vvEPA
EPA Document No.
EPA-815-R-24-001
Economic Analysis for the Final Per- and Polyfluoroalkyl Substances National
Primary Drinking Water Regulation
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Economic Analysis for the Final Per- and Polyfluoroalkyl Substances National
Primary Drinking Water Regulation
Prepared by:
U.S. Environmental Protection Agency
Office of Water
Office of Ground Water and Drinking Water
Standards and Risk Management Division
Washington, DC 20460
EPA Document Number: EPA-815-R-24-001
APRIL 2024
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Disclaimer
This document has been reviewed in accordance with EPA policy and approved for publication.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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Authors, Contributors, and Reviewers
This document was prepared by the Office of Water (OW) of the U.S. Environmental Protection
Agency (EPA). The agency gratefully acknowledges the valuable contributions of the EPA
scientists and economists from the OW's Standards and Risk Management Division, Water
Economics Center, Office of Science and Technology, and the Office of Policy's National
Center for Environmental Economics. This document was prepared by Katherine Foreman,
Rachel Gonsenhauser, Austin Heinrich, Erik Helm, Kirsten Studer, and Morgan Webster. The
EPA scientists and economists who provided valuable contributions to the development of the
document include Lena Abu-Ali, Carlye Austin, Wes Austin, Keelan Baldwin, Elizabeth Berg,
Adam Cadwallader, Stanley Gorzelnik, Ashley Greene, Hannah Holsinger, Won Hyung Lee,
Brittany Jacobs, Rajiv Khera, Alexis Lan, Casey Lindberg, Gregory Miller, Michael Trombley,
and Holly Young. The agency gratefully acknowledges the valuable technical reviews by Chris
Dockins and Ruth Etzel and the executive direction from Ryan Albert and Eric Burneson.
This document was prepared in collaboration with ICF under the U.S. EPA Contracts EP-C-16-
011 and 68HE0C18D0001. This document was prepared by Anna Bel ova, Elena Besedin, Sorina
Eftim, Brad Firlie, Andre Kiesel, Kate Munson, and Jerry Stedge.
This document was prepared in collaboration with Abt Associates under the BPA Number
68HERC21A0011. Contributors from Abt Associates include Holly Bender, Emma Glidden-
Lyon, Claire Lay, Shannon Ragland and Patrick Ransom.
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Contents
1 Executive Summary 1-1
2 Introduction 2-1
2.1 Summary of the Final PFAS Rule and Regulatory Alternatives 2-2
2.2 Economic Analysis Assumptions 2-3
2.2.1 Compliance Schedule and Period of Analysis for Final Rule 2-3
2.2.2 Dollar Year and Discount Rates 2-3
2.2.3 Annualization 2-4
2.2.4 Population 2-4
2.2.5 Valuation 2-4
2.3 Document Organization 2-5
2.4 Supporting Documentation 2-6
3 Need for the Rule 3-1
3.1 Previous EPA Nonregulatory and Regulatory Actions Potentially Affecting PFAS
Drinking Water Management 3-1
3.1.1 PFAS Council and PFAS Strategic Roadmap 3-1
3.1.2 Final Regulatory Determinations on the Fourth Drinking Water Contaminant
Candidate List 3-1
3.1.3 Proposed PFAS National Primary Drinking Water Rule and Regulatory
Determinations for PFHxS, PFNA, HFPO-DA, PFBS, and their Mixtures 3-2
3.1.4 Unregulated Contaminant Monitoring Rule 3-2
3.2 Statutory Authority for Promulgating the Rule 3-3
3.3 Economi c Rati onal e 3-4
4 Baseline Drinking Water System Conditions 4-1
4.1 Introduction 4-1
4.2 Data Sources 4-1
4.2.1 SDWIS/Fed 2021 4-2
4.2.2 Unregulated Contaminant Monitoring Rule 4-5
4.2.3 Independent State Sampling Programs 4-5
4.2.4 Six-Year Review Data 4-5
4.2.5 Geometries and Characteristics of Public Water Systems (2000) 4-6
4.2.6 Community Water System Survey (2006) 4-6
4.3 Drinking Water System Baseline/Industry Profile 4-7
4.3.1 Water System Inventory 4-7
4.3.2 Population and Households Served 4-10
4.3.3 Treatment Plant Characterization/Production Profile 4-13
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4.3.4 Public Water System Labor Rates 4-17
4.3.5 Cost of Capital 4-19
4.4 Occurrence of PFAS 4-21
4.4.1 Overview of UCMR 3 Data 4-21
4.4.2 Overview of State PFAS Data 4-21
4.4.3 Overview of PFAS Co-Occurrence 4-24
4.4.4 Summary of PFAS Occurrence Data Analysis 4-25
4.4.5 Summary of National PFAS Occurrence 4-27
4.5 Uncertainties in the Baseline and Compliance Characteristics of Systems 4-43
5 Cost Analysis 5-1
5.1 Introducti on 5-1
5.1.1 Chapter Overview 5-1
5.1.2 Uncertainty Characterization 5-1
5.1.3 Summary of Quantified National Cost Estimates of the Final Rule 5-2
5.2 Overview of SafeWater Multi-Contaminant Benefit Cost Model (MCBC) 5-7
5.2.1 Modeling PWS Variability in SafeWater MCBC 5-8
5.3 Estimating Public Water System Costs 5-10
5.3.1 PWS Treatment Costs 5-10
5.3.2 Estimating PWS Administrative and Monitoring Costs 5-29
5.4 Estimating Primacy Agency Costs 5-36
5.5 PWS-Level Cost Estimates 5-38
5.6 Household-Level Cost Estimates 5-39
5.7 Discussion of Data Limitations and Uncertainty 5-39
6 Benefits Analysis 6-1
6.1 Introduction 6-1
6.1.1 Chapter Overview 6-2
6.1.2 Uncertainty Characterization 6-2
6.1.3 Summary of Quantified National Benefits Estimates of the Final Rule 6-3
6.1.4 Life Table Modeling Background 6-6
6.2 Overview of Benefit Categories 6-7
6.2.1 Availability of Pharmacokinetic (PK) Models 6-14
6.2.2 Benefits of PFOA and PFOS Exposure Reduction 6-14
6.2.3 Summary of Health Information Considered in the Economic Analysis 6-25
6.2.4 Nonquantifiable Benefits of PFAS in Final Rule and PFAS Expected to be
Co-Removed 6-25
6.2.5 Sensitive Populations 6-31
6.2.6 Co-Removal of Additional Contaminants 6-32
6.3 Blood Serum Concentration Modeling for PFAS 6-33
6.3.1 Introduction 6-33
6.3.2 Application of PK Models to Benefits Analyses 6-33
6.3.3 Contributions from Other Sources 6-35
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6.4 Developmental Effects 6-35
6.4.1 Overview of the Birth Weight Risk Reduction Analysis 6-36
6.4.2 Estimation of Birth Weight Changes Between Baseline and Regulatory
Alternatives 6-38
6.4.3 Estimation of Birth Weight Impacts 6-41
6.4.4 Valuation of Reduced Birth Weight Impacts 6-50
6.4.5 Results 6-54
6.5 Cardiovascular Disease 6-55
6.5.1 Overview of the Cardiovascular Disease Risk Analysis 6-55
6.5.2 Cardiovascular Disease Exposure-Response Analyses 6-58
6.5.3 Estimation of Cardiovascular Disease Risk Reductions 6-61
6.5.4 Valuation of Cardiovascular Disease Risk Reductions 6-71
6.5.5 Results 6-73
6.6 Renal Cell Carcinoma 6-74
6.6.1 Overview of the RCC Risk Reduction Analysis 6-74
6.6.2 RCC Exposure-Response Modeling 6-77
6.6.3 Estimation of RCC Risk Reductions 6-78
6.6.4 Valuation of RCC Risk Reductions 6-79
6.6.5 Results 6-81
6.7 Benefits from Co-Removal of Disinfection Byproducts 6-83
6.7.1 Overview of Reduced Disinfection Byproduct Formation 6-84
6.7.2 Estimation of Bladder Cancer Risk Reductions 6-105
6.7.3 Results 6-112
6.8 Limitations and Uncertainties of the Benefits Analysis 6-113
7 Comparison of Costs to Benefits 7-1
8 Environmental Justice Analysis 8-11
8.1 Introducti on 8-11
8.2 Literature Review 8-12
8.2.1 Methods 8-12
8.2.2 Findings 8-12
8.2.3 Discussion and Limitations 8-17
8.3 EJ PFAS Exposure Analysis 8-17
8.3.1 Data Sources and Approach 8-18
8.3.2 EJ Exposure Analysis Results 8-27
8.4 SafeWater EJ Analysis of Final Rule and Regulatory Alternatives 8-64
8.4.1 Methodology 8-64
8.4.2 SafeWater EJ Analysis Results 8-66
8.5 Conclusions 8-80
8.5.1 EJ PFAS Exposure Analysis 8-80
8.5.2 SafeWater EJ Analysis of Regulatory Options 8-81
8.5.3 Overall Environmental Justice Conclusion 8-82
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9 Statutory and Administrative Requirements 9-1
9.1 Executive Order 12866: Regulatory Planning and Review and Executive Order
14094: Modernizing Regulatory Review 9-1
9.2 Additional Analysis Pursuant to EO 12866 9-2
9.3 Paperwork Reduction Act 9-12
9.3.1 Primacy Agency Activities 9-12
9.3.2 Public Water System Activities 9-13
9.4 The Final Regulatory Flexibility Analysis 9-14
9.4.1 Need for, Objectives, and Legal Basis of the Rule 9-15
9.4.2 Summary of the SBAR Comments and Recommendations 9-16
9.4.3 Summary of the Final Rule and Public Comments on the Impacts to Small
Entities 9-18
9.4.4 Number and Description of Small Entities Affected 9-19
9.4.5 Description of Compliance Requirements of the Final Rule 9-20
9.4.6 Analysis of Impact of Regulatory Options on Small System Costs 9-21
9.4.7 The EPA's Steps to Minimize the Significant Economic Impact of the Final
Rule on Small Systems 9-23
9.5 Unfunded Mandates Reform Act 9-26
9.6 Executive Order 13132: Federalism 9-28
9.7 Executive Order 13175: Consultation and Coordination with Indian Tribal
Governments 9-29
9.8 Executive Order 13045: Protection of Children from Environmental Health and
Safety Risks 9-30
9.9 Executive Order 13211: Actions That Significantly Affect Energy Supply,
Distribution, or Use 9-31
9.9.1 Energy Supply 9-31
9.9.2 Energy Distribution 9-31
9.9.3 Energy Use 9-31
9.10 National Technology Transfer and Advancement Act 9-32
9.11 Executive Order 12898: Federal Actions to Address Environmental Justice in
Minority Populations and Low-Income Populations, Executive Order 14096:
Revitalizing our Nation's Commitment to Environmental Justice for All 9-32
9.12 Consultations with the Science Advisory Board, National Drinking Water Council,
and the Secretary of Health and Human Services 9-33
9.12.1 Science Advisory Board 9-33
9.12.2 National Drinking Water Advisory Council 9-33
9.12.3 Secretary of Health and Human Services 9-34
9.13 Affordability Analyses 9-34
9.13.1 National Small System Affordability Determination 9-35
9.13.2 Supplemental Affordability Analyses 9-38
10 References 10-1
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Tables
Table 4-1: Data Sources Used to Develop the Water System Characteristics 4-2
Table 4-2: Inventory of CWSs 4-8
Table 4-3: Inventory of NTNCWSs 4-9
Table 4-4: Population and Number of Households Served by CWSs 4-11
Table 4-5: Population Served by NTNCWSs 4-12
Table 4-6: Frequency Distribution of EP Inputs for CWSs 4-15
Table 4-7: Frequency Distribution of EP Inputs for NTNCWSs 4-15
Table 4-8: Functions for Design and Average Daily Flow by System Types 4-16
Table 4-9: Design and Average Daily Flow for CWSs 4-17
Table 4-10: Design and Average Daily Flow for NTNCWSs 4-17
Table 4-11: Hourly Wage Rates Based on CWSS Data ($2007) 4-18
Table 4-12: Hourly Labor Costs Including Wages Plus Benefits ($2007) 4-18
Table 4-13: Hourly Labor Costs Escalated to $2022 4-19
Table 4-14: Weighted Average Cost of Capital by PWS Ownership and Size Category 4-20
Table 4-15: Non-Targeted State PFAS Finished Water Data - Summary of Samples with
Detections of PFAS Included in Final Regulation 4-23
Table 4-16: Non-Targeted State PFAS Finished Water Data - Summary of Systems with
Detections of Select PFAS 4-24
Table 4-17: State PFAS Regulations 4-26
Table 4-18: Total Systems Impacted, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) 4-28
Table 4-19: Total Systems Impacted, Option la (PFOA and PFOS MCLs of 4.0 ppt) 4-29
Table 4-20: Total Systems Impacted, Option lb (PFOA and PFOS MCLs of 5.0 ppt) 4-30
Table 4-21: Total Systems Impacted, Option lc (PFOA and PFOS MCLs of 10.0 ppt) 4-31
Table 4-22: Total Entry Points Impacted, Final Rule (PFOA and PFOS MCLs of 4.0 ppt
each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) 4-32
Table 4-23: Total Entry Points Impacted, Option la (PFOA and PFOS MCLs of 4.0 ppt) 4-33
Table 4-24: Total Entry Points Impacted, Option lb (PFOA and PFOS MCLs of 5.0 ppt) 4-34
Table 4-25: Total Entry Points Impacted, Option lc (PFOA and PFOS MCLs of 10.0 ppt).... 4-35
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Table 4-26: Total Population at PWSs Impacted, Final Rule (PFOA and PFOS MCLs of 4.0
ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) 4-36
Table 4-27: Total Population at PWSs Impacted, Option la (PFOA and PFOS MCLs of 4.0
ppt) 4-37
Table 4-28: Total Population at PWSs Impacted, Option lb (PFOA and PFOS MCLs of 5.0
ppt) 4-38
Table 4-29: Total Population at PWSs Impacted, Option lc (PFOA and PFOS MCLs of
10.0 ppt) 4-39
Table 4-30: Total Population at Entry Points Impacted, Final Rule (PFOA and PFOS MCLs
of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1).... 4-40
Table 4-31: Total Population at Entry Points Impacted, Option la (PFOA and PFOS MCLs
of 4.0 ppt) 4-41
Table 4-32: Total Population at Entry Points Impacted, Option lb (PFOA and PFOS MCLs
of 5.0 ppt) 4-42
Table 4-33: Total Population at Entry Points Impacted, Option lc (PFOA and PFOS MCLs
of 10.0 ppt) 4-43
Table 4-34: Limitations and Uncertainties that Apply to the Baseline Characteristics of
Systems for the Final PFAS Rule 4-44
Table 5-1: Quantified Sources of Uncertainty in Cost Estimates 5-2
Table 5-2: National Annualized Costs, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) (Million $2022) 5-4
Table 5-3: National Annualized Costs, Option la (PFOA and PFOS MCLs of 4.0 ppt)
(Million $2022) 5-5
Table 5-4: National Annualized Costs, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
(Million $2022) 5-6
Table 5-5: National Annualized Costs, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
(Million $2022) 5-7
Table 5-6: Model PWS Variability Characteristics and Data Sources 5-9
Table 5-7: Frequency Distribution to Estimate Influent TOC in mg/L 5-13
Table 5-8: Initial Compliance Forecast Including POU RO 5-14
Table 5-9: Initial Compliance Forecast Excluding POU Devices 5-15
Table 5-10: Estimated Parameter Values for Technology-Specific Bed Life Equations 5-16
Table 5-11: Cost Elements Included in All WBS Models 5-21
Table 5-12: Technology-Specific Cost Elements Included in the GAC Model 5-23
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Table 5-13: Technology-Specific Cost Elements Included in the PFAS-Selective IX Model.. 5-25
Table 5-14: Technology-Specific Cost Elements Included in the Nontreatment Model 5-26
Table 5-15: Implementation Administration Startup Costs ($2022) 5-30
Table 5-16: Modeled Initial and Long-Term Sampling Frequencies Per System Entry Point.. 5-32
Table 5-17: Sampling Costs ($2022) 5-33
Table 5-18: Treatment Administration Costs ($2022) 5-35
Table 5-19: Public Notification Burden Estimate 5-36
Table 5-20: Primacy Agency Costs ($2022) 5-37
Table 5-21: Limitations that Apply to the Cost Analysis for the Final PFAS Rule 5-39
Table 6-1: Quantified Sources of Uncertainty in Benefits Estimates 6-3
Table 6-2: National Annualized Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt
each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1)
(Million $2022) 6-4
Table 6-3: National Annualized Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt)
(Million $2022) 6-5
Table 6-4: National Annualized Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
(Million $2022) 6-5
Table 6-5: National Annualized Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
(Million $2022) 6-6
Table 6-6: Overview of Health Benefits Categories Considered in the Analysis of Changes
in PFAS Drinking Water Levels 6-9
Table 6-7: Overview of Epidemiology and Toxicology Evidence of PFAS Effects on
Health Outcomes 6-12
Table 6-8: Summary of Studies Relating PFOA or PFOS to Birth Weight 6-39
Table 6-9: Serum Exposure-Birth Weight Response Estimates 6-40
Table 6-10: Race/Ethnicity- and Gestational Age-Specific Birth Weight Marginal Effects
and Odds Ratios from the Mortality Regression Models 6-45
Table 6-11: Simulated Cost Changes for Birth Weight Increases ($2022) (Based on Klein
and Lynch, 2018 Table 8) 6-52
Table 6-12: National Birth Weight Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt
each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1)
(Million $2022) 6-54
Table 6-13: National Birth Weight Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt)
(Million $2022) 6-54
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Table 6-14: National Birth Weight Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
(Million $2022) 6-55
Table 6-15: National Birth Weight Benefits, Option lc (PFOA and PFOS MCLs of 10.0
ppt) (Million $2022) 6-55
Table 6-16: Studies Selected for Inclusion in the Meta-Analyses 6-59
Table 6-17: Estimated Shares of Fatal and Non-Fatal First Hard CVD Events Based on
MEPS and HCUP Data 6-68
Table 6-18: Estimated Risk of Post-Acute CVD Mortality Following the First Non-Fatal
Hard CVD Event 6-71
Table 6-19: Cost of Illness of Non-Fatal First CVD Event Used in Modeling 6-72
Table 6-20: National CVD Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million
$2022) 6-73
Table 6-21: National CVD Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt) 6-73
Table 6-22: National CVD Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt) 6-74
Table 6-23: National CVD Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt) 6-74
Table 6-24: RCC Morbidity Valuation 6-81
Table 6-25: National RCC Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million
$2022) 6-82
Table 6-26: National RCC Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt) 6-82
Table 6-27: National RCC Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt) 6-83
Table 6-28: National RCC Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt) 6-83
Table 6-29: Data Sources and How the Information Derived from each Source is Used in
the DBP Co-Removal Analysis 6-85
Table 6-30: DBP ICR (1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Summary of
Raw Water TOC Annual System Means for Ground Water Systems 6-89
Table 6-31: DBP ICR (1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Summary of
Raw Water TOC Annual System Means for Surface Water Systems 6-89
Table 6-32: SYR3 ICR (2011) and SYR4 ICR (2019) - Summary of Finished Water TOC
Annual System Means for Ground Water Systems 6-90
Table 6-33: SYR3 ICR (2011) and SYR4 ICR (2019) - Summary of Finished Water TOC
Annual System Means for Surface Water Systems 6-90
Table 6-34: DBP ICR (Aux 1; 1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Finished
Water Annual System Mean TOC; Common Surface Water Systems 6-91
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Table 6-35: Summary of THM4 Baseline Comparing DBP ICR and SYR4 ICR 6-92
Table 6-36: DBP ICR (Aux 1) Summary of THM4 Concentrations Based on Disinfectant
and Source Water Type 6-93
Table 6-37: TOC Reduction for All Waters (Both Surface Water and Ground Water) with
GAC EBCT of 20 Min and a 2-year Replacement Time 6-98
Table 6-38: Estimation of ATHM4 in Surface Water with a 20 Min EBCT, and a 2-year
GAC Replacement Time 6-99
Table 6-39: Estimation of ATHM4 in Ground Water with a 20 Min EBCT, and a 2-year
GAC Replacement Time 6-99
Table 6-40: Selected Distribution Systems from SYR4 Based on Outlined Criteria 6-101
Table 6-41: Information on Selected Distribution System and Corresponding ATHM4
Values 6-103
Table 6-42: Comparison Between ICR TSD Conservative ATHM4 and SYR4 ATHM4 for
Surface Water Systems 6-104
Table 6-43: Bladder Cancer Morbidity Valuation 6-111
Table 6-44: National Bladder Cancer Benefits, Final Rule (PFOA and PFOS MCLs of 4.0
ppt each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1)
(Million $2022) 6-112
Table 6-45: National Bladder Cancer Benefits, Option la (PFOA and PFOS MCLs of 4.0
ppt) 6-112
Table 6-46: National Bladder Cancer Benefits, Option lb (PFOA and PFOS MCLs of 5.0
ppt) 6-113
Table 6-47: National Bladder Cancer Benefits, Option lc (PFOA and PFOS MCLs of 10.0
ppt) 6-113
Table 6-48: Limitations and Uncertainties that Apply to Benefits Analyses Considered for
the Final PFAS Rule 6-114
Table 6-49: Limitations and Uncertainties in the PK Model Application 6-118
Table 6-50: Limitations and Uncertainties in the Analysis of Birth Weight Benefits Under
the Final Rule 6-119
Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule 6-122
Table 6-52: Limitations and Uncertainties in the Analysis of RCC Benefits Under the Final
Rule 6-127
Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule 6-129
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Table 7-1: Annualized Quantified National Costs and Benefits, Final Rule (PFOA and
PFOS MCLs of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each,
and HI of 1) (Million $2022) 7-2
Table 7-2: Annualized Quantified National Costs and Benefits, Option la (PFOA and
PFOS MCLs of 4.0 ppt) (Million $2022) 7-3
Table 7-3: Annualized Quantified National Costs and Benefits, Option lb (PFOA and
PFOS MCLs of 5.0 ppt) (Million $2022) 7-3
Table 7-4: Annualized Quantified National Costs and Benefits, Option lc (PFOA and
PFOS MCLs of 10.0 ppt) (Million $2022) 7-4
Table 7-5: Summary of Quantified and Nonquantified Benefits and Costs in the National
Analysis 7-6
Table 7-6: Potential Impact of Nonquantifiable Benefits and Costs 7-7
Table 8-1: Categorizing of PWSs Based on Data Availability for PFAS Occurrence and
PWS Service Area Boundaries 8-19
Table 8-2: Data Sources for Predelineated PWS Service Areas 8-22
Table 8-3: Number of Category 1 and 2 PWSs and Populations Served by Size and State 8-29
Table 8-4: Population Served by Category 1 and 2 PWSs Compared to Percent of U.S.
Population by Demographic Group 8-31
Table 8-5: Baseline Scenario: Population Served by Category 1 and 2 PWS Service Areas
Above Baseline Thresholds and as a Percent of Total Population Served 8-35
Table 8-6: Modeled Average PFAS Concentrations (ppt) by Demographic Group in the
Baseline, Category 1 and 2 PWS Service Areas 8-36
Table 8-7: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above UCMR 5 MRLs and as
a Percent of Total Population Served 8-39
Table 8-8: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a
Hypothetical Regulatory Scenario with Maximum Contaminant Level at the
UCMR 5 MRLs, Category 1 and 2 PWS Service Areas 8-40
Table 8-9: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above 10.0 ppt and as a
Percent of Total Population Served 8-43
Table 8-10: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a
Hypothetical Regulatory Scenario with Maximum Contaminant Level at 10.0
ppt, Category 1 and 2 PWS Service Areas 8-44
Table 8-11: Population Served by Category 1 and 2 PWSs and Percent of U.S. Population
by Demographic Group, Large Systems 8-46
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Table 8-12: Population Served by Category 1 and 2 PWSs and Percent of U.S. Population
by Demographic Group, Small Systems 8-47
Table 8-13: Baseline Scenario: Demographic Breakdown of Population Served by Category
1 and 2 PWS Service Areas Above Baseline Thresholds and as a Percent of
Total Population Served, Large Systems 8-50
Table 8-14: Baseline Scenario: Demographic Breakdown of Population Served by Category
1 and 2 PWS Service Areas Above Baseline Thresholds and as a Percent of
Total Population Served, Small Systems 8-51
Table 8-15: Modeled Average PFAS Concentrations (ppt) by Demographic Group and
System Size in the Baseline, Category 1 and 2 PWS Service Areas 8-52
Table 8-16: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above UCMR 5 MRLs and as
a Percent of Total Population Served, Large Systems 8-56
Table 8-17: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above UCMR 5 MRLs and as
a Percent of Total Population Served, Small Systems 8-57
Table 8-18: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a
Hypothetical Regulatory Scenario with Maximum Contaminant Levels at the
UCMR 5 MRLs, Category 1 and 2 PWS Service Areas 8-58
Table 8-19: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above 10.0 ppt and as a
Percent of Total Population Served, Large Systems 8-61
Table 8-20: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population
Served by Category 1 and 2 PWS Service Areas Above 10.0 ppt and as a
Percent of Total Population Served, Small Systems 8-62
Table 8-21: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a
Hypothetical Regulatory Scenario with Maximum Contaminant Levels at 10.0
ppt, Category 1 and 2 PWS Service Areas 8-63
Table 8-22: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each, PFHxS, PFNA,
HFPO-DA MCLs of 10 ppt each and HI of 1) 8-69
Table 8-23: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option la (PFOA and PFOS MCLs of 4.0 ppt) 8-70
Table 8-24: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option lb (PFOA and PFOS MCLs of 5.0 ppt) 8-71
Table 8-25: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option lc (PFOA and PFOS MCLs of 10.0 ppt) 8-71
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Table 8-26: Annualized Population Weighted Household Cost by PWS Size Category and
Race/Ethnicity Group ($2022) 8-75
Table 8-27: Annualized Population Weighted Household Cost by PWS Size Category and
Income Level ($2022) 8-76
Table 8-28: Annualized Population-Weighted Household Cost for Treating PWSs by Size
Category and Race/Ethnicity Group 8-79
Table 8-29: Annualized Population Weighted Household Cost for Treating PWSs by PWS
Size Category and Income Level ($2022) 8-80
Table 9-1: Estimates of the Social Cost of CO2, 2020-2080 (2020$ per metric ton CO2) 9-5
Table 9-2: Entry Point Level Electricity Consumption Range by System Size and
Technology (MWh/year) 9-7
Table 9-3: National Electricity Use (MWh/year) by Technology and System Size 9-8
Table 9-4: CO2 Emissions per MWh Calculated from Post-IRA 2022 IPM Reference Case... 9-10
Table 9-5: CO2 emissions per Year from Operating Treatment Technologies to Comply
with the PFAS NPDWR 9-11
Table 9-6: Annualized Monetized Climate Disbenefits Associated with Operating
Treatment Technologies to Comply with the Final PFAS NPDWR ($2022) 9-12
Table 9-7: Average Annual Burden, Costs, and Responses for the Final Rule Information
Collection Request 9-13
Table 9-8: Total Burden, Costs, and Responses for Each Required Activity 9-14
Table 9-9: Inventory of Small CWSs 9-20
Table 9-10: Inventory of Small NTNCWSs 9-20
Table 9-11: Cost-Revenue Ratio for Small CWSs, Final Rule (PFOA and PFOS MCLs of
4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1)
(Commercial Cost of Capital) 9-23
Table 9-12: Annual Costs by PWS Size and Ownership, Final Rule (Million $2022)
(Commercial Cost of Capital) 9-28
Table 9-13: SSCT Affordability Analysis Results - Technologies that Meet Effectiveness
Criterion 9-36
Table 9-14: Expenditure Margins for SSCT Affordability Analysis 9-37
Table 9-15: Total Annual Cost per Household for Candidate Technologies 9-37
Table 9-16: Total Annual Cost per Household Assuming Hazardous Waste Disposal 9-38
Table 9-17: Potential Annual Expenditure Margins for SSCT Affordability Analysis 9-40
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Table 9-18: Affordability Analysis Results Using a 1.0% of Annual Median Household
Income Expenditure Margin 9-40
Table 9-19: Affordability Analysis Results Using a 2.5% of Lowest Quintile of Annual
Household Income Expenditure Margin 9-41
Table 9-20: Annual Cost per Household for Candidate Technologies Assuming 100%
Financial Assistance for Technology Capital Costs 9-44
Table 9-21: Affordability Analysis Results Using a 2.5% of Annual Median Household
Income Minus the Baseline Median Annual Drinking Water Cost Expenditure
Margin and Assuming 100% Financial Assistance for Technology Capital Costs. 9-45
Table 9-22: Affordability Analysis Results Using a 1.0% of Annual Median Household
Income Expenditure Margin and Assuming 100% Financial Assistance for
Technology Capital Costs 9-46
Table 9-23: Affordability Analysis Results Using a 2.5% of Lowest Quintile of Annual
Household Income Expenditure Margin and Assuming 100% Financial
Assistance for Technology Capital Costs 9-46
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Figures
Figure 5-1: Approach Used by SafeWater MCBC to Model PWS Variability 5-10
Figure 6-1: Overview of Analysis of Birth Weight-Related Benefits 6-38
Figure 6-2: Comparison of Change in Incidence of Infant Death per 1 g Increase in Birth
Weight by Gestational Age Category and Race/Ethnicity (Deaths per 1,000
Births) 6-44
Figure 6-3: Weighted Mortality Odds Ratios Based on Populations of Infants Falling into
100 g Birth Weight Increments and Four Gestational Age Categories 6-47
Figure 6-4: Piecewise Medical Cost Function Calculated by Klein and Lynch (2018) for
Three Increments in Increased Birth Weight (18 g, 50 g, and 100 g) 6-51
Figure 6-5. Interpolated Cost of Illness at Baseline Average Birth Weights, by Estimated
Change in Birth Weight Under the Final Rule 6-53
Figure 6-6: Overview of the CVD Risk Model 6-57
Figure 6-7: Overview of Life Table Calculations in the CVD Model 6-63
Figure 6-8: CVD Model Calculations for Ages 40+ Tracking CVD 6-65
Figure 6-9: Overview of Analysis of Reduced RCC Risk 6-76
Figure 6-10: Overview of Analysis of Co-Removal Benefits 6-87
Figure 6-11: Estimated TOC Percent Removal in Ground Water Using GAC Based on
Logistic Equation Model 6-96
Figure 6-12: Estimated TOC Percent Removal in Surface Water Using GAC Based on
Logistic Equation Model 6-97
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Acronyms and Abbreviations
ACS
American Community Survey
AFFF
Aqueous Film Forming Foam
AIX
Anion Exchange
ANSI
American National Standards Institute
AOC
Assimilable Organic Carbon
ARIC
Atherosclerosis Risk in Communities
ATSDR
Agency for Toxic Substances and Disease Registry
AWWA
American Water Works Association
BAT
Best Available Technology
BIL
Bipartisan Infrastructure Law
BLS
Bureau of Labor Statistics
BP
Blood Pressure
BV
Bed Volumes
CARDIA
Coronary Artery Risk Development in Young Adults
CBX
SafeWater Cost Benefit Model
CCL
Contaminant Candidate List
CCR
Consumer Confidence Report
CDC
Centers for Disease Control and Prevention
CERCLA
Comprehensive Environmental Response, Compensation, and Liability Act
CFR
Code of Federal Regulations
CHMS
Canadian Health Measures Survey
COI
Cost of Illness
CPI
Consumer Price Index
CVD
Cardiovascular Disease
CWSs
Community Water Systems
CWSS
Community Water System Survey
DBP
Disinfection Byproduct
DHS
Department of Homeland Security
DWSRF
Drinking Water State Revolving Fund
EA
Economic Analysis
EBCT
Empty Bed Contact Time
ECEC
Employer Cost for Employee Compensation
ECI
Employment Cost Index
ECTT
Error Code Tracking Tool
EC-SDC
Emerging Contaminants in Small or Disadvantaged Communities
EJ
Environmental Justice
EP
Entry Point
EPA
U.S. Environmental Protection Agency
FR
Federal Register
GAC
Granular Activated Carbon
GDP
Gross Domestic Product
GHG
Greenhouse Gas
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gpm
GWUDI
HDLC
HESD
HFPO-DA
HHS
HI
HRRCA
HTN
HUC
ICR
ICR TSD
IHS
IPM
IRA
IRFA
IS
IWG
IX
LBW
LDLC
LRAA
MCBC
MCLGs
MCLs
MCMC
MGD
MHI
MI
MRL
NCHS
NCWSs
NDWAC
NF
NHANES
NOM
NPDWR
NSF
NTNCWSs
NTTAA
O&M
OEHHA
OES
OIRA
APRIL 2024
Gallons per Minute
Ground Water Under the Direct Influence
High-Density Lipoprotein Cholesterol
Health Effects Support Document
Hexafluoropropylene Oxide Dimer Acid
Department of Health and Human Services
Hazard Index
Health Risk Reduction and Cost Analysis
Hypertension
Hydraulic Unit Code
Information Collection Request
Information Collection Rule Treatment Study Database
Indian Health Service
Integrated Planning Model
Inflation Reduction Act
Initial Regulatory Flexibility Analysis
Ischemic Stroke
Interagency Working Group
Ion Exchange
Low Birth Weight
Low-Density Lipoprotein Cholesterol
Locational Running Annual Average
Multi-Contaminant Benefit-Cost Model
Maximum Contaminant Level Goals
Maximum Contaminant Levels
Markov Chain Monte Carlo
Million Gallons Per Day
Median Household Income
Myocardial Infarction
Minimum Reporting Level
National Center for Health Statistics
Non-Community Water Systems
National Drinking Water Advisory Council
Nanofiltration
National Health and Nutrition Examination Survey
Natural Organic Matter
National Primary Drinking Water Regulation
National Sanitation Foundation
Non-Transient Non-Community Water Systems
National Technology Transfer and Advancement Act
Operation and Maintenance
California Environmental Protection Agency's Office of Environmental Health Hazard
Assessment
Occupational Employment Survey
Office of Information and Regulatory Affairs
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OMB
ORD
OSHA
PAF
PBPK
PFAA
PFAS
PFBS
PFHpA
PFHxS
PFNA
PFOA
PFOS
PK
POU
POURO
PRA
PWS
PWSID
PWSS
Q
RCC
RCRA
RFA
RIA
RO
RO/NF
RSSCT
SAB
SBA
SBAR
SBREFA
SDWA
SDWIS
SEER
SER
SGA
SISNOSE
soc
SSCTs
T&C
T3
T4
TC
APRIL 2024
Office of Management and Budget
Office of Research and Development
Occupational Safety and Health Administration
Population Attributable Fraction
Physiological-Based Pharmacokinetic
Perfluorinated Alkyl Acids
Per- And Polyfluoroalkyl Substances
Perfluorobutanesulfonic Acid
Perfluoroheptanoic Acid
Perfluorohexanesulfonic Acid
Perfluorononanoic Acid
Perfluorooctanoic Acid
Perfluorooctane Sulfonatic Acid
Pharmacokinetic
Point-of-Use
Point-of-Use Reverse Osmosis
Paperwork Reduction Act
Public Water System
Public Water System Identifier
Public Water System Supervision
Design Flow
Renal Cell Carcinoma
Resource Conservation and Recovery Act
Regulatory Flexibility Act
Regulatory Impact Analysis
Reverse Osmosis
Reverse Osmosis/Nanofiltration
Rapid Small-Scale Column Test
Science Advisory Board
Small Business Administration
Small Business Advocacy Review
Small Business Regulatory Enforcement Fairness Act
Safe Drinking Water Act
Safe Drinking Water Information System
Surveillance, Epidemiology, And End Results
Small Entity Representatives
Small for Gestational Age
Significant Economic Impact on a Substantial Number of Small Entities
Synthetic Organic Compounds
Small System Compliance Technologies
Technologies and Costs
T riiodo thy ro nine
Thyroxine
Total Cholesterol
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TDP
THM4
TNCWSs
TOC
TRI
TSH
UCMR
UCMR3
UCMR 4
UMRA
VOCs
VSL
WBS
WIFIA
WIIN
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Technology Design Panel
Four Regulated Trihalomethanes
Transient Non-Community Water Systems
Total Organic Carbon
Toxics Release Inventory
Thyroid Stimulating Hormone
Unregulated Contaminant Monitoring Rule
Third Unregulated Contaminant Monitoring Rule
Fourth Unregulated Contaminant Monitoring Rule
Unfunded Mandates Reform Act
Volatile Organic Compounds
Value of a Statistical Life
Work Breakdown Structure
Water Infrastructure Finance and Innovation
Water Infrastructure Improvements for the Nation Act
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1 Executive Summary
Under the Safe Drinking Water Act (SDWA), the U.S. Environmental Protection Agency (EPA
or "the agency") has the authority to set enforceable National Primary Drinking Water
Regulations (NPDWRs) for drinking water contaminants and require monitoring of public water
supplies. The EPA is finalizing a NPDWR for per-and polyfluoroalkyl substances (PFAS) (EPA-
HQ-OW-2022-0114). The agency initiated the process for developing a NPDWR for PFAS
compounds in March 2021, when the EPA published the fourth regulatory determination for
contaminants on the fourth Contaminant Candidate List (CCL), which included a final
determination to regulate perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid
(PFOS) in drinking water. Additionally, in the EPA's final regulatory determination for PFOA
and PFOS, as well as its PFAS Strategic Roadmap, the agency committed to evaluating
additional PFAS beyond PFOA and PFOS and considering actions to address groups of PFAS
(86 FR 12272) (U.S. EPA, 2021b; U.S. EPA, 202le). In March of 2023, the EPA made a
preliminary regulatory determination for four additional PFAS and their mixtures:
perfluorononanoic acid (PFNA), hexafluoropropylene oxide dimer acid (HFPO-DA) and its
ammonium salt (also known as GenX chemicals)1, perfluorohexanesulfonic acid (PFHxS), and
perfluorobutanesulfonic acid (PFBS). Additionally, the EPA proposed a NPDWR and health-
based Maximum Contaminant Level Goals (MCLGs) for PFOA, PFOS and these four additional
PFAS and their mixtures (88 FR 18638). The final NPDWR is one of several actions consistent
with the agency's commitment to address these long-lasting "forever chemicals" that occur in
drinking water supplies and impact communities across the U.S.
