workplaces where degreasing activities could be performed; however, note degreasing
is unlikely to be a primary operation for many of these industries. Therefore, using such a broad
range of NAICS codes likely result in an overestimate.
Table_Apx F-12 presents BLS occupation codes among the relevant NAICS sectors where
workers are potentially exposed to degreasing solvents. EPA/OPPT designated repairers,
mechanics, production workers and assemblers as "Worker (W)" because they are likely to
work directly with the degreasing equipment. In addition, EPA/OPPT assumed engineers,
technicians, and production supervisors could be "Occupational non-user (N)". There are
general uncertainties in how the job duties in these sectors relate to degreasing; it is possible
that employees within a single occupation code perform work both as a "worker" and as an
"occupational non-user".
Table_Apx F-ll NAICS Codes for All Degreasing Types
NAICS
314999
321113
323111
325180
325998
326299
331110
331210
331410
BLS NAICS
314900
321100
323100
325100
325900
326200
331100
331200
331400
Industry
All Other Miscellaneous Textile Product Mills
Sawmills
Commercial Printing (except Screen and Books)
Other Basic Inorganic Chemical Manufacturing
All Other Miscellaneous Chemical Product and Preparation Manufacturing
All Other Rubber Product Manufacturing
Iron and Steel Mills and Ferroalloy Manufacturing
Iron and Steel Pipe and Tube Manufacturing from Purchased Steel
Nonferrous Metal (except Aluminum) Smelting and Refining
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Table_Apx F-ll NAICS Codes for All Degreasing Types
NAICS
331420
332111
332112
332119
332117
332215
332216
332311
332313
332431
332510
332618
332721
332722
332811
332812
332813
332912
332913
332919
332994
332996
332999
333132
333249
333318
333410
333415
333921
333994
333999
334220
334413
334416
334417
334419
334513
334515
335120
335121
BIS NAICS
331400
332100
332100
332100
332100
332200
332200
332300
332300
332400
332500
332600
332720
332720
332800
332800
332800
332900
332900
332900
332900
332900
332900
333100
333200
333300
333400
333400
333900
333900
333900
334200
334400
334400
334400
334400
334500
334500
335100
335100
Industry
Copper Rolling, Drawing, Extruding, and Alloying
Iron and Steel Forging
Nonferrous Forging
Metal Crown, Closure, and Other Metal Stamping (except Automotive)
Powder Metallurgy Part Manufacturing
Metal Kitchen Cookware, Utensil, Cutlery, and Flatware (except Precious)
Manufacturing
Saw Blade and Handtool Manufacturing
Prefabricated Metal Building and Component Manufacturing
Plate Work Manufacturing
Metal Can Manufacturing
Hardware Manufacturing
Other Fabricated Wire Product Manufacturing
Precision Turned Product Manufacturing
Bolt, Nut, Screw, Rivet, and Washer Manufacturing
Metal Heat Treating
Metal Coating, Engraving (except Jewelry and Silverware), and Allied Services to
Manufacturers
Electroplating, Plating, Polishing, Anodizing, and Coloring
Fluid Power Valve and Hose Fitting Manufacturing
Plumbing Fixture Fitting and Trim Manufacturing
Other Metal Valve and Pipe Fitting Manufacturing
Small Arms, Ordnance, and Ordnance Accessories Manufacturing
Fabricated Pipe and Pipe Fitting Manufacturing
All Other Miscellaneous Fabricated Metal Product Manufacturing
Oil and Gas Field Machinery and Equipment Manufacturing
Other Industrial Machinery Manufacturing
Other Commercial and Service Industry Machinery Manufacturing
Ventilation, Heating, Air-Conditioning, and Commercial Refrigeration Equipment
Manufacturing
Air-Conditioning and Warm Air Heating Equipment and Commercial and Industrial
Refrigeration Equipment Manufacturing
Elevator and Moving Stairway Manufacturing
Industrial Process Furnace and Oven Manufacturing
All Other Miscellaneous General Purpose Machinery Manufacturing
Radio and Television Broadcasting and Wireless Communications Equipment
Manufacturing
Semiconductor and Related Device Manufacturing
Capacitor, Resistor, Coil, Transformer, and Other Inductor Manufacturing
Electronic Connector Manufacturing
Other Electronic Component Manufacturing
Instruments and Related Products Manufacturing for Measuring, Displaying, and
Controlling Industrial Process Variables
Instrument Manufacturing for Measuring and Testing Electricity and Electrical
Signals
Lighting Fixture Manufacturing
Residential Electric Lighting Fixture Manufacturing
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Table_Apx F-ll NAICS Codes for All Degreasing Types
NAICS
335210
335310
335312
335313
335911
335921
335929
335999
336320
336340
336410
336411
336413
336414
336510
337125
337127
339114
339990
339992
339995
339999
488111
493110
811310
BIS NAICS
335200
335300
335300
335300
335900
335900
335900
335900
336300
336300
336400
336400
336400
336400
336500
337120
337120
339100
339900
339900
339900
339900
488100
493100
811300
Industry
Small Electrical Appliance Manufacturing
Electrical Equipment Manufacturing
Motor and Generator Manufacturing
Switchgear and Switchboard Apparatus Manufacturing
Storage Battery Manufacturing
Fiber Optic Cable Manufacturing
Other Communication and Energy Wire Manufacturing
All Other Miscellaneous Electrical Equipment and Component Manufacturing
Motor Vehicle Electrical and Electronic Equipment Manufacturing
Motor Vehicle Brake System Manufacturing
Aerospace Product and Parts Manufacturing
Aircraft Manufacturing
Other Aircraft Parts and Auxiliary Equipment Manufacturing
Guided Missile and Space Vehicle Manufacturing
Railroad Rolling Stock Manufacturing
Household Furniture (except Wood and Metal) Manufacturing
Institutional Furniture Manufacturing
Dental Equipment and Supplies Manufacturing
All Other Miscellaneous Manufacturing
Musical Instrument Manufacturing
Burial Casket Manufacturing
All Other Miscellaneous Manufacturing
Air Traffic Control
General Warehousing and Storage
Commercial and Industrial Machinery and Equipment (except Automotive and
Electronic) Repair and Maintenance
Table_Apx F-12 SOC Codes for Worker Exposure in the Degreasing Sector
SOC
17-2000
17-3000
19-4000
49-1000
49-2000
49-3000
49-9010
49-9020
49-9040
49-9060
49-9070
49-9090
51-2000
51-9192
Occupation
Engineers k "^
Drafters, Engineering Technicians, and Mapping Technicians
Life, Physical, and Social Science Technicians
Supervisors of Installation, Maintenance, and Repair Workers
Electrical and Electronic Equipment Mechanics, Installers, and Repairers
Vehicle and Mobile Equipment Mechanics, Installers, and Repairers
Control and Valve Installers and Repairers
Heating, Air Conditioning, and Refrigeration Mechanics and Installers
Industrial Machinery Installation, Repair, and Maintenance Workers
Precision Instrument and Equipment Repairers
Maintenance and Repair Workers, General
Miscellaneous Installation, Maintenance, and Repair Workers
Assemblers and Fabricators
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders
Exposure
Designation
N
N
N
N
W
W
W
W
W
W
W
W
W
W
Source: (U.S. BLS, 2015) W - worker, N - occupational non-user
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There are 109,966 establishments among the industry sectors presented in Table_Apx F-ll. The
number of businesses that use 1-BP for vapor degreasing is estimated at 500 to 2,500
businesses (U.S. EPA, 2007c). This translates to a 1-BP market penetration of 0.5 percent to 2.3
percent.
Table_Apx F-13 presents the estimated number of workers and occupational non-users based
on industry- and occupational-specific employment data. The low-end estimates correspond to
a 0.5 percent market penetration, while the high-end estimates correspond to a 2.3 percent
market penetration. The total number of potentially exposed workers and occupational non-
users range from 4,712 to 23,558.
Table_Apx F-13 Estimated Number of Workers Potentially Exposed to 1-BP in Degreasing Uses
Exposed
Workers
Exposed
Occupational
non-users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
non-users per
Site
Low-end
3,245
1,466
4,712
500
6
3
High-end
16,226
7,332
23,558
2,500
6
K 3
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. Values are rounded to the nearest integer.
F-4 Estimates for Number of Workers Potentially Using
Aerosol Degreasing
Table_Apx F-14 presents the NAICS industry sectors relevant to aerosol degreasing. These
NAICS codes cover repair and maintenance shops where aerosol degreasing is likely to occur.
Table_Apx F-12 of Section F-3 presents BLS occupation codes where workers are potentially
exposed to degreasing solvents. EPA/OPPT assumed the types of occupation with potential
solvent exposure are similar between vapor degreasing and aerosol degreasing.
Table_Apx F-14 NAICS Codes for Aerosol Degreasing
NAICS
811111
811112
811113
811118
811121
811122
811191
811192
811198
811211
811212
811213
811219
BLS NAICS
811110
811110
811110
811110
811120
811120
811190
811190
811190
811200
811200
811200
811200
Industry
General Automotive Repair
Automotive Exhaust System Repair
Automotive Transmission Repair
Other Automotive Mechanical and Electrical Repair and Maintenance
Automotive Body, Paint, and Interior Repair and Maintenance
Automotive Glass Replacement Shops
Automotive Oil Change and Lubrication Shops
Car Washes
All Other Automotive Repair and Maintenance
Consumer Electronics Repair and Maintenance
Computer and Office Machine Repair and Maintenance
Communication Equipment Repair and Maintenance
Other Electronic and Precision Equipment Repair and Maintenance
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Table_Apx F-14 NAICS Codes for Aerosol Degreasing
NAICS
811310
811411
811490
451110
BLS NAICS
811300
811400
811400
451110
Industry
Commercial and Industrial Machinery and Equipment (except Automotive and
Electronic) Repair and Maintenance
Home and Garden Equipment Repair and Maintenance
Other Personal and Household Goods Repair and Maintenance
Sporting Goods Stores
There are 222,940 establishments among the industry sectors presented in Table_Apx F-14. The
EPA market report on 1-BP estimated that "1,000 to 5,000 businesses used 1-BP-based aerosol
solvents in 2002 (U.S. EPA. 2007c). as cited in (U.S. EPA. 2013c)". This translates to a market
penetration of approximately 0.4 percent to 2.2 percent. Based on these estimates,
approximately 2,466 to 12,329 workers and occupational non-users are potentially exposed to
1-BP as an aerosol degreasing solvent. It is unclear whether the number of establishments using
1-BP-based aerosol solvents has increased since 2002.
Table_Apx F-15 Estimated Number of Workers Potentially Exposed to 1-BP in Aerosol Degreasing
Exposed
Workers
Exposed
Occupational
non-users
Total Exposed
Estimated
Number of
Establishments
Workers per Site
Occupational
non-users per
^ Site
Low-end
2,227
238
2,466
1,000
^. 2
0.2
High-end "^
11,137
1,192
12,329
5,000
2
0.2
Note: Number of workers and occupational non-users per site are calculated by dividing the exposed number of
workers or occupational non-users by the number of establishments. The number of workers per site is rounded to
the nearest integer. The number of occupational non-users per site is shown as 0.2, as it rounds down to zero.
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Appendix G APPROACH USED TO COLLECT MONITORING DATA
AND INFORMATION ON MODEL PARAMETERS
EPA/OPPT conducted a comprehensive literature search to identify worker exposure monitoring
data relevant to 1-BP use in degreasing, dry cleaning, and spot cleaning applications. In addition,
EPA/OPPT searched for information on model parameters for the purpose of 1-BP exposure
modeling. Some of these model parameters include, but are not limited to, 1-BP use rate, use
volume, and vapor generation rate.
For each 1-BP use scenario, EPA/OPPT developed scenario-specific primary and secondary
keywords to be used for the literature search. EPA/OPPT searched the following data sources:
• Standard engineering sources used by OPPT/RAD for occupational exposure assessments.
• Internet literature search (e.g. ScienceDirect.com)
All search results were reviewed, compared to the data quality criteria, and documented.
Table_Apx G-l presents the data quality criteria and corresponding acceptance specifications for
the literature review.
Table_Apx G-l Data Quality Criteria and Acceptance Specifications for 1-BP Literature Review for
Monitoring Data and Information on Model Parameters
Quality Criterion
Description/Definition
Acceptance Specification
Currency (up to
date)
The information reflects present conditions.
Data from all years are acceptable.
Geographic Scope
The information reported reflects an area
relevant to the assessment.
Data for the modeling input parameters for
the commercial scenarios in scope in the
United States and the rest of world are
acceptable.
Reliability
The information reported is reliable. For
example, this criterion may include the
following acceptance specifications:
The information or data are from a peer-
reviewed, government, or industry-specific
source.
The source is published.
The author is engaged in a relevant field
such that competent knowledge is expected
(i.e., the author writes for an industry trade
association publication versus a general
newspaper).
The information was presented in a
technical conference where it is subject to
review by other industry experts.
Data are reliable if they are from one of the
following sources:
U.S. or other government publication.
Sources by an academic researcher where:
• Publication is in peer-reviewed journal; or
• Presented at a technical conference; or
• Source has documented qualifications or
credentials to discuss particular topic.
Sources by an industry expert or trade group
where:
• Presented at a technical conference where
the information is subject to review by
other industry experts; or
• Source has documented qualifications or
credentials to discuss particular topic; or
• Source represents a large portion of the
industry of interest.
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Table_Apx G-l Data Quality Criteria and Acceptance Specifications for 1-BP Literature Review for
Monitoring Data and Information on Model Parameters
Unbiased
Comparability
Representativeness
Applicability
The information is not biased towards a
particular product or outcome.
The data are comparable to other sources
that have been identified.
The data reflect the typical industry
practices. The data are based on a large
industry survey or study, as opposed to a
case study or sample from a limited number
of sites.
For surrogate data, the data are expected to
be similar for the industry or property of
interest.
• Objective of the information is clear.
• Methodology is designed to answer a
specific question.
Data sources will not be accepted or rejected
based on their comparison to data from other
sources.
Literature sources are not rejected based on
the sample size of sites. Large industry surveys
as well as case studies and limited sample
sizes are acceptable.
Surrogate data deemed applicable only if
approved by EPA.
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Appendix H EQUATIONS FOR CALCULATING ACUTE AND
CHRONIC EXPOSURES FOR NON-CANCER AND CANCER
This report assesses 1-BP exposures to workers in occupational settings, presented as 8-hr time
weighted average (TWA) exposure. The 8-hr TWA exposures are then used to calculate acute
exposure, 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 (8-hr
TWA).
ADC and LADC are used to estimate workplace exposures for non-cancer and cancer risks,
respectively. These exposures are estimated as follows:
Equation_Apx H-l ADC and LADC
AT^ T ATA/- CxEDxEFxWY
ADC or LADC = —
where:
ADC = average daily concentration (8-hr TWA) used for chronic non-cancer risk
calculations
LADC = lifetime average daily concentration (8-hr TWA) used for chronic cancer risk
calculations
C = contaminant concentration in air (8-hr TWA)
ED = exposure duration (8 hr/day)
EF = exposure frequency (260 days/yr)
WY = working years per lifetime (40 yr)
AT = averaging time (LT x 260 days/yr x 8 hrs/day; where LT = lifetime; LT = 40 yr for
non-cancer risks; LT=70 yr for cancer risks)
The parameter values inTable_Apx H-l are used to calculate each of the above exposure estimates
with the exception that the multi-zone dry cleaning model varies the exposure frequency from 250
to 312 days per year. The AC, ADC, and LADC calculations are integrated into the Monte Carlo
simulation for dry cleaning.
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Table_Apx H-l Parameter Values for Calculating Exposure Estimates
Parameter Name
Exposure Duration (acute)
Averaging Time (acute)
Exposure Duration (chronic)
Exposure Frequency (chronic)
Working Years per Lifetime
(chronic)
Lifetime (chronic, non-cancer)
Lifetime (chronic, cancer)
Averaging Time (chronic, non-
cancer)
Averaging Time (chronic,
cancer)
Symbol
EDAcute
ATAcute
EDchronic
EFchronic
WYchronic
LTchronic, Non-Cancer
LTchronic, Cancer
ATchronic, Non-Cancer
ATchronic, Cancer
Value
8
24
8
260
40
40
70
83,200
145,600
Unit
hr/day
hr/day
hr/day
day/yr
yr
yr
yr
hr
hr
Example AC, ADC and LADC calculations for 1-BP use in spray adhesives:
1-BP pre-EC 8-hr TWA exposure for sprayers, 95th percentile: 253.26 ppm (see Appendix I below for
explanation of this estimate)
CxED 253.26 ppm x 8 hr
AC = ——— = — = 253.26 ppm
AT
8hr
CXEDXEFXWY 253.26 ppm x 8^ x 260^ x 40 yr
ADC = — = „„„!„. = 253.26 ppm
AT
83,200 hr
~
C x ED x EF x WY 253'26 PPm x 8 dt X 26° ~W X 40 yr
LADC = - — - = - . . , L . . - - - = 144.72 ppm
AT
145,600 hr
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Appendix I EXAMPLE OF MONITORING DATA ANALYSIS FOR
SPRAY ADHESIVE USE
This appendix describes how EPA/OPPT analyzed the exposure monitoring data for the spray
adhesive use scenario. The following data sources were included in EPA/OPPT's analysis:
• A 1998 NIOSH Health Hazard Evaluation (HHE) of Custom Products, Inc. in Mooresville,
North Carolina (NIOSH. 2002a):
• A 2002 NIOSH HHE of STN Cushion Company in Thomasville, North Carolina (NIOSH.
2002b);
• A 2003 NIOSH HHE of Marx Industries, Inc. in Sawmills, North Carolina (NIOSH. 2003):
• OSHA IMIS data from OSHA inspection of Foamex International, Franklin Corp, Royale
Comfort Seating, Inc., Starr Aircraft Products Inc., and Willard Packaging Company Inc.
(OSHA. 2013).
Table_Apx 1-1 shows how EPA/OPPT categorized each employee as either sprayer, non-sprayer, or
occupational non-user. EPA/OPPT defined "sprayers" as employees who perform manual spraying
of the 1-BP adhesive as a regular part of the job. EPA/OPPT defined "non-sprayers" as employees
who are not sprayers, but either handle the adhesive or spend the majority of their shift working in
an area where spraying occurs (e.g. employees who work in the Assembly department where
spraying regularly occurs). EPA/OPPT defined "occupational non-users" as employees who do not
regularly work in a department/area where spraying occurs (e.g. employees in the Saw and Sew
departments).
Table_Apx 1-1 Categorization of Employees as Sprayers, Non-Sprayers, or Occupational Non-Users
Data Source
NIOSH (2002a)
NIOSH (2002b)
NIOSH (2003)
OSHA (2013):
Foamex International
OSHA (2013):
Franklin Corp
OSHA (2013): Royale
comfort Seating Inc.
OSHA (2013): Starr
Aircraft Products Inc.
OSHA (2013): Willard
Packaging Company
Inc.
Category
Sprayer
Sprayer (Assembly and
Covers department)
Sprayer (Fabrication
department)
Sprayer (Glue line and
Spring line)
Sprayer (Gluing area)
Gluer
Sprayer
Sprayer (Bonding and
Blocking area)
Sprayer (Spray booth)
Non-sprayer
Assembler and Supervisor
in Assembly department
Floater (Fabrication
department)
Doffer, supervisor, baler,
and foam set-up worker
(Glue line and Spring line)
No data
No data
No data
No data
No data
Occupational Non-user
Operator and Supervisor in Saw
department
No data
Accounting, blowing, customer
service, fiber cutting, foam cutting,
maintenance, and supervisor
worker (work areas other than
Glue line and Spring line)
No data
No data
No data
No data
No data
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Table_Apx 1-2 shows how EPA/OPPT categorized the exposure monitoring data into either "pre-EC"
or "post-EC" scenarios. EPA/OPPT categorized the data into "pre-EC" scenario if the facility had
little to no engineering control to reduce 1-BP vapor at the time of monitoring. EPA/OPPT
categorized the data as "post-EC" if specific engineering controls were implemented to reduce
1-BP exposure.
Table_Apx 1-2 Categorization of Exposure Data into Pre-EC and Post-EC Scenarios
Data Source
NIOSH (2002a)
NIOSH (2002b)
NIOSH (2003)
OSHA(2013):Foamex
International
OSHA (2013): Franklin Corp
OSHA (2013): Royale comfort
Seating Inc.
OSHA (2013): Starr Aircraft
Products Inc.
OSHA (2013): Willard Packaging
Company Inc.
Scenario
Pre-EC
Initial worker exposure assessment
in 1998 is categorized as "pre-EC"
due to ineffective control
Initial worker exposure assessment
in 2000 is categorized as "pre-EC"
due to ineffective control
All data categorized as "pre-EC" due
to ineffective control. Facility only
had exhaust fans located on outside
walls of spray rooms.
All data categorized as "pre-EC".
Facility had overhead canopy hood
with two exhaust fans above the
work stations, but it was unclear
whether these fans were effective in
controlling 1-BP vapor.
All data categorized as "pre-EC".
Facility had no local exhaust
ventilation.
All data categorized as "pre-EC".
Facility had poor to no ventilation.
There were three wall fans that
exhaust air to outside.
All data categorized as "pre-EC".
Workers conducted spraying either
in spray booths or at table top. No
additional engineering controls were
described.
All data categorized as "pre-EC".
Facility had a spray booth. No
additional engineering controls were
described.
Post-EC
Follow-up assessment in 2000 is
categorized as "post-EC" after the facility
improved spray booths with hoods and
filters and removed excess adhesive from
exhaust system
Follow-up assessment in 2001 2000 is
categorized as "post-EC" after facility
improved ventilation and enclosed all
spray stations to create spray booths
No data. Facility did not implement
engineering controls in between the two
studies.
No data.
No data
No data
No data
No data
For example, EPA/OPPT determined the initial assessment in NIOSH (2002a) to be representative
of a "pre-EC" scenario, where there is insufficient engineering control to prevent worker exposure
to 1-BP. Data from the initial assessment are presented in Table_Apx 1-3. The sample duration of
the personal breathing zone measurements are approximately 8 hours; therefore, we assume
these data are representative of 8-hr TWA values.
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Subsequent to NIOSH's initial assessment, the facility installed new spray booths with local exhaust
ventilation for all adhesive spraying operations (Assembly and Covers departments) based on
NIOSH recommendations. EPA/OPPT determined exposure data in the follow-up assessment to be
representative of a "post-EC" scenario. These data are presented in Table_Apx 1-4.
Table_Apx 1-3 Personal Breathing Zone Monitoring Data for Sprayers, Initial NIOSH Assessment (Pre-EC
Scenario)
Department
Assembly
Covers
Covers
Assembly
Covers
Covers
Covers
Covers
Assembly
Assembly
Covers
Covers
Assembly
Covers
Covers
Covers
Assembly
Assembly ^B
Assembly
Covers
Assembly
Covers
Covers
Assembly
Covers
Assembly
Assembly
Covers
Covers
Covers
Assembly
Assembly
Assembly
Covers
Covers
Covers
Worker Job
Description
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer A
Sprayer ^
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer <.
Sprayer
Sprayer 1
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sample Date
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
11/11/1998
Sample Duration
8hr (0710-1518)
8.25hr(0720-1533)
8.25hr(0716-1533)
8hr(0732-1527)
8.25hr(0719-1533)
8.25hr(0724-1533)
8.5hr(0713-1533)
8.5hr(0708-1533)
8hr(0715-1521)
2hr(0722-0928)
8.5hr(0700-1533)
8.5hr(0710-1533)
8hr(0735-1533)
8.5hr(0711-1533)
8.5hr(0713-1533)
8.5hr(0709-1533)
8hr(0732-1529)
8hr(0730-1529)
8hr(0730-1532)
8.5hr(0712-1533)
5.75hr(0730-1310)
8.25hr(0715-1533)
8.5hr(0700-1533)
6.75hr(0730-1410)
8.25hr(0716-1533)
6hr(0729-1333)
8hr(0735-1521)
8.5hr(0700-1533)
8.5hr(0701-1533)
8.5hr(0705-1533)
3.75hr(1100-1443)
8hr(0729-1522)
8hr(0732-1526)
8.5hr(0710-1533)
8.5hr(0701-1533)
8.5hr(0700-1533)
Exposure
Concentration
(ppm)
115.3
117.3
126.8
132.8
140.5
142.8
142.9
147.3
150.3
156.5
157
161
171.4
176.3
180.7
181.4
184.8
188
190.8
194.5
198.9
203.3
211.1
211.7
221
225.8
227.1
232.7
235
241.1
242
249.1
250.7
264.8
278.5
381.2
Source: (NIOSH, 2002a), (Appendix 2)
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Table_Apx 1-4 Personal Breathing Zone Monitoring Data for Sprayers, Follow-up NIOSH Assessment (Post-
EC Scenario)
Department
Covers-2
Covers-6
Assembly-1
Assembly-3
Covers-6
Covers-3
Covers-1
Covers-4
Assembly-2
Covers-2
Covers-5
Covers-5
Covers-3
Covers-1
Worker Job
Description
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sprayer
Sample Date
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
11/16/2000
Sample Duration
8hr (0723-1520)
8hr (0730-1520)
8hr (0710-1517)
8hr (0715-1520)
8hr (0730-1521)
8hr (0722-1522)
8hr (0720-1521)
6.25hr (0904-1519)
8.25hr (0708-1519)
8hr (0723-1522)
8hr (0727-1521)
8hr (0730-1520)
8hr (0725-1523)
8hr (0719-1520)
Exposure
Concentration (ppm)
5.4
13.9
14.9
18.1
23.2
25.3
26.5
28.2
32
33.7
36.8
45.3
51.6
58
Source: (NIOSH, 2002a), (Appendix 3)
For each employee category (sprayer, non-sprayer, and occupational non-user) and exposure
scenario (pre-EC or post-EC), EPA/OPPT calculated the 95th and 50th percentile exposure levels16
from the observed data set. The 95th percentile exposure concentration represents high-end
exposure to 1-BP across the distribution of exposure data. The 50th percentile exposure
concentration represents a typical exposure level. Table_Apx 1-5 presents the analysis results.
Table_Apx 1-5 Summary of Inhalation Exposure Monitoring Data for Spray Adhesives
Category
Acute and Chro
Exposures (8-Ho
ACl-BP, 8-hrTWAan
95th Percentile
nic, Non-Cancer
jrTWAs in ppm)
i ADCl-BP, 8-hr TWA
50th Percentile
Chronic, Cancer
LADCi-B
95th Percentile
Exposures (ppm)
',8-hr TWA
50th Percentile
Data Points
Sprayers
PreEC
Post ECa
253.26
41.90
131.40
17.81
144.72
23.94
75.09
10.18
85
49
Non-sprayers b
PreEC
Post ECa
210.85
28.84
127.20
18.00
120.49
16.48
72.69
10.29
31
9
Occupational non-users0
PreEC
Post ECa
128.66
5.48
3.00
2.00
73.52
3.13
1.71
1.14
39
17
Notes: AC = Acute Concentration; ADC = Average Daily Concentration and LADC = Lifetime Average Daily Concentration. EC =
Engineering controls.
Sources: (OSHA. 2013: NIOSH. 2003. 2002a. b)
a Engineering controls implemented: Enclosing spray tables to create "spray booths" and/or improve ventilation.
b Non-Sprayer refers to those employees who are not sprayers, but either handle the adhesive or spend the majority of their shift
working in an area where spraying occurs.
c Occupational non-user refers to those employees who do not regularly work in a department/area where spraying occurs (e.g.,
employees in Saw and Sew departments.
16 Using Microsoft Excel
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Appendix J OCCUPATIONAL EXPOSURE MODELING (NEAR-
FIELD/FAR-FIELD) APPROACH
This appendix presents the modeling approach and model equations used in the 1-BP
assessment. All of the models in this assessment use a near-field / far-field approach (Keil et al.,
2009), where a vapor generation source located inside the near-field diffuses into the
surrounding environment. Workers are assumed to be exposed to 1-BP vapor concentrations in
the near-field, while occupational non-users are exposed at concentrations in the far-field.
In general, the ventilation rate, indoor air speed, near-field size, and other environmental
conditions (e.g. temperature, pressure) are assumed to be the same across all use scenarios.
However, a targeted literature search was conducted to identify chemical- and industry-specific
use rate information to calculate vapor generation rates for each scenario. Where information
is available, the far-field room size and number of working hours per day are also varied to
provide more realistic results for that given scenario. The specific values used for each scenario
are presented in the body of the report.
An individual model input parameter could either have a discrete value or a distribution of
values. EPA/OPPT assigned statistical distributions based on available literature data.
A Monte Carlo simulation (a type of stochastic simulation) was conducted to capture variability
in the model input parameters. The simulation was conducted using the Latin hypercube
sampling method in @Risk Professional Edition, Version 6.2.0. 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. With the exception of the multi-zone model, the number of
iterations was arbitrarily selected to be one million to capture the range of possible input
values (i.e., including values with low probability of occurrence). For the multi-zone dry cleaning
model, the number of iterations was selected to be 5,000 such that the simulation can be
completed within a reasonable time period.
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.
Vapor DeRreasinR, Cold CleaninR, and Spot CleaninR Exposure ModelinR Equations
Near-Field Mass Balance
Equation_Apx J-l Near-Field Mass Balance for Vapor Degreasing, Cold Cleaning and Spot Cleaning
— CNFQNF + G
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Far-Field Mass Balance
Equation_Apx J-2 Far-Field Mass Balance for Vapor Degreasing, Cold Cleaning and Spot Cleaning
dCff
dt
Where:
— CFFQNF — CFFQFF
• VNF is the near-field volume;
• VFF is the far-field volume;
• QNF is the near-field ventilation rate;
• QFp is the far-field ventilation rate;
• CNF is the average near-field concentration;
• CFF is the average far-field concentration;
• G is the average vapor generation rate; and
• t is the elapsed time.
Both of the previous equations can be solved for the time-varying concentrations in the near-
field and far-field as follows (Keil et al.. 2009):
Equation_Apx J-3 Instantaneous Near-Field Concentration as a Function of Time
CNF = G(ki + kze^t - k3e^')
Equation_Apx J-4 Instantaneous Far-Field Concentration as a Function of Time
Where:
Equation_Apx J-5 Regrouping of Parameters into Parameter
QNF
Equation_Apx J-6 Regrouping of Parameters into Parameter k2
=
Equation_Apx J-7 Regrouping of Parameters into Parameter k3
=
Equation_Apx J-8 Regrouping of Parameters into Parameter k4
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Equation_Apx J-9 Regrouping of Parameters into Parameter k5
Equation_Apx J-10 Eigenvalue
fc5= ( "^ )fc3
0.5
V
ti^£(0)VF + OFF)\ JQNFVFF + VNF(QNF + QFF)\ A {QNFQFF-\
iv^ J+Jl v^ ) -4fed
Equation_Apx J-ll Eigenvalue \2
iQNFVFF + VNF(QNF + QFF-)\ liQNFVFF
1, = 0.5
QFF~)\ (QNFQFF\
\VNFVFF
EPA/OPPT calculated the hourly TWA concentrations in the near-field and far-field using the
following equations. Note that the numerator and denominator of Equations E-12 and E-13 use
two different sets of time parameters. The numerator is based on operating times for the
scenario (e.g., 2 hours for vapor degreasing, see Table_Apx K-2) while the denominator is fixed
to an average time span, tavg, of 8 hours. 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 8 hours regardless of the values
selected for txand t2.
Equation_Apx J-12 Near-field Hourly TWA Concentration
f 2 C rlf f 2 C'('I? -U I? ah-\t }r o^-2^\r\i-
I LifflpLlL I *-* \ ^l ' **• 2 **"3 ^ \\A-\f
r — _£l — ci
k3eX2t2\
J-
^"2 /
~G kiti-H
Lavg
Equation_Apx J-13 Far-field Hourly TWA Concentration
J^2 CFFdt J^2
dt
<-avg
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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/OPPT 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 as:
Equation_Apx J-14 Free Surface Area
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 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_Apx J-15 Near-Field Ventilation Rate
QNF =
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_Apx J-16 Far-Field Ventilation Rate
QFF = VFFAER
Aerosol DeRreasinR Exposure ModelinR Equations
Near-Field Mass Balance
Equation_Apx 1-17 Near-Field Mass Balance for Aerosol Degreasing
Far-Field Mass Balance
Equation_Apx J-18 Far-Field Mass Balance for Aerosol Degreasing
— CFFQNF — CFFQFF
Where:
• VNF is the near-field volume;
• VFF is the far-field volume;
• QNF is the near-field ventilation rate;
• QFF is the far-field ventilation rate;
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• CNF is the average near-field concentration at a given point in time; and
• CFF is the average far-field concentration at a given point in time.
The aerosol degreasing model assumes a spontaneous "burst" in the near-field concentration,
CNF, at the time of each 1-BP application. Solving Equation_Apx J-17 and
Far-Field Mass Balance
Equation_Apx J-18 in terms of the time-varying concentrations in the near-field and far-field
yields Equation_Apx J-19 and Equation_Apx J-20, which EPA/OPPT applied to each of the eight
1-hour increments. For each 1-hour increment, EPA/OPPT calculated the initial near-field
concentration at the top of the hour (tn), accounting for both the burst of 1-BP from the
degreaser application and the residual near-field concentration remaining after the previous 1-
hour increment (tn_i, except for n = 1, in which case there would be no residual 1-BP from a
previous application). The initial far-field concentration is equal to the residual far-field
concentration remaining after the previous 1-hour increment. EPA/OPPT then calculated the
decayed concentration in the near-field and far-field at the bottom of the hour, just before the
degreaser application at the top of the next hour (tn+1). EPA/OPPT also calculated a 1-hour
TWA exposure for the near-field and far-field, representative of the worker's and occupational
non-users' exposures to the airborne concentrations during each 1-hour increment. Note that
the k coefficients (Equation_Apx J-21 through Equation_Apx J-24) are a function of the initial
near-field and far-field concentrations, and therefore are re-calculated at the top of each hour.
Equation_Apx J-19 Instantaneous Near-Field Concentration as a Function of Time
Equation_Apx J-20 Instantaneous Far-Field Concentration as a Function of Time
Where:
Equation_Apx J-21 Regrouping of Parameters into Parameter ki
0, n = 0
n) . „ „ . r , „
, n = 1,2, 3, 4, 5, 6, or 7
VNF( A ! - A 2)
Equation_Apx J-22 Regrouping of Parameters into Parameter k2
0, n = 0
n) -193Ac;fi 7
— , n — 1, Z, 6, 4, b, b, or /
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Equation_Apx J-23 Regrouping of Parameters into Parameter k3
o, n = o
o o /I C /-
*•' *> 4< 5< 6< or
tn = )(QNF+^lVNF)[QNF(CFF,o(tn)-CNF,o(tn))-^2VNFCNF,o(tn)]
- — ~ — - ' n =
Equation_Apx J-24 Regrouping of Parameters into Parameter k4
0, n = 0
(CNF,o(tn)-CFF,o(tn))+^-lVNFCNF,o(tn)] -1TQ/I C a r^
- - - - , n — i,/, Ji 4, b, o, or
QNFVNpUl-^2)
Equation_Apx J-25 Near-field Concentration at the Moment of Aerosol Degreaser Application for each
of the Seven Applications
0, n = 0
Amt /1,000 mg\
~~' U
^NF.O^n) —
+ kun-i6*1*2 + fytn^e*2*2' n = 2,3,4, 5,6, or 7
Equation_Apx J-26 Far-field Concentration at the Moment of Aerosol Degreaser Application for each
of the Seven Applications
0, n = 0 or 1
fc4|tn_1e;i2t2, n = 2, 3,4, 5,6, or 7
Equation_Apx J-27 Eigenvalue \i
^ = 0. 5
PI ~v /v r ' r r ' ~/vrv"v/vr • ^ F F s \
, „ „ .+ II rr— I -4|
Equation_Apx J-28 Eigenvalue
, =05- NFFFNFNFFF _ NFFFNFNFFF *
\ VNFVFF \ VNFVFF \VNFVFF
Equation_Apx J-29 Near-Field Concentration, 1-hr TWA
tn-! ^t fcz.^! ^t \ /*l.tn_M t k2,tn
Equation_Apx J-30 Far-Field Concentration, 1-hr TWA
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After calculating all near-field/far-field 1-hour TWA exposures (i.e., CNF^.hrTWAitn and
CpF.-L-hr TWA,tn^or eacn hour from tn = 1 to tn =8), EPA/OPPT calculated the near-field/far-field
8-hour TWA concentration by summing the 1-hour TWA exposures and dividing the respective
totals by tavg (i.e., 8 hours for an 8-hour TWA), as denoted by the equations below:
Equation_Apx J-31 Near-Field Concentration, 8-hr TWA
r _ £n=l CNF,1 -hr TWA,tn
LNF,8-hrTWA ~ ~ ~~t
i-avg
Equation_Apx J-32 Far-Field Concentration, 8-hr TWA
r _ £n=l CFF,1 -hr TWA,tn
LFF,8-hrTWA —~ ~T~
lavg
EPA/OPPT used the acute and chronic exposure equations presented in Equation_Apx J-l and
Equation_Apx J-2 for aerosol degreasing to obtain the final exposure results.
Dry CleaniriR Exposure ModelinR Equations
Near-Field Mass Balance
Equation_Apx J-33 Near-Field Mass Balance for Spot Cleaning (Multi-Zone)
s
Equation_Apx J-34 Near-Field Mass Balance for Finishing (Multi-Zone)
Equation_Apx J-35 Near-Field Mass Balance for Dry Cleaning Machine (Multi-Zone)
Far-Field Mass Balance
Equation_Apx J-36 Far-Field Mass Balance for Dry Cleaning Facility (Multi-Zone)
= CSQS + CFQF + CDQD — CFFQS — CppQp — CFFQD — CFFQFF
Where:
• Vs is the near-field volume for spot cleaning;
• VF is the near-field volume for finishing;
• VD is the near-field volume for unloading dry cleaning machine;
• VFF is the far-field volume;
• Qs is the near-field ventilation rate for spot cleaning;
• QF is the near-field ventilation rate for finishing;
• QD is the near-field ventilation rate for dry cleaning machine;
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is the far-field ventilation rate;
• Cs is the average near-field concentration for spot cleaning;
• CF is the average near-field concentration for finishing;
• CD is the average near-field concentration for dry cleaning machine;
• CFF is the average far-field concentration;
• Gs is the average vapor generation rate for spot cleaning;
• GF is the average vapor generation rate for finishing; and
• t is the elapsed time.
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 may not be
equal to the surface area of the entire near-field.
For spot-cleaning, EPA/OPPT 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
as:
Equation_Apx J-37 Free Surface Area for Spot Cleaning
FSAS = 2(LSHS) + 2(WSHS) + (LSWS)
For finishing, EPA/OPPT defined the near-field zone to be a rectangular box covering the upper
body of a worker:
Equation_Apx J-38 Free Surface Area for Finishing
FSAF = 2(LNFHNF) + 2(WNFHNF) + 2(LNFWNF)
For dry cleaning, EPA/OPPT defined the near-field zone to be a hemispheric area projecting
from the door of the dry cleaning machine:
Equation_Apx J-39 Free Surface Area for Dry Cleaning Machine
FSAD = 2nrD2
Where:
• FSAS is the free surface area for spot-cleaning;
• FSAF is the free surface area for finishing;
• FSAD is the free surface area for dry cleaning machine;
• Ls is the near-field length for spot-cleaning;
• Hs is the near-field height for spot-cleaning;
• Ws is the near-field width for spot-cleaning;
• LF is the near-field length for finishing;
• HF is the near-field height for finishing;
• WF is the near-field width for finishing; and
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• rD is the radius of the dry cleaning machine door opening.
The near-field ventilation rates, Qs, QD, and QF are calculated 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. The near-field indoor wind speed
is assumed to be the same across all three near fields:
Equation_Apx J-40 Near-Field Ventilation Rate for Spot Cleaning
Qs=^vNFFSAs
*•
Equation_Apx J-41 Near-Field Ventilation Rate for Finishing
^H
QF = ivNFFSAF
Equation_Apx J-42 Near-Field Ventilation Rate for Dry Cleaning Machine
QD = ^vNFFSAD
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_Apx J-43 Far-Field Ventilation Rate for Dry Cleaning Facility
QFF = VFFAER
The model results in the following four, coupled ordinary differential equations (DDEs):
Equation_Apx J-44 Differential Equation for Spot Cleaning Near-Field Concentration
dcs _ Qs r , Qs r , GS
IT -^CS + ^CFF + ^
Equation_Apx J-45 Differential Equation for Finishing Near-Field Concentration
dcF _ QF r , QF r , GF
~d7= ~^CF + ^CFF + ^
Equation_Apx J-46 Differential Equation for Dry Cleaning Machine Near-Field Concentration
dCD _ QD QD
~dT ~^CD+^CFF
Equation_Apx J-47 Differential Equation for Far-Field Concentration at Dry Cleaning Facility
dCFF _ Qs QF QD (Qs+Qp+Qp+QpF) r
--Cs + CF+CD- — CFF
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When solving coupled DDEs, it is common to transform the equations into a standard
mathematical format. This standard mathematical format allows one to more easily identify
appropriate solution methodologies from standard mathematical references. EPA/OPPT
transformed these four DDEs into the following format:
Equation_Apx J-48 Alternative Representation for the Spot Cleaning Near-Field Concentration
Differential Equation
yi = anyi + ai4y4 + gi
Equation_Apx J-49 Alternative Representation for the Finishing Near-Field Concentration Differential
Equation
y2 = 322yz + a24y4 + g2
Equation_Apx J-50 Alternative Representation for the Dry Cleaning Machine Near-Field Concentration
Differential Equation
y3 = assy? + ^34y4
Equation_Apx J-51 Alternative Representation for the Far-Field Concentration Differential Equation
a42y2 + a43y3 + a44y4
Where:
dcs
dCF
dt
dt
And:
Cs = yi CF = y2 CD = y3 CFF = y4
= -^ a™ = v;
QD n -Q"
~~ ~
_ Qs _ QF _ QD
"41 — 7T~ "42 — 7T~ "43 — ;T~ "44 — ~~
Vpp VFF Vpp
Gs Gp
ffi=- 32 =
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These ordinary differential equations can be solved using a numerical integration method.
EPA/OPPT used the fourth-order Runge-Kutta method (RK4). RK4 numerically integrates a
system of coupled ordinary differential equations from time step n to n+1 with a constant time
step size of h using the following equations (shown for generic variables yi, y2, ys, and y4 as a
function of t).
Equation_Apx J-52 Redefinition of Time Derivative as Function of Independent and Dependent
Variables (yi')
Equation_Apx J-53 Redefinition of Time Derivative as Function of Independent and Dependent
Variables (y2')
Equation_Apx J-54 Redefinition of Time Derivative as Function of Independent and Dependent
Variables (y3')
Equation_Apx J-55 Redefinition of Time Derivative as Function of Independent and Dependent
Variables (y4')
= /4(t.yi.y2.y
Where, for each ODEj = I, 2, 3, 4 (where 1 = spot cleaning, 2 = finishing, 3 = dry cleaning machine,
and 4 = far field):
^L
Equation_Apx J-56 RK4 Beginning-of-lnterval Slope
k{ = fj(t,y1,y2,y3,y4)
Equation_Apx 1-57 RK4 First-Midpoint Slope
Equation_Apx J-58 RK4 Second-Midpoint Slope
k( = fj(t + l/i,yi + -k2h,y2 +
•5 J J \ 2 ' ±. i £ ' J £
Equation_Apx J-59 RK4 End-of-lnterval Slope
k{ = fj(t + h,y^+ k\h,y2 + k%h,y3 + /c|/i,y4
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Equation_Apx J-60 RK4 Calculation of the Dependent Variable, y, at the Next Time Step
yn+i = yn +
RK4 is an explicit integration method, meaning it solves for the dependent variables at step n+1
explicitly using the dependent variables at step n. RK4 is a fourth-order method, which means
the local truncation error at a single integration step is on the order of h5, while the total global
error is on the order of h4.