The final PFAS NPDWR is a significant regulatory action that was submitted to the Office of
Management and Budget (OMB) for review. An economic analysis (EA) is required for all
significant rules under Executive Order (EO) 12866 (Regulatory Planning and Review). In
addition, Section 1412(b)(3)(C) of the 1996 Amendments to the SDWA requires the EPA to
prepare a Health Risk Reduction and Cost Analysis (HRRCA) in support of any NPDWRs that
include a maximum containment level (MCL). This EA addresses these and other regulatory
reporting requirements, including those that direct the EPA to conduct distributional and
environmental justice analysis. With respect to the SDWA HRRCA requirements, this document
provides the following:
• Quantifiable and nonquantifiable health risk reduction benefits for which there is a
factual basis in the rulemaking record to conclude that such benefits are likely to occur as
the result of compliance with each level of treatment (Chapter 6);
• Quantifiable and nonquantifiable health risk reduction benefits for which there is a
factual basis in the rulemaking record to conclude that such benefits are likely to occur
from reductions in co-occurring contaminants that may be attributed solely to compliance
with the final MCL, excluding benefits resulting from compliance with other proposed or
promulgated regulations (Chapter 6);
1 The EPA notes that the chemical HFPO-DA is used in a processing aid technology developed by DuPont to make
fluoropolymers without using PFOA. The chemicals associated with this process are commonly known as GenX Chemicals and
the term is often used interchangeably for HFPO-DA along with its ammonium salt.
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• Quantifiable and nonquantifiable costs for which there is a factual basis in the rulemaking
record to conclude that such costs are likely to occur solely as a result of compliance with
the final MCL, including monitoring, treatment, and other costs, and excluding costs
resulting from compliance with other proposed or promulgated regulations (Chapter 2);
• Incremental costs and benefits associated with each alternative MCL considered (Chapter
7);
• Effects of the contaminant on the general population and on groups within the general
population, such as sub-populations identified as likely to be at greater risk of adverse
health effects due to exposure to contaminants in drinking water than the general
population (Chapters 6 and 8);
• Any increased health risk that may occur as the result of compliance, including risks
associated with co-occurring contaminants (Chapter 6); and
• Other relevant factors, including the quality and extent of the information, uncertainties
in the analysis, and factors related to the degree and nature of the risk (Chapters 5-7).
The final NPDWR will reduce PFAS concentrations in the drinking water distributed by public
water systems (PWSs) from the current baseline to drinking water concentrations that are in
compliance with MCLs of 4.0 parts per trillion (ppt; also expressed as ng/L) for PFOA, 4.0 ppt
for PFOS, and a unitless hazard index (HI) of 1 for the group including PFNA, HFPO-DA,
PFHxS, PFBS. Additionally, the EPA is finalizing individual MCLs for HFPO-DA, PFHxS, and
PFNA at 10 ppt each. See Sections III and V of the PFAS NPDWR for further discussion (U.S.
EPA, 2024h). These impacts are assessed in comparison to the baseline scenario, which reflects
the PFAS occurrence and exposure conditions expected in the absence of finalizing a PFAS
drinking water regulation. This EA presents the incremental costs and benefits associated with
the final rule (PFOA, PFOS, HI, PFHxS, PFNA, and HFPO-DA MCLs) and three regulatory
alternatives that only include MCLs for PFOA and PFOS. The regulatory alternative MCLs are
referred to as Option la (MCL of 4.0 ppt for PFOA and 4.0 ppt for PFOS), Option lb (MCL of
5.0 ppt for PFOA and 5.0 ppt for PFOS), and Option lc (MCL of 10.0 ppt for PFOA and 10.0
ppt for PFOS). The regulatory alternative MCLs for PFOA and PFOS (Options la, lb, and lc)
do not directly regulate additional PFAS, thereby limiting public health protection and benefits
relative to the final rule.
In this EA, the EPA presents the quantified and nonquantifiable health benefits expected from
reductions in PFAS exposures resulting from the final rule. Quantified benefits are assessed as
avoided cases of illness and deaths (or morbidity and mortality, respectively) associated with
exposure to PFAS contaminants. Adverse human health outcomes associated with PFAS
exposure that cannot be quantified and monetized are assessed as nonquantifiable benefits.
Additionally, this EA presents the costs associated with the final NPDWR. Costs presented
include those expenses incurred by PWSs to (1) monitor for PFAS, (2) inform consumers, (3)
install and operate treatment technologies, and (4) perform record-keeping and reporting to
comply with the PFAS NPDWR; and the costs incurred by primacy agencies (typically states)
with authority to implement and enforce SDWA regulations. The EPA presents annualized
quantified benefits and costs discounted at a 2 percent discount rate, consistent with OMB
guidance (OMB Circular A-4, 2023).
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Quantified economic benefits analyses consider the strength of evidence for associations
between PFAS exposure and each adverse health effect and the availability of data to quantify
the morbidity and mortality impacts associated with that adverse health effect. To identify health
effects that are associated with PFAS exposure, the EPA relied on the assessment of adverse
health effects associated with PFOA and PFOS exposure in the final human health toxicity
assessments for PFOA and PFOS (U.S. EPA, 2024f; U.S. EPA, 2024e). The EPA provides a
national-level quantitative estimate of avoided morbidity and mortality related to cardiovascular
disease (CVD; both PFOA and PFOS), low birth weight (both PFOA and PFOS), and renal cell
carcinoma (RCC; PFOA only) associated with reductions in PFAS consistent with the final rule.
Additional quantified benefits estimates for low birth weight (PFNA) and liver cancer (PFOS)
are presented in sensitivity analyses in Appendix K and Appendix O, respectively.
As required by SDWA, the EPA also provides a qualitative assessment of potential benefits for
adverse health effects that are associated with PFAS exposure but lack the economic or other
information needed for a quantitative analysis. In this EA, a qualitative discussion is provided for
other adverse health effects and potential avoided diseases associated with PFOA, PFOS, and the
four PFAS compounds included in the HI group (PFHxS, PFNA, PFBS, and HFPO-DA). The
agency anticipates that the nonquantifiable human health benefits associated with reductions in
drinking water PFAS exposure are substantial and may reasonably exceed the benefits the
agency was able to quantify for this final rule.
As part of its HRRCA, the EPA is directed by SDWA to evaluate quantifiable and
nonquantifiable health risk reduction benefits for which there is a factual basis in the rulemaking
record to conclude that such benefits are likely to occur from reductions in co-occurring
contaminants that may be attributed solely to compliance with the final MCL (SDWA
1412(b)(3)(C)(II)). These co-occurring contaminants are expected to include additional PFAS
contaminants not directly regulated by the final PFAS NPDWR, co-occurring chemical
contaminants such as other synthetic organic compounds (SOCs), volatile organic compounds
(VOCs), and disinfection byproduct (DBP) precursors. The EPA has quantified costs associated
with reduction in DBP precursors, and has considered health risk reduction benefits for other
PFAS, SOCs, and VOCs qualitatively.
The agency anticipates that because of the PFAS NPDWR, some community water systems
(CWSs) and non-transient non-community water systems (NTNCWSs) will need to reduce their
PFAS concentrations to comply with the rule. This EA describes the costs associated with
activities PWSs are expected to undertake to comply with the final rule (e.g., installation of
treatment technologies to remove PFAS), and the costs associated with primacy agency
implementation and administration of the final rule. National quantified cost estimates are
provided for PFOA, PFOS, and PFHxS treatment. In the national cost analysis, the EPA
quantified the national treatment and monitoring costs for PFHxS individual MCL exceedances
and HI MCL exceedances where PFHxS is present above its HBWC while one or more other HI
PFAS is also present in that same mixture. In instances where concentrations of PFNA, PFBS,
and HFPO-DA are high enough to cause or contribute to a HI exceedance when the
concentrations of PFOA, PFOS, and PFHxS would not have already otherwise triggered
treatment, the national quantified costs may be underestimated; however, these costs are
considered quantitatively in a sensitivity analysis. Additional discussion of the methodology and
results of this analysis can be found in Chapter 5, Section 5.3.1.4, and Appendix N.3. See section
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XII. A.4 of the final rule preamble for more information about how EPA considered HI, PFNA,
and HFPO-DA MCL costs.
The EPA identified effective treatment technologies as part of the NPDWR, and consistent with
SDWA requirements found in Section 1412(b)(3)(C)(II) to consider benefits likely to occur from
reductions in co-occurring compounds, the EPA estimated expected benefits from reductions in
co-occurring compounds as a result of PFAS treatment. Moreover, the EPA developed a
quantitative analysis for reductions in bladder cancer morbidity and mortality that stem from
removal of DBP precursors. DBPs, specifically trihalomethanes, are formed when disinfectants
interact with organic material in drinking water distribution systems. Since PFAS treatment has
been demonstrated to remove DBP precursors, the agency anticipates that DBPs, including
trihalomethanes, will be reduced with PFAS treatment. The EPA provides a qualitative
discussion of benefits for other potential water quality improvements that stem from PFAS
treatment, including those benefits associated with reductions in other co-occurring contaminants
besides DBPs.
The tables below present quantified benefits and costs of the final NPDWR ("final rule") and
alternative MCLs considered. Compared to the economic analysis for the proposed PFAS
NPDWR, which presented costs in 2021 dollars, the EPA presents costs for the final rule in 2022
dollars. Table ES-1 presents the total estimated national annualized benefits associated with the
final rule and regulatory alternatives considered. Table ES-2 presents the total estimated national
annualized costs associated with the final rule and regulatory alternatives considered.
Quantitative estimates are presented using a 2 percent discount rate. Throughout this EA,
benefits and costs are presented using mean (or "expected value"), 5th, and 95th percentile
results to characterize key sources of uncertainty, including but not limited to PFAS baseline
occurrence and health effect slope factor uncertainty, which is consistent with OMB and EPA
guidance (OMB Circular A-4, 2003; U.S. EPA, 2010a). All significant limitations and
uncertainties of this economic analysis are described in the pages that follow.
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Table ES-1: Quantified Total National Annualized Benefits, All Options (Million $2022)
2% Discount Rate3
Option
5th Percentileb Expected Value 95th Percentileb
Final rulec $920.91 $1,549.40 $2,293.80
Option lad $913.05 $1,542.74 $2,280.10
Option lbe $768.55 $1,296.84 $1,919.30
Option lcf $397.28 $664.45 $970.70
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates. Quantified total national annualized benefits do not include quantified sensitivity analysis results for
PFNA effects on birth weight and PFOS effects on liver cancer, and as such, the quantified total national annualized benefits
may be underestimated. See appendices K and O for PFNA birth weight and PFOS liver cancer sensitivity analysis results,
respectively.
aSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
bThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 6.1.2 and Table 6-1
for benefits. This range does not include the uncertainty described in Table 6-48 for benefits.
The final rule sets PFOA and PFOS MCLs of 4.0 ppt each, an HI of 1, and MCLs for HFPO-DA, PFNA, and PFHxS of 10
ppt each.
Option la sets PFOA and PFOS MCLs only, at 4.0 ppt each.
eOption lb sets PFOA and PFOS MCLs only, at 5.0 ppt each.
fOption lc sets PFOA and PFOS MCLs only, at 10.0 ppt each.
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Table ES-2: Quantified Total National Annualized Costs, All Options (Million $2022)
2% Discount Ratea'b
Opti°n 5th Percentile0 .Mean 95th Percentile0
Final rule46
$1,435.70
$1,548.64
$1,672.10
Option laf
$1,423.60
$1,537.07
$1,660.30
Option lbg
$1,102.60
$1,192.13
$1,291.40
Option lch
$462.87
$499.29
$540.68
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
bPFAS-contaminated wastes are not considered regulatory under the Resource Conservation and Recovery Act (RCRA) or
characteristic hazardous wastes at this time and therefore total costs reported in this table do not include costs associated with
hazardous waste disposal of spent filtration materials. To address stakeholder concerns about potential costs for disposing
PFAS-contaminated wastes as hazardous should they be regulated as such in the future, the EPA conducted a sensitivity
analysis with an assumption of hazardous waste disposal for illustrative purposes only. See Appendix N, Section N.2 for
additional detail.
The 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1 for
costs. This range does not include the uncertainty described in Table 5-21 for costs.
Quantified national costs do not include quantified sensitivity analysis results for PFNA, PFBS, and HFPO-DA. Including the
costs of treating for these compounds increases total annualized cost of the final rule to $ 1,631.05 million. These benefits and
costs are considered quantitatively in the sensitivity analysis. See Appendix N.3 for more information.
eThe final rule sets PFOA and PFOS MCLs of 4.0 ppt each, an HI of 1 and MCLs for HFPO-DA, PFNA, and PFHxS of 10 ppt
each.
fOption la sets PFOA and PFOS MCLs of 4.0 ppt each.
gOption lb sets PFOA and PFOS MCLs of 5.0 ppt each.
•'Option lc sets PFOA and PFOS MCLs of 10.0 ppt each.
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2 Introduction
PFAS are a class of synthetic chemicals that have been manufactured and in use since the 1940s
(AAAS, 2020; U.S. EPA, 2022h). PFAS are or were most commonly used to make products
resistant to water, heat, and stains and are consequently found in industrial and consumer
products like clothing, food packaging, cookware, cosmetics, carpeting, and fire-fighting foam
(AAAS, 2020). PFAS manufacturing and processing facilities, facilities using PFAS in the
production of other products, airports, and military installations have been associated with PFAS
releases into the air, soil, and water (U.S. EPA, 2016b; U.S. EPA, 2016c). People may be
exposed to PFAS by using certain consumer products, through occupational exposure, and/or
through consuming contaminated food or contaminated drinking water (Domingo & Nadal,
2019; Fromme et al., 2009).
PFOS and PFOA are part of a subset of PFAS referred to as perfluorinated alkyl acids (PFAA)
and are two of the most widely studied and longest-used PFAS. Due to their widespread use and
persistence in the environment, most people have been exposed to PFAS, including PFOA and
PFOS (U.S. EPA, 2016b; U.S. EPA, 2016c). PFOA and PFOS have been detected in up to 98
percent of blood serum samples taken in biomonitoring studies that are representative of the U.S.
general population (CDC, 2019). Following the voluntary phase-out of PFOA by eight major
chemical manufacturers and processors in the U.S. under the EPA's 2010/2015 PFOA
Stewardship Program and reduced manufacturing of PFOS (last reported in 2002 under Chemical
Data Reporting), serum concentrations have been declining. The National Health and Nutrition
Examination Survey (NHANES) data exhibited that 95th-percentile serum PFOS concentrations
have decreased over 75 percent, from 75.7 [j,g/L in the 1999-2000 cycle to 18.3 [j,g/L in the 2015-
2016 cycle (CDC, 2019; Jain, 2018; Calafat et al., 2007; Calafat et al., 2019).
Despite voluntary phase-outs and reduced exposure to some PFAS chemicals, PFAS are still
used in a wide range of consumer products and industrial applications. The EPA's analysis of
drinking water monitoring data shows widespread occurrence of PFAS compounds in multiple
geographic locations. Most known exposures are relatively low, but some can be high,
particularly when people are exposed to a concentrated source over long periods of time. Studies
indicate that PFAS exposure above certain levels may result in adverse health effects, including
developmental effects to fetuses during pregnancy or to breast-fed infants, cancer, and other
immunologic-related effects.
Under SDWA, the EPA is regulating PFAS in drinking water distributed by all CWSs2 and
NTNCWSs. In 2021, the EPA determined that a NPDWR for PFAS would result in a meaningful
opportunity to reduce health risks (U.S. EPA, 2021b). In March of 2023, the EPA proposed a
NPDWR with health-based MCLGs and enforceable MCLs for PFOA, PFOS and four PFAS and
their mixtures. Section 2.1 provides further detail on the final NPDWR for PFAS.
2 Systems that supply water to the same population year-round.
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2.1 Summary of the Final PFAS Rule and Regulatory Alternatives
The EPA is regulating six PFAS in finished drinking water: (1) PFOS, (2) PFOA, (3) PFNA, (4)
HFPO-DA and its ammonium salt (also known as GenX chemicals), (5) PFHxS, and (6) PFBS.
The final regulation utilizes compound-specific MCLs for PFOA, PFOS, PFNA, HFPO-DA, and
PFHxS and a group MCL based on a HI for PFNA, HFPO-DA, PFHxS, and PFBS. This
regulatory approach utilizes the mixtures framework peer reviewed by the EPA's Science
Advisory Board (SAB; U.S. EPA, 2022i) and builds a framework for inclusion of additional
PFAS through future rulemaking as new data become available (U.S. EPA, 2024d). For more
information on the HI approach, see the EPA's Framework for Estimating Noncancer Health
Risks Associated with Mixtures of PFAS (U.S. EPA, 2024d).
Based on the best available scientific information on the health effects, the EPA is finalizing
MCLGs of 0 ppt for PFOA and PFOS each, an MCLG of 1 for the HI, and MCLGs of 10 ppt for
HFPO-DA, PFHxS, and PFNA each. The EPA has determined that it is feasible to set
enforceable MCLs for PFOA and PFOS at 4.0 ppt each and MCLs for HFPO-DA, PFHxS, and
PFNA at 10 ppt each. Additionally, the EPA has determined it is feasible to set an MCL for four
PFAS with a HI limit of 1. As such, the EPA is finalizing enforceable MCLs of 4.0 ppt for
PFOA, 4.0 ppt for PFOS, 10 ppt for HFPO-DA, 10 ppt for PFHxS, and 10 ppt for PFNA and a
unitless HI of 1 for the group including PFNA, HFPO-DA, PFHxS, and PFBS. For additional
details about the MCLGs and MCLs in the final rule, see the Federal Register Notice for this
rulemaking.
Additionally, in this EA, the EPA presents benefits and costs for the final rule as well as three
regulatory alternatives. For the proposed rule, the agency received comments on whether
establishing traditional MCLGs and MCLs for PFHxS, HFPO-DA, PFNA, and PFBS instead of
or in addition to the HI approach would change public health protection, improve clarity for the
rule, or change costs. See Section V of the Federal Register Notice for further discussion of why
the EPA added individual MCLs for HFPO-DA, PFHxS, and PFNA. For the final rule, the EPA
has also included estimates of the marginal costs for the individual PFHxS, PFNA, and HFPO-
DA MCLs in the absence of the HI (See Section 5.1.3 and Appendix N.4 for details). This
analysis confirms that the treatment burden from the individual MCLs is fully considered in the
HI cost estimates in Appendix N.3 (and as discussed above, the individual PFHxS, PFNA, and
HFPO-DA MCL marginal costs are lower in the absence of the HI MCL).
The regulatory alternatives that the EPA evaluated present individual MCLG and enforceable
MCL values for PFOA and PFOS. MCL values for PFOA and PFOS vary for each alternative
considered: 4.0 ppt in Option la, 5.0 ppt in Option lb, and 10.0 ppt in Option lc. The EPA
evaluated benefits and costs for Option la to determine the difference in costs between
alternatives for PFOA and PFOS MCLs only versus MCLs for PFOA and PFOS and an HI for
four additional PFAS. The EPA considered benefits and costs under Option lb—MCLs of 5.0
ppt for PFOA and PFOS—because it is 25 percent above the compliance quantitation limit of 4.0
ppt established for the final rule. Lastly, the EPA considered benefits and costs of Option lc—
MCLs of 10.0 ppt for PFOA and PFOS—to provide information on whether the agency should
consider utilizing its authority under Section 1412(b)(6) to set an alternative MCL at the level at
which the benefits would justify the costs.
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2.2 Economic Analysis Assumptions
2.2.1 Compliance Schedule and Period of Analysis for Final Rule
For purposes of this EA, the EPA assumes that the NPDWR will be promulgated in 2024. As the
final rule will grant a 2-year nationwide extension of the date for MCL compliance, this analysis
assumes that capital improvements (i.e., installation of treatment technologies) for systems
taking action under the rule take effect five years after the date on which the regulation is
promulgated, or in 2029. All other requirements, including initial monitoring, are assumed to be
completed within three years of rule promulgation. In addition to this initial time window, the
EPA's period of analysis includes the 80 years following the assumed compliance date.3 This
time span is based on an assumed median human lifespan of 80 years. In this EA, the EPA
evaluates costs and benefits under the final rule for the period of analysis from 2024 through
2105. The EPA selected this period of analysis to estimate human health risk reduction to capture
health effects from chronic illnesses that are typically experienced later in life (i.e.,
cardiovascular disease and cancer). Capital costs for installation of treatment technologies are
spread over the useful life of the technologies. The EPA does not capture effects of compliance
with the final rule beyond the year 2105.
2.2.2 Dollar Year and Discount Rates
The EPA presents estimated costs and benefits under the final rule in 2022 U.S. dollars.
Appendix J provides additional details on the price indices used for inflation adjustments.
The final rule analysis estimates the annualized value of future benefits and costs using a 2
percent discount rate. The U.S. White House and OMB recently finalized and re-issued the A-4
and A-94 benefit-cost analysis guidance (see OMB Circular A-4, 2023), and the update includes
new guidance to use a social discount rate of 2 percent. The updated OMB Circular A-4 states
that the discount rate should equal the real (inflation-adjusted) rate of return on long-term U.S.
government debt, which provides an approximation of the social rate of time preference. This
rate for the past 30 years has averaged around 2.0 percent per year in real terms on a pre-tax
basis. OMB arrived at the 2 percent discount rate figure by considering the 30-year average of
the yield on 10-year Treasury marketable securities, and the approach taken by OMB produces a
real rate of 1.7 percent per year, to which OMB added a 0.3 percent per-year rate to reflect
inflation as measured by the personal consumption expenditure (PCE) inflation index. The OMB
guidance states that Agencies must begin using the 2 percent discount rate for draft final rules
that are formally submitted to the Office of Information and Regulatory Affairs (OIRA) after
December 31, 2024. The updated OMB Circular A-4 guidance further states that "to the extent
feasible and appropriate, as determined in consultation with OMB, agencies should follow this
Circular's guidance earlier than these effective dates." Given the updated default social discount
rate prescribed in the OMB Circular A-4 and also public input received on the discount rates
considered by the EPA in the proposed NPDWR for this final rule (see response to comment
3 When calculating the present value of costs over the 82-year period of analysis, the EPA uses the useful life of the technology to
determine when the capital components will need to be replaced. So, for example, if a PWS installs a technology in year 7 of the
analysis that has an average useful life of 18 years, and costs $1M, the PWS accrues capital costs of SIMin each of the following
years: 7,25, 43, 61, and 79. It also accrues O&M costs every year of the analysis beginning in year 7.
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document Section 13.2), the EPA estimated national benefits and costs at the 2 percent discount
rate for the final rule and incorporated those results into the final economic analysis. Since the
EPA proposed this NPDWR with the 3 and 7 percent discount rates based on guidance in the
previous version of OMB Circular A-4, the EPA has kept the presentation of results using these
discount rates in Appendix P. The Administrator reaffirms his determination that the benefits of
the rule justify the costs. The EPA's determination is based on its analysis under in SDWA
section 1412(b)(3)(C) of the quantifiable benefits and costs at the 2 percent discount rate, in
addition to at the 3 and 7 percent discount rate, as well as the nonquantifiable benefits and costs.
The EPA found that significant nonquantifiable benefits are likely to occur from the final PFAS
NPDWR.
The same discount rate is used for both benefits and costs. All future cost and benefit values are
discounted back to the initial year of the analysis, 2024, providing the present value of the cost or
benefit.
2.2.3 Annualization
Consistent with the timing of the final rule and associated reductions in PFAS levels, the EPA
uses the following equation to annualize the future costs and benefits:
Equation 1:
r(PV)
AV = —
(1 + r)[l — (1 + r) n]
Where AV is the annualized value, PV is the present value,4 r is the discount rate (2%), and n is
the number of years (82 years).
2.2.4 Population
To determine the number of people expected to benefit from actions under the final rule, the
EPA uses population data from the Safe Drinking Water Information System Federal version
(SDWIS/Fed) 2021 Quarter 4 (Q4) database (U.S. EPA, 202lh). The SDWIS/Fed data provide
the population served by each PWS in the U.S. For analyses that rely on age-, sex-, and
race/ethnicity-specific populations, the EPA uses county-level population proportions based on
2021 estimates from the U.S. Census Bureau (2020a). The EPA does not consider population
growth during the period of analysis (2024-2105). For more information on the SDWIS/Fed and
U.S. Census Bureau (2020a) data, see Appendix B.
2.2.5 Valuation
To estimate the economic value of avoided premature deaths, the EPA uses Value of Statistical
Life estimates. The EPA follows Guidelines for Preparing Economic Analyses (U.S. EPA,
2010a) and approximates Value of Statistical Life growth using a compound annual growth rate
of projected Value of Statistical Life values to obtain a Value of Statistical Life suitable for
4 The present value is the current value of a future sum of benefits given a specified discount rate. The present value represents
the expected value of benefits determined at the date of valuation.
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valuation of mortality risk reductions during the period of analysis, 2024-2105. As the base
value, the EPA used the Value of Statistical Life estimate of $4.8 million ($1990, 1990 income
year), which is the central tendency of the Value of Statistical Life distribution recommended for
use in the EPA's regulatory impact analyses (U.S. EPA, 2010a). The base Value of Statistical
Life estimate is adjusted for inflation and income growth as described in Appendix J. The Value
of Statistical Life estimates employed in the EPA's analysis range from $11.6 million ($2022) in
2024 to $19.1 million ($2022) in 2105.5
To estimate the economic value of avoided morbidity (i.e., non-fatal heart attacks and ischemic
strokes, birth weight decrements, and cancers), the EPA used the cost of illness (COI) valuation
approach. The COI-based values used in this analysis reflect medical care expenditures and
opportunity costs associated with managing/treating the condition. The health endpoint-specific
morbidity valuation details are provided in Sections 6.4.4, 6.5.4, 6.6.4, and 6.7.2.5. The EPA
received public comments on the proposed rule that recommended the EPA incorporate
willingness to pay metrics in addition to COI in its final estimates of non-fatal health effects
associated with reduced PFAS exposure. To address these comments, the EPA developed a
sensitivity analysis in Appendix O to illustrate the impact to benefits results when using available
willingness to pay information to monetize cancer morbidity.
2.3 Document Organization
The remainder of this EA is organized into the following chapters:
• Chapter 2: Introduction summarizes the final PFAS rule and regulatory alternatives,
including the economic assumptions made in developing the rule.
• Chapter 3: Need for the Rule summarizes the statutory requirements, regulatory actions,
and national EPA initiatives affecting PFAS in drinking water. It also explains the
contributors to the PFAS rule, statutory authority, and the economic rationale for the
regulatory approach.
• Chapter 4: Baseline Drinking Water System Conditions describes the systems subject to
the final PFAS rule, PFAS water concentration levels, and data sources used to
characterize the baseline before the EPA models estimated changes that result from
complying with the final PFAS requirements.
• Chapter 5: Estimating Public Water System Costs provides a description of the estimated
costs for the final regulatory changes affecting systems and Primary Agencies.
• Chapter 6: Benefits Analysis provides an estimate of the potential health benefits of the
final PFAS rule and regulatory alternatives relative to the baseline, including
quantification and monetization where possible.
5 Income growth projections from the U.S. Energy Information Administration (2021) are available through 2050. The EPA uses
these projections to calculate annual VSL values in $2022 from years 2024 to 2050 as described in Equation J-l in Appendix J.
The EPA uses these calculated VSL values to estimate a compound annual growth rate (see Equation J-2 in Appendix J) and
applies this growth rate to estimate annual VSL values beyond year 2050 (see Equation J-3 in Appendix J).
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• Chapter 7: Comparison of Costs to Benefits provides a summary of costs and benefits
associated with the provisions of the final PFAS rule.
• Chapter 8: Environmental Justice Analysis provides a description of how the final PFAS
rule addresses Executive Order 12898: Federal Actions to Address Environmental Justice
in Minority Populations and Low-Income Populations.
• Chapter 9: Statutory and Administrative Requirements discusses analyses performed to
evaluate the effects of the final PFAS rule and regulatory alternatives on different
segments of the population in accordance with 12 federal mandates and statutory reviews,
including but not limited to the Final Regulatory Flexibility Analysis/Small Business
Regulatory Enforcement Fairness Act (RFA/SBREFA), Unfunded Mandates Reform Act
(UMRA), and Executive Order 14008: Tackling the Climate Crisis at Home and Abroad.
• Chapter 10: References includes a list of references cited throughout the final PFAS rule
economic analysis.
2.4 Supporting Documentation
This EA involves numerous detailed and complex analyses, and the following appendices are
provided to help the reader understand how those analyses were conducted and their underlying
data and assumptions:
• Appendix A: Framework of Bayesian Hierarchical Markov Chain Monte Carlo
Occurrence Model
• Appendix B: Affected Population
• Appendix C: Cost Analysis Results
• Appendix D: PFOA and PFOS Serum Concentration-Birth Weight Relationship
• Appendix E: Effects of Reduced Birth Weight on Infant Mortality
• Appendix F: Serum Cholesterol Dose-Response Functions
• Appendix G: CVD Benefits Model Details and Input Data
• Appendix H: Cancer Benefits Model Details and Input Data
• Appendix I: Trihalomethane Co-Removal Model Details and Analysis
• Appendix J: Value of a Statistical Life Updating
• Appendix K: Benefits Sensitivity Analyses
• Appendix L: Uncertainty Characterization Details and Input Data
• Appendix M: Environmental Justice
• Appendix N: Supplemental Cost Analyses
• Appendix O: Supplemental Benefits Analyses
• Appendix P: Additional Model Outputs
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Appendix Q: Appendix References
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3 Need for the Rule
This section provides the statutory and economic rationales for choosing a regulatory approach
to address the public health consequences of PFAS contamination in drinking water. The EPA's
statutory requirements, regulatory actions, and agency initiatives impacting PFAS in drinking
water are discussed.
3.1 Previous EPA Nonregulatory and Regulatory Actions
Potentially Affecting PFAS Drinking Water Management
This section provides a summary of actions and initiatives affecting PFAS in drinking water
prior to the publication of the final NPDWR for PFAS. Additionally, states have begun
proposing and promulgating their own regulatory and non-regulatory standards for PFAS in
drinking water. For more information on these state actions, see the Environmental Council of
the States' Processes & Considerations for Setting State PFAS Standards (ECOS, 2022).
3.1.1 PFAS Council and PFAS Strategic Roadmap
EPA Administrator Michael Regan established the EPA Council on PFAS in April 2021 and
charged it to develop a bold, strategic, whole-of-EPA strategy to protect public health and the
environment from the impacts of PFAS. The Council comprises senior technical and policy
leaders from across EPA program offices and regions and is chaired by Assistant Administrator
for Water Radhika Fox and Acting Region 1 Administrator Deb Szaro (U.S. EPA, 2021e).
On October 18, 2021, Administrator Regan announced the agency's PFAS Strategic Roadmap,
developed by the PFAS Council to lay out the EPA's whole-of-agency approach to tackling
PFAS. The PFAS Strategic Roadmap sets timelines by which the EPA plans to take specific
actions and commits to bolder new policies to safeguard public health, protect the environment,
and hold polluters accountable. Described in the Roadmap are key commitments the agency
made toward addressing these contaminants in the environment. With this final rule, the EPA is
delivering on a key commitment in the Roadmap to "establish a National Primary Drinking
Water Regulation" (U.S. EPA, 2021e).
3.1.2 Final Regulatory Determinations on the Fourth Drinking
Water Contaminant Candidate List
Section 1412(b)(l)(B)(i) of SDWA requires the EPA to publish the CCL every five years after
public notice and an opportunity to comment. The CCL is a list of contaminants which are not
subject to any final or promulgated NPDWRs but are known or anticipated to occur in PWSs and
may require regulation under SDWA. SDWA Section 1412(b)( 1 )(B)(ii) directs the EPA to
determine, after public notice and an opportunity to comment, whether to regulate at least five
contaminants from the CCL every five years.
Under Section 1412(b)(1)(A) of SDWA, the EPA will regulate a contaminant in drinking water
if the EPA Administrator determines that:
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a) The contaminant may have an adverse effect on the health of persons;
b) The contaminant is known to occur or there is a substantial likelihood that the contaminant
will occur in PWSs with a frequency and at levels of public health concern; and
c) In the sole judgment of the Administrator, regulation of such contaminant presents a
meaningful opportunity for health risk reduction for persons served by PWSs.
If after considering public comment on a preliminary determination, the EPA decides to regulate
a contaminant, the EPA will initiate the process to propose and promulgate a NPDWR. In that
case, the statutory time frame provides for agency proposal of a regulation within 24 months and
action on a final regulation within 18 months of proposal.
On March 10, 2020, the EPA published preliminary positive regulatory determinations for PFOS
and PFOA (85 FR 14098) (U.S. EPA, 2020a). On March 3, 2021, the EPA published final
regulatory determinations for PFOS and PFOA (86 FR 12272) (U.S. EPA, 2021b). In doing so,
the EPA also committed to evaluating a broader range of PFAS, including new monitoring and
occurrence data, and other information being developed by the EPA, other federal agencies, state
governments, international organizations, industry groups, and other stakeholders (U.S. EPA,
2021b).
3.1.3 Proposed PFAS National Primary Drinking Water Rule and
Regulatory Determinations for PFHxS, PFNA, HFPO-DA,
PFBS, and their Mixtures.
On March 14th, 2023, the EPA announced the PFAS NPDWR and requested comments on all
aspects of the proposed rule. This action included determinations to regulate PFHxS, HFPO-DA
and its ammonium salt (also known as a GenX chemicals), PFNA, and PFBS, and mixtures of
these PFAS as contaminants. A summary of major public comments and agency responses to
those comments are presented in the preamble for the final rule (U.S. EPA, 2024h). The agency's
detailed response to the comments received are presented in the document "Response to
Comments on the EPA's Proposed PFAS NPDWR" which is available in the public docket for
this rule. The EPA received approximately 122,000 comments on these regulatory
determinations and proposed NPDWR and considered commenter input in finalizing the rule and
this economic analysis.
3.1.4 Unregulated Contaminant Monitoring Rule
As part of its responsibilities under the SDWA, the EPA implements Section 1445(a)(2),
Monitoring Program for Unregulated Contaminants. This section requires that once every five
years, the EPA issues a list of no more than 30 unregulated contaminants to be monitored by
PWSs. This monitoring is implemented through the Unregulated Contaminant Monitoring Rule
(UCMR), which collects data from community water systems and NTNCWS. For each UCMR
cycle, the EPA establishes a new list of contaminants for monitoring, specifies which systems are
required to monitor, identifies the sampling locations, and defines the analytical methods to be
used.
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The third Unregulated Contaminant Monitoring Rule (UCMR 3) was published on May 2, 2012.
UCMR 3 required monitoring for six PFAS: PFOA, PFOS, PFNA, PFHxS, PFBS, and
perfluoroheptanoic acid (PFHpA). UCMR 3 data were used in the development of this economic
analysis. See Sections 4.2.2 and 4.4 for further discussion of these data.
On December 17, 2021, the EPA Administrator Michael Regan signed the final Revisions to the
Unregulated Contaminant Monitoring Rule (UCMR 5) for Public Water Systems, and the rule
was subsequently published in the Federal Register on December 27, 2021 (86 FR 73131). The
five-year UCMR 5 cycle spans from 2022 to 2026, with preparations in 2022, sample collection
from 2023 to 2025, and completion of data reporting in 2026. UCMR 5 includes all 29 PFAS
that are within the scope of EPA Methods 533 and 537.1 (U.S. EPA, 2021b). Initial sampling
results for UCMR 5 are available at: https://www.epa.gov/dwucmr/occurrence-data-unregulated-
contaminant-monitoring-rule#5 and discussed in PFAS Occurrence & Contaminant Background
Support Document (U.S. EPA, 2024g). In addition to information on occurrence data from
previous rounds of UCMR sampling, when completed, permanent results for UCMR 5 will be
available at: https://www.epa.gov/dwucmr/occurrence-data-unregulated-contaminant-
monitoring-rule.
3.2 Statutory Authority for Promulgating the Rule
Section 1412(b)(1)(A) of SDWA authorizes the EPA to establish NPDWRs for contaminants that
may have an adverse public health effect, that are known to occur or that present a substantial
likelihood of occurring in PWSs at a frequency and level of public health concern, and that
present a meaningful opportunity for health risk reduction for persons served by PWSs.
Section 1445(a) of SDWA authorizes the EPA Administrator to establish monitoring,
recordkeeping, and reporting regulations that the Administrator can use to establish regulations
under the SDWA, determine compliance with SDWA, and advise the public of the risks of
unregulated contaminants (42 U.S.C. § 300j-4(a)). In requiring a PWS to monitor under Section
1445(a), the Administrator may take into consideration the water system size and the
contaminants likely to be found in the system's drinking water (42 U.S.C. § 300j-4(a)). Section
1445(a)(1)(C) of the SDWA provides that "every person who is subject to a national primary
drinking water regulation" under Section 1412 must provide such information as the
Administrator may reasonably require to assist the Administrator in establishing regulations
under Section 1412 (42 U.S.C § 3OOj-4(a)(1)(C)).
Section 1413(a)(1) of the SDWA allows the EPA to grant a state primary enforcement
responsibility ("primacy") for NPDWRs when the EPA has determined that the state has, among
other things, adopted regulations that are no less stringent than the EPA's (42 U.S.C. § 300g-
2(a)(1)). To obtain primacy for this rule, states must adopt comparable regulations within two
years of the EPA's promulgation of the final rule, unless the EPA grants the state a two-year
extension (40 CFR 142.12(b)). State primacy requires, among other things, adequate
enforcement (including monitoring and inspections) and reporting. The EPA must approve or
deny state primacy applications within 90 days of submission to the EPA (42 U.S.C. § 300g-
2(b)(2)). In some cases, a state submitting revisions to adopt aNPDWR has interim primary
enforcement authority for the new regulation while the EPA's decision on the revision is pending
(42 U.S.C. § 300g-2(c)).
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Section 1450 of the SDWA authorizes the Administrator to prescribe such regulations as are
necessary or appropriate to carry out his or her functions under the Act (42 U.S.C § 300j-9).
3.3 Economic Rationale
Section 1(b) of Executive Order 12866, "The Principles of Regulation," provides that each
agency, as applicable and permitted by law: "shall identify the problem that it intends to address
(including, where applicable, the failures of private markets or public institutions that warrant
new agency action) as well as assess the significance of that problem." This section describes the
types of market failures that NPDWRs address.
In a perfectly competitive market, market forces guide buyers and sellers to attain the most
efficient social outcome. A perfectly competitive market occurs when both buyers and sellers are
price takers, usually when there are many producers and buyers of a product and both producers
and buyers have complete knowledge about that product. Also, there must not be any barriers to
entry into the industry, and existing producers in the industry must not have any advantage over
potential new producers. Several factors in the public water supply industry preclude it from
being a perfectly competitive market and lead to market failures that may require regulation.
First, it is not economically efficient to have multiple suppliers who would, for example,
compete by building multiple systems of pipelines, reservoirs, wells, and other facilities. Instead,
economic efficiency leads to a single firm or government entity performing these functions
generally under public control. Under these monopoly conditions, consumers are provided only
one level of service with respect to drinking water quality. If consumers do not believe that the
quality of tap water is adequate, they cannot simply switch to another water utility. Consumers
may purchase bottled water, but this option can be much more expensive due to the inefficiencies
of bottling and transporting bottled water. Consumers may also install and operate home
treatment systems, but this can also be considerably more expensive without the economies of
scale of large, centralized water systems. Additionally, home treatment systems potentially can
lead to increased health risks when not regularly maintained by the consumer.
Second, high information and transaction costs impede the public's understanding of health and
safety issues concerning drinking water quality. The health risks potentially posed by trace
quantities of drinking water contaminants requires the EPA to analyze and distill complex
toxicological and health sciences data. The EPA promulgated the Consumer Confidence Report
(CCR) rule to make water quality information more easily available to consumers. The CCR rule
requires CWSs to mail their customers an annual report on local drinking water quality.
The report provides customers with information on levels of detected contaminants in their
drinking water, limited health risk information associated with contaminant exposure when
levels exceed MCLs, and utility contact information. Even if informed consumers can engage
utilities regarding these health issues, the costs of such engagement, known as "transaction
costs" (in this case measured in personal time and commitment), can be a barrier to efficient
market outcomes.
SDWA regulations are intended to provide a level of protection from exposure to drinking water
contaminants that would not otherwise occur in the existing market environment of public water
supply. The regulations set minimum performance requirements for all public water supplies to
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reduce the risk confronted by all consumers from exposure to drinking water contaminants.
SDWA regulations are not intended to restructure market mechanisms or establish competition in
supply; rather, SDWA standards establish the level of service needed to better reflect the public's
preference for safety. Federal regulations remove the high information and transaction costs by
acting on behalf of all consumers in balancing the risk reduction and social costs of achieving
this reduction.
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4 Baseline Drinking Water System Conditions
4.1 Introduction
In its Guidelines for Preparing Economic Analyses, the EPA characterizes the baseline as a
reference point that reflects the world without the regulation (U.S. EPA, 2010a); this baseline is
the starting point for estimating the potential benefits and costs of the final PFAS NPDWR.
This chapter presents a characterization of PWSs and their current operations (i.e., the baseline)
before changes are made to meet the final PFAS NPDWR. Section 4.2 identifies each major data
source used to develop the baseline. Section 4.3 explains the derivation of each baseline
characteristic and presents results in detailed tables. Section 4.4 describes the Bayesian model
developed to estimate national PFAS occurrence in drinking water supplies. Section 4.5
summarizes limitations of the major data sources and uncertainties in the baseline
characterization (both quantified and nonquantifiable) in table format.
4.2 Data Sources
The EPA used a variety of data sources to develop the baseline. Section 4.2.1 explains the
relevant information provided in the federal version of the SDWIS/Fed and measures the EPA
took to verify the data. Section 4.2.2 describes the purpose of UCMR 3 data. Section 4.2.3
describes the independent state sampling program data. Sections 4.2.4 and 4.2.5 describe two
data sources used to develop key characteristics of system treatment plants. Section 4.2.6
explains the purpose of the 2006 Community Water System Survey (CWSS) and the
representativeness of the data. Table 4-1 identifies each major data source and the baseline data
element(s) derived from them.
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Table 4-1: Data Sources Used to Develop the Water System Characteristics
Data Source
Baseline Data Derived from the Source
SDWIS/Fed fourth quarter
2021 Q4 "frozen" dataset3
• Water System Inventory (Section 4.3.1): PWS inventory, including system
unique identifier, population served, number of service connections,
source water type, and system type.
• Population and Households Served (Section 4.3.2): PWS population
served.
• Treatment Plant Characterization (Section 4.3.3.1): Number of unique
treatment plant facilities per system, which are used as a proxy for entry
points (EPs) when UCMR 3 sampling site data are not available.
UCMR 3 (U.S. EPA, 2017)
• Treatment Plant Characterization (Section 4.3.3): Number of unique EP
sampling sites, which are used as a proxy for EPs.
• Treatment Plant Characterization (Section 4.3.3): PFAS concentration data
collected as part of UCMR 3.
Independent state sampling
programs
• Treatment Plant Characterization (Section 4.3.3): PFAS concentration data
collected by states. These data supplemented the occurrence modeling for
systems included in UCMR 3.
SYR4 ICR Occurrence
Dataset (2012-2019)
• Treatment Plant Characterization (Section 4.3.3): TOC.
Geometries and
Characteristics of PWSs
(U.S. EPA, 2000)
• Treatment Plant Characterization (Section 4.3.3): Design and average
daily flow per system.
2006 CWSS (U.S. EPA,
2009)
• PWS Labor Rates (Section 4.3.4): PWS labor rates.
Abbreviations: CWSS - Community Water System Survey; ICR - Information Collection Request; PFAS - per- and
polyfluoroalkyl substances; PWS - public water system; SDWIS/Fed - Safe Drinking Water Information System/federal
version; SYR - Six-Year Review; TOC - total organic carbon; UCMR 3 - Third Unregulated Contaminant Monitoring Rule.
Note:
Contains information extracted on January 14,2022.
4.2.1 SDWIS/Fed 2021
SDWIS/Fed (U.S. EPA, 2021h) is the EPA's national regulatory compliance database for the
drinking water program. It contains system inventory, treatment facility, violation, and
enforcement information for PWSs as reported by primacy agencies, EPA regions, and EPA
headquarters personnel. Primacy agencies report data quarterly to the EPA. The information
presented in the EA is based on the fourth quarter 2021 "frozen" dataset that was extracted on
January 14, 2022.
SDWIS/Fed contains information to characterize the inventory of PWSs, namely: system name
and location; retail population served, source water type, and PWS type.
4.2.1.1 PWS Type
The EPA defines a PWS as a system that provides water for human consumption through pipes
or other constructed conveyances to at least 15 service connections or regularly serves an
average of at least 25 individuals per day for at least 60 days per year (U.S. EPA, 2021h).
Systems are categorized as follows:
• CWSs are systems that supply water to the same population year-round.
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• Non-community water systems (NCWSs) are systems that supply water to a varying
population or one that is served less than year-round; these are sub-categorized as:
o Non-transient non-community water systems (NTNCWSs) are systems that
are not CWSs and that regularly supply water to at least 25 of the same
people at least six months per year (e.g., schools),
o Transient non-community water systems (TNCWSs) are NCWSs that do not
meet the non-transient criterion; they provide water in places such as gas
stations or seasonal campgrounds where people do not remain for long
periods of time.
A final rule to limit PFAS in drinking water would not apply to TNCWSs. Therefore, system
inventories in this analysis are classified into two categories: CWSs and NTNCWSs.
4.2.1.1.1 Population Served
Systems are also categorized by the number of people they serve.6 The following nine categories
of populations served by systems are used throughout this EA:
• < 100
• 101-500
• 501-1,000
• 1,001-3,300
• 3,301-10,000
• 10,001-50,000
• 50,001-100,000
• 100,001-1,000,000 (1M)
• >1M
The EPA uses these system size categories based on distinctions in the way systems operate as
the amount of water supplied and number of service connections increases. Systems within each
size category can be expected to face similar implementation and cost challenges when
complying with the new regulatory requirements for this final rule.
4.2.1.1.2 Source Water Type
SDWIS/Fed classifies system by source water using the following six categories:
• Ground water
• Ground water purchased
6 SDWIS/Fed classifies systems according to "retail" population that does not include the population served by other systems that
purchase water from them.
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• Ground water under the direct influence (GWUDI)7
• Ground water under the direct influence purchased (purchased GWUDI)
• Surface water
• Surface water purchased
For this analysis, the EPA broadly categorized systems as surface water if any of their sources
are surface water, surface water purchased, GWUDI, or purchased GWUDI. Systems are
classified as ground water if they exclusively used ground water or purchased ground water.8
4.2.1.1.3 Facilities
SDWIS/Fed provides additional information on system facilities, including the type of facility,
its activity status, and a unique facility identification number.
4.2.1.2 Verification of SDWIS/Fed Data
The EPA routinely conducts program reviews to verify whether information in the primacy
agencies' databases and files, such as inventory and violations for all regulations are correctly
represented in SDWIS/Fed. Between 2006 and 2016, the EPA recorded the findings from these
reviews in the national Error Code Tracking Tool (ECTT) (U.S. EPA, 2007b). The ECTT
contains, as individual records, all actions assessed during each program review. The EPA
identifies records as confirmed actions (correct compliance determinations and correct reporting
to SDWIS/Fed), compliance determination discrepancies (incorrect compliance determinations),
or data flow discrepancies (correct compliance determination but incorrect reporting). This
section presents data from the ECTT from program reviews conducted from 2006 to 2016 related
to system inventory.
It is important to note that treatment data (objective codes and process codes for plants in
SDWIS/Fed) are not evaluated during program reviews and therefore have more uncertainty
associated with the data as compared to inventory and compliance data.
4.2.1.2.1 System Inventory
From 2006 to 2016 the EPA evaluated inventory data for a total of 2,180 systems. Prior to
August 2007, the program reviews evaluated eight inventory fields: system type, system status,
activity status, source type, population, service connection, administrative contact, and
administrative address. After August 2007, the reviews did not include administrative contact or
address. In addition, in August 2007, the review policy changed so that discrepancies for
7 40 CFR Section 141.2 defines ground water under the direct influence of surface water as "any water beneath the surface of the
ground with significant occurrence of insects or other macroorganisms, algae, or large-diameter pathogens such as Giardia
lamblia or Cryptosporidium, or significant and relatively rapid shifts in water characteristics such as turbidity, temperature,
conductivity, or pH which closely correlate to climatological or surface water conditions."
8 23 CWS and 11 NTNCWS have an unknown primary water source. For purposes of this analysis, the EPA assigned these
systems to the source type ground water.
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inventory were only identified if they affected monitoring requirements (e.g., a change in
population that would increase or decrease the minimum number of required samples).
Of the inventory fields evaluated from 2006 to 2016, only 82 (<1%) inventory discrepancies
were identified. Furthermore, some of these discrepancies, such as those related to administrative
contact and address, may not impact the PWS baseline characterization. The inventory data in
ECTT indicate a high degree of completeness and accuracy in SDWIS/Fed as of 2016, and the
EPA expects that the information is largely representative of the regulated PWS.
4.2.2 Unregulated Contaminant Monitoring Rule
Every five years, the EPA issues a new list of no more than 30 unregulated contaminants to be
monitored by PWSs. UCMR 3 was published in 2012 and required monitoring for six PFAS
from 2013-2015: PFOA, PFOS, PFBS, PFNA, PFHxS, and PFHpA. The final UCMR 3 dataset
of analytical results was released in January 2017.
Under UCMR 3, all CWSs and NTNCWSs with more than 10,000 retail customers and a
representative sample of 800 systems serving 10,000 or fewer retail customers were required to
conduct assessment monitoring to collect occurrence data for the listed contaminants suspected
to be present in drinking water but that do not have health-based standards set under the SDWA.
Systems conducted assessment monitoring over one consecutive 12-month period between
January 2013 and December 2015. Ground water systems were required to monitor twice during
that period, with sampling events occurring five to seven months apart. Surface water systems
were required to monitor in four consecutive quarters, with sampling events occurring three
months apart. For the PFAS compounds, sampling was conducted at the entry point (EP) to the
distribution system post treatment.
The fifth UCMR (UCMR 5), published December 2021, requires sample collection and analysis
for 29 PFAS to occur between January 2023 and December 2025 using analytical methods
developed by the EPA and consensus organizations. In the Federal Register Notice for this
rulemaking, the EPA describes the small subset (7%) of data released as of August 2023, the
limitations with considering an incomplete dataset, and that findings from analyses of these data
are generally confirmatory of the EPA's other occurrence analyses. Because of the partial nature
of this dataset, the EPA has not used it to characterize baseline occurrence in this EA.
4.2.3 Independent State Sampling Programs
The EPA used state monitoring data from 20 states (Alabama, Colorado, Illinois, Indiana,
Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire,
New Jersey, New York, North Dakota, Ohio, South Carolina, Tennessee, Vermont, and
Wisconsin). These states conducted non-targeted monitoring (i.e., random sampling) of finished
drinking water for one or more of the four PFAS in this analysis.
4.2.4 Six-Year Review Data
The EPA used information from the fourth Six-Year Review Information Collection Request
(ICR) Dataset ("SYR4 ICR dataset") to characterize the total organic carbon (TOC) level for
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individual systems. The SYR4 ICR dataset is the most comprehensive and current national
drinking water occurrence dataset, containing millions of records of water system compliance
monitoring data and treatment technique information for regulated chemical, radiological, and
microbiological contaminants collected from 2012 through 2019. The portion of the dataset
containing the TOC information was made publicly available in August 2022.9
4.2.5 Geometries and Characteristics of Public Water Systems
(2000)
An important factor in determining costs of treatment is average daily flow and design flow,
measured in gallons per day or million gallons per day (MGD), at a treatment plant. The EPA
estimated the average daily flow and design flow for each EP in the system based on the
relationship between retail population and flow as derived in the EPA's Geometries and
Characteristics of Public Water Systems report (U.S. EPA, 2000).
Utilizing data from the 1995 CWSS, the EPA conducted an extensive data-cleaning process10 to
develop a dataset of 1,734 records with paired responses for population and total average daily
flow. These data were then weighted to account for non-responses to individual questions from
the CWSS. The EPA used this dataset to develop regression equations that predict average daily
flow based on retail population served (for both publicly-owned and privately-owned systems).
The data show a strong correlation as indicated by a high R-squared value of 0.90. Additional
information and background data are provided in Chapter 4 of the Geometries and
Characteristics of Public Water Systems report (U.S. EPA, 2000).
4.2.6 Community Water System Survey (2006)
The EPA periodically conducts the CWSS to obtain data to support the agency's development
and evaluation of drinking water regulations. The 2006 CWSS is the most recent survey. For this
EA, the EPA relied on the national average estimates of unit labor from the 2006 CWSS to
derive the unit labor rates.
The EPA selected the CWSS as a data source because it is based on a nationally representative
sample of CWSs. The sample was drawn from SDWIS/Fed, which includes approximately
50,000 systems in the 50 states and the District of Columbia. The survey used a stratified random
sample design to ensure the sample was representative. The EPA selected a survey sample of
2,210 systems, including all systems serving populations of 100,000 or more. In the 2006 CWSS,
the agency took additional steps to improve response rates, ensure accurate responses, and
reduce the burden of the survey on systems, especially systems serving 3,300 or fewer persons.
The EPA sent water system experts to collect data from systems serving 3,300 or fewer persons.
For systems serving more than 3,300 people, the agency mailed the survey, made available a
spreadsheet and Web-based version of the questionnaire, and provided extensive assistance
through e-mail and a toll-free telephone hotline. The survey was designed to collect data for the
9 Available at: https://www.epa.gov/dwsixyearTeview/microbial-and-disinfection-byproduct-data-files-2012-2019-epas-fourth-
six-year
10 The EPA adjusted the dataset to remove non-zero values; adjusted flow if needed to represent retail flow only removing
wholesale water flow; and adjusted for reporting discrepancies in population, flow, or service connections.
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year 2006. Full-scale data collection occurred from June to December 2007. The overall
response rate was 59 percent with a total of 1,314 systems responding; 95 percent of selected
systems serving 3,300 or fewer persons (representing 571 of 600 systems sampled) participated
in the survey (U.S. EPA, 2009).
4.3 Drinking Water System Baseline/Industry Profile
This section presents the following baseline characterizations for the purposes of estimating costs
and benefits for the final rule. Section 4.3.1 provides a characterization of the inventory of
systems subject to the final rule (CWSs and NTNCWSs). Section 4.3.2 includes the population
served by CWSs and NTNCWSs and the number of households served by CWSs. Section 4.3.3
provides treatment plant characteristics used to determine treatment costs. Section 4.3.4
describes the derivation of PWS labor rates. Finally, Section 4.3.5 describes the cost of capital
rates used to estimate household-level costs. Each section includes a characterization of the
baseline for CWSs, followed by NTNCWSs, if applicable, and a characterization of data
limitations and uncertainty. TNCWSs are not subject to the final rule.
4.3.1 Water System Inventory
A key component of the baseline is the inventory of systems—both CWSs and NTNCWSs—
subject to the final rule. As shown in Table 4-2, approximately 81 percent of all CWSs serve
3,300 or fewer people (39,746 of the total systems), and those serving 500 or fewer account for
about 54 percent of all CWSs (26,742 of the total systems). CWSs serving 3,301-50,000 people
represent about 17 percent of all CWSs (8,422 of the total systems), and those serving more than
50,000 people account for only about 2 percent (1,025 of the total systems). Most CWSs (about
77 percent or 37,733 systems) use ground water as their primary source. Most systems serving
more than 10,000 people, however, are classified as surface water systems (about 63 percent or
2,817 systems).
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Table 4-2: Inventory of CWSs
CWSs'
System Size (Population
Served)
Ground Water
Surface Water
Total
A
B
C = A + B
< 100
10,654
739
11,393
101-500
13,037
2,042
15,079
501-1,000
4,132
1,179
5,311
1,001-3,300
5,503
2,460
7,963
3,301-10,000
2,784
2,223
5,007
10,001-50,000
1,385
2,030
3,415
50,001-100,000
162
417
579
100,001-1M
74
347
421
> 1M
2
23
25
TOTAL
37,733
11,460
49,193
Abbreviations: CWS - community water systems.
Note:
includes 23 CWSs serving 10,000 or fewer people for which no primary source water type was reported to SDWIS/Fed.
The EPA assigned these systems to the source type of ground water.
Source: SDWIS/Fed fourth quarter 2021 "frozen " dataset that contains information reported through January 14, 2022.
Includes all active CWSs.
As shown in Table 4-3, approximately 99 percent of all NTNCWSs serve 3,300 or fewer people
(17,135 of the total). NTNCWSs serving 3,301 - 50,000 people account for about 1 percent of all
NTNCWSs (200 of the total). Only two NTNCWSs serve more than 50,000 people, and none
serve more than 1 million people. Most NTNCWSs (about 95 percent or 16,531 systems) use
ground water as their primary source. Approximately 51 percent (21 systems) of those serving
10,001-100,000 people use surface water versus ground water and the one system serving
100,001-1 million people is classified as a surface water system.
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Table 4-3: Inventory of NTNCWSs
NTNCWSs'
System Size (Population
Served)
Ground Water
Surface Water
Total
A
B
C=A+B
< 100
101-500
501-1,000
1,001-3,300
3,301-10,000
10,001-50,000
50,001-100,000
100,001-1M
> 1M
TOTAL
16,531
8,084
6,111
1,476
743
97
20
0
0
0
252
257
91
121
63
20
1
1
0
806
17,337
8,336
6,368
1,567
864
160
40
1
1
0
Abbreviations: NTNCWS - non-transient non-community water systems.
Notes:
includes 11 NTNCWSs serving 3,300 or fewer people for which no primary source type was reported to SDWIS/Fed. The
EPA assigned these systems to the source water type of ground water.
Source: SDWIS/Fed fourth quarter 2021 "frozen " dataset that contains information reported through January 14, 2022.
Includes all active NTNCWSs.
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There is uncertainty in the approach used to assign source water type to the 23 CWSs and 11
NTNCWSs where no primary source type was reported to SDWIS/Fed. This analysis assumes
that these systems have ground water as their primary source based on the preponderance of
ground water systems in the inventory. This could result in an under- or overestimate of costs in
those instances where the cost model inputs vary by source type (e.g., number of EPs per
system); however, the EPA expects the impact to be low because the systems without a source
type in SDWIS/Fed represent a small proportion of systems subject to the rule (23 of the total
49,193 CWSs and 11 of the total 17,337 NTNCWSs or 0.05 percent of all systems subject to the
rule) and all serve fewer than 10,000 people.
4.3.2 Population and Households Served
It is necessary to have an accurate characterization of population served by water systems when
assessing the potential benefits of a final regulation. Population is also an input for estimating
treated water volumes and associated granular activated carbon (GAC) or ion exchange (IX)
costs.
SDWIS/Fed tracks "retail" population served, meaning that it counts only the population that
purchases water directly from the water system, not the population of a system's wholesale
customers. The systems that purchase water appear in SDWIS/Fed as a separate system with a
unique PWS identification (PWSID) number.
Table 4-4 and Table 4-5 show the total population served and average population served per
system by size category for CWSs and NTNCWSs, respectively. Each exhibit is organized by
source water type (surface water or ground water) and is based on the SDWIS/Fed fourth quarter
2021 "frozen" dataset that contains information reported by primacy agencies through January
14, 2022.
Because systems often pass some or all of their costs onto customers in the form of rate
increases, the final rule cost analysis also includes analyses to assess the impact of the rule
requirements on annual household expenditures. The EPA estimated the number of households
served by affected CWSs by dividing the population for each system size category by the
average number of people per household. For CWSs, the EPA assumed an average of 2.53
persons per household based on 2020 U.S. Census data (U.S. Census Bureau, 2020b). This
information is also included in Table 4-4 by system size and source type. NTNCWSs do not
serve households, thus, this information is not included in Table 4-5.
As shown in Table 4-4, although CWSs serving 3,300 or fewer people account for approximately
81 percent of all CWSs, they serve fewer than 8 percent of the population and households that
receive their water from a CWS. Although CWSs serving more than 50,000 people account for
only 2 percent of all CWSs, they serve more than half (59 percent) of the population and
households that receive their water from a CWS.
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Table 4-4: Population and Number of Households Served by CWSs
Ground Water0
Surface Water
TOTAL
System Size
(Population
Served)
Population
Served
Average
Population
Per System
Number of
Households
Served
Population
Served
Average
Population
Per System
Number of
Households
Served
Population
Served
Average
Population
Per System
Number of
Households
Served
A
Ba
C = A/2.53b
D
Ea
F = D/2.53b
G
Ha
I = G/2.53b
< 100d
652,335
61
257,840
45,231
61
17,878
697,566
61
275,718
101-500
3,254,293
250
1,286,282
576,601
282
227,906
3,830,894
254
1,514,187
501-1,000
3,032,366
734
1,198,564
883,656
749
349,271
3,916,022
737
1,547,835
1,001-3,300
10,264,020
1,865
4,056,925
4,935,965
2,006
1,950,974
15,199,985
1,909
6,007,899
3,301-10,000
15,794,291
5,673
6,242,803
13,633,206
6,133
5,388,619
29,427,497
5,877
11,631,422
10,001-50,000
28,665,202
20,697
11,330,119
46,262,480
22,789
18,285,565
74,927,682
21,941
29,615,685
50,001-100,000
10,889,918
67,222
4,304,315
29,350,794
70,386
11,601,104
40,240,712
69,500
15,905,420
100,001-1M
15,082,760
203,821
5,961,565
84,675,709
244,022
33,468,660
99,758,469
236,956
39,430,225
> 1M
3,400,000
1,700,000
1,343,874
44,266,001
1,924,609
17,496,443
47,666,001
1,906,640
18,840,317
TOTALe
91,035,185
2,413
35,982,287
224,629,643
19,601
88,786,420
315,664,828
6,417
124,768,707
Abbreviations: CWS - community water systems.
Notes:
aB, E, and H: Derived by dividing the population served by the number of systems presented in Table 4-2.
bC, F, and I: The average of 2.53 persons per household is from 2020 U.S. Census data (Table AVG1. Average Number of People per Household, by Race and Hispanic Origin/1,
Marital Status, Age, and Education of Householder: 2020).
cCWSs with unreported primary source were assumed to be ground water systems. Thus, the ground water column reflects an additional 23 CWSs with unreported primary source
type.
dThe EPA removed any CWS wholesaler serving less than 25 people from the analysis and assumed that any remaining CWS had a minimum possible population of 25.
eNumbers may not sum to total because of rounding.
Source for A, D, and G: SDWIS/Fed fourth quarter 2021 "frozen " dataset that contains information reported through January 14, 2022.
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As previously discussed, NTNCWSs serving 3,300 or fewer people account for approximately
99 percent of all NTNCWSs. As shown in Table 4-5, these systems serve approximately 70
percent of the population that receives their water from an NTNCWS. Those serving 3,301—
50,000 people and more than 50,000 people serve approximately 26 percent and 4 percent of the
population that receives water from an NTNCWS, respectively.
Table 4-5: Population Served by NTNCWSs
Ground Waterb Surface Water TOTAL
System Size
(Population Served)
Population
Served
Average
Population
Per System
Population
Served
Average
Population
Per
System
Population
Served
Average
Population
Per
System
A
Ba
D
Ea
F
Ga
< 100c
452,516
56
12,534
50
465,050
56
101-500
1,513,562
248
69,046
269
1,582,608
249
501-1,000
1,049,638
711
68,235
750
1,117,873
713
1,001-3,300
1,241,973
1,672
239,516
1,979
1,481,489
1,715
3,301-10,000
511,494
5,273
377,219
5,988
888,713
5,554
10,001-50,000
397,246
19,862
414,099
20,705
811,345
20,284
50,001-100,000
0
0
71,963
71,963
71,963
71,963
100,001-1M
0
0
203,375
203,375
203,375
203,375
> 1M
0
0
0
0
0
0
TOTALd
5,166,429
313
1,455,987
1,806
6,622,416
382
Abbreviations: NTNCWS - non-transient non-community water systems.
Notes:
aB, E, and G: Derived by dividing the population served by the number of systems presented in Table 4-3.
bNTCWSs with unreported primary source were assumed to be ground water systems. Thus, the "Ground Water" column
reflects an additional 11 NTCWSs with unreported primary source type.
The EPA assumed any non-wholesale NTNCWS had a minimum possible population of 25.
dNumbers may not sum to total because of rounding.
Source for A, D, and F: SDWIS/Fed fourth quarter 2021 "frozen" dataset that contains information reported through
January 14,2022.
As noted previously, the EPA consistently classifies systems in SDWIS/Fed according to the
retail population served by the system and does not include the population served by wholesale
customers. Wholesale customers who purchase water from another system and meet the PWS
definition have their own unique PWSID, retail population, and associated regulatory
requirements under SDWA. The EPA uses retail population to estimate design and average daily
flow parameters, which are then used to estimate treatment costs associated with the rule. Use of
retail population may overestimate aggregate costs by assuming that each system will have an
individual treatment plant instead of the more common scenario of the seller having one large
plant and selling treated water to their wholesale customers. Because of returns to scale in
treatment capital costs, the cost of a single large plant will be less than the sum of the costs
across several small plants treating the same aggregate flow.
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In addition, given that some of the reported population values would create inconsistencies in the
analysis, the EPA removed any CWS wholesaler serving less than 25 people from its analysis
and assumed that any remaining CWS had a minimum possible population of 25. The EPA
assumed any non-wholesale NTNCWS had a minimum possible population of 25.
4.3.3 Treatment Plant Characterization/Production Profile
This section explains the baseline inputs for the following treatment-related PWS characteristics.
Section 4.3.3.1 discusses the EPs per system characterization. Section 4.3.3.2 discusses the
EPA's TOC baseline assumptions and Section 4.3.3.3 presents the estimation method and the
computed average daily flows and design flows by system type and size.
4.3.3.1 Entry Points Per System
EPs are the point of compliance for the final rule and systems can have multiple EPs. The EPA
developed estimates of EPs per system using UCMR 3 unique sampling points, SDWIS/Fed
facility data, and a modeled frequency distribution.
UCMR 3 required a subset of CWSs and NTNCWSs to conduct assessment monitoring for six
PFAS compounds.11 The data record a unique identifying number for the EP sample location(s)
for each system. Given the information provided, the EPA assumes that the number of unique
sample point IDs per system approximates the total number of EPs per system.
For systems without UCMR 3 occurrence data, the EPA developed estimates based on
SDWIS/Fed facilities data. The SDWIS/Fed data include unique identification numbers for
system facilities, as well as facility type and activity status. This analysis relies on active
facilities identified as treatment plants. Using the assumption that treatment plants are associated
with one EP, the SDWIS/Fed facility data provide an approximation for the number of EPs per
system when a system does not have UCMR 3 occurrence data. The EPA considers the UCMR 3
sampling point data to be of higher quality than the SDWIS/Fed treatment facility data. If the
SDWIS/Fed treatment facility data value for a system exceeded the maximum number found for
the equivalent system size and source water combination in the UCMR 3 data, the EPA limited
the system EP value to the UCMR3 maximum number of EPs.
For systems without UCMR 3 occurrence data or SDWIS/Fed facility data, the EPA relies on an
estimate of the number of EPs. The estimated value for each system with missing EP count data
was imputed from known EP counts for stratified SDWIS/Fed data. Within each stratum, defined
by a combination of system size and source water, the EPA sampled from systems with known
EP counts. Sampling was done with replacement after truncating the EP counts to the maximum
recorded in UCMR 3. For reproducibility, the EPA performed this sample-based imputation in R
using the 'base::sample' function (R Core Team, 2021).
11 UCMR 3 required all systems serving more than 10,000 people to collect and analyze samples for PFOA, PFOS, PFNA,
PFHxS, PFBS, and PFHpA at each distribution system entry point. The EPA also identified a stratified random sample of 800
small systems serving up to 10,000 people to collect samples for these six PFAS.
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Following this process, the EPA relies on sample point values recorded in UCMR 3 for 5,419
systems, SDWIS/Fed facility data for 43,563 systems, and imputed EP values for 17,523
systems. All systems have at least one EP. Among CWSs, the maximum number of EPs is 202,
and the mean is 1.80. Among NTNCWSs, the maximum number of EPs is 22, and the mean is
1.31.
Table 4-6 summarizes the final frequency distribution of EP input ranges for each CWS stratum
of size and source water combination. Table 4-7 summarizes the final frequency distribution of
EP input ranges for each NTNCW stratum of size and source water combination. These
distributions are used to proportionally assign numbers of EPs to systems in each system size and
type category.12
12 The SDWIS/Fed data provide information on the PWS characteristics that typically define PWS categories, or strata, for which
the EPA develops costs in rulemakings. These characteristics include system type (CWS, NTNCWS), number of people served
by the PWS, PWS's primary raw water source (ground water or surface water), PWS's ownership type (public or private), and
PWS state. For more information on the use of baseline and compliance characteristics to define model systems in the EPA's cost
analysis, please see Section 5.2.