The choice of step size h is such to allow a successful integration of the system of differential
equations. If parameter values are chosen such that the differential equation coefficients (the a
terms in Equations J-48 through J-51) are sufficiently large, the differential equations may
become stiff. Stiff differential equations would require sufficiently small time step sizes to allow
their integration. Stiffness can be difficult to predict. If stiffness is encountered, meaning if the
solution diverges to unrealistic values, such as infinity, the step size should be reduced to see if
that allows for successful integration.
Exposure Estimate Equations
Dry cleaning facilities are often small business that may operate up to twelve hours a day. For
the purpose of modeling worker exposure, dry cleaning employees are assumed to work 8-hr
shifts. EPA/OPPT assumed the first work shift covers hour 0 through hour 8, and the second
work shift covers hour 4 through hour 12, such that there is a 4-hr period overlap between the
two shifts. For each shift, one worker is assumed to perform each category of work bulleted
below. Specific assumptions on each worker category are as follow:
• Spot cleaning is performed from hour 2 through hour 10 of the operating day, such that
the first shift worker is exposed for six hours and the second shift worker is exposed for
two hours. For example, the first-shift spot cleaning worker is exposed at concentration
CFF from hour 0 to hour 2, and is exposed at concentration Cs from hour 2 through hour
• Machine unloading, garment finishing and pressing are performed at regular intervals
throughout the operating day, and the frequency of this activity varies depending on the
number of loads dry cleaned each day. Each machine and finishing worker is exposed to
concentrations CD and CF for the duration of these activities, and is exposed at
concentration CFF for the remainder of the 8-hr shift. During the 4-hr overlap where
both shifts are present, loads are assigned to the first shift if the load can be completed
before first shift leaves at hour 8. EPA/OPPT defines a load as being "completed" when
that load of garment is completely unloaded, finished and pressed. If the load cannot be
completed during the first shift, it is assigned to the second shift.
• Occupational non-user who only spends time in the far-field is exposed at concentration
CFF for the entirety of the 8-hour shift.
Acute workplace exposures are estimated as follows:
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Equation_Apx J-61 Acute Concentration for Dry Cleaning Model (Multi-Zone)
CxED
AC =
AT
where:
AC
C
ED
AT
= acute concentration (8-hr TWA)
= contaminant concentration in air (8-hr TWA)
= exposure duration (8 hr/day)
= averaging time (8 hr/day)
The average daily concentration (ADC) and lifetime average daily concentration (LADC) are used
to estimate workplace exposures for non-cancer and cancer risks, respectively. These exposures
are estimated as follows:
Equation_Apx J-62 ADC and LADC for Dry Cleaning Model (Multi-Zone)
AT^ T ATA/- CxEDxEFxWY
ADC or LADC = -
where:
AT
ADC = average daily concentration (8-hr TWA) used for chronic non-cancer risk
calculations
LADC = lifetime average daily concentration (8-hr TWA) used for chronic cancer risk
calculations
C = contaminant concentration in air (8-hr TWA)
ED = exposure duration (8 hr/day)
EF = exposure frequency (250 to 312 days/yr)
WY = working years per lifetime (40 yr)
AT = averaging time (LT x 365 days/yr x 12 hr/day; where LT = lifetime; LT = 40 yr
for non-cancer risks; LT=70 yr for cancer risks)
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Appendix K OCCUPATIONAL EXPOSURE MODELING
PARAMETERS
This appendix presents the modeling input parameters. Table_Apx K-l summarizes the input
parameters and their assumptions common to all degreasing scenarios. Table_Apx K-2 summarizes
input parameters specific to the vapor degreasing near-field/far-field model, while Table_Apx K-3
summarizes input parameters specific to the aerosol degreasing near-field/far-field model.
Table_Apx K-4 summarizes input parameters and their assumptions used to model all scenarios at
dry cleaning facilities. Table_Apx K-5 through Table_Apx K-7 summarizes parameters for the multi-
zone dry cleaning model, while Table_Apx K-8 summarizes parameters for the stand-alone spot
cleaning model.
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Table_Apx K-l Summary of Environmental Parameters for Degreasing Facilities
Input
Parameter
Near-field
indoor wind
speed
Operating days
per year
Near-field
volume
Engineering
controls
effectiveness
Symbol
VNF
OD
VNF
EC
Unit
cm/s
(ft/s)
day/yr
ft3
%
Model Parameter
Values
Value
10
(1,181)
260
600
90
Basis
50th
percentile
—
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
0
—
— 1
—
Upper
Bound
GO
—
—
—
Mode
—
—
—
—
Distribution
Type
Lognormal,
u= 17.5
cm/s
o= 25.3
cm/s
—
—
—
Comments
Baldwin and Maynard (1998) surveyed the
wind speeds in 55 work areas covering a
wide range of workplaces. The study states
that the pooled distribution of all surveys
and the distributions of each survey, in
general, could be approximated by a
lognormal distribution. EPA/OPPT fitted the
data set, and the fitted mean and standard
deviation are 17.5 cm/s and 25.3 cm/s,
respectively.
The 2001 EPA Generic Scenario on the Use
of Vapor Degreasers estimates that
degreasers of all sizes operate 260 days per
year (ERG, 2001).
EPA applied the same dimensions used in
the final TCE risk assessment (i.e., 10 ft for
LNF and WNF and 6 ft for HNF) (U.S. EPA,
2014c). Value supported by Demou et al.
(2009).
Value supported by Wadden et al. (1989).
The study indicates local exhaust ventilation
can reduce workplace emissions by 90
percent. The estimate is based on an LEV
system for an open-top vapor degreaser
(lateral exhaust hoods installed on two
sides of the tank).
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Table_Apx K-2 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Vapor Degreasing
Input
Parameter
Far-field
volume
Air exchange
rate
Starting time
Exposure
Duration
Averaging
time
Emission
factor
Symbol
VFF
AER
ti
t2
tavg
EF
Unit
ft3
hr1
hr
hr
hr
Ib/employ
ee-yr
Model Parameter
Values
Value
10,594
2
m
^^"^^
—
8
—
Basis
Minimum
Minimum
—
k —
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
10,594
2
—
—
—
0
Upper
Bound
70,629
20
—
—
—
GO
Mode
17,657
3.5
—
—
—
—
Distribution
Type
Triangular
Triangular
^
—
—
—
Lognormal,
u= 10.4
o= 17.2
Comments
Per von Grote et al. (2003), volumes at
European metal degreasing facilities can
vary from 300 to several thousand cubic
meters. They noted smaller volumes are
more typical, and assumed 400 and 600 m3
in their models (von Grote et al., 2003).
EPA/OPPT assumed 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, or 17,657 ft3)
Hellweg et al. (2009) identifies average AER
for occupational settings utilizing
mechanical ventilation systems to be
between 3 and 20 hr1. The EPATCE RA peer
review comments indicate values around 2
to 5 hr1 may be more likely (SCG, 2013). A
triangular distribution is used with the
mode equal to the midpoint of the range
provided by the RA peer reviewers.
Constant value.
Equal to operating hours per day.
Constant value.
To develop the California Solvent Cleaning
Emissions Inventories, CARB surveyed
solvent cleaning facilities and gathered site-
specific information for 213 facilities. CARB
estimated a 1-BP emission factor of 10.43
Ib/employee-yr with a standard deviation of
17.24 Ib/employee-yr (CARB, 2011). CARB
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Input
Parameter
Number of
employees
per site
Units per site
Vapor
generation
rate
Operating
hours per day
Equipment
substitution
effectiveness
Symbol
EMP
U
G
OH
ES
Unit
employee/
site
unit/site
kg/unit-hr
hr/day
%
Model Parameter
Values
Value
—
—
m
—
2
98
Basis
—
—
—
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
0
1
—
—
—
Upper
Bound
CO
1.2
—
—
—
Mode
—
-
—
—
—
Distribution
Type
Weibull
a=1.1165
P- 34.175
Discrete
^
N/A
Discrete
—
Comments
estimated that more than 98 percent of 1-
BP emissions were attributed to vapor
degreasing for the solvent cleaning facilities
surveyed.
EPA/OPPT applied a lognormal distribution
to account for uncertainty in the CARB
emission factor.
Data based on 2007 Economic Census for
the vapor degreasing NAICS codes identified
in the TCE RA (U.S. EPA, 2014c). EPA/OPPT
fitted a Weibull distribution to the Census
data set.
The EPA TCE RA (2014c) estimated 1
unit/site for small vapor degreasing
facilities, and 1.2 unit/site for large facilities
based on analysis of the National Emissions
Inventory (NEI). Because NEI data are not
available for 1-BP, EPA/OPPT assumes equal
probability of small versus large facilities.
Calculated as the following:
G = EFxEMP/(2.2xOHxODxU)
The 2001 Generic Scenario on the Use of
Vapor Degreasers assumes degreasers
operate 2 hours per day, regardless of unit
size (ERG, 2001).
Value supported by NEWMOA (2001), as
used in the EPA TCE RA (2014c). The study
states that "air emissions can be reduced 98
percent or more when [a closed-loop
degreaser is] compared with an open-top
vapor degreaser".
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Table_Apx K-3 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Aerosol Degreasing
Input
Parameter
Far-field
volume
Air exchange
rate
Starting time
Exposure
Duration
Averaging
time
Applications
per day
Amount per
application
Symbol
VFF
AER
ti
t2
tavg
APD
AMT
Unit
ft3
hr1
hr
hr
hr
applications/
day
g/
application
Model Parameter Values
Value
10,594
1
0
1
8
7
27.5
Basis
Midpoint
Minimum
—
—
—
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
10,594
1
—
—
—
—
—
Upper
Bound
37,328
20
—
—
—
—
—
Mode
—
3.5
—
—
—
—
—
Distribution
Type
Uniform
Triangular
^v
—
—
—
—
—
Comments
Golsteijn et al. (2014) indicates a characteristic
volume of 300 m3 (10,594 ft3). Demou et al.
(2009) indicates a characteristic volume of
1,057 m3 (37,328 ft3) for aerosol degreasing at
automotive repair shops.
Demou et al. (2009) identifies typical AERs of 1
hr1 and 3 to 20 hr1 for occupational settings
with and without mechanical ventilation
systems, respectively. Golsteijn, et al. (2014)
indicates a characteristic AER of 4 hr"1. RA peer
review comments indicate values around 2 to
5 hr"1 may be more likely (SCG, 2013), in
agreement with Golsteijn, et al. (2014). A
triangular distribution is used with the mode
equal to the midpoint of the range provided by
the RA peer reviewer (3.5 is the midpoint of
the range 2 to 5 hr"1)
Constant value.
EPA assumed aerosol degreasers are applied in
hourly increments.
Value supported by Golsteijn, et al. (2014).
EPA assumed aerosol degreasers are applied
once per hour, and that no applications occur
during the first hour of the 8-hour work day.
Aerosol degreasing facilities use 192.2 g
degreaser/day Golsteijn, et al. (2014).
Assuming an APD of 7 and 100% 1-BP in the
degreaser yields an AMT of 27.5 g 1-
BP/application.
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Table_Apx K-4 Summary of Environmental Parameters at Dry Cleaning Facilities
Input
Parameter
Far-field
volume
Near-field
indoor wind
speed
Air exchange
rate
Symbol
VFF
VNF
AER
Unit
ft3
cm/s
(ft/s)
hr1
Model Parameter
Values
Value
2,472
10
(1,181)
1
Basis
Minimum
50th
percentile
Minimum
Uncertainty Analysis
Assumptions
Lower
Bound
2,472
0
1
Upper
Bound
105,944
GO
19
Mode
26,600
—
3.5
Distribution
Type
Triangular
^
Lognormal,
u.= 17.5 cm/s
o= 25.3 cm/s
Triangular
Comments
Cal/EPA (2007) indicated a mean volume of
26,600 ft3 for dry cleaning facilities in
California, von Grote et al. (2006) indicated
volumes at German dry cleaning facilities
ranging from 70 to 3,000 m3 (2,472 to 105,944
ft3) with a mean of 618 m3 (21,825 ft3). Klein
and Kurz (1994) indicated volumes at German
dry cleaning facilities ranging from 200 to 630
m3 (7,063 to 22,248 ft3) with a mean of 362
m3 (12,784 ft3) (as cited in von Grote et al.
(2006)).
EPA/OPPT assumes a triangular distribution
bound from 70 to 3,000 m3 (2,472 to 105,944
ft3) with a mode of 26,600 ft3, the mean
reported by Cal/EPA (2007).
Baldwin and Maynard (1998) surveyed the
wind speeds in 55 work areas covering a wide
range of workplaces. The study states that the
pooled distribution of all surveys and the
distributions of each survey, in general, could
be approximated by a lognormal distribution.
EPA/OPPT fitted the data set, and the fitted
mean and standard deviation are 17.5 cm/s
and 25.3 cm/s, respectively. For model input,
the distribution is capped at 202 cm/s, the
maximum average wind speed observed in
the study.
von Grote et al. (2006) indicated typical AERs
of 5 to 19 hr1 for German dry cleaning
facilities. Klein and Kurz (1994) indicated AERs
of 1 to 19 h"1, with a mean of 8 h"1 for German
dry cleaning facilities (as cited in von Grote et
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Input
Parameter
Engineering
controls
effectiveness
Operating
hours per day
(multi-zone)
Operating days
per year
(multi-zone)
Symbol
EC
OH
OD
Unit
%
hr/day
day/yr
Model Parameter
Values
Value
90
12
300
Basis
—
—
Mode
Uncertainty Analysis
Assumptions
Lower
Bound
—
—
250
Upper
Bound
—
—
312
Mode
—
—
300
Distribution
Type
—
—
9
Triangular
Comments
al. (2006)). The EPA TCE RA peer review
comments indicate values around 2 to 5 hr1
may be more likely (SCG, 2013). A triangular
distribution is used with the mode equal to
the midpoint of the range provided by the RA
peer reviewer (3.5 is the midpoint of the
range 2 to 5 hr1
Wadden et al. (1989) indicates LEV systems
for an open-top vapor degreaser can reduce
workplace emissions by 90 percent. Because
no data on LEV effectivenss were found for
dry cleaners, the Wadden et al. (1989) value is
cited.
EPA/OPPT assumed a typical dry cleaner
operates 12 hr/day based on engineering
judgment.
Low-end value based on 5 days per week and
50 weeks per year. Mode is based on 6 days
per week and 50 weeks per year.
High-end value based on 6 days per week and
52 weeks per year, assuming the dry cleaner is
open year-round.
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Table_Apx K-5 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for 1-BP, Unloading Dry Cleaning Machines
(Multi-Zone Model)
Input
Parameter
Machine door
diameter
Machine door
radius
Near-field
volume
Free surface
area for dry
cleaning
machine
Number of
loads per day
Cylinder
concentration
Symbol
D
ro
VD
FSAo
LD
Cc
Unit
in
ft
ft3
ft2
loads/day
ppm
Model Parameter
Values
Value
25
1.04
2.37
6.82
14
—
Basis
EPA/OPPT
estimate
EPA/OPPT
estimate
—
—
Maximum
—
Uncertainty Analysis
Assumptions
Lower
Bound
—
—
—
—
1
300
Upper
Bound
—
—
—
—
14
8,600
Mode
—
—
—
—
—
Distribution
Type
—
—
—
—
Uniform
Uniform
Comments
EPA/OPPT determined an approximate
door diameter by reviewing images of
several 4th generation PERC machine
models manufactured by Bowe and
Firbimatic.
Calculated as rD = "A (D/ 12 in/ft)
Workers are likely to bend over while
retrieving garments, such that their
breathing zones are at or near the
machine opening. EPA/OPPT assumes the
near-field consists of a hemispherical
volume surrounding the machine door
opening, VD = n * (D / 12 in/ft)3/ 12
Calculated as the surface area of the
hemisphere, FSAD = 2 x n x ro2
EPA/OPPT will assume the number of
loads ranges from one to 14 based on the
number of loads observed in NIOSH
(2010) and Blando et al. (2010).
Low-end value based on 4th generation
machine (300 ppm solvent; (CDC, 1997)).
High-end value based on 3rd generation
machines, which reduce cylinder
concentration to 2,000 to 8,600 ppm
(ERG, 2005).
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Input
Parameter
Cylinder
volume
Initial, spiked
concentration
Starting time
Exposure
Duration
Averaging time
Symbol
Vc
CD,O
ti
t2
tavg
Unit
m3
mg/m3
hr
hr
hr
Model Parameter
Values
Value
—
0
0.08
8
Basis
Calculated
—
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
0.24
—
—
—
—
Upper
Bound
0.64
—
—
—
—
Mode
—
—
—
—
Distribution
Type
Uniform
—
—
—
—
Comments
Value based characteristic sizes provided
by von Grote et al. (2003). EPA/OPPT
does not have U.S. distribution of
machine sizes. Therefore, a uniform
distribution is assumed.
Calculated as CD,o = (Cc x Vc) / VD with unit
conversions.
Constant value.
Based on engineering judgment,
EPA/OPPT assumed workers take 5
minutes to retrieve garments after each
load.
Work activities are assumed to be split
across two 8-hr shifts over each operating
day, such that a single worker is exposed
for 8 hours a day.
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Table_Apx K-6 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Finishing (Multi-Zone Model)
Input
Parameter
Near-field
volume
Free surface
area for
finishing
Residual
solvent
Load size
Loading factor
Symbol
VF
FSAF
R
LS
F
Unit
ft3
ft2
g/kg
kg/load
Unitless
Model Parameter
Values
Value
300
320
3.75
4
32
0.79
Basis
—
Maximum
Maximum
Average
Uncertainty Analysis
Assumptions
Lower
Bound
—
—
0.26
12
—
Upper
Bound
—
—
3.75
32
—
Mode
—
—
—
—
—
Distribution
Type
—
—
Discrete
Uniform
—
Comments
For length and width, EPA/OPPT
applied the same dimensions used in
the final TCE risk assessment (i.e., 10 ft
for LF and W» (U.S. EPA, 2014c).
EPA/OPPT assumes a height of 3 ft for
HF to cover the upper body of the
worker, because workers typically
perform finishing while standing.
Surface area of the near-field,
calculated as: FSAF = 2(LF x W» + 2(LF x
HF) + 2(WF x HF)
Assume 80% of loads have 0.26 g/kg
residual (normal loads) and 20% of
loads have 3.75 g/kg residual (off-the-
peg loads), per von Grote et al. (2003).
EPA/OPPT assumed the same
distribution of load types in the United
States. These estimates correspond to
a non-vented, dry-to-dry machine (3rd
generation), which is likely conservative
because 4th generation machines may
also be used.
Range of capacities for five
characteristic machine sizes (von Grote
et al., 2003). The data were obtained
from a 2002 dry cleaner survey in
Germany. EPA/OPPT assumed the
cylinder volumes and capacities are
similar to those in U.S. machines.
Because good cleaning results can only
be obtained when the machine is not
overloaded, EPA/OPPT assumed the
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Input
Parameter
Number of
loads per day
Exposure
Duration
Vapor
generation
rate
Symbol
LD
ts
GF
Unit
loads/day
hr
mg/hr-load
Model Parameter
Values
Value
14
0.33
—
Basis
Maximum
—
Calculated
Uncertainty Analysis
Assumptions
Lower
Bound
1
—
—
Upper
Bound
14
—
—
Mode
—
—
—
Distribution
Type
Uniform
—
^
—
Comments
each load is not filled to the maximum
capacity. The loading factor is an
average value derived in a survey
carried out by Klein and Kurz (1994).
EPA/OPPT assumed the number of
loads ranges from one to 14 based on
the NIOSH (2010) and Blando et al.
(2010).
EPA/OPPT assumed workers take 20
minutes to press and finish each load.
This estimate is approximately
consistent with Von Grote et al. (2003),
which estimated that residual solvent
will evaporate continuously over a
period of approximately 11 to 20
minutes.
Calculated as: GF = R x 1,000 mg/g x LS x
F/b
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Table_Apx K-7 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Spot Cleaning (Multi-Zone Model)
Input
Parameter
Near-field
volume
Free surface
area for spot-
cleaning
Starting time
Exposure
Duration
Averaging time
Use rate
Vapor
generation
rate
Symbol
Vs
FSAs
ti
t2
tavg
UR
Gs
Unit
ft3
ft2
hr
hr
hr
gal/yr
mg/hr
Model Parameter
Values
Value
600
340
0
8
8
16
Basis
—
Maximum
Calculated
Uncertainty Analysis
Assumptions
Lower
Bound
—
13.92
Upper
Bound
—
16
Mode
—
Distribution
Type
—
Constant
^
Uniform
Comments
Same dimensions used in the final risk
assessment (i.e., 10 ft for LNF and WNF
and 6 ft for HNF) (U.S. EPA, 2014c).
Surface area of the near-field,
calculated as: FSAF = (LF x W» + 2(LF x
HF) + 2(W> x HF)
Constant value.
Assumes the activity is performed from
hour 2 to hour 10 of each operating
day.
Constant value. Work activities are
assumed to be split across two 8-hr
shifts over each operating day. The first
shift worker spot cleans from hour 2 to
hour 8, while the second shift worker
spot cleans from hour 8 to hour 10.
A MassDEP comparative analysis
worksheet provides an example case
study for a facility, which spends $60
per month on spot cleaner (MassDEP,
2013). The cost of 1-BP is estimated at
$45 per gallon (Blando et al., 2009).
These numbers translate to 16 gallons
per year. We assume the 1-BP
concentration could vary uniformly
from 87 to 100 percent (Enviro Tech
International, 2013).
Density of DrySolv is 1.33 kg/L (Enviro
Tech International, 2013).
Gs = UR x (3.785 L/gal) x (1.33 kg/L) x
(10s mg/kg) / [(8 hr/day) x OD]
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Table_Apx K-8 Input Near-Field/Far-Field Model Parameters and Monte Carlo Simulation Assumptions for Spot Cleaning (Stand-Alone Model)
Input
Parameter
Near-field
volume
Starting time
Exposure
Duration
Averaging time
Use rate
Vapor
generation
rate
Operating
hours per day
Operating days
per year
Symbol
VNF
ti
t2
tavg
UR
G
OH
OD
Unit
ft3
hr
hr
hr
gal/yr
mg/hr
hr/yr
day/yr
Model Parameter
Values
Value
600
0
8
8
16
38,723
8
260
Basis
—
—
—
Maximum
Maximum
—
—
Uncertainty Analysis
Assumptions
Lower
Bound
—
—
—
13.92
33,689
—
—
Upper
Bound
—
—
—
16
38,723
—
—
Mode
—
—
—
—
—
Distribution
Type
—
Constant
—
Uniform
Uniform
—
—
Comments
EPA/OPPT applied the same dimensions
used in the EPATCE final risk
assessment (i.e., 10 ft for LNF and WNF
and 6 ft for HNF) (U.S. EPA, 2014c).
Constant value.
Equal to operating hours per day.
Constant value.
$60 spot cleaner per month (MassDEP,
2013) at a cost of $45 per gallon
(Blando et al., 2009) translates to 16
gallons per year. We assume the 1-BP
concentration could vary uniformly
from 87 to 100 percent (Enviro Tech
International, 2013).
G is set equal to UR with appropriate
unit conversions. Density of DrySolv is
1.33 kg/L (Enviro Tech International,
2013).
EPA/OPPT assumed 8 hr/day.
EPA/OPPT assumed 260 day/yr.
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Appendix L CONSUMER EXPOSURE ASSESSMENT
L-l Default Parameters Used in GEM for Emission and
Household Characteristics
The Exposure and Fate Assessment Screening Tool Version 2 (E-FAST2) Consumer Exposure
Module (CEM) performs assessments of exposures to common products to consumers. This section
describes the values that were chosen for the modeling parameters in CEM to provide more
support for the 1-BP exposure assessment. This material is also described in the E-FAST2 manual
available at http://www.epa.gov/tsca-screening-tools/e-fast-exposure-and-fate-assessment-
screening-tool-version-2014.
The default parameters used for household characteristics were all set to mean or median values
based on data found in the available literature and these were used in the 1-BP assessment.
Consumer behavior patterns were not set to E-FAST2's default settings, alternatively, a
hypothetical scenario was created for users of products containing 1-BP. Data from the Westat
(1987) survey aligned with the description of the products chosen for modeling in this exposure
assessment.
Table_Apx L-l summarizes the selection and justification of exposure parameters for CEM for
the purposes of estimation of indoor air concentrations of 1-BP.
L-2 Air Exchange Rate
The air exchange rate used by OPPT for the 1-BP model runs was the E-FAST/CEM default value of
0.45 air changes per hour (ACH). This choice is consistent with the recommended central tendency
value per the current and prior editions of the Exposure Factors Handbook, as shown below in
Table_Apx L-l. (U.S. EPA. 2011.1997b).
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Table_Apx L-l Summary of Parameters Used for Estimation of Indoor Air Concentrations of 1-BP
Modeling Input
Air exchange rate
(air exchanges/hr)
Overspray fraction
(unitless)
Whole House Volume (m3)
Emission rate constant
(hrs-1)
Inhalation rate
(mVhr)
Body weight
(kg)
Interzonal airflow rate
(mVhr)
Value
0.45
0.01
492
183.09
0.74
(During use)
0.61
(After use)
80
81.73
Justification/Source
Recommended 50th percentile value of residential air
exchange rate for all regions within the United States (U.S.
EPA, 1995)
Selection based on professional judgment (U.S. EPA, 2007a).
Recommended whole house volume from the EFH (2011),
central estimate.
Estimated using Chinn's algorithm (DTIC DLA. 1981) based on E-
FAST model documentation. This algorithm utilizes molecular
weight and vapor pressure to estimate emission rates.
Inhalation rate during product use based on short-term
exposure at light activity level (U.S. EPA, 2011)
Short term inhalation values during light activity (male and
female combined) were taken from the following age groups
and averaged to create an estimate for inhalation rate during
product use. 21 to <31 years; 31 to <41 years; 41 to <51 years;
51 to <61 years; 61 to <71 years; and 71 to <81 years.
After product use: 0.611 mVhr (U.S. EPA, 2011)
Mean value of body weights for all adults (>21 yrs), male and
female combined. Value based on EPA analysis of NHANES
1999-2006 data (U.S. EPA, 2011)
Air flow rate between the room of use (utility room or zone 1)
and the rest of the house (zone 2; (U.S. EPA, 1995)
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Figure_Apx L-l Screen Capture of Summary of Recommended Values for Residential Building
Parameters from the Exposure Factors Handbook (2011).
Table 19-1. Summary of Recommended Values for Residential Building Parameters
Mean 10 Percentile Source
Volume of Residence2 492 in3 (central estimate)" 1 54 in3 (lower percentile)' U.S. EPA 2010 analysis of U.S. DOE.
2008a
Air Exchange Rate 0.45 ACH (central estimate)'' 0.18 ACH (lower percentile)' Koontz and Rector. 1995
Volumes vary with type of housing For specific housing type volumes, see Table 19-6.
Mean value presented in Table 19-6 recommended for use as a central estimate for all single family homes, including
mobile homes and multifamilv units.
lO* percentile value from Table 19-8 recommended to be used as a lower percentile estimate.
Median value recommended to be used as a central estimate based across all U.S. census regions (see Table 19-24).
10th percentile value across all U.S. census regions recommended to be used as a lower perceutile value (see
Table 19-24).
ACH = Air changes per hour.
L-3 Overspray Fraction
The selection of a default overspray fraction of 0.01 in CEM was based on professional judgement
(as cited in E-FAST). We are only using the peak concentration as a model diagnostic in this
assessment, not as a tool to understand exposures for any time scale longer than 10 seconds.
L-4 Emission Rate
The emitted mass was addressed in CEM in two ways. When an aerosol product is used, some of
the product does not reach the intended application surface but remains in the air. This portion,
commonly known as the overspray, was assumed to be 1% of the product emitted during use. This
results in the constant emission of 1-BP to the room air over the duration of use. The remaining
fraction (99%) was assumed to strike the intended application surface forming a film. This film is
treated as an incremental source, as described below (Figure_Apx L-2 Screen Capture of E-FAST
Equations for Estimation of Emission Rate).
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Figure_Apx L-2 Screen Capture of E-FAST Equations for Estimation of Emission Rate
For a product that is applied to surface, such as a general purpose cleaner or a latex paint, an
incremental source model is used. This model assumes a constant application rate over the specified
duration of use, each instantaneously applied segmenthas an emission rate that declines exponentially
over time, at a rate that depends on the chemical's molecular weight (MW) and vapor pressure (VP).
In the case of a general purpose cleaner, the equation for exponentially declining emissions for
each instantaneously applied segment is as follows:
E(t)=E(0)xecp(-kt) (Eq. 3-41)
where E (t) is the emission rate (mass/time) at time t (in hours), E (0) is the initial emission rate at time 0,
k is a first-order rate constant for the emissions decline (inverse hours), and t is elapsed time (hours). The
value of k is determined from an empirical relationship, developed by Chinn (1981), between the time (in
hours) required for 90 percent of a pure chemical film to evaporate (EvapTime) and the chemical's
molecular weight and vapor pressure:
145
EvapTime = -™- (Eq. 3-42)
The value of k is determined from the 90 per cent evaporation time as follows:
In(lO)
- ^-J—
EvapTime
In(lO)
k = - ^-J— (Eq. 3-43)
Using Equation 3-42 to calculate EvapTime:
145
EvapTime =
(123xl46.3)°-9546
Where,
Molecular weight (MW) = 123 g/mole
Vapor Pressure (VP) = 146.3 torn
Hence, EvapTime = 0.0126 hrs or 0.75 min
Using Equation 3-43 to calculate Emission Rate Constant (k):
,_ MM
K ~ 1.36
Hence, Emission Rate Constant (k) = 183.09 hrs"1 or 3.05 min"1
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Because Chirm's algorithm (DTIC DLA, 1981) assumes a pure chemical film, it tends to produce a
lower-bound estimate of the evaporation time; thus, overestimates the peak concentration. In
products that are a mixture of chemicals, interaction forces between the different chemicals
could alter the evaporation rate of individual constituents.
In the simulation done for this assessment, the outcome was not expected to be strongly
dependent on the exact value of k due to the long time period the consumer spent in the room
of use after the period of product application. All of the 1-BP mass was expected to enter the air
before the user leaves the room even if the k value was adjusted to be less conservative.
Currently the evaporation time for 90% of the 1-BP in the film on the application surface
(0.0126 hrs or 0.75 min) was much less than the time the user spent in the room of use. Even if
this value were to increase, due to intermolecular interactions within a more complicated
mixture decreasing the emission rate, it would likely still be less than the time spent in the room
of use.
L-5 Room and House Volume and Movement Within the Home
The CEM within E-FAST2 currently uses a default house volume (523 m3) that is based on the
calculated volume of a single attached home in the Formaldehyde Indoor Air Model (FIAM).
The 2011 edition of the EFH recommends a house volume of 492 m3 (U.S. EPA, 2011). While the
default house volume used in CEM is slightly larger than the average value presented in the
EFH, the difference is less than 10% and as noted in the sensitivity analysis, the house volume is
an import factor, but is not the largest contributor to potential differences in predicted air
concentrations.
The exposure values for the user could be more impacted by the size of the room selected
during use. The volume assigned to the room of use was 20 m3 for a utility room where the
volume was represented by a 9 ft x 10 ft room with 8 ft ceilings (720 ft3 = 20.4 m3) (U.S. EPA,
2014c), 48 m3 for the living room (U.S. EPA, 2011), and 118 m3 for the "garage" (Batterman et
al., 2007). The garage volume was used based on an indoor air quality study (Batterman et al.,
2007) which included attached garages of 15 homes in Michigan, with a median volume of 118 m3.
The room of use is Zone 1 in the CEM simulations; Zone 2 is the rest of the house (492 m3). The
user and bystander move about the home according to a hypothetical behavior pattern
constructed to represent a day spent mostly indoors. Since the behavior patterns do not involve
the residents entering the room of use except to use the product, the user spends the rest of
the time either in Zone 2 or outside (where there is no expected chemical exposure) and the
non-user spends the entire 24 hours either in Zone 2 or outside.
L-6 Inhalation Rate and Body Weight
The inhalation rate and body weight values for the simulation were taken from the 2011 EFH
(U.S. EPA, 2011). These values were based on the NHANES data (1999-2006) and correspond to
the age groups reported in the (U.S. EPA, 2011). It is important to note that in the exposure
assessment only the exposure doses will be affected by these parameters. Indoor air
concentrations are determined by the product use patterns, the volume of the room and of the
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house, and the physical-chemical properties of 1-BP. Body weight and inhalation rate do not
change the calculated indoor air concentrations.
L-7
Consumer Behavior Patterns
E-FAST2/CEM requires the input of consumer behavior pattern information, including mass of
product used, duration of use, time spent in the room of use and the volume of the room of
use. By default, E-FAST2/CEM uses pre-set, high-end values for a variety of consumer use
scenarios when use information is not available for specific products. Under these conditions,
the model results tend to over predict the exposure.
EPA/OPPT did not have consumer behavior pattern information for the specific branded
products being evaluated in this assessment. Rather than using the E-FAST2/CEM's default
inputs, EPA/OPPT relied upon professional judgment and the Household Solvent Products
Survey prepared by Westat for EPA in (1987) to inform the selection of input parameters and
assumptions representing the consumers' behavior patterns. Table_Apx L-2 provides a
summary of the information provided in the Westat (1987) survey, with a comparison to the
values used in this assessment.
Table_Apx L-2 Comparison of Westat Survey Data and Simulation Values for 1-BP
Spray Adhesives
Time spent using product
Time spent in room after use3
Amount of product used per
event
Weight fraction 1-BP in
product**
Room of use
Mean
15 min
69 min
2.98 oz
(84.5 g)
Median
(50th %)
4 min
10 min
0.25oz
(7.1 g)
90th %
30 min
180 min
2.0 oz
(56.7 g)
Garage 6%
Living Room 12%
H Inside Room 61%
Simulation values*
50th %
4 min
60 min
0.25oz
(7.1 g)
0.60
90th %
30 min
180 min
2.0 oz
(56.7 g)
0.85
Utility room
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Table_Apx L-2 Comparison of Westat Survey Data and Simulation Values for 1-BP
Spot Removers
Time spent using product
Time spent in room after useb
Amount of product used per
event
Weight fraction 1-BP in
product**
Room of use
Mean
llmin
44min
3.49 oz
(98.9 g)
Median
(50th %)
5 min
5 min
1.33 oz
(37.7 g)
90th %
30 min
120 min
7.5 oz
(212.6 g)
Basement 9%
Living Room 20%
Inside Room 57%
Simulation values
50th %
5 min
60 min
1.33 oz
(37.7 g)
0.55
90th %
30 min
120 min
7.5 oz
(212.6 g)
0.95
Utility room
Engine Degreasers
Time spent using product
Time spent in room after usec
Amount of product used per
event
Weight fraction 1-BP in
product**
Room of use
Mean
29min
5min
18.7 oz
(530 g)
Median
(50th %)
15 min
Omin
11.6 oz
(329 g)
90th %
60 min
Omin
32 oz
(907 g)
Garage and Outside 1%
Garage 8%
Outside 89 %
Simulation values
50th %
15 min
60 min
11.6 oz
(329 g)
0.75
90th %
60 min
120 min
32 oz
(907 g)
0.90
Garage
Brake Cleaners
Time spent using product
Time spent in room after used
Amount of product used per
event ^^
Weight fraction 1-BP in
product**
Room of use
Mean
23 min
lOmin
6oz
(170 g)
Median
(50th %)
15 min
Omin
4oz
(113 g)
90th %
50 min
30 min
12 oz
(340 g)
Garage and Outside 3%
Garage 18%
Outside 77%
Simulation values
50th %
15 min
60 min
4oz
(113 g)
0.75
90th %
50 min
120 min
12 oz
(340 g)
0.95
Garage
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Table_Apx L-2 Comparison of Westat Survey Data and Simulation Values for 1-BP
Electronics Cleaners
Mean
Median
(50th %)
90th %
Simulation values
50th %
90th %
Time spent using product
9min
2min
20m in
2 min
20 min
Time spent in room after use6
60 min
60 min
300 min
60 min
300 min
Amount of product used per
event
1.8 oz
(51 g)
0.5 oz
(14 g)
3.5 oz
(IQOg)
0.5 oz
(14 g)
3.5 oz
(100 g)
Weight fraction 1-BP in
product**
0.35
0.75
Room of use
Basement 6%
Other Inside Room36%
Living Room 48 %
Living Room
Notes:
*Simulation values for time spent in room of use are for total time in room of use and can only be modeled in increments of
1 hour, with a minimum value of 1 hour. Therefore, for scenarios where survey data indicated that users left the room of use
immediately following application, if the application duration was less than one hour, time spent in room of use was modeled as
one hour.
** Weight fraction in products based on information from available products as described in Table_Apx A-3.
aPercentile rankings included respondents who said they used contact cements, super glues or spray adhesives but did not spenc
any time in the room of use. In comparison, median time spent in the room of use including only those who spent time in the
room of use was 20 minutes and the 90th percentile value was 240 minutes.
bPercentile rankings included respondents who said they used spot removers but did not spend any time in the room of use. In
comparison, median time spent in the room of use including only those who spent time in the room of use was 10 minutes and
the 90th percentile value was 180 minutes.
cPercentile rankings included respondents who said they used engine degreasers but did not spend any time in the room of use.
In comparison, median time spent in the room of use including only those who spent time in the room of use was 60 minutes
and the 90th percentile value was 120 minutes.
Percentile rankings included respondents who said they used brake quieters/cleaners but did not spend any time in the room of
use. In comparison, median time spent in the room of use including only those who spent time in the room of use was
30 minutes, and the 90th percentile value was 120 minutes.
Percentile rankings included respondents who said they used specialized electronic cleaners but did not spend any time in the
room of use. In comparison, median time spent in the room of use including only those who spent time in the room of use was
60 minutes and the 90th percentile value was 300 minutes.
L-8 Use Data for Contact Cement, Super Glues or Spray
Adhesives
The description of this product category in the Westat (1987) survey matches reasonably well
with the simulated scenario, however no distinction in the survey statistics was made between
the three types of products and therefore it is unknown if these statistics are skewed more
towards one product or another. More than 60% of the 4917 respondents in the survey said that
they had ever used contact cement, super glues or spray adhesives. Of the 2686 respondents who
had recently used any of these products, only 2.9% stated that the product was in the aerosol form.
More than 60% of the respondents who used these products stated that they used it in "another
inside room", thus EPA/OPPT chose the room of use as the default utility room within the CEM
model. The majority of the users (59%) stated they did not have an open window or door for
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ventilation and 91% of the users stated that they did use an exhaust fan during use. Furthermore,
75.1% of respondents stated that the door of the room of use was open to the rest of the house.
This information supports the assumptions of no ventilation and a second zone with potential
bystander exposure used in modeling the indoor air concentration in this assessment. The 90th
percentile values for the the mass used and time spent in the room of use were used to present a
conservative estimate however this was also balanced by presenting a central tendency estimate by
using 50th percentile input values.
L-9 Use Data for Spot Removers
The description of this product category in the survey matches reasonably well with the
simulated scenario. Nearly half (43.9%) of the 1388 respondents to the Westat (1987) survey that
said that they had recently used spot removers stated that the product was in the aerosol form.
More than 57% of the respondents who used these products stated that they used it in "another
inside room", thus EPA/OPPT chose the room of use as the default utility room within the CEM
model. The majority of the users (55%) stated they did not have an open window or door for
ventilation and nearly 91% of the users stated that they did not use an exhaust fan during use.
Furthermore, over 80% stated that the door of the room of use was open to the rest of the house.
This information supports the assumptions of no ventilation and a second zone with potential
bystander exposure used in modeling the indoor air concentration in this assessment. The 90th
percentile values for the the mass used and time spent in the room of use were used to present a
conservative estimate however this was also balanced by presenting a central tendency estimate by
using 50th percentile input values.
L-10 Use Data for Engine Degreasers
The description of this product category in the survey matches reasonably well with the
simulated scenario, with some exceptions. More than three quarters (78.9%) of the 577
respondents to the Westat (1987) survey that said that they had recently used engine degreasers
stated that the product was in the aerosol form. More than 89% of the respondents who used
these products stated that they used it outside, with 7.8% reporting that they used the product in
their garage. Although it is clear that the main location of use is outside, E-FAST/CEM does out have
the ability to model air concentrations outdoors, thus EPA/OPPT chose the room of use as a garage.
The CEM model does not have a default garage volume therefore the utility room was used as a
proxy with an adjusted volume. The garage volume was used based on an indoor air quality study
(Batterman et al., 2007) which included attached garages of 15 homes in Michigan, with a median
volume of 118 m3. The 90th percentile values for the mass used and time spent in the room of use
were used to present a conservative estimate however this was also balanced by presenting a
central tendency estimate by using 50th percentile input values.
L-ll Use Data for Brake Quieters/Cleaners
The description of this product category in the survey matches reasonably well with the
simulated scenario, with some exceptions. More than half (65.6%) of the 94 respondents to the
Westat (1987) survey that said that they had recently used brake quieter/cleaner stated that the
product was in the aerosol form. This sampling is not large, therefore there is may be some
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uncertainty associated with this use; however, EPA/OPPT was not able to identify other available
data to better inform this scenario. More than 77% of the respondents who used these products
stated that they used it outdoors, with nearly 18% reporting that they used the product in their
garage. As mentioned in Section L-5, E-FAST/CEM does not have the ability to model air
concentrations outdoors, thus EPA/OPPT chose the room of use as a garage. Because E-FAST does
not have a designated "garage" as a room of use in its default scenarios, EPA/OPPT chose to use the
utility room in E-FAST as a proxy by adjusting the room volume.The 90th percentile values for the
the mass used and time spent in the room of use were used to present a conservative estimate
however this was also balanced by presenting a central tendency estimate by using 50th percentile
input values.
L-12 Use Data for Specialized Electronic Cleaners
The description of this product category in the survey matches reasonably well with the
simulated scenario, with some exceptions. Less than half (47.5%) of the 541 respondents to the
Westat (1987) survey that said that they had recently used specialized electronic cleaners stated
that the product was in the aerosol form. Nearly half (47.5%) of the respondents who used these
products stated that they used it in the living room with another 36% reporting that they used it in
"another inside room", thus EPA/OPPT chose the room of use as the default living room within the
CEM model. The majority of the users (66%) stated they did not have an open window or door for
ventilation and nearly 94% of the users stated that they did not use an exhaust fan during use.
Furthermore, over 70% stated that the door of the room of use was open to the rest of the house.
This information supports the assumptions of no ventilation and a second zone with potential
bystander exposure used in modeling the indoor air concentration in this assessment. The 90th
percentile values for the the mass used and time spent in the room of use were used to present a
conservative estimate however this was also balanced by presenting a central tendency estimate by
using 50th percentile input values.
L-13 Converting E-FAST ADRs to Air Concentrations
The Exposure and Fate Assessment Screening Tool Version 2 (E-FAST2) Consumer Exposure
Module (CEM) performs assessments of exposures to common products to consumers. The
ADRs generated using the E-FAST/CEM models are shown in the table below in mg/kg-bw/day
(Table_Apx L-3). The only output in the acute exposure scenario expressed as a concentration
was the peak concentration, which represented the maximum concentration in air calculated by
the model during any 10-second time step during (in this case) 24 hrs. This value did not
realistically describe a 24-hr exposure, even as a worst-case scenario.