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Table 4-6: Frequency Distribution of EP Inputs for CWSs
Ground Water
Surface Water
System Size
1 EP
2-5
EP
6-10
EP
11-15
EP
16-20
EP
21-
100
EP
>100
EP
1 EP
2-5
EP
6-10
EP
11-15
EP
16-20
EP
21-
100
EP
>100
EP
< 100
90%
10%
0.1%
0
0
0
0
87%
13%
0
0
0
0
0
101-500
76%
24%
0
0
0
0
0
84%
16%
0
0
0
0
0
501-1,000
62%
38%
0.5%
0
0
0
0
76%
23%
0.8%
0
0
0
0
1,001-3,300
48%
50%
1%
0
0
0
0
70%
30%
0.7%
0
0
0
0
3,301-10,000
32%
59%
8%
0.9%
0.1%
0
0
54%
43%
3%
0.5%
0.04%
0
0
10,001-50,000
3%
58%
28%
7%
3%
1%
0.07%
3%
82%
10%
2%
1%
0.6%
0
50,001-100,000
0
51%
25%
8%
8%
9%
0
0.2%
74%
13%
6%
2%
4%
0
100,001-1M
0
34%
22%
11%
8%
24%
1%
0.3%
67%
13%
4%
9%
6%
0.3%
Abbreviations: CWS - community water systems; EP - entry point.
Table 4-7: Frequency Distribution of EP Inputs for NTNCWSs
Ground Water
Surface Water
System Size
1 EP
2-5 EP
6-10 EP
11-20 EP
>20 EP
1 EP
2-5 EP
6-10 EP
11-20 EP
> 20 EP
< 100
84%
16%
0.4%
0
0
82%
18%
0
0
0
101-500
81%
19%
0
0
0
74%
26%
0
0
0
501-1,000
0
0
0
0
0
0
0
0
0
0
1,001-3,300
68%
30%
2%
0
0
61%
31%
8%
0
0
3,301-10,000
53%
44%
2%
1%
0
35%
44%
14%
6%
0
10,001-50,000
10%
80%
0
10%
0
30%
40%
5%
20%
5%
50,001-100,000
0
0
0
0
0
0
100%
0
0
0
100,001-1M
0
0
0
0
0
0
100%
0
0
0
Abbreviations: NTNCWS - non-transient non-community water systems; EP - entry point.
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4.3.3.2 Total Organic Carbon
The effectiveness of the GAC treatment process varies with the level of TOC in the influent
water. There is no national dataset containing TOC values for every CWS or NTNCWS.
Therefore, the EPA randomly assigned a TOC level to each system based on two distributions of
TOC in 'finished' water. The agency developed distributions using TOC data voluntarily
submitted by states in response to the SYR4 ICR drinking water regulations. Because TOC
levels in ground water are lower on average than TOC levels in surface water, the EPA separated
the data by system primary source water. TOC levels can also vary throughout a system. Source
water TOC measurements can be higher than finished water estimates if a treatment process
removes TOC. For each system, the EPA identified TOC measurements that best represented
finished water quality. Using the resulting distribution of ground water or surface water
estimates, the EPA identified decile midpoint values to randomly assign to each system.
4.3.3.3 Average Daily Production Flow and Design Flow
Average daily production flow and design flow per system are based on regression equations
from the EPA's Geometries and Characteristics of Public Water Supplies report (U.S. EPA,
2000). The average daily flow and design flow are functions of the population served, with
different equations for source water type (surface water or ground water). Table 4-8 presents
these flow equations. The flow was then divided by the number of EPs to calculate the flow per
treatment plant for the system (assuming each EP has one treatment plant). The EPA does not
have comparable flow-population regression equations for NTNCWSs and, therefore, used the
CWS relationships to estimate flow for NTNCWSs.
Table 4-8: Functions for Design and Average Daily Flow by System Types
Design Flow Functions (kgal)
Surface water system
Ground water system
Design Flow = 0.59028 x Population0 94573
(or 2 x Average Flow, whichever is greater)
Design Flow = 0.54992 x Population0 95538
(or 2 x Average Flow, whichever is greater)
Average Daily Flow Functions (kgal)
Surface water system
Average Flow = 0.14004 x Population0 99703
Ground water system
Average Flow = 0.08575 x Population1 05839
Abbreviations: kgal - 1000 gallons.
As an example, Table 4-9 shows the design flow and average daily flow results when applying
the regression equations to the average population per system for each CWS system stratum. The
results for NTNCWSs are in Table 4-10. Note that these results are examples only. In practice,
the EPA applied the regression equations to the population served of individual systems, instead
of the stratum average population. In addition, for systems serving more than 1 million people,
the EPA obtained publicly available system-specific information on the average daily flow and
design flow for each EP whenever possible (e.g., annual Consumer Confidence Reports).
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Table 4-9: Design and Average Daily Flow for CWSs
Ground Water
Surface Water
System Size
Average
Design
Flow
Average
Flow
Average
Design
Flow
Average
Flow
Population
(MGD)
(MGD)
Population
(MGD)
(MGD)
< 100
61
0.028
0.007
61
0.029
0.008
101-500
250
0.107
0.030
282
0.123
0.039
501-1,000
734
0.301
0.093
749
0.309
0.103
1,001-3,300
1,865
0.733
0.248
2,006
0.784
0.275
3,301-10,000
5,673
2.121
0.806
6,133
2.255
0.837
10,001-50,000
20,697
7.305
3.171
22,789
7.804
3.098
50,001-100,000
67,222
22.512
11.031
70,386
22.671
9.535
100,001-1M
203,821
71.371
35.685
244,022
73.470
32.937
Abbreviations: CWS - community water systems; MGD - million gallons per day.
Table 4-10: Design and Average Daily Flow for NTNCWSs
Ground Water
Surface Water
System Size
Average
Design
Flow
Average
Flow
Average
Design
Flow
Average
Flow
Population
(MGD)
(MGD)
Population
(MGD)
(MGD)
< 100
56
0.026
0.006
50
0.024
0.007
101-500
248
0.107
0.029
269
0.117
0.037
501-1,000
711
0.292
0.089
750
0.309
0.103
1,001-3,300
1,672
0.660
0.221
1,979
0.774
0.271
3,301-10,000
5,273
1.978
0.746
5,988
2.205
0.817
10,001-50,000
19,862
7.023
3.035
20,705
7.127
2.815
50,001-100,000
Not
Not
Not
71,963
23.151
9.748
applicable
applicable
applicable
100,001-1M
Not
Not
Not
203,375
61.841
27.465
applicable
applicable
applicable
Abbreviations: NTNCWS - non-transient non-community water systems; MGD - million gallons per day.
4.3.4 Public Water System Labor Rates
The EPA recognizes that there may be variation in labor rates across all systems. However, for
purposes of this EA, the EPA used national average estimates of unit labor from the 2006 CWSS,
with a few modifications described below. Prior labor unit costs for managerial, technical, and
clerical labor in the EPA's work breakdown structure13 (WBS) were based on a review of data
from three sources:
• The Occupational Employment Survey (OES), a semi-annual Bureau of Labor Statistics
(BLS) survey that provides hourly wage estimates by occupation and industry (BLS,
2022b).
13 To estimate treatment costs, the EPA uses several engineering models using a bottom-up approach known as work breakdown
structure (WBS). The WBS models derive system-level costs and provide the EPA with comprehensive, flexible and transparent
tools to help estimate treatment costs.
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• The Water Utility Compensation Survey, an annual American Water Works Association
(AWW A) survey that provides hourly wage estimates for the water and wastewater
industry by occupation. Data are in 2008 dollars.
• The 2006 CWSS, a periodic EPA survey that obtains employment information from a
sample of CWSs.
There are more recent wage data from the OES and AWWA surveys, but there has not been a
CWSS since 2006. A 2020 review of the WBS labor rates found that the WBS wage rates in
2019 dollars overstate labor costs for clerical labor hours as well as potentially overstate labor
costs for technical labor hours (Abt Associates, 2020). Following these findings, the EPA
adjusted the labor costs used in the WBS to reflect occupation-specific escalation factors rather
than the seasonally adjusted employment cost index (ECI) for all civilian employees. The WBS
labor costs for managerial hours were not clearly over- or understated compared to OES data but
were consistently lower than the AWWA wage estimates (Abt Associates, 2020).
Table 4-11 presents the labor rate estimates used in the WBS in 2007 dollars. Labor rates were
calculated for three occupation categories: technical, managerial, and clerical. The rates do not
include benefits.
Table 4-11: Hourly Wage Rates Based on CWSS Data ($2007)
Occupation
<500
501-
3,300
3,301-
10,000
10,001-
50,000
50,001-
100,000
> 100,000
Technical
$16.97
$16.97
$18.10
$19.11
$19.95
$23.32
Managerial
$24.06
$24.06
$27.52
$30.65
$35.76
$38.21
Clerical
$16.21
$16.21
$16.21
$20.93
$20.93
$20.93
Abbreviations: CWSS - Community Water System Survey.
Source: Abt Associates, 2020
A review of updated BLS Employer Cost for Employee Compensation (ECEC) data indicated
that benefits account for a higher proportion of total compensation today than they did at the end
of 2006 (Abt Associates, 2020). The WBS assumes a benefit multiplier of 1.45, which is the
2020 multiplier for all civilians working in service-producing industries (Abt Associates, 2020).
The benefit-loaded wage rates are shown in Table 4-12.
Table 4-12: Hourly Labor Costs Including Wages Plus Benefits ($2007)
Occupation
<500
501-
3,300
3,301-
10,000
10,001-
50,000
50,001-
100,000
> 100,000
Technical
$24.61
$24.61
$26.25
$27.71
$28.93
$33.81
Managerial
$34.89
$34.89
$39.90
$44.44
$51.85
$55.40
Clerical
$23.50
$23.50
$23.50
$30.35
$30.35
$30.35
Source: Abt Associates, 2020
Because the WBS relies on 2020 dollar values, the EPA escalated the CWSS values using the
OES occupation-specific change in mean wage rate from 2007 to 2020 instead of the general
civilian ECI escalation rate. The escalation for the technical rate is 35.2 percent and the
escalation for the clerical rate is 36.3 percent. The WBS managerial wage rates are consistent
with OES rates, but slightly lower than AWWA rates (Abt Associates, 2020). At the time of the
analysis in 2020, the OES occupation-specific wage escalation rate for the managerial rate was
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comparable to the ECI rate (Abt Associates, 2020). Therefore, the WBS retains the ECI escalated
managerial labor rates, which for 2020 is 41.4 percent. The national cost-benefit analysis method
described in Section 5.2 presents all values in 2022 dollars. The method uses the gross domestic
product (GDP) implicit price deflator to adjust values in other dollar years to 2022 dollars.
Therefore, the labor costs including wages and benefits in 2022 dollars shown in Table 4-13
reflect an additional adjustment for dollar year. The EPA applied the same system labor rates to
both CWSs and NTNCWSs.
Table 4-13: Hourly Labor Costs Escalated to $2022
Occupation
<500
501-
3,300
3,301-10,000
10,001-
50,000
50,001-
100,000
> 100,000
Technical
35.48
35.48
37.84
39.94
41.70
48.74
Managerial
52.60
52.60
60.16
67.02
78.19
83.55
Clerical
34.17
34.17
34.17
44.12
44.12
44.12
There is uncertainty in the derivation of labor rates that could result in an over- or underestimate
of national costs of the final rule. The mean labor rate is based on findings of the 2006 CWSS.
The labor rate mix may have changed since the time of the survey. The EPA accounted for
general changes in cost of labor by adjusting 2007 values to 2020 using occupation-specific
escalators and the ECI where appropriate. There is also uncertainty in assuming a 1.45 benefits
multiplier; this may cause an under- or overestimation of cost of the final rule.
4.3.5 Cost of Capitai
For the social cost-benefit analysis, the EPA uses a social discount rate of 2 percent to discount
future values and annualize discounted present value over the period of analysis. This rates is in
accordance with OMB Circular A-4 (OMB, 2023).
When evaluating the economic impacts on PWSs and households, however, the EPA uses
estimated cost of capital to discount future costs and annualize the discounted present value over
the analysis period. This rate best represents the actual costs of compliance that systems will
incur over time. To estimate PWS cost of capital, the EPA used data from the 2006 CWSS. The
CWSS defined the following categories of funding sources:
• Current revenue;
• Equity or other funds from private investors;
• Department of Homeland Security (DHS) grant;
• Other government grants;
• Drinking Water State Revolving Fund (DWSRF), including loans and Principal
Repayment Forgiveness;
• Other borrowing from public sector sources; and
• Borrowing from private sectors sources.
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The EPA calculated the overall weighted average cost of capital (across all funding sources and
loan periods) for each size/ownership category, weighted by the percentage of funding from each
source.14 Table 4-14 shows the cost of capital for each CWS size category and ownership type.
Similar cost of capital information is not available for NTNCWS. Therefore, the EPA used the
CWS cost of capital when calculating the annualized cost per NTNCWS.
Table 4-14: Weighted Average Cost of Capital by PWS Ownership and Size Category
Size Category Publicly Owned CWS Privately Owned CWS
<100
3.8%
7.8%
101-500
5.5%
8.2%
501-1,000
4.0%
8.6%
1,001-3,300
4.7%
7.1%
3,301-10,000
5.8%
7.0%
10,001-50,000
6.1%
7.0%
50,001-100,000
4.9%
6.9%
100,001-500,000
4.7%
3.9%
Over 500,000
3.7%
7.8%
Abbreviations: PWS - public water system; CWS - community water system.
Since the CWSS data collection, Congress established new programs and expanded funding for
existing programs. These funding sources allow PWSs to lower their cost of capital. These
include the DWSRF, the Water Infrastructure Finance and Innovation (WIFIA) program, the
Water Infrastructure Improvements for the Nation Act of 2016 (WIIN Act), and the Bipartisan
Infrastructure Law of 2021 (BIL).
Through the DWSRF Program, the EPA allocates annual capitalization grants to states. The
grants, along with state matching monies, support a dedicated loan fund to finance eligible water
system infrastructure improvement projects. States are permitted to use funding from their
DWSRF to help PWS finance water treatment through low-interest loans. These loans range
from zero percent to market rate. The weighted average interest rate across all signed DWSRF
loans in the past ten fiscal years (2013 through 2023) has been below 2% each year, with the
weighted average for the 2023 state fiscal year of 1.6%. EPA notes these weighted averages
reflect the rates signed into final loans, not the range of possible rates offered during those years.
The WIFIA program provides creditworthy PWSs access to low-interest direct federal loans for
water treatment investment. The WIIN Act established a grant program to help small,
underserved, and disadvantaged communities achieve compliance with drinking water standards.
Additionally, the BIL (P.L. 117-58) authorizes $5 billion as part of the Emerging Contaminants
in Small or Disadvantaged Communities (EC-SDC) grants program that can be used to reduce
PFAS in drinking water in communities facing disproportionate impacts. BIL funds will be
provided as grants and loan forgiveness associated with PFAS drinking water treatment capital
expenditures. Overall, the actual cost of capital faced by some PWSs may be lower than those
used in this analysis.
14 See "Cost of Capital Approach.doc" in the docket for details of how the cost of capital estimates were developed.
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4.4 Occurrence of PFAS
The EPA's PbAS Occurrence & Contaminant Background Support Document provides
estimates of the baseline PFAS occurrence in PWSs (U.S. EPA, 2024g). After reviewing the
available data on PFAS in drinking water, the EPA determined that the data from the UCMR 3
are the best available nationally representative data to characterize the occurrence of multiple
PFAS in drinking water. Consistent with the agency's commitment in the final regulatory
determination for PFOA and PFOS and the EPA's PFAS Strategic Roadmap to present the best
available occurrence information, the agency supplemented the UCMR 3 data with data collected
by states that have made their data publicly available (U.S. EPA, 2021b; U.S. EPA, 2021e).
This section summarizes the EPA's PFAS occurrence analysis (U.S. EPA, 2024g). Section 4.4.1
provides an overview of UCMR 3 and its PFAS occurrence data. Section 4.4.2 provides an
overview of state PFAS monitoring data. Section 4.4.4 summarizes the EPA's analysis of PFAS
drinking water occurrence data. Section 4.4.5 summarizes the national PFAS occurrence
estimates used in the cost and benefit analyses.
4.4.1 Overview of UCMR 3 Data
The UCMR is a national drinking water monitoring program administered by the EPA. The
UCMR 3 monitoring cycle included a census of all large CWSs and NTNCWSs (i.e., those
serving more than 10,000 people) and a statistical sample of 800 small CWSs and NTNCWSs
(i.e., those serving 10,000 people or fewer). Monitoring under UCMR 3 occurred from 2013 to
2015. More information on the UCMR 3 study design and data analysis can be found in U.S.
EPA (2012) and U.S. EPA (2019c).
The EPA collected the UCMR 3 data from PWSs in all 50 states and seven additional primacy
agencies. UCMR 3 monitoring occurrence data are available for six PFAS: PFOS, PFOA, PFNA,
PFHxS, PFHpA, and PFBS. For the individual PFAS contaminants, the EPA collected nearly
37,000 finished water samples from 4,920 PWSs.
Systems collected PFAS samples at each EP to their customer distribution system. EPs are the
point of compliance for the final rule, and systems can have multiple EPs. The sampling
frequency varied by source water: four quarterly samples in a one-year period for surface water
systems, and two samples at least six months apart for ground water systems.
The EPA's I'l'AS Occurrence & Contaminant Background Support Document (U.S. EPA,
2024g) describes the data and analyses that the EPA used to develop national estimates of PFAS
occurrence in public drinking water systems using UCMR 3 data.
4.4.2 Overview of State PFAS Data
Outside of the UCMR 3 data collection, many states have undertaken individual efforts to
monitor for PFAS in both source and finished drinking water. The EPA collected data from 32
states that have made their data available and represents sampling conducted on or before May
2023. The EPA notes that this data collection cutoff was made to allow sufficient time for the
agency to conduct analyses on the state information for the final NPDWR. Due to the limitations
in representation and reporting of some of the available data, the EPA conducted technical
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analyses using a subset of the available state data from 20 states. These more recent state data,
collected using improved analytical methods that have lower reporting limits than under UCMR
3, show widespread occurrence of PFOA, PFOS, PFNA, and PFHxS and co-occurrence of these
four PFAS and PFBS in multiple geographic locations. These data also show that these PFAS
occur with substantial frequency at lower concentrations than were analyzed under UCMR 3, as
demonstrated within the EPA's PbAS Occurrence & Contaminant Background Support
Document (U.S. EPA, 2024g). Furthermore, these state data include results for more PFAS than
were included in the UCMR 3, including HFPO-DA. Please see Sections III and VI of the
Federal Register Notice for discussion about how these data enhanced and supported the EPA's
occurrence analyses and were confirmatory of the EPA's findings and conclusions in the
proposed PFAS NPDWR.
The EPA's analysis of state PFAS data shows occurrence in multiple geographic locations
consistent with what was observed during UCMR 3 monitoring. The agency notes that the data
vary in terms of quantity and coverage; for example, some of these available data are from
targeted sampling efforts (i.e., monitoring in areas of known or potential contamination) and thus
may not be representative of levels found in all PWSs within the state. Summaries on the non-
targeted state PFAS finished water data are available in Table 4-15 and Table 4-16. Specifically,
a summary on the percent of samples in state datasets that were above reporting thresholds for
select PFAS is provided in Table 4-15, and a summary on the number of systems in state datasets
that had detections for select PFAS is available in Table 4-16. Comprehensive summaries of
state data are available within the EPA's I'l'AS Occurrence & Contaminant Background Support
Document (U.S. EPA, 2024g).
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Table 4-15: Non-Targeted State PFAS Finished Water Data - Summary of Samples with
Detections of PFAS Included in Final Regulation
State
PFHxS3
PFNAa
PFBSa
HFPO-DAab
Colorado
10.8%
0.9%
11.0%
0.2%
Illinois
13.4%
0.6%
17.6%
0.0%
Indiana
1.5%
0.2%
5.6%
0.0%
Kentucky
8.6%
2.5%
12.3%
13.6%
Maine
3.0%
3.5%
10.1%
N/A
Maryland
18.2%
2.3%
19.3%
0.0%
Massachusetts
23.6%
2.9%
39.8%
0.1%
Michigan
4.3%
0.6%
7.5%
0.1%
Missouri
3.3%
0.0%
6.1%
0.0%
New Hampshire
16.8%
3.3%
32.1%
3.8%
New Jersey
26.2%
7.7%
28.1%
N/A
New York
21.6%
8.6%
28.8%
0.7%
North Dakota
5.3%
0.0%
8.8%
0.0%
Ohio
6.6%
0.3%
5.0%
0.1%
South Carolina
8.1%
0.1%
13.7%
1.3%
Tennessee
0.0%
0.0%
0.0%
N/A
Vermont
4.2%
2.5%
7.1%
0.2%
Wisconsin
27.2%
2.2%
28.0%
0.0%
Abbreviations: PFAS - per- and polyfluoroalkyl substances.
Note:
a0.0 % indicates that monitoring data were available for the compound/state but there were no detections above minimum
reporting limits. Detections are determined by individual state reporting limits which are not defined consistently across all
states.
bN/A indicates that no data are available.
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Table 4-16: Non-Targeted State PFAS Finished Water Data - Summary of Systems with
Detections of Select PFAS
State
PFHxS3
PFNAa
PFBSa
HFPO-DAab
Colorado
13.4%
1.0%
13.4%
0.3%
Illinois
4.6%
0.5%
8.0%
0.0%
Indiana
1.3%
0.3%
6.5%
0.0%
Kentucky
9.5%
2.7%
13.5%
12.2%
Maine
2.8%
3.9%
10.3%
N/A
Maryland
12.7%
3.2%
12.7%
0.0%
Massachusetts
18.1%
4.4%
27.8%
0.3%
Michigan
4.1%
0.6%
7.9%
0.3%
Missouri
2.7%
0.0%
6.2%
0.0%
New Hampshire
22.5%
5.5%
38.1%
5.1%
New Jersey
32.9%
16.5%
35.2%
N/A
New York
25.0%
9.7%
36.7%
1.1%
North Dakota
5.4%
0.0%
9.0%
0.0%
Ohio
2.2%
0.3%
2.4%
0.1%
South Carolina
13.7%
0.3%
22.1%
2.0%
Tennessee
0.0%
0.0%
0.0%
N/A
Vermont
2.7%
0.9%
6.0%
0.5%
Wisconsin
31.8%
3.9%
33.9%
0.0%
Abbreviations: PFAS - per-and polyfluoroalkyl substances.
Note:
a0.0 % indicates that monitoring data were available for the compound/state but there were no detections above minimum
reporting limits. Detections are determined by individual state reporting limits which are not defined consistently across all
states.
bN/A indicates that no data are available.
4.4.3 Overview of PFAS Co-Occurrence
Co-occurrence of multiple PFAS has been reported in drinking water, ambient surface waters,
aquatic organisms, biosolids (sewage sludge), and other environmental media. PFOA and PFOS
have historically been target analytes, which has partly contributed to their prevalence in
environmental monitoring studies, although some recent monitoring studies have begun to focus
on additional PFAS via advanced analytical instruments/methods and non-targeted analysis
(McCord & Strynar, 2019; McCord et al., 2020).
The EPA's analysis on PFAS co-occurrence using UCMR 3 data found that 4 percent of PWSs
reported results for which one or more of the six UCMR 3 PFAS were measured at or above their
respective UCMR 3 minimum reporting levels (MRL). Additionally, several studies have
demonstrated PFAS co-occurrence in finished drinking water (Adamson et al., 2017;
Cadwallader et al., 2022; Guelfo & Adamson, 2018; Smalling et al., 2023). One study in
particular used UCMR 3 data to demonstrate that two or more of the six PFAS monitored under
UCMR 3 co-occurred in 48 percent (285/598) of sampling events with PFAS detected, and
PFOA and PFOS co-occurred in 27 percent (164/598) of sampling events with two or more
PFAS detected (Guelfo & Adamson, 2018).
For additional discussion and analysis on PFAS co-occurrence, reference the EPA's PFAS
Occurrence & Contaminant Background Support Document (U.S. EPA, 2024g).
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4.4.4 Summary of PFAS Occurrence Data Analysis
Identifying the systems and population exposed to PFAS exceeding the limits under the final rule
and the three regulatory alternatives is a key step to estimating benefits and costs of the final
NPDWR. The EPA used a Bayesian hierarchical Markov chain Monte Carlo (MCMC)
occurrence model to estimate national PFAS occurrence in PWSs. The EPA used the MCMC
occurrence model output to estimate the PWSs and EPs with PFAS occurrence exceeding the
limits under the final rule and regulatory alternatives. The EPA assumed that the populations
served by these PWSs were exposed to the PFAS concentration estimates generated by the
MCMC occurrence model.
This section summarizes the occurrence model and the EPA's use of the model to identify the
systems and EPs with PFAS occurrence exceeding the regulatory alternatives considered within
the EA, as well as the corresponding populations exposed. Further details on the MCMC model
are available in Appendix A, Cadwallader et al. (2022), and U.S. EPA (2024g).
Data collected under UCMR 3 served as the primary dataset for the MCMC occurrence model
due to its nationally representative design. Additionally, the EPA incorporated state PFAS
monitoring datasets to supplement UCMR 3 data in the occurrence model. These state datasets,
for which the monitoring has been conducted more recently than UCMR 3, generally have lower
reporting limits because the analytical methods have improved over the last 10 years, allowing
laboratories to reliably measure PFAS at concentrations approximately 5 to 20 times lower than
for UCMR 3. Thus, state datasets with lower reporting limits than those in UCMR 3 helped
inform the model by enabling observation of PFAS occurrence at lower concentrations. State
datasets also consist of more recent samples than UCMR 3, which broadened the temporal range
of data used to fit the model. The supplemental state data were limited to samples collected from
systems that were also in UCMR 3 to prevent biasing the dataset toward states for which the data
from additional PWSs were available as well as maintain the nationally representative set of
systems selected for UCMR 3. Using these criteria, 28 states were identified as having some
state monitoring data to be included in fitting the national occurrence model.
The dataset used to fit the model included all data available in the final UCMR 3 dataset for
PFOS, PFOA, PFHpA, and PFHxS. This amounted to 36,972 samples each for PFOS, PFOA,
and PFHpA, and 36,971 samples for PFHxS. Of these four PFAS, 1,114 samples had results
reported at or above the UCMR 3 MRLs. The additional state datasets included to supplement
the UCMR 3 data contained 18,091 PFOS samples, 18,082 PFOA samples, 14,458 PFHpA
samples, and 14,906 PFHxS samples collected at systems that were included in UCMR 3. Of
these samples, 7,156 (40%) were reported values for PFOS, 8,257 (46%) were reported values
for PFOA, 4,496 (31%) were reported values for PFHpA, and 5,041(34%) were reported values
for PFHxS. The remainder were listed as being below their respective reporting limits. A
summary of the state data used in the occurrence model, including system and sample counts, is
available in Appendix A.
Some states have promulgated drinking water standards for PFAS since the UCMR 3
monitoring. The EPA reviewed state websites and identified states with enacted standards for the
PFAS compounds considered within the regulatory alternatives discussed in the EA. Table 4-17
summarizes state regulations on PFAS in drinking water, which are current as of May 2023. The
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state PFAS regulation summary in Table 4-17 is reflective of only those states that have
promulgated PFAS drinking water regulations and does not include information from states that
have proposed PFAS drinking water regulations or issued guidance for PWSs.
Table 4-17: State PFAS Regulations
Regulated PFAS Levels (ppt)
State
PFOA
PFOS
PFBS PFHpA PFHxA
PFHxS
PFNA PFDA
HFPO-
DA Sum
New Jersey
14
13
13
Vermont3
*
*
*
*
*
20
New
Hampshire
12
15
18
11
Massachusetts3
*
*
*
*
* *
20
Michigan
8
16
420 400,000
51
6
370
New York
10
10
Pennsylvania
14
18
Wisconsin
70
70
Rhode Island3
*
*
*
*
* *
20
Abbreviations: PFAS - per-and polyfluoroalkyl substances.
Notes:
Asterisks (*) indicate states that regulate PFAS compounds at an overall threshold value, as indicated in the Sum column.
Sources: Slate websites are as follows - New Jersey
(https://www.nj.gov/health/ceohs/documents/pfas_drinking%20water.pdJ), Vermont (https://dec.vermont.gov/water/drinking-
water/water-quality-monitoring/pfas), New Hampshire (https://www.nhwwa.org/wp-content/uploads/NHWWA-Water-is-
Essential-Seminar-Oct-20-2020-PFAS-Arsenic-Rule-Updates.pdf, Massachusetts (https://www.mass.gov/lists/development-
of-a-pfas-drinking-water-standard-mcl#final-pfas-mcl-regulations-), Michigan
(https://www.michigan.gov/pfasresponse/drinking-water/mcl), New York
(https://www.health.ny.gov/environmental/water/drinking/docs/water_supplierJact_sheet_new_mcls.pdf.
To estimate the costs and benefits of the final rule, the EPA assumed that all MCMC occurrence
model estimates exceeding state limits are equivalent to the state-enacted limit. For these states,
the EPA assumed that the state MCL is the maximum baseline PFAS occurrence value for all
EPs in the state. This adjustment was made to the MCMC occurrence model PFAS estimates for
PFOA, PFOS, and PFHxS in this EA. In the three states where PFAS is regulated at a combined
threshold level (Vermont, Massachusetts, and Rhode Island), the EPA did not make any
adjustment to the estimated PFAS occurrence values from the MCMC model. Since the final rule
standards are more stringent than current state drinking water standards, systems in states with
PFAS regulations are still expected to incur incremental costs to comply with the final rule,
although the estimated compliance costs will be less compared to costs that do not adjust the
MCMC occurrence data to reflect the state MCLs. Similarly, populations served by PWSs in the
states with PFAS regulations are expected to benefit from further reductions in PFAS exposures,
although the incremental benefits for these populations will be less compared to benefits that do
not adjust the MCMC occurrence data to reflect the state MCLs.
The EPA used system-level distributions, as described in Cadwallader et al. (2022), to simulate
EP concentrations and estimate PFAS occurrence relative to the regulatory alternatives and final
rule limits. The EPA assumed EP concentrations were constant. Simulated sample data are
composed of a set of 4,000 iterations with the number of simulated samples per system within
each iteration equal to the number of EPs. The EPA estimated within system variation from all
available samples within each system as part of the model fitting process. Although the data used
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to fit the model may have included multiple samples over time or EPs, this simulation strategy
assumes that all within-system variability is across EPs.
For 4,920 systems with means fitted by the model (i.e., systems with PFAS data in UCMR 3),
the EPA simulated system-specific samples based on the best-fit model. The EPA simulated
from the high level multivariate normal distribution to produce means for each chemical at each
non-UCMR system and then used those distributions to simulate system-specific samples. The
agency then generated random samples from the multivariate distribution and the value of the
fixed parameters for each iteration. The exception to this approach was systems serving more
than 1 million people. For these systems, the EPA used UCMR 3 and more recent monitoring
data to identify the EPs that might require PFAS removal. These relatively few very large
systems have the potential to affect aggregate costs and, therefore, require more precision in
baseline occurrence estimates.
4.4.5 Summary of National PFAS Occurrence
Using the MCMC occurrence model, the EPA estimated baseline occurrence to understand
changes in occurrence and exposure for the final rule and the regulatory alternative MCLs under
Options la - lc. These estimates vary across the 4,000 MCMC occurrence model iterations,
thereby characterizing baseline occurrence uncertainty. In addition, for PWSs in states with
existing MCLs for PFOA, PFOS, and PFHxS, the EPA capped contaminant concentrations at the
state MCLs. The EPA notes that the baseline occurrence estimates presented herein differ from
those presented under the proposed rule, which is due to the EPA's incorporation of additional
state data for the final rule. Additionally, the final rule requirements for the number of significant
digits used to assess compliance also impacts the baseline occurrence estimates.
The estimated number of PWSs with at least one EP above the PFHxS MCL and, by definition
the PFHxS HBWC are provided in Table 4-18 through Table 4-21, while the total estimated
number of EPs above the MCLs are provided in Table 4-22 through Table 4-25. In Table 4-26
through Table 4-29, the EPA provides the population served by PWSs with at least one EP above
the MCLs. The population served by EPs above the MCLs are provided in Table 4-30 through
Table 4-33. Each table provides expected value estimates as well as 5th percentile and 95th
percentile estimates that characterize the uncertainty of baseline PFAS occurrence.
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Table 4-18: Total Systems Impacted, Final Rule (PFOA and PFOS MCLs of 4.0
ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1)
5th 95th
Percentile ean Percentile
Small Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs With PFHxS MCL and/or PFHxS HB WC
Exceedanceab
PWSs That Exceed One or More MCLs
Large Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs With PFHxS MCL and/or PFHxS HB WC
Exceedanceab
PWSs That Exceed One or More MCLs
All Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs With PFHxS MCL and/or PFHxS HB WC
Exceedanceab
PWSs That Exceed One or More Limits
62,048 62,048 62,048
1,929 2,854 3,942
1,903 2,759 3,791
51 110 194
2,797 3,872 5,217
4,482 4,482 4,482
912 969 1,025
992 1,049 1,107
92 105 120
1,207 1,266 1,328
66,530 66,530 66,530
2,874 3,823 4,958
2,924 3,808 4,825
154 215 297
4,023 5,139 6,427
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level; PFHxS - perfluorohexane sulfonate; HI - hazard index; HBWC -
Health Based Water Concentration.
Note: Detail may not add exactly to total due to independent rounding.
aThe national level exceedance estimates for PFHxS are reflective of both the total national PFHxS individual
MCL exceedances and HI MCL exceedances where PFHxS is present above its HBWC while one or more other
HI PFAS is also present in that same mixture. Total national exceedance values do not include the exceedances
associated with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional HI and
individual MCLs for PFNA and HFPO-DA exceedances associated with occurrence of HFPO-DA, PFBS, and
PFNA in aquantified sensitivity analysis; see Appendix N, SectionN.3 for the analysis and Section XII.A.4 of
the final rule preamble for more information about how the EPA considered HI, PFNA, and HFPO-DA MCL
costs.
bExceedance of both the PFHxS MCL as well as the PFHxS HBWC is triggered by PFHxS occurrence estimates
above 10 ppt from the MCMC occurrence model.