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Table_Apx L-3 Estimated Acute Dose Rates from Consumer Use
Acute Dose Rate (mg/kg-bw/day) - High End
Age (yrs)
21 to 78
16 to <21
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This simplification is reasonable since the averaging time for acute exposure is one day (24 hrs).
In both scenarios, the frequency is just once per day. Although the duration of the event for the
two consumer scenarios is 0.5 hrs, for the purposes of this exercise and to convert the model
output to a more useable exposure value to compare to the hazard value, there is no correction
for this difference. This assumption is still conservative since the values generated were
reasonably high exposures that probably overestimated the actual exposures.
An example calculation is presented below, since the final value is in mg/m3 and the desired
units will be in ppm. All calculated values are presented in Table_Apx L-3 and Table_Apx L-4.
For example, the spray adhesive use, 21- to 78-yr-old user:
ADRpot = 5.12 mg/kg-bw/day
InhR (during use; 0.5 hrs) = 0.74 m3/hr
InhR (other times; 23.5 hrs) = 0.611 m3/hr
BW = 80 kg [using 2011 EFH (U.S. EPA. 2011)1
And calculating for Cair:
Cair = (5.12 mg/kg-bw/day) x (80 kg)
[(0.74 m3/hr x 0.5 hr) + (0.611 m3/hr x 23.5 m3/hr)]
= 27.81 mg/ m3;
= 5.5 ppm (rounded to 6 ppm to use a single significant figure given the assumptions in
the back-calculation).
However, for the user in all scenarios, the inhalation rates were slightly higher during use of the
product, as stipulated in the model outputs. Thus, for example, for the spray adhesive use, an
inhalation rate of 0.74 m3/hr (for 21 to 78 year olds, 0.72 m3/hr for the 16 to 20 year olds) was
used for one 0.5 hrs, and 0.611 m3/hr (for 21 to 78 yr olds, 0.679 m3/hr for the 16 to 20 yr olds)
for the remaining 23.5 hrs. This correction was not performed for any non-user scenario.
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Table_Apx L-4 Estimated Acute Air Concentrations from Consumer Use (rounded to one significant
figure)
Acute Air Concentration (ppm) - High End
Age (yrs)
21 to 78
16 to <21
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Table_Apx L-5 Plausible range for input parameters for Tier 1 analysis
Parameters
Room of Use
Whole House
Volume
Consumer
Product Weight
Fraction
Mass of Product
1 \rv 1 I.-I1
used per use
Air Exchange
Rate
Inhalation Rate -
during use
Low
18 m3
(Bathroom)
ocn ««3
oby m
75%
0.25
ounces
0.18
0.58
Medium
(Baseline)
36 m3
(Utility Room)
A m ««3
i|.yz rn
85%
1.33 ounces
0.45
0.74
High
48m3
(Living room)
~7O~7 ««3
/o / m
95%
7.5 ounces
1.26
0.95
Source
FIAM1
(Appendix A)
EPA (2011,
1997a)
EPA
Westat (1987)
EPA (2011)
EPA (2011)
Selection Justification
Room volumes obtained from the Formaldehyde Indoor Air Model
(FIAM). In the FIAM model, the bathroom is the smallest room and the
living room is the largest room.
Low volume selected from EFH (U.S. EPA, 1997a) whole house
volume. Medium volume selected from EFH (U.S. EPA, 2011) median
house volume. High volume was interpolated based on an
approximately 40% increase in volume size from the baseline
scenario. The 40% increase is based on the difference in volumes
between the low and medium values.
Medium and high values selected from spray adhesive and spot
remover scenarios. The low value was interpolated based on the
difference (10%) between the medium and high values.
Low, medium, and high values selected from the 10 percentile,
median, and 90th percentile values of mass (in ounces) of chemical
used for spot remover scenario. Data obtained from survey conducted
by Westat in 1987 from 1275 recent users ((Westat, 1987) - Table C-
18 -page 5-49).
Values were selected from the summary statistics for residential air
exchange rates (in air changes per hour) table (Table 19-24) in EPA's
EFH (U.S. EPA, 2011). The low value is the 10th percentile, the medium
value is the 50th percentile and the high value is the 90th percentile.
The medium and high values were selected from Table 6-2 (page 6-4)
in EPA's EFH (U.S. EPA, 2011). The medium value represents the
average of mean short-term exposure values for inhalation during
light activity (males and females combined) for the age classes 21 to
<31 years; 31 to <41 years; 41 to <51 years; 51 to <61 years; 61 to <71
years; and 71 to <81 years. The high value represented the average of
the 95th percentile short-term exposure values.
The low value was interpolated based on an approximately 28.3%
increase in volume size between the medium and high values. Thus,
the low volume was estimated to be approximately 28.3% lower than
the medium value.
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Table_Apx L-5 Plausible range for input parameters for Tier 1 analysis
Parameters
Low
Medium
(Baseline)
High
Source
Selection Justification
Inhalation Rate-
after use
0.463
0.611
0.807
The medium and high values were selected from Table 6-1 (page 6-3)
in EPA's EFH (U.S. EPA, 2011). The medium value represents the
average of mean long-term exposure values for inhalation during light
activity (males and females combined) for the age classes 21 to <31
years; 31 to <41 years; 41 to <51 years; 51 to <61 years; 61 to <71
years; and 71 to <81 years. The high value represented the average of
the 95th percentile long-term exposure values.
The low value was interpolated based on an approximately 32%
increase in volume size between the medium and high values. Thus,
the low volume was estimated to be approximately 32% lower than
the medium value.
Body weight
65.5
80
104
Values were selected from Table 8-3 in EPA's EFH (U.S. EPA, 2011)
providing mean and percentile body weights derived from NHANES
(1999-2006) males and females combined. The low value is the 25th
percentile average of ages 21 and over, the medium value is the
average of the mean values and the high value is the average of the
90th percentiles.
Event duration
(central
tendency/high
end)
0.25
0.5
Professional
Judgment
It is assumed that a typical DIY project with spray adhesives would last
no more than 30 minutes. The low value was assumed to be half this
time and high value was assumed to be double this time.
Note: FIAM - USEPA's Formaldehyde Indoor Air Model
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The plausible inputs for each parameter were varied one at a time and the model responses
(i.e., changes in the ADR and acute concentration values) were noted. The results were first
ranked by their output differences using the maximum response value minus the minimum
response value of the plausible range and then by their index of sensitivity. The "index of
sensitivity" was calculated by dividing the percent change in ADR by the percent change of the
input values for each parameter. The rankings from both were averaged for an overall rank for
each parameter tested. This exercise was repeated for the acute air concentration results.
The resulting ADRs (mg/kg-bw) and acute air concentrations (ppm) along with the rankings for
each of the tested parameters are provided in Table_Apx L-6 and Table_Apx L-7.
Table_Apx L-6 Tier 1 Sensitivity Rankings for Acute Dose Rate
Parameter
Room of Use
Whole House Volume
Consumer Product weight fraction
Mass of Product used per use
Air exchange rate
Inhalation Rate - during use
inhalation rate - after use
Body weight
Event Duration (central/high
tendency)
Notes:
ADR (mg/kg-bw)
Low
3.06
3.32
2.32
0.496
4.83
2.16
2.53
3.22
2.72
Medium
(baseline)
2.63
2.63
2.63
2.63
2.63
2.63
2.63
2.63
2.63
High
2.40
2.03
2.94
14.9
1.17
3.26
2.77
2.03
2.18
Difference
Ranking
6
3
7
1
2
5
9
4
8
Index of
Sensitivity
Ranking
6
4
1.5
1.5
5
4
7
3
8
Tierl
Overall
Rank
6
3.5
4.25
1.25
3.5
4.5
8
3.5
8
Bold indicates selected parameters for the Tier 2 pure sensitivity analysis.
Ranking from 1 to 9 with 1 being the most sensitive parameter
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Table_Apx L-7 Tier 1 Sensitivity Rankings for Acute Air Concentration
Parameter
Room of Use
Whole House Volume
Consumer Product
weight fraction
Mass of Product used per use
Air exchange rate
Inhalation Rate - during use
inhalation rate - after use
Body weight
Event Duration
(central/high tendency)
Notes:
Acute Air Concentration
(ppm)
Low
3.30
3.58
2.50
0.536
5.21
Medium
(baseline)
2.84
2.84
2.84
2.84
2.84
High
2.59
2.19
3.17
16.1
1.26
N/A ^
N/A
N/A
2.94
2.84
2.34
Difference
Ranking
4
3
5
1
2
Index of
Sensitivity
Ranking
5
3
1.5
1.5
4
Tierl
Overall
Rank
4.5
3.0
3.3
1.3
3.0
N/A
N/A
N/A
6
6
6.0
Acute air concentration is not affected by inhalation rate or body weight changes.
Bold indicates selected parameters for the Tier 2 pure sensitivity analysis.
Ranking from 1 to 6 with 1 being most sensitive parameter.
The Tier 1 analysis indicated that the four most sensitive parameters affecting the ADR and the
acute air concentration were as follows:
Acute Dose Rate
1. mass of product used per use;
2. whole house volume;
3. air exchange rate; and
4. body weight.
Acute air concentration
1. mass of product used per use;
2. whole house volume;
3. air exchange rate; and
4. consumer product weight fraction.
The parameter most influential in determining the acute dose rate and acute air concentration
is the mass of product applied per use. The emission rate is directly dependent upon the
chemical properties and therefore the mass of product used strongly influences the air
concentration and dose rate. Because the modeled scenario follows the user over a 24 hour
period limiting the period of use to 0.5 hrs in the utility room, the whole house volumes (the
remaining 23.5 hours) plays a larger factor in influencing the final acute dose rate and acute air
concentration. As shown in Table_Apx L-6 and Table_Apx L-7, the air exchange rate and product
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weight fraction can influence the contaminant concentration but do not play as large a role in
the final outcome. The above-mentioned 5 input parameters were chosen for the Tier 2
analysis.
Tier 2 Analysis
For the Tier 2 analysis, all the parameters were adjusted by equal increments from the base
value. All of the baseline input values were adjusted by -10% and +10% to calculate sensitivity
near the baseline value and by -50% and +50% to calculate sensitivity for values farther
removed from the baseline value. The baseline scenario was the same baseline scenario that
was used for the Tier 1 analysis with the exception of the consumer product weight fraction.
Due to a limitation with this value (since the baseline consumer weight fraction was 85% and
we could not increase that by 50% as the model would only consider weight fractions that were
less than 100%) the consumer product weight fraction was lowered from 85% to 50% for the
baseline scenario. The inputs for the Tier 2 analysis are provided in Table_Apx L-8.
Table_Apx L-8 Range of Input Parameters for Tier 2 Analysis
Parameters
Whole House Volume (m3)
Mass of Product used per use (g)
Air exchange rate
Body weight (kg)
Consumer Product Weight Fraction
-50%
262
18.9
0.225
40
0.25
-10%
471
33.9
0.405
72
0.45
Baseline
523
37.7
0.450
80
0.50
+10%
575
41.5
0.495
88
0.55
+50%
785
56.6
0.675
120
0.75
Similar to the protocol followed in the Tier 1 analysis, the input parameters were varied one at
a time and the model responses (ADR and acute concentration) were recorded. There were a
total of four variable runs for each parameter. The sensitivity was calculated near the base
value (-10% and +10%) and farther removed from the base value (-50% and +50%) for each of
the tested parameters. Table_Apx L-9 provides the calculated sensitivities for the parameters
affecting the ADR and Table_Apx L-10 provides the calculated sensitivities for the parameter
affecting the acute air concentration.
Table_Apx L-9 Tier 2 Sensitivity Results for ADR
Parameters
Whole House
Volume (m3)
Mass of Product
used per use (g)
Air exchange rate
Body weight (kg)
ADR (mg/kg-bw)
-50%
2.37
0.78
2.47
3.10
-10%
1.67
1.39
1.67
1.72
Baseline
1.55
1.55
1.55
1.55
+10%
1.45
1.71
1.45
1.41
+50%
1.13
2.33
1.15
1.03
Average percent
change of the -10%
and +10% values
from the baseline
11.0%
16.0%
11.0%
15.5%
Average percent
change of the -50%
and +50% values
from the baseline
62.0%
77.7%
66.0%
104%
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Table_Apx L-10 Tier 2 Sensitivity Results for Acute Air Concentration
Parameter
Consumer Product
weight fraction
Whole House
Volume (m3)
Mass of Product
used per use (g)
Air exchange rate
Acute air concentration (ppm)
-50%
0.84
2.56
0.84
2.67
-10%
1.50
1.80
1.50
1.80
Baseline
1.7
1.7
1.7
1.7
+10%
1.84
1.57
1.85
1.57
+50%
2.50
1.22
2.52
1.24
Average percent
change of the -10%
and +10% values
from baseline
16.7%
13.4%
17.3%
13.4%
Average percent
change of the -50%
and +50% values
from baseline
83.4%
66.9%
83.8%
71%
Results of the Tier 2 analysis indicate that the CEM model is most sensitive to changes in body
weight when using the ADR as the model output. When the acute concentration is used as the
model output, it was the mass of product used that the CEM model is most sensitive to. It
should be noted that the sensitivity analysis was conducted using some hypothetical values that
were based solely on mathematical interpolation. Although some of these values might not
correspond to specific product uses based on aerosol spray adhesive, aerosol spot remover, or
aerosol degreaser and cleaner scenarios, they lend themselves in the overall understanding of
the model sensitivity.
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Appendix M STUDY QUALITY AND SELECTION
CONSIDERATIONS
Toxicological studies were evaluated for quality, considering soundness, applicability and utility,
clarity and completeness and uncertainty and variability (U.S. EPA, 2014b). Specifically, each
laboratory animal-based study was reviewed considering the following factors:
• the adequacy of study design,
• test animals (e.g., species, strain, source, sex, age/lifestage/embryonic stage),
• environment (e.g., husbandry, culture medium),
• test substance (e.g., identification, purity, analytical confirmation of stability and
concentration),
• treatment (e.g., dose levels, controls, vehicle, group sizes, duration, route of
administration),
• endpoints evaluated (e.g., schedule of evaluation, randomization and blinding
procedures, assessment methods) and
• reporting (quality and completeness)
The evaluation also included a number of considerations, as described below in Table_Apx M-l.
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Table_Apx M-l Study Quality Considerations
Feature
Example Questions
Exposure
Quality
• Were the exposures well designed and tightly
controlled?
•Was the test article/formulation adequately identified
and characterized? Are co-exposures expected as a
result of test article composition?
• Is the administration route relevant to human
exposure?
•Are the exposure levels relevant?
• Inhalation exposure: Were analytical concentrations in
the test animals' breathing zone measured and reported
(i.e., not just target or nominal concentrations)?
• Inhalation exposure: For aerosol studies, were the mass
median aerodynamic diameter and geometric standard
deviation reported?
• Inhalation exposure: Was the chamber type appropriate?
Dynamic chambers should be used; static chambers are not
recommended.
• Inhalation exposure: Were appropriate methods used to generate
the test article and measure the analytical concentration?
• Diet/Water Exposure: Was consumption measured to allow for
accurate dose determinations? Were stability and homogeneity
of the test substance maintained? Was palatability an issue?
•Oavage Exposure: Was an appropriate vehicle used? Are there
any toxicokinetic differences due to bolus dosing? Consider
relevance to human exposures.
Test Animals
•Were the test animals appropriate for evaluation of the
specified effect(s)?
•Were the species, strain, sex, and/or age of the test
animals appropriate for the effect(s) measured?
•Were the control and exposed populations matched in
all aspects other than exposure?
•Were an appropriate number of animals examined, based on
what is known about the particular endpoint(s) in question ?
•Were there any notable issues regarding animal housing or food
and water consumption?
Study Design
• Is the study design appropriate for the effect(s) and
chemical analyzed?
•Were exposure frequency and duration appropriate for
the effect(s) measured?
•Were anticipated confounding factors caused by
selection bias controlled for in the study design (e.g.,
correction for potential litter bias; randomization of
treatment groups)?
•Was the timing of the endpoint evaluation (e.g., latency
from exposure) appropriate?
• Was it a Good Laboratory Practices (GLP) study?
•Was it designed according to established guidelines (e.g., EPA,
OECO)? Was it designed to specifically test the endpoint(s) in
question?
• Did the study design include other experimental procedures (e.g.,
surgery) that may influence the results of the toxicity endpoint(s)
in question? Were they controlled for?
•Was the study design able to detect the most sensitive effects in
the most sensitive population(s)?
•Were multiple exposure groups tested? Was justification for
exposure group spacing given? Was recovery or adaptation
tested?
Toxicity
Endpoints
•Are the protocols used for evaluating a specific
endpoint reliable and the study endpoints chosen
relevant to humans?
•Are the endpoints measured relevant to humans? Do
the endpoints evaluate an adverse effect on the health
outcome in question?
•Were the outcomes evaluated according to established
protocols? If not, were the approaches biologically
sound? Were any key protocol details omitted?
•Were all necessary control experiments performed to allow for
selective examination of the endpoint in question?
•As appropriate, were steps taken to minimize experimenter bias
(e.g., blinding)?
• Does the methodology employed represent the most appropriate
and discriminating option for the chosen endpoint?
Data
Presentation
and Analysis
•Were statistical methods and presentation of data
sufficient to accurately define the direction and
magnitude of the observed effect(s)?
•Are the statistical methods and comparisons
appropriate?
•Was sufficient sampling performed to detect a
biologically relevant effect (e.g., appropriate number of
slides examined)?
• Does the data present pooled groups that should be displayed
separately (e.g., pooled exposure groups; pooled sexes) and/or
analyzed separately?
•Was an unexpectedly high/low level of within-study variability
and/or variation from historical measures reported or explained?
•As appropriate, were issues such as systemic and maternal
toxicity (e.g., body weight) considered?
Reporting
•Are descriptions of study methods and results for all
endpoints sufficient to allow for study quality
evaluations?
•Were the details of the exposure protocols and
equipment provided?
•Were test animal specifics adequately presented?
•Are the protocols for all study endpoints clearly
described? Is sufficient detail provided to reproduce the
experiment(s)?
•Are the statistical methods applied for data analysis provided and
applied in a transparent manner? Was variability reported?
• Did the study evaluate a unique cohort of animals (i.e., are
multiple studies linked)?
•Are group sizes and results reported quantitatively for each
exposure group, time-point, and endpoint examined?
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Appendix N TOXICOKINETICS
The studies summarized in this section were identified for consideration in the human health
hazard assessment, as described in Section 3.1.
Empirical evidence from rodent toxicity studies and from occupational exposure studies
indicate that 1-BP is absorbed by both inhalation and dermal routes. Additional evidence of the
systemic uptake of 1-BP via the oral route has been reported (Lee et al., 2007). Absorption by
all routes is rapid, and a significant portion of the absorbed dose (39% to 48% in mice and 40%
to 70% in rats) is eliminated in exhaled breath as unspecified volatile organic compounds (VOC)
(Garner etal., 2006; Jones and Walsh, 1979). Garner and Yu (2014) provided supplemental
evidence on the toxicokinetics of BP in rodents. Rodents exposed to 1-BP via either IV injection
or inhalation exhibited rapid system clearance and elimination that decreased as the dose
increased. Previous studies showed that the remaining absorbed dose is eliminated,
unchanged, in urine humans or as metabolites in the urine and exhaled breath of all species
studied (Garner et al., 2006; Kawai et al., 2001). Available toxicokinetic data indicate that
glutathione (GSH) conjugation and oxidation via cytochrome P450 (CYP450) significantly
contribute to the metabolism of 1-BP (Garner and Yu, 2014; Garner etal., 2006).
N-l Absorption
The detection of carbon-containing metabolites and elevated bromide ion concentrations in
urine samples of workers exposed to 1-BP by inhalation and dermal contact provides qualitative
evidence that 1-BP is absorbed by the respiratory tract and the skin in humans (Hanley et al.,
2010, 2009; Valentine et al., 2007; Hanleyetal., 2006). In addition, reports of neurological and
other effects in occupationally exposed subjects provide indirect evidence of absorption of 1-BP
(Samukawaetal.. 2012: CDC. 2008: Majersik et al.. 2007: Raymond and Ford. 2007: NIOSH.
2003: Ichihara etal..2002: Sclar. 1999).
Dermal absorption characteristics estimated in human epidermal membranes mounted on
static diffusion cells included steady-state fluxes averaging 625-960 u.g cm"2 hour1 with pure
1-BP and 441-722 u.g cm"2 hour"1 with a commercial dry cleaning solvent, an average dermal
penetration of about 2% from an applied dose of 13.5 mg/cm2 under non-occluded conditions,
and a dermal permeability coefficient for 1-BP in water of 0.257 cm/hour (Frasch et al., 2011).
Qualitative evidence of absorption by the gastrointestinal and respiratory tracts comes from
animal studies (Garner et al., 2006; Jones and Walsh, 1979). 13C-labeled metabolites were
detected in urine collected from rats and mice exposed by inhalation to 800 ppm
[1,2,3-13C]-1-BP for 6 hours (Garner et al., 2006). A number of mercapturic acid derivative
metabolites were detected in pooled urine samples collected from rats given oral doses of
200 mg 1-BP/kg/day in arachis oil for 5 days (Jones and Walsh, 1979).
No other human or animal studies were located that determined the rate or extent of
absorption of 1-BP following inhalation, oral, or dermal exposure.
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N-2 Distribution
Metabolic disposition studies in rats and mice given single intravenous injections of
[1,2,3-13C]-1-BP indicate that 1-BP is not expected to accumulate in tissues (Garner et al., 2006).
Following intravenous injection of [1-14C]-1-BP at nominal doses of 5, 20, or 100 mg/kg,
radioactivity remaining in the carcass 48 hours after dose administration accounted for about 6,
6, and 2% of the administered dose in rats, and 4, 2, and 4% in mice (Garner et al., 2006). In
these studies, most of the administered radioactivity was exhaled as parent material or
metabolized C02 or excreted as metabolites in the urine.
N-3 Metabolism
The metabolism of 1-BP in mammals involves: (1) conjugation, principally with glutathione,
leading to release of the bromide ion and formation of mercapturic acid derivatives and
(2) oxidation (catalyzed by cytochrome P-450) of parent material and metabolites leading to
metabolites with hydroxyl, carbonyl, and sulfoxide groups, and to C02. These concepts are
based on studies of urinary metabolites in workers exposed to 1-BP (Hanley et al., 2010, 2009;
Valentine et al., 2007; Hanley et al., 2006), in vivo metabolic disposition studies in rats and mice
(Garner etal., 2007; Garner et al., 2006; Ishidao et al., 2002; Jones and Walsh, 1979; Barnsleyet
al., 1966), and in vitro metabolism studies with rat liver preparations (Kaneko et al., 1997;
Tachizawa et al., 1982; Jones and Walsh, 1979).
N-Acetyl-S-propylcysteine has been identified in urine samples from workers in a 1-BP
manufacturing plant (Valentine et al., 2007), in foam fabricating plants using spray adhesives
containing 1-BP (Hanley et al., 2010, 2009; Hanley et al., 2006), and in degreasing operations in
plants using 1-BP as a cleaning solvent in the manufacture of aerospace components, hydraulic
equipment, optical glass, and printed electronic circuit assemblies (Hanley et al., 2009). Other
urinary metabolites identified in 1-BP workers are the bromide ion (Hanley et al., 2010) and
three oxygenated metabolites present at lower urinary concentrations than N-acetyl-S-
propylcysteine: N-acetyl-S-propylcysteine-S-oxide (also known as N-acetyl-S-(propylsulfinyl)
alanine), N-acetyl-S-(2-carboxyethyl) cysteine, and N-acetyl-S-(S-hydroxy-propyl) cysteine
(Cheever et al., 2009; Hanley et al., 2009). The correlations between time weighted average
workplace air concentrations of 1-BP and urinary levels of bromide and N-acetyl-S-
propylcysteine (Hanleyet al., 2010, 2009; Valentine et al., 2007; Hanleyetal., 2006) support
the hypothesis that conjugation with glutathione is an important pathway in humans (see
Figure 3-3). The detection of oxygenated metabolites in urine samples indicates that oxidation
pathways also exist in humans (see Figure 3-3 for structures of identified oxygenated
metabolites).
Results from metabolic disposition studies in rats and mice illustrate that the metabolism of
1-BP in mammals is complex, involving initial competing conjugation or oxidation steps,
followed by subsequent conjugation, oxidation, or rearrangement steps. Figure 3-5 presents
proposed metabolic pathways based on results from studies of F-344 rats and B6C3F1 mice
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exposed to [1-14C]-1-BP by intravenous injection or [1,2,3-13C]-1-BP by inhalation or intravenous
injection (Garner et al., 2006).
The metabolic scheme shows an oxidation path to C02 involving cytochrome P450 (CYP)
oxidation steps to l-bromo-2-propanol and bromoacetone. This path is proposed based on
several findings:
1. Following intravenous injection of 14C-1-BP at nominal doses of 5, 20, or 100 mg/kg,
radioactivity in C02 exhaled in 48 hours accounted for approximately 28, 31, and 10% of
the administered dose in rats, and 22, 26, and 19% in mice (Garner et al., 2006). (These
data also indicate that oxidative metabolism of 1-BP in rats is more dependent on dose
than oxidative metabolism in mice; the decrease in percentage dose exhaled as C02 at
the highest dose is greater in rats than mice.)
2. Pretreatment of rats with 1-aminobenzotriazole (ABT) before administration of single
intravenous doses of ~20 mg/kg 14C-1-BP or inhalation exposure to 800 ppm 13C-1-BP for
6 hours caused decreased exhalation of radioactivity as C02 and decreased formation of
oxidative urinary metabolites (Garner et al., 2006). ABT is an inhibitor of a number of CYP
enzymes (Emoto et al., 2003).
3. Urinary metabolites derived from l-bromo-2-propanol accounted for over half of all
carbon-containing urinary metabolites identified in rats and mice exposed by inhalation
or intravenous injection of 13C-1-BP, and no l-bromo-2-propanol-derived metabolites
were found in urine of ABT-pretreated rats exposed to 13C-1-BP (Garner et al., 2006).
l-Bromo-2-propanol and bromoacetone themselves were not detected in urine of
1-BP-exposed.
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Figure_Apx N-l Formation of N-Acetyl-S-Propylcysteine from 1-Bromopropane Via Conjugation with
Reduced Glutathione (GSH)
HN-Acetyl
Glutathione
Bi\
H2c;
CH,
1-Bromopropane
Br-
HC-
-COOH
H2Cx
,CH,
CH,
CH,
N-Acetyl-S-propylcysteine
Figure_Apx N-2 Mercapturic Acid Metabolites with a Sulfoxide Group or a Hydroxyl or Carbonyl Group
on the Propyl Residue Identified in Urine Samples of 1-Bromopropane-Exposed Workers
HC-
\
HN-Acetyl
-COOH
O:
\
CH,
H2C
CH3
N-Acetyl-S-propylcysteine-S-oxide
[also known as
N-acetyl-3-(propylsulfinyl)alanine]
HN-Acetyl
HC-
-COOH
.CH,
H2C
\
-OH
N-Acetyl-S-(3-hydroxypropyl)cysteine
HN-Acetyl
HC COOH
H2C
\(
\
.CH,
COOH
N-Acetyl-S-(2-carboxyethyl)cysteine
Sources: (Cheever et al., 2009; Hanleyetal., 2009)
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Results from animal metabolic disposition studies indicate that 1-BP is eliminated from the
body by exhalation of the parent material and metabolically derived C02 and by urinary
excretion of metabolites (Garner et al., 2006; Jones and Walsh, 1979). Following single
intraperitoneal injections of 200 mg/kg doses of [1-14C]-1-BP in rats, about 60 and 1.4% of the
administered dose was in parent material and C02 in air expired within 6 hours, respectively,
and about 15% of the administered dose was in urine collected for 48 hours (Jones and Walsh,
1979). Following intravenous injection of [1-14C]-1-BP at nominal doses of 5, 20, or 100 mg/kg,
radioactivity in C02 exhaled in 48 hours accounted for about 28, 31, and 10% of the
administered dose in rats, and 22, 26, and 19% in mice (Garner et al., 2006). Radioactivity in
exhaled parent material accounted for about 25, 32, and 71% of the administered dose in rats,
and 45, 39, and 48% in mice (Garner et al., 2006). Radioactivity in urine collected for 48 hours
accounted for about 17,19, and 13% of the administered dose in rats, and 23, 19, and 14% in
mice (Garner et al., 2006). Radioactivity in feces accounted for <4% of administered doses,
regardless of dose level, in both species (Garner et al., 2006).
Animal studies also show that the elimination of 1-BP from the body is rapid and accumulation
in the body is not expected (Garner and Yu, 2014; Garner et al., 2006; Ishidao et al., 2002).
Following intravenous injection of [1-14C]-1-BP at nominal doses of 5, 20, or 100 mg/kg,
radioactivity remaining in the carcass 48 hours after dose administration accounted for about 6,
6, and 2% of the administered dose in rats, and 4, 2, and 4% in mice (Garner et al., 2006).
(Garner et al., 2006) proposed that radioactivity remaining in the carcass could represent
covalently bound residues from reactive metabolites or incorporation of 14C into cellular
macromoleculesfrom intermediate metabolic pathways. Following intravenous injection of 5 or
20 mg 1-BP/kg doses into rats, the mean half-times of elimination of 1-BP from the blood were
0.39 and 0.85 hours, respectively (Garner and Yu, 2014) . In gas uptake studies with male and
female rats, calculated half-times of elimination for 1-BP were rapid and decreased with
increasing air concentrations of 1-BP (Garner and Yu, 2014). Terminal elimination half-times
were 0.5, 0.6, 1.1, and 2.4 hours for males, and 1.0, 1.0, 2.0, and 6.1 hours for females, exposed
to initial air concentrations of 70, 240, 800, and 2,700 ppm, respectively. Pretreatment of
female rats with ABT to inhibit CYP metabolism (intraperitoneal injection of 50 mg 1-BP/kg
4 hours prior to gas uptake measurements) or buthionine sulfoxime, an inhibitor of glutathione
synthesis (1,000 mg 1-BP/kg/day orally for 3 days before gas uptake), resulted in longer
elimination half-times: 9.6 hours with ABT and 4.1 hours with D,L-butionine(S,R)-sulfoximine
(BSD), compared with 2.0 hours in untreated females at 800 ppm 1-BP in the gas uptake
chamber (Garner and Yu, 2014). The results with the inhibitors show that both CYP mediated
oxidative metabolism and glutathione conjugation play important roles in the elimination of
1-BP. Levels of 1-BP in blood decreased rapidly to detection limits within 0.7 hours after
exposure stopped in Wistar rats exposed to 700 or 1,500 ppm 1-BP 6 hours/day for >3 weeks
(Ishidao et al., 2002). Clearance of the bromide ion from blood and urine, however, showed
slower elimination kinetics: elimination half-times for bromide were 4.7-15.0 days in blood and
5.0-7.5 days in urine (Ishidao et al., 2002) .
Based on urinary metabolites identified with nuclear magnetic resonance (NMR) spectroscopy,
liquid chromatography-tandem mass spectrometry (LC-MS/MS), and high-performance liquid
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chromatography (HPLC) radiochromatography (Garner et al., 2006), the scheme in Figure 3-5
also shows an initial conjugation of 1-BP with glutathione leading to N-acetyl-S-propylcysteine,
an oxidation step from l-bromo-2-propanol to alpha-bromohydrin, a glucuronic acid
conjugation step from l-bromo-2-propanol to l-bromo-2-hydroxypropane-O-glucuronide, and
glutathione conjugation of l-bromo-2-propanol and bromoacetone followed by oxidation steps
leading to metabolites with sulfoxide groups (e.g., N-acetyl-3-[(2-hydroxypropyl)sulfinyl]
alanine). The steps involving oxidation of sulfur in the glutathione conjugate derivatives were
proposed to be catalyzed by CYP oxygenases or flavin-containing monooxygenases (FMO) as
suggested by Krause et al. (2002).
Catalysis of the oxidation steps by a number of CYP enzymes is supported by results from
metabolic disposition studies in wild-type and Cyp2el-/- knock-out mice (Fl hybrids of 129/Sv
and C57BL/6N strains) exposed by inhalation to 800 ppm 13C-1-BP for 6 hours (Garner et al.,
2007). Three major metabolites were identified in urine collected from wild-type mice during
exposure: N-acetyl-S-(2-hydroxypropyl) cysteine (34 u.moles in collected urine), 1-bromo-
hydroxypropane-0-glucuronide (5 u.moles), and N-acetyl-S-propylcysteine (8 u.moles). In
Cyp2el-/- mice, the amounts of these metabolites in collected urine were changed to 21, 2, and
24 u.moles, respectively. The ratio of 2-hydroxylated metabolites to N-acetyl-S-propylcysteine
was approximately 5:1 in wild-type and 1:1 Cyp2el-/- mice. The results indicate that the
elimination of CYP2E1 increased the relative importance of the glutathione conjugation
pathway, but did not eliminate the formation of oxygenated metabolites, suggesting the
involvement of other CYP enzymes, in addition to CYP2E1, in oxidation steps illustrated in
Figure 3-5.
Evidence for the initial conjugation of 1-BP with glutathione leading to the formation of
N-acetyl-S-propylcysteine comes from a number of studies in rats and mice (Garner et al., 2007;
Garner etal., 2006; Khan and OBrien, 1991; Jones and Walsh, 1979).
1. N-Acetyl-S-propylcysteine was detected in the urine of wild-type and Cyp2el-/- mice
exposed to 800 ppm 1-BP for 6 hours, at molar ratios to hydroxylated metabolites of 5:1
and 1:1 (Garner et al., 2007).
2. N-Acetyl-S-propylcysteine and N-acetyl-3-(propylsulfinyl) alanine (i.e., N-acetyl-
S-propylcysteine-S-oxide) accounted for approximately 39 and 5% of excreted urinary
metabolites, respectively, in urine collected for 24 hours after inhalation exposure of
rats to 800 ppm 1-BP for 6 hours (Garner et al., 2006).
3. N-Acetyl-S-propylcysteine was a relatively minor urinary metabolite in rats given single
5-mg 1-BP/kg intravenous doses, but accounted for >80% of urinary metabolites
following administration of 100 mg 1-BP/kg (Garner et al., 2006).
4. N-Acetyl-S-propylcysteine and N-acetyl-S-propylcysteine-S-oxide were among the six
mercapturic acid derivatives identified in urine from rats given 200 mg 1-BP/kg by
gavage (in arachis oil) for 5 days (Jones and Walsh, 1979). The structures of the other
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four mercapturic acid derivatives identified were consistent with glutathione
conjugation of oxygenated metabolites of 1-BP, rather than 1-BP itself. These included
N-acetyl-S-(2-hydroxypropyl) cysteine, N-acetyl-S-(S-hydroxypropyl) cysteine, and N-
acetyl-S-(2-carboxyethyl) cysteine (Jones and Walsh, 1979). The techniques used in this
study did not determine the relative amounts of the urinary mercapturic acid
derivatives.
5. Isolated hepatocytes incubated for 60 minutes with 1-BP showed a decrease in
glutathione content (from 58.4 to 40.8 nmol/106cells), consistent with the importance
of glutathione conjugation in metabolic disposition of 1-BP in mammals (Khan and
OBrien. 1991).
Other studies have identified other metabolites, not included in Figure 3-5, in urine from rats
and mice exposed to 1-BP Ishidao (Ishidao et al., 2002; Jones and Walsh, 1979) and in in vitro
systems (Kaneko et al., 1997; Tachizawa etal., 1982; Jones and Walsh, 1979). (Jones and Walsh,
1979) reported detecting metabolites in urine from rats orally exposed to 1-BP that are
consistent with the initial oxidation of the 3-C of 1-BP: N-acetyl-S-(S-hydroxypropyl) cysteine,
3-bromopropionic acid, and N-acetyl-S-(2-carboxyethyl) cysteine. (Garner et al., 2006) were not
able to detect these metabolites in urine following administration of single intravenous doses
up to 100 mg 1-BP/kg in rats or exposure to 800 ppm for 6 hours in rats or mice. (Garner et al.,
2006) proposed that the apparent discrepancy may have been due to an amplification of minor
metabolites from the pooling, concentration, and acid hydrolysis processes used in the earlier
study. Glycidol (l,2-epoxy-3-propanol) was detected in urine of Wistar rats exposed by
inhalation 6 hours/day to 700 ppm for 3 or 4 weeks or 1,500 ppm for 4 or 12 weeks; but no
determination of the amount of this compound was made, and the report did not mention the
detection of any other carbon-containing metabolites (Ishidao et al., 2002). (Kaneko et al.,
1997) monitored the formation of n-propanol during incubation of rat liver microsomes with
1-BP. 3-Bromopropanol and 3-bromopropionic acid were detected when 1-BP was incubated in
an in vitro oxidizing system, but 1-BP metabolism with rat liver homogenates was not examined
due to the low water solubility of 1-BP (Jones and Walsh, 1979). Propene, 1,2-epoxypropane,
1,2-propanediol, and proprionic acid were detected when liver microsomes from
phenobarbital-treated rats were incubated with 1-BP, and the addition of glutathione to the
reaction mixture led to formation of S-(l' propyl)glutathione and S-(2' hydroxyl-1' propyl)
glutathione (Tachizawa et al., 1982). (Garner et al., 2006) reported that propene, propylene
oxide, propanediol, and propionic acid were not detected in liver homogenate incubations with
1-BP; they suggested that the use of phenobarbital as a CYP inducer may have resulted (in the
(Tachizawa et al., 1982) studies) in the formation of metabolites not generated by constituitive
CYP enzymes.
N-4 Elimination
Results from animal metabolic disposition studies indicate that 1-BP is eliminated from the
body by exhalation of the parent material and metabolically derived C02 and by urinary
excretion of metabolites (Garner et al., 2006; Jones and Walsh, 1979). Following single
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intraperitoneal injections of 200 mg/kg doses of [114C]-1-BP in rats, about 60 and 1.4% of the
administered dose was in parent material and C02 in air expired within 6 hours, respectively,
and about 15% of the administered dose was in urine collected for 48 hours (Jones and Walsh,
1979). Following intravenous injection of [114C] 1 bromopropane at nominal doses of 5, 20, or
100 mg/kg, radioactivity in C02 exhaled in 48 hours accounted for about 28, 31, and 10% of the
administered dose in rats, and 22, 26, and 19% in mice (Garner et al., 2006). Radioactivity in
exhaled parent material accounted for about 25, 32, and 71% of the administered dose in rats,
and 45, 39, and 48% in mice (Garner et al., 2006). Radioactivity in urine collected for 48 hours
accounted for about 17,19, and 13% of the administered dose in rats, and 23, 19, and 14% in
mice (Garner et al., 2006). Radioactivity in feces accounted for <4% of administered doses,
regardless of dose level, in both species (Garner et al., 2006).
Animal studies also show that the elimination of 1-BP from the body is rapid and accumulation
in the body is not expected (Garner and Yu, 2014; Garner et al., 2006; Ishidao et al., 2002).
Following intravenous injection of [1-14C]-1-BP at nominal doses of 5, 20, or 100 mg/kg,
radioactivity remaining in the carcass 48 hours after dose administration accounted for about 6,
6, and 2% of the administered dose in rats, and 4, 2, and 4% in mice (Garner et al., 2006).
(Garner et al., 2006) proposed that radioactivity remaining in the carcass could represent
covalently bound residues from reactive metabolites or incorporation of 14C into cellular
macromoleculesfrom intermediate metabolic pathways. Following intravenous injection of 5 or
20 mg 1-BP/kg doses into rats, the mean half-times of elimination of 1-BP from the blood were
0.39 and 0.85 hours, respectively (Garner and Yu, 2014). In gas uptake studies with male and
female rats, calculated half-times of elimination for 1-BP were rapid and decreased with
increasing air concentrations of 1-BP (Garner and Yu, 2014). Terminal elimination half-times
were 0.5, 0.6, 1.1, and 2.4 hours for males, and 1.0, 1.0, 2.0, and 6.1 hours for females, exposed
to initial air concentrations of 70, 240, 800, and 2,700 ppm, respectively. Pretreatment of
female rats with ABT to inhibit CYP metabolism (intraperitoneal injection of 50 mg 1-BP/kg
4 hours prior to gas uptake measurements) or buthionine sulfoxime, an inhibitor of glutathione
synthesis (1,000 mg 1-BP/kg/day orally for 3 days before gas uptake), resulted in longer
elimination half-times: 9.6 hours with ABT and 4.1 hours with D,L-butionine(S,R)-sulfoximine
(BSD), compared with 2.0 hours in untreated females at 800 ppm 1 bromopropane in the gas
uptake chamber (Garner and Yu, 2014). The results with the inhibitors show that both CYP
mediated oxidative metabolism and glutathione conjugation play important roles in the
elimination of 1-BP. Levels of 1-BP in blood decreased rapidly to detection limits within
0.7 hours after exposure stopped in Wistar rats exposed to 700 or 1,500 ppm 1-BP 6 hours/day
for >3 weeks (Ishidao et al., 2002). Clearance of the bromide ion from blood and urine,
however, showed slower elimination kinetics: elimination half-times for bromide were 4.7-
15.0 days in blood and 5.0-7.5 days in urine (Ishidao et al., 2002).
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Appendix 0 ANIMAL AND HUMAN TOXICITY STUDIES
CONSIDERED FOR USE IN RISK ASSESSMENT
0-1 Reproductive Toxicity
A two-generation reproduction study in rats reported adverse effects on male and female
reproductive parameters (WIL Research, 2001). The majority of these effects exhibited a dose-
response beginning at 250 ppm, with statistical significance at 500 ppm. The Fo generation
experienced significant dose-related decreases in male and female fertility indices at 500 ppm,
and in mating indices at 750 ppm (fertility was 52% and 0% at 500 and 750 ppm, respectively).
A significant increase in the number of females that displayed evidence of mating without
delivery was also observed at 500 (10 of 25, 40%) and 750 ppm (17 of 25, 68%) in the F0
generation. In the Fi generation, the number of females that displayed evidence of mating
without delivery at 500 ppm was greater than controls, but not statistically significant (8 of 25,
32% versus 3/25, 12% in treated and control dams, respectively). The number of males in the Fo
generation that did not sire a litter numbered 2, 0, 3,12 and 25 (8, 0, 12, 48 and 100%) in the
control, 100, 250 and 750 ppm groups respectively. In addition, two females treated at 500
ppm showed evidence of mating, and were gravid, but did not deliver litters. The number of
implantation sites, actual number of litters produced, and live litter size were significantly
reduced at 500 ppm in the Fo and Fi generations.
Significant changes in female reproductive parameters included a decrease in absolute and
relative ovary weights at 750 ppm in the Fo generation and an increase in estrous cycle length in
Fo and Fi females (500 ppm). Estrous cycling was not observed in two Fo females in the 500 ppm
group, three Fo females in the 750 ppm group, three Fi females in the 250 ppm group, and four
Fi females in the 500 ppm group. This finding is supported by an inhalation study which showed
significant treatment-related effects on estrous cycling in female rats and mice following three
months of 1-BP inhalation exposure at > 250 ppm (NTP, 2011).
The toxicological significance of these findings is underscored by related findings at comparable
doses in Fo and Fi generations:
• Decreased fertility (significant in 500 and 750 ppm groups). Because both males and
females were treated, the observed decreases in fertility could be due in part, to dose-
related impairment of male reproductive function.
• An increase in the number of primordial follicles at the highest dose evaluated (750 ppm
in Fo and 500 ppm in Fi) and a decrease in the number of corpora lutea in Fo females at
> 500 ppm (significant at 750 ppm; endpoint was not measured at 100 or 250 ppm).
• No difference in the numbers of corpora lutea was observed in Fi females treated at
500 ppm as compared to control (no other doses were evaluated for this endpoint).
• A significantly decreased number of implantation sites in Fo and Fi females at > 500 ppm
(no implantations observed at 750 ppm).
• Decreased live litter size (significant at 500 ppm in Fo and Fi treatment groups).
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Statistically significant changes in male reproductive and spermatogenic endpoints included:
• Decreased sperm motility and morphologically normal sperm in the Fo (^ 500 ppm) and
Fi generations (500 ppm)
• Reduced absolute weight of the left and right cauda epididymides at > 500 ppm in Fo/Fi
• Decreased absolute prostate weight in Fo (^ 250 ppm) and Fi males (500 ppm)
• Decreased seminal vesicle weight in Fo (750 ppm) and Fi males (250 ppm)
• Decreased mean epididymal sperm numbers in Fo males at 750 ppm
These findings positively correlate with the negative effects on fertility observed at 500 ppm,
and the complete lack of fertility observed in Fo mating pairs treated at 750 ppm.
0-2 Neurotoxicity
A number of laboratory animal studies report that both acute and repeated inhalational
exposure to high concentrations of 1-BP produce peripheral neurotoxicity indicated by changes
in both function and structure of the peripheral nervous system. The degree or severity of
peripheral neurotoxicity produced by 1-BP depends on the concentration as well as duration of
exposure. Most studies using concentrations of >1000 ppm report ataxia progressing to
severely altered gait, hindlimb weakness or loss of hindlimb control, convulsions, and death
(e.g., (Banu etal., 2007; Yu et al., 2001; Fueta etal., 2000; Ichihara etal., 2000a; Ohnishi et al.,
1999; ClinTrials, 1997a, b). Concentrations of 400-1000 ppm produce neuropathological
changes including peripheral nerve degeneration, myelin sheath abnormalities, and spinal cord
axonal swelling (Wang etal., 2002; Yu etal., 2001; Ichihara etal., 2000a).
Physiological and behavioral measures have been used to characterize and develop dose-
response data for this peripheral neurotoxicity. Motor nerve conduction velocity and latency
measured in the rat tail nerve were altered at >800 ppm with progressive changes observed
from 4 to 12 weeks of exposure (Yu et al., 2001; Ichihara et al., 2000a). These findings in rats
agree with neurological symptoms reported in exposed humans, including peripheral weakness,
tingling in extremities, and gait disturbances. The nerve conduction velocity endpoint that was
altered in rats (Yu et al., 2001; Ichihara et al., 2000a) is directly comparable to the increased
latencies and lower conduction velocity measured in a population of female factory workers
exposed to 1-BP (Li etal.. 2010a: Li et al.. 2010b: Ichihara et al.. 2004b).
Behavioral tests such as grip strength, landing foot splay, traction (hang) time, and gait score
provide dose-response data and appear somewhat more sensitive than neuropathology or
physiological changes. Ichihara et al. (2000aJ reported progressively worsening effects over
13 weeks of exposure at 400 and 800 ppm including decreased hindlimb and forelimb grip
strength, and inability to walk on a slightly-sloped plane; exposure at 200 ppm significantly
decreased hindlimb grip only at 4 weeks and otherwise was without effect. Hindlimb grip was
preferentially decreased compared to forelimb as is often observed with peripheral
neuropathy. Similarly decreased hindlimb strength was reported by Banu et al. (2007) after
6 weeks of 1-BP exposure at 1000 ppm (but not 400 ppm); these changes had not recovered at
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14 weeks post exposure. Honma et al. (2003) measured the time for a rat to hang onto a
suspended bar, which they called a traction test. The average time to hang appeared to be
decreased following 21 days of exposure to 50 ppm, and was significantly so with 200 and
1000 ppm; these changes were still evident when animals were tested 7 days later. The ability
to stay on a rotarod was not altered in these rats, suggesting that the weakness is peripherally
mediated.
Results reported following oral dosing with 1-BP are similar to those reported following
inhalation exposure. Over 16 weeks of dosing (200-800 mg/kg/d), Wang et al. (2012), reported
progressively decreased hindlimb grip strength, wider landing foot splay, and increased gait
abnormalities. The high-dose group was too debilitated to test after 14 weeks, but at that time
their grip strength was decreased by 42%, somewhat comparable to the 56% decrease reported
with 13 weeks of 800 ppm inhalational exposure (Ichihara et al., 2000a). Rats exposed to the
lowest concentration of 200 mg/kg/d showed less, but still statistically significant changes in
gait and decreased (9%) hindlimb grip strength. Subcutaneous administration of 455 or
1353 mg/kg/d (said to be equal to inhalation of 300 or 1000 ppm) over a 4 week period also
produced changes in tail motor nerve function (Zhao et al., 1999) similar to the effects reported
by others following inhalation exposure.
Some behavioral assays conducted in rats exposed to 1-BP reflect involvement of central as well
as peripheral nervous systems. Increased motor activity levels were measured following
inhalation of 50 or 200 ppm for three weeks (Honma et al., 2003). Spatial learning and memory
measured in a Morris water maze was severely impaired while rats were receiving oral doses of
200 mg/kg/d and greater (Guo et al., 2015; Zhonget al., 2013). Guo et al. (2015) also reported
that these cognitive deficits correlated with lowered levels of neuroglobin and glutathione
depletion indicative of oxidative stress in the same rats. During inhalational exposure, water
maze performance was impaired at concentrations of 200 ppm and above (Honma et al., 2003).
However, these concentrations also produced neuromotor difficulties, which would interfere
with performance of the task. There were no changes in water maze performance when
training was initiated after exposure ended. Furthermore, there were no differences in memory
of a passive avoidance task when the initial learning took place before exposures began
(Honma et al.. 2003).
A number of features reflecting CNS neurotoxicity have been reported for 1-BP. Brain pathology
has been reported in several, but not all, studies, which may be due to experimental differences
such as tissue sampling, staining, and measurement. Histological examination of the brain
showed widespread pathology at 1000 and 1600 ppm, and mild myelin vacuolization at
400 ppm, following 28 days of exposure (ClinTrials, 1997b); however, the same testing
laboratory reported no neuropathology with exposures up to 600 ppm for 13 weeks (ClinTrials,
1997a). In the cerebellum, exposure at 400 ppm and higher produced degeneration of Purkinje
cells (Mohideen et al., 2013; Ohnishi et al., 1999) without morphological changes in the
hippocampus (Mohideen et al., 2013). Similar exposure levels decreased noradrenergic but not
serotonergic axonal density in frontal cortex and amygdala (Guo et al., 2015; Mohideen et al.,
2011). In contrast to these reports, no degeneration was observed across several brain sections
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up to 800 ppm despite marked peripheral and spinal cord changes in the same rats (Wang et
al., 2002; Ichihara et al., 2000a). In two other studies conducted in the same laboratory, one
reported no histological or morphological changes in brain following exposures up to 1250 ppm
for 13 weeks (Sohn et al., 2002) and another reported no neuropathology after daily exposures
of 1800 ppm for up to eight weeks (Kim et al., 1999a), even though in the latter study other
indicators of neurotoxicity were observed.
Decreased absolute brain weight has been reported in several studies, both in the context of
adult exposures and long-term exposures during a 2-generation reproductive study. Studies
involving exposures from 4 to 12 weeks reported decreased brain weight at 800 and 1000 ppm
(Subramanian et al., 2012; Wangetal., 2003; Ichihara etal., 2000a). Kim et al. (1999a) also
reported decreased brain weight at 300 ppm for 8 weeks, but only provided relative brain:body
weight data. In the parental generation of a 2-generation study, exposure for at least 16 weeks
also produced brain weight changes, with males being more sensitive (NOAEL=100 ppm,
LOAEL=250 ppm) than females (NOAEL=250 ppm) (WIL Research, 2001). The Fi generation,
which was exposed during gestation and at least 16 weeks after weaning, had lower brain
weight at 100 ppm in males, and again females were less sensitive (NOAEL=250 ppm).
Histopathological evaluations in the WIL study revealed no correlative macroscopic or
microscopic alterations in unperfused brain tissue. Two studies have measured brain weight
and reported no effects: 1) (Wang et al., 2002), in which exposure was only 7 days and may not
have been a sufficient exposure duration, and 2) the 13-wk study of (ClinTrials, 1997a), even
though the same laboratory reported decreased brain weight at the same concentration with
only 4 weeks of exposure.
^
Fueta and colleagues (Fueta etal., 2007; Uenoetal., 2007; Fueta etal., 2004; Fueta etal.,
2002a; Fueta et al., 2002b; Fueta et al., 2000), reported a series of studies using
electrophysiological measures of hippocampal slices (dentate gyrus and CA1 regions) from rats
exposed to 1-BP for four to 12 weeks. Concentrations of 400 ppm and higher showed
disinhibition in paired-pulse population spikes, and the effect was dependent on exposure
concentration and duration. This hyperexcitability appeared to be due to a reduction in
feedback inhibition rather than a change in excitatory synaptic drive. There was a moderate
correlation with the level of bromide ion in the brain. Pharmacological probes, proteins and
receptor mRNA levels suggest that these effects are related to actions on the GABA and NMDA
neural systems, and/or intracellular signaling cascades (Ueno et al., 2007; Fueta et al., 2004;
Fueta et al., 2002a; Fueta et al., 2002b). A recent Society of Toxicology presentation (abstract
only available) reported similar effects in hippocampal slices from 14-day old rat pups whose
mothers were exposed to 400 or 700 ppm during gestation (Fueta et al., 2013).
A number of investigators have probed potential molecular mechanisms for some of these CNS
effects. Exposures of 200 ppm and greater produce changes in biomarkers and proteome
expressions suggesting alterations in the function and maintenance of neural and astrocytic cell
populations. Some of these include indicators of oxidative stress (reactive oxygen species,
glutathione depletion), ATP loss, protein damage, altered apoptotic signaling, neurotransmitter
dysregulation, decreased hippocampal neurogenesis, and others (Huang et al., 2015; Mohideen
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et al., 2013; Zhang etal., 2013; Zhongetal., 2013; Huang etal., 2012; Subramanian et al., 2012;
Huang etal., 2011; Yoshida etal., 2007; Wang etal., 2003; Wang etal., 2002). Concentrations
as low as 50 ppm for three weeks were reported to decrease levels of the serotonin metabolite
5-HIAA in frontal cortex and taurine in midbrain, while concentrations of 200 ppm and greater
impacted additional markers (protein levels, mRNA) of monoaminergic and amino acid
neurotransmitter systems (Zhang et al., 2013; Mohideen et al., 2009; Suda et al., 2008; Ueno et
al., 2007). Overall these data suggest several and perhaps overlapping cellular and molecular
mechanisms that could contribute to the functional and structural alterations reported for
1-BP.
0-3 Human Case Reports
Several case studies have reported various neurological effects in workers exposed to 1-BP
(Samukawa etal., 2012; CDC, 2008; Majersik et al., 2007; Raymond and Ford, 2007; Ichihara et
al., 2002; Sclar, 1999). Some of the neurological effects experienced by workers included
peripheral neuropathy, muscle weakness, pain, headaches, numbness, gait disturbance,
confusion, ocular symptoms, slowed mental activity, and dizziness. In some instances, the
effects were still observed many months after exposure had ceased or had been reduced.
Workers described in the case reports were exposed to 1-BP in the following activities: metal
cleaning, circuit board cleaning, and gluing foam cushions or furniture. In almost all of the cases
reported in the table below, personal protective equipment was not used and air
concentrations of 1-BP, when available, were greater than 100 ppm. Bromide levels, both
serum and in a few cases, urinary, were provided in some of the studies and are included in the
table below. Bromide concentrations have been used as a biomarker of exposure to 1-BP. A
description of the use of bromide levels and the investigation into using other biomarkers of
exposure are included in Section 2.3 of the 1-BP Report.
(Raymond and Ford, 2007) reported high levels of urinary arsenic, as well as serum bromide, in
the workers described in their case report of four employees who required hospitalization,
suggesting arsenic and bromide synergism. All four of the workers had total (organic and
inorganic) urinary arsenic levels greater than 200 u.g/L, but the source of the arsenic could not
be identified. NIOSH reported on these 4 employees in a HHE on a plant where workers applied
spray adhesive to cushions, and concluded that the exposure was likely not occupational and
could not have been the sole cause of ataxia and paresthesias that the four hospitalized
workers experienced
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Table_Apx O-l Case Reports on 1-BP
Reference
(Majersik et
al., 2007)
(Sclar. 1999)
(CDC. 2008)
(Samukawa
etal., 2012)
(Raymond
and Ford,
2007)
(4 cases
from NIOSH
(2003)
HHE report
on Marx
Industries)
(NIOSH.
2003)
# Cases
6
1
2
1
4
16
(incl. 4
from
Raymond
(2007)
Primary Symptoms
Headache, nausea,
dizziness, lower
extremity
numbness, pain,
paresthesias,
difficulty
walking/balance
Peripheral
neuropathy,
weakness of lower
extremities and
hand, numbness,
dysphagia
Confusion,
dysarthria,
dizziness,
paresthesias, ataxia;
Headache, nausea,
dizziness, malaise
Muscle weakness,
pain, numbness in
lower extremities,
gait disturbance
Dizziness, anorexia,
dysesthesias,
nausea, numbness,
ocular symptoms,
unsteady gait,
weakness, weight
loss
Headache, anxiety,
feeling "drunk",
numbness and "pins
and needles"
Activity
Foam
cushion
gluing at
furniture
plant (glue
contained
70% 1-BP)
Metal
stripping
(degreasing
and cleaning)
Cleaning
circuit boards
(spray)
Solvent in
dry cleaning
Metal
cleaning
Gluing in
furniture
making
Spray
application
of glue to
polyurethane
foam to
Air levels
130 ppm
(range,
91-176);
TWA 108
ppm (range,
92-127)
Not available
178 ppm
75-250x
background
levels
553 ppm,
mean TWA
(range,
353-663)
Mean 107
ppm (range,
58-254 ppm)
collected 9
months after
workers
became ill
1999 (16
personal
breathing
zone
samples):
Serum
Bromide
levels
(mg/dL)1
Peak range:
44-170
Not
available
48 mg/dL
and not
available for
case #2
58 u.g/mL
(peak)
3.0-12.5
mEq/L (100
mg/dL)
Arsenic
levels > 200
u.g/L for all
4
employees2
Serum GM:
4.8 mg/dL
(2.7-43.5;
n=39);
Urinary:
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Table_Apx O-l Case Reports on 1-BP
Reference
(Ichihara et
al.. 2002)
# Cases
3
Primary Symptoms
sensation in legs
and feet
Staggering gait,
paresthesia in lower
extremities,
numbness in legs,
headache, urinary
incontinence, deer
in vibration sense in
legs
Activity
make
cushions
Spray
application
of glue to
polyurethane
foam to
make
cushions
Air levels
GM 81.2 ppm
(range,
18-254 ppm);
2001 (13 PBZ
samples):
GM 45.7 ppm
(range, 7-281
ppm)
Mean 133
ppm, (range,
60-261 ppm
daily TWA);
avg over 11
days 133 ± 67
ppm— after
ventilation
improved
Serum
Bromide
levels
(mg/dL)1
46.5 mg/dL
(15.4-595.4,
n=40)
Includes
both
exposed
and
"unexposed
workers
Not
available
Biomarker Studies also Containing Case Report Data
(Hanlev et
al.. 2006)
(Ichihara et
al.. 2004b;
Ichihara et
al.. 2004a)
13
24
female
13 male
China
(focused on
exposure and
urinary Br)
Nose, throat, eye
irritation; malaise,
headache, dizziness
Spray
application
of glue to
polyurethane
foam to
make
cushions
1-BP
production
Mean
92 ppm
(range,
45-200 ppm)
3.3-90.2 ppm
No severe
neurological
effects
< 170 ppm
Urinary: 190
(43-672;
composite
of 2 days)
Urinary
bromide
measured
but not
reported
1Serum bromide unless otherwise indicated; Reference ranges vary by report
2Arsenic Reference range: <0.06
0-4 Human Epidemiology Studies
Three studies of workers occupationally exposed to 1-BP were located in the literature (Li et al.,
2010a; Toraason et al., 2006; Ichihara et al., 2004b), two of the studies report neurologic effects
and the third describes DNA damage in workers' leukocytes.
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Twenty-three female workers involved in 1-BP production in China were surveyed in 2001 and
compared with age-matched controls from a beer factory located in the same city (Ichihara et
al., 2004b). The study authors did not report the method of recruitment. Neurological tests
(vibration sensation, electrophysiologic studies), blood tests, neurobehavioral tests and
postural sway tests were administered. Passive sampling indicated individual exposure levels
ranging from 0.34 - 49.2 ppm in an 8-hour shift (median 1.61 ppm; geometric mean 2.92 ppm).
Some of the employees in this plant were also exposed to 2-BP and were analyzed separately.
Although some of the neurologic measures indicated reduced function in exposed workers
compared to controls, because of the past exposures to 2-BP and the small number of cases
who entered the study after 2-BP was no longer used (n= 12 pairs), it was difficult to interpret
the results of this study. In workers who were employed at the plant after 2-BP was no longer
used, Benton visual memory test scores, POMS depression, and POMS fatigue were significantly
different. It is not clear whether this indicates a lack of power to detect differences in the larger
group or whether the exposure to 2-BP affected the results.
As a follow-up to the Ichihara et al. 2004 study described above, (Li et al., 2010a) combined
data from three 1-BP production facilities in China to analyze a larger sample of workers. Sixty
female and 26 male workers and controls from other types of factories matched by age, sex and
geographic region were analyzed from four time periods (2001, 2003, 2004, 2005). Data were
collected over 3 days between 2001 and 2005. The authors did not describe the recruitment
process, and it is not clear whether the same workers included in the Ichihara 2004 study were
recruited for this study. The authors reported that none of the workers had a history of
diabetes.
Exposures were measured for each plant using passive samplers. Exposure was measured
either once or twice over 8 or 12 hour work shifts. TWA exposure concentrations to 1-BP
ranged from 0.07-106.4 ppm for female workers and 0.06-114.8 ppm for male workers. It was
reported that none of the workers wore gloves or masks in the plant. However, the authors
later clarified that some workers wore gloves (Ichihara et al., 2011). Employees were placed
into low-, medium-, and high-exposure groups (for females) to include equal numbers. Median
exposures for the three groups (n=20 per group) were 1.28, 6.60 and 22.58 ppm for females
and 1.05 (low) and 12.5 (high) ppm for males (n=13 per group). Ambient exposure levels varied
by job and by plant and were collected in different years for each plant. For example, the
ambient concentrations of "raw product collection" were more than 3 times higher at the
Yancheng plant (analyzed in 2003) than at the Yixing plant (Li et al., 2010a).
Clinical chemistries were obtained, and electrophysiological studies and neurological and
neurobehavioral tests were conducted for each employee. A single neurologist performed most
of the neurological assessments except for those collected in 2004 from one plant, which
included 5 female workers. Electrophysiological tests conducted included: motor nerve
conduction velocity, distal latency (DL), F-wave conduction velocity in the tibial nerve, sensory
nerve conduction velocity in the sural nerve (SNCV), and amplitude of the electromyogram
induced by motor nerve stimulation, F-wave, and potential of sensory nerve. Vibration sense,
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reflex, and muscle strength were measured using a tuning fork on the big toe. Neurobehavioral
tests and blood tests were also performed.
In regression analyses, the authors reported a statistically significant increase (p<0.05) in mean
tibial motor distal latency and a decrease in mean sural nerve conduction velocity in women in
the middle exposure group only (compared to controls). Statistically significant decreased
vibration sense in toes (vibration loss) was reported in all exposure groups compared to
controls. In addition, thyroid stimulating hormone (TSH) was significantly different in the middle
and high exposure groups compared to controls and FSH in low and medium exposure groups in
females. Red blood cell count was significantly decreased in all exposure groups compared to
controls in females. In males, the only statistically significant difference between the high
exposure group and controls was for blood urea nitrogen.
Analyses of cumulative exposure measures (exposure level x duration) indicated statistically
significant (p<0.05) increases in vibration sense in toes in females across all exposure levels
when compared to controls (5.6 ± 4.3, 6.4 ± 3.8, and 6.5 ± 3.4 sees, mean ± SD for low, medium
and high cumulative exposure groups, respectively). In females, only the high cumulative
exposure group for tibial motor DL was statistically higher than in controls and only the low
cumulative exposure group for sural NCV. Analyses to adjust for other factors that could
influence vibration loss (examining neurologist, age, height, body weight, alcohol consumption)
were conducted using analysis of covariance in female workers. The effect of 1-BP exposure on
vibration loss was significant (p = 0.0001 or p = 0.0002) based on cumulative exposures as well
as exposures not considering duration of exposure, respectively, but the effect of examining
neurologist was also significant (p < 0.0001).
Both of the neurological studies described above (Li et al., 2010a; Ichihara et al., 2004b) showed
neurological effects related to 1-BP exposure. The co-exposures to 2-BP and the small sample
size of workers exposed only to 1-BP was a limitation in the Ichihara et al. 2004 study. Li et al.
(2010aJ selected workers exposed to 1-BP from 3 plants to include more study participants;
however, the exposure data reported by plant were limited, the job activities were somewhat
different between plants (but for those jobs with similar activities between plants, some
exposures were more than 3 times higher at one plant than another), and ambient exposure
levels of 1-BP and 2-BP reported by job and by plant were collected in different years for each
plant. Several of these issues could lead to exposure misclassification of the workers. TWAs (8-
and 12- hour) were used to assign exposure groups, based on either 1 or 2 samples. Using the
TWA does not account for the fluctuations or potential peaks that may have occurred during
the shift. In addition, the median exposure level of the high exposure group for females was
22.58 ppm but the range was 15.28-106.4 ppm, indicating that some of the workers were
exposed to levels much higher than the lowest exposed workers in that group. In addition, the
cumulative exposure measures were based on only 1-3- day measurements of individual
exposure levels.
Skin temperature is important when conducting electrophysiological studies; however, the only
control for temperature in this study was to acclimate study participants to 24° C in a room for
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30 minutes. Individual skin temperatures should have been taken at the site of the test (on the
foot) because the results are affected by temperature. Vibration sense can be influenced by
BMI, but it was not reported or controlled in the study. As acknowledged in the report by the
study authors, vibration sense is inherently imprecise (based on the sensitivity of the subject
relative to the examiner). Evidence of a high degree of variability was shown in the large
standard deviations reported for vibration sense in females (2.9 ± 3.9, mean ± SD for controls;
5.6 ± 4.4, low exposure group). Other than RBC, only vibration sense in females using the
cumulative exposure measure was concentration-dependent. RBC in females could have been
influenced by other factors (e.g., menstruation, dehydration) that were not examined in the
study.
Toraason et al. (2006) analyzed DNA damage in peripheral leukocytes of workers exposed to
1-BP during spray application of adhesives in the manufacture of foam cushions for upholstered
furniture. Sixty-four workers (18 males, 46 females) at two plants were included in the analysis.
There were no unexposed groups. Fifty of 64 workers wore personal air monitors for 1-3 days.
Workers employed as sprayers had the highest exposures; 1-BP 8-hr TWA concentrations were
substantially higher (4 times) for sprayers at one of the plants than the other. TWA exposures
ranged from 0.2 to 271 ppm across both plants. DNA damage was assessed using comet assay.
DNA damage was measured by tail moment in leukocytes of workers. At both the start and end
of the work week, DNA leukocyte damage was higher for sprayers than non-sprayers but the
increases were not statistically significant. In addition, the facility with lower exposures had
higher measures of DNA damage than the higher exposure facility at the beginning of the week
but not the end. Tail moment dispersion coefficients did not indicate an exposure-response
relationship. Three different biomarkers of exposure, 1-BP TWA concentrations and serum and
urinary bromide levels, were evaluated in multivariate analyses. After controlling for various
potential confounders, starting and ending work week comet tail moments in leukocytes were
significantly associated with serum bromide quartiles and ending work week values of 1-BP
TWA concentrations. None of the models that examined associations between DNA damage
and dispersion coefficients was statistically significant. There was a slight risk for DNA damage
in workers' leukocytes in vitro in workers exposed to 1-BP but the results of the in vivo data
were not consistent.
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Table_Apx O-2 Summary of the E
Target Organ/
System1
Death
Death
Death
Death
Death
Death
Death
Death
Body weight
Species2
Rat
(n=10/group)
Rat
(n=10/group)
Rat
(male)
(n=50/group)
Mouse
(male)
(n=5/group)
Mouse
(male)
(n=24/group)
Mouse
(female)
(n=8/group)
Mouse
(n=20/group)
Rat
(n=10/group)
Rat
(male)
(n=10/group)
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lidemiological and Toxicological Database for 1-BP
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Oral
Inhalation
Concentration3
6040, 7000, 7400
or 8500 ppm
11,000, 13,000,
15,000 or
17,000 ppm
125, 250 or
500 ppm
125, 250, 500,
1000 or 2000 ppm
50, 110 or 250
ppm
125, 250 or
500 ppm
62.5, 125, 250 or
500 ppm
2000 mg/kg
6040, 7000, 7400
or 8500 ppm
Duration4
4 hours
^1
4 hours
6.2 hours/day,
5 days/week
for 105 weeks
6.2 hours/day,
5 days/week
for 17 days
8 hours/day,
7 days/week
for 4 weeks
6.2 hours/day,
5 days/week for
4 or 10 weeks
6.2 hours/day,
5 days/week
for 14 weeks
Single exposure
4 hours
POD5 (ppm or
mg/kg-day)
LC5o=7000
LC5o= 14,374
NOAEL=250
NOAEL=250
NOAEL= 110
NOAEL=250
NOAEL=250
LD5o>2000
NOAEL= 8500
Effect6
Death (acute
inflammatory
response and
alveolar edema)
Death
Decreased
survival
Decreased
survival
Death (two of
three strains
affected)
Death (first
week on study)
Decreased
survival rate
Death
No effects on
body weight
Reference7
(Elf Atochem
S.A., 1997)
(Kimetal.,
1999b)
(NTP, 2011)
(NTP, 2011)
(Liuetal.,
2009)
(Anderson et
al., 2010)
(NTP, 2011)
(Elf Atochem
S.A., 1993a)
(Elf Atochem
S.A., 1997)
Comments8
GLP study -
provides evidence
of a steep
concentration-
response curve for
lethality
GLP study -
evidence of steep
concentration
response curve for
lethality
GLP study - cause
of death attributed
to neoplasms not
related to 1-BP
exposure
GLP study- cause
of death was not
specified
GLP study -
hepatocellular
necrosis observed
at 250 ppm in all
strains
GLP study - cause
of death not
specified
GLP study - cause
of death was not
specified
GLP study- no
route-to-route
extrapolation
GLP study
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Target Organ/
System1
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Species2
Rat
(male)
(n=9/group)
Rat
(male)
(n=12/group)
Rat
(n=10/group)
Rat
(male)
(n=5/group)
Rat
(female)
(n=7-8/group)
Rat
(n=20/group)
Rat
(male)
(n=12/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=24/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 ppm
400, 800 or
1000 ppm
125, 250, 500,
1000 or 2000 ppm
10, 50, 200 or
1000 ppm
50, 200 or
1000 ppm
398, 994 or
1590 ppm
400, 800 or
1000 ppm
1000 ppm
400, 800 or
1000 ppm
50, 300 or
1800 ppm
200, 400 or
800 ppm
Duration4
8 hours/day
for 7 days
8 hours/day
for 7 days
6.2 hours/day,
5 days/week
for 16 days
8 hours/day,
7 days/week
for 3 weeks
8 hours/day,
7 days/week
for 3 weeks
6 hours/day,
5 days/week
for 4 weeks
8 hours/day,
7 days/week
for 4 weeks
8 hours/day,
7 days/week
for 5 or 7 weeks
8 hours/day,
7 days/week
for 6 weeks
6 hours/day,
5 days/week
for 8 weeks
8 hours/day,
7 days/week
for 12 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=400
NOAEL=400
NOAEL= 1000
NOAEL= 50
NOAEL= 1000
NOAEL=398
NOAEL=800
LOAEL= 1000
NOAEL=400
NOAEL=300
NOAEL=200
Effect6
Decreased body
weight
Decreased body
weight
Decreased body
weight
Increased body
weight
No effects on
body weight
Decreased
weight gain
Decreased body
weight
Decreased body
weight
Decreased body
weight
Decreased body
weight
Decreased body
weight
Reference7
(Wang et al.,
2QQ2)
(Zhang et al.,
2013)
(NTP, 2011)
(Honma et al.,
2QQ3)
(Sekiguchi et
al., 2002)
(ClinTrials,
1997b)
(Subramanian
etal.,2012)
(Yu et al.,
2001)
(Banu et al..
2QQ7)
(Kimetal.,
1999a)
(Ichihara et
al., 2000a)
Comments8
GLP study- data on
food consumption
not provided
GLP study- data on
food consumption
not provided
GLP study - data on
food consumption
not provided
GLP study -
corresponding
changes in food
consumption noted
GLP study - data on
food consumption
not provided
GLP study -
decreased food
consumption noted
GLP study - data on
food consumption
not provided
Not suitable for
dose-response
analysis because
only one exposure
group was used
GLP study- data on
food consumption
were not provided
GLP study- no
change in food
consumption
GLP study - data on
food consumption
not provided
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Target Organ/
System1
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Species2
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Rat
(n=30/group)
Rat
(n=10/group)
Rat
(male)
(n=10/group)
Rat
(n=100/group)
Rat
(female)
(n=10/group)
Rat
(female)
(n=25/group)
Rat
(female)
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 ppm
200, 400 or
800 ppm
100, 200, 400
or 600 ppm
200, 500, or
1250 ppm
62.5, 125, 250
or 1000 ppm
125, 250
or 500 ppm
100, 199, 598
or 996 ppm
103, 503 or
1005 ppm
100, 400 or
800 ppm
Duration4
8 hours/day,
7 days/week
for 12 weeks
8 hours/day, 7
days/week for
up to 12 weeks
6 hours/day, 5
days/week for 13
weeks
6 hours/day,
5 days/week
for 13 weeks
6.2 hours/day,
5 days/week
for 14 weeks
6.2 hours/day,
5 days/week
for 105 weeks
6 hours/day on
CDs 6-19
and lactation
days 4-20
6 hours/day on
CDs 6-19
8 hours/day
during gestation
(CDs 0-20)
and lactation
(PNDs 1-20)
POD5 (ppm or
mg/kg-day)
NOAEL=200
NOAEL=400
NOAEL=600
NOAEL= 1250
NOAEL=500
NOAEL=500
NOAEL= 100
NOAEL= 103
NOAEL=400
Effect6
Decreased body
weight
Decreased body
weight
No effects on
body weight
No effects on
body weight
Decreased body
weight
No effects on
body weight
Decreased body
weight gain
during gestation
Decreased body
weight gain
during gestation
Decreased body
weight at PND
21
Reference7
(Wang et al.,
2QQ3)
(Yamada et
al., 2003)
(ClinTrials,
1997a)
(Sohn et al..
2QQ2)
(NTP, 2011)
(NTP, 2011)
(Huntingdon
Life Sciences,
1999)
(Huntingdon
Life Sciences,
2001)
(Furuhashi et
al., 2006)
Comments8
GLP study- data on
food consumption
were not provided
GLP study- data on
food consumption
were not provided
GLP study - no
change in food
consumption
GLP study
GLP study data on
food consumption
not provided
GLP study data on
food consumption
not provided
GLP study -
observed body
weight changes
were not
statistically
significant
GLP study
Quantitative body
weight data
provided for the
high-exposure
group only
Page 283 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Species2
Rat
(female)
(n =
8-25/group)
Mouse
(male)
(n=5/group)
Mouse
(male)
(n=24/group)
Mouse
(female)
(n=8/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Rat
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Oral
Concentration3
100, 250, 500
or 750 ppm
125, 250, 500,
1000 or 2000 ppm
50, 110 or 250
ppm
125, 250 or
500 ppm
62.5, 125, 250
or 500 ppm
62.5, 125 or
250 ppm
2000 mg/kg
Duration4
6 hrs/day during
pre-mating
(> 70 days),
through mating,
and until
sacrifice in
males; or until
GD20
and from PND5
until weaning of
offspring (~PND
21) in females
6.2 hours/day, 5
days/week for 17
51 days
8 hours/day, 7
days/week for 4
weeks
6.2 hours/day, 5
days/week for 4
or 10 weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
Single exposure
POD5 (ppm or
mg/kg-day)
NOAEL=250
NOAEL=500
NOAEL=250
LOAEL= 125
NOAEL=500
NOAEL=250
NOAEL=2000
Effect6
Decreased body
weight (Fo and
Fi)
Decreased body
weight gain
No effects on
body weight
Decreased body
weight
No effects on
body weight
No effects on
body weight
No effects on
body weight
Reference7
(WIL
Research,
2001)
(NTP, 2011)
(Liuetal.,
2009)
(Anderson et
al., 2010)
(NTP, 2011)
(NTP, 2011)
(Elf Atochem
S.A., 1993a)
Comments8
GLP study peer-
reviewed by NTP
GLP study -effect
not observed in
females
GLP study
GLP study - effects
on body weight
only observed in
mice exposed for 4
weeks
GLP study - body
weights appeared
to stay within 10%
of controls based
on data presented
graphically in the
study report
GLP study
GLP study - not
able to do route-
to-route
extrapolation
Page 284 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Body weight
Body weight
Body weight
Body weight
Body weight
Body weight
Cardiovascular
Species2
Rat
(male)
(n=10/group)
Rat
(male)
(n=7/group)
Rat
(male)
(n=10/group)
Rat
(male)
(n=14/group)
Mouse
(female)
(n=5/group)
Mouse
(male)
(n=20/group)
Rat (male)
(n=9/group)
Exposure
Route
Oral
Oral
Oral
Oral
Oral
Oral
Inhalation
Concentration3
200, 400 or
800 mg/kg-day
1000 mg/kg-day
200, 400 or
800 mg/kg-day
100, 200, 400 or
800 mg/kg-day
200, 500 or
1000 mg/kg
300 or 600
mg/kg-day
200, 400 or
800 ppm
Duration4
12 days
14 days
16 weeks
12 days
Single exposure
for 6, 12, 24 or
48hrs
Exposed for 10
days prior to
mating
8 hours/day, 7
days/week for 12
weeks
POD5 (ppm or
mg/kg-day)
NOAEL=400
LOAEL= 1000
NOAEL=400
NOAEL=400
NOAEL= 1000
NOAEL=600
NOAEL=800
Effect6
Decreased final
body weight.
Used for weight
of evidence; no
route-to-route
extrapolation.
Decreased body
weight
Decreased body
weight
Decreased body
weight
No effects on
body weight
No effects on
body weight
No effects on
heart weight or
histopathology
Reference7
(Zhong et al.,
2013)
(Xinetal.,
2010)
(Wang et al.,
2012)
(Quo et al..
2015)
(Lee et al..
2QQZ)
(Yu et al.,
2008)
(Ichihara et
al., 2000b)
Comments8
GLP study - not
able to do route-
to-route
extrapolation
GLP study -not
able to do route-
to-route
extrapolation
Abstract in English,
with partial
translation of study
methods and
results provided by
primary author.
GLP study - not
able to do route-
to-route
extrapolation
GLP study - not
able to do route-
to-route
extrapolation
GLP study - not
able to do route-
to-route
extrapolation
GLP study - not
able to do route-
to-route
extrapolation
GLP study -
conducted in males
only, peer
reviewed literature
Page 285 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Cardiovascular
Cardiovascular
Cardiovascular
Cardiovascular
Cardiovascular
Cardiovascular
Cardiovascular
Dermal
Dermal
Dermal
Dermal
Dermal
Species2
Rat
(n=20/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Mouse
(male)
(n=5/group)
Mouse
(n=20/group)
Mouse
(n=50/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
50, 300 or
1800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
125, 250, 500,
1000 or 2000 ppm
62.5, 125, 250
or 500 ppm
62.5, 125 or
250 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250, 500
or 1000 ppm
125, 250 or
500 ppm
62.5, 125, 250
or 500 ppm
62.5, 125 or
250 ppm
Duration4
6 hours/day, 5
days/week for 8
weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6.2 hours/day, 5
days/week for 17
days
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
NOAEL= 1800
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL= 2000
NOAEL=500
NOAEL=250
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL=500
NOAEL=250
Effect6
No effects on
heart weight or
histopathology
No effects on
heart weight or
histopathology
No effects on
heart weight
No effects on
histopathology
Decreased
absolute and
relative heart
weight
No effects on
heart weight or
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
Reference7
(Kimetal.,
1999a)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
Comments8
GLP study, peer
reviewed literature
GLP study
GLP study
GLP study
GLP study
GLP study
GLP study
GLP study- no
route-to-route
extrapolation
GLP study- no
route-to-route
extrapolation
GLP study - no
route-to-route
extrapolation
GLP study- no
route-to-route
extrapolation
GLP study- no
route-to-route
extrapolation
Page 286 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Endocrine
Endocrine
Endocrine
Endocrine
Endocrine
Endocrine
Endocrine
Species2
Human
(60 female;
26 male)
Rat
(male)
(n=12/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Rat
(n=30/group)
Rat
(n=20/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
0.06-114.8 ppm (8-
hrTWA
concentration)
400, 800 or
1000 ppm {
50, 300 or
1800 ppm
200, 400 or
800 ppm
200, 400 or
800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
Duration4
About 40 months
^
8 hours/day for 7
days
6 hours/day, 5
days/week for 8
weeks
8 hours/day, 7
days/week for 12
weeks
8 hours/day, 7
days/week for
up to 12 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
POD5 (ppm or
mg/kg-day)
NOAEL= 1000
NOAEL= 1800
NOAEL=800
NOAEL=800
NOAEL=600
NOAEL= 1000
Effect6
Statistically
significant
Increase in
serum TSH in
middle and high
exposure group
compared to
controls; FSH
higher in low
and middle
exposure group
in females
No effects on
adrenal gland
weight or
plasma
corticosterone
No effects on
organ weights
or
histopathology
No effects on
organ weights
or
histopathology
No effects on
organ weights
or
histopathology
No effects on
organ weights
or
histopathology
No effects on
organ weights
Reference7
(Lietal.,
2010a)
(Zhang et al..
2013)
(Kimetal.,
1999a)
(Ichihara et
al., 2000a;
Ichihara et al..
2000b)
(Yamada et
al., 2003)
(ClinTrials,
1997a)
(NTP, 2011)
Comments8
Limited evaluation
of worker
exposure, peer
reviewed literature
GLP study, peer
reviewed literature
GLP study, peer
reviewed literature
GLP study -
conducted in males
only, peer
reviewed literature
GLP study -
conducted in
females only, peer
reviewed literature
GLP study
GLP study
Page 287 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Endocrine
Endocrine
Endocrine
Endocrine
Gastrointestinal
Gastrointestinal
Gastrointestinal
Gastrointestinal
Species2
Rat
(n=100/group)
Rat
(male)
(n=24/group)
Mouse
(female)
(n=10/group)
Mouse
(n=100/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Mouse
(n=20/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
125, 250 or
500 ppm
100, 250, 500
or 750 ppm
62.5, 125
or 500 ppm
62.5, 125
or 250 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250
or 500 ppm
62.5, 125, 250
or 500 ppm
Duration4
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDSuntil
^weaning of ^
offspring (~PND
21) in females
6.2 hours/day,
5 days/week
for 14 weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6.2 hours/day, 5
days/week for 14
weeks
POD5 (ppm or
mg/kg-day)
NOAEL=500
NOAEL=500
NOAEL=250
NOAEL=250
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL=500
Effect6
No effects on
histopathology
Decreased
absolute
weights of
adrenals and
pituitary (Fi)
Necrosis of
adrenal cortex
(moderate to
marked)
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
Reference7
(NTP, 2011)
(WIL
Research,
2001)
(NTP, 2011)
(NTP, 2011)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
Comments8
GLP study
GLP study peer-
reviewed by NTP -
these effects were
not observed in
females
GLP study- no
exposure-related,
non-neoplastic
changes were
observed in other
endocrine glands
GLP study
GLP study
GLP study
GLP study
GLP study
Page 288 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Gastrointestinal
Hematological
Hematological
Hematological
Hematological
Hematological
Hematological
Hematological
Species2
Mouse
(n=100/group)
Human
(n = 43)
Human
(60 female;
26 male)
Human
(n = 43)
Rat
(male)
(n=10/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Rat
(n=20/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
62.5, 125 or
250 ppm
81.2 ppm GM;
range 18-254 pm)
0.06-114.8 ppm (8-
hrTWA
concentration)
168.9 ppm; (mean)
6040, 7000, 7400
or 8500 ppm
398, 994 or
1590 ppm
1000 ppm
50, 300 or
1800 ppm
Duration4
6.2 hours/day, 5
days/week for
105 weeks
2 weeks to 2
months
^1
About 40 months
4-9 years
4 hours
6 hours/day, 5
days/week for 4
weeks
8 hours/day, 7
days/week for 5
or 7 weeks
6 hours/day, 5
days/week for 8
weeks
POD5 (ppm or
mg/kg-day)
NOAEL=250
NOAEL=8500
NOAEL=398
LOAEL= 1000
NOAEL=300
Effect6
No effects on
histopathology
No effects on
hematology
parameters
RBC in females
only
significantly
decreased
across exposure
groups
No statistically
significant
effects on
hematology
parameters
No effects on
hematology
parameters
Decreased
erythrocyte
parameters
Decreased
mean
corpuscular
volume
Decreased
WBCs, RBCs,
hematocrit and
MCV; increased
HgbandMCH
Reference7
(NTP, 2011)
(NIOSH, 2003)
(Lietal.,
2010a; Li et
al., 2010b)
(NIOSH,
2002a)
(Elf Atochem
S.A., 1997)
(ClinTrials,
1997b)
(Yu et al.,
2001)
(Kimetal.,
1999a)
Comments8
GLP study
No effects noted;
not used in
quantitative
analysis
No other related
clinical chemistries
affected; limited
evaluation of
worker exposure.
Not used in
quantitative
analysis.