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Table 4-19: Total Systems Impacted, Option la (PFOA and PFOS MCLs of 4.0
ppt)
Small Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
Large Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
All Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
5th
Percentile
62,048
1,935
1,903
2,795
4,482
916
987
1,203
66,530
2,875
2,930
4,018
Mean
62,048
2,854
2,759
3,870
4,482
969
1,049
1,266
66,530
3,823
3,808
5,136
95th
Percentile
62,048
3,972
3,800
5,097
4,482
1,026
1,109
1,328
66,530
4,952
4,828
6,441
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-20: Total Systems Impacted, Option lb (PFOA and PFOS MCLs of 5.0
ppt)
Small Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
Large Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
All Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
5th
Percentile
62,048
1,336
1,217
1,936
4,482
741
779
981
66,530
2,142
2,058
2,945
Mean
62,048
2,075
1,867
2,768
4,482
791
827
1,033
66,530
2,865
2,693
3,801
95th
Percentile
62,048
2,932
2,636
3,733
4,482
841
877
1,084
66,530
3,753
3,443
4,809
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-21: Total Systems Impacted, Option lc (PFOA and PFOS MCLs of 10.0
ppt)
5th
Percentile
Small Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
Large Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
All Systems
Total Number of PWSs
PWSs With PFOS Exceedance
PWSs With PFOA Exceedance
PWSs That Exceed One or More MCLs
62,048
391
250
505
4,482
338
300
444
66,530
750
570
977
Mean
62,048
648
421
806
4,482
366
323
473
66,530
1,014
744
1,279
95th
Percentile
62,048
987
645
1,188
4,482
395
347
503
66,530
1,348
958
1,658
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-22: Total Entry Points Impacted, Final Rule (PFOA and PFOS MCLs
of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1)
5th
Mean
95th
Percentile
Percentile
Small Systems
Total Number of Entry Points
88,938
88,938
88,938
Entry Points With PFOS Exceedance
2,455
3,623
5,006
Entry Points With PFOA Exceedance
2,368
3,448
4,706
Entry Points With PFHxS MCL and/or PFHxS
59
126
219
HBWC exceedanceab
Entry Points That Exceed One or More MCLs
3,672
5,122
6,884
Large Systems
Total Number of Entry Points
23,264
23,264
23,264
Entry Points With PFOS Exceedance
2,306
2,438
2,572
Entry Points With PFOA Exceedance
2,388
2,518
2,651
Entiy Points With PFHxS MCL and/or PFHxS
273
298
327
HBWC exceedanceab
Entry Points That Exceed One or More MCLs
3,742
3,921
4,086
All Systems
Total Number of Entry Points
112,202
112,202
112,202
Entry Points With PFOS Exceedance
4,852
6,061
7,520
Entry Points With PFOA Exceedance
4,856
5,966
7,248
Entiy Points With PFHxS MCL and/or PFHxS
349
425
524
HBWC exceedanceab
Entry Points That Exceed One or More MCLs
7,546
9,043
10,759
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level; PFHxS - perfluorohexane sulfonate; HI - hazard index; HBWC -
health based water concentration.
Note: Detail may not add exactly to total due to independent rounding.
aThe national level exceedance estimates for PFHxS are reflective of both the total national PFHxS individual
MCL exceedances and HI MCL exceedances where PFHxS is present above its HBWC while one or more other
HI PFAS is also present in that same mixture. Total national exceedance values do not include the exceedances
associated with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional HI and
individual MCLs for PFNA and HFPO-DA exceedances associated with occurrence of HFPO-DA, PFBS, and
PFNA in a quantified sensitivity analysis; see Appendix N, Section N.3 for the analysis and Section XII.A.4 of
the final rule preamble for more information about how the EPA considered HI, PFNA, and HFPO-DA MCL
costs.
bExceedance of both the PFHxS MCL as well as the HBWC is triggered by PFHxS occurrence estimates above
10 ppt from the MCMC occurrence model.
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Table 4-23: Total Entry Points Impacted, Option la (PFOA and PFOS MCLs
of 4.0 ppt)
5th 95th
Percentile ean Percentile
Small Systems
Total Number of Entry Points 88,938 88,938 88,938
Entry Points With PFOS Exceedance 2,456 3,623 5,007
Entry Points With PFOA Exceedance 2,368 3,448 4,709
Entry Points That Exceed One or More MCLs 3,666 5,115 6,858
Large Systems
Total Number of Entry Points 23,264 23,264 23,264
Entry Points With PFOS Exceedance 2,305 2,438 2,572
Entry Points With PFOA Exceedance 2,386 2,518 2,651
Entry Points That Exceed One or More MCLs 3,701 3,878 4,056
All Systems
Total Number of Entry Points 112,202 112,202 112,202
Entry Points With PFOS Exceedance 4,853 6,061 7,511
Entry Points With PFOA Exceedance 4,862 5,966 7,247
Entry Points That Exceed One or More MCLs 7,497 8,993 10,711
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-24: Total Entry Points Impacted, Option lb (PFOA and PFOS MCLs
of 5.0 ppt)
5th 95th
Percentile ean Percentile
Small Systems
Total Number of Entry Points 88,938 88,938 88,938
Entry Points With PFOS Exceedance 1,735 2,620 3,794
Entry Points With PFOA Exceedance 1,532 2,321 3,234
Entry Points That Exceed One or More MCLs 2,567 3,643 4,967
Large Systems
Total Number of Entry Points 23,264 23,264 23,264
Entry Points With PFOS Exceedance 1,821 1,928 2,043
Entry Points With PFOA Exceedance 1,784 1,884 1,982
Entry Points That Exceed One or More MCLs 2,900 3,038 3,185
All Systems
Total Number of Entry Points 112,202 112,202 112,202
Entry Points With PFOS Exceedance 3,627 4,548 5,661
Entry Points With PFOA Exceedance 3,399 4,204 5,135
Entry Points That Exceed One or More MCLs 5,550 6,682 8,007
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-25: Total Entry Points Impacted, Option lc (PFOA and PFOS MCLs of
10.0 ppt)
5th 95th
Percentile ean Percentile
Small Systems
Total Number of Entiy Points 88,938 88,938 88,938
Entry Points With PFOS Exceedance 475 809 1,221
Entry Points With PFOA Exceedance 308 520 780
Entry Points That Exceed One or More MCLs 676 1,051 1,547
Large Systems
Total Number of Entry Points 23,264 23,264 23,264
Entry Points With PFOS Exceedance 787 842 903
Entry Points With PFOA Exceedance 609 649 693
Entry Points That Exceed One or More MCLs 1,177 1,244 1,312
All Systems
Total Number of Entry Points 112,202 112,202 112,202
Entry Points With PFOS Exceedance 1,320 1,651 2,069
Entry Points With PFOA Exceedance 955 1,170 1,435
Entry Points That Exceed One or More MCLs 1,900 2,295 2,780
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-26: Total Population at PWSs Impacted, Final Rule (PFOA and PFOS
MCLs of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI
of 1)
5th 95th
Percentile ean Percentile
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
PFHxS HBWC Exceedanceab
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
PFHxS HBWC Exceedanceab
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
PFHxS HBWC Exceedanceab
Population Impacted by One or More MCL
Exceedances
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level; PFHxS - perfluorohexane sulfonate; HI - hazard index; HBWC -
Heath Based Water Concentration.
Notes: Detail may not add exactly to total due to independent rounding.
aThe national level exceedance estimates for PFHxS are reflective of both the total national PFFlxS individual
MCL exceedances and HI MCL exceedances where PFHxS is present above its HBWC while one or more other
HI PFAS is also present in that same mixture. Total national exceedance values do not include the exceedances
associated with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional HI and
individual MCLs for PFNA and HFPO-DA exceedances associated with occurrence of HFPO-DA, PFBS, and
PFNA in aquantified sensitivity analysis; see Appendix N, SectionN.3 for the analysis and Section XII.A.4 of
the final rule preamble for more information about how the EPA considered HI, PFNA, and HFPO-DA MCL
costs.
bExceedance of both the PFHxS MCL as well as the HI is triggered by PFHxS occurrence estimates above 10 ppt
from MCMC occurrence model.
58,607,697
2,240,600
2,362,000
83,044
3,314,000
263,679,547
51,819,000
55,099,000
6,372,000
67,160,000
322,287,244
54,945,000
58,326,000
6,508,600
71,354,000
58,607,697
3,286,600
3,309,200
177,250
4,494,200
263,679,547
56,096,000
59,554,000
7,499,900
71,789,000
322,287,244
59,383,000
62,863,000
7,677,100
76,283,000
58,607,697
4,520,200
4,393,900
296,240
5,848,200
263,679,547
60,482,000
64,109,000
8,864,500
76,869,000
322,287,244
64,025,000
67,423,000
9,025,300
81,397,000
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Table 4-27: Total Population at PWSs Impacted, Option la (PFOA and PFOS
MCLs of 4.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
2,268,500
2,342,800
3,176,300
263,679,547
51,819,000
55,205,000
66,940,000
322,287,244
54,951,000
58,313,000
71,316,000
Mean
58,607,697
3,286,700
3,309,200
4,489,900
263,679,547
56,098,000
59,554,000
71,747,000
322,287,244
59,385,000
62,863,000
76,237,000
95th
Percentile
58,607,697
4,520,100
4,372,800
5,816,300
263,679,547
60,417,000
64,109,000
76,805,000
322,287,244
63,997,000
67,420,000
81,338,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-28: Total Population at PWSs Impacted, Option lb (PFOA and PFOS
MCLs of 5.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
1,616,400
1,557,200
2,360,900
263,679,547
42,546,000
44,201,000
55,498,000
322,287,244
44,997,000
46,406,000
58,436,000
Mean
58,607,697
2,422,200
2,294,100
3,270,600
263,679,547
46,436,000
47,952,000
59,542,000
322,287,244
48,858,000
50,246,000
62,812,000
95th
Percentile
58,607,697
3,346,900
3,119,500
4,284,100
263,679,547
50,371,000
51,786,000
64,103,000
322,287,244
52,916,000
54,145,000
67,277,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-29: Total Population at PWSs Impacted, Option lc (PFOA and PFOS
MCLs of 10.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
494,310
345,510
663,970
263,679,547
19,723,000
18,531,000
26,477,000
322,287,244
20,510,000
19,034,000
27,545,000
Mean
58,607,697
792,790
566,290
1,009,300
263,679,547
22,216,000
20,713,000
29,287,000
322,287,244
23,009,000
21,280,000
30,296,000
95th
Percentile
58,607,697
1,154,300
841,210
1,428,300
263,679,547
24,811,000
23,109,000
32,179,000
322,287,244
25,642,000
23,717,000
33,118,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-30: Total Population at Entry Points Impacted, Final Rule (PFOA and
PFOS MCLs of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each
and HI of 1)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
Hazard Index Exceedance3
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
Hazard Index Exceedance3
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by PFHxS MCL and/or
Hazard Index Exceedance3
Population Impacted by One or More MCL
Exceedances
5th
Percentile
Mean
58,607,697
1,592,500
1,553,100
34,900
2,423,800
263,679,547
22,266,000
24,109,000
1,641,800
35,505,000
322,287,244
24,476,000
26,227,000
1,723,000
38,658,000
58,607,697
2,389,900
2,282,900
80,968
3,394,500
263,679,547
23,923,000
25,766,000
1,953,000
37,817,000
322,287,244
26,313,000
28,049,000
2,034,000
41,212,000
95th
Percentile
58,607,697
3,324,700
3,157,000
143,530
4,582,500
263,679,547
25,634,000
27,448,000
2,316,400
40,155,000
322,287,244
28,238,000
29,959,000
2,388,100
43,817,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; MCL - maximum
contaminant level; PFHxS - perfluorohexane sulfonate; HI - hazard index.
Notes: Detail may not add exactly to total due to independent rounding.
aExceedance of both the PFHxS MCL as well as the HI is triggered by PFHxS occurrence estimates above 10 ppt
from the MCMC occurrence model.
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Table 4-31: Total Population at Entry Points Impacted, Option la (PFOA and
PFOS MCLs of 4.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
1,595,900
1,553,000
2,422,500
263,679,547
22,295,000
24,014,000
35,131,000
322,287,244
24,482,000
26,221,000
38,390,000
Mean
58,607,697
2,390,000
2,282,900
3,389,700
263,679,547
23,923,000
25,765,000
37,547,000
322,287,244
26,313,000
28,048,000
40,937,000
95th
Percentile
58,607,697
3,320,100
3,157,400
4,576,500
263,679,547
25,634,000
27,504,000
39,930,000
322,287,244
28,242,000
29,959,000
43,524,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; MCL - maximum
contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-32: Total Population at Entry Points Impacted, Option lb (PFOA and
PFOS MCLs of 5.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
1,128,900
1,006,000
1,661,300
263,679,547
17,664,000
17,229,000
27,557,000
322,287,244
19,282,000
18,650,000
29,830,000
Mean
58,607,697
1,725,500
1,534,300
2,411,500
263,679,547
19,054,000
18,563,000
29,479,000
322,287,244
20,780,000
20,097,000
31,890,000
95th
Percentile
58,607,697
2,455,000
2,154,900
3,279,500
263,679,547
20,404,000
19,877,000
31,476,000
322,287,244
22,362,000
21,605,000
34,032,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; MCL - maximum
contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
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Table 4-33: Total Population at Entry Points Impacted, Option lc (PFOA and
PFOS MCLs of 10.0 ppt)
Small Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
Large Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
All Systems
Total Population
Population Impacted by PFOS Exceedance
Population Impacted by PFOA Exceedance
Population Impacted by One or More MCL
Exceedances
5th
Percentile
58,607,697
315,170
195,280
434,870
263,679,547
8,341,500
5,758,200
11,901,000
322,287,244
8,850,300
6,089,500
12,555,000
Mean
58,607,697
531,480
341,450
691,810
263,679,547
9,048,100
6,399,500
12,819,000
322,287,244
9,579,600
6,741,000
13,511,000
95th
Percentile
58,607,697
797,030
528,460
1,007,800
263,679,547
9,820,200
7,097,400
13,810,000
322,287,244
10,391,000
7,435,600
14,539,000
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; PWS - public water
system; MCL - maximum contaminant level.
Note: Detail may not add exactly to total due to independent rounding.
4.5 Uncertainties in the Baseline and Compliance
Characteristics of Systems
This section summarizes limitations and uncertainties of the baseline analysis. In the chapter, the
EPA described how the quantitative analysis incorporates some sources of uncertainty. The
agency also noted data limitations that introduce uncertainty because information is not available
for the baseline analysis. Table 4-34 provides a summary of sources that have quantifiable
uncertainty and data limitations.
The EPA notes that in most cases it is not possible to determine the extent to which a particular
limitation or uncertainty can affect the magnitude of the baseline conditions. The EPA notes the
potential direction of the impact on baseline inputs to the costs and/or benefits analysis when
possible, but the agency does not prioritize the entries with respect to the impact magnitude.
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Table 4-34: Limitations and Uncertainties that Apply to the Baseline Characteristics of
Systems for the Final PFAS Rule
Uncertainty/ Assumption
Effect on Quantitative
Analysis
Notes
The agency assigned
ground water as the source
to systems missing source
water information.
Underestimate costs
The design and average flow equations for ground water
systems result in lower flow estimates than the equations
for surface water systems. If any of the systems assigned
ground water source are in fact surface water systems, then
the flow estimates used in the cost analysis will be
underestimated. In addition, initial monitoring costs will be
underestimated for small surface water systems that are
assigned as a ground water source.
SDWIS/Fed retail
populations used for
baseline analysis
Overestimate costs
The EPA did not reallocate populations for purchased
water systems to the wholesale suppliers. All systems are
in the inventory with their respective retail populations. In
general, this will result in extra systems with small
populations in the analysis and smaller populations at the
wholesale systems. Both results will tend to increase cost
estimate because the cost curves reflect economies of
scale.
SDWIS/Fed data quality
Uncertain impact on
baseline number of systems
and EPs
The EPA periodically reviews inventory information in
SDWIS/Fed (U.S. EPA, 2021h) and has generally found a
high level of completeness and accuracy. There is
uncertainty, however, in some of the population and
facility data reported per system. To address this, the EPA
removed any CWS wholesaler serving fewer than 25
people from the analysis and assumed any remaining
CWSs had a minimum possible population of 25. The EPA
also assumed any non-wholesale NTNCWSs had a
minimum possible population of 25. The maximum
number of EPs per system was limited to the maximum
number found for the equivalent system size and source
water combination in the UCMR 3 data.
Flow relationships for
CWS
Uncertain impact on flow
inputs to cost analysis
The equations used to estimate design and average daily
flow based on service population may over- or
underestimate actual system flows. In general, average per
capita household water consumption has declined since the
source data were collected because of increased water
efficiency.3 The change in nonresidential consumption is
unknown.
CWS flow curves applied
to NTNCWS
Uncertain impact on flow
inputs to cost analysis
The EPA applied the CWS population-flow equations to
NTNCWSs. This approach may result in an over- or
underestimate of flow, and therefore cost for NTNCWSs.
Uniform EP population
distribution
Uncertain impact on flow
inputs to cost analysis and
population inputs to benefits
analysis
The EPA assumed a uniform distribution of system
population across system EPs. Actual EP population may
be greater or lower than the modeled estimates.
System wage rates are
based on old survey data
Uncertain impact on cost
analysis
National average wage rates are based on CWSS data
finalized in 2006. The EPA escalated the values to $2022
to reflect current national industry averages, but actual
wage rates at affected systems may be greater or less than
national averages.
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Table 4-34: Limitations and Uncertainties that Apply to the Baseline Characteristics of
Systems for the Final PFAS Rule
Uncertainty/ Assumption
Effect on Quantitative
Analysis
Notes
Baseline occurrence based
on MCMC occurrence
model outputs
Uncertain effect on
occurrence and exposure
The iterative MCMC approach (4,000 iterations)
probabilistically estimates parameters for system-level
distributions to capture uncertainty. The simulated EP
concentrations then reflect the system-level distribution
from which they are drawn across 4,000 iterations. Further
details on the MCMC model are available in Cadwallader
et al. (2022).
UCMR 3 data for PFBS
and PFNA and no UCMR 3
data for HFPO-DA were
available to incorporate
into the Bayesian
hierarchical occurrence
model
Underestimate occurrence
and exposure
Excluding occurrence estimates for PFNA, HFPO-DA, and
PFBS underestimates the number of systems that would
exceed the MCLs based on occurrence of these three
compounds. Due to occurrence data limitations, cost
estimates for PFNA, PFBS, and HFPO-DA are less precise
relative to those for PFOA, PFOS, and PFHxS compounds,
and as such, the EPA performed a quantitative sensitivity
analysis of the national cost impacts associated with
exceedances resulting from PFNA, PFBS, and HFPO-DA
in Appendix N.3 to consider the potential magnitude of
costs associated with treating these regulated PFAS.
Abbreviations: CWS - community water systems; CWSS- community water system survey; HI- hazard index; MCMC -
Markov chain Monte Carlo; NTNCWS - non-transient, non-community water systems; PFAS - per- and polyfluoroalkyl
substances; PFOA- perfluorooctanoic acid; PFOS- perfluorooctane sulfonate; SDWIS/Fed- safe drinking water information
system federal version.
Note:
aThere is uncertainty in using the equations from the EPA's Geometries and Characteristics of Public Water Systems report (U.S.
EPA, 2000) to predict future average daily and design flow based on a system's retail population. Water use efficiency has
increased substantially since the 1980s, with a major improvement between 2005 and 2010 (Rockaway et al., 2011). A 2016
Water Research Foundation study reported a 22 percent decline in indoor water use (Water Research Foundation, 2016). Several
factors have contributed to increases in water efficiency. Technological changes, supported by policy, increased the efficiency of
water use. For example, the Energy Policy Act of 1992 required water efficiency standards for fixtures, including shower heads,
toilets, and washing machines. Water recycling and increased efficiency of power generation also reduces freshwater use. The
economic downturn of 2008 contributed to the drop in water use and the increase in use of water-efficient fixtures and
xeriscaping. Other demand-side management measures contributed to reduction in per capita use as well. The trend of lower
residential water use could result in lower flow per population and lower treatment costs as compared to predicted values in this
EA.
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5 Cost Analysis
5.1 Introduction
In this chapter, the EPA presents its cost analysis for the final PFAS NPDWR (the final rule) and
other alternative rule options considered by the agency as part of the rulemaking process
(Options la through lc). The contents include the national cost estimates for the final rule as
well as options and the approach the EPA used to derive those estimates. The estimates include
the cost that PWSs, households, and primacy agencies may incur in response to the final rule
requirements.
5.1.1 Chapter Overview
This chapter has seven main sections including this introductory section. Section 5.2 provides an
overview of the EPA's approach to estimate the cost of the final rule and options. In Section 5.3,
the EPA provides the data and algorithms used to calculate the cost of activities PWSs will
undertake to comply with the final rule. Section 5.4 provides the data and assumptions used to
calculate the cost of activities primacy agencies will undertake to implement and administer the
final rule. Sections 5.1.3, 5.5, and 5.6 provide the cost estimates at the national, PWS, and
household level, respectively. As indicated below, some additional details on the approach and
data used to calculate the costs of the final rule are in Appendix C.
5.1.2 Uncertainty Characterization
Many of the input values used to calculate the costs of drinking water regulations are not known
with certainty. For example, estimated technology unit costs and contaminant occurrence values
are uncertain to some degree given imperfect information. The EPA determined it does have
enough information about the level or distribution of uncertainty to conduct a full Monte-Carlo
based uncertainty analysis as part of the SafeWater Multi-Contaminant Benefit-Cost Model
(MCBC). With respect to the cost analysis, the EPA modeled the sources of uncertainty
summarized in Table 5-1.
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Table 5-1: Quantified Sources of Uncertainty in Cost Estimates
Source
Description of Uncertainty
EP concentration
ofPFAS
compounds
TOC concentration
Compliance
technology unit
cost curve selection
Abbreviations: MCBC -
organic carbon.
The concentration and co-occurrence at each PWS EP of each modeled compound is
unknown. The cost analysis uses EP concentrations simulated with system level
distributions produced by the Bayesian hierarchical MCMC occurrence model. The
iterative MCMC approach (4,000 iterations) probabilistically estimates parameters for
system-level distributions to capture uncertainty. The simulated EP concentrations then
reflect the system-level distribution from which they are drawn across 4,000 iterations.
Further details on the MCMC model are available in Cadwallader et al. (2022). For more
information on the application of the model in this analysis, see Section 4.4 and Appendix
A. For more information on the data and analyses that the EPA used to develop national
estimates ofPFAS occurrence in public drinking water systems see U.S. EPA (2024g).
The TOC value assigned to each system is from a distribution derived from the fourth Six-
Year Review Information Collection Request database (see Section 5.3.1.1)
Cost curve selection varies with baseline PFAS concentrations and also includes a random
selection from a distribution across feasible technologies (see Section 5.3.1.1), and a
random selection from a triangular distribution of low-, mid-, and high-cost equipment
(25%, 50%, and 25%, respectively).
- Multi-Contaminant Benefit-Cost Model; PFAS - per- and polyfhioroalkyl substances; TOC - total
For each iteration, SafeWater MCBC assigned new values to the three sources of modeled
uncertainty as described in Table 5-1, and then calculated costs for each of the model PWSs.
This was repeated 4,000 times to reach an effective sample size for each parameter. At the end of
the 4,000 iterations, SafeWater MCBC outputs the expected value as well as the 90 percent
confidence interval for each cost metric (i.e., bounded by the 5th and 95th percentile estimates
for each cost component). Detailed information on the data used to model uncertainty is provided
in Appendix A and Appendix L.
5.1.3 Summary of Quantified National Cost Estimates of the
Final Rule
In Table 5-2, the EPA summarizes the total annualized cost of the final rule at a 2 percent
discount rate. The first three rows show the annualized PWS sampling costs, the annualized PWS
implementation and administrative costs, and the annualized PWS treatment costs. The fourth
row shows the sum of the annualized PWS costs. Expected annualized PWS costs are $1.54
billion. The quantified uncertainty range for annualized PWS costs is $1.43 billion to $1.67
billion. Finally, annualized primacy agency implementation and administrative costs are added to
the annualized PWS costs to calculate the total annualized cost of the final rule. Expected total
annualized cost of the final rule is $1.55 billion with an uncertainty range of $1.44 billion to
$1.67 billion.
The difference in the costs between the final rule and Option la provides the marginal cost of the
PFHxS standards. As shown in Table 4-18 and 4-22, the EPA estimates that 215 water systems
(425 EPs) will exceed the PFHxS MCL of 10 ppt and by definition the HBWC of 10 ppt for the
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HI.15 Of the water systems estimated to exceed the PFHxS regulatory thresholds, many are also
anticipated to exceed the PFOA and PFOS MCLs. The EPA estimates that 3 water systems with
50 EPs will be triggered into corrective action for PFHxS alone while 212 systems (375 EPs)
will treat for PFHxS in addition to PFOA and/or PFOS, and the national annualized marginal
costs of all PFHxS exceedances, including at systems with and without PFOA/PFOS
exceedances, is $11.57 million dollars. This is the estimated contribution of costs from PFHxS to
the overall costs of the rule, not in addition to the costs presented in Table 5-2. As discussed in
U.S. EPA (2024g), PFHxS is observed to strongly cooccur with PFOA and PFOS; therefore,
there are significantly more systems with PFHxS, PFOA, and PFOS present with two or more of
these PFAS above their respective MCLs than systems with PFHxS above the MCL alone.
Furthermore, this pattern is accentuated because the PFHxS MCL of 10 ppt is 2.5 times higher
than either the PFOA or PFOS MCLs of 4.0 ppt. Additionally, since the PFHxS MCL is one
significant figure, whereas PFOA and PFOS are two significant figures, for purposes of
estimating compliance, PFHxS would not be deemed to be in exceedance until above 15 ppt. All
told, this means that the PFHxS MCL (and its contributions to the HI) adds important public
health protection for a modest additional cost.
15 Note that results above a single HBWC for a single PFAS does not constitute an HI exceedance (see Section V.B.III of the
preamble for the final rule for more information).
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Table 5-2: National Annualized Costs, Final Rule (PFOA and PFOS MCLs of 4.0 ppt
each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) (Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized PWS Sampling Costs
Annualized PWS Implementation and
Administration Costs
Annualized PWS Treatment Costs
Total Annualized PWS Costs
Primacy Agency Rule Implementation
and Administration Cost
Total Annualized Rule Costsb'c'd
$33.63
$1.33
$1,395.23
$1,431.00
$4.35
$1,435.70
$36.23
$1.33
$1,506.44
SI,544.00
$4.65
SI,548.64
$39.03
$1.33
$1,627.65
$1,667.10
$4.97
$1,672.10
Abbreviations: PWS - public water system.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P, Section P.2 for results presented at 3
and 7 percent discount rates. 5th and 95th percentile values for total rule costs are not additive across cost category as the
categories are not completely correlated.
The 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1.
This range does not include the uncertainty described in Table 5-21.
bSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
The national level cost estimates for PFHxS are reflective of both the total national cost for PFHxS individual MCL
exceedances, and HI MCL exceedances where PFHxS is present above its HBWC while one or more other HI PFAS is also
present in that same mixture. Total quantified national cost values do not include the incremental treatment costs associated
with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional national costs of the HI and
individual MCLs associated with HFPO-DA, PFNA, and PFBS occurrence in a quantified sensitivity analysis; See Appendix
N, Section N.3 for the analysis and more information.
dPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
In Table 5-3, Table 5-4, and Table 5-5 the EPA summarizes the total annualized cost of Options
la, lb, and lc, respectively.
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Table 5-3: National Annualized Costs, Option la (PFOA and PFOS MCLs of 4.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized PWS Sampling Costs
$33.37
$35.98
$38.77
Annualized PWS Implementation and
Administration Costs
$1.33
$1.33
$1.33
Annualized PWS Treatment Costs
$1,383.33
$1,495.14
$1,616.15
Total Annualized PWS Costs
$1,419.20
$1,532.44
$1,654.80
Primacy Agency Rule Implementation
and Administration Cost
$4.34
$4.63
$4.95
Total Annualized Rule Costsb'c
$1,423.60
$1,537.07
$1,660.30
Abbreviations: PWS - public water system.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P, Section P.2 for results presented at 3
and 7 percent discount rates. 5tli and 95th percentile values for total rule costs are not additive across cost category as the
categories are not completely correlated.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1.
This range does not include the uncertainty described in Table 5-21.
bSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
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Table 5-4: National Annualized Costs, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized PWS Sampling Costs
Annualized PWS Implementation and
Administration Costs
Annualized PWS Treatment Costs
Total Annualized PWS Costs
Primacy Agency Rule Implementation
and Administration Cost
Total Annualized Rule Costsb'c
$31.07
$1.33
$1,065.30
$1,098.40
$3.98
$1,102.60
$33.29
$1.33
$1,153.31
SI, 187.92
$4.21
$1,192.13
$35.71
$1.33
$1,250.22
$1,286.50
$4.47
$1,291.40
Abbreviations: PWS - public water system.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P, Section P.2 for results presented at 3
and 7 percent discount rates. 5th and 95th percentile values for total rule costs are not additive across cost category as the
categories are not completely correlated.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1.
This range does not include the uncertainty described in Table 5-21.
bSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
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Table 5-5: National Annualized Costs, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized PWS Sampling Costs
$26.11
$27.48
$28.97
Annualized PWS Implementation and
Administration Costs
$1.33
$1.33
$1.33
Annualized PWS Treatment Costs
$431.37
$467.12
$507.50
Total Annualized PWS Costs
$459.50
$495.93
$537.21
Primacy Agency Rule Implementation
and Administration Cost
$3.27
$3.37
$3.48
Total Annualized Rule Costsb'c
$462.87
$499.29
$540.68
Abbreviations: PWS - public water system.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P, Section P.2 for results presented at 3
and 7 percent discount rates. 5th and 95th percentile values for total rule costs are not additive across cost category as the
categories are not completely correlated.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1.
This range does not include the uncertainty described in Table 5-21.
bSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
5.2 Overview of SafeWater Multi-Contaminant Benefit Cost
Model (MCBC)
The SafeWater Cost Benefit Model (SafeWater CBX) was designed to calculate the costs and
benefits associated with setting a new or revised MCL. Since the final PFAS rule simultaneously
regulates multiple PFAS contaminants, the EPA developed a new model version called the
SafeWater MCBC to estimate the costs and benefits associated with regulating more than one
contaminant. The following modifications were made to the SafeWater CBX model to create the
SafeWater MCBC model:
1. Instead of tracking a single contaminant's level and comparing that to the MCL options
to determine if the PWS must take compliance actions, SafeWater MCBC tracks each
PWS's level of multiple PFAS contaminants and compares them against MCL options for
each contaminant (or group of contaminants). The PWS will need to take corrective
action if any of its EP's contaminant levels are above any of the MCLs. In this case the
EP will incur treatment costs and will accrue health benefits.
2. The structure of the occurrence data input to the model was updated to not only handle
multiple contaminants, but to incorporate all information from the PFAS occurrence
model on the predicted co-occurrence of contaminants.
3. The model structure was also adjusted to allow for assignment of one or more compliance
technologies that achieve all regulatory requirements and estimates costs and benefits
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associated with multiple PFAS contaminant reductions and calculates before and after
treatment concentrations of each contaminant for use in the estimation of benefits.
5.2.1 Modeling PWS Variability in SafeWater MCBC
The costs incurred by a PWS depend on water system characteristics. The data describing some
of these characteristics for PWSs are in SDWIS/Fed. The SDWIS/Fed data provide information
on the PWS characteristics that typically define PWS categories, or strata, for which the EPA
develops costs in rulemakings:
• System type (CWS, NTNCWS);
• Number of people served by the PWS;
• PWS's primary raw water source (ground water or surface water);
• PWS's ownership type (public or private); and
• State in which PWS is located.
Because the EPA does not have complete PWS-specific data across the 49,193 CWSs and 17,337
NTNCWS in SDWIS/Fed for many of the baseline and compliance characteristics necessary to
estimate costs and benefits, such as design and average daily flow rates, water quality
characteristics, treatment in-place, and labor rates, the EPA adopted a "model PWS" approach.
SafeWater MCBC creates model PWSs by combining the PWS-specific data available in
SDWIS/Fed with data on baseline and compliance characteristics available at the PWS category
level. In some cases, the categorical data are simple point estimates. In this case, every model
PWS in a category is assigned the same value. In other cases, where more robust data
representing system variability are available, the category-level data include a distribution of
potential values. In the case of distributional information, SafeWater MCBC assigns each model
PWS a value sampled from the distribution. These distributions are assumed to be independent.
Table 5-6 provides a list of all the PWS characteristics that impact model PWS compliance costs.
These data include inventory data specific to each system and categorical data for which
randomly assigned values are based on distributions that vary by category (e.g., ground water
and surface water TOC distributions or compliance forecast distributions that vary by system
size category).
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Table 5-6: Model PWS Variability Characteristics and Data Sources
APRIL 2024
PWS Characteristic
Data Type and Description
System Type
Known SDWIS/Fed Inventory
Primary Source Water
Known: SDWIS/Fed Inventory
Ownership
Known: SDWIS/Fed Inventory
Population Served
Known: SDWIS/Fed Inventory
Number of EPs
Known: UCMR 3, SDWIS/Fed Inventory, and modeled from SDWIS/Fed
Inventory distribution (see Section 4.3.3.1)
PFAS Contaminant Concentration
Sampled from EPA Occurrence Model (see Section 4.3.3.2)
at each EP
Influent TOC Level
Assigned from distribution derived from fourth Six-Year Review
Information Collection Request database (see Section 5.3.1.1)
Compliance Technology Forecast
Assigned from distribution derived from full-scale compliance actions
at each EP
analyzed by the EPA (see Section 5.3.1.1)
Abbreviations: EPA - U.S. Environmental Protection Agency; PFAS - per-and polyfluoroalkyl substances; SDWIS/Fed -
Safe Drinking Water Information System/Federal version; TOC - total organic carbon; UCMR 4 - Fourth Unregulated
Contaminant Monitoring Rule.
As illustrated in Figure 5-1, once all the model PWSs are created and assigned baseline and
compliance characteristics, SafeWater MCBC estimates the quantified costs and benefits of
compliance for each model PWS under the final rule. Because of this model PWS approach,
SafeWater MCBC does not output any results at the PWS-level. Instead, the outputs are cost and
benefit estimates for 36 PWS categories, or strata. Each PWS category is defined by the system
type (CWS and NTNCWS), primary water source (ground or surface), and size category (there
are nine). Note the EPA does not report state specific strata although state location is utilized in
the SafeWater MCBC model (e.g., current state level regulatory limits on PFAS in drinking
water).
For each PWS category, the model then calculates summary statistics that describe the costs and
quantified benefits associated with the final rule compliance. These summary statistics include
total quantified costs of the final regulatory requirements, total quantified benefits of the final
regulatory requirements, the variability in PWS-level costs (i.e., 10th, 25th, 50th, 75th and 90th
percentile system costs), and the variability in household-level costs (i.e., 10th, 25th, 50th, 75th
and 90th percentile household costs). In addition, SafeWater MCBC characterizes the
uncertainty in the estimated costs and benefits by calculating the expected value and 90th
percentile confidence interval (5th and 95th percentile values) for each output metric.
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A
r
Figure 5-1: Approach Used by SafeWater MCBC to Model PWS Variability
5.3 Estimating Public Water System Costs
The EPA estimated PWS compliance activities that result in treatment costs and administrative
and monitoring costs associated with the final rule. Each major regulatory component consists of
required activities, which the EPA details here. The EPA presents the costs associated with
treatment addition and nontreatment actions that could be taken in lieu of treatment in Section
5.3.1. The EPA presents the costs associated with the administrative and monitoring
requirements of the final rule in Section 5.3.2.
This section describes how the EPA estimated costs associated with:
• Engineering, installing, operating, and maintaining PFAS removal treatment
technologies, including treatment media replacement and spent media destruction or
disposal; and
• Nontreatment actions that some PWSs might take in lieu of treatment, such as
constructing new wells in an uncontaminated aquifer or interconnecting with and
purchasing water from a neighboring PWS.
The EPA used SafeWater MCBC to apply costs for one of these treatment technologies or
nontreatment alternatives at each EP in a PWS estimated to be out of compliance with the
regulatory option under consideration. First, for each affected EP, SafeWater MCBC selected
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from among the compliance alternatives using the decision tree procedure described in Section
5.3.1.1. Next, SafeWater MCBC estimated the cost of the chosen compliance alternative using
inputs from the EPA's WBS cost estimating models. Specifically, SafeWater MCBC used cost
equations generated from the following models:16
• The GAC WBS model;
• The PFAS-selective IX WBS model; and
• The nontreatment WB S model.
The national cost analysis reflects that PFAS-contaminated wastes are not considered Resource
Conservation and Recovery Act (RCRA) regulatory or characteristic hazardous wastes.