No statistically
significant effects
reported
GLP study
GLP study
Not suitable for
dose-response
analysis because
only one exposure
group was used
GLP study - peer
reviewed literature
biological
relevance
uncertain
Page 289 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Hematological
Hematological
Hematological
Hematological
Immune
Immune
Immune
Immune
Species2
Rat
(male)
(n=9/group)
Rat
(female)
(n=15/group)
Rat
(n=20/group)
Mouse
(n=20/group)
Rat
(female)
(n=8/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
62.5, 125, 250
or 500 ppm
250, 500 or
1000 ppm
50, 300 or
1800 ppm
200, 400 or
800 ppm
200, 400 or
800 ppm
Duration4
8 hours/day, 7
days/week for 12
weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for 14
weeks ^^
6.2 hours/day, 5
days/week for 4
or 10 weeks
6 hours/day, 5
days/week for 8
weeks
8 hours/day, 7
days/week for 12
weeks
8 hours/day, 7
days/week for
up to 12 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=400
NOAEL=400
NOAEL= 1000
NOAEL=500
NOAEL=500
NOAEL= 1800
NOAEL=800
NOAEL=800
Effect6
Decreased
MCHC;
increased MCV
Decreased WBC
and absolute
lymphocytes (at
6 weeks)
No effects on
hematology
parameters
No effects on
hematology
parameters
Decreased
spleen IgM
response to
SRBC;
decreased T
cells
No effects on
histopathology
(thymus and
spleen)
No effects on
organ weights
or
histopathology
(spleen and
thymus)
No effects or
organ weights
or
histopathology
(spleen and
thymus)
Reference7
(Ichihara et
al., 2000b)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(Anderson et
al., 2010)
(Kimetal.,
1999a)
(Ichihara et
al., 2000b)
(Yamada et
al., 2003)
Comments8
GLP study -
conducted in males
only, peer
reviewed literature
GLP study - effects
not observed after
13 weeks of
exposure
GLP study
GLP study
GLP study - IgM
response occurred
in the absence of
effects on spleen
cellularity or serum
IgM (at 10 weeks)
GLP study, peer
reviewed literature
GLP study -
conducted in males
only, peer
reviewed literature
GLP study -
conducted in
females only, peer
reviewed literature
Page 290 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Immune
Immune
Immune
Immune
Immune
Immune
Species2
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Rat
(n=25/group)
Mouse
(female)
(n=8/group)
Mouse
(n=20/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
100, 250, 500
or 750 ppm
125, 250 or
500 ppm
62.5, 125, 250
or 500 ppm
Duration4
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
6.2 hours/day, 5
days/week for 4
or 10 weeks
6.2 hours/day, 5
days/week for 14
weeks
POD5 (ppm or
mg/kg-day)
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL=750
LOAEL= 125
NOAEL=500
Effect6
No immune
effects
No effects on
histopathology
(lymphoreticula
r tissues)
No effects on
histopathology
(lymphoreticula
r tissues)
Increased
brown pigment
in the spleen
Decreased
spleen IgM
response to
SRBC
No effects on
histopathology
(lymphoreticula
r tissues)
Reference7
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(WIL
Research,
2001)
(Anderson et
al., 2010)
(NTP, 2011)
Comments8
GLP study
GLP study
GLP study
GLP study peer-
reviewed by NTP
GLP study - effect
occurred in the
absence of an
effect on serum
IgM. Quantitative
data not available.
GLP study
Page 291 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Immune
Immune
Liver
Liver
Liver
Liver
Liver
Species2
Mouse
(n=100/group)
Mouse
(female)
(n=5/group)
Human
(60 female, 26
male)
Rat
(male)
(n=5/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=10/group)
Rat
(male)
(n=10/group)
Exposure
Route
Inhalation
Oral
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
62.5, 125, 250,
500 or 250 ppm
200, 500 or
1000 mg/kg
0.06-114.8 ppm (8-
hrTWA
concentration)
125, 250, 500,
1000 or 2000 ppm
1000 ppm
50, 300 or
1800 ppm
700 or 1500 ppm
Duration4
6.2 hours/day, 5
days/week for
105 weeks
Single exposure
for 6, 12, 24 or
48hrs
^
About 40 months
6.2 hours/day, 5
days/week for 16
days
8 hours/day, 7
days/week for 5
or 7 weeks
6 hrs/day,
5 days/wk for 8
wks
6 hours/day, 5
days/week for 4
and 12 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=250
LOAEL=200
NOAEL= 125
LOAEL= 1000
NOAEL= 50
LOAEL=700
Effect6
No effects on
histopathology
(lymphoreticula
r tissues)
Reduced
antibody
response to T-
antigen. Used
for weight of
evidence; no
route-to-route
extrapolation.
No effects on
liver clinical
chemistry
parameters
Increased
absolute and
relative liver
weights
No effects on
histopathology
Increased
relative liver
weight
Decreased
plasma ALT
activity
Reference7
(NTP, 2011)
(Lee et al..
2QQ7)
(Lietal.,
2010a)
(NTP, 2011)
(Yu et al..
2001)
(Kimetal.,
1999b)
(Fuetaetal.)
Comments8
GLP study
GLP study - not
able to do route-
to-route
extrapolation
No effects noted;
not used in
quantitative
analysis
GLP study -
evidence of
histopathological
changes observed
in the liver
Not suitable for
dose-response
analysis because
only one exposure
group was used
GLP study - no
histopathology or
clinical chemistry
changes indicative
of liver damage
were identified
GLP study- no
microscopic
examination of
liver conducted,
peer reviewed
literature
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Target Organ/
System1
Liver
Liver
Liver
Liver
Liver
Liver
Species2
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Rat
(male)
(n=15/group)
Rat
(female)
(n=10/group)
Rat
(n=100/group)
Rat
(male)
(n=25/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 ppm
200, 400 or
800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
100, 250, 500
or 750 ppm
Duration4
8 hours/day, 7
days/week for 12
weeks
8 hours/day, 7
days/week for
up to 12 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day
during pre-
mating (> 70
j. days),
throughout
mating, and until
sacrifice
POD5 (ppm or
mg/kg-day)
NOAEL=400
LOAEL=200
LOAEL=
100
NOAEL= 125
NOAEL=500
NOAEL=100
Effect6
Increased
absolute and
relative liver
weight
Increased
absolute and
relative liver
weight
Increased
incidence of
cytoplasmic
vacuolization
Increased liver
weight;
increased
incidence of
cytoplasmic
vacuolization
No effects on
histopathology
Increased
incidence of
vacuolization of
centrilobular
hepatocytes (Fo)
Reference7
(Ichihara et
al., 2000a)
(Yamada et
al., 2003)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(WIL
Research,
2001)
Comments8
GLP study -
conducted in males
only, peer
reviewed literature
GLP study -
conducted in
females only; liver
histopathology
observed at the
highest exposure
concentration
GLP study
GLP study
GLP study
GLP study peer-
reviewed by NTP
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Target Organ/
System1
Liver
Liver
Liver
Liver
Liver
Liver
Species2
Rat
(female)
(n=25/group)
Mouse
(male)
(n=5/group)
Mouse
(male)
(n=24/group)
Mouse
(male)
(n=8/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
100, 250, 500
or 750 ppm
125, 250, 500,
1000 or 2000 ppm
50, 110
or 250 ppm
100 or 300 ppm
62.5, 125, 250
or 500 ppm
62.5, 125
or 250 ppm
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
GD 20; from PND
5 until weaning
of offspring
(-PND21)
6.2 hours/day, 5
days/week for 17
days
8 hours/day, 7
days/week for 4
weeks
8 hours/day, 7
days/week for 4
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=250
NOAEL=250
LOAEL= 50
LOAEL= 100
NOAEL=250
NOAEL=250
Effect6
Increased
incidence of
vacuolization of
centrilobular
hepatocytes (Fo)
Centrilobular
necrosis (mild
to moderate)
Hepatocellular
degeneration
and focal
necrosis
Necrosis and
hepatocyte
degeneration
Necrosis and
hepatocyte
degeneration
No effects on
histopathology
Reference7
(WIL
Research,
2001)
(NTP, 2011)
(Liuetal.,
2009)
(Liuetal.,
2010)
(NTP, 2011)
(NTP, 2011)
Comments8
GLP study peer-
reviewed by NTP
GLP study -
increased liver
weight reported in
males and females
at this dose
GLP study - one of
three strains
affected at this
concentration,
peer reviewed
literature
GLP study -
magnitude of
change was small
(< 1%), but
statistically
significant
GLP study
GLP study
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Target Organ/
System1
Liver
Liver
Metabolic
Musculoskeletal
Musculoskeletal
Musculoskeletal
Musculoskeletal
Musculoskeletal
Musculoskeletal
Species2
Rat
(male)
(n=10/group)
Mouse
(female)
(n=5/group)
Rat
(n=30/group)
Rat
(male)
(n=ll/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Exposure
Route
Oral
Oral
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 mg/kg-day
200, 500 or
1000 mg/kg
100, 200, 400
or 600 ppm
200, 400
or 800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
62.5, 125,
250 or 500 ppm
62.5, 125,
or 250 ppm
Duration4
16 weeks
Single exposure
for 6, 12, 24 or
48hrs
6 hours/day, 5
days/week for 13
weeks
8 hours/day, 7
days/week for 12
weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
LOAEL=200
NOAEL=200
NOAEL=600
NOAEL=400
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL=500
NOAEL=250
Effect6
Increased
relative liver
weight. Used
for weight of
evidence; no
route:to:route
extrapolation
Centrilobular
hepatocyte
swelling
No effects on
electrolyte or
glucose levels
Alteration in
soleus muscle
myofilaments
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
Reference7
(Wang et al.,
2012)
(Lee et al..
2QQZ)
(ClinTrials,
1997a)
(Ichihara et
al., 2000a)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
Comments8
GLP study - not
able to do route-
to-route
extrapolation
GLP study -not
able to do route-
to-route
extrapolation
GLP study
GLP study
GLP study
GLP study
GLP study
GLP study
GLP study
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Human
(3 female, 1
male)
Human
(female, n = 3)
Human (1
male)
Human (23
female cases;
23 controls)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
107 ppm
(geometric mean;
range: 58-254
ppm)
133 ppm (daily
TWA
concentration);
range: 60-261 ppm
533 ppm (TWA
concentration)
range, 353-663
ppm)
2.92 ppm
(geometric mean;
range 0.34 -49. 2
pm)
Duration4
< 2 weeks
^1
2 to 12 months
18 months
27 months
(mean)
POD5 (ppm or
mg/kg-day)
N/A
N/A
N/A
Effect6
Clinical signs of
neurotoxicity
(including
headache,
dizziness,
numbness,
weakness)
Staggering,
lower extremity
paresthesias
and
dysesthesia,
ataxia,
numbness in
back, legs, hips,
weakness,
autonomic
dysfunction,
mood changes)
Severe ataxia,
motor and
sensory
impairments,
axonal damage
(based on sural
nerve biopsy)
Increased distal
latency,
decreased
vibration sense
in toes,
decreased
Benton visual
memory test
scores
Reference7
(Raymond and
Ford, 2007)
(Ichihara et
al., 2002)
(Samukawa et
al.,2012)
(Ichihara et
al., 2004b)
Comments8
Case reports.
Arsenic levels
(from unidentified
source) also high in
all 4 cases
Case reports. Air
samples collected
(for 1 case) only
after ventilation
was improved
Case report on 1
male. Symptoms
subsided after
several months
without exposure
Some exposure to
both 1-BP and 2-
BP. Only 12 pairs
exposed to 1-BP
only; may lack
statistical power to
detect differences
in this subgroup.
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Species2
Human
(n = 32)
Human
(4 female, 2
male)
Human (60
female, 26
male)
Exposure
Route
Inhalation
Inhalation
Inhalation
Concentration3
81.2 ppm GM;
(range 18-254
ppm)
108 ppm (7-hr
TWA
concentration)
0.06-114.8 ppm (8-
hrTWA
concentrations)
Duration4
2 weeks to 2
months
^1
> 3 years
About 40 months
POD5 (ppm or
mg/kg-day)
Effect6
Dizziness, lower
extremity
weakness,
difficulty
standing or
walking,
paresthesias.
Inability to
walk, spastic
paraparesis
distal sensory
loss,
hyperreflexia
Stat signif
decreased
vibration sense
in toes in all
exposure
groups
compared to
controls;
increased tibial
motor distal
latency and
decreased sural
nerve
conduction
velocity
compared to
controls but
stat signif in
middle
exposure group
only
Reference7
(NIOSH, 2003)
(Majersiket
al., 2007)
(Lietal.,
2010a)
Comments8
Health Hazard
Evaluation
reporting health
effects from 1-BP
and2-BP
exposures. Urinary
arsenic levels were
also high in
workers.
Case reports
No clear dose-
response
relationship for DL
orSNCV-not
statistically
significant; possible
exposure
misclassification by
combination of
plants collected
over years based
on 1 or 2 samples;
skin temperature
not taken
individually and
BMI not adjusted
for
electrophysiologica
1 tests.
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Human (n=43)
Rat
(male)
(n=10/group)
Rat
(n=10/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=6-
12/group)
Rat
(male)
(n=6-
13/group)
Rat
(male)
(n=9/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
168.9 ppm (mean)
50, 300, or
1800 ppm
11,000, 13,000,
15,000 or
17,000 ppm
200, 400 or
800 ppm
200, 400, 800
or 1000 ppm
1500 ppm
400 or 1000 ppm
Duration4
4-9 years
6 hours/day, 5
days/week for 8
weeks
4 hours
8 hours/day for 7
days
8 hours/day for 7
or 28 days
6 hours/day, 5
days/week for 1,
3, or 4 weeks
8 hours/day, 7
days/week for 1
or 4 weeks
POD5 (ppm or
mg/kg-day)
NOAEL= 50
LOAEL=11,000
LOAEL= 200
LOAEL=200
LOAEL= 1500
LOAEL=400
Effect6
Headache,
tingling in hands
or feet, tremor
Decreased
relative brain
weight
Ataxia,
lacrimation,
decreased
activity
Altered neuron-
specific proteins
and ROS
Decreased
hippocampal
glucocorticoid
receptor
expression
Paired pulse
disinhibition
(DG and CA1
pyramidal
neuron);
behavioral
abnormalities
Altered
regulation and
expression of
hippocampal
proteins
Reference7
(NIOSH,
2002a)
(Kimetal.,
1999b)
(Kimetal.,
1999b)
(Wang et al..
2002)
(Zhang et al.,
2013)
(Fueta et al..
2002a; Fueta
et al., 2002b)
(Huang et al..
2011)
Comments8
The study is limited
by small sample
size of employees
reporting
symptoms
Data only provided
as brain:body
weight ratio; peer-
reviewed literature
Effects were
observed at both
exposure
concentrations
(incidence not
reported). Lethality
at > 13,000 ppm;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(male)
(n=9/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=4/group)
Rat
(male)
(n=5/group)
Rat
(male)
(n=4-5/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=6/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
400 or 1000 ppm
400 or 1000 ppm
10, 50 or 200 ppm
10, 50, 200
or 1000 ppm
50, 200
or 1000 ppm
398, 994 or
1590 ppm
400 to 1000 ppm
400 to 1000 ppm
Duration4
8 hours/day, 7
days/week for 1
or 4 weeks
8 hours/day, 7
days/week for 1
or 4 weeks
8 hours/day, 7
days/week for 3
weeks
8 hours/day, 7
days/week for 3
weeks
8 hours/day, 7
days/week for 3
weeks
6 hours/day, 5
days/week for 4
weeks
8 hours/day, 7
days/week for 4
weeks
8 hours/day, 7
days/week for 4
weeks
POD5 (ppm or
mg/kg-day)
LOAEL=400
LOAEL=400
NOAEL= 10
NOAEL= 50
LOAEL= 50
LOAEL=
398
NOAEL=400
NOAEL=400
Effect6
Increased
hippocampal
ROS levels
Altered
regulation and
expression of
hippocampal
proteins
Increased
spontaneous
locomotor
activity
Decreased time
hanging from a
suspended bar
Altered
neurotransmitt
er and
metabolites
Histopathologic
al abnormalities
intheCNS
Changes in the
mRNA
expression of
serotonin,
dopamine, and
GABA receptors
Decreased
density of
noradrenergic
axons in frontal
cortex and
amygdala
Reference7
(Huang et al.,
2012)
(Huang et al.,
2015)
(Honma et al..
2QQ3)
(Honma et al..
2QQ3)
(Suda et al..
2008)
(ClinTrials,
1997b)
(Mohideen et
al., 2009)
(Mohideen et
al.,2011)
Comments8
Mechanistic data;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
Activity changes
persisted 3-4 days
after exposure
ended; peer-
reviewed literature
Data selected by
EPA for dose-
response analysis;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
GLP study
Mechanistic data;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
Page 299 of 403
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(male)
(n=12/group)
Rat
(male)
(n=12/group)
Rat
(male)
(n=8/group)
Rat
(male)
(n=13/group)
Rat
(male)
(n=7-
14/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
400 to 1000 ppm
400, 800 or
1000 ppm
1500 ppm
1500 ppm
700 ppm
Duration4
8 hours/day, 7
days/week for 4
weeks
^1
8 hours/day, 7
days/week for 4
weeks
6 hours/day, 5
days/week for 4
weeks
6 hours/day, 5
days/week for 4
weeks
6 hours/day, 5
days/week for 4,
8, or 12 weeks
POD5 (ppm or
mg/kg-day)
LOAEL= 400
LOAEL=400
LOAEL= 1500
LOAEL= 1500
LOAEL=700
Effect6
Increased
astrogliosis
Morphological
changes in
cerebellar
microglia and
increased ROS
Decreased
activity,
behavioral
abnormalities,
movement
disorders,
histopathologic
al changes in
Purkinje cells
Paired pulse
disinhibition,
neuronal
dysfunction in
dentate gyrus;
convulsive
behaviors
Paired pulse
disinhibition in
ex vivo
hippocampal
slices (DG and
CA1 pyramidal
neuron)
Reference7
(Mohideen et
al.,2013)
(Subramanian
etal.,2012)
(Ohnishi etal..
1999)
(Fueta et al..
2002b)
(Fueta et al..
2QQ4)
Comments8
Mechanistic data.
Only 3
rats/exposure
group were
subjected to
microscopic
evaluations; peer-
reviewed literature
Mechanistic data;
peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used;
peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used;
peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used;
peer-reviewed
literature
Page 300 of 403
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(n=9/group)
Rat
(male)
(n=24/group)
Rat
(male)
(n=12/group)
Rat
(male)
(n=6/group)
Rat
(n=20/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
1000 ppm
400, 800 or
1000 ppm \
700 ppm
200 or 400 ppm
50, 300 or
1800 ppm
Duration4
8 hours/day, 7
days/week for 5
or 7 weeks
fl
^
8 hours/day, 7
days/week for 6
weeks
6 hours/day, 5
days/week for 8
weeks
6 hours/day, 5
days/week for 8
or 12 weeks
6 hours/day, 5
days/week for 8
weeks
POD5 (ppm or
mg/kg-day)
LOAEL= 1000
NOAEL=400
LOAEL=700
NOAEL=200
NOAEL= 1800
Effect6
Movement
disorder,
altered motor
nerve
conduction
velocity and
distal nerve
latency in tail
nerve);
histopathologic
al changes to
CNSandPNS
Movement
disorder,
decreased hind
limb grip
strength
Paired pulse
disinhibition in
ex vivo
hippocampal
slices (DG and
CA1 pyramidal
neuron);
increased
protein kinase
activities
Paired pulse
disinhibition in
ex vivo
hippocampal
slices (DG and
CA1 pyramidal
neuron)
No effects on
brain
histopathology
Reference7
(Yu et al..
2001)
(Banu et al..
2QQZ)
(Fueta et al..
2002a)
(Fueta et al..
2QQ7)
(Kimetal.,
1999a)
Comments8
Not suitable for
dose-response
analysis because
only one exposure
group was used.
Same data
reported in two
publications. Peer-
reviewed literature
Peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used;
peer-reviewed
literature
This study was
conducted in males
only; peer-
reviewed literature
Peer-reviewed
literature
Page 301 of 403
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(male)
(n=ll/group)
Rat
(male)
(n=9/group)
Rat
(male)
(n=6/group)
Rat
(male)
(n=8/group)
Rat
(n=30/group)
Rat
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 ppm
200, 400 or
800 ppm
400 ppm
400 ppm
100, 200, 400
or 600 ppm
200, 500 or
1250 ppm
Duration4
8 hours/day, 7
days/week for 12
weeks
8 hours/day, 7
days/week for 12
weeks
6 hours/day, 5
days/week for 12
weeks
6 hours/day, 5
days/week for 12
weeks
6 hours/day, 5
days/week for 13
weeks
6 hours/day, 5
days/week for 13
weeks
POD5 (ppm or
mg/kg-day)
LOAEL=200
LOAEL=200
LOAEL=400
LOAEL=400
NOAEL=600
NOAEL= 1250
Effect6
Decreased hind
limb grip
strength
Altered neuron-
specific proteins
and increased
ROS
Changes in gene
expression of
anti-apoptotic
proteins in
astrocytes
Decreased
paired pulse
inhibition in ex
vivo
hippocampal
slices (dentate
gyrus)
No changes
based on
functional
observational
battery, motor
activity, organ
weight, or
histopathology
No effects
histopathology
of central or
peripheral
nervous tissues
Reference7
(Ichihara et
al., 2000a)
(Wang et al..
2QQ3)
(Yoshidaetal.,
2QQ7)
(Ueno et al..
2QQZ)
(ClinTrials,
1997a)
(Sohn et al..
2QQ2)
Comments8
Lowest
concentration
significant during
but not at end of
exposure; peer-
reviewed literature
Mechanistic data;
peer-reviewed
literature
Mechanistic data;
peer-reviewed
literature
Not suitable for
dose-response
analysis because
only one exposure
group was used.
Peer-reviewed
literature
GLP study
Peer-reviewed
literature
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(n=10/group)
Rat
(n=20/group)
Rat
(n=100/group)
Rat
(n=25/group)
Rat
(male)
(n=24-
25/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
125, 250, 500,
1000 or 2000 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250
or 500 ppm
100, 250, 500
or 750 ppm
100, 250, 500
or 750 ppm
Duration4
6.1 hours/day, 5
days/week for 16
days
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
POD5 (ppm or
mg/kg-day)
NOAEL=1000
ppm
NOAEL= 1000
NOAEL=500
NOAEL= 100
LOAEL= 100
Effect6
Hindlimb splay
No effects
No effects
Decreased brain
weight (Fo)
Decreased brain
weight
(weanling and
adult Fi)
Reference7
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(WIL
Research,
2001)
(WIL
Research,
2001)
Comments8
No mention of
brain
histopathology
results; GLP study
No mention of
brain
histopathology
results; GLP study
No mention of
brain
histopathology
results; GLP study
GLP study peer-
reviewed by NTP
GLP study peer-
reviewed by NTP.
Females less
sensitive.
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Target Organ/
System1
Nervous System
Nervous System
Nervous System
Nervous System
Nervous System
Species2
Rat
(n=15-
22/group)
Mouse
(n=10/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Rat
(male)
(n=14/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Oral
Concentration3
100, 250, 500
or 750 ppm
125, 250, 500,
1000 or 2000 ppm
62.5, 125, 250
or 500 ppm
62.5, 125, 250
or 250 ppm
100, 200, 400 or
800 mg/kg-day
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in (
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
6.1 hours/day, 5
days/week for 17
days
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
12 days
POD5 (ppm or
mg/kg-day)
NOAEL=250
NOAEL=2000
ppm
NOAEL=500
NOAEL=250
LOAEL=100
Effect6
Decreased brain
weight
(weanling Fz)
No effects
No effects
No effects
Impaired spatial
learning and
memory;
neuron loss in
prelimbic
cortex;
increased ROS
in cerebral
cortex
Reference7
(WIL
Research,
2001)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(Quo et al..
2015)
Comments8
GLP study peer-
reviewed by NTP
No mention of
brain
histopathology
results; GLP study
No mention of
brain
histopathology
results; GLP study
No mention of
brain
histopathology
results; GLP study
Not able to do
route-to-route
extrapolation;
peer-reviewed
literature
Page 304 of 403
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Nervous System
Nervous System
Nervous System
Ocular
Ocular
Ocular
Ocular
Ocular
Species2
Rat
(male)
(n=10/group)
Rat
(male)
(n=10/group)
Rat (male)
(n=7-9/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Exposure
Route
Oral
Oral
Subcutaneous
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or
800 mg/kg-day
200, 400 or
800 mg/kg-day
3.7 or 11
mmol/kg-day
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
62.5, 125,
250 or 500 ppm
62.5, 125 or
250 ppm
Duration4
12 days
16 weeks
^r
4 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
LOAEL=200
LOAEL=200
LOAEL= 3.7
NOAEL=600
NOAEL= 1000
NOAEL=500
NOAEL=500
NOAEL=250
Effect6
Impaired spatial
learning and
memory. Used
for weight of
evidence
Decreased
hindlimb grip
strength;
increased gait
score. Used for
weight of
evidence; no
route-to-route
extrapolation.
Increased tail
motor nerve
latency
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
No effects on
histopathology
Reference7
(Zhong et al.,
2013)
(Wang et al.,
2012)
(Zhao et al.,
1999)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
Comments8
Not able to do
route-to-route
extrapolation;
peer-reviewed
literature
English abstract
and partial
translation of
methods and
results. Used for
weight of evidence.
Peer-reviewed
literature able to
do route-to-route
Not able to do
route-to-route
extrapolation;
peer-reviewed
literature
GLP study
GLP study
GLP study
GLP study
GLP study
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Target Organ/
System1
Kidney
Kidney
Kidney
Kidney
Kidney
Species2
Human
(n = 43)
Human
(n= 60 female,
26 male)
Rat
(female)
(n=5/group)
Rat
(n=20/group)
Rat
(male)
(n=9/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
81.2 ppm GM;
(range 18-254
ppm)
0.06-114.8 ppm (8-
hrTWA
concentration)
125, 250, 500,
1000 or 2000 ppm
398, 994 or
1590 ppm
1000 ppm
Duration4
2 weeks to 2
months
About 40 months
6.2 hours/day, 5
days/week for 16
days
6 hours/day, 5
days/week for 4
weeks
8 hours/day, 7
days/week for 5
or 7 weeks
POD5 (ppm or
mg/kg-day)
LOAEL= 125
NOAEL=398
NOAEL= 1000
Effect6
No effects on
clinical
chemistry
parameters for
kidney effects
No effects on
clinical
chemistry
parameters
related to
kidney
Increased
relative kidney
weight
Changes in
BUN, total
bilirubin, and
total protein
levels
No effects on
histopathology
Reference7
(NIOSH, 2003)
(Lietal.,
2010a)
(NTP, 2011)
(ClinTrials,
1997b)
(Yu et al..
2QQ1)
Comments8
No effects noted;
not used in
quantitative
analysis
Limited evaluation
of worker exposure
GLP study - Effects
occurred at all
concentrations no
NOAELwas
identified
GLP study -
Histopathological
changes reported
at the highest
exposure
concentration (in
the absence of
effects on
urinalysis
parameters)
Not suitable for
dose-response
analysis because
only one exposure
group was used.
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Target Organ/
System1
Kidney
Kidney
Kidney
Kidney
Kidney
Kidney
Species2
Rat
(n=10/group)
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Rat
(male)
(n=15/group)
Rat
(female)
(n=10/group)
Rat
(n=100/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
50 to 1800 ppm
200, 400
or 800 ppm
200, 400
or 800 ppm
100, 200, 400
or 600 ppm
62.5, 125, 250,
500 or 1000 ppm
125, 250 or
500 ppm
Duration4
6 hours/day, 5
days/week for 8
weeks
^1
8 hours/day, 7
days/week for 12
weeks
8 hours/day, 7
days/week for
up to 12 weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=300
NOAEL=800
LOAEL=200
NOAEL=600
NOAEL=500
NOAEL=500
Effect6
Decreased
urobilinogen
(males);
increased
bilirubin
(females)
No effects on
kidney weight
or
histopathology
Increased
absolute and
relative kidney
weight
No effects on
urinalysis
parameters, or
organ weights
Increased
absolute and
relative kidney
weights
No effects on
histopathology
Reference7
(Kimetal.,
1999b)
(Ichihara et
al., 2000b)
(Yamada et
al., 2003)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
Comments8
Accompanying
effects included
tubular casts in
females (incidence
not reported),
increased relative
kidney weight (no
data on absolute
kidney weight)
GLP study -peer
reviewed literature
Effects occurred at
all exposure
concentrations; no
NOAELwas
identified. Renal
histopathology
observed only at
the highest
exposure
concentration.
GLP study
GLP study -There
were no supporting
effects on clinical
chemistry
parameters or
kidney
histopathology
GLP study
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Target Organ/
System1
Kidney
Kidney
Kidney
Kidney
Kidney
Reproductive
System
Species2
Rat
(male)
(n=25/group)
Rat
(female)
(n=25/group)
Mouse
(female)
(n=5/group)
Mouse
(female)
(n=10/group)
Mouse
(n=100/group)
Human (n = 9
males)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
100 to 750 ppm
100, 250, 500
or 750 ppm
125, 250, 500,
1000 or 2000 ppm
62.5, 125, 250
or 500 ppm
62.5, 125
or 250 ppm
81.2 ppm GM;
(range 18-254
ppm)
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
GD 20; from PND
5 until weaning
of offspring
(-PND21)
6.2 hours/day, 5
days/week for 17
days
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
2 weeks - 2
months
POD5 (ppm or
mg/kg-day)
NOAEL=100
NOAEL=100
^k
NOAEL=500
NOAEL=250
NOAEL=250
Effect6
Increased
incidence of
pelvic
mineralization
(Fo)
Increased
incidence of
pelvic
mineralization
(Fo)
Increased
absolute and
relative kidney
weights
Increased
absolute and
relative kidney
weights
No effects on
histopathology
No decrease in
sperm number,
shape, or
motility
Reference7
(WIL
Research,
2001)
(WIL
Research,
2QQ1)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(NIOSH, 2003)
Comments8
GLP study peer-
reviewed by NTP
GLP study peer-
reviewed by NTP
GLP study -These
effects were not
observed in males;
no evidence of
renal
histopathology
GLP study -These
effects were not
observed in males;
no evidence of
renal
histopathology
GLP study
No effects noted;
not used in
quantitative
analysis
Page 308 of 403
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Target Organ/
System1
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Species2
Rat
(male)
(n=5/group)
Rat
(female)
(n=7-8/group)
Rat
(male)
(n=10/group)
Rat
(male)
(n=24/group)
Rat
(male)
(n=9/group)
Rat
(female)
(n=10/group)
Rat
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
6040, 7000,
7400 or 8500 ppm
50, 200 or
1000 ppm
398, 994 or
1590 ppm
400, 800 or
1000 ppm
1000 ppm
200, 400
or 800 ppm
50 to 1800 ppm
Duration4
4 hours
8 hours/day, 7
days/week for 3
weeks
6 hours/day, 5
days/week for 4
weeks
8 hours/day, 7
days/week for 6
weeks
8 hours/day, 7
days/week for 5
or 7 weeks
8 hours/day, 7
days/week for
up to 12 weeks
6 hours/day, 5
days/week for 8
weeks
POD5 (ppm or
mg/kg-day)
NOAEL= 8500
NOAEL= 1000
NOAEL=994
LOAEL=400
NOAEL= 1000
LOAEL=200
NOAEL=300
Effect6
No effects on
histopathology
of the testes
No effects on
number of days
per estrous
cycle or ovary
and uterus
weights
Microscopic
lesions in male
reproductive
system
Decreased
epididymal
sperm count
No effects on
testis
histopathology
Decreased
number of
antral follicles
Increased
relative ovary
weight
Reference7
(Elf Atochem
S.A., 1997)
(Sekiguchi et
al., 2002)
(ClinTrials,
1997b)
(Banu et al..
2QQ7)
(Yu et al.,
2001)
(Yamada et
al., 2003)
(Kimetal.,
1999b)
Comments8
No effects noted
No effects noted
GLP study - Specific
tissues and lesions
not described
GLP study -
conducted in males
only. Effects
occurred at both
exposure
concentrations; no
NOAELwas
identified.
Not suitable for
dose-response
analysis because
only one exposure
group was used
Peer reviewed
literature
Peer reviewed
literature. Effect
confounded by
decreased body
weight at the same
exposure
concentration
Page 309 of 403
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Target Organ/
System1
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Species2
Rat (male)
(n=9/group)
Rat
(n=30/group)
Rat (male)
(n=10/group)
Rat (female)
(n=10/group)
Rat
(n=100/group)
Rat (male)
(n=25/group)
Rat (male)
(n=25/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
200, 400 or 800
ppm
100, 200, 400 or
600 ppm
62.5, 125, 250, 500
or 1000 ppm
62.5, 125, 250, 500
or 1000 ppm
125, 250 or 500
ppm
100, 250, 500 or
750 ppm
100, 250, 500 or
750 ppm
Duration4
8 hours/day, 7
days/week for 12
weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
6 hours/day
during pre-
mating (> 70
j. days),
throughout
mating, and until
sacrifice
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice
POD5 (ppm or
mg/kg-day)
LOAEL = 200
NOAEL=600
LOAEL=250
LOAEL=250
NOAEL=500
NOAEL=250
NOAEL=250
Effect6
Decreased
relative seminal
vesicle weight
No effects on
organ weights
Decreased
sperm motility
Alterations in
estrous cycles
No effects on
histopathology
of reproductive
organs
Decreased
percent motile
sperm (Fo)
Decreased
percent normal
sperm
morphology (Fo)
Reference7
(Ichihara et
al., 2000b)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(WIL
Research,
2001)
(WIL
Research,
2001)
Comments8
GLP study -peer
reviewed literature
GLP study
Not all exposure
groups were
evaluated for
reproductive
effects; a NOAEL
could be not
identified
Not all exposure
groups were
evaluated for
reproductive
effects; a NOAEL
could be not
identified
GLP study
GLP study peer-
reviewed by NTP
GLP study peer-
reviewed by NTP
Page 310 of 403
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Target Organ/
System1
Reproductive
System
Reproductive
System
Reproductive
System
Species2
Rat (male)
(n=25/group)
Rat (female)
(n=25/group)
Mouse (male)
(n=24/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Concentration3
100, 250, 500 or
750 ppm
100, 250, 500 or
750 ppm
50, 110 or 250
ppm
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice
^
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
GD 20; from PND
5 until weaning
of offspring
(-PND21)
8 hours/day, 7
days/week for 4
weeks
POD5 (ppm or
mg/kg-day)
NOAEL= 100
NOAEL=250
LOAEL= 50
Effect6
Decreased
absolute
prostate weight
(Fo)
Increase in
estrous cycle
length (Fo)
Decreased
sperm count
and motility
and/or
increased
abnormal
sperm
Reference7
(WIL
Research,
2001)
(WIL
Research,
2001)
(Liuetal.,
2009)
Comments8
GLP study peer-
reviewed by NTP.
Absolute prostate
weights were
decreased by about
the same amount
at all exposure
concentrations (i.e.
there was no dose-
response); there
were also no
significant effects
on relative prostate
weight.
There were no
significant changes
in absolute or
relative prostate
weights in Fl
adults.
GLP study peer-
reviewed by NTP
Effects occurred at
all exposure
concentrations;
peer reviewed
literature. No
NOAELwas
identified
Page 311 of 403
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Target Organ/
System1
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Reproductive
System
Respiratory
Species2
Mouse (male)
(n=10/group)
Mouse
(female)
(n=10/group)
Mouse
(n=100/group)
Rat (male)
(n=7/group)
Mouse (male)
(n=20/group)
Rat
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Oral
Oral
Inhalation
Concentration3
62.5, 125, 250 or
500 ppm
62.5, 125, 250 or
500 ppm
62.5, 125 or 250
ppm
1000 mg/kg-day
300 or 600 mg/kg-
day
6040, 7000, 7400
or 8500 ppm
Duration4
6.2 hours/day, 5
days/week for 14
weeks
^1
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
14 days
Exposed for 10
days prior to
mating
4 hours
POD5 (ppm or
mg/kg-day)
NOAEL= 125
LOAEL= 125
NOAEL=250
LOAEL= 1000
LOAEL=600
NOAEL=6040
Effect6
Decreased
epididymis
weight and
sperm motility
Alterations in
estrous cycles
No effects on
histopathology
of reproductive
organs
Decreased
epididymal
sperm count;
decreased
epididymis and
prostate +
seminal vesicle
weights
Degeneration of
pachytene
spermatocytes.
Used for weight
of evidence; no
route-to-route
extrapolation.
Pulmonary
edema and
emphysema
Reference7
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(Xinetal.,
2010)
(Yu et al..
2008)
(Elf Atochem
S.A., 1997)
Comments8
Not all exposure
groups were
evaluated for
reproductive
effects. There were
no effects on
reproductive organ
weights or
histopathology.
Not all exposure
groups were
evaluated for
reproductive
effects; a NOAEL
could be not
identified
GLP study
Not able to do
route-to-route
extrapolation. Peer
reviewed literature
No able to do
route-to-route
extrapolation. Peer
reviewed literature
GLP study
Page 312 of 403
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Target Organ/
System1
Respiratory
Respiratory
Respiratory
Respiratory
Respiratory
Respiratory
Respiratory
Species2
Rat (male)
(n=5/group)
Rat
(n=20/group)
Rat (male)
(n=9/group)
Rat
(n=20/group)
Rat
(n=30/group)
Rat
(n=20/group)
Rat
(n=100/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
125, 250, 500, 100
or 2000 ppm
398, 994 or 1590
ppm
200, 400 or 800
ppm
50, 300 or 1800
ppm
100, 200, 400 or
600 ppm
62.5, 125, 250, 500
or 1000 ppm
125, 250 or 500
ppm
Duration4
6.2 hours/day, 5
days/week for 16
days
6 hours/day, 5
days/week for 4
weeks
8 hours/day, 7
days/week for 12
weeks
6 hours/day, 5
days/week for 8
weeks
6 hours/day, 5
days/week for 13
weeks
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
POD5 (ppm or
mg/kg-day)
NOAEL=250
NOAEL=994
NOAEL=800
NOAEL= 1800
NOAEL=600
NOAEL= 1000
LOAEL= 125
Effect6
Nasal lesions
(including
suppurative
inflammation
and respiratory
epithelial
necrosis)
Histopathologic
al changes in
nasal cavities
No effects on
lung weight or
histopathology
No effects on
lung weight or
histopathology
No effects on
lung weight or
histopathology
No effects on
lung weight or
histopathology
Chronic active
nasal
inflammation
and squamous
metaplasia in
the larynx
Reference7
(NTP, 2011)
(ClinTrials,
1997b)
(Ichihara et
al., 2000a)
(Kimetal.,
1999a)
(ClinTrials,
1997a)
(NTP, 2011)
(NTP, 2011)
Comments8
These effects were
not observed in
females. There
were no significant
changes in lung
weight.
GLP study
Study conducted in
males only; peer
reviewed literature
GLP study
GLP study
GLP study
GLP study
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Target Organ/
System1
Respiratory
Respiratory
Respiratory
Respiratory
Developmental
Effects
Species2
Rat
(n=25/group)
Mouse
(n=10/group)
Mouse
(n=20/group)
Mouse
(n=100/group)
Rat
(n=10/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
100, 250, 500 or
750 ppm
125, 250, 500,
1000 or 2000 ppm
62.5, 125, 250 or
500 ppm
62.5, 125 or 250
ppm
100 , 400 or 800
ppm
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in Fo
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in Fo females
6.2 hours/day, 5
days/week for 17
days
6.2 hours/day, 5
days/week for 14
weeks
6.2 hours/day, 5
days/week for
105 weeks
8 hours/day
during gestation
(CDs 0-21) and
lactation (PNDs
1-21)
POD5 (ppm or
mg/kg-day)
NOAEL=750
NOAEL=250
NOAEL=250
LOAEL=62.5
NOAEL= 100
Effect6
No effects on
lung weight or
histopathology
Lesions in the
lung and nose
Cytoplasmic
vacuolization in
the nose,
larynx, trachea,
and lung
Histopathologic
al lesions in the
nasal
respiratory
epithelium,
larynx, trachea,
and bronchioles
Decreased
survival during
lactation
Reference7
(WIL
Research,
2001)
(NTP, 2011)
(NTP, 2011)
(NTP, 2011)
(Furuhashi et
al., 2006)
Comments8
GLP study peer-
reviewed by NTP
GLP study
Lesions were
detected almost
exclusively in
animals that died
early
Effects occurred at
all exposure
concentrations; no
NOAELwas
identified
Quantitative data
not available for
pup survival, peer
reviewed literature
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Target Organ/
System1
Developmental
Effects
Developmental
Effects
Developmental
Effects
Developmental
Effects
Species2
Rat
(n=10/group)
Rat
(n=25/group)
Rat
(n=25/group)
Rat
(n=10-
24/group)
Exposure
Route
Inhalation
Inhalation
Inhalation
Inhalation
Concentration3
100, 199, 598 or
996 ppm
103, 503 or 1005
ppm
100, 250 or 500
ppm
100, 250 or 500
ppm
Duration4
6 hours/day on
CDs 6-19; PNDs
4-20
6 hours/day on
CDs 6-19; PNDs
4-20
A
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDS until
weaning of
offspring (~PND
21) in females
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDS until
weaning of
offspring (~PND
21) in females
POD5 (ppm or
mg/kg-day)
NOAEL= 199
LOAEL=103
NOAEL=250
NOAEL=100
Effect6
Decreased body
weight gain in
pups
Decreased fetal
weight
Decreased live
litter size (Fi
females)
Decreased pup
body weights
(FiPND28
males)
Reference7
(Huntingdon
Life Sciences,
1999)
(Huntingdon
Life Sciences,
2001)
(WIL
Research,
2QQ1)
(WIL
Research,
2001)
Comments8
GLP study - effect
coincided with
decreased body
weight gains during
gestation (at > 199
ppm)
GLP study - effect
coincided with
decreased body
weight gains during
gestation (503 ppm
and above)
GLP study peer-
reviewed by NTP
GLP study peer-
reviewed by NTP
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Target Organ/
System1
Developmental
Effects
Developmental
Effects
Species2
Rat
(n=10-
24/group)
Rat
(n=10-
24/group)
Exposure
Route
Inhalation
Inhalation
Concentration3
100, 250 or 500
ppm
100, 250 or 500
ppm
Duration4
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in (
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
6 hours/day
during pre-
mating (> 70
days),
throughout
mating, and until
sacrifice in
males; or until
GD 20 and from
PNDSuntil
weaning of
offspring (~PND
21) in females
POD5 (ppm or
mg/kg-day)
NOAEL = 250
NOAEL = 250
Effect6
Decreased pup
body weights
(F2PNDsl4and
21 males)
Decreased pup
body weights
(F2PNDsl4and
21 females)
Reference7
(WIL
Research,
2001)
(WIL
Research,
2001)
Comments8
GLP study peer-
reviewed by NTP
GLP study peer-
reviewed by NTP
Notes: 'Inclusion of an entry in this table was based on availability of data deemed reliable from selected secondary sources. Therefore, this table is not comprehensive; additional information may be available from
primary sources.
2Species (and sex in which the effect(s) at the POD were observed, if reported in only one sex). Studies were conducted in both sexes unless indicated otherwise in the Comments column.
3Control concentrations or doses are not included in the table.
"Acute exposures defined as those occurring within a single day. Chronic exposures defined as 10% or more of a lifetime (U.S. EPA, 2011).
5POD type can be LD5o, LC5o, NOAEL, LOAEL, or BMDL. Units are ppm for inhalation exposure and mg/kg-day for oral exposure. For repeated-dose studies, the preference was BMDL> NOAEL> LOAEL.
6The effect(s) listed were the most sensitive effects observed for that target organ/system in that study (i.e., the effect(s) upon which the POD was based).
This column lists the primary reference for the reported data. The secondary source(s) from which the data were extracted are listed in the Comments column. Full citations for the primary references can be
found in the associated secondary source(s).
8lnformation included in this column is variable, depending on the nature and extent of information provided in the secondary source(s) from which the entry was extracted.
GD = gestational day; PND = postnatal day
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0-5 Carcinogenicity and Mutagenicity
There are no epidemiological studies on the effects of 1-BP exposure on human cancer.