Additionally, this PFAS NPDWR does not require drinking water treatment residuals to be
managed in any specific way. The EPA understands that the current practice for drinking water
systems to manage their spent treatment media is generally to reactivate GAC and to dispose of
ion exchange treatment residuals as non-hazardous waste. As shown below in Table 5-9, the
EPA estimates that 52-89% of systems will use GAC and 11-48% of systems will use IX,
depending on system size and water quality. The national cost analysis assumes the spent GAC
media is reactivated off-site under current RCRA non-hazardous waste regulations. The WBS
model uses a unit cost for reactivation that includes transportation to the reactivation facility and
back to the treatment plant. To account for losses in the reactivation and replacement process, it
also adds the cost of replacing 30 percent of the spent GAC with virgin media. The national cost
analysis assumes the spent IX resin is incinerated off-site under current RCRA non-hazardous
waste regulations. The WBS model uses a unit cost for non-hazardous incineration that includes
transportation to the incineration facility. For purposes of the cost analysis, EPA does not assume
any facilities will utilize Subtitle D Landfills. EPA notes that if the agency were to assume some
or all facilities would utilize Subtitle D landfills to dispose of spent IX resin, estimated spent
resin treatment residual disposal costs attributable to the PFAS NDPWR would have been lower.
For more information on GAC and IX residuals management unit cost estimates for PFAS see
Section 7.2 and 7.3 of the Technologies and Costs (T&C) document (U.S. EPA, 2024i).
The EPA proposed PFOA and PFOS be designated as Comprehensive Environmental Response,
Compensation, and Liability Act (CERCLA) hazardous substances to require reporting of PFOA
and PFOS releases, enhance the availability of data, and ensure agencies can recover cleanup
costs (U.S. EPA, 2022). Stakeholders have expressed concern to the EPA that a hazardous
substance designation for certain PFAS may limit their disposal options for drinking water
treatment residuals (e.g., spent media, concentrated waste streams) and/or potentially increase
costs. Designation of PFOA and PFOS as CERCLA hazardous substances would not require
waste (e.g., biosolids, treatment residuals, etc.) to be treated in any particular fashion, nor
disposed of at any specific particular type of landfill. The designation also would not restrict,
change, or recommend any specific activity or type of waste at landfills. Although designating
16 At this time, the EPA is not including point-of-use (POU) devices in the national cost estimates because the final rule requires
treatment to concentrations below the current NSF/ANSI certification standard for POU devices. However, POU treatment is
reasonably anticipated to become a compliance option for small systems in the future if NSF/ANSI or other independent third-
party certification organizations develop a new certification standard that mirrors the EPA's final regulatory standard. In the
event POU treatment becomes a valid compliance option, national costs could be lower than estimated in this application of the
SafeWater MCBC.
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chemicals as hazardous substances under CERCLA would not result in new requirements for
disposal of PFAS drinking water treatment residuals, to address stakeholder concerns, including
those raised during the SBREFA process, the EPA conducted a sensitivity analysis with an
assumption of hazardous waste disposal for illustrative purposes only. The EPA acknowledges
that if in the future PFAS-contaminated wastes are required to be handled as hazardous wastes,
the residuals management costs are expected to be higher. For a discussion of the findings from
this sensitivity analysis, see Appendix N, Section N.2.
Section 5.3.1.2 describes the WBS models. Section 5.3.1.2.2 describes the form of the resulting
cost equations and their application in SafeWater MCBC. The T&C document (U.S. EPA, 2024i)
provides a comprehensive discussion of each of the treatment technologies, their effectiveness,
and the WBS cost models. It also presents the cost equations themselves in tabular form. These
models are available on the EPA's website at https://www.epa.gov/sdwa/drinking-water-
treatment-technology-unit-cost-models as well as in the docket for this rulemaking.
5.3.1.1 Decision Tree for Technology Selection
For EPs at which baseline PFAS concentrations exceed regulatory thresholds, SafeWater MCBC
selects a treatment technology or nontreatment alternative using a two-step process that:
1. Determines whether to include or exclude each alternative from consideration given the
EP's characteristics and the regulatory option selected; and
2. Selects from among the alternatives that remain viable based on percentage distributions
derived, in part, from data on recent PWS actions in response to PFAS contamination.
Inputs to SafeWater MCBC used in the Step 1 include the following:
• Influent concentrations of individual PFAS contaminants in ppt (ng/L);
• EP design flow in MGD; and
• TOC influent to the new treatment process in mg/L.
Section 4.4 describes the EPA's method for estimating PFAS influent concentrations and Section
4.3.3.3 describes how the EPA derived EP flow estimates. SafeWater MCBC selects influent
TOC using the distribution shown in Table 5-7.
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Table 5-7: Frequency Distribution to Estimate Influent TOC in mg/L
APRIL 2024
Percentile Surface Water Ground Water
0.05
0.65
0.35
0.15
1.1
0.48
0.25
1.38
0.5
0.35
1.6
0.5
0.45
1.85
0.58
0.5
1.97
0.69
0.55
2.14
0.75
0.65
2.54
1
0.75
3.04
1.39
0.85
3.63
2.01
0.95
4.81
3.8
Abbreviations: TOC - total organic carbon.
Source: The EPA's analysis of total organic carbon concentrations in the fourth Six-Year Review Information Collection
Request database.
In Step 1, SafeWater MCBC uses these inputs to determine whether to include or exclude each
treatment alternative from consideration in the compliance forecast. For the treatment
technologies (GAC and IX), this determination is based on estimates of each technology's
performance given available data about influent water quality and the regulatory option under
consideration. Section 5.3.1.1.1 describes this process for GAC and IX.
The EPA assumes a small number of PWSs may be able to take nontreatment actions in lieu of
treatment. The viability of nontreatment actions (interconnection with neighboring system or
new wells) is likely to depend on the quantity of water being replaced. Therefore, SafeWater
MCBC considers nontreatment only for EPs with design flows less than or equal to 3.536 MGD.
In Step 2, SafeWater MCBC selects a compliance alternative for each EP from among the
alternatives that remain in consideration after Step 1. Table 5-8 shows the initial compliance
forecast that is the starting point for this step. The percentages in Table 5-8 consider data
presented in the T&C document (U.S. EPA, 2024i) on actions PWSs have taken in response to
PFAS contamination.
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Table 5-8: Initial Compliance Forecast Including POU RO
Design Flow Less than 1 Design Flow 1 to Less Design Flow Greater than
MGD than 10 MGD or Equal to 10 MGD
Compliance
Alternative
TOC Less
than or
Equal to 1.5
mg/L
TOC
Greater
than 1.5
mg/L
TOC Less
than or
Equal to 1.5
mg/L
TOC
Greater
than 1.5
mg/L
TOC Less
than or
Equal to 1.5
mg/L
TOC
Greater
than 1.5
mg/L
GAC
68%
53%
81%
52%
89%
52%
PFAS-selective IX
11%
26%
11%
40%
11%
48%
Central RO/NF
0%
0%
0%
0%
0%
0%
POU devices
13%
13%
0%
0%
0%
0%
Interconnection
6%
6%
6%
6%
0%
0%
New Wells
2%
2%
2%
2%
0%
0%
Abbreviations: GAC - granular activated carbon; PFAS - per-and polyfluoroalkyl substances; MGD - million gallons per
day; IX - ion exchange; RO/NF - reverse osmosis/nanofiltration; POU - point-of-use; TOC - total organic carbon.
Source: The EPA's analysis of total organic carbon concentrations in the fourth Six-Year Review Information Collection
Request database.
To date, the majority of PWSs for which data are available have installed GAC (U.S. EPA,
2024i). U.S. EPA (2024i) includes data for 52 systems, 34 of which (65%) have installed GAC.
The first full-scale system treating drinking water using PFAS-selective IX began operation in
2017 (WWSD, 2018). The data in the T&C document (U.S. EPA, 2024i) also suggest that an
increasing share of PWSs have selected IX in response to PFAS since that first installation.
Specifically, for systems installed prior to 2017, 78% used GAC. The EPA expects this trend to
continue, so the initial percentages include adjustments to account for this expectation. In
addition, as discussed in Section 5.3.1.1.1, the performance of GAC is affected by the presence
of TOC. Accordingly, the table includes adjusted distributions for systems with higher influent
TOC.
While central reverse osmosis/nanofiltration (RO/NF) remains a best available technology
(BAT) for the final rule, the EPA does not anticipate water systems will select this technology to
comply with the rule, largely due to the challenges presented by managing the treatment
residuals from this process.
The initial percentages in Table 5-8 reflect the fact that some small systems could choose point-
of-use reverse osmosis (POU RO) as a compliance alternative. At this time, the EPA is not
including POU devices in the national cost estimates because the regulatory options under
consideration require treatment to concentrations below 70 ppt PFOA and PFOS summed, the
current certification standard for POU devices.17 Therefore, SafeWater MCBC excludes POU
devices from consideration and proportionally redistributes the percentages among the other
alternatives. Table 5-9 shows the final compliance forecast after this redistribution.
17 POU treatment might become a compliance option for small systems in the future if independent third-party certification
organizations, such as NSF or ANSI develop a new certification standard that mirrors the EPA's proposed regulatory standard. In
the event POU treatment becomes a valid compliance option, national costs could be lower than estimated here.
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Table 5-9: Initial Compliance Forecast Excluding POU Devices
Design Flow Less than 1 Design Flow 1 to Less Design Flow Greater than
MGD than 10 MGD or Equal to 10 MGD
Compliance
TOC Less
TOC
TOC Less
TOC
TOC Less
TOC
Alternative
than or
Greater
than or
Greater
than or
Greater
Equal to 1.5
than 1.5
Equal to 1.5
than 1.5
Equal to 1.5
than 1.5
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
GAC
79%
62%
81%
52%
89%
52%
PFAS-selective IX
12%
29%
11%
40%
11%
48%
Central RO/NF
0%
0%
0%
0%
0%
0%
Interconnection
7%
7%
6%
6%
0%
0%
New Wells
2%
2%
2%
2%
0%
0%
Abbreviations: GAC - granular activated carbon; PFAS - per-and polyfluoroalkyl substances; MGD - million gallons per
day; IX - ion exchange; RO/NF - reverse osmosis/nanofiltration; POU - point-of-use; TOC - total organic carbon.
If all the compliance alternatives (other than POU devices and Centralized RO) remain in
consideration after Step 1, the decision tree uses the forecast shown in Table 5-9. If GAC or IX is
not viable for a particular EP due to performance limitations (see Section 5.3.1.1.1), SafeWater
MCBC proportionally redistributes the percentages among the remaining alternatives and uses
the redistributed percentages.
5.3.1.1.1 Estimating GAC and IX Performance
The viability of GAC and IX depends on bed life, which is the length of time the technology can
maintain a target removal percentage (e.g., 80 percent, 95 percent). Bed life can vary depending
on factors including type of media used (GAC or IX), specific PFAS contaminants targeted,
influent water quality, and removal performance required to meet regulatory option thresholds.
Bed life determines media replacement frequency and, therefore, affects both the practicality and
operation and maintenance (O&M) cost of these technologies. This analysis estimates bed life in
bed volumes (BV), which is a measure of throughput: the volume of water treated during the bed
life divided by the volume of the media bed.
The bed life estimates use linear equations derived as described in the T&C document (U.S.
EPA, 2024i). The EPA estimated the equations based on pooled data from several studies of
GAC as well as IX performance and reflect central tendency results under varying water quality
conditions. As such, the EPA believes they represent the best approach currently available for
use in a national cost estimation. However, they should not be used in lieu of site-specific
engineering analyses or pilot studies to guide the design or operation of specific treatment
systems.
The bed life equations are technology-specific and shown below:
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Equation 2:
BVcontam,GAC ~ ^TOC ^ TOC + Ar ^ °^°^contam Bcontain,GAC
BVcontam,IX ~ ^PFAS ^ P^^^total ^R,IX ^ °^°^contam Bcontam,IX
Where:
BVcontam.tech = bed life of the given technology for a given PFAS contaminant in BV; tech =
GAC or IX
TOC = TOC influent to the new treatment process in mg/L
PFAStotai = total influent concentration of all PFAS contaminants (regulated or unregulated) in
ppt
°/oRCOntam = target percent removal of a given PFAS as a decimal (e.g., 0.8, 0.95)
Bcontain,tech = constant; tech = GAC or IX
Table 5-10 shows the estimated values of the parameter coefficients AT0C, Apfas, AR tech, and
intercepts B^contamXech ¦
Table 5-10: Estimated Parameter Values for Technology-Specific Bed Life Equations
Parameter
GAC Model Value
IX Model Value
Atoc
-37,932
Not applicable3
Apfas
Not applicable3
-6.04
Ar
-36,309
-198,242
BhFPO-DA
113,034
Data not available
BpFHxA
113,967
212,867
BpFBS
129,357
439,515
BpFHpA
129,357
319,511
BpFHxS
129,357
439,515
BpFOA
139,862
390,787
BpFOS
143,731
439,515
Note:
aTotal PFAS is not a significant parameter in GAC performance; TOC is not a significant parameter in IX performance.
Source: Technical Support Document - Technologies and Cost for Removing Per- and Polyfluoroalkyl Substances (PFAS)
from Drinking Water (U.S. EPA, 2024i)
The bed life equations are only applicable over a specific range of water quality conditions (TOC
up to 3.2 mg/L for GAC; total PFAS up to 7,044 ppt for IX). Data are not available to estimate
performance beyond these limits. Therefore, SafeWater MCBC excludes GAC from
consideration if an EP's influent TOC concentration is greater than 3.2 mg/L. It excludes IX if
total influent PFAS is greater than 7,044 ppt. No PWS meets both of these exclusionary
conditions.
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If GAC and/or IX remain in consideration, SafeWater MCBC calculates the percent removal
required for the regulatory option under consideration and uses the linear equations above to
estimate bed life. These calculations vary depending on the regulatory option. Section 5.3.1.1.1.1
describes the calculations for PFOA and PFOS. Section 5.3.1.1.1.2 describes the calculations
under the final rule (individual MCLs for PFOS, PFOA, PFNA, PFHxS, and HFPO-DA plus the
group HI MCL).
Based on data presented in the T&C document (U.S. EPA, 2024i), specifically the maximum
removal effectiveness values reported in EPA's Drinking Water Treatability Database plus the
full set of removal data used to develop the bed life equations presented in Table 5-10, the EPA
assumes the maximum PFAS removal achievable by GAC or IX is 99.5% percent. Therefore, if
the relevant regulatory option requires removal at an EP greater than this maximum, SafeWater
MCBC removes GAC and IX from consideration, as described in the sections below.
Additionally, the EPA assumes that bed lives less than 5,000 BV for GAC and less than 20,000
BV for IX are impractical. These bed lives correspond to media replacement frequencies of two
to five months depending on the average flow of the EP. If the relevant regulatory option results
in a final operating bed life below these limits, SafeWater MCBC removes the corresponding
technology from consideration. Finally, the EPA assumes that the maximum bed life for GAC is
75,000 BV and the maximum bed life for IX is 260,000 BV. While some water systems treating
for PFAS may have performance that exceeds these values, the EPA included this assumption to
more conservatively estimate operational costs. If the calculated bed life is greater than 75,000
BV for GAC or greater than 260,000 BV for IX, then SafeWater MCBC sets the bed life at
75,000 BV for GAC and 260,000 BV for IX. For EPs that ultimately select GAC or IX, the final
operating bed life is also an input to the cost estimates (see Section 5.3.1.3) and the calculation of
post-treatment PFAS concentrations used to estimate reduction in health risks).18
5.3.1.1.1.1 Bed Life for PFOA and PFOS
Under Options la-c, PWSs must meet individual MCLs for PFOS and PFOA. For these options,
SafeWater MCBC calculates the percent removal required to meet each individual MCL in the
following equation:
Equation 3:
n/ n _ Q),contain ~ MCLCOntam ^ SF
'°Kcontam ~
.contam
Where:
°/oRCOntam = target percent removal of a given PFAS as a decimal (e.g., 0.8, 0.95)
C0,contam = influent concentration of the given PFAS in ppt
MCLcontam = MCL for the given PFAS in ppt
18 As shown in Equation 2, bed life and percent removal are directly related. SafeWater uses the same equation to back-calculate
final percent removal for each PFAS compound from final operating bed life. It then uses the final removal efficiency to calculate
post-treatment concentrations.
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SF = 0.8, a safety factor that assumes PWSs will design and operate treatment processes to
achieve 80 percent of the MCL (i.e. to 20 percent below the MCL value).
SafeWater MCBC performs this calculation for each contaminant that occurs at an EP and has an
MCL in the regulatory option, even if the contaminant occurs at a concentration below the MCL.
Including contaminants that are below their respective MCLs helps to account for
chromatographic peaking;19 which is a concern in GAC along with IX and is discussed in greater
detail in the T&C document (U.S. EPA, 2024i). The calculations here are designed to account for
and avoid it.
If the percent removal required for any contaminant (%RCOntam) is greater than 0.99 (99
percent), SafeWater MCBC removes GAC and IX from consideration. If the technologies remain
in consideration, SafeWater MCBC estimates the bed life for each contaminant using the linear
equations presented in Section 5.3.1.1.1. The final operating bed life is the minimum of the
individual contaminant-specific bed life estimates. If this final operating bed life is less than
5,000 BV for GAC or less than 20,000 BV for IX, SafeWater MCBC removes the corresponding
technology from consideration.
5.3.1.1.1.2 Bed Life Under the Final Rule
The final rule utilizes compound-specific MCLs for PFOA, PFOS, PFNA, HFPO-DA, and
PFHxS and an HI MCL for mixtures containing at least two or more of PFNA, HFPO-DA,
PFHxS, and PFBS. Due to limitations in occurrence data, the national cost estimates summate
costs only for the occurrence of PFOA, PFOS and PFHxS. The EPA notes that the costs for the
HI MCL and the individual MCLs for PFNA and HFPO-DA, are included and considered in the
Appendix N, Section N.3 sensitivity analysis. Therefore, for this option, SafeWater MCBC
calculates the percent removal required to meet the individual health benchmark for PFHxS
using the following equation:
Equation 4:
n/ n _ Cqpphxs ~ HBPFHxS X SF
/oK PFHxS — T.
L0 ,PFHxS
Where:
%Rpfhxs= target percent removal of PFHxS as a decimal (e.g., 0.8, 0.95)
Co,pfhxs = influent concentration of PFHxS in ppt
HBPFHxS = heath benchmark for PFHxS in ppt
SF = 0.8, a safety factor that assumes PWSs will design and operate treatment processes to
achieve 80 percent of the health benchmark.
19 Chromatographic peaking is a phenomenon in which less strongly sorbed contaminants are detached from sorbents by more
strongly bound sorbents and the less tightly bound sorbent re-enters drinking water. Direct competition with stronger sorbing
constituents can lead to effluent PFAS concentrations temporarily exceeding influent concentrations. Some PFAS species sorb
more strongly than other PFAS species which can cause more weakly sorbed species to re-enter drinking water.
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SafeWater MCBC performs this calculation even when PFHxS occurs at a concentration below
its health benchmark. Including contaminants that are below their respective MCLs prevents the
subsequent bed life calculations from selecting a bed life that results in a preferred PFAS
displacing a less preferred PFAS from the treatment media to the extent that the less preferred
PFAS periodically exceeds its MCL. This phenomenon is sometimes a concern in GAC as well
as IX design and operation and is discussed in greater detail in the T&C document (U.S. EPA,
2024i). The calculations here are designed to account for and avoid it.
If the percent removal required to meet the MCL and health benchmark for PFHxS is greater
than 0.99 (99 percent), SafeWater MCBC removes GAC and IX from consideration. If the
technologies remain in consideration, SafeWater MCBC estimates the bed life for PFHxS using
the linear equations presented in Section 5.3.1.1.1. It also calculates the bed lives necessary to
meet the individual MCLs for PFOS and PFOA, as described in Section 5.3.1.1.1.1. The final
operating bed life is the minimum of all the bed life estimates resulting from the calculations for
all three contaminants (PFOS, PFOA, and PFHxS). If this final operating bed life is less than
5,000 BV for GAC or less than 20,000 BV for IX, SafeWater MCBC removes the corresponding
technology from consideration. Finally, if the calculated bed life is greater than 75,000 for GAC,
or greater than 260,000 for IX, then SafeWater MCBC sets the bed life at 75,000 for GAC and
260,000 for IX.
53.1.2 WBS Models
The WBS models are spreadsheet-based engineering models for individual treatment
technologies, linked to a central database of component unit costs. The EPA developed the WBS
model approach as part of an effort to address recommendations made by the Technology Design
Panel (TDP), which convened in 1997 to review the agency's methods for estimating drinking
water compliance costs (U.S. EPA, 1997). The TDP consisted of nationally recognized drinking
water experts from the EPA, water treatment consulting companies, public as well as private
water utilities along with suppliers, equipment vendors, and Federal along with State regulators
in addition to cost estimating professionals.
In general, the WBS approach involves breaking a process down into discrete components for
the purpose of estimating unit costs. The WBS models represent improvements over past cost
estimating methods. By adopting a WBS-based approach to identify the components that should
be included in a cost analysis, the models produce a more comprehensive, flexible, and
transparent assessment of the capital and operating requirements for a treatment system.
Section 5.3.1.2.1 is a brief overview of the common elements of all the WBS models. Section
5.3.1.2.2 provides information on the anticipated accuracy of the models. Sections 5.3.1.2.3
through 5.3.1.2.5 identify technology-specific cost elements included in each model and discuss
key inputs. The documentation for the individual WBS models (U.S. EPA, 2023i; U.S. EPA,
2023k; U.S. EPA, 2023j), provides more complete details on the structure, content, and use of
each model.
5.3.1.2.1 Common Model Components and Inputs
Each WBS model contains the work breakdown for a particular treatment process and
preprogrammed engineering criteria and equations that estimate equipment requirements for
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user-specified design requirements (e.g., system size and influent water quality). Each model
also provides unit and total cost information by component (e.g., individual items of capital
equipment) and totals the individual component costs to obtain a direct capital cost. Additionally,
the models estimate add-on costs (e.g., permits and land acquisition), indirect capital costs, and
annual O&M costs, thereby producing EPA's best estimates of complete compliance cost.
Primary inputs common to all the WBS models include design flow and average daily flow in
MGD. Each WBS model has default designs (input sets) that correspond to specified categories
of flow, but the models can generate designs for many other combinations of flows. To estimate
costs for PFAS compliance, the EPA fit cost curves to the WBS estimates across a range of flow
rates, as described in Section 5.3.1.3.
Another input common to all the WBS models is "component level" or "cost level." This input
drives the selection of materials for items of equipment that can be constructed of different
materials. For example, a low-cost system might include fiberglass pressure vessels and
polyvinyl chloride (PVC) piping. A high-cost system might include stainless steel pressure
vessels and stainless-steel piping. The component level input also drives other model
assumptions that can affect the total cost of the system, such as building quality and heating and
cooling. The component level input has three possible values: low cost, mid cost, and high cost.
To estimate costs for PFAS treatment, the EPA generated separate cost equations for each of the
three component levels, thus creating a range of cost estimates for use in national compliance
cost estimates.
The third input common to all the WBS models is system automation, which allows the design of
treatment systems that are operated manually or with varying degrees of automation (i.e., with
control systems that reduce the need for operator intervention). The cost equations described in
Section 5.3.1.3 are for systems that are fully automated, minimizing the need for operator
intervention and reducing operator labor costs.
The WBS models generate cost estimates that include a consistent set of capital, add-on, indirect,
and O&M costs. Table 5-11 identifies these cost elements, which are common to all the WBS
models and included in the cost estimates below. Sections 5.3.1.2.3 through 5.3.1.2.5 identify the
technology-specific cost elements included in each model. The documentation for the WBS
models (U.S. EPA, 2023i; U.S. EPA, 2023k; U.S. EPA, 20231; U.S. EPA, 2023j) provide more
information on the methods and assumptions used in the WBS models to estimate the costs for
both the technology-specific and common cost elements.
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Table 5-11: Cost Elements Included in All WBS Models
APRIL 2024
Cost Category
Components Included
Direct Capital
• Technology-specific equipment (e.g., vessels, basins, pumps, treatment media,
Costs
piping, valves)
• Instrumentation and system controls
• Buildings
• Residuals management equipment
Add-on Costs
• Land
• Permits
• Pilot testing
Indirect Capital
• Mobilization and demobilization
Costs
• Architectural fees for treatment building
• Equipment delivery, installation, and contractor's overhead and profit
• Sitework
• Yard piping
• Geotechnical
• Standby power
• Electrical infrastructure
• Process engineering
• Contingency
• Miscellaneous allowance
• Legal, fiscal, and administrative
• Sales tax
• Financing during construction
• Construction management
O&M Costs:
• Operator labor for technology-specific tasks (e.g., managing backwash and media
Technology-
replacement)
specific
• Materials for O&M of technology-specific equipment
• Technology-specific chemical usage
• Replacement of technology-specific equipment that occurs on an annual basis
(e.g., treatment media)
• Energy for operation of technology-specific equipment (e.g., mixers)
O&M Costs: Labor
• Operator labor for O&M of process equipment
• Operator labor for building maintenance
• Managerial and clerical labor
O&M Costs:
• Materials for maintenance of booster or influent pumps
Materials
• Materials for building maintenance
O&M Costs:
• Energy for operation of booster or influent pumps
Energy
• Energy for lighting, ventilation, cooling, and heating
O&M Costs:
• Residuals management operator labor, materials, and energy
Residuals
• Residuals disposal and discharge costs
Abbreviations: O&M - operation & maintenance; WBS - work breakdown structure.
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5.3.1.2.2 WBS Model Accuracy
Costs for a given system can vary depending on site-specific conditions (e.g., raw water quality,
climate, local labor rates, and location relative to equipment suppliers). The costs presented here
are based on national average assumptions and include a range (represented by low-, mid-, and
high-cost equations) intended to encompass the variation in costs that systems would incur to
remove PFAS. To validate the engineering design methods used by the WBS models and
increase the accuracy of the resulting cost estimates, the EPA has subjected the individual
models to a process of external peer review by nationally recognized technology experts.
The GAC model underwent peer review in 2006. Two of the three reviewers expressed the
opinion that resulting cost estimates would be in the range of budget estimates (+30 to -15
percent). The other reviewer did not provide a precise estimate of the model's accuracy range but
commented that the resulting cost estimates were reasonable. The EPA made substantial
revisions to the GAC model in response to the peer review.
The IX model underwent peer review in 2005, during an early stage of its development. One peer
reviewer responded that resulting cost estimates were in the range of budget estimates (+30 to -
15 percent). The other two reviewers thought the estimates were order of magnitude estimates
(+50 to -30 percent), with an emphasis on the estimates being high. The IX model has since
undergone extensive revision, both in response to the peer review and to adapt it for PFAS
treatment using selective resin.
The EPA received peer review comments on the nontreatment model in May 2012. The first
reviewer responded that cost estimates resulting from the nontreatment model were in the range
of budget estimates (+30 to -15 percent). The second reviewer thought the cost estimates were
order of magnitude estimates (+50 to -30 percent). The third reviewer felt the cost estimates were
definitive (+15 to -5 percent), except for land costs, which were difficult to assess due to regional
variations. The EPA revised the nontreatment model in response to the peer review
recommendations.
5.3.1.2.3 GAC Model
Work Breakdown Structure-Based Cost Model for Granular Activated Carbon Drinking Water
Treatment provides a complete description of the engineering design process used by the WBS
model for GAC (U.S. EPA, 2023i). The model can generate costs for two types of design:
• Pressure designs where the GAC bed is contained in stainless steel, carbon steel, or
fiberglass pressure vessel; and
• Gravity designs where the GAC bed is contained in open concrete basins.
Table 5-12 shows the technology-specific capital equipment and O&M requirements included in
the GAC model. These items are in addition to the common WBS cost elements listed in Table
5-11.
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Table 5-12: Technology-Specific Cost Elements Included in the GAC Model
Cost Category
Major Components Included
Direct Capital
Costs
Booster pumps for influent water
Contactors (either pressure vessels or concrete basins) that contain the GAC bed
Tanks and pumps for backwashing the contactors
GAC transfer and storage equipment
Spent GAC reactivation facilities (if on-site reactivation is selected)
Associated piping, valves and instrumentation
O&M Costs: Labor
Operator labor for contactor maintenance (for gravity GAC designs)
Operator labor for managing backwash events
Operator labor for backwash pump maintenance (if backwash occurs weekly or
more frequently)
Operator labor for GAC transfer and replacement
O&M Costs:
Materials
Materials for contactor maintenance (accounts for vessel relining in pressure
designs, because GAC can be corrosive, and for concrete and underdrain
maintenance in gravity designs)
Materials for backwash pump maintenance (if backwash occurs weekly or more
frequently)
Replacement virgin GAC (loss replacement only if reactivation is selected)
O&M Costs:
Energy
Operating energy for backwash pumps
O&M Costs:
Residuals
Discharge fees for spent backwash
Fees for reactivating spent GAC (if off-site reactivation is selected)
Labor, materials, energy, and natural gas for regeneration facility (if on-site
reactivation is selected)
Disposal of spent GAC (if disposal is selected)
Abbreviations: GAC - granular activated carbon; O&M - operation & maintenance; WBS - work breakdown structure.
For small systems (less than 1 MGD) using pressure designs, the GAC model assumes the use of
package treatment systems that are pre-assembled in a factory, mounted on a skid, and
transported to the site. These assumptions are based on common vendor practice for these
technologies, for example, see Khera et al. (2013), which says "...small systems are often built
as packaged, pre-engineered, or skid-mounted systems." The model estimates costs for package
systems by costing all individual equipment line items (e.g., vessels, interconnecting piping and
valves, instrumentation, and system controls) in the same manner as custom-engineered systems.
This approach is based on vendor practices of partially engineering these types of package plants
for specific systems (e.g., selecting vessel size to meet flow and treatment criteria). The model
applies a variant set of design inputs and assumptions that are intended to simulate the use of a
package plant and that reduce the size and cost of the treatment system. U.S. EPA (2023i)
provides complete details on the variant design assumptions used for package plants.
To generate the cost equations discussed in Section 5.3.1.3, the EPA used the following key
inputs in the GAC model:
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• For pressure designs, two vessels in series with a minimum total empty bed contact time
(EBCT) of 20 minutes;
• For gravity designs, contactors in parallel with a minimum total EBCT of 20 minutes;
and
• Bed life varying over a range from 5,000 to 75,000 BV, estimated as discussed in Section
5.3.1.1.1.
The EPA generated separate cost equations for two spent GAC management scenarios:
• Off-site reactivation under current RCRA non-hazardous waste regulations; and
• Off-site disposal as a hazardous waste and replacement with virgin GAC (i.e., single use
operation).
The T&C document (U.S. EPA, 2024i) provides a comprehensive discussion of these and other
key inputs and assumptions.
5.3.1.2.4 PFAS-selective IX Model
Work Breakdown Structure-Based Cost Model for Ion Exchange Treatment of Per- and
Polyfluoroalkyl Substances (PFAS) in Drinking Water provides a complete description of the
engineering design process used by the WBS model for PFAS-selective IX (U.S. EPA, 2023j).
Table 5-13 shows the technology-specific capital equipment and O&M requirements included in
the model. These items are in addition to the common WBS cost elements listed in Table 5-11.
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Table 5-13: Technology-Specific Cost Elements Included in the PFAS-Selective IX Model
Cost Category
Major Components Included
Direct Capital
• Booster pumps for influent water
Costs
• Pre-treatment cartridge filters
• Pressure vessels that contain the resin bed
• Tanks and pumps for initial rinse and (optionally) backwash of the resin bed
• Tanks (with secondary containment), pumps and mixers for delivering sodium
hydroxide for use in post-treatment corrosion control (optional)
• Associated piping, valves, and instrumentation
O&M Costs: Labor
• Operator labor for pre-treatment filters
• Operator labor for managing backwash/rinse events
• Operator labor for backwash pump maintenance (only if backwash occurs weekly
or more frequently)
• Operator labor for resin replacement
O&M Costs:
• Replacement cartridges for pre-treatment filters
Materials
• Materials for backwash pump maintenance (only if backwash occurs weekly or
more frequently)
• Chemical usage (if post-treatment corrosion control is selected)
• Replacement virgin PFAS-selective resin
O&M Costs:
Energy
• Operating energy for backwash/rinse pumps
O&M Costs:
• Disposal of spent cartridge filters
Residuals
• Discharge fees for spent backwash/rinse
• Disposal of spent resin
Abbreviations: IX - ion exchange; O&M - operation & maintenance; PFAS - per-and polyfluoroalkyl substances.
For small systems (less than 1 MGD), the PFAS-selective IX model assumes the use of package
treatment systems that are pre-assembled in a factory, mounted on a skid, and transported to the
site. The IX model estimates costs for package systems using an approach similar to that
described for the GAC model, applying a variant set of inputs and assumptions that reduce the
size and cost of the treatment system (see Section 5.3.1.2.3). U.S. EPA (2023j) provides
complete details on the variant design assumptions used for IX package plants.
To generate the cost equations discussed in Section 5.3.1.3, the EPA used the following key
inputs in the PFAS-selective IX model:
• Two vessels in series with a minimum total EBCT of 6 minutes; and
• Bed life varying over a range from 20,000 to 260,000 BV, estimated as discussed in
Section 5.3.1.1.
The EPA generated separate cost equations for two spent resin management scenarios:
• Spent resin managed as non-hazardous and sent off-site for incineration; and
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• Spent resin managed as hazardous and sent off-site for incineration.
The T&C document (U.S. EPA, 2024i) provides a comprehensive discussion of these and other
key inputs and assumptions.
5.3.1.2.5 Nontreatment Model
Work Breakdown Structure-Based Cost Model for Nontreatment Options for Drinking Water
Compliance provides a complete description of the engineering design process used by the WBS
model for nontreatment actions (U.S. EPA, 2023k). The model can estimate costs for two
nontreatment alternatives: interconnection with another system and drilling new wells to replace
a contaminated source. Table 5-14 shows the technology-specific capital equipment and O&M
requirements included in the model for each alternative. The interconnection alternative does not
include any buildings. It includes all the indirect capital costs shown in Table 5-14 except for
yard piping, site work, and architectural fees. The new well alternative includes a small shed or
other low-cost building at the well site along with materials and labor for maintenance of this
building. It includes all the indirect capital costs shown in Table 5-14 except for yard piping.
Table 5-14: Technology-Specific Cost Elements Included in the Nontreatment
Model
Cost Category
Major Components Included for
Interconnection
Major Components Included for
New Wells
Direct Capital
• Booster pumps or pressure
• Well casing, screens, and
Costs
reducing valves (depending on
plugs
pressure at supply source)
• Well installation costs
• Concrete vaults (buried) for
including drilling,
booster pumps or pressure
development, gravel pack, and
reducing valves
surface seals
• Interconnecting piping
• Well pumps
(buried) and valves
• Piping (buried) and valves to
connect the new well to the
system
O&M Costs: Labor
• Operator labor for O&M of
booster pumps or pressure
reducing valves (depending on
pressure at supply source) and
interconnecting valves
• Operator labor for operating
and maintaining well pumps
and valves
O&M Costs:
• Cost of purchased water
Materials
• Materials for maintaining
• Materials for maintaining well
booster pumps (if required by
pumps
pressure at supply source)
O&M Costs:
Energy
• Energy for operating booster
pumps (if required by pressure
at supply source)
• Energy for operating well
pumps
Abbreviations: O&M - operation & maintenance.
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To generate the cost equations discussed in Section 5.3.1.3, the EPA used the following key
inputs in the nontreatment model for interconnection:
• An interconnection distance of 10,000 feet;
• Includes booster pumps designed to account for friction loss in interconnecting piping;
and
• An average cost of purchased water of $3.35 per thousand gallons in 2022 dollars.
For new wells, the EPA used the following key inputs:
• A maximum well capacity of 500 gallons per minute (gpm), such that one new well is
installed per 500 gpm of water production capacity required;
• A well depth of 250 feet; and
• 500 feet of distance between the new wells and the distribution system.