The carcinogenicity of 1-BP has been studied in rats and mice in a two-year bioassay by the
National Toxicology Program (NTP, 2011). Groups of 50 male and 50 female rats and mice were
exposed to 1-BP vapor at concentrations of 62.5,125, or 250 ppm (mice) and 125, 250, or
500 ppm (rats), 6 hours per day, 5 days per week for up to 105 weeks. Similar groups of 50
animals were exposed to clean air in the same inhalation chambers as the control groups. All
animals were observed twice daily. Clinical findings were recorded for all animals every 4 weeks
through week 93, every 2 weeks thereafter, and at the end of the studies. Rats and mice were
weighed initially, weekly for the first 13 weeks, then every 4 weeks through week 93, every
2 weeks thereafter, and at the end of the studies. Complete necropsies and microscopic
examinations were performed on all rats and mice.
At the end of the two-year bioassay, there were treatment-related skin tumors in male rats and
large intestine tumors in female rats. Significantly increased incidence of lung tumors was
found in female mice. Based on increased incidences of tumors in rats and mice, at multiple
sites and the occurrence of rare tumors, it has been concluded that there is sufficient evidence
of carcinogenicity in experimental animals for 1-BP. Each of these tumor types is described
below.
0-5-1 Skin Tumors
In male rats, there were exposure concentration treatment-related increased incidences of
keratoacanthoma, keratoacanthoma or squamous cell carcinoma (combined); and
keratoacanthoma, basal cell adenoma, basal cell carcinoma, or squamous cell carcinoma
(combined). The incidences of keratoacanthoma and of keratoacanthoma or squamous cell
carcinoma (combined) in 250 ppm (12%) and 500 ppm (12%) males were significantly increased
as compared to the controls (0% and 2%), and exceeded the historical control ranges (0-8%) for
inhalation studies. The incidences of keratoacanthoma, basal cell adenoma, basal cell
carcinoma, or squamous cell carcinoma (combined) were significantly increased in all exposed
groups of males (125 ppm: 14%; 250 ppm: 18%; and 500 ppm: 20%) as compared to the
controls (2%) and exceeded the historical control range (0-10%) for inhalation studies. In female
rats, there were increased incidences of squamous cell papilloma, keratoacanthoma, basal cell
adenoma, or basal cell carcinoma (combined) in the 500 ppm group (8%) as compared to the
control (2%). Although the increased incidences were not significant, they exceeded the
respective historical control ranges for inhalation studies.
0-5-2 Large Intestine Tumors
Large intestine tumors are rare tumors in the rat. The incidence of adenoma of the large
intestine (colon or rectum) in 500 ppm females (5/50,10%) was significantly greater than that
in the controls (0%). The incidences in the 250 ppm (2%) and 500 ppm (4%) groups of females
exceeded the historical controls in inhalation studies (0.1%). In 250 (4%) and 500 (2%) ppm
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males, the incidences of adenoma of the large intestine were slightly increased compared to
that in the controls (0%); although the increases were not statistically significant, the incidence
in the 250 ppm group (4%) exceeded the historical control ranges (0-2%) for inhalation studies.
0-5-3 Lung Tumors
In the female mice, there were treatment-related increased incidences of alveolar/bronchiolar
adenoma, alveolar/bronchiolar carcinoma, and alveolar/bronchiolar adenoma or carcinoma
(combined). The incidence of alveolar/bronchiolar adenoma in 250 ppm females (20%) and the
incidences of alveolar/bronchiolar carcinoma in 62.5 ppm (14%) and 125 ppm (10%) females
were significantly increased as compared to the controls (0-2%). The incidences of
alveolar/bronchiolar adenoma or carcinoma (combined) were significantly increased in all
exposed groups (18%, 16% and 28% in low-, mid- and high-dosed groups) as compared to the
controls (2%).
0-5-4 Pancreatic Tumors
The evidence that 1-BP exposure was associated with an increased incidence of pancreatic islet
adenoma in male rats was equivocal. Although the incidences of pancreatic islet adenoma were
significantly increased in all exposed groups compared to the chamber controls (0%, 10%, 8%,
10%), the incidences were within the historical control ranges for inhalation studies (0% to
12%). The incidences of pancreatic islet carcinoma in exposed male rats were not significantly
different from that in the chamber controls and were not considered treatment related. The
incidences of pancreatic islet adenoma or carcinoma (combined) were significantly increased
only in the low-dose (20%) and mid-dose groups (18%) as compared with the chamber controls
(6%); only the incidence in the low-dose group (20%) exceeded the historical control ranges for
inhalation studies (6% to 18%).
0-5-5 Malignant Mesothelioma
There were increased incidences of malignant mesothelioma in male rats exposed to 1-BP as
compared to the chamber controls: control, 0%; low-dose, 4%; mid-dose, 4%; and high-dose,
8%. The incidence of malignant mesothelioma in high-dose group (8%) was significantly greater
than that of the chamber controls (0%) and exceeded that of the historical controls (0-6%) in
inhalation studies. The overall strength of this evidence was considered equivocal because the
increased incidence in the high-dose (500 ppm) group was s barely outside the historical control
range(0%to6%).
Under the conditions of these 2-year inhalation studies, there was clear evidence of
carcinogenic activity of 1-BP in female F344/N rats based on increased incidences of adenoma
of the large intestine. Increased incidences of skin neoplasms may also have been related to
1-BP exposure. There was some evidence of carcinogenic activity of 1-BP in male F344/N rats
based on the increased incidences of epithelial neoplasms of the skin (keratoacanthoma,
squamous cell carcinoma, and basal cell neoplasms). Increased incidences of malignant
mesothelioma and pancreatic islet adenoma and carcinoma (combined) may also have been
related to 1-BP exposure. There was clear evidence of carcinogenic activity of 1-BP in female
B6C3F1 mice based on increased incidences of alveolar/bronchiolar neoplasms. There was no
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evidence of carcinogenic activity of 1-BP in male B6C3F1 mice exposed to concentrations of
62.5, 125, or 250 ppm 1-BP. Based on increased incidences of tumors in rats and mice, at
multiple sites and the occurrence of rare tumors, it has been concluded that there is sufficient
evidence of carcinogenicity in experimental animals for 1-BP. The compound has been
considered to be "reasonably to be anticipated as a human carcinogen" and will be listed in the
next issue of Report on Carcinogens of the National Toxicology Program (NTP, 2013).
The tumor data on the skin, large intestine and lung in male and female rats and female mice
(Table_Apx 0-3) may be used for quantitative assessment of the potential risk of humans
exposed to 1-BP.
Table_Apx O-3 Tumors induced by 1-BP in Rats and Mice
Animal
F344/N rats, male
F344/N rats, female
B6C3F1 mice, female
Tumor
Skin (keratoacanthoma,
squamous-cell carcinoma, basal-cell
adenoma or carcinoma combined)
Large intestine (colon or rectum
adenoma)
Lung (alveolar /bronchiolar adenoma
or carcinoma combined)
^L
Concentration
(ppm)
0
125
250
500
Trend
0
125
250
500
Trend
0
62.5
125
250
Trend
Incidence
1/50 (2%)
7/50* (14%)
9/50** (18%)
10/50** (20%)
p=0.003
0/50 (0%)
1/50 (2%)
2/50 (4%)
5/50* (10%)
p=0.004
1/50 (2%)
9/50** (18%)
8/50* (16%)
14/50*** (28%)
p<0.001
p<0.05; **p<0.01; ***p<0.001
0-5-6 Genotoxicity
1-BP has been shown to bind covalently to DNA to form N7-guanine adducts in an in vitro
system using calf thymus DNA (Lee et al., 2007). This is supportive of possible genotoxic
potential; however, further studies are needed to identify the DNA adducts in animals exposed
to 1-BP, particularly in in vivo studies, to provide information for mode of action consideration.
Mixed results have been reported in genotoxicity tests using bacteria. 1-BP was mutagenic in a
dose-dependent manner in Salmonella typhimurium (S. typhimurium) strains TA100 and
TA1535 when the assay was conducted using closed chambers/desiccators specifically designed
for testing volatile substances (Barber et al., 1981). The data suggest that 1-BP may be a direct-
acting mutagen since similar responses were observed both with and without metabolic
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activation. A number of other studies reported negative responses in S. typhimurium and
Escherichia coli (E. coli) but some of these studies were not conducted using the ppropriate
methodology (i.e., treatment and incubation in a closed chamber) for testing a volatile
substance (NTP. 2011: Kimetal.. 1998: Elf Atochem S.A.. 1993b). An NTP peer review
committee considered the Barber (1981) study to be a well conducted, strong study (NTP,
2013).
1-BP was shown to induce base-pair mutations in the L5178Y mouse lymphoma cell assay, with
and without S9 metabolic activation (Elf Atochem S.A., 1996b). Using the comet assay,
(Toraason et al., 2006) demonstrated DNA damage in human leukocytes exposed to 1 mM 1-BP
in vitro; there was also limited evidence that leukocytes from workers exposed to 1-BP may
present a small risk for increased DNA damage. In contrast to these in vitro studies, negative
results were reported with in vivo micronucleus assays in mice exposed to 1-BP via
intraperitoneal (ip) injection, and in rats exposed via inhalation (Kim et al., 1998) and (NTP,
2011; Elf Atochem S.A., 1995). It should be noted, however, that a recent compilation of in vivo
micronucleus data by (Benigni et al., 2012) showed that overall, a low correlation exists
between in vivo micronucleus data and carcinogenicity, suggesting a potential for false negative
carcinogenicity predictions. 1-BP was also negative in dominant lethal mutation assays
conducted in ICR mice (Yu et al., 2008) and Sprague-Dawley rats (Saito-Suzuki et al., 1982).
Several known or proposed metabolites of 1-BP have been shown to be mutagenic (NTP, 2014;
IARC, 2000, 1994). For example, both glycidol and propylene oxide are mutagenic in bacteria,
yeast, Drosophila, and mammalian cells. These compounds have also been shown to induce
DNA and chromosomal damage in rodent and human cells, and can form DNA adducts in vitro.
a-Bromohydrin and 3-bromo-l-propanol were mutagenic in the S. typhimurium reversion
assay, and 3-bromo-l-propanol and l-bromo-2-propanol induced DNA damage in E. coli. The
available in vivo test results for glycidol indicate that it induces micronucleus formation, but not
chromosomal aberrations in mice. Studies of propylene oxide indicated chromosomal damage
evidenced by positive responses for micronucleus induction in mouse bone marrow and
chromosomal aberration tests; DNA damage was evident in the sister chromatid exchange (SCE)
assay.
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Table_Apx O-4 Genotoxicity of 1-BP In Vitro
Results
Species (test system)
End point
With Without
activation activation Reference
Prokaryotic organisms:
S. typhimurium TA98,
TA100, TA1535, TA1537,
TA1538
S. typhimurium TA100,
TA1535
S. typhimurium TA97, TA98,
TA100, TA 1535
Escherichia coli Wp2
uvrA/pKMlOl
Mammalian cells:
Human hepatoma cell-line
(HepG2)
Human hepatoma cell-line
(HepG2)
Human leukocyte cells
Reverse mutation - -
(open test (open test
system) system)
Reverse mutation + +
(closed (closed test
test system)
system)
Reverse mutation - -
Reverse mutation - -
DNA damage and -
repair, single
strand breaks
DNA damage and -
repair, repair
activity
DNA damage and +
repair
(Barber etal.. 1981)
(Barber etal.. 1981)
(NTP. 2011)
(NTP. 2011)
(Hasspieler et al.,
2006)
(Hasspieler et al.,
2006)
(Toraason et al.,
2006)
+ = positive results; - = negative results
0-5-7 Metabolism, Structure-Activity Relationships and
Mechanism/Mode of Action
Studies in experimental animals and humans indicate that 1-BP can be absorbed following
inhalation, oral, or dermal exposure (Cheever et al., 2009; NIOSH, 2007). Metabolism studies
show that oxidation by P450 enzymes (e.g., CYP2E1) and glutathione conjugation are the
primary metabolic pathways (Garner et al., 2006; Ishidao et al., 2002). Over 20 metabolites
have been identified in rodent studies, including the four metabolites that can be detected in
urine samples of workers exposed to 1-BP (Hanley et al., 2009). Besides being a direct-acting
alkylating agent, 1-BP can also be metabolically activated to the epoxide intermediate via
hydroxylation at the 2-position followed by dehydrobromination. Mice appear to have a greater
capacity to oxidize 1-BP than rats (Garner et al., 2006). This species difference in metabolic
capacity may explain why mice were found to be more sensitive to 1-BP toxicity than rats. The
identified or putative reactive intermediates for 1-BP include glycidol, propylene oxide,
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a-bromohydrin and 2-oxo-l-BP (NTP. 2014: Ishidao et al.. 2002: Mitchell et al.. 1998).
Detoxification of 1-BP metabolites occurs primarily via glutathione-S-transferase (GST) -
mediated conjugation with glutathione (NTP, 2014; Liuetal., 2009; Garner etal., 2006).
1-BP is expected to be a good alkylating agent because bromine is a good leaving group. Two of
its closest homologs, bromoethane and 1-bromobutane, were both shown to be mutagenic in
the Ames Salmonella test; in both cases, use of desiccators was needed to show positive results
(NTP, 1989; Simmon et al., 1977). Bromoethane is a known carcinogen via the inhalation route
of exposure (NTP, 1989), whereas 1-bromobutane has not been tested for carcinogenic activity.
1-BP is a relatively soft electrophile which is expected to preferentially react with sulfhydryl
(-SH) residues on glutathione and proteins before binding to DNA. Besides being a direct-acting
alkylating agent, 1-BP may be metabolically activated to genotoxic intermediates (see above). A
number of other structurally-related halogenated alkanes such as 1,2-dibromoethane (ethylene
dibromide) (IARC. 1999e). dichloromethane (IARC. 1999d). 1,2-dichloroethane (IARC. 1999b).
l,2-dibromo-3-chloropropane (IARC, 1999a) and 1,2,3-trichloropropane (IARC, 1999c) have
been classified as "probably carcinogenic to humans (group 2A)" or "possibly carcinogenic to
human carcinogens" (group 2B) by the International Agency for Research on Cancer; however,
some of these chemicals may have different mechanisms.
The exact mechanism/mode of action for 1-BP carcinogenesis is not clearly understood. More
research (e.g., organ-specific in vivo DNA adduct studies, oxidative stress) is needed to identify
key molecular events. Since 1-BP can induce tumors in multiple organs and can act directly as
an alkylating agent, as well as indirectly via metabolically activated reactive intermediates such
as glycidol and propylene oxide, it may have different mechanisms in different target organs. At
least four possible mechanisms—genotoxicity, oxidative stress, immunosuppression, and cell
proliferation—have been suggested (NTP, 2013). These mechanisms can act synergistically to
complete the multi-stage process of carcinogenesis.
As discussed in the previous section on genotoxicity, 1-BP and its genotoxic reactive
intermediates can induce DNA mutations and/or chromosome aberrations. Although the results
are not as clear cut for 1-BP itself, some of the discrepancies may be explained by testing
limitations. Available in vitro DNA binding studies and structure-activity relationship analyses
support the genotoxic potential of 1-BP. The induction of tumors in multiple targets by 1-BP is
also a common characteristic of genotoxic carcinogens. Overall, there is a justifiable basis to
support a probable mutagenic mode of action for 1-BP carcinogenesis.
Oxidative stress due to cellular glutathione depletion could contribute to the carcinogenicity of
1-BP (Morgan et al., 2011). Oxidative stress is an important epigenetic mechanism that can
contribute to all three stages of carcinogenesis - oxidation can induce initiation (as a result of
DNA damage), promotion (as a result of compensatory cell proliferation in response to cell
necrosis), and progression (via oxidative changes in signal transduction and gene expression;
rev. (Woo and Lai, 2003). Exposure to 1-BP has also been shown to deplete glutathione in
various tissues (e.g., (Liuetal., 2009; Lee et al., 2007; Wang etal., 2003), which can lead to a
loss of protection against electrophiles.
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Besides genotoxicity and oxidative stress, 1-BP has been shown to cause immunosuppression in
rodents (Anderson et al., 2010; Lee et al., 2007). Immunosuppression can facilitate tumor
progression by lowering the immunosurveillance process against tumor growth. There is also
some evidence that 1-BP can cause y-aminobutyric acid (GABA) dysfunction and thereby impact
cell proliferation, differentiation and migration of neuronal cells (NTP, 2013).
Appendix P BENCHMARK DOSE ANALYSIS
BMD modeling was performed using USEPA's BMD Software package (BMDS Version 2.6), in a
manner consistent with EPA Benchmark Dose Technical Guidance. Continuous models were
used to fit dose-response data and BMRs were selected for each endpoint individually. In
particular a BMR of 5% was used for developmental endpoints (Kavlock et al., 1995). The dose
metric for all endpoints was the exposure concentration in ppm.
P-l Benchmark Dose Modeling of Non-Cancer Effects for
Acute Exposures
EPA/OPPT selected the decreased live litter size observed in the 2-generation reproductive and
developmental study by WIL Research (2001) as the most relevant endpoint for calculating risks
associated with acute worker and consumer scenarios. A BMR of 5% was used to address the
relative severity of this endpoint (U.S. EPA, 2012a) see section 3.4.1. For comparison the
modeling results with a BMR of 1 standard deviation and 1% relative deviation are also shown.
The doses and response data used for the modeling are presented in Table_Apx P-l.
Table_Apx P-l Litter Size Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
Number of litters
23
25
22
11
Mean litter size
14.4
13.3
12.3
8.3
Standard Deviation
2.21
3.72
4.47
4.1
The best fitting model was selected based on Akaike information criterion (AIC; lower value
indicates a better fit), chi-square goodness of fit p-value (higher value indicates a better fit),
ratio of the BMCBMCL (lower value indicates less model uncertainty) and visual inspection.
Comparisons of model fits obtained are provided in Table_Apx P-2. The best-fitting model
(Exponential M2), based on the criteria described above, is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-l, the model version number, model form,
benchmark dose calculation, parameter estimates and estimated values are shown. Although
the means were well-modeled the variances are not well modeled by the non-homogeneous
variance model (the non-homogeneous variance model was used because the BMDS test 2 p-
value = 0.0130). To investigate the effect of the poor modeling of the variances on the BMDL,
the models were run using the smallest dose standard deviation (2.21), highest (4.47) and
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pooled (3.54) for all dose levels and the results are summarized in Table_Apx P-3. As shown in
the last column of Table_Apx P-3 the ratios BMDLs for the lowest to the highest variance for the
two best fitting models the Linear and Exponential (M2) models are 1.15 and 1.20, respectively.
Overall the adjustment of the variances from most-variable to least-variable for all of the models
makes little difference on the BMDL. This is strong evidence that the poor variance modeling for
the original data is not substantially impacting the BMDL estimates. It is reasonable to use the
non-homogeneous Exponential M2 model for the original data because it has the lowest AIC of
all the model choices for the original data and therefore a BMDL of 41 ppm (40.7 ppm rounded
to two significant figures) was selected for this endpoint.
Table_Apx P-2 Summary of BMD Modeling Results for Reduced Litter Size in F0 Generation Exposed to
1-BP by Inhalation; BMRs of 1 Standard Deviation, and 5% and 1% Relative Deviation From Control
Mean.
Model"
Exponential (M2)
Exponential (M3)b
Power0
Polynomial 3°d
Polynomial 2°e
Linear
Hill
Exponential (M4)
Exponential (M5)f
Good ness of fit
p-value
0.533
0.433
0.722
0.622
AIC
291.10
291.51
291.96
292.08
BMDiso
(ppm)
256
281
178
181
BMDLiso
(ppm)
158
189
error8
69.4
BMDsRD
(ppm)
61.3
69.9
35.8
40.4
BMDLsRD
(ppm)
40.7
49.8
10.4
17.8
BMD
1RD
(ppm)
12.0
14.0
6.36
7.48
BMDL
1RD
(ppm)
7.97
9.95
1.69
3.23
Basis for
model
selection
The
Exponential
(M2) model
was
selected
based on
the lowest
AIC and
adequate fit
by visual
inspection.
a Modeled variance case presented (BMDS Test 2 p-value = 0.0130), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were -0.16, -0.05, 0.66, -0.76, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
g BMDL computation failed for this model.
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Exponential 2 Model, with BMR of 0.05 Rel. Dev. for the BMD and 0.95 Lower Confidence Limit for the BMDL
MDL BMD
14:24 11/20 2015
Figure_Apx P-l Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M2) Model
with Modeled Variance for Reduced Litter Size in F0 Generation Exposed to 1-BP by Inhalation; BMR =
5% Relative Deviation from Control Mean.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * exp(sign * b * dose)
A modeled variance is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 61.3264
BMDL at the 95% confidence level = 40.6605
Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
10.4606
-3.14328
14.4915
0.000836398
n/a
n/a
Default Initial
Parameter Values
6.08025
-1.44632
10.5312
0.00102437
0
1
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
23
25
22
11
Obs Mean
14.4
13.3
12.3
8.3
Est Mean
14.49
13.33
11.76
9.54
Obs Std Dev
2.21
3.72
4.47
4.1
Est Std Dev
2.8
3.19
3.88
5.4
Scaled Resid
-0.1569
-0.04505
0.6554
-0.7614
Likelihoods of Interest
Model
Al
A2
A3
R
2
Log(likelihood)
-143.3786
-137.9879
-140.9173
-153.5054
-141.5475
# Param's
5
8
6
2
4
AIC
296.7571
291.9758
293.8347
311.0108
291.095
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
31.03
10.78
5.859
1.26
Test df
6
3
2
2
p-value
<0.0001
0.01297
0.05343
0.5325
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Table_Apx P-3 BMD Modeling Results for Reduced Litter Size in F0 Generation Following Inhalation Exposure of Parental Rats to 1
Two-Generation Study with Variances Fixed at Smallest, Pooled and Highest Values.
,-BP in a
Model"
Linear
Exponential
(M2)
Exponential
(M4)
Polynomial 3°
Polynomial 2°
Power
Exponential
(M3)
Exponential
(M5)
Hill
Smallest Standard Deviation
Goodness of fit
p-value
0.279
0.112
0.112
0.506
0.393
0.303
0.239
0.239
N/Ab
AIC
213.92
215.74
215.74
213.81
214.09
214.43
214.75
214.75
216.43
BMDsRD
(ppm)
63.5
54.9
54.9
96.4
105
115
127
127
115
BMDLsRD
(ppm)
53.5
44.1
42.6
58.4
57.4
56.4
56.1
56.1
56.4
Pooled Standard Deviation
Good ness of fit
p-value
0.605
0.420
0.420
0.678
0.593
0.519
0.461
N/Ab
N/Ab
AIC
288.69
289.42
289.42
289.86
289.97
290.10
290.23
292.23
292.10
BMDsRD
(ppm)
63.5
54.9
54.9
96.4
105
115
127
127
116
BMDLsRD
(ppm)
49.2
39.4
34.4
51.1
50.8
50.5
42.6
42.6
50.3
Largest Standard Deviation
Goodness of fit
p-value
0.729
0.579
0.579
0.742
0.672
0.609
0.559
0.559
N/Ab
AIC
326.11
326.57
326.57
327.58
327.65
327.74
327.82
327.82
329.74
BMDsRD
(ppm)
63.5
54.9
54.9
96.4
105
115
127
127
116
BMDLsRD
(ppm)
46.6
36.7
29.1
47.8
47.6
47.4
38.7
33.0
47.2
Ratio
BMDLs
Smallest to
Largest Std
Dev
1.15
1.20
1.46
1.22
1.21
1.19
1.45
1.70
1.19
a Constant variance case presented (BMDS Test 2 p-value = 1.000, BMDS Test 3 p-value = 1.000), no model was selected as a best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
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P-2 Benchmark Dose Modeling of Non-Cancer Effects for
Chronic Exposures
EPA/OPPT selected multiple endpoints for quantitative dose-response analysis with BMDS and
calculating risks associated with chronic worker scenarios including: include liver toxicity, kidney
toxicity, neurotoxicity, reproductive toxicity, and developmental toxicity. The doses, response
data and BMD modeling results are presented below by effect.
P-2-1 Increased Incidence of Vacuolization of
Centrilobular Hepatocytes in Males
Increased incidence of vacuolization of centrilobular hepatocytes was observed in males of the
Fo generation of the reproductive and developmental study by WIL Laboratories (2001).
Dichotomous models were used to fit dose response data. A BMR of 10% added risk was
choosen per EPA Benchmark Dose Technical Guidance (U.S. EPA, 2012a). The doses and
response data used for the modeling are presented in Table_Apx P-4.
Table_Apx P-4 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response
Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
25
25
Incidence
0
0
7
22
24
The BMD modeling results for vacuolization of centrilobular hepatocytes are summarized in
Table_Apx P-5. The best fitting model was the LogLogistic based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure_Apx P-2. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
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Table_Apx P-5 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Male F0 Rats
Following Inhalation Exposure to 1-BP in a Two-Generation Study
Model"
LogLogistic
LogProbit
Gamma
Multistage 2°
Weibull
Logistic
Probit
Quantal-Linear
Goodness of fit
p-value
0.939
0.907
0.691
0.538
0.360
0.146
0.0542
0.0025
AIC
60.974
60.980
61.912
63.187
64.026
65.548
66.345
81.794
BMDlOPctAdd
(ppm)
188
185
178
129
158
186
177
41.1
BMDLlOPctAdd
(ppm)
143
142
130
98.5
110
142
^133 ^
32.2
Basis for model selection
LogLogistic model was selected
based on the lowest AIC, highest
goodness of fit p-value and
adequate fit by visual inspection.
a Selected model in bold; scaled residuals for selected model for doses 0,100, 250, 500, and 750 ppm were
0, -0.45, 0.12, 0.15, -0.41, respectively.
Log-Logistic Model, with BMP of 1O% Added Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
17:49 12/092015
Figure_Apx P-2 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (LogLogistic)
for Vacuolization of Centrilobular Hepatocytes in Male Rats Exposed to 1-BP Via Inhalation in ppm;
BMR 10% Added Risk.
Logistic Model. (Version: 2.14; Date: 2/28/2013)
The form of the probability function is: P[response] = background+(l-background)/[l+EXP(-
intercept-slope*Log(dose))]
Slope parameter is restricted as slope >= 1
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Benchmark Dose Computation.
BMR = 10% Added risk
BM 0 = 187.639
BMDL at the 95% confidence level = 143.489
Parameter Estimates
Variable
background
intercept
slope
Estimate
0
-2.4067E+01
4.17795
Default Initial
Parameter Values
0
-2.0600E+01
3.60147
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-28.2
-28.49
-85.19
# Pa ram's
5
2
1
Deviance
0.58301
113.996
Test d.f.
3
4
p-value
0.9
<.0001
AIC: = 60.9741
Goodness of Fit Table
Dose
0
100
250
500
750
Est. Prob.
0
0.0079
0.2693
0.8696
0.9732
Expected
0
0.199
6.731
21.74
24.33
Observed
0
0
7
22
24
Size
25
25
25
25
25
Scaled Resid
0
-0.45
0.12
0.15
-0.41
ChiA2 = 0.41 d.f = 3 p-value = 0.9391
P-2-2 Increased Incidence of Vacuolization of
Centrilobular Hepatocytes in Males
Increased incidence of vacuolization of centrilobular hepatocytes was observed in males of the
ClinTrials study (1997a). Dichotomous models were used to fit dose response data. A BMR of
10% added risk was choosen per EPA Benchmark Dose Technical Guidance (U.S. EPA, 2012a).
The doses and response data used for the modeling are presented in Table_Apx P-6.
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Table_Apx P-6 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response
Modeling for 1-BP
Dose (ppm)
0
100
200
400
800
Number of animals
15
15
15
15
15
Incidence
0
0
0
3
6
The BMD modeling results for vacuolization of centrilobular hepatocytes are summarized
inTable_Apx P-7. The best fitting model was the LogLogistic based on Akaike information
criterion (AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher
value indicates a better fit) and visual inspection. For the best fitting model a plot of the model
is shown in Figure_Apx P-3. The model version number, model form, benchmark dose
calculation, parameter estimates and estimated values are shown below.
Table_Apx P-7 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Male Rats
Following Inhalation Exposure to 1-BP
Model"
Multistage 3°
Multistage 2°
LogProbit
Gamma
LogLogistic
Weibull
Probit
Logistic
Goodness of fit
p-value
0.955
0.898
0.951
0.919
0.903
0.872
0.773
0.662
AIC
38.189
39.202
39.678
39.874
40.003
40.180
40.585
41.195
BMDlOPctAdd
(ppm)
346
289
345
349
349
351
370
382
BMDLlOPctAdd
(ppm)
226
198
225
227
224
222
275
290
Basis for model selection
Multistage 3° model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection.
a Selected model in bold; scaled residuals for selected model for doses 0,100, 200, 400, and 600 ppm were 0, -0.2, -0.56, 0.54,
-0.18, respectively.
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Multistage Model, with BMP of 1O% Added Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
19:16 12/O9 2O15
Figure_Apx P-3 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Multistage
3°) for Vacuolization of Centrilobular Hepatocytes in Male Rats Exposed to 1-BP Via Inhalation in ppm;
BMR 10% Added Risk.
Multistage Model. (Version: 3.4; Date: 05/02/2014)
The form of the probability function is: P[response] = background + (l-background)*[l-EXP(-
betal*doseAl-beta2*doseA2...)]
Benchmark Dose Computation.
BMR = 10% Added risk
BMD = 345.704
BMDL at the 95% confidence level = 226.133
Parameter Estimates
Variable
Background
Beta(l)
Beta(2)
Beta(3)
Estimate
0
0
0
2.5502E-09
Default Initial
Parameter Values
0
0
1.4788E-06
0
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-17.6
-18.09
-27.52
# Pa ram's
5
1
1
Deviance
0.986987
19.8363
Test d.f.
4
4
p-value
0.91
0
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AIC: = 38.1894
Goodness of Fit Table
Dose
0
100
200
400
600
Est. Prob.
0
0.0025
0.0202
0.1506
0.4235
Expected
0
0.038
0.303
2.259
6.353
Observed
0
0
0
3
6
Size
15
15
15
15
15
Scaled Resid
0
-0.2
-0.56
0.54
-0.18
ChiA2 = 0.67 d.f = 4 p-value = 0.9552
P-2-3 Increased Incidence of Vacuolization of
Centrilobular Hepatocytes in Females
Increased incidence of vacuolization of centrilobular hepatocytes was observed in females of
the Fo generation of the reproductive and developmental study by WIL Laboratories (2001).
Dichotomous models were used to fit dose response data. A BMR of 10% added risk was
choosen per EPA Benchmark Dose Technical Guidance (U.S. EPA, 2012a). The doses and
response data used for the modeling are presented in Table_Apx P-8.
Table_Apx P-8 Incidence of Vacuolization of Centrilobular Hepatocytes Selected for Dose-Response
Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
25
25
Incidence
0
0
0
6
16
The BMD modeling results for vacuolization of centrilobular hepatocytes are summarized in
Table_Apx P-9. The best fitting model was the LogProbit based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure_Apx P-4. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
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Table_Apx P-9 BMD Modeling Results for Vacuolization of Centrilobular Hepatocytes in Female F0 Rats
Following Inhalation Exposure to 1-BP in a Two-Generation Study
Model"
Log Pro bit
Gamma
LogLogistic
Weibull
Probit
Logistic
Multistage 2°
Quantal-Linear
Goodness of fit
p-value
0.988
0.965
0.945
0.879
0.826
0.661
0.410
0.0134
AIC
64.438
64.648
64.843
65.283
65.496
66.491
68.583
80.285
BMDlOPctAdd
(ppm)
415
416
415
411
423
431
279
153
BMDLlOPctAdd
(ppm)
322
320
320
310
335
347
228
109
Basis for model selection
LogProbit model was selected
based on the lowest AIC, highest
goodness of fit p-value and
adequate fit by visual inspection.
a Selected model in bold; scaled residuals for selected model for doses 0,100, 250, 500, and 750 ppm were 0, 0, -0.29, 0.19,
-0.11, respectively.
LogProbit Model, with BMP of 1O% Added Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
LogProbit
17:56 12/092015
Figure_Apx P-4 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (LogLogistic)
for Vacuolization of Centrilobular Hepatocytes in Female Rats Exposed to 1-BP Via Inhalation in ppm;
BMR 10% Added Risk.
Probit Model. (Version: 3.3; Date: 2/28/2013)
The form of the probability function is: P[response] = Background + (1-Background) *
CumNorm(lntercept+Slope*Log(Dose)),where CumNorm(.) is the cumulative normal
distribution function
Slope parameter is not restricted
Benchmark Dose Computation.
BMR = 10% Added risk
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BMD = 415.388
BMDL at the 95% confidence level = 322.058
Parameter Estimates
Variable
background
intercept
slope
Estimate
0
-1.8305E+01
2.82354
Default Initial
Parameter Values
0
-7.9627E+00
1.1917
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-30.11
-30.22
-58.16
# Pa ram's
5
2
1
Deviance
0.213311
56.0935
Test d.f.
3
4
p-value
0.98
<.0001
AIC: = 64.4382
Goodness of Fit Table
Dose
0
100
250
500
750
Est. Prob.
0
0
0.0033
0.2242
0.6505
Expected
0
0
0.083
5.605
16.263
Observed
0
0
~ °^L
6
16
Size
25
25
25
25
25
Scaled Resid
0
0
-0.29
0.19
-0.11
ChiA2 = 0.13 d.f = 3 p-value = 0.9879
P-2-4 Increased Incidence of Renal Pelvic Mineralization
in Males
Increased incidence of renal pelvic mineralization was observed in males of the Fo generation of
the reproductive and developmental study by WIL Laboratories (2001). Dichotomous models
were used to fit dose response data. A BMR of 10% added risk was choosen per EPA Benchmark
Dose Technical Guidance (U.S. EPA, 2012a). The doses and response data used for the modeling
are presented in Table_Apx P-10.
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Table_Apx P-10 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for
1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
25
25
Incidence
1
0
1
2
6
The BMD modeling results for vacuolization of renal pelvic mineralization are summarized
inTable_Apx P-ll. The best fitting model was the Multistage 3° based on Akaike information
criterion (AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher
value indicates a better fit) and visual inspection. For the best fitting model a plot of the model
is shown in Figure_Apx P-5. The model version number, model form, benchmark dose
calculation, parameter estimates and estimated values are shown below.
Table_Apx P-ll BMD Modeling Results for Renal Pelvic Mineralization in Male F0 Rats Following
Inhalation Exposure to 1-BP in a Two-Generation Study
Model"
Multistage 3°
Multistage 2°
Logistic
Probit
Weibull
LogLogistic
Gamma
LogProbit
Quantal-Linear
Goodness of fit
p-value
0.789
0.668
0.629
0.567
0.603
0.602
0.597
0.597
0.326
AIC
63.835
64.258
64.260
64.488
65.825
65.835
65.856
65.894
66.496
BMDlOPctAdd
(ppm)
571
527
545
526
581
579
575
577
507
BMDLlOPctAdd
(ppm)
386
368
434
408
375
371
371
355
284
Basis for model selection
Multistage 3° model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection
a Selected model in bold; scaled residuals for selected model for doses 0,100, 250, 500, and 750 ppm were 0.6, -0.76, 0.26,
-0.18, 0.07, respectively.
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Multistage Model, with BMP of 1O% Added Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
19:O3 12/O9 2O15
Figure_Apx P-5 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Multistage
3°) for Renal Pelvic Mineralization in Male Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10%
Added Risk.
Multistage Model. (Version: 3.4; Date: 05/02/2014)
The form of the probability function is: P[response] = background + (l-background)*[l-EXP(-
betal*doseAl-beta2*doseA2...)]
Benchmark Dose Computation.
BMR = 10% Added risk
BMD = 571.342
BMDL at the 95% confidence level = 385.532
Parameter Estimates
Variable
Background
Beta(l)
Beta(2)
Beta(3)
Estimate
0.0222219
0
0
5.7848E-10
Default Initial
Parameter Values
0.00963337
0
0
5.8917E-10
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-29.14
-29.92
-34.85
# Pa ram's
5
2
1
Deviance
1.5483
11.4055
Test d.f.
3
4
p-value
0.67
0.02
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AIC: = 63.8352
Goodness of Fit Table
Dose
0
100
250
500
750
Est. Prob.
0.0222
0.0228
0.031
0.0904
0.234
Expected
0.556
0.57
0.776
2.261
5.849
Observed
1
0
1
2
6
Size
25
25
25
25
25
Scaled Resid
0.6
-0.76
0.26
-0.18
0.07
ChiA2 = 1.05 d.f = 3 p-value = 0.7887
P-2-5 Increased Incidence of Renal Pelvic Mineralization
in Females
Increased incidence of renal pelvic mineralization was observed in females of the Fo generation
of the reproductive and developmental study by WIL Laboratories (2001). Dichotomous models
were used to fit dose response data. A BMR of 10% added risk was choosen per EPA Benchmark
Dose Technical Guidance (U.S. EPA, 2012a). The doses and response data used for the modeling
are presented in Table_Apx P-12.
Table_Apx P-12 Incidence of Renal Pelvic Mineralization Selected for Dose-Response Modeling for
1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
24
25
Incidence
2
3
5
12
14
The BMD modeling results for vacuolization of renal pelvic mineralization are summarized in
Table_Apx P-13. The best fitting model was the LogProbit based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. For the best fitting model a plot of the model is
shown in Figure_Apx P-6. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
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Table_Apx P-13 BMD Modeling Results for Renal Pelvic Mineralization in Female F0 Rats Following
Inhalation Exposure to 1-BP in a Two-Generation Study
Model"
Probit
Quantal-Linear
Logistic
LogProbit
LogLogistic
Gamma
Weibull
Multistage 2°
Goodness of fit
p-value
0.708
0.703
0.664
0.735
0.728
0.683
0.662
0.610
AIC
130.24
130.32
130.43
131.49
131.51
131.63
131.70
131.86
BMDlOPctAdd
(ppm)
212
113
228
195
187
182
174
164
BMDLlOPctAdd
(ppm)
174
79.3
186
70.4
69.9
82.8
82.5
81.6
Basis for model selection
Probit model was selected based
on the lowest AIC, highest
goodness of fit p-value and
adequate fit by visual inspection.
a Selected model in bold; scaled residuals for selected model for doses 0,100, 250, 500, and 750 ppm were -0.17, -0.15, -0.16,
0.99, -0.58, respectively.
Probit Model, with BMP of 1O% Added Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
18:44 12/092015
Figure_Apx P-6 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Probit) for
Renal Pelvic Mineralization in Female Rats Exposed to 1-BP Via Inhalation in ppm; BMR 10% Added
Risk.
Probit Model. (Version: 3.3; Date: 2/28/2013)
The form of the probability function is: P[response] = CumNorm(lntercept+Slope*Dose), where
CumNorm(.) is the cumulative normal distribution function
Slope parameter is not restricted
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Benchmark Dose Computation.
BMR = 10% Added risk
BMD = 212.127
BMDL at the 95% confidence level = 174.256
Parameter Estimates
Variable
background
intercept
slope
Estimate
n/a
-1.3432E+00
0.00218661
Default Initial
Parameter Values
0
-1.3433E+00
0.00218429
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-62.44
-63.12
-74.7
# Pa ram's
5
2
1
Deviance
1.36613
24.5328
Test d.f.
3
4
p-value
0.71
<.0001
AIC: = 130.239
Goodness of Fit Table
Dose
0
100
250
500
750
Est. Prob.
0.0896
0.1304
0.2129
0.4013
0.6167
Expected
2.24
3.26
5.321
9.632
15.417
Observed
2
3
5
12
14
Size
25
25
25
24
25
Scaled Resid
-0.17
-0.15
-0.16
0.99
-0.58
ChiA2 = 1.39 d.f = 3 p-value = 0.7082
P-2-6 Decreased Seminal Vesicle Weight
Decreased relative and absolute seminal vesicle weights were observed in (Ichihara et al.,
2000b). Continuous models were used to fit dose-response data for both absolute and relative
seminal vesicle weights. A BMR 1 standard deviation was choosen per EPA Benchmark Dose
Technical Guidance (U.S. EPA, 2012a). Both absolute and relative organ weights may be relevant
for reproductive organs like the seminal vesicle as described in EPA's Guidelines for
Reproductive Toxicity Risk Assessment (U.S. EPA, 1996). In this case by coincidence the BMDL
was the same (38 ppm) for both absolute and relative seminal vesicle weights and therefore
this endpoint is refered to as absolute/relative seminal vesicle weight in Table 3- and the
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following text and tables. The doses, response data and BMD modeling results are presented
for relative and then absolute seminal vesicle weights below.
P-2-6-1 Decreased Relative Seminal Vesicle Weight
r-^-\j-i t»c«,icd3cu ixciauve semmai vesicie weigni
The doses and response data used for relative seminal vesicle weight are presented in
Table_ApxP-14.
P-14 Relative Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP
(ppm) Number of animals Relative Weight (mg/g BW) Standard Deviation
Table_Apx
Dose (ppm)
0
4.35
0.62
200
3.23
0.55
400
3.17
0.67
800
2.62
0.87
Comparisons of model fits obtained are provided in Table_Apx P-15. Models with
homogeneous variance were used because the BMDS Test 2 p-value was 0.543. The Hill model
was excluded because the BMD to BMDL ratio was 7.34. Of the remaining models the best
fitting model (Exponential (M4)) was selected based on Akaike information criterion (AIC; lower
values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a better
fit) and visual inspection. The Exponential (M4) model had an acceptable BMD to BMDL ratio of
3.2 and is indicated in bold. For the best fitting model a plot of the model is shown in
Figure_Apx P-7. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
Table_Apx P-15 Summary of BMD Modeling Results for Relative Seminal Vesicle Weight in Rats
Exposed to 1-BP by Inhalation
Model"
Hill
Exponential (M4)
Exponential (M5)b
Exponential (M2)
Exponential (M3)c
Powerd
Polynomial 2°e
Linear'
Polynomial 3°g
Goodness of fit
p-value
0.298
0.221
0.107
0.0604
0.0604
AIC
13.857
14.274
15.240
16.386
16.386
BMDioRD
(ppm)
57.2
73.1
170
213
213
BMDLioRD
(ppm)
6.72
21.4
123
165
165
BMDisD
(ppm)
101
124
301
376
376
BMDLiso
(ppm)
13.7
38.1
199
267
267
Basis for model
selection
For models with
BMD to BMDL
ratios less than 5
(this excludes the
Hill model), the
Exponential (M4)
model was
selected based on
the lowest AIC,
highest goodness
of fit p-value and
adequate fit by
visual inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.543), selected model in bold; scaled residuals for selected model
for doses 0, 200, 400, and 800 ppm were 0.15, -0.68, 0.92, -0.37, respectively.
b For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M4) model.
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c For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
d For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f The Linear model may appear equivalent to the Polynomial 3° model, however differences exist in digits not displayed in the
table.
g The Polynomial 3° model may appear equivalent to the Power model, however differences exist in digits not displayed in the
table. This also applies to the Polynomial 2° model. This also applies to the Linear model.