The T&C document (U.S. EPA, 2024i) provides a comprehensive discussion of these and other
key inputs and assumptions.
53.13 WBS Cost Equations
The EPA developed the cost estimates for PFAS treatment using outputs from the WBS models.
Outputs from these models are point estimates of total capital and O&M cost that correspond to a
given set of inputs that include design flow and average daily flow in MGD. Separately for total
capital and annual O&M cost, the EPA fit cost equations to the WBS outputs for up to 49
different flow rates. The EPA choose from among several possible equation forms: linear,
quadratic, cubic, power, exponential, and logarithmic. For each equation, the EPA selected the
form that resulted in the best correlation coefficient (R2), subject to the requirement that the
equation must be monotonically increasing over the appropriate range of flow rates (i.e., within
the flow rate category, the equation must always result in higher estimated costs for higher flow
systems than for lower flow systems). The resulting cost equations take one of the following
forms, identified by which coefficients (CI through C10) are nonzero:
Equation 5:
Cost = C1 QC2
or = C3 Ln(Q) + C4
or = C5 e(C6Q)
or = C7 Q3 + C8 Q2 + C9 Q + C10
In each case, Q is design flow in MGD for total capital costs, or average flow in MGD for annual
O&M costs. The resulting costs are in 2022 dollars.
Final PFAS Rule Economic Analysis
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The equations are categorized by water source (surface water or ground water) and component
level (low, mid, or high cost). The EPA developed separate equations for small, medium, or large
systems. These equations apply as follows:
• Small system equations apply where design flow (Q) is less than 1 MGD;
• Medium system equations apply where design flow (Q) is 1 MGD or greater, but less
than 10 MGD; and
• Large system equations apply where design flow (Q) is 10 MGD or greater.
SafeWater MCBC selects from among the small, medium, and large equations and applies the
equations using the treated flow of the EP. For GAC, IX, and nontreatment alternatives, the
treated flow is the entire flow of the EP.
For GAC and IX, the EPA developed separate equations that vary according to the estimated bed
life. These equations are in increments of 5,000 BV for GAC and 20,000 BV for IX. Each bed
life increment corresponds to a change in media replacement frequency of two to five months,
depending on the average flow of the EP. For EPs using GAC or IX, SafeWater MCBC selects
from among these equations based on the final operating bed life calculated as described in
Section 5.3.1.1.1, rounded down to the nearest increment of 5,000 BV for GAC and 20,000 BV
for IX.
For GAC, there are separate equations for pressure designs and gravity designs. For ground
water EPs using GAC, the EPA assumed PWSs would always use pressure designs to maintain
their existing pressure head. For surface water EPs using GAC, the EPA assumed PWSs would
choose between pressure and gravity based on the design that results in the lower annualized
cost.
In total, there are more than 2,600 individual cost equations across the categories of capital and
O&M cost, water source, component level, flow, bed life (for GAC and IX), residuals
management scenario (for GAC and IX), and design type (for GAC). The T&C document (U.S.
EPA, 2024i) presents the equations in tabular form.
5.3.1.4 Incremental Treatment Costs of PFNA, PFBS, and HFPO-DA
The EPA has estimated the national level costs of the final rule associated with PFOA, PFOS and
PFHxS. As discussed in Chapter 4 and detailed in the Technical Support Document for PFAS
Occurrence and Contaminant Background Chapter 10.1 and 10.3, there are limitations with
nationally representative occurrence information for the other compounds in the final rule
(PFNA, HFPO-DA, and PFBS; U.S. EPA, 2024g). Specifically, HFPO-DA does not currently
have a completed nationally representative dataset while PFNA and PFBS were not included in
the national occurrence model because of limited results reported above the minimum reporting
levels in UCMR 3. As described in the Technical Support Document for PFAS Occurrence and
Contaminant Background Chapter 10.3 non-targeted state monitoring datasets were used for
extrapolation of PFNA, HFPO-DA, and PFBS in lieu of a nationally representative dataset (U.S.
EPA, 2024g). EPA used conservative assumptions in this extrapolation to generate conservative
cost estimates. As demonstrated in this analysis, the HI, PFNA, and HFPO-DA MCLs
meaningfully increase public health protection at modest additional costs.
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Because of the increased uncertainty associated with PFNA, HFPO-DA and PFBS, the additional
treatment cost from co-occurrence of PFNA, HFPO-DA, PFBS at systems already required to
treat because of PFOA, PFOS, or PFHxS MCL and HI exceedances are not quantitatively
assessed in the national cost estimates. These HI treatment costs are summarized here in this
section and detailed in Appendix N, Section N.3. Likewise, treatment costs for systems that
exceed the HI based on the combined occurrence of PFNA, HFPO-DA, PFBS, and PFHxS
(where PFHxS itself does not exceed its HBWC of 10 ppt) are not included in the national
monetized cost estimates and are also summarized in this section and detailed in Appendix N,
Section N.3.
In the EA for the proposed PFAS NPDWR, the EPA used a model system approach to illustrate
the potential incremental costs for removing PFAS not included in the national economic model.
After considering public comments on the incremental cost analysis, the EPA decided to further
explore the incremental costs associated with the HI and MCLs with a national level sensitivity
analysis in the final rule.
When the modeled occurrence data for PFNA, HFPO-DA, PFBS is incorporated into the
SafeWater MCBC model, the estimated number of EPs exceeding one or more MCLs, and
therefore required to treat or use a different water source, increases to 9,471 from 9,043. This
results in an increase in the expected national costs. Under the primary analyses, the expected
total national cost is $1,549 million over EPA's period of analysis (2024-2105) for the PFOA,
PFOS and PFHxS MCLs (which as discussed in Section XII. A.4 of the preamble for today's rule,
accounts for a portion of the HI costs). When considering the additional incremental national
cost impacts of the HI MCL based on occurrence of PFNA, HFPO-DA, and PFBS and individual
MCLs for PFNA and HFPO-DA based on their individual occurrence the expected national costs
of the final rule increase to $1,631 million, or approximately a 5 percent national cost increase.
For further detail on the assumptions and findings of the EPA's analysis of incremental costs of
other PFAS, see Appendix N, Section N.3.
53.2 Estimating PWS Administrative and Monitoring Costs
This section details how the EPA estimated the costs of compliance with system administrative
and sampling activities associated with the final rule. In section 5.3.2, the EPA organizes and
presents the cost information based on the series of activities that are required to comply with the
final PFAS NPDWR, with tables for each data element used to calculate the final rule component
costs. These tables include the data element name and a description of the data variable, as well
as any relevant sources for the data. The EPA presents the costs categorized as follows:
• Administrative costs associated with implementation (Section 5.3.2.1)
• Sampling costs (Section 5.3.2.2)
• Administrative costs associated with treatment (Section 5.3.2.3)
Consistent with standard agency practice, the EPA assumes compliance with the rule throughout
the economic analysis, and as a result, SafeWater MCBC does not accrue costs to any system for
the Tier 2 and 3 public notifications. Nevertheless, the EPA presents a qualitative discussion of
the public notification costs potentially associated with the final rule in Section 5.3.2.4.
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5.3.2.1 Implementation Administration Costs
Systems conduct the following one-time actions to begin implementation of the rule:
• Reading and understanding the rule; and
• Attending training provided by primacy agencies.
The average unit costs for PWSs are based on the following burden assumptions: 1) The EPA
anticipates that the majority of water systems will likely not read the entirety of the rule
preamble (as they are not required to do so) but focus their time and attention on understanding
the regulatory requirements through the Code of Federal Regulations (CFR) regulatory text,
relevant portions of the preamble, the EPA provided fact sheets and small system guidance
documents, and state provided summaries documents; 2) Additionally, the EPA anticipates that
system staff will attend primacy agency PFAS rule trainings to reenforce the systems'
understanding of the final rule. The EPA assumes that systems will conduct these activities
during years one through three of the period of analysis. Table 5-15 lists the data elements and
provides descriptions, values, and sources for these costs. The cost per system for each activity is
the product of the hourly labor cost (labor sys rate) and the hours (hrssysadoptrule and
hrs_sys_initial_ta), which vary by system size. The total cost is the sum of per-system costs.
Table 5-15: Implementation Administration Startup Costs ($2022)
Data Element Name
Data Element
Source
laborsysrate
The labor rate per $36.43 (systems <3,300)
hour for systems $38.84 (systems 3,301-10,000)
WBS Technical
Labor Cost
$41.00 (systems 10,001-50,000)
$42.81 (systems 50,001-
100,000)
$50.03 (systems >100,000)
hrssysadoptrule
The average hours 4 hours per system
per system to read
and adopt the rule
Arsenic in Drinking
Water Rule
Economic Analysis
(EPA 815-R-00-
026)
Arsenic in Drinking
Water Rule
Economic Analysis
(EPA 815-R-00-
026)
hrssysinitialta
The average hours 16 hours per system (systems
per system to attend <3,300)
one-time training 32 hours per system (systems
provided by primacy >3,300)
agencies
Abbreviation: WBS - work breakdown structure.
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53.2.2 Sampling Costs
The final rule requires initial and long-term monitoring. As Table 5-16 shows, surface and
ground water systems serving greater than 10,000 people will collect one sample each quarter, at
each EP, during the initial 12-month monitoring period. Surface water systems serving 10,000 or
fewer people are also required to collect a quarterly sample at each EP during the initial 12-
month period. Ground water systems that serve 10,000 or fewer people will be required to
sample once at each EP on a semi-annual basis for the first 12-month monitoring period.
Long-term monitoring schedules are based on specific EP sampling results (i.e., water systems
can have different EPs within the system on different monitoring schedules). Long-term
monitoring requirements differ based on whether a system can demonstrate during the initial
monitoring period or once conducting long-term monitoring that an EP is below the trigger levels
for regulated PFAS. The trigger levels are set as one-half each of the MCLs: 2.0 ppt for PFOA
and PFOS 5 ppt for PFHxS, HFPO-DA, and PFNA and 0.5 for the HI. EPs below the trigger
level values during the initial 12-month monitoring period and in future long-term monitoring
periods may conduct triennial monitoring and collect one triennial sample at that EP. For EPs
with concentration values at or above a trigger level, a quarterly sample must be taken at that EP
following initial monitoring. EPs that demonstrate they are "reliably and consistently"20 below
the MCLs following four consecutive quarterly samples are eligible to conduct annual
monitoring. After three annual samples at that EP showing no results at or above a trigger level,
the location can further reduce to triennial monitoring.
For any samples that have a detection, the system will analyze the field reagent blank samples
collected at the same time as the monitoring sample. Systems that have an MCL exceedance will
collect one additional sample from the relevant EP to confirm the results (i.e., a confirmation
sample) (U.S. EPA, 2004).
20 The definition of reliably and consistently below the MCL means that each of the samples contains regulated PFAS
concentrations below the applicable MCLs. For the PFAS NPDWR, this demonstration of reliably and consistently below the
MCL would include consideration of at least four quarterly samples at an EP below the MCL, but states will make their own
determination as to whether the detected concentrations are reliably and consistently below the MCL.
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Table 5-16: Modeled Initial and Long-Term Sampling Frequencies Per System Entry
Point
Initial Monitoring
Long Term Monitoring3
System Size
Category
Sample Number
and Frequency
PFAS Detection >
MCLs
PFAS Detection >
Trigger Levels and <
MCLsb
PFAS Detection <
Trigger Levels
< 10,000
>10,000
Surface water: 1
sample every
quarter
Groundwater: 1
sample every 6-
month period
Surface water and
Groundwater: 1
sample every
quarter
1 sample every
quarter
1 sample every
quarter
1 sample every year
(following four
consecutive quarterly
samples reliably and
consistently below the
MCL)
1 sample every year
(following four
consecutive quarterly
samples reliably and
consistently below the
MCL)
1 triennial sample
1 triennial sample
Abbreviations: MCL- maximum contaminant level; PFAS - per-and polyfluoroalkyl substances.
Note:
aThe EPA used the following thresholds to distinguish whether PFAS concentrations are reliably and consistently below the
MCL: If after four consecutive quarterly samples, a system is below the MCLs (PFOA and PFOS - 4.0 ppt, PFHxS, HFPO-
DA, PFNA - 10 ppt, HI - 1).
bSystems are not eligible for annual monitoring until after four consecutive quarterly samples are collected following initial
monitoring.
For the national cost analysis, the EPA assumes that systems with either UCMR 5 data or
monitoring data in the State PFAS Database will not conduct the initial year of monitoring (See
Section 3.1.4). As a simplifying assumption for the cost analysis, the EPA assumes all systems
serving a population of greater than 3,300 have UCMR 5 data and those with 3,300 or less do
not. For the State PFAS Database, the EPA relied on the PWSIDs stored in the database and
exempted those systems from the first year of monitoring in the cost analysis.
The EPA assumes that systems with an MCL exceedance will implement actions to comply with
the MCL by the compliance date. As indicated in Section 5.3.1, the EPA assumes a treatment
target, for systems required to treat for PFAS, that includes a margin of safety so finished water
PFAS levels at these systems are 80 percent of the MCLs and HI. In the final rule, in order to
reduce burden associated with monitoring, the EPA is adding an annual tier of sampling for any
system with concentrations reliably and consistently below the MCL but not consistently below
the trigger level. The EPA believes this tier would likely apply to most systems treating their
water for regulated PFAS, at least for the first three years of treatment. Therefore, in the model,
the EPA assumes EPs that have installed treatment will take one year of quarterly samples, then
continue to sample on an annual basis after that. The final rule allows EPs showing no results at
or above a trigger level after three annual samples to further reduce to triennial monitoring. In
the national cost analysis, the EPA does not model this possibility nor does the EPA model
instances where water systems are triggered back into quarterly monitoring after installing
treatment.
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For all systems, the activities associated with the sample collection in the initial 12-month
monitoring period are the labor burden and cost for the sample collection and analysis, as well as
a review of the sample results. Table 5-17 presents the data needs associated with the
implementation monitoring period. The cost per EP for each sampling activity is the product of
the hourly labor cost and the hours plus the laboratory analysis cost. The laboratory analysis cost
will include the additional field blank cost when occurrence values exceed method detection
limits. The total cost is the sum of per-EP costs.
Table 5-17: Sampling Costs ($2022)
Data Element Name Data Element Description
Data Element Value
Data Element
Source
laborsysrate
The labor rate per hour for
systems
$36.43 (systems <3,300)
$38.84 (systems 3,301-
10,000)
$41.00 (systems 10,001-
50,000)
$42.81 (systems 50,001-
100,000)
$50.03 (systems >100,000)
WBS Technical
Labor Cost
numbinitialsamples
numb quarterly samples
numb annual samples
numbtrienniallsamples
hrssamp
EPA537 cost
The number of samples per
EP per monitoring round for
the initial monitoring in Year
1
The number of samples per
EP per long-term monitoring
year for EPs with finished
water concentrations > MCLs
(i.e., Systems not reliably and
consistently below the
MCLs)
The number of samples per
EP per long-term monitoring
year for EPs with finished
water concentrations < MCLs
but > the trigger levels for
four consecutive quarterly
samples
The number of samples per
EP per long-term monitoring
round for EPs with finished
water concentrations < the
trigger levels
The hours per sample to
travel to sampling locations,
collect samples, record any
additional information,
submit samples to a
laboratory, and review results
The laboratory analysis cost
per sample for EPA Method
537.lb
4 samples per system3
2 samples (ground water
systems < 10,000)
4 samples per year
Final rule
Final rule
1 sample per year
1 sample every 3 years
1 hour
$309
Final rule
UCMR5 ICR (EPA-
HQ-OW-2020-
0530-00141)
UCMR5 ICR (EPA-
HQ-OW-2020-
0530-0141)
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Table 5-17: Sampling Costs ($2022)
Data Element Name Data Element Description Data Element Value
Data Element
Source
EPA537 fieldblank cost The laboratory analysis cost $273c
per sample for the field
reagent blank under EPA
Method 537.1
Abbreviations: EPA - U.S. Environmental Protection Agency; ICR - Information Collection Request; UCMR - Unregulated
Contaminant Monitoring Rule; WBS - work breakdown structure.
Notes:
aSystems greater than 3,300 will rely on UCMR 5 data and a subset of other systems will rely on data in the State PFAS
Monitoring Database.
bThe EPA assumes that while both methods provide the required data to demonstrate compliance, water systems will select the
least costly analytical method (which is Method 537.1).
This incremental sample cost applies to all samples that exceed the method detection limit.
5.3.2.3 Treatment Administration Costs
As described in Section 5.3.1, any system with an MCL exceedance adopts either a treatment or
nontreatment alternative to comply with final rule. The majority of systems are anticipated to
install treatment technologies while a subset, described in Section 5.3.1.1, will choose alternative
methods. The EPA assumes that systems will have administrative costs associated with obtaining
permits for either the treatment or nontreatment methods. The costs vary depending on whether
the system installs treatment or selects a nontreatment method. For the economic analysis, the
EPA assumes that systems install treatment in the fifth year of the period of analysis. In addition,
after installation of treatment, the EPA assumes that systems will spend an additional 2 hours per
treating EP compiling data for and reviewing treatment efficacy with their primacy agency
during their triennial sanitary survey.
Table 5-18 presents the data elements and sources for these costs. The cost per EP requiring
treatment or changing water source is the product of the hourly labor cost and the hours per the
relevant permit request and sanitary survey review. The total cost is the sum of per-EP costs.
Final PFAS Rule Economic Analysis
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Table 5-18: Treatment Administration Costs ($2022)
Data Element Name
Data Element
Description
Data Element Value
Data Element
Source
laborsysrate
The labor rate per hour
$36.43 (systems <3,300)
WBS Technical
for systems
$38.84 (systems 3,301-
Labor Cost
10,000)
$41.00 (systems 10,001-
50,000)
$42.81 (systems 50,001-
100,000)
$50.03 (systems
>100,000)
hrssystreat
The hours per EP for a
3 hours (systems <100)
Lead and Copper
system to notify,
5 hours (systems 101-
Rule Revisions
consult, and submit a
500)
Support Material
permit request for
7 hours (systems 501-
(EPA-HQ-OW-
treatment installation3
1,000)
2017-0300-1701)
12 hours (systems 1,001 -
3,300)
22 hours (systems 3,301 -
50,000)
42 hours (systems >=
50,001)
hrs_ss_incrcmcnt
The additional hours
2 hours per EP that
Lead and Copper
per EP the system will
installs treatment every 3
Rule Revisions
spend every 3 years
years post-installation
Support Material
after PFAS-related
(EPA-HQ-OW-
treatment is installed
2017-0300-1701)
during a sanitary
survey.
hrssyssource
The hours per EP for a
6 hours
Lead and Copper
system to notify,
Rule Revisions
consult, and submit a
Support Material
permit request for
(EPA-HQ-OW-
source water change or
2017-0300-1700)
alternative method3
Abbreviations: WBS - work breakdown structure.
Note:
aThe Lead and Copper Rule Revisions presents this burden per system, but the EPA applied the cost per EP for this
economic analysis because the notification, consultation, and permitting process occurs for individual EPs.
5.3.2.4 Public Notification Costs
While the EPA assumes full compliance with the rule and does not include public notification
costs in the cost estimates, there are public notification requirements in the final rule for systems
with certain violations. The final rule designates MCL violations for PFAS as Tier 2, which
requires systems to provide public notification as soon as practical, but no later than 30 days
after the system learns of the violation. The system must repeat notice every three months if the
violation or situation persists unless the primacy agency determines otherwise. At a minimum,
systems must give repeat notice at least once per year.
The final rule designates monitoring and testing procedure violations as Tier 3, which requires
systems to provide public notice not later than one year after the system learns of the violation.
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The system must repeat the notice annually for as long as the violation persists. Community
water systems may deliver Tier 3 PNs in their CCR if the timing, content, and delivery
requirements are met according to 40 CFR 141.204(d). Using the CCR to deliver Tier 3 PNs can
minimize the burden on systems by reducing delivery costs.
To provide an approximate estimate of the burden associated with the Tier 2 and 3 violations, the
EPA reviewed the ICR for the Public Water System Supervision (PWSS) Program (U.S. EPA,
2011), which includes Tier 2 and 3 notifications. Table 5-19 presents the PWSS Program ICR
burdens for the preparation and delivery of the Tier 2 and 3 public notifications.
Table 5-19: Public Notification Burden Estimate
Data Element"
Data Element Value
Data Element Source
Preparation of initial Tier 2 notices
3.5 hours
PWSS Program ICR (EPA-HQ-
OW-2011-0433-0003)
Preparation of initial Tier 3 notices
3 hours (CWS)
3.5 hours (NTNCWS)
PWSS Program ICR (EPA-HQ-
OW-2011-0433-0003)
Delivery of initial Tier 2 notices
9 hours (CWS <500)
30 hours (CWS >500)
9 hours (NTNCWS)
PWSS Program ICR (EPA-HQ-
OW-2011-0433-0003)
Development and delivery of
repeated Tier 2 and 3 notices
3 hours
PWSS Program ICR (EPA-HQ-
OW-2011-0433-0003)
Abbreviations: CWS - community water systems; NTNCWS - non-transient non-community water systems; PWSS - public
water systems supervision; ICR - information collection request.
Note:
aDelivery of Tier 3 notices must occur not later than one year after the system learns of the violation. The EPA assumes
systems will include this notice with the Consumer Confidence Reports sent to all customers annually, therefore Tier 3
delivery costs are assumed to be zero.
5.4 Estimating Primacy Agency Costs
In addition to the PWS costs associated with the rule implementation, the EPA assumes primacy
agencies will have upfront implementation costs as well as ongoing administrative costs and
costs associated with the system actions related to sampling and treatment. The activities
associated with primacy agencies under the final rule include:
• Reading and understanding the rule, providing internal primacy agency officials training
for the rule implementation, updating sanitary survey standard operating procedures,
• Primacy package application, including making state regulatory changes to the federal
rule where applicable
• Providing systems with training and technical assistance during the rule implementation;
• Reporting to the EPA on an ongoing basis any PFAS-specific information under 40 CFR
142.15 regarding violations as well as enforcement actions and general operations of
public water supply programs;
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Performing inspection of PFAS related treatment during sanitary surveys every three
years21
• Reviewing the sample results during the initial monitoring period and the long-term
monitoring period; and
• Reviewing and consulting with systems on the installation of treatment technology or
alternative methods, including source water change.
For the last three activities listed above, primacy agency burdens are incurred in response to an
action taken by a system. For example, the cost to primacy agencies of reviewing any sample
result depends on the number of samples taken at each EP by each system under the jurisdiction
of the primacy agency. Table 5-20 presents the data elements and sources for all primacy agency
costs. The data element descriptions indicate whether the cost is per primacy agency, per sample,
per system, or per EP. In each instance, the primacy agency labor rate is multiplied by the
number of relevant hours and the activity frequency.
Table 5-20: Primacy Agency Costs ($2022)
Data Element Name Data Element Description
Data Element Value
Data Element
Source
labor_pa_rate
hrs_pa_adopt_rule
hrs pa write reg
hrspainitialta
hrs sdwis
The labor rate per hour for
primacy agencies
The average hours per
primacy agency to read and
understand the rule, update
sanitary survey standard
operating procedures, and
train internal staff.
The average hours for a
primacy agency to develop
state-level regulations
The average hours per
primacy agency to provide
initial training and technical
assistance to systems
The average hours per
primacy agency to report
annually to the EPA
information under 40 CFR
142.15 regarding violations,
variances and exemptions,
enforcement actions and
general operations of State
public water supply programs
$59.69
4,020 hours per primacy
agency
300 hours per primacy
agency
1,500 hours per primacy
agency
Loaded labor rate
(including the cost of
benefits) derived from
the Bureau of Labor
Statistics3
ASDWA, 2023
ASDWA, 2023
ASDWA, 2023
The EPA assumes
that the final PFAS
rule will have no
discernable
incremental burden
for quarterly or
annual reports to
SDWIS/Fed
21 Sanitary surveys are required for CWS every three years, except for CWS with outstanding performance based on prior
sanitary surveys for which they are required every 5 years. Sanitary surveys are required for NCWS at least every 5 years. As a
simplifying assumption in the national cost analysis, the EPA set the sanitary survey frequency to three years for all systems
expected to install treatment to comply with the PFAS rule.
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Table 5-20: Primacy Agency Costs ($2022)
Data Element Name Data Element Description
Data Element Value
Data Element
Source
hrspareportep
hrspatreat
The hours per sample for a
primacy agency to review
sample results
The hours per EP for a
primacy agency to review and
consult on installation of a
treatment technique15
1 hour
80 hours (systems <3,300)
70 hours (systems serving
3,301 -50,000)
50 hours (systems serving >
50,000)
Arsenic in Drinking
Water Rule Economic
Analysis (EPA 815-
R-00-026)
ASDWA, 2023
hrs_pa_ss_incrc merit
The additional hours per EP
the primacy agency will spend
every 3 years after PFAS-
related treatment is installed
during a sanitary survey.
2 hours per EP that installs
treatment every 3 years post-
installation.
Lead and Copper
Rule Revisions
Support Material
(EP A-HQ-0 W-2017-
0300-1701)
hrs_pa_source
The hours per EP for a
primacy agency to review and
consult on a source water
changeb
4 hours
Lead and Copper
Rule Revisions
Support Material
(EP A-HQ-0 W-2017-
0300-1700)
Abbreviations: PFAS - per-and polyfluoroalkyl substances; SDWIS/Fed - Safe Drinking Water Information System Federal
Version; ASDWA - Association of State Drinking Water Administrators.
Notes:
aState employee wage rate of $33.91 from National Occupational Employment and Wage Estimates, United States, BLS SOC
Code 19-2041, "State Government, excluding schools and hospitals - Environmental Scientists and Specialists, Including
Health," hourly mean wage rate. May 2020 data (published in March 2021): https://www.bls.gov/oes/curTent/oesl92041.htm.
Wages are loaded using a factor of 62.2 from the BLS Employer Costs for Employee Compensation report, Table 3, March
2020. Percent of total compensation - Wages and Salaries - All Workers - State and Local Government Workers
(https://www.bls.gOv/news.release/archives/ecec_06182020.pdf). See worksheet BLS Table 3. The final loaded wage is
adjusted for inflation.
bThe Lead and Copper Rule Revisions present this burden per system, but the EPA has applied the cost per EP for this
economic analysis because the notification, consultation, and permitting process occurs for individual EPs.
In addition to the costs described above, a primacy agency may also have to review the
certification of any Tier 2 or 3 public notifications sent out by systems. The EPA assumes full
compliance with the final rule but provides a brief discussion of the possible system costs
associated with this component in Section 5.3.2.4. The public notification burden associated with
primacy agencies is between 0.33 and 0.5 hours per system to review the system certification of
the public notification. The burden is derived from the Lead and Copper Rule Revisions
estimates for a similar activity.
5.5 PWS-Level Cost Estimates
PWS-level cost estimates for the final rule and other regulatory options are provided in Appendix
C. PWS-level cost are provided for all PWSs by PWS-type, size category, primary source water
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type, and ownership. In addition, a second set of PWS-level costs are provided for PWSs that
must take action to comply with the rule (treat or change water source).
5.6 Household-Level Cost Estimates
Household-level cost estimates for the final rule and other regulatory options are provided in
Appendix C. Household-level cost are provided for all CWSs by size category, primary source
water type, and ownership. In addition, a second set of household-level costs are provided for
households served by CWSs that must take an action to comply with the rule (treat or change
water source).22
5.7 Discussion of Data Limitations and Uncertainty
The preceding sections identify the nonquantifiable costs and the uncertainty information
incorporated in the quantitative cost analysis. There are also data limitations that could not be
incorporated in this analysis. Chapter 7 and Table 7-6 outline the nonquantifiable costs
associated with the regulatory requirements of the final rule as well as Options la-c. Table 5-21
lists the data limitations and characterizes the impact on the quantitative cost analysis. The EPA
notes that in most cases it is not possible to judge the extent to which a particular limitation or
uncertainty could affect the cost analysis. The EPA provides the potential direction of the impact
on the cost estimates when possible but does not prioritize the entries with respect to the impact
magnitude.
Table 5-21: Limitations that Apply to the Cost Analysis for the Final PFAS Rule
Uncertainty/ Assumption
Effect on Quantitative
Analysis
Notes
WBS engineering cost
model assumptions and
component costs
Uncertain
The WBS engineering cost models require many design
and operating assumptions to estimate treatment process
equipment and operating needs. Section 5.3.1 addressed
the bed life assumption. The Technologies and Costs
document (U.S. EPA, 2024i) and individual WBS models
in the rule docket provide additional information. The
component-level costs approximate national average costs,
which can over- or under-estimate costs at systems affected
by the final rule.
Compliance forecast
Uncertain
The forecast probabilities are based on historical full-scale
compliance actions. Site-specific water quality conditions,
changes in technology, and changes in market conditions
can result in future technology selections that differ from
the compliance forecast.
22 Note that the EPA does compute per household technology cost values in the separate national small system affordability
determination analysis. These household values are distinct from the values generated in the national cost estimates as they
include only small system compliance technology cost. For three small system size categories (systems serving 25-500, 501 -
3,300, and 3,301-10,000) The EPA estimates a per household treatment technology cost range including the minimum and
maximum cost values. These cost estimates are based on system characteristics, contaminant reduction requirements, and
technology efficacy, across the set of small system compliance technology options. See Chapter 9.12 for additional information
on the national small system affordability determination.
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Table 5-21: Limitations that Apply to the Cost Analysis for the Final PFAS Rule
Uncertainty/ Assumption
Effect on Quantitative
Analysis
Notes
TOC concentration
Uncertain
The randomly assigned values from the two national
distributions are based on a limited dataset. Actual TOC
concentrations at systems affected by the final rule can be
higher or lower than the assigned values.
Insufficient UCMR 3 data
for PFBS and PFNA and no
UCMR 3 data for HFPO-
DA were available to
incorporate into the
Bayesian hierarchical
occurrence model
Underestimate
The final rule regulates PFBS, PFNA, and HFPO-DA in
addition to the PFAS modeled in the primary analysis
(PFOA, PFOS and PFHxS). In instances when
concentrations of PFBS, PFNA, and/or HFPO-DA are high
enough to cause or contribute to HI exceedances or PFNA
and/or HFPO-DA are high enough to cause individual
MCL exceedances, the modeled costs in the primary
analysis may be underestimated. If these PFAS occur in
isolation at levels that affect treatment decisions, or if they
occur in sufficient concentration to result in an exceedance
when the concentration of PFHxS alone would be below
the HBWC, then costs would be underestimated. Note that
the EPA has conducted an analysis of and considered the
potential changes in national level treatment cost
associated with the occurrence of PFBS, PFNA, and
HFPO-DA, which is discussed in detail in Appendix N,
Section N.3.
POU not included in
compliance forecast
Overestimate
If POU devices can be certified to meet concentrations that
satisfy the final rule, then small systems may be able to
reduce costs by using a POU compliance option instead of
centralized treatment or source water changes.
Process wastes not
classified as hazardous
Underestimate
The national cost analysis reflects the assumption that
PFAS-contaminated wastes are not considered RCRA
regulatory or characteristic hazardous wastes. To address
stakeholder concerns, including those raised during the
SBREFA process, the EPA conducted a sensitivity analysis
with an assumption of hazardous waste disposal for
illustrative purposes only. As part of this analysis, the EPA
generated a second full set of unit cost curves that are
identical to the curves used for the national cost analysis
with the exception that spent GAC and spent IX resin are
considered hazardous. The EPA acknowledges that if in
the future PFAS-contaminated wastes require handling as
hazardous wastes, the residuals management costs in the
WBS treatment cost models are expected to be higher. See
Appendix N, Section N.2 for a sensitivity analysis
describing the potential increase in costs associated with
hazardous waste disposal at 100 percent of systems
treating for PFAS. The costs estimated in Appendix N,
Section N.2 are consistent with EPA OLEM's "Interim
Guidance on the Destruction and Disposal of
Perfluoroalkyl and Polyfluoroalkyl Substances and
Materials Containing Perfluoroalkyl and Polyfluoroalkyl
Substances" (U.S. EPA, 2020b).
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Table 5-21: Limitations that Apply to the Cost Analysis for the Final PFAS Rule
Uncertainty/ Assumption
Effect on Quantitative
Analysis
Notes
Population served held
constant over time
Uncertain
All PWS populations served were held constant over the
period of analysis as not all locations have reliable
information on population changes over time. If population
served by affected PWSs increases (or decreases), then the
estimated costs are likely underestimated (or
overestimated).
Abbreviations: WBS - work breakdown structure; TOC - total organic carbon; HFPO-DA - hexafluoropropylene oxide dimer
acid; PFAS - per and polyfluoroalkyl substances; PFBS - perfluorobutanesulfonic acid; PFNA - perfluorononanoic acid; PFHxS
- perfluorohexanesulfonic acid; MCL - maximum contaminant level; HI - hazard index; HBWC- health based water
concentration; POU - point-of-use; RCRA - Resource Conservation and Recovery Act; SBREFA - Small Business Regulatory
Enforcement Fairness Act; GAC - granulated activated carbon; IX - ion exchange; OLEM - Office of Land Energy and
Management.
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6 Benefits Analysis
6.1 Introduction
This chapter discusses the potential quantified and nonquantifiable23 benefits to human health
resulting from changes in PFAS levels in drinking water due to implementation of the final rule,
as well as several regulatory alternatives. The EPA's quantification of health benefits resulting
from reduced PFAS exposure in drinking water was driven by PFAS occurrence estimates,
pharmacokinetic (PK) model availability, information on exposure-response relationships, and
economic data to monetize the impacts. The EPA either quantitatively assesses or qualitatively
discusses health endpoints associated with exposure to PFAS. The EPA assesses potential
benefits quantitatively if there is evidence of an association between PFAS exposure and health
effects if it is possible to link the outcome to risk of a health effect, and if there is no overlap in
effect with another quantified endpoint in the same outcome group. Only a subset of the avoided
morbidity and mortality stemming from reduced PFAS levels in drinking water can be quantified
and monetized. The monetized benefits evaluated in the economic analysis for the final rule
include changes in human health risks associated with CVD and infant birth weight from reduced
exposure to PFOA and PFOS in drinking water and RCC from reduced exposure to PFOA.24 The
EPA also quantified benefits from reducing bladder cancer risk due to the co-removal of non-
PFAS pollutants via the installation of drinking water treatment, discussed in greater detail in
Section 6.7. The EPA was not able to quantify or monetize other benefits, including those related
to possible immune, hepatic, endocrine, metabolic, reproductive, musculoskeletal, or other
outcomes. The EPA discusses these benefits qualitatively in more detail below in Section 6.2 of
the economic analysis.
The EPA analyzes the quantified costs and benefits of the final rule MCLs of 4.0 ppt for PFOA,
4.0 ppt for PFOS, and a unitless HI of 1 for the group including PFNA, HFPO-DA, PFHxS, and
PFBS. The analysis of costs and benefits associated with the HI also express the costs and
benefits of the individual MCLs for PFNA, HFPO-DA, and PFHxS. Additionally, the EPA
presents the incremental costs and benefits associated with three regulatory alternative MCLs for
PFOA and PFOS at 4.0 ppt, 5.0 ppt, and 10.0 ppt, referred to as Options la through lc
respectively. As discussed in Section 2.1, the regulatory options include treatment thresholds that
would reduce PFAS levels in finished drinking water by various amounts. The change in PFAS
levels at a particular water system depends on baseline PFAS levels estimated using the
occurrence model (Section 4.4) and the PFAS treatment threshold specified under each
regulatory alternative.
The EPA notes that the quantified benefits alone of this analysis are a significant underestimate
of the total benefits expected to result from this rule because the EPA was not able to
quantitatively monetize all benefits. Hence, as mandated by SDWA Section 1412(b)(3)(C), the
23 Nonquantifiable benefits are discussed qualitatively.
24 Benefits to human health in terms of reduced liver cancer incidence are described in Appendix O. This analysis is presented as
a supplemental analysis for the final rule in response to public comments received on the proposed rule requesting that the EPA
quantify additional health benefits.