Exponential 4 Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
5OO 6OO
1O:24 1O/3O 2O15
Figure_Apx P-7 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model
with Constant Variance for Relative Seminal Vesicle Weight; BMR = 1 Standard Deviation Change from
Control Mean.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * [c-(c-l) * exp(-b * dose)]
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1.0000 Estimated standard deviations from control
BMD = 123.644
BMDL at the 95% confidence level = 38.1407
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Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
-0.820732
n/a
4.31581
0.00406673
0.611025
n/a
Default Initial
Parameter Values
-0.863617
0
4.5675
0.00345735
0.546303
1
Table of Data and Estimated Values of Interest
Dose
0
200
400
800
N
8
9
9
9
Obs Mean
4.35
3.23
3.17
2.62
Est Mean
4.32
3.38
2.97
2.7
Obs Std Dev
0.62
0.55
0.67
0.87
Est Std Dev
0.66
0.66
0.66
0.66
Scaled Resid
0.1458
-0.6845
0.9177
-0.3705
Likelihoods of Interest
Model
Al
A2
A3
R
4
Log(likelihood)
-2.386703
-1.313327
-2.386703
-13.55019
-3.137185
# Param's
5
8
5
2
4
AIC
14.77341
18.62665
14.77341
31.10038
14.27437
Tests of Interest
Test
Testl
Test 2
Tests
Test 6a
-2*log(Likelihood
Ratio)
24.47
2.147
2.147
1.501
Test df
6
3
3
1
p-value
0.0004272
0.5425
0.5425
0.2205
P-2-6-2 Decreased Absolute Seminal Vesicle Weight
The doses and response data used for the modeling are presented in Table_Apx P-16.
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Table_Apx P-16 Absolute Seminal Vesicle Weight Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
200
400
800
Number of animals
8
9
9
9
Seminal Vesicle Absolute Weight (mg)
1.88
1.38
1.27
1.00
Standard Deviation
0.27
0.26
0.25
0.36
Comparisons of model fits obtained are provided in Table_Apx P-17. Models with
homogeneous variance were used because the BMDS Test 2 p-value was 0.653. The best fitting
model (Hill) was selected based on Akaike information criterion (AIC; lower values indicates a
better fit), chi-square goodness of fit p-value (higher value indicates a better fit) and visual
inspection. The Hill model had an acceptable BMD to BMDL ratio of 2.5 and is indicated in bold.
For the best fitting model a plot of the model is shown in Figure_Apx P-8. The model version
number, model form, benchmark dose calculation, parameter estimates and estimated values
are shown below.
Table_Apx P-17 Summary of BMD Modeling Results for Seminal Vesicle Absolute Weight in Rats
Exposed to 1-BP by Inhalation
Model"
Hill
Exponential (M4)
Exponential (M5)b
Exponential (M2)
Exponential (M3)c
Powerd
Polynomial 3°e
Polynomial 2°f
Linear
Goodness of fit
p-value
0.429
0.337
0.159
0.0576
AIC
-47.533
-47.235
-46.484
-44.450
BMDiso
(ppm)
97.3
112
219
299
BMDLisD
(ppm)
38.4
58.4
152
222
Basis for model selection
The Hill model was selected
based on the lowest AIC, highest
goodness of fit p-value and
adequate fit by visual inspection
a Constant variance case presented (BMDS Test 2 p-value = 0.653), selected model in bold; scaled residuals for selected model
for doses 0, 200, 400, and 800 ppm were 0.07, -0.43, 0.61, -0.24, respectively.
b For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M4) model.
c For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M2)
model.
d For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
e For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
f For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
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Hill Model, with BMP of 1 Std. Dev. for the BMD and 0.95 Lower Confidence Limit for the BMDL
2.2 r Hill
400
dose
14:19 11/3O 2O15
Figure_Apx P-8 Plot of Mean Response by Dose in ppm with Fitted Curve for Hill Model with Constant
Variance for Seminal Vesicle Absolute Weight; BMR = 1 Standard Deviation Change from Control
Mean.
Hill Model. (Version: 2.17; Date: 01/28/2013)
The form of the response function is: Y[dose] = intercept + v*doseAn/(kAn + doseAn)
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1 Estimated standard deviations from the control mean
BMD = 97.2583
BMDL at the 95% confidence level = 38.4029
Parameter Estimates
Variable
alpha
rho
intercept
V
n
k
Estimate
0.0752711
n/a
1.87362
-1.2008
1
328.422
Default Initial
Parameter Values
0.0834806
0
1.88
-0.88
1.5698
176
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Table of Data and Estimated Values of Interest
Dose
0
200
400
800
N
8
9
9
9
Obs Mean
1.88
1.38
1.27
1
Est Mean
1.87
1.42
1.21
1.02
Obs Std Dev
0.27
0.26
0.25
0.36
Est Std Dev
0.27
0.27
0.27
0.27
Scaled Resid
0.0658
-0.428
0.61
-0.244
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
28.078773
28.894036
28.078773
27.766532
13.387326
# Param's
5
8
5
4
2
AIC
-46.157546
-41.788073
-46.157546
-47.533065
-22.774652
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
31.0134
1.63053
1.63053
0.624482
Test df
6
3
3
1
p-value
<0.0001
0.6525
0.6525
0.4294
P-2-7 Decreased Percent Normal Sperm Morphology
Decreased percent normal sperm morphology was observed in the Fo generation of the
reproductive and developmental study by WIL Laboratories (2001). The doses and response
data used for the modeling are presented in Table_Apx P-18.
Table_Apx P-18 Sperm Morphology Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
24
24
% normal
99.7
99.7
99.3
98.2
90.6
Standard Deviation
0.6
0.52
0.83
2.59
8.74
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Comparisons of model fits obtained are provided in Table_Apx P-19. The best fitting model
(Exponential (M2) with homogeneous variance because the BMDS Test 2 p-value was 0.144)
was selected based on Akaike information criterion (AIC; lower values indicates a better fit), chi-
square goodness of fit p-value (higher value indicates a better fit) and visual inspection. The
best-fitting model is indicated in bold. For the best fitting model a plot of the model is shown in
Figure_Apx P-9. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
Table_Apx P-19 Summary of BMD Modeling Results for Sperm Morphology in the F0 Generation
Exposed to 1-BP by Inhalation
Model"
Exponential (M2)
Exponential (M3)b
Power0
Polynomial 3°d
Polynomial 2°e
Linear
Exponential (M4)
Hill
Exponential (M5)
Goodness of fit
p-value
0.787
0.780
0.534
N/Af
N/Af
AIC
-401.21
-401.19
-399.30
-397.69
-397.69
BMDiso
(ppm)
472
473
459
482
463
BMDLiso
(ppm)
327
331
230
124
112
Basis for model selection
The Exponential (M2) model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.144), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, and 500 ppm were -0.05, 0.39, -0.53, 0.19, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f No available degrees of freedom to calculate a goodness of fit value.
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Exponential 2 Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
Exponential 2
09:56 10/302015
Figure_Apx P-9 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M2) Model
with Constant Variance for Sperm Morphology in F0 Rats Exposed to 1-BP by Inhalation; BMR = 1
Standard Deviation Change from Control Mean.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * exp(sign * b * dose)
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1.0000 Estimated standard deviations from control
BMD = 471.627
BMDL at the 95% confidence level = 326.935
Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
-5.07205
n/a
1.97082
0.0000869453
n/a
n/a
Default Initial
Parameter Values
-5.07685
0
1.89939
0.000086769
0
1
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
25
25
25
25
Obs Mean
1.97
1.96
1.92
1.89
Est Mean
1.97
1.95
1.93
1.89
Obs Std Dev
0.08
0.07
0.07
0.1
Est Std Dev
0.08
0.08
0.08
0.08
Scaled Resid
-0.05174
0.3941
-0.5332
0.1908
Likelihoods of Interest
Model
Al
A2
A3
R
2
Log(likelihood)
203.8426
206.5452
203.8426
196.2377
203.6027
# Param's
5
8
5
2
3
AIC
-397.6852
-397.0903
-397.6852
-388.4753
-401.2054
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
20.62
5.405
5.405
0.4799
Test df
6
3
3
2
p-value
0.002151
0.1444
0.1444
0.7867
P-2-8 Decreased Percent Motile Sperm
A decrease in motile sperm was observed in the Fo generation in the reproductive and
developmental study by WIL Laboratories (2001). The doses and response data used for the
modeling are presented in Table_Apx P-20.
Table_Apx P-20 Sperm Motility Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
23
15
Mean sperm motility (% motile)
86.8
88.8
83.4
71.9
53.2
Standard Deviation
11.90
7.22
10.41
9.27
19.59
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The BMD modeling results for sperm motility with non-homogeneous variance (BMDS test 2 p-
value = 0.0001749) are summarized in Table_Apx P-21. Although the means are sufficiently fit
for some models (e.g. the Polynomial 2° model has p-value of 0.516) the variances are not well
modeled BMDS Test 3 p-value = 0.0426. This result suggests that due to the poor variance
modeling for the data it is not reasonable to use BMDS for this endpoint. Instead the NOAEL of
250 ppm was used.
Table_Apx P-21 BMD Modeling Results for Sperm Motility F0 Male Rats Following Inhalation Exposure
to 1-BP
Model"
Polynomial 2°
Power
Polynomial 3°
Exponential (M3)
Hill
Polynomial 4°
Exponential (M5)
Linear
Exponential (M2)
Exponential (M4)b
Goodness of fit
p-value
0.516
0.334
0.330
0.324
0.139
0.137
0.133
0.00132
2.10E-04
AIC
657.83
659.73
659.76
659.80
661.73
661.76
661.80
671.22
675.10
BMDiso
(ppm)
386
399
397
402
400
397
402
237
226
BMDLisD
(ppm)
346
313
315
317
323
314
317
192
178
Basis for model selection
Due to unacceptable fitting of
the variances no model was
selected.
a Modeled variance case presented (BMDS Test 2 p-value = 1.75E-04, BMDS Test 3 p-value = 0.0426), no model was selected
as a best-fitting model.
b For the Exponential (M4) model, the estimate of c was 0 (boundary). The models in this row reduced to the Exponential (M2)
model.
To investigate the effect of the poor modeling of the variances on the BMDL the observed
standard deviations were considered and the standard deviation at the highest dose is much
larger than at the other dose groups. The data set was investigated with the highest dose
dropped. The model fits with non-homogeneous variance (BMDS test 2 p-value = 0.0966) are
summarized in Table_Apx P-22. Although the means are sufficiently fit for some models (e.g. the
Polynomial 2° model has p-value of 0.676) the variances are not well modeled BMDS Test 3 p-
value = 0.0426.
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Table_Apx P-22 BMD Modeling Results for Sperm Motility F0 Male Rats Following Inhalation Exposure
to 1-BP with the Highest Dose Dropped
Model"
Polynomial 3°
Polynomial 2°
Hill
Exponential (M3)
Power
Exponential (M5)
Linear
Exponential (M2)c
Exponential (M4)d
Polynomial 4°
Goodness of fit
p-value
0.676
0.676
0.529
0.386
0.376
N/Ab
0.107
0.0743
0.0743
error
AIC
551.25
551.25
552.86
553.22
553.25
554.86
554.94
555.67
555.67
error
BMDiso
(ppm)
394
394
271
391
395
267
315
310
310
error6
BMDLiso
(ppm)
345
302
255
294
296
253
^241 ^
231
231
error6
Basis for model selection
Due to unacceptable fitting of
the variances no model was
selected.
a Modeled variance case presented (BMDS Test 2 p-value = 0.0966, BMDS Test 3 p-value = 0.0426), no model was selected as a
best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
c The Exponential (M2) model may appear equivalent to the Exponential (M4) model, however differences exist in digits not
displayed in the table.
d The Exponential (M4) model may appear equivalent to the Exponential (M2) model, however differences exist in digits not
displayed in the table.
e BMD or BMDL computation failed for this model.
P-2-9 Decreased Left Cauda Epididymis Weight
A decrease in left cauda epididymis absolute weight was observed in the Fo generation in the
reproductive and developmental study by (WIL Research, 2001). The absolute weights are used
for BMD modeling of the epididymis as described in EPA's Guidelines for Reproductive Toxicity
Risk Assessment (U.S. EPA, 1996). The doses and response data used for the modeling are
presented in Table_Apx P-23.
Table_Apx P-23 Left Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling
for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
23
22
Left Cauda Epididymis Weight (mg)
0.3252
0.3242
0.3050
0.2877
0.2401
Standard Deviation
0.03673
0.03149
0.03556
0.03170
0.03529
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The BMD modeling results for left cauda epididymis absolute weight with homogeneous
variance (BMDS test 2 p-value =0.911) are summarized in Table_Apx P-24. The best fitting
model (Polynomial 4°) was selected based on Akaike information criterion (AIC; lower values
indicates a better fit), chi-square goodness of fit p-value (higher value indicates a better fit) and
visual inspection. The Polynomial 4° model had an acceptable BMD to BMDL ratio of 1.4 and is
indicated in bold. For the best fitting model a plot of the model is shown in Figure_Apx P-10.
The model version number, model form, benchmark dose calculation, parameter estimates and
estimated values are shown below.
Table_Apx P-24 BMD Modeling Results for Left Cauda Epididymis Absolute Weight F0 Male Rats
Following Inhalation Exposure to 1-BP
Model"
Polynomial 4°
Polynomial 3°
Polynomial 2°
Power
Exponential (M3)
Linear
Hill
Exponential (M5)
Exponential (M2)
Exponential (M4)
Goodness of fit
p-value
0.622
0.565
0.47
0.430
0.382
0.133
0.193
0.166
0.0636
0.0636
AIC
-714.88
-714.69
-714.32
-714.14
-713.91
-712.23
-712.14
-711.91
-710.55
-710.55
BMDiso
(ppm)
438
440
437
444
446
307
444
446
289
289
BMDLisD
(ppm)
313
316
315
317
320
256
317
320
236
235
Basis for model selection
The Polynomial 4° model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.911), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, 500, and 750 ppm were -0.21, 0.64, -0.65, 0.26, -0.04, respectively.
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Polynomial Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
12:36 11/3O 2O15
Figure_Apx P-10 Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model
with Constant Variance for Left Cauda Epididymis Absolute Weight; BMR = 1 Standard Deviation
Change from Control Mean.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose + beta_2*doseA2 + ...
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1 Estimated standard deviations from the control mean
BMD = 438.482
BMDL at the 95% confidence level = 313.325
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
beta_2
beta_3
beta_4
Estimate
0.00113284
n/a
0.326617
-0.0000672194
0
-6.09563E-33
-1.13164E-13
Default Initial
Parameter Values
0.0011711
0
0.3252
0
-0.00000139519
0
-2.44944E-12
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
750
N
25
25
25
25
25
Obs Mean
0.32
0.32
0.3
0.29
0.24
Est Mean
0.33
0.32
0.31
0.29
0.24
Obs Std Dev
0.04
0.03
0.04
0.03
0.04
Est Std Dev
0.03
0.03
0.03
0.03
0.03
Scaled Resid
-0.21
0.641
-0.649
0.262
-0.044
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
361.914605
362.410744
361.914605
361.438986
322.608827
# Param's
6
10
6
4
2
AIC
-711.829209
-704.821488
-711.829209
-714.877972
-641.217655
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
79.6038
0.992278
0.992278
0.951238
Test df
8
4
4
2
p-value
<0.0001
0.911
0.911
0.6215
P-2-10 Decreased Right Cauda Epididymis Weight
A decrease in right cauda epididymis absolute weight was observed in the Fo generation in the
reproductive and developmental study by (WIL Research, 2001). The absolute weights are used
for BMD modeling of the epididymis as described in EPA's Guidelines for Reproductive Toxicity
Risk Assessment (U.S. EPA, 1996). The doses and response data used for the modeling are
presented in Table_Apx P-25.
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Table_Apx P-25 Right Cauda Epididymis Absolute Weight Data Selected for Dose-Response Modeling
for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
23
22
Left Cauda Epididymis Weight (mg)
0.3327
0.3311
0.3053
0.2912
0.2405
Standard Deviation
0.03631
0.04453
0.04188
0.05206
0.04804
The BMD modeling results for right cauda epididymis absolute weight with homogeneous
variance (BMDS test 2 p-value =0.455) are summarized in Table_Apx P-26. The best fitting
model (Polynomial 4°) was selected based on Akaike information criterion (AIC; lower values
indicates a better fit), chi-square goodness of fit p-value (higher value indicates a better fit) and
visual inspection. The Polynomial 4° model had an acceptable BMD to BMDL ratio of 1.4 and is
indicated in bold. For the best fitting model a plot of the model is shown in Figure_Apx P-ll.
The model version number, model form, benchmark dose calculation, parameter estimates and
estimated values are shown below.
Table_Apx P-26 BMD Modeling Results for Right Cauda Epididymis Absolute Weight F0 Male Rats
Following Inhalation Exposure to 1-BP
Model"
Polynomial 4°
Polynomial 3°
Linear
Polynomial 2°
Power
Exponential (M3)
Exponential (M2)
Exponential (M4)
Hill
Exponential (M5)
Goodness of fit
p-value
0.493
0.442
0.296
0.376
0.340
0.304
0.196
0.196
0.142
0.123
AIC
-646.60
-646.38
-646.32
-646.06
-645.86
-645.63
-645.33
-645.33
-643.85
-643.63
BMDiso
(ppm)
485
480
371
472
474
473
350
350
474
473
BMDLisD
(ppm)
338
334
303
327
323
317
277
270
323
317
Basis for model selection
The Polynomial 4° model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.455), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, 500, and 750 ppm were -0.09, 0.63, -0.9, 0.44, -0.08, respectively.
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Polynomial Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
12:13 11/3O 2O15
Figure_Apx P-ll Plot of Mean Response by Dose in ppm with Fitted Curve for Polynomial 4° Model
with Constant Variance for Right Cauda Epididymis Absolute Weight; BMR = 1 Standard Deviation
Change from Control Mean.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose + beta_2*doseA2 + ...
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1 Estimated standard deviations from the control mean
BMD = 484.978
BMDL at the 95% confidence level = 338.42
^L
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
beta_2
beta_3
beta_4
Estimate
0.00195609
n/a
0.333498
-0.0000793692
-2.2991E-28
-2.18866E-31
-1.03676E-13
Default Initial
Parameter Values
0.00201467
0
0.3327
0
-0.00000198872
0
-3.6281E-12
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
750
N
25
25
25
25
25
Obs Mean
0.33
0.33
0.3
0.29
0.24
Est Mean
0.33
0.33
0.31
0.29
0.24
Obs Std Dev
0.04
0.04
0.04
0.05
0.05
Est Std Dev
0.04
0.04
0.04
0.04
0.04
Scaled Resid
-0.0902
0.627
-0.899
0.437
-0.0754
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
328.007576
329.833395
328.007576
327.300407
299.119376
# Param's
6
10
6
4
2
AIC
-644.015151
-639.66679
-644.015151
-646.600813
-594.238753
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
61.428
3.65164
3.65164
1.41434
Test df
8
4
4
2
p-value
<0.0001
0.4552
0.4552
0.493
P-2-11 Increased Estrus Cycle Length
An increase estrus cycle length was observed in the Fo generation in the reproductive and
developmental study by (WIL Research, 2001). The doses and response data used for the
modeling are presented in Table_Apx P-27.
Table_Apx P-27 Estrus Cycle Length Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
23
22
Estrus cycle Length (days)
4.2
4.5
4.7
5.5
5.6
Standard Deviation
0.49
1.05
0.9
2.17
1.79
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The BMD modeling results for estrus cycle length with non-homogeneous variance (BMDS test 2
p-value = <0.0001) are summarized in Table_Apx P-28. The means are not adequately fit for any
of the models as shown by the goodness of fit where the model with the highest p-value is
0.0065 for the Exponential M4 and M5 models (excluding the Hill model because a BMDL could
not be calculated). This result suggests that due to the poor model fit to the data it is not
reasonable to use BMDS for this endpoint. Instead the NOAEL of 250 ppm was used.
Table_Apx P-28 BMD Modeling Results for Estrus Cycle Length F0 Female Rats Following Inhalation
Exposure to 1-BP
Model"
Hill
Exponential (M4)
Exponential (M5)c
Powerd
Polynomial 4°e
Polynomial 3°f
Polynomial 2°g
Linear
Exponential (M2)
Exponential (M3)h
Goodness of fit
p-value
0.00656
0.00650
0.00169
7.68E-04
AIC
160.04
160.05
163.13
164.81
BMDiso
(ppm)
145
157
300
344
BMDLisD
(ppm)
errorb
79.5
205
244
Basis for model selection
Due to inadequate fit of the
models to the data means
(shown by the goodness of fit p-
value) no model was selected.
a Modeled variance case presented (BMDS Test 2 p-value = <0.0001, BMDS Test 3 p-value = 0.506), no model was selected as a
best-fitting model.
b BMD or BMDL computation failed for this model.
c For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
d For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
e For the Polynomial 4° model, the b4 and b3 coefficient estimates were 0 (boundary of parameters space). The models in this
row reduced to the Polynomial 2° model. For the Polynomial 4° model, the b4, b3, and b2 coefficient estimates were 0
(boundary of parameters space). The models in this row reduced to the Linear model.
f For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
g For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
h For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
P-2-12 Decreased Antral Follical Count
A decreased antral follicle count was observed in the study of female reproductive function by
(Yamada et al., 2003). The doses and response data used for the modeling are presented in
Table_Apx P-29. The highest dose was not included for modeling because all the rats in the
highest dose group (800 ppm) were seriously ill and were sacrificed during the 8th week of the
12 week study.
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Table_Apx P-29 Antral Follicle Count Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
200
400
Number of animals
8
9
9
Antral Follicle Count
30.1
12.6
7.44
Standard Deviation
22.4
4.82
6.52
The BMD modeling results for antral follical count with non-homogeneous variance (BMDS test
2 p-value = <0.0001) are summarized in Table_Apx P-30. The means are not adequately fit for
any of the models as shown by the goodness of fit where the model with the highest p-value is
0.0404 for the Exponential M2 model. This result suggests that due to the poor model fit to the
data it is not reasonable to use BMDS for this endpoint. Instead the LOAEL of 200 ppm was used.
Table_Apx P-30 BMD Modeling Results for Antral Follical Count in Female Rats Following Inhalation
Exposure to 1-BP
Model"
Exponential (M4)
Exponential (M2)
Power0
Lineard
Polynomial 2°e
Exponential (M3)
Goodness of fit
p-value
N/Ab
0.0404
0.00496
0.00496
N/Ab
AIC
148.31
150.51
154.21
154.21
179.12
BMDiso
(ppm)
189
270
410
410
1.8E+05
BMDLiso
(ppm)
0.651
117^
233
233
754
Basis for model selection
Due to inadequate fit of the
models to the data means
(shown by the goodness of fit p-
value) no model was selected.
a Modeled variance case presented (BMDS Test 2 p-value = <0.0001, BMDS Test 3 p-value = 0.0545), no model was selected as
a best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d The Linear model may appear equivalent to the Polynomial 2° model, however differences exist in digits not displayed in the
table.
e The Polynomial 2° model may appear equivalent to the Power model, however differences exist in digits not displayed in the
table. This also applies to the Linear model.
P-2-13 Decreased Male and Female Fertility Index
A decrease in the male and female fertility index was observed in the Fo generation in the
reproductive and developmental study by WIL Laboratories (2001). The doses and response
data are presented in Table_Apx P-31 as a percentage and incidence. The incidence represents
the number of males that did not sire a litter which is equal to the number of nongravid
females. The incidence was used for modeling as a dichotomous endpoint.
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Table_Apx P-31 Fertility Index Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
750
Number of animals
25
25
25
23
22
Fertility Index (%)
92
100
88
52
0
Number Nongravid Females = Males
that did not Sire a Litter
2
0
3
12
25
The BMD modeling results for the fertility index are summarized in Table_Apx P-32. The best
fitting models were the LogLogistic and Dichotomous-Hill based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. Dichotomous-Hill model had a warning about the
BMDL computation and the LogLogistic model did not so the LogLogistic model was selected.
The LogLogistic and Dichotomous-Hill models had nearly the same BMDLs with LogLogistic
slightly lower (356 ppm) than Dichotomous-Hill (363 ppm). For the best fitting model a plot of
the model is shown in Figure_Apx P-12. The model version number, model form, benchmark
dose calculation, parameter estimates and estimated values are shown below.
Table_Apx P-32 BMD Modeling Results for Fertility Index of F0 Rats Following Inhalation Exposure of
Parental Rats to 1-BP in a Two-Generation Study
Model"
LogLogistic
Dichotomous-Hill
Multistage 4°
Weibull
Gamma
LogProbit
Multistage 3°
Logistic
Probit
Multistage 2°
Quantal-Linear
Goodness of fit
p-value
0.388
0.388
0.355
0.253
0.256
0.223
0.161
0.0103
0.0031
0.0152
0
AIC
75.396
75.396
75.682
77.024
77.045
77.357
78.153
80.981
82.358
85.979
106.73
BMDiopct
(ppm)
448
448
306
361
361
461
250
238
208
173
68.4
BMDLiopct
(ppm)
356
363
219
252
260
352
202
182
159
143
52.1
Basis for model selection
The Dichotomous-Hill and
LogLogistic models had the
lowest AIC, highest goodness of
fit p-value and adequate fit by
visual inspection. The
Dichotomous-Hill model had a
warning about the BMDL
computation and the LogLogistic
model did not so the LogLogistic
model was selected.
a Selected model in bold; scaled residuals for selected model for doses 0,100, 250, 500, and 750 ppm were 0.27, -1.34,1.07,
-0.01, 0.14, respectively.
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Log-Logistic Model, with BMP of 1O% Extra Risk for the BMD and O.95 Lower Confidence Limit for the BMDL
17:13 12/O3 2O15
Figure_Apx P-12 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(LogLogistic) for Fertility Index in Rats Exposed to 1-BP Via Inhalation in ppm BMR 10% Extra Risk.
Logistic Model. (Version: 2.14; Date: 2/28/2013)
The form of the probability function is: P[response] = background+(l-background)/[l+EXP(-
intercept-slope*Log(dose))]
Slope parameter is restricted as slope >= 1
%
Benchmark Dose Computation.
BMR = 10% Extra risk
BMD = 448.13
BMDL at the 95% confidence level = 356.183
Parameter Estimates
Variable
background
intercept
slope
Estimate
0.0666427
-1.1209E+02
18
Default Initial
Parameter Values
0.08
-2.1668E+01
3.62868
Analysis of Deviance Table
Model
Full model
Fitted model
Reduced
model
Log(likelihood)
-33.45
-35.7
-79.79
# Pa ram's
5
2
1
Deviance
4.4943
92.6846
Test d.f.
3
4
p-value
0.21
<.0001
AIC: = 75.3964
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Goodness of Fit Table
Dose
0
100
250
500
750
Est. Prob.
0.0666
0.0666
0.0666
0.4809
0.9992
Expected
1.666
1.666
1.666
12.022
24.98
Observed
2
0
3
12
25
Size
25
25
25
25
25
Scaled Resid
0.27
-1.34
1.07
-0.01
0.14
ChiA2 = 3.02 d.f = 3 p-value = 0.3884
P-2-14 Decreased Implantations Sites
A decrease in the number of implantations sites was observed in the Fo generation in the
reproductive and developmental study by (WIL Research, 2001). The doses and response data
used for modeling are presented in Table_Apx P-33. The highest dose group was not included
because none of the dams had implantations sites.
Table_Apx P-33 Implantations Site Data Selected for Dose-Response Modeling for 1-BP
Dose (ppm)
0
100
250
500
Number of animals
23
^ 25
22
11
Average Numer of Sites
15.3
14.3
13.8
9.0
Standard Deviation
2.53
3.09
4.23
4.54
The BMD modeling results for the number of implantations sites are summarized in Table_Apx
P-34. The best fitting models were the Linear and Power based on Akaike information criterion
(AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher value
indicates a better fit) and visual inspection. Based on the parameter estimate for the Power
model it reduced to the Linear, so the Linear model was selected. For the best fitting model a
plot of the model is shown in Figure_Apx P-13. The model version number, model form,
benchmark dose calculation, parameter estimates and estimated values are shown below.
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Table_Apx P-34 BMD Modeling Results for Implantations Sites in F0 Rats Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Linear
Powerb
Exponential (M2)
Exponential (M4)
Polynomial 3°
Polynomial 2°
Hill
Exponential (M3)
Exponential (M5)
Goodness of fit
p-value
0.936
0.901
0.901
0.741
0.724
0.715
0.669
N/AC
AIC
284.66
284.74
284.74
286.64
286.66
286.67
286.71
288.71
BMDsRD
(ppm)
80.8
74.1
74.1
85.5
84.3
80.6
82.3
82.3
BMDLsRD
(ppm)
56.1
48.1
37.3
56.2
56.1
55.8
48.2
48.2
BMDiso
(ppm)
282
270
270
295
289
282
278
278
BMDLiso
(ppm)
188
166
138
188
188
195
167
167
Basis for model selection
Linear and Power
models were selected
based on the lowest AIC,
highest goodness of fit
p-value and adequate fit
by visual inspection.
a Modeled variance case presented (BMDS Test 2 p-value = 0.0493), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were -0.17, -0.23,1, -1, respectively.
b For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
c No available degrees of freedom to calculate a goodness of fit value.
Linear Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
19:50 12/032015
Figure_Apx P-13 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Implantation Sites in Rats Exposed to 1-BP Via Inhalation in ppm BMR 1 Standard Deviation.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose
A modeled variance is fit
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Benchmark Dose Computation.
BMR = 1 Estimated standard deviations from the control mean
BM 0 = 282.359
BMDL at the 95% confidence level = 188.047
Parameter Estimates
Variable
lalpha
rho
beta_0
beta_l
Estimate
12.2915
-3.77194
15.393
-0.00952791
Default Initial
Parameter Values
2.51459
0
15.7286
-0.01237
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
23
25
22
11
Obs Mean
15.3
14.3
13.8
9
Est Mean
15.4
14.4
13
10.6
Obs Std Dev
2.53
3.09
4.23
4.54
Est Std Dev
2.69
3.03
3.69
5.41
Scaled Resid
-0.166
-0.231
1
-0.999
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
-140.289933
-136.366566
-138.26616
-138.332408
-151.740933
# Pa ram's
5
8
6
4
2
AIC "^^
290.579865
288.733132
288.532319
284.664816
307.481866
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2* log( Likelihood
Ratio)
30.7487
7.84673
3.79919
0.132497
Test df
6
3
2
2
p-value
<0.0001
0.04929
0.1496
0.9359
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P-2-15 Decreased Pup Body Weight
Decreased pup body weight was observed in the 2-generation reproductive and developmental
study by (WIL Research, 2001). Statistically significant decreases in pup body weight were noted
for males in the Fi generation at PND 28 and in the F2 generation in both sexes at PNDs 14 and
21. Continuous models were used to fit-dose response data for decreased pup body weights. A
BMR of 5% was used because this is a developmental endpoint (Kavlock et al., 1995). A BMR of
1 standard deviation is also shown for comparison per EPA Benchmark Dose Technical Guidance
(U.S. EPA, 2012a). The doses, response data and BMD modeling results for decreased pup body
weight are presented below at each time point.
P-2-15-1 Decreased Body Weight in Fl Male Pups at PND 28
The doses and response data from the WIL Laboratories (WIL Research, 2001) study were used
for the modeling and are presented in Table_Apx P-35.
Table_Apx P-35 Pup Body Weight Data in Fi Males at PND 28 for Dose-Response Modeling
Number of litters
Mean pup wt (g)
Standard deviation (g)
Concentration (ppm)
0
23
88.1
7.60
100 ^1
24
82.8
7.74
250
21
80.3
9.04
^ 500
10
76.0
9.45
A comparison of the model fits obtained for pup body weight changes is provided in Table_Apx
P-36. The best fitting model was selected based on Akaike information criterion (AIC; lower
values indicates a better fit), visual inspection and comparison with the BMD/BMDLs among the
data for decreased pup weights at other time points. There is a large spread in BMC/L values
among the models and EPA procedures allow for selecting the lowest BMDL is this case (the Hill
model) however the Exponential (M2) was selected because it is in line with the results from
the pup body weight decreases observed at the other time points in this data set. The best-
fitting model is indicated in bold. For the best fitting model a plot of the model is shown in
Figure_Apx P-14. The model version number, model form, benchmark dose calculation,
parameter estimates and estimated values are shown below.
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Table_Apx P-36 BMD Modeling Results for Body Weight of Fi Male Rat Pups on PND 28 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Exponential
(M2)
Exponential
(M3)b
Power0
Polynomial 3°d
Polynomial 2°e
Linear
Hill
Exponential
(M4)
Exponential
(M5)f
Good ness of fit
p-value
0.449
0.406
0.578
0.512
AIC
411.4
6
411.6
6
412.1
7
412.2
9
BMDiso
(ppm)
334.07
345.22
234.74
238.92
BMDLiso
(ppm)
228.77
242.64
85.21
95.80
BMDsRD
(ppm)
174
183
92.2
101
BMDLsRD
(ppm)
123
133
23.2
36.8
Basis for model selection
The Exponential (M2) model
was selected based on the
lowest AIC.
a Constant variance case presented (BMDS Test 2 p-value = 0.785), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, and 500 ppm were 0.77, -0.88, -0.17, 0.44, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential (M4)
model.
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Exponential Model 2, with BMR of 0.05 Rel. Dev. for the BMD and 0.95 Lower Confidence Level for BMDL
90
85
80
75
70
0
Exponential
BMD
BMD
100
400
500
200 300
dose
10:5309/022015
Figure_Apx P-14 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR 5%
Relative Deviation.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * exp(sign * b * dose)
A constant variance model is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 173.561
BMDL at the 95% confidence level = 122.612
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Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
4.19824
n/a
86.7871
0.000295534
n/a
n/a
Default Initial
Parameter Values
4.17769
0
78.9392
0.000288601
0
1
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
23
24
21
10
Obs Mean
88.1
82.8
80.3
76
Est Mean
86.79
84.26
80.61
74.87
Obs Std Dev
7.6
7.74
9.04
9.45
Est Std Dev
8.16
8.16
8.16
8.16
Scaled Resid
0.7717
-0.8765
-0.1719
0.4398
Likelihoods of Interest
Model
Al
A2
A3
R
2
Log(likelihood)
-201.9297
-201.395
-201.9297
-210.4356
-202.7313
# Param's
5
8
5
2
3
AIC
413.8595
418.7901
413.8595
424.8712
411.4626
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood Ratio)
18.08
1.069
1.069
1.603
Test df
6
3
3
2
p-value
0.006033
0.7845
0.7845
0.4486
P-2-15-2 Decreased Body Weight in F2 Female Pups at PND 14
The doses and response data used for the modeling are presented in Table_Apx P-37.
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Table_Apx P-37 Pup Body Weight Data in F2 Females at PND 14 from Selected for Dose-Response
Modeling
Number of litters
Mean pup wt (g)
Standard deviation (g)
Concentration (ppm)
22
27.6
2.29
100
17
26.9
2.11
250
15
27.3
3.87
500
15
23.7
3.70
The BMD modeling results for decreased pup weight in F2 females at PND 14 with non-
homogeneous variance (BMDS test 2 p-value = 0.0218) are summarized in Table_Apx P-38.
Although the variances are non-homogeneous and not well modeled for any of the non-
homogeneous variance models the means were well-modeled (the highest p-value is 0.904 for
the linear model with non-homogeneous variances).
Table_Apx P-38 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Linear
Exponential (M2)
Exponential (M4)
Exponential (M3)
Power
Polynomial 3°b
Polynomial 2°c
Exponential (M5)
Hill
Polynomial 4°
Goodness of fit
p-value
0.904
0.893
0.893
0.715
0.708
0.687
0.687
N/Ad
N/Ad
error
AIC
221.02
221.05
221.05
222.96
222.96
222.98
222.98
224.82
224.82
error
BMDsRD
(ppm)
228
224
224
244
245
245
245
228
226
error6
BMDLsRD
(ppm)
145
138
104
139
146
145
145
107
105
error6
a Modeled variance case presented (BMDS Test 2 p-value = 0.0218, BMDS Test 3 p-value = 0.0438), no model was selected as
a best-fitting model.
b The Polynomial 3° model may appear equivalent to the Polynomial 2° model, however differences exist in digits not
displayed in the table.
c The Polynomial 2° model may appear equivalent to the Polynomial 3° model, however differences exist in digits not
displayed in the table.
d No available degrees of freedom to calculate a goodness of fit value.
e BMD or BMDL computation failed for this model.
To investigate the effect of the poor modeling of the variances on the BMDL, the models were
run using the smallest dose standard deviation (2.29), highest (3.87) and pooled (2.89) for all
dose levels and the modeling results are summarized in Table_Apx P-39.
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Table_Apx P-39 BMD Modeling Results for Body Weight of F2 Female Rat Pups on PND 14 Following Inhalation Exposure of Parental Rats to
1-BP in a Two-Generation Study with Variances Fixed at Smallest, Pooled and Highest Values.
Model"
Polynomial 3°
Polynomial 2°
Power
Exponential
(M3)
Hill
Exponential
(M5)
Linear
Exponential
(M2)
Exponential
(M4)
Smallest Standard Deviation
Goodness of fit
p-value
0.518
0.318
0.331
0.331
N/Ab
N/Ab
0.0533
0.0443
0.0443
AIC
186.54
187.51
188.16
188.16
190.16
190.16
191.08
191.45
191.45
BMDsRD
(ppm)
360
304
465
473
466
470
193
188
188
BMDLsRD
(ppm)
274
199
247
249
248
249
146
139
131
Pooled Standard Deviation
Goodness of fit
p-value
0.661
0.485
0.441
0.441
N/Ab
N/Ab
0.154
0.137
0.137
AIC
218.16
218.78
219.93
219.93
221.93
221.93
221.07
221.31
221.31
BMDsRD
(ppm)
360
304
465
470
465
470
193
188
188
BMDLsRD
(ppm)
183
260
200
202
200
202
138
131
115
Largest Standard Deviation
Good ness of fit
p-value
0.793
0.667
0.564
0.564
N/Ab
N/Ab
0.348
0.325
0.325
AIC
258.09
258.44
259.96
259.96
261.96
261.96
259.74
259.88
259.88
BMDsRD
(ppm)
360
304
460
473
442
473
193
188
188
BMDLsRD
(ppm)
145
140
148
143
138
139
127
119
90.2
Ratio
BMDLs
Smallest to
Largest Std
Dev
1.9
1.4
1.7
1.7
1.8
1.8
1.1
1.2
1.5
a Constant variance case presented (BMDS Test 2 p-value = 1., BMDS Test 3 p-value = 1.), no model was selected as a best-fitting model.
b No available degrees of freedom to calculate a goodness of fit value.
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A comparison across the full suite of BMD models shows the BMDL is sensitive to the adjustment
of the variances and for the model that fit the constant variance data best, the Polynomial 3°
model the ratio of BMDLs was 1.9. This result suggests that due to the poor variance modeling
for the original data it is not reasonable to use BMDS for this endpoint. Instead the NOAEL of
250 ppm was used.
P-2-15-3 Decreased Body Weight in F2 Female Pups at PND 21
The doses and response data used for the modeling are presented in Table_Apx P-40.
Table_Apx P-40 Pup Body Weight Data in F2 Females at PND 21 from Selected for Dose-Response
Modeling
Number of litters
Mean pup wt (g)
Standard deviation (g)
Concentration (ppm)
0
22
46.6
4.05
100
17
44.7
3.80
250
15
45.6 ^
K 5.60
500
15
39.7
6.13
Comparisons of model fits obtained are provided in Table_Apx P-41. The best fitting model
(Polynomial 2° with constant variance) was selected based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a
better fit) and visual inspection. The best-fitting model is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-15. The model version number, model
form, benchmark dose calculation, parameter estimates and estimated values are shown
below.
Table_Apx P-41 BMD Modeling Results for Body Weight of F2 Females on PND 21 Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Polynomial 2°
Linear
Power
Exponential (M3)
Polynomial 3°
Exponential (M2)
Exponential (M4)
Exponential (M5)
Hill
Goodness of fit
p-value
0.372
0.176
0.216
0.216
0.213
0.160
0.160
N/Ab
N/Ab
AIC
291.28
292.77
292.83
292.83
292.85
292.97
292.97
294.83
294.83
BMDiso
(ppm)
436.24
386.50
475.29
474.45
449.22
385.88
385.88
474.45
475.10
BMDLisD
(ppm)
299.79
269.95
314.36
316.27
313.20
261.10
250.91
316.27
314.77
BMDsRD
(ppm)
303
187
407
406
336
181
181
406
406
BMDLsRD
(ppm)
148
135
155
152
154
127
105
152
150
Basis for model
selection
The Polynomial 2°
model was
selected based on
the lowest AIC,
highest goodness
of fit p-value and
adequate fit by
visual inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.144), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were 0.4, -1.06, 0.8, -0.15, respectively.
b No available degrees of freedom to calculate a goodness of fit value.
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Polynomial Model, with BMP of O.O5 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
13:2O 1O/29 2O15
Figure_Apx P-15 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Polynomial 2°) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative
Deviation.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose + beta_2*doseA2 + ...
A constant variance model is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 302.794
BMDL at the 95% confidence level = 148.282
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
beta_2
Estimate
22.9776
n/a
46.1877
0
-0.0000251884
Default Initial
Parameter Values
23.7017
0
45.9942
0
-0.000029911
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
22
17
15
15
Obs Mean
46.6
44.7
45.6
39.7
Est Mean
46.2
45.9
44.6
39.9
Obs Std Dev
4.05
3.8
5.6
6.13
Est Std Dev
4.79
4.79
4.79
4.79
Scaled Resid
0.403
-1.06
0.797
-0.154
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
-141.651019
-138.944287
-141.651019
-142.640988
-150.681267
# Param's
5
8
5
3
2
AIC
293.302038
293.888574
293.302038
291.281976
305.362534
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
23.474
5.41346
5.41346
1.97994
Test df
6
3
3
2
p-value
0.0006523
0.1439
r 0.1439
0.3716
P-2-15-4 Decreased Body Weight in F2 Male Pups at PND 14
The doses and response data used for the modeling are presented in Table_Apx P-42.
Table_Apx P-42 Pup Body Weight Data in F2 Males at PND 14 from Selected for Dose-Response
Modeling
Number of litters
Mean pup wt (g)
Standard deviation (g)
Concentration (ppm)
0
22
29.2
2.77
100
17
28.1
2.43
250
15
28.4
3.65
500
16
24.5
4.14
Comparisons of model fits obtained are provided in Table_Apx P-43. The best fitting model
(Polynomial 2° with constant variance) was selected based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a
better fit) and visual inspection. The best-fitting model is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-16. The model version number, model
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form, benchmark dose calculation, parameter estimates and estimated values are shown
below.
Table_Apx P-43 BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 14 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Polynomial 2°
Linear
Polynomial 3°
Power
Exponential (M3)
Exponential (M2)
Exponential (M4)
Hill
Exponential (M5)
Goodness of fit
p-value
0.509
0.236
0.316
0.290
0.289
0.209
0.209
N/Ab
N/Ab
AIC
238.45
239.99
240.11
240.22
240.23
240.23
240.23
242.22
242.23
BMDiso
(ppm)
427.44
367.99
439.96
457.39
456.58
365.77
365.77
457.31
456.58
BMDLisD
(ppm)
290.47
261.73
300.66
297.00
297.67
251.63
241.42
296.92
297.67
BMDsRD
(ppm)
288
168
314
358
358
161
161
358
358
BMDLsRD
(ppm)
136
124
140
138
134
115
95.6
138
134
Basis for model
selection
The Polynomial 2°
model was
selected based on
the lowest AIC,
highest goodness
of fit p-value and
adequate fit by
visual inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.116), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were 0.35, -0.89, 0.64, -0.12, respectively.
b No available degrees of freedom to calculate a goodness of fit value.
Polynomial Model, with BMP of O.O5 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
14:31 10/292015
Figure_Apx P-16 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Polynomial 2°) for Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative
Deviation.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose + beta_2*doseA2 + ...