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EPA has considered both quantifiable and nonquantifiable benefits in informing its decision
making that the costs of this rule are clearly justified by the benefits.
6.1.1 Chapter Overview
Section 6.2 provides an overview of the health benefit categories considered in the analysis of
reductions of PFAS in drinking water. In addition to describing the benefits that the EPA is able
to quantify, this section includes a robust qualitative discussion of nonquantifiable benefits.
Because of the broad adverse health impacts of PFAS on many endpoints, the nonquantifiable
benefits of this final rule are likely substantial. Section 6.3 describes the application of the EPA's
PK models for PFAS to estimate changes in blood serum concentrations under each regulatory
alternative. Section 6.4 presents the methodology and results of the impacts of the PFAS
regulatory alternatives on a subset of developmental outcomes, namely infant birth weight.
Section 6.5 presents the methodology and results of the impacts of the PFAS regulatory
alternatives on CVD incidence. Section 6.6 presents the methodology and results of the impacts
of the PFAS regulatory alternatives on the incidence of RCC, one of the cancers associated with
PFOA exposure. Section 6.7 presents the methodology and results of the impacts of the PFAS
regulatory alternatives on DBP formation and the associated incidence of bladder cancer. Finally,
Section 6.8 describes limitations and uncertainties of the benefits analyses.
6.1.2 Uncertainty Characterization
The EPA characterizes sources of uncertainty in its analysis of potential quantified benefits
resulting from changes in PFAS levels in drinking water. The analysis reports uncertainty bounds
for benefits estimated in each health endpoint category modeled for the final rule. Each lower
(upper) bound value is the 5th (95th) percentile of the category-specific benefits estimate
distribution represented by 4,000 Monte Carlo draws. Table 6-1 provides an overview of the
specific sources of uncertainty that the EPA quantified in this benefits analysis. In addition to
these sources of uncertainty, reported uncertainty bounds also reflect the following upstream
sources of uncertainty: baseline PFAS occurrence (Section 4.4), affected population size and
demographic composition (Section 4.3), and the magnitude of PFAS concentration reductions
(Section 4.4). These analysis-specific sources of uncertainty are further described in Appendix L.
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Table 6-1: Quantified Sources of Uncertainty in Benefits Estimates
Source Description of Uncertainty
Health effect slope factors The slope factors that express the effects of serum PFOA, serum PFOS,
and THM4 on health outcomes (birth weight, CVD.1 RCC, and bladder
cancer) are based either on the EPA meta-analyses or medium- or high-
confidence studies that provide a central estimate and a confidence
interval. To characterize uncertainty, the EPA assumed that these slope
factors have a normal distribution with a mean set at the central estimate
and the standard deviation set at the estimated standard error.
RCC risk reduction cap The EPA implemented a cap on the cumulative RCC risk reductions due to
reductions in serum PFOA based on the population attributable fraction
(PAF) estimates for a range of cancers and environmental contaminants.
This parameter is treated as uncertain; its uncertainty is characterized by a
log-uniform distribution with a minimum set at the smallest PAF estimate
identified in the literature and a maximum set at the largest PAF estimate
identified in the literature. The central estimate for the PAF is the mean of
this log-uniform distribution.
Abbreviations: PFAS - per- and polyfluoroalkyl substances; PFOA - perfluorooctanoic acid; PFOS - perfluorooctane sulfonic
acid; RCC - renal cell carcinoma; PAF - population attributable fraction, THM4 - four regulated trihalomethanes.
Note:
aThe slope factors contributing to the CVD benefits analysis include the relationship between total cholesterol and PFOA and
PFOS, and the relationship between blood pressure and PFOS.
The EPA did not characterize the following sources of potentially quantifiable uncertainty in the
national-level quantified benefits analysis: U.S. population life tables (see Section 6.1.4), annual
all-cause and health outcome-specific incidence and mortality rates, coefficients of the CVD risk
model linking total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), and blood
pressure (BP) to cardiovascular event incidence (Goff et al., 2014), CVD risk model predictors
(e.g., share of smokers) estimated from health survey data, prevalence of CVD event history in
the U.S. population, distribution of CVD events by type, the estimated infant mortality-birth
weight slope factor (See Section 6.4.3.1), state-level distributions of infant births and infant
deaths over discrete birth weight ranges, the 200-g cap on birth weight changes estimated under
the rule, cost of illness estimates for all modeled non-fatal health outcomes, the Value of
Statistical Life reference value, the Value of Statistical Life income elasticity value used to
approximate the Value of Statistical Life income growth adjustment, and the gross domestic
product per capita projection used for the Value of Statistical Life income growth adjustment
(see Appendix J). The EPA expects that the sources listed in Table 6-1, in addition to uncertainty
surrounding the estimates of PFAS occurrence, affected population size, and the magnitude of
PFAS reduction, account for a substantial portion of the uncertainty in the benefits analysis.
6.1.3 Summary of Quantified National Benefits Estimates of the
Final Rule
This section provides summary outputs for the benefits analysis of the final rule as well as
Options la-c. Total annual benefits include human health risk reduction benefits for the health
outcomes listed in Section 6.1.1. The EPA annualized benefit values for each endpoint at a 2
percent discount rate. Both the expected value and the 90% confidence interval (CI) are
provided.
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As discussed in Section 2.1, for purposes of this analysis, the EPA is considering the benefits
analysis to be representative of the final rule utilizing individual MCLs for PFOA, PFOS, PFNA,
HFPO-DA, and PFHxS and a group MCL based on a HI for PFNA, HFPO-DA, PFHxS, and
PFBS.
Table 6-2: National Annualized Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt
each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million $2022)
Annualized CVD Benefits
Annualized Birth Weight
Benefits
Annualized RCC Benefits
Annualized Bladder
Cancer Benefits
Total Annualized Rule
Benefitsb
5th Percentile3
$140.66
$124.85
$61.33
$300.64
$920.91
2% Discount Rate
Expected Value
$606.09
$209.00
$353.90
$380.41
SI,549.40
95th Percentile"
$1,069.40
$292.78
$883.55
$463.74
$2,293.80
Abbreviations: CVD - cardiovascular disease; HI - hazard index; RCC - renal cell carcinoma.
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. 5th and 95th percentile values for total rule benefits are not additive across benefit category as the categories
are not completely correlated. Quantifiable benefits are increased under final rule table results relative to the other options
presented because of modeled PFHxS occurrence, which results in additional quantified benefits from co-removed PFOA and
PFOS.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
When using willingness to pay instead of cost of illness values to monetize cancer morbidity
impacts, annualized RCC benefits are $360.97 million, whereas annualized bladder cancer
benefits are $456.28 million (see Appendix O). If used in the national benefits analysis, these
willingness to pay estimates would result in approximately 83 million dollars additional
quantified benefits from those presented in Table 6-2, resulting in an increase in quantified
benefits of approximately 5.4%.
Additionally, in Appendix O, the EPA presents several sensitivity analyses, including an analysis
evaluating liver cancer benefits. Quantified benefits associated with reduction of liver cancer
from PFOS could increase total benefits from $1,549.40 million to $1,554.19 million (see
Appendix O).
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Table 6-3: National Annualized Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized CVD Benefits
$140.12
$602.72
$1,059.60
Annualized Birth Weight
Benefits
$124.82
$207.82
$291.00
Annualized RCC Benefits
$60.90
$351.79
$877.47
Annualized Bladder
Cancer Benefits
$301.06
$380.41
$462.73
Total Annualized Rule
Benefitsb
$913.05
$1,542.74
$2,280.10
Abbreviations: CVD - cardiovascular disease; RCC - renal cell carcinoma.
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. 5tli and 95th percentile values for total rule benefits are not additive across benefit category as the categories
are not completely correlated.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-4: National Annualized Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3
Expected Value
95th Percentile3
Annualized CVD
$119.18
$513.27
$900.13
Benefits
Annualized Birth Weight
$107.34
$178.97
$250.00
Benefits
Annualized RCC
$48.41
$290.72
$730.99
Benefits
Annualized Bladder
$246.48
$313.88
$383.32
Cancer Benefits
Total Annualized Rule
$768.55
$1,296.84
$1,919.30
Benefitsb
Abbreviations: CVD - cardiovascular disease; RCC - renal cell carcinoma.
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. 5th and 95th percentile values for total rule benefits are not additive across benefit category as the categories
are not completely correlated.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
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Table 6-5: National Annualized Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
(Million $2022)
2% Discount Rate
5th Percentile3 Expected Value 95th Percentile3
Annualized CVD Benefits $66.97 $267.56 $469.05
Annualized Birth Weight $60.24 $98.97 $137.75
Benefits
Annualized RCC Benefits $21.20 $137.30 $352.07
Annualized Bladder $120.97 $160.62 $202.14
Cancer Benefits
Total Annualized Rule $397.28 S664.45 $970.70
Benefitsb
Abbreviations: CVD - cardiovascular disease; RCC - renal cell carcinoma.
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. 5th and 95th percentile values for total rule benefits are not additive across benefit category as the categories
are not completely correlated.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
6.1.4 Life Table Modeling Background
The EPA uses a life table modeling approach to evaluate reductions in CVD and cancer risk.
This approach allows for internally consistent estimation of the path-dependent health effects for
regulatory alternatives, including annual incidence of CVD events or cancers among those
without prior history of these conditions, which is dependent on the population prevalence of
these chronic conditions and survival over time.
The life table is a statistical tool used to analyze the mortality experience of a population over
time. Specifically, using data on the age-specific probability of death and the initial population
size (e.g., 100,000 persons), the life table computes the number of persons surviving to a specific
age, the number of deaths occurring at a given age, the number of person-years lived at a given
age, the number of person-years lived beyond a given age, and age-specific life expectancy. The
details of standard life table calculations can be found in Anderson (1999).
The life table modeling approach extends the standard life table calculations to characterize
populations with respect to their chronic condition status and estimate transitions into the
subpopulation affected by the chronic condition.25 The EPA has previously used life table
approaches in regulatory analyses, including the analysis of lead-associated health effects in the
2015 Benefit and Cost Analysis for the Effluent Limitations Guidelines, Standards for the Steam
25 For example, a benefits model that evaluates the impact of contaminant exposure on incidence of cancer—a chronic
condition—would need to estimate the number of persons who are cancer free and, therefore, are eligible for the estimation of
new cancer risk (i.e., the risk of transition into the subpopulation affected by the chronic condition).
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Electric Power Generating Point Source Category (U.S. EPA, 2015), and PM2.5-related health
effects in revisions to the National Ambient Air Quality Standards for ground-level ozone (U.S.
EPA, 2008). Other examples of the use of a life table approach among federal agencies include
the EPA's analysis of Benefits and Costs of the Clean Air Act from 1990 to 2020 (U.S. EPA,
201 la) and the Occupational Safety and Health Administration (OSHA) assessment of lifetime
excess lung cancer, nonmalignant respiratory disease mortality, and silicosis risks from exposure
to respirable crystalline silica (OSHA, 2010; OSHA, 2016). Additionally, the agency sought
advice from the EPA SAB on the use of the life table in this application and they supported this
approach (U.S. EPA, 2022i). See Appendix G for details on application of the life table for the
CVD benefits analysis. See Appendix H for details on application of the life table for cancer
benefits analyses.
6.2 Overview of Benefit Categories
The EPA's decision to quantify health benefits resulting from reduced PFAS exposure in
drinking water is driven by the availability of PFAS-related occurrence estimates, PK models,
and information on exposure-response relationships. In this benefits analysis, the EPA either
quantitatively assesses or qualitatively discusses the health endpoints associated with exposure to
PFAS; the EPA assesses potential benefits quantitatively if (1) there is indicative evidence of a
relationship between exposure and a health effect response, (2) it is possible to link the health
outcome (e.g., CVD) to risk of a health effect (e.g., increased total cholesterol), and (3) there is
no overlap in effect with another quantified endpoint in the same outcome group.
The EPA describes occurrence modeling information in Section 4.4. Table 6-6 presents an
overview of the categories of health benefits expected to result from the implementation of
treatment that reduces PFAS levels in drinking water. The PFAS compounds that the EPA
identified as having indicative evidence linking exposure to a particular health endpoint, as well
as compounds having reliable PK models estimating the distribution to PFAS compounds
throughout the body, include PFOA, PFOS, and PFNA.26
As seen in Table 6-6, only a small subset of the potential health effects of reduced PFAS levels
in drinking water can be quantified and monetized. The monetized benefits evaluated in the
national-level quantified analysis for the final rulemaking include CVD, infant birth weight, and
RCC. The EPA also quantified benefits from reducing bladder cancer risk due to the reduction of
DBP formation as a result of the co-removal of organic carbon via the installation of additional
treatment for PFAS (Cantor et al., 1998; Crittenden et al., 1993; Regli et al., 2015; Weisman et
al., 2022). The EPA also quantified benefits associated with PFOS effects on liver cancer and
PFNA effects on birth weight in sensitivity analyses, available in appendices O and K,
respectively. The EPA notes that the agency anticipates additional benefits resulting from
installing drinking water treatment for PFAS chemicals and the subsequent removal of co-
occurring non-PFAS contaminants, including source water metals (e.g., chromium (VI)), organic
regulated and unregulated contaminants, (e.g., cyanotoxins (Foreman et al., 2021)), and certain
pesticides. The EPA was not able to quantify or monetize other benefits, including those related
to possible immune, hepatic, endocrine, metabolic, reproductive, musculoskeletal, many cancers,
26 The EPA relies on the serum PFNA calculator from Lu and Bartell (2020). PFNA effects are described as part of a sensitivity
analysis for birth weight-related benefits in Appendix K.
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or other outcomes discussed in Section 6.1.2. The EPA discusses these benefits qualitatively in
Sections 6.2.2.2 and 6.2.4.
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Table 6-6: Overview of Health Benefits Categories Considered in the Analysis of Changes in PFAS Drinking Water Levels
Health Outcome
PFAS Compounda'M
Benefits Analysis
Category
Endpoint
PFOA
PFOS
Discussed
Discussed
Quantitatively
Qualitatively
Lipids
Total cholesterol (TC)
X
X
X
High-density lipoprotein cholesterol (HDLC)
xc
xc
X
Low-density lipoprotein cholesterol (LDLC)
X
X
X
CVD
Blood pressure (BP)
X
X
Developmental
Birth weight
X
X
X
Small for gestational age (SGA), non-birth weight developmental
X
X
Hepatic
Alanine transaminase (ALT)
X
X
X
Immune
Antibody response (tetanus, diphtheria)
X
X
X
Metabolic
Leptin
X
X
Musculoskeletal
Osteoarthritis, bone mineral density
X
X
Cancer
Renal Cell Carcinoma (RCC)
X
X
Liver
X
xe
Testicular
X
X
Abbreviations: PFAS - per- and polyfluoroalkyl substances; PFOA - perfluorooctanoic acid; PFOS - perfluorooctane sulfonic acid
Notes:
^Fields marked with "X" indicate the PFAS compound for which there is evidence of an association with a given health outcome in humans.
bOutcomes with indicative evidence of an association between a PFAS compound and a health outcome are assessed quantitatively unless (1) there is an overlap within the same
outcome group (e.g., low density lipoprotein cholesterol overlaps with total cholesterol and small for gestational age overlaps with low birth weight), or (2) it is not possible to
link the outcome to the risk of the health effect (e.g., evidence is inconclusive regarding the relationship between PFOS exposure, leptin levels and associated health outcomes).
Such health outcomes are discussed qualitatively.
cAlthough evidence of associations between HDLC and PFOA and PFOS was mixed, certain individual studies reported robust associations in general adult populations (See
Section 6.2.2.1.2 on Cardiovascular Effects). Based on comments and recommendations from the EPA SAB (U.S. EPA, 2022i), the EPA assessed F1DLC in a sensitivity
analysis (see Appendix K).
''Note that only PFOA and PFOS effects were modeled in the assessment of benefits under the final rule. For another PFAS in the rule, PFNA, the best available finalized
analysis is based on studies published before 2018 (ATSDR, 2021). The EPA notes that new evidence since the release of the current, best available peer reviewed scientific
assessment for PFNA (ATSDR, 2021) provides further justification for the EPA's analysis of potential economic benefits of PFNA exposure reduction and avoided birth weight
effects. More recent epidemiological studies that evaluated PFNA and birth weight, including key studies modeled for PFOA and PFOS (Sagiv et al., 2018; Wikstrom et al.,
2020), as well as a recently published meta-analysis of mean birth weight that indicates the birth weight results for PFNA are robust and consistent, even if associations in some
studies may be small in magnitude (Wright et al., 2023). PFNA was modeled in a sensitivity analyses of birth weight benefits. This modeling relied on epidemiological studies
published before 2018, representing the best available finalized human health analysis of PFNA (ATSDR, 2021) and the approach by Lu and Bartell (2020) was used for
estimating PFNA blood serum levels resulting from PFNA exposures in drinking water (see Appendix K).
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Table 6-6: Overview of Health Benefits Categories Considered in the Analysis of Changes in PFAS Drinking Water Levels
Health Outcome
PFAS Compounda'M
Benefits Analysis
Category
Endpoint
PFOA PFOS
Discussed Discussed
Quantitatively Qualitatively
eLiver cancer benefits are not included in the national-level quantified benefits analysis. See Appendix O for the liver cancer benefits analysis results.
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In Table 6-7, the EPA presents an overview of the epidemiology and toxicology evidence
regarding the effects of exposure to PFAS compounds on health outcomes that were examined in
various EPA and Agency for Toxic Substances and Disease Registry's (ATSDR) assessments.
Health outcomes are classified as having:
• No evidence of an association27 (signified with a dot in the table);
• Evidence of an association noted as suggestive or slight (signified with an X in the table);
or
• Indicative evidence of an association (signified with a green-highlighted X in the table).
Health outcomes that have indicative (likely) associations and that are quantified in the benefits
analysis for the final rule are signified with X*. The EPA further describes the associations, and
supporting evidence of associations, in Section 6.2.2 for PFOA and PFOS and in Section 6.2.4
for additional PFAS compounds.
27 No evidence of an association is listed in instances where an absence of evidence precludes definitive conclusions about the
relationship between exposure and a given health effect or when there is evidence demonstrating that exposure does not result in
a given health effect.
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Table 6-7: Overview of Epidemiology and Toxicology Evidence of PFAS Effects on Health Outcomes
PFAS
Evidence Type
Health Outcomes
Data Source(s)
Notes
Lipids
CVD
Developmental
Hepatic
Immune
Endocrine
Metabolic
Renal
Reproductive
Musculoskeletal
Hematologic
Other non-
cancer
Cancer
u
H
111)1X
LDLC
BPa (human)
Heart histopathology (animal)
Birth weight
SGA, non-birth weight developmental
ALT (human) Organ weight,
cell death (animal)
AbRa (tetanus, diphtheria) (human)
Various endpoints (animal)
Thyroid hormone disruption
Leptin, body weight (human)Body fat,
body weight (animal)
Uric acid (human)
Organ weight (animal)
Gestational hypertension/pre-
eclampsia (human)Various endpoints
(animal)
Osteoarthritis, bone mineral density
Vitamin D levels, hemoglobin levels,
albumin levels
Other non-cancer
u
Testicular
Liver
Other
PFOA
Epi
X*
X
X
X*
X
X
X
X
X
X
X
X*
Xb
X
U.S. EPA 2024b, 2024d; ATSDR
2021; NASEM, 2022
Other non-cancer: neurological effects,
respiratory effects, gastrointestinal
Tox
X
X
X
X*
X
X
X
X
X
X
X
X
U.S. EPA 2024b, 2024d; ATSDR
2021
Other non-cancer: neurological effects,
respiratory effects, gastrointestinal
PFOS
Epi
X*
X
X
x*c
X
X
X
xd
X
X
X
U.S. EPA 2024a, 2024c; ATSDR
2021; NASEM, 2022
Other non-cancer: neurological effects,
gastrointestinal
Tox
X
X
X
X
X
X
X
X
X
U.S. EPA 2024a, 2024c; ATSDR
2021
Other non-cancer: neurological effects,
gastrointestinal
PFBA
Epi
IRIS Assessment 2022; ATSDR
2021; NASEM, 2022
No associations in humans
Tox
X
X
X
IRIS Assessment 2022; ATSDR 2021
Other non-cancer: ocular, respiratory
(ATSDR)
PFNA
Epi
X
X
xe
ATSDR 2021; NASEM, 2022
Other non-cancer: respiratory effects
Tox
X
X
X
X
X
X
ATSDR 2021
Other non-cancer: general toxicity
PFDA
Epi
X
X
X
X
X
X
X
X
ATSDR 2021; NASEM, 2022
Tox
X
X
X
X
X
X
ATSDR 2021
PFHxS
Epi
X
X
ATSDR 2021; NASEM, 2022
Tox
X
X
X
X
X
ATSDR 2021
Other non-cancer: respiratory effects
PFHxA
Epi
IRIS Assessment 2023; ATSDR
2021; NASEM, 2022
No associations in humans
Tox
X
X
X
X
IRIS Assessment 2023; ATSDR 2021
Other non-cancer: nervous (IRIS,
ATSDR), respiratory (ATSDR)
PFBS
Epi
EPA Human Health Toxicity Study
2021; ATSDR 2021; NASEM, 2022
No associations in humans
Tox
X
X
X
X
X
X
EPA Human Health Toxicity Study
2021; ATSDR 2021
Other non-cancer: respiratory effects
(ATSDR)
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PFAS
Evidence Type
Health Outcomes
Data Source(s)
Notes
Lipids
CVD
Developmental
Hepatic
Immune
Endocrine
Metabolic
Renal
Reproductive
Musculoskeletal
Hematologic
Other non-cancer
Cancer
TC
HDLC
LDLC
BPa (human)
Heart histopathology (animal)
Birth weight
SGA, non-birth weight developmental
ALT (human) Organ weight,
cell death (animal)
AbR (tetanus, diphtheria) (human) Various
endpoints (animal)
Thyroid hormone disruption
Leptin, body weight (human)Body fat,
body weight (animal)
Uric acid (human)
Organ weight (animal)
Gestational hypertension/pre-
eclampsia (human)Various endpoints (animal
Osteoarthritis, bone mineral density
Vitamin D levels, hemoglobin levels, albumin
levels
Other non-cancer
u
Testicular
Liver
Other
PFHpA
Epi
AT SDR 2021; NASEM, 2022
No associations in humans
Tox
AT SDR 2021
PFUnA
Epi
X
AT SDR 2021; NASEM, 2022
Tox
X
AT SDR 2021
PFDoDA
Epi
AT SDR 2021; NASEM, 2022
Tox
AT SDR 2021
FOSA
Epi
AT SDR 2021; NASEM, 2022
Tox
AT SDR 2021
HFPO-DAf
Epi
EPA HFPO-DA 2021 final toxicity
assessment
No data from epidemiology studies
Tox
X
X
X
X
X
X
X
X
EPA HFPO-DA 2021 final toxicity
assessment
Notes:
• Health outcomes examined, no evidence of associations (also noted as inadequate, or equivocal evidence).
X Health outcomes examined, slight or suggestive evidence of associations.
X Health outcomes examined, moderate or indicative evidence of associations (also noted as supports a hazard in IRIS assessments, evidence indicates, or evidence demonstrates).
X* Health outcomes quantified in benefits analyses, indicative evidence of associations.
[Blank cell] Health outcome was not examined.
a AbR: antibody response; BP: blood pressure; Epi: epidemiology; Tox: toxicology; RCC: renal cell carcinoma.
b Supported based on PFOA HESD (2016) and Bartell et al. (2021) meta-analysis.
c Supported byDzierlenga eta I. (2020) meta-analysis.
d Also supported by recent meta-analysis from Gao et al. (2021) (PFOS and preeclampsia risk).
e Also supported by recent meta-analysis from Wright etal. (2023) (PFNAand birth weight).
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6.2.1 Availability of Pharmacokinetic (PK) Models
PK models are tools for quantifying the relationship between external measures of exposure and
internal measures of dose. The EPA evaluated existing PFOA and PFOS PK models for their
utility in predicting internal doses for use in both cancer and non-cancer dose-response
assessments (U.S. EPA, 2024e; U.S. EPA, 2024f). PFOA and PFOS PK models typically take
one of three forms:
• Classical compartment models, where modelers define the body as a one- or two-
compartment system with volumes and intercompartmental transfer fit specifically to the
PFAS PK dataset. The most common approach for prediction of serum PFAS levels is to
apply a simple single-compartment model.
• Modified compartment models, where modelers attempt to characterize absorption,
distribution, metabolism, and/or excretion through protein-binding, cardiac output, and
known renal elimination. These models also rely on fitting PFAS data to non-
physiological parameters.
• Physiological-based pharmacokinetic (PBPK) models, where tissues and organs of the
body are described as physiological-based compartments. In these models, transport
between compartments is informed by measures of blood flow and tissue perfusion.
These models are fit to time-course concentration data.
The EPA's Final Human Health Toxicity Assessment for PFOA (U.S. EPA, 2024f) and Final
Human Health Toxicity Assessment for PFOS (U.S. EPA, 2024e)28 describe existing PFOA and
PFOS PK models and modifications made to existing PK models to derive points of departure in
the assessments. Briefly, the EPA updated a modified single-compartment PK model for adult
males and females to estimate blood serum PFOA and PFOS concentrations. These models are
described in Section 4.1.3.2 of U.S. EPA (2024e; 2024f), and the application of these models in
health risk benefits modeling is described in Section 6.3.
6.2.2 Benefits of PFOA and PFOS Exposure Reduction
This section provides an overview of the potential health benefits of reduced exposure to PFOA
and PFOS in drinking water. These benefits are expected to be realized as avoided adverse health
effects as a result of the final NPDWR, in addition to the benefits that the EPA has quantified.
The EPA identified a wide range of potential health effects associated with exposure to PFOA
and PFOS using five comprehensive federal government health effects assessments that
summarize the recent literature on PFAS (mainly PFOA and PFOS, although many of the same
health effects have been observed for the other PFAS in this rule) exposure and its health
impacts: the EPA's Health Effects Support Document for PFOA and Health Effects Support
Document for PFOS, hereafter referred to as the EPA HESDs (U.S. EPA, 2016e; U.S. EPA,
2016f); the EPA's Final Human Health Toxicity Assessments for PFOA and PFOS (U.S. EPA,
2024e; U.S. EPA, 2024f); and the U.S. Department of Health and Human Services ATSDR
28 For brevity, these documents are described throughout as the EPA's Final Human Health Toxicity Assessments for PFOA and
PFOS.
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Toxicological Profile for Perfluoroalkyls (ATSDR, 2021). Each source presents comprehensive
literature reviews on adverse health effects associated with PFOA and PFOS.
The most recent literature reviews on PFAS exposures and health impacts, which are included in
the EPA's Final Human Health Toxicity Assessments for PFOA and PFOS, describe the weight
of evidence supporting PFOA and PFOS associations with health outcomes as either
demonstrative, indicative (likely), suggestive, inadequate, or strong evidence supportive of no
effect according to the evidence integration judgments outlined in the Office of Research and
Development (ORD) Staff Handbook for Developing IRIS Assessments (U.S. EPA, 2022g; U.S.
EPA, 2024e; U.S. EPA, 2024f). For the purposes of the reviews conducted to develop the Final
Human Health Toxicity Assessments for PFOA and PFOS, an association is deemed
demonstrative when there is a strong evidence base demonstrating that the chemical exposure
causes a health effect in humans. The association is deemed indicative (likely) when the
evidence base indicates that the chemical exposure likely causes a health effect in humans,
although there might be outstanding questions or limitations that remain, and the evidence is
insufficient for the higher conclusion level. The association is suggestive if the evidence base
suggests that the chemical exposure might cause a health effect in humans, but there are very few
studies that contributed to the evaluation, the evidence is very weak or conflicting, or the
methodological conduct of the studies is poor. The association is inadequate if there is a lack of
information or an inability to interpret the available evidence (e.g., findings across studies). The
association supports no effect when extensive evidence across a range of populations and
exposure levels has identified no effects/associations. Note that the EPA considered information
available as of September 2023 for the analyses presented herein. Section 6.2.2.1 discusses
PFOA and PFOS-related health effects that were considered quantitatively (modeled and
monetized) in the benefits analysis, while Section 6.2.2.2 discusses PFOA and PFOS-related
health effects that were considered only qualitatively in the benefits analysis. These sections
specify whether evidence is based on animal (toxicology) or human (epidemiology) studies, or
both.
6.2.2.1 Quantitative Benefits of PFOA and PFOS Exposure Reduction
In this section, the EPA discusses some of the health benefits expected to result from reduced
exposure to PFOA and PFOS in drinking water. These benefits are expected to be realized as
avoided adverse health effects as a result of the final NPDWR and are quantified in Sections 6.4,
6.5, and 6.6 respectively.
6.2.2.1.1 Developmental Effects
Exposure to PFOA and PFOS is linked to developmental effects such as decreased infant birth
weight, birth length, head circumference at birth, and other effects (Steenland et al., 2018;
Dzierlenga et al., 2020; Verner et al., 2015; U.S. EPA, 2016e; U.S. EPA, 2016f; Negri et al.,
2017; Waterfield et al., 2020; U.S. EPA, 2024e; U.S. EPA, 2024f). Low birth weight (LBW) is
an important health outcome because it is a significant factor in survival rates and medical care
costs among infants (ATSDR, 2021). Infants are exposed prenatally to PFOA and PFOS through
maternal serum via the placenta (U.S. EPA, 2024e, U.S. EPA, 2024f).
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Because data on the cost of incremental changes in birth weight are available from Klein and
Lynch (2018), the EPA selected decreased birth weight as a key developmental health effect
when assessing the economic impacts of reduced PFOA and PFOS exposures. Epidemiology
studies on PFOA were associated with an increased risk of decreased BW in infants with PFOA
exposures (U.S. EPA, 2024f). Similarly, epidemiology studies on PFOS were associated with an
increased risk of decreased BW in infants with increasing PFOS exposures (U.S. EPA, 2024e).
As described in the toxicity assessments for PFOA and PFOS (see Section 3.4.4.1.4 of the final
toxicity assessments; U.S. EPA, 2024e; U.S. EPA, 2024f), many epidemiology studies
evaluating the association between maternal serum PFOA/PFOS and birth weight reported
inverse associations (i.e., increased exposure is associated with decreased birth weight) (Darrow
et al., 2013; Verner et al., 2015; Govarts et al., 2016; Negri et al., 2017; Starling et al., 2017;
Sagiv et al., 2018; Chu et al., 2020; Dzierlenga et al., 2020; Wikstrom et al., 2020; Yao et al.,
2021).29 Toxicology studies on PFOA further supported an association between decreased
offspring weight and PFOA exposure; several studies conducted on rodents showed decreased
fetal and pup weight with gestational PFOA exposure (U.S. EPA, 2024f). Toxicology studies
also reported that increased exposure to PFOS was associated with decreased body weight in
rodent fetuses and pups (U.S. EPA, 2024e). For additional details on developmental effects
studies and their individual outcomes, see Chapter 3.4.4 (Developmental) in U.S. EPA (2024e)
and U.S. EPA (2024f). See Section 6.4 for the EPA's analysis of avoided infant birth weight
impacts estimated as attributable to reduced PFOA and PFOS exposure from the final rule.
6.2.2.1.2 Cardiovascular Effects
CVD is one of the leading causes of premature mortality in the U.S. (D'Agostino et al., 2008;
Goff et al., 2014; Lloyd-Jones et al., 2017). As discussed in the EPA's Final Human Health
Toxicity Assessments for PFOA and PFOS, exposure to PFOA and PFOS through drinking
water contributes to increased serum PFOA and PFOS concentrations and elevated levels of TC,
as well as suggestive changes in levels of HDLC and elevated levels of systolic BP (U.S. EPA,
2024e; U.S. EPA, 2024f). Changes in TC, HDLC, and BP are associated with changes in
incidence of CVD events such as myocardial infarction (MI, i.e., heart attack), ischemic stroke
(IS), and cardiovascular mortality occurring in populations without prior CVD event experience
(D'Agostino et al., 2008; Goff et al., 2014; Lloyd-Jones et al., 2017).
Overall, epidemiology evidence indicated a positive association between PFOS/PFOA exposure
and TC levels (i.e., increased exposure is associated with increased TC levels) (ATSDR, 2021;
U.S. EPA, 2024e; U.S. EPA, 2024f). Epidemiology studies observed relatively consistent
positive associations between PFOA and LDLC (U.S. EPA, 2024f). Most epidemiology studies
on PFOS exposure reported a positive association between exposure and TC levels in the general
population (ATSDR, 2021; U.S. EPA, 2024e). There was also some evidence of this association
in children and pregnant women (U.S. EPA, 2024e). Consistent positive associations were also
observed between PFOS and LDLC in general population adults. Toxicology studies often
reported decreases in serum lipids from oral exposure to PFOA and PFOS (U.S. EPA, 2024e;
U.S. EPA, 2024f). Although the biological significance of the decrease in various serum lipid
29 Recent evidence indicates that relationships between maternal serum PFOA/PFOS and birth weight may be impacted by
changes in pregnancy hemodynamics, however exact patterns are not completely understood (Sagiv et al., 2018; Steenland et al.,
2018).
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levels observed in animal models regardless of species, sex, or exposure paradigm is unclear,
these effects do indicate a disruption in lipid metabolism, which is consistent with effects
observed in humans. For additional details on the TC studies and their individual outcomes, see
Chapter 3.4.3 (Cardiovascular) in U.S. EPA (2024e) and U.S. EPA (2024f).
Existing epidemiology and toxicology studies provided inconsistent evidence of associations
between PFOA and PFOS exposures and HDLC levels, with a mix of positive and some inverse
associations in adult populations (ATSDR, 2021; U.S. EPA, 2024e; U.S. EPA, 2024f). Two
studies reported a positive association between PFOA and HDLC in pregnant women (Starling et
al., 2017; Dalla Zuanna et al., 2021). In children, prenatal exposure to PFOA was associated with
lower HDLC in some studies, especially in boys, whereas childhood exposure was not
consistently associated with higher HDLC (ATSDR, 2021; U.S. EPA, 2024f). Similarly, studies
did not report consistent associations between PFOS and HDLC levels (ATSDR, 2021; U.S.
EPA, 2024e). Most of the evidence in adults involved cross-sectional assessments, although
associations between PFOS and lower HDLC were also observed in the cohort study by Lin et al.
(2019). Studies examining PFOS and HDLC in pregnant women provided mixed evidence (U.S.
EPA, 2024e). Although evidence of associations between PFOA and PFOS exposures and
HDLC is mixed, certain individual studies reported robust associations in general adult
populations. Based on comments and recommendations from the EPA SAB on the EPA's
analysis of CVD risk reductions resulting from changes in PFOA/PFOS exposures (U.S. EPA,
2021a), the EPA assessed HDLC in a sensitivity analysis (see Appendix K). For additional
details on the HDLC studies and their individual outcomes, see Chapter 3.4.3 (Cardiovascular) of
U.S. EPA (2024e) and U.S. EPA (2024f).
Epidemiology studies observed inconsistent associations between PFOA exposure and BP
(ATSDR, 2021; U.S. EPA, 2024f). In adults, some epidemiology studies reported positive
associations between PFOA exposure and changes in BP or risk of hypertension (defined as
elevated BP) (U.S. EPA, 2024f). Studies in children, adolescents, and pregnant women suggested
no association between PFOA exposure and elevated BP (U.S. EPA, 2024f). In adults, there was
consistent evidence of positive associations between PFOS exposure and BP, although the results
were not always consistent between systolic BP and diastolic BP, and one study reported an
inverse association (U.S. EPA, 2024e). However, there was overall consistent evidence of an
association between PFOS and BP in studies conducted in general adult populations