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A constant variance model is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BM 0 = 287.938
BMDL at the 95% confidence level = 135.688
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
beta_2
Estimate
10.1836
n/a
28.9615
0
-0.000017466
Default Initial
Parameter Values
10.5942
0
28.8658
0
-0.000019675
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
22
17
15
16
Obs Mean
29.2
28.1
28.4
24.5
Est Mean
29
28.8
27.9
24.6
Obs Std Dev
2.77
2.43
3.65
4.14
Est Std Dev
3.19
3.19
3.19
3.19
Scaled Resid
0.35
-0.887
0.643
-0.119
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
-115.551371
-112.600048
-115.551371
-116.227119
-125.255153
# Param's
5
8
5
3
2
AIC
241.102743
241.200097
241.102743
238.454239
254.510306
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
25.3102
5.90265
5.90265
1.3515
Test df
6
3
3
2
p-value
0.0002991
0.1164
0.1164
0.5088
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P-2-15-5 Decreased Body Weight in F2 Male Pups at PND 21
The doses and response data from the WIL Laboratories (2001) study was used for the
modeling and are presented in Table_Apx P-44.
Table_Apx P-44 Pup Body Weight Data in F2 Males at PND 21
Number of litters
Mean pup wt (g)
Standard deviation (g)
Concentration (ppm)
0
22
49.5
5.14
100
17
46.9
5.03
250
15
^ 47.6
5.40
500
16
40.8
6.70
Comparisons of model fits obtained are provided in Table_Apx P-45. The best fitting model
(Linear with homogeneous variance) was selected based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a
better fit) and visual inspection. The best-fitting model is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-17. The model version number, model
form, benchmark dose calculation, parameter estimates and estimated values are shown
below.
Table_Apx P-45 BMD Modeling Results for Body Weight of F2 Male Rat Pups on PND 21 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Linear
Exponential (M2)
Exponential (M4)
Polynomial 3°
Polynomial 2°
Power
Exponential (M3)
Hill
Exponential (M5)
Goodness of fit
p-value
0.218
0.194
0.194
0.194
0.153
0.150
0.148
N/Ab
N/Ab
AIC
315.14
315.38
315.38
315.78
316.14
316.17
316.19
318.17
318.19
BMDiso
(ppm)
344.43
339.42
339.42
418.75
404.48
435.13
436.20
435.26
436.20
BMDLiso
(ppm)
249.00
237.32
220.01
271.24
264.17
263.67
257.18
262.98
257.18
BMDsRD
(ppm)
155
147
147
273
252
313
318
314
318
BMDLsRD
(ppm)
116
107
84.8
125
122
122
115
121
115
Basis for model
selection
The Linear model
was selected
based on the
lowest AIC,
highest goodness
of fit p-value and
adequate fit by
visual inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.614), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were -0.04, -0.78,1.44, -0.54, respectively.
b No available degrees of freedom to calculate a goodness of fit value.
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Linear Model, with BMP of O.O5 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
15:O3 1O/29 2O15
Figure_Apx P-17 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Pup Body Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 5% Relative Deviation.
Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose
A constant variance model is fit
Benchmark Dose Computation.
BMR = 5% Relative deviation
BMD = 154.623
BMDL at the 95% confidence level = 116.114
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
Estimate
30.4578
n/a
49.5516
-0.0160234
Default Initial
Parameter Values
30.9275
0
49.615
-0.0160705
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
22
17
15
16
Obs Mean
49.5
46.9
47.6
40.8
Est Mean
49.6
47.9
45.5
41.5
Obs Std Dev
5.14
5.03
5.4
6.7
Est Std Dev
5.52
5.52
5.52
5.52
Scaled Resid
-0.0439
-0.784
1.44
-0.536
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Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
-153.048201
-152.146228
-153.048201
-154.572024
-163.858303
# Param's
5
8
5
3
2
AIC
316.096402
320.292456
316.096402
315.144048
331.716606
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
23.4241
1.80395
1.80395
3.04765
Test df
6
3
3
2
p-value
0.0006662
0.6141
0.6141
0.2179
P-2-16 Decreased Brain Weight
Decreased brain weights were observed in the 2-generation reproductive and developmental
study by (WIL Research, 2001). Statistically significant decreases in brain weights were noted for
both sexes in the Fo generation, Fi generation as adults and in the F2 generation at PNDs 21.
Continuous models were used to fit-dose response data for decreased brain weights. A BMR of
5% was used because this is a developmental endpoint (Kavlock et al., 1995). A BMR of 1
standard deviation is also shown for comparison per EPA Benchmark Dose Technical Guidance
(U.S. EPA, 2012a). The doses, response data and BMD modeling results for decreased pup brain
weight are presented below at each time point.
P-2-16-1 Decreased Brain Weight in Fo Females
The doses and response data from the WIL Laboratories (2001) study was used for the
modeling and are presented in Table_Apx P-46.
Table_Apx P-46 Brain Weight Data in F0 Females for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
0
25
1.96
0.078
100
25
1.92
0.094
250
25
1.94
0.084
500
25
1.89
0.105
750
25
1.86
0.072
Comparisons of model fits obtained are provided in Table_Apx P-47. The best fitting model
(Linear with homogeneous variance) was selected based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a
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better fit) and visual inspection. The best-fitting model is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-18. The model version number, model
form, benchmark dose calculation, parameter estimates and estimated values are shown
below.
Table_Apx P-47 BMD Modeling Results for Brain Weight of F0 Females Following Inhalation Exposure
to 1-BP
Model"
Linear
Exponential (M2)
Exponential (M4)
Polynomial 4°b
Polynomial 3°
Polynomial 2°
Power
Exponential (M3)
Exponential (M5)
Hill
Goodness of fit
p-value
0.444
0.441
0.441
0.273
0.271
0.263
0.261
0.101
0.100
AIC
-480.77
-480.75
-480.75
-478.85
-478.84
-478.77
-478.76
-476.76
-476.75
BMDiso
(ppm)
711
711
711
717
718
715
716
716
error0
BMDLisD
(ppm)
509
504
434
511
511
509
504
504
error0
Basis for model selection
The Linear model was selected
based on the lowest AIC, highest
goodness of fit p-value and
adequate fit by visual inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.340), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, 500, and 750 ppm were 0.41, -1.2,1.01, -0.12, -0.1, respectively.
b For the Polynomial 4° model, the b4 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 3° model.
c BMD or BMDL computation failed for this model.
Linear Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confiden
3 Limit for the BMDL
18:44 1O/O5 2O15
Figure_Apx P-18 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Linear) for
Brain Weight in F0 Female Rats Exposed to 1-BP Via Inhalation in ppm BMR = 1 Standard Deviation.
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Polynomial Model. (Version: 2.20; Date: 10/22/2014)
The form of the response function is: Y[dose] = beta_0 + beta_l*dose
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1 Estimated standard deviations from the control mean
BMD = 711.056
BMDL at the 95% confidence level = 508.985
Parameter Estimates
Variable
alpha
rho
beta_0
beta_l
Estimate
0.00749034
n/a
1.95295
-0.000121716
Default Initial
Parameter Values
0.007637
0
1.95295
-0.000121716
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
750
N
25
25
25
25
25
Obs Mean
1.96
1.92
1.94
1.89
1.86
Est Mean
1.95
1.94
1.92
1.89
1.86
Obs Std Dev
0.08
0.09
0.08
0.1
0.07
Est Std Dev
0.09
0.09
0.09
0.09
0.09
Scaled Resid
0.407
-1.2
1.01
-0.121
-0.096
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
244.723276
246.984613
244.723276
243.383815
234.782134
# Param's
6
10
6
3
2
AIC
-477.446552
-473.969225
-477.446552
-480.76763
-465.564268
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Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
24.405
4.52267
4.52267
2.67892
Test df
8
4
4
3
p-value
0.001959
0.3399
0.3399
0.4438
P-2-16-2 Decreased Brain Weight in Fo Males
The doses and response data from the WIL Laboratories (2001) study was used for the
modeling and are presented in Table_Apx P-48.
Table_Apx P-48 Brain Weight Data in F0 Males for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
0
25
2.19
0.091
100
25
2.15
0.114
250^^
25
2.08
0.087
500
25
2.1
0.177
750
25
2.05
0.091
The BMD modeling results for decreased brain weight in Fo males with non-homogeneous
variance (BMDS test 2 p-value = 0.000386) are summarized in Table_Apx P-49. Although the
variances are non-homogeneous and not well modeled for any of the non-homogeneous
variance models the means were well-modeled (the highest p-value is 0.618 for the Exponential
(M4) model with non-homogeneous variances).
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Table_Apx P-49 BMD Modeling Results for Brain Weight of F0 Males Following Inhalation Exposure to
1-BP
Model"
Exponential (M4)
Hill
Exponential (M5)
Exponential (M2)
Exponential (M3)b
Power0
Polynomial 4°d
Polynomial 2°e
Linear'
Polynomial 3°g
Good ness of fit
p-value
0.618
0.340
0.152
0.0868
0.0804
0.0804
AIC
-408.61
-406.66
-405.52
-405.00
-404.83
-404.83
BMDsRD
(ppm)
372
354
115
636
644
644
BMDLsRD
(ppm)
159
107
102
453
463
463
a Modeled variance case presented (BMDS Test 2 p-value = 3.86E-04, BMDS Test 3 p-value = 5.66E-04), no model was selected
as a best-fitting model.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 4° model, the b4 and b3 coefficient estimates were 0 (boundary of parameters space). The models in this
row reduced to the Polynomial 2° model. For the Polynomial 4° model, the b4, b3, and b2 coefficient estimates were 0
(boundary of parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f The Linear model may appear equivalent to the Polynomial 3° model, however differences exist in digits not displayed in the
table.
g The Polynomial 3° model may appear equivalent to the Power model, however differences exist in digits not displayed in the
table. This also applies to the Polynomial 4° model. This also applies to the Polynomial 2° model. This also applies to the
Linear model.
To investigate the effect of the poor modeling of the variances on the BMDL, the models were
run using the smallest dose standard deviation (0.091), highest (0.177) and the pooled (0.0907)
for all dose levels and the modeling results are summarized in Table_Apx P-50.
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Table_Apx P-50 BMD Modeling Results for Brain Weight of F0 Male Rats Following Inhalation Exposure to 1-BP in a Two-Generation Study
with Variances Fixed at Smallest, Pooled and Highest Values.
Model"
Exponential
(M4)
Hill
Exponential
(M5)
Exponential
(M2)
Exponential
(M3)
Power
Polynomial 4°
Polynomial 2°
Linear
Polynomial 3°
Smallest Standard Deviation
Good ness of fit
p-value
0.0893
0.0423
0.0398
0.0238
0.0238
0.0223
0.0223
0.0223
0.0223
0.0223
AIC
-477.73
-476.44
-476.34
-475.11
-475.11
-474.96
-474.96
-474.96
-474.96
-474.96
BMDsRD
(ppm)
375
289
246
669
669
674
674
674
674
674
BMDLsRD
(ppm)
164
106
104
515
515
523
523
523
523
523
Pooled Standard Deviation
Goodness of fit
p-value
0.108
0.0513
0.0484
0.0332
0.0332
0.0312
0.0312
0.0312
0.0312
0.0312
AIC
-467.70
-466.35
-466.26
-465.43
-465.43
-465.29
-465.29
-465.29
-465.29
-465.29
BMDsRD
(ppm)
375
289
246
669
669
674
674
674
674
674
BMDURD
(ppm)
159
106
103
510
510
518
518
518
518
518
Largest Standard Deviation
Good ness of fit
p-value
0.553
0.315
0.309
0.503
0.503
0.496
0.496
0.496
0.496
0.496
AIC
-303.82
-302.00
-301.97
-304.65
-304.65
-304.62
-304.62
-304.62
-304.62
-304.62
BMDsRD
(ppm)
375
289
246
669
669
674
674
674
674
674
BMDURD
(ppm)
78.7
70.4
82.4
420
420
430
430
430
430
430
Ratio
BMDLs
Smallest to
Largest Std
Dev
2.1
1.5
1.3
1.2
1.2
1.2
1.2
1.2
1.2
1.2
a Constant variance case presented (BMDS Test 2 p-value = 1., BMDS Test 3 p-value = 1.), no model was selected as a best-fitting model.
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A comparison across the full suite of BMD models shows the BMDL is sensitive to the adjustment
of the variances and for the model that fit the constant variance data best, the Exponential (M4)
model the ratio of BMDLs was 2.1. This result suggests that due to the poor variance modeling
for the original data it is not reasonable to use BMDS for this endpoint. Instead the NOAEL of
100 ppm was used.
P-2-16-3 Decreased Brain Weight in Fi Females as Adults
The doses and response data used for the modeling are presented in Table_Apx P-51.
Table_Apx P-51 Brain Weight Data in Fi Females as Adults from Selected for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
0
25
1.97
0.076
100
25
1.96
0.073
^^^250
25
1.92
0.067 ^
500
25
1.89
0.102
Comparisons of model fits obtained are provided in Table_Apx P-52. The best fitting model
(Exponential (M2) with homogeneous variance) was selected based on Akaike information
criterion (AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher
value indicates a better fit) and visual inspection. The best-fitting model is indicated in bold. For
the best fitting model a plot of the model is shown in Figure_Apx P-19. The model version
number, model form, benchmark dose calculation, parameter estimates and estimated values
are shown below.
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Table_Apx P-52 BMD Modeling Results for Brain Weight of Fi Female Rats as Adults Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Exponential (M2)
Exponential (M3)b
Power0
Polynomial 3°d
Polynomial 2°e
Linear
Exponential (M4)
Hill
Exponential (M5)
Good ness of fit
p-value
0.787
0.780
0.534
N/Af
N/Af
AIC
-401.21
-401.19
-399.30
-397.69
-397.69
BMDiso
(ppm)
472
473
459
482
463
BMDLi
SD
(ppm)
327
331
230
230
112
BMDsRD
(ppm)
590
589
619
error8
error8
BMDLs
RD
(ppm)
416
419
363
error8
0
BMDiRD
(ppm)
116
118
94.7
138
141
BMDLi
RD
(ppm)
81.5
83.8
35.1
33.1
37.6
Basis for
model
selection
The
Exponential
(M2) model
was
selected
based on
the lowest
AIC, highest
good ness of
fit p-value
and
adequate fit
by visual
inspection.
a Constant variance case presented (BMDS Test 2 p-value = 0.144), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, and 500 ppm were -0.05, 0.39, -0.53, 0.19, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f No available degrees of freedom to calculate a goodness of fit value.
g BMD or BMDL computation failed for this model.
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Exponential 2 Model, with BMP of O.O1 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
13:46 11/O6 2O15
Figure_Apx P-19 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Brain Weight in Fi Female Rats as Adults Exposed to 1-BP Via Inhalation in ppm
BMR = 1% Relative Deviation.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * exp(sign * b * dose)
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1% Relative deviation
BMD = 115.594
BMDL at the 95% confidence level = 81.5083
Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
-5.07205
n/a
1.97082
0.0000869453
n/a
n/a
Default Initial
Parameter Values
-5.07685
0
1.89939
0.000086769
0
1
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
25
25
25
25
Obs Mean
1.97
1.96
1.92
1.89
Est Mean
1.97
1.95
1.93
1.89
Obs Std Dev
0.08
0.07
0.07
0.1
Est Std Dev
0.08
0.08
0.08
0.08
Scaled Resid
-0.05174
0.3941
-0.5332
0.1908
Likelihoods of Interest
Model
Al
A2
A3
R
2
Log(likelihood)
203.8426
206.5452
203.8426
196.2377
203.6027
# Param's
5
8
5
2
3
AIC
-397.6852
-397.0903
-397.6852
-388.4753
-401.2054
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
20.62
5.405
5.405
0.4799
Test df
6
3
3
2
p-value
0.002151
0.1444
0.1444
0.7867
P-2-16-4 Decreased Brain Weight in Fi Males as Adults
The doses and response data used for the modeling are presented in Table_Apx P-53.
Table_Apx P-53 Brain Weight Data in Fi Males as Adults from Selected for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
A °
24
2.21
0.092
100
25
2.11
0.111
250
25
2.12
0.109
500
24
2.01
0.079
The data were not adequately fit by any of the models, the means goodness of fit p-values were
less than 0.05 for all of the models. Comparisons of model fits obtained are provided in
Table_Apx P-54. Since no model was selected a plot of the model, BMD and BMDL calculations
and other output are not presented. BMRs other than 5% relative deviation are not shown
because the fit to the means are not different and therefore also inadequate. Instead the LOAEL
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of 100 ppm was used because there was no NOAEL observed in the WIL Laboratories (2001)
study.
Table_Apx P-54 BMD Modeling Results for Brain Weight of Fi Male Rats as Adults Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Exponential (M2)
Exponential (M3)b
Power0
Polynomial 3°d
Polynomial 2°e
Linear
Hill
Exponential (M4)
Exponential (M5)f
Good ness of fit
p-value
0.0320
0.0312
0.00968
0.00932
AIC
-346.71
-346.66
-344.90
-344.84
BMDsRD
(ppm)
308
314
265
279
BMDLsRD
(ppm)
245
252
112
144
a Constant variance case presented (BMDS Test 2 p-value = 0.310, BMDS Test 3 p-value = 0.310), no model was selected as a
best-fitting model.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Power model, the power parameter estimate was 1. The models in this row reduced to the Linear model.
d For the Polynomial 3° model, the b3 coefficient estimates was 0 (boundary of parameters space). The models in this row
reduced to the Polynomial 2° model. For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Linear model.
e For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
f For the Exponential (M5) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M4) model.
P-2-16-5 Decreased Brain Weight in F2 Females at PND 21
The doses and response data used for the modeling are presented in Table_Apx P-55.
Table_Apx P-55 Brain Weight Data in F2 Females at PND 21 from Selected for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
0
22
1.3957
0.06491
100
17
1.3903
0.08882
250
15
1.3673
0.12231
500
15
1.3089
0.1004
Comparisons of model fits obtained are provided in Table_Apx P-56. The best fitting model
(Exponential (M2) with non-homogeneous variance) was selected based on Akaike information
criterion (AIC; lower values indicates a better fit), chi-square goodness of fit p-value (higher
value indicates a better fit) and visual inspection. The best-fitting model is indicated in bold. For
the best fitting model a plot of the model is shown in Figure_Apx P-20. The model version
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number, model form, benchmark dose calculation, parameter estimates and estimated values
are shown below.
Table_Apx P-56 BMD Modeling Results for Brain Weight of F2 Female Rats at PND 21 Following
Inhalation Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Exponential (M2)
Exponential (M3)b
Power
Polynomial 3°c
Lineard
Polynomial 2°e
Exponential (M4)
Hill
Exponential (M5)
Good ness of fit
P-
value
0.634
0.621
0.566
0.566
0.702
N/Af
N/Af
AIC
-257.31
-257.27
-257.27
-257.27
-256.08
-254.41
-254.41
BMDiso
(ppm)
454
456
456
456
643
error8
error8
BMDLi
SD
(ppm)
260
266
266
266
130
error8
0
BMDsRD
(ppm)
426
427
427
427
1149
error8
error8
BMDLs
RD
(ppm)
256
261
261
261
170
error8
0
BMDiRD
(ppm)
83.4
85.3
85.3
85.3
48.5
85.7
81.2
BMDLi
RD
(ppm)
50.1
52.1
52.1
52.1
12.6
6.27
14.9
Basis for
model
selection
The
Exponential
(M2) model
was selected
based on the
lowest AIC
and adequate
fit by visual
inspection.
a Modeled variance case presented (BMDS Test 2 p-value = 0.0643), selected model in bold; scaled residuals for selected
model for doses 0,100, 250, and 500 ppm were -0.31, 0.32, 0.34, -0.32, respectively.
b For the Exponential (M3) model, the estimate of d was 1 (boundary). The models in this row reduced to the Exponential
(M2) model.
c For the Polynomial 3° model, the b3 and b2 coefficient estimates were 0 (boundary of parameters space). The models in this
row reduced to the Linear model.
d The Linear model may appear equivalent to the Polynomial 2° model, however differences exist in digits not displayed in the
table.
e The Polynomial 2° model may appear equivalent to the Polynomial 3° model, however differences exist in digits not
displayed in the table. This also applies to the Linear model.
f No available degrees of freedom to calculate a goodness of fit value.
g BMD or BMDL computation failed for this model.
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Exponential 2 Model, with BMP of O.O1 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
BMDL BMD
13:15 11/O6 2O15
Figure_Apx P-20 Plot of Mean Response by Dose with Fitted Curve for the Selected Model
(Exponential (M2)) for Brain Weight in F2 Female Exposed to 1-BP Via Inhalation in ppm BMR = 1%
Relative Deviation.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * exp(sign * b * dose)
A modeled variance is fit
Benchmark Dose Computation.
BMR = 1% Relative deviation
BMD = 83.4282
BMDL at the 95% confidence level = 50.1098
^L
Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
-0.0282712
-15.3239
1.40066
0.000120467
n/a
n/a
Default Initial
Parameter Values
-1.99881
-8.92906
1.33604
0.000129477
0
1
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Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
22
17
15
15
Obs Mean
1.4
1.39
1.37
1.31
Est Mean
1.4
1.38
1.36
1.32
Obs Std Dev
0.06
0.09
0.12
0.1
Est Std Dev
0.07
0.08
0.09
0.12
Scaled Resid
-0.3121
0.3231
0.3377
-0.3236
Likelihoods of Interest
Model
Al
A2
A3
R
2
Log(likelihood)
131.2578
134.8828
133.1137
126.819
132.6574
# Param's
5
8
6
2
4
AIC
-252.5155
-253.7656
-254.2275
-249.638
-257.3148
Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
16.13
7.25
3.538
0.9127
Test df
6
3
2
2
p-value
0.01309
0.06434
r 0.1705
0.6336
P-2-16-6 Decreased Brain Weight in F2 Males at PND 21
The doses and response data from the WIL Laboratories (2001) study was used for the
modeling are presented in Table_Apx P-57.
Table_Apx P-57 Brain Weight Data in F2 Males at PND 21 for Dose-Response Modeling
Number of animals
Brain wt (g)
Standard deviation (g)
Concentration (ppm)
0
22
1.4728
0.07836
100
17
1.4253
0.07679
250
15
1.4668
0.05971
500
16
1.3629
0.09581
Comparisons of model fits obtained are provided in Table_Apx P-58. The best fitting model
(Power with homogeneous variance) was selected based on Akaike information criterion (AIC;
lower values indicates a better fit), chi-square goodness of fit p-value (higher value indicates a
better fit) and visual inspection. The best-fitting model is indicated in bold. For the best fitting
model a plot of the model is shown in Figure_Apx P-21. The model version number, model
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form, benchmark dose calculation, parameter estimates and estimated values are shown
below.
Table_Apx P-58 BMD Modeling Results for Brain Weight of F2 Male Rats as Adults Following Inhalation
Exposure of Parental Rats to 1-BP in a Two-Generation Study
Model"
Power
Polynomial 3°
Polynomial 2°
Exponential
(M3)
Hill
Linear
Exponential
(M2)
Exponential
(M4)
Exponential
(M5)
Goodness of fit
p-value
0.137
0.0961
0.0647
0.0463
0.0463
0.0306
0.0294
0.0294
N/AC
AIC
-279.68
-278.97
-278.18
-277.68
-277.68
-276.68
-276.60
-276.60
-275.68
BMDiso
(ppm)
495
472
459
495
495
430
431
431
495
BMDLi
SD
(ppm)
395
353
383
396
281
293
289
278
272
BMDsRD
(ppm)
493
459
440
493
493
393
393
393
493
BMDLs
RD
(ppm)
374
331
370
376
errorb
274
269
250
376
BMDiRD
(ppm)
451
269
197
450
450
78.6
76.9
76.9
449
BMDLi
RD
(ppm)
97.6
67.1
166
102
errorb
54.8
52.8
36.9
102
Basis for model
selection
The Power
model was
selected based
on the lowest
AIC, highest
goodness of fit
p-value and
adequate fit by
visual
inspection
a Constant variance case presented (BMDS Test 2 p-value = 0.337), selected model in bold; scaled residuals for selected model
for doses 0,100, 250, and 500 ppm were 0.99, -1.62, 0.52, 0, respectively.
b BMD or BMDL computation failed for this model.
c No available degrees of freedom to calculate a goodness of fit value.
Power Model, with BMP of O.O1 Pel. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
13:32 11/O6 2O15
Figure_Apx P-21 Plot of Mean Response by Dose with Fitted Curve for the Selected Model (Power) for
Brain Weight in Rats Exposed to 1-BP Via Inhalation in ppm BMR = 1% Relative Deviation.
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Power Model. (Version: 2.18; Date: 05/19/2014)
The form of the response function is: Y[dose] = control + slope * doseApower
A constant variance model is fit
Benchmark Dose Computation.
BMR = 1% Relative deviation
BM 0 = 450.983
BMDL at the 95% confidence level = 97.5507
Parameter Estimates
Variable
alpha
rho
control
slope
power
Estimate
0.00621258
n/a
1.45618
-2.44527E-50
18
Default Initial
Parameter Values
0.00622577
0
1.3629
0.0048117
-9999
Table of Data and Estimated Values of Interest
Dose
0
100
250
500
N
22
17
15
16
Obs Mean
1.47
1.43
1.47
1.36
Est Mean
1.46
1.46
1.46
1.36
Obs Std Dev
0.08
0.08
0.06
0.1
Est Std Dev
0.08
0.08
0.08
0.08
Scaled Resid
0.989
-1.62
0.522
-0.00000182
Likelihoods of Interest
Model
Al
A2
A3
fitted
R
Log(likelihood)
144.826466
146.516124
144.826466
142.841294
135.116612
# Param's
5
8
5
3
2
AIC
-279.652932
-277.032248
-279.652932
-279.682588
-266.233223
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Tests of Interest
Test
Testl
Test 2
Tests
Test 4
-2*log(Likelihood
Ratio)
22.799
3.37932
3.37932
3.97034
Test df
6
3
3
2
p-value
0.0008667
0.3368
0.3368
0.1374
P-2-17 Decreased Hang Time
EPA/OPPT selected decreased time hanging from a suspended bar from the (Honma et al.,
2003) study as a relevant endpoint for calculating risks associated with chronic worker
scenarios. Since this is a continuous endpoint and in the absence of a basis for selecting a BMR
a default selection of 1 standard deviation was used in accordance with EPA Benchmark Dose
Technical Guidance (U.S. EPA, 2012a). The doses and response data used for the modeling are
presented in Table_Apx P-59.
Table_Apx P-59 Hang Time from a Suspended Bar Data for Dose-Response Modeling for 1-BP
Dose (ppm)
0
10
50
200
1000
Number of animals
5
5
5
5
5
Mean traction time (sec)
25.2
23.8
15.2
5.2
4.4
Standard Deviation
15.25
7.53
5.54
3.42
3.65
The best fitting model was selected based on Akaike information criterion (AIC; lower value
indicates a better fit), chi-square goodness of fit p-value (higher value indicates a better fit),
ratio of the BMCBMCL (lower value indicates less model uncertainty) and visual inspection.
Comparisons of model fits obtained are provided in
Table_Apx P-60. The best-fitting model (Exponential M4), based on the criteria described
above, is indicated in bold. For the best fitting model a plot of the model is shown in Figure_Apx
P-22. The model version number, model form, benchmark dose calculation, parameter
estimates and estimated values are shown.
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Table_Apx P-60 Summary of BMD Modeling Results for Hang Time from a Suspended Bar; BMR = 1
std. dev. change from control mean
Model"
Exponential (M4)
Exponential (M5)
Hill
Exponential (M2)c
Exponential (M3)d
Power6
Polynomial 2°f
Linear8
Polynomial 3°
Polynomial 4°
Goodness of fit
p-value
0.955
0.766
0.467
0.00443
0.00443
2.22E-04
2.22E-04
<0.0001
N/Ah
AIC
122.13
124.12
124.57
133.13
133.13
139.47
139.47
188.00
192.45
BMDiso
(ppm)
36.9
37.7
45.0
47.4
47.4
799
799
-9999
-9999
BMDLiso
(ppm)
18.2
18.2
errorb
20.8
20.8
525
525
errorb
errorb
Basis for model selection
The Exponential (M4) model was
selected based on the lowest
AIC, highest goodness of fit p-
value and adequate fit by visual
inspection.
a Modeled variance case presented (BMDS Test 2 p-value = 0.00293), selected model in bold; scaled residuals for selected
model for doses 0,10, 50, 200, and 1000 ppm were -0.34, 0.12, 0.44, -0.07, -0.17, respectively.
b BMD or BMDL computation failed for this model.
c The Exponential (M2) model may appear equivalent to the Exponential (M3) model, however differences exist in digits not
displayed in the table.
d The Exponential (M3) model may appear equivalent to the Exponential (M2) model, however differences exist in digits not
displayed in the table.
e The Power model may appear equivalent to the Polynomial 2° model, however differences exist in digits not displayed in the
table. This also applies to the Linear model.
f For the Polynomial 2° model, the b2 coefficient estimate was 0 (boundary of parameters space). The models in this row
reduced to the Linear model.
z The Linear model may appear equivalent to the Power model, however differences exist in digits not displayed in the table.
h No available degrees of freedom to calculate a goodness of fit value.
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Exponential 4 Model, with BMP of 1 Std. Dev. for the BMD and O.95 Lower Confidence Limit for the BMDL
17:15 O8/1O 2O15
Figure_Apx P-22 Plot of Mean Response by Dose in ppm with Fitted Curve for Exponential (M4) Model
with Modeled Variance for Hang Time from a Suspended Bar; BMR = 1 Standard Deviation Change
from Control Mean.
Exponential Model. (Version: 1.10; Date: 01/12/2015)
The form of the response function is: Y[dose] = a * [c-(c-l) * exp(-b * dose)]
A modeled variance is fit
Benchmark Dose Computation.
BMR = 1.0000 Estimated standard deviations from control
BMD = 36.9173
BMDL at the 95% confidence level = 18.2429
Parameter Estimates
Variable
Inalpha
rho
a
b
c
d
Estimate
-0.107405
1.46448
26.8244
0.0174245
0.172048
n/a
Default Initial
Parameter Values
0.415293
1.29675
26.46
0.00510395
0.15837
1
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Table of Data and Estimated Values of Interest
Dose
0
10
50
200
1000
N
5
5
5
5
5
Obs Mean
25.2
23.8
15.2
5.2
4.4
Est Mean
26.82
23.27
13.91
5.3
4.62
Obs Std Dev
15.25
7.53
5.54
3.42
3.65
Est Std Dev
10.54
9.5
6.51
3.21
2.9
Scaled Resid
-0.3447
0.1241
0.4434
-0.0668
-0.1656
Likelihoods of Interest
Model
Al
A2
A3
R
4
Log(likelihood)
-62.64066
-54.60856
-56.01777
-73.64274
-56.06343
# Param's
6
10
7
2
5
AIC
137.2813
129.2171
126.0355
151.2855
122.1269
Tests of Interest
Test
Testl
Test 2
Tests
Test 6a
-2*log(Likelihood
Ratio)
38.07
16.06
2.818
0.09133
Test df
8
4
3
2
p-value
<0.0001
0.002934
0.4205
0.9554
P-3 Benchmark Dose Modeling of Tumors
EPA/OPPT selected 1-BP-induced tumors observed in mice and rats in the chronic inhalation
bioassay by NTP (2011) for BMD modeling with EPA's BMDS. The three tumor sites were
selected for modeling were alveolar/bronchiolar adenomas and carcinomas (i.e. lung tumors) in
female mice (Section P-3-1), adenomas of the large intestine in female rats (Section P-3-2), and
keratoacanthoma and squamous cell carcinomas of the skin in male rats (Section P-3-3). All of
the models in the BMDS suite of dichotomous models were applied the gamma, logistic, log-
logistic, multistage, probit, log-probit, quantal-linear and Weibull models. A BMR of 0.1% (1 in
1,000) added risk was used and the 95% lower confidence limit was calculated. Models were
determined to be adequate or not in a manner consistent with EPA Benchmark Dose Technical
Guidance (U.S. EPA, 2012a). Briefly the AIC, goodness of fit p-values (0.1 or greater) and a visual
assessment of fit are important criteria. The data were further modeled by using a model-
averaging (MA) technique with the Model Averaging for Dichotomous Response Benchmark
Dose (MADr-BMD) software as described in Wheeler and Bailer (2008). The models in the
averaging technique are weighted on the basis of model fit. The models selected for averaging
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are the multistage, log-probit and Weibull based on the observation that this 3 model suite
performed better in bias and coverage in the analysis by Wheeler and Bailer (2007). Confidence
limits in the model were determined with a bootstrapping method. The doses, tumor incidence
data, BMD modeling results and model averaging results are presented below for each tumor
site. Further a sensitivity analysis by quantitatively comparing the impact of alternative model
selections is presented for each of the tumor data sets.
P-3-1 Lung Tumors in Female Mice
The doses and response data from the NTP (2011) study that were used for the modeling are
presented in Table_Apx P-61.
Table_Apx P-61 Incidence of Lung Tumors in Female Mice
Dose (ppm)
0
62.5
125
250
Number of animals
50
50
50
50
Number of Animals
with Tumors
1
9
8
14
Comparisons of model fits obtained from BMD modeling of the NTP (2011) study are provided
in Table_Apx P-62. The loglogistic, gamma, Weibull, quantal-linear, multistage and logprobit all
had acceptable fits to the data by p-value, visual inspection and similar AIC values. A summary
of the model average results are shown for comparison with the BMDS results in Table_Apx
P-62. Detailed output of the model average run are shown below. The model average result
was selected because the model has an adequate p-value and model-averaging has been shown
to have reduced bias and better coverage in some cases (Wheeler and Bailer, 2007). In a
sensitivity analysis alternative model selections include the single best benchmark dose model
based on p-value, visual inspection and lowest AIC the loglogistic model or the multistage
model per EPA Benchmark Dose Technical Guidance for cancer datasets (U.S. EPA, 2012a). The
BMDL of the loglogistic model is 0.42 ppm and multistage model is 0.522 ppm, both are similar
(within a factor of 2) of the the model average BMDL of 0.63 ppm.
Table_Apx P-62 Summary of BMD Modeling Results for Lung Tumors in Female Mice
Model
LogLogistic
Gammab
Weibull0
Quantal-Linearb
Multistage 3°d
Multistage 2°b
Good ness of fit
p-value
0.283
0.218
0.218
0.218
AIC
166.52
166.97
166.97
166.97
BMDo.lPctAdd
(ppm)
0.649
0.772
0.772
0.772
BMDLo.ipctAdd
(ppm)
0.423
0.522
0.522
0.522
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LogProbit
Probit
Logistic
Model average
(multistage, log-
probit and
Weibull)
0.343
0.0956
0.0889
0.1298
167.13
169.23
169.51
0.0391
1.94
2.16
0.849
error6
1.47
1.64
0.634
b The Gamma model may appear equivalent to the Weibull model, however
differences exist in digits not displayed in the table. This also applies to the Multistage
3° model. This also applies to the Multistage 2° model. This also applies to the
Quantal-Linear model.
c For the Weibull model, the power parameter estimate was 1. The models in this row
reduced to the Quantal-Linear model.
d For the Multistage 3° model, the beta coefficient estimates were 0 (boundary of
parameters space). The models in this row reduced to the Multistage 2° model.
e BMD or BMDL computation failed for this model.
Summary of Model Averaging Fit Statistics
Model
Multistage, 3°
Weibull
Log-Probit
Weight
0.245
0.665
0.091
-2log(L)
162.97
162.97
166.96
AIC
170.97
168.97
172.96
BIC
184.16
178.87
182.85
Average-Model Benchmark Dose Estimate:
Nominally Specified Confidence Level:0.950
Weighting Criterion: AIC
BMD Calculation: Added Risk
BMR: 0.001000
BMD: 0.849148762733
BMDL(BCa):0.400888479370
BMDL(Percentile):0.634308392327
Acceleration: 0.043517
Bootstrap Resamples: 5000
Random Seed: 102210
Average-Model Goodness of Fit Test
Test Statistic: 3.274559
Bootstrap p-value: 0.129800
Parameter Estimates
Model
Multistage, 3°
Weibull
Parameter
gamma
beta(l)
beta(2)
beta(3)
gamma
alpha
Estimate
0.03348013
0.001340506
0
0
0.033480
1.0
Standard Error
0.02882729
0.0003669969
N/A
N/A
0.028840
N/A
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Log-Probit
beta
gamma
alpha
beta
0.001341
0.079419089201
-6.191081
1.0
0.000367
0.034577
0.272037
N/A
P-3-2 Large Intestine Adenomas in Female Rats
The doses and response data from the NTP (2011) study that were used for the modeling are
presented in Table_Apx P-63.
Table_Apx P-63 Incidence of Large Intestine Adenomas in Female Rats
Dose (ppm)
0
125
250
500
Number of animals
50
50
50
50
Number of Animals with
Tumors
0
1
2
5
Comparisons of model fits obtained from BMD modeling of the NTP (2011) study are provided
in Table_Apx P-64. All of the models tested had acceptable fits to the data acceptable by p-
value and visual inspection. The quantal-linear model had the lowest AIC value. A summary of
the model average results are shown for comparison with the BMDS results in Table_Apx P-64.
Detailed output of the model average run are shown below. The model average result was
selected because the model has an adequate p-value and model-averaging has been shown to
have reduced bias and better coverage in some cases (Wheeler and Bailer, 2007). In a
sensitivity analysis alternative model selections include the single best benchmark dose model
based on p-value, visual inspection and lowest AIC the quantal-linear model or the multistage
model per EPA Benchmark Dose Technical Guidance for cancer datasets (U.S. EPA, 2012a). The
BMDL of the quantal-linear model is 3.1 ppm and multistage model is 3.14 ppm, both are
similar (within a factor of 2) of the the model average BMDL of 5.005 ppm.
Table_Apx P-64 Summary of BMD Modeling Results for Large Intestine Adenomas in Female Rats
Model
Quantal-Linear
Multistage 3°
Multistage 2°
Weibull
Gamma
LogLogistic
LogProbit
Good ness of fit
p-value
0.989
0.999
0.996
0.991
1.0
0.989
0.979
AIC
61.234
63.109
63.115
63.126
63.1
63.128
63.150
BMDo.lPctAdd
(ppm)
5.27
6.56
7.44
11.8
12.2
12.5
22.5
BMDLo.ipctAdd
(ppm)
3.10
3.14
3.14
3.13
3.1
2.97
3.05E-10
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Probit
Logistic
Model average
(multistage, log-
probit and
Weibull)
0.758
0.722
0.824
63.982
64.145
20.4
21.9
13.5
10.3
11.4
5.005
Summary of Model Averaging Fit Statistics
Model
Multistage, 3°
Weibull
Log-Probit
Weight
0.191
0.514
0.295
-2log(L)
59.11
59.13
60.24
AIC
67.11
65.13
66.24
BIC
80.30
75.02
76.13
Average-Model Benchmark Dose Estimate:
Nominally Specified Confidence Level:0.950
Weighting Criterion: AIC
BMD Calculation: Added Risk
BMR: 0.001000
BMD: 13.472617282689
BMDL(BCa): 2.445277845095
BMDL(Percentile): 5.005030327500
Acceleration: -0.149668
Bootstrap Resamples: 5000
Random Seed: 331201
Average-Model Goodness of Fit Test
Test Statistic: 0.139777
Bootstrap p-value: 0.824400
Parameter Estimates
Model
Multistage, 3°
Weibull
Log-Probit
Parameter
gamma ^^k
beta(l)
beta(2)
beta(3)
gamma
alpha
beta
gamma
alpha
beta
Estimate
0.0
0.0001525544
0
2.307482E-10
0.0
1.238098
0.000047
0.006136953057
-7.449471
1.0
Standard Error
N/A
0.00006655318
N/A
N/A
N/A
0.739784
0.000206
0.011787
0.263198
N/A
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P-3-3 Keratoacanthoma and Squamous Cell Carcinomas in
Male Rats
The doses and response data from the NTP (2011) study that were used for the modeling are
presented in Table_Apx P-65.
Table_Apx P-65 Incidence of Keratoacanthoma and Squamous Cell Carcinomas in Male Rats
'animals Number of Animals
Dose (ppm)
0
125
250
500
Number of animals
50
50
50
50
Number of Animals
with Tumors
1
4
6
8
Comparisons of model fits obtained from BMD modeling of the NTP (2011) study are provided
in Table_Apx P-66. All of the models tested had acceptable fits to the data acceptable fits to the
data by p-value and visual inspection. The quantal-linear had the lowest AIC value. A summary
of the model average results are shown for comparison with the BMDS results in Table_Apx
P-66. Detailed output of the model average run are shown below. The model average result
was selected because the model has an adequate p-value and model-averaging has been shown
to have reduced bias and better coverage in some cases (Wheeler and Bailer, 2007). In a
sensitivity analysis alternative model selections include the single best benchmark dose model
based on p-value, visual inspection and lowest AIC the loglogistic model or the multistage
model per EPA Benchmark Dose Technical Guidance for cancer datasets (U.S. EPA, 2012a). The
BMDL of the loglogistic model is 1.58 ppm and multistage model is 1.78 ppm, both are similar
(within a factor of 2) of the the model average BMDL of 2.26 ppm.
Table_Apx P-66 Summary of BMD Modeling Results for Keratoacanthoma and Squamous Cell
Carcinomas in Male Rats
Model
LogLogistic
Gammab
Multistage 3°c
Multistage 2°b
Weibulld
Quantal-Linearb
Probit
Logistic
LogProbit
Good ness of fit
p-value
0.843
0.802
0.802
0.802
0.503
0.471
0.913
AIC
122.68
122.78
122.78
122.78
123.82
123.99
124.35
BMDo.lPctAdd
(ppm)
2.72
2.96
2.96
2.96
6.80
7.54
1.25
BMDLo.ipctAdd
(ppm)
1.58
1.78
1.78
1.78
4.76
5.31
error6
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PEER REVIEW DRAFT - DO NOT QUOTE OR CITE
Model average
(multistage, log-probit
andWeibull)
0.7077
3.73
2.26
b The Gamma model may appear equivalent to the Weibull model, however differences exist in digits not
displayed in the table. This also applies to the Multistage 3° model. This also applies to the Multistage 2°
model. This also applies to the Quantal-Linear model.
c For the Multistage 3° model, the beta coefficient estimates were 0 (boundary of parameters space). The
models in this row reduced to the Multistage 2° model.
d For the Weibull model, the power parameter estimate was 1. The models in this row reduced to the
Quantal-Linear model.
e BMD or BMDL computation failed for this model.
Summary of Model Averaging Fit Statistics
Model
Multistage, 3°
Weibull
Log-Probit
Weight
0.213
0.580
0.207
-21og(L)
118.78
118.78
120.84
AIC
126.78
124.78
126.84
BIC
139.97
134.67
136.74
Average-Model Benchmark Dose Estimate:
Nominally Specified Confidence Level:0.950
Weighting Criterion: AIC
BMD Calculation: Added Risk
BMR: 0.001000
BMD: 3.732432783338
BMDL(BCa): 1.505273123061
BMDL(Percentile): 2.260265766150
Acceleration: 0.030873
Bootstrap Resamples: 5000
Random Seed: 257515
Average-Model Goodness of Fit Test
Test Statistic: 0.707725
Bootstrap p-value: 0.586800
Parameter Estimates
Model
Multistage, 3°
Weibull
Log-Probit
Parameter
gamma
beta(l)
beta(2)
beta(3)
gamma
alpha
beta
gamma
alpha
beta
Estimate
0.02541313
0.0003467654
0
0
0.025414
1.0
0.000347
0.050387778679
-7.271630
1.0
Standard Error
0.02238034
0.0001309450
N/A
N/A
0.022401
N/A
0.000131
0.025518
0.311627
N/A
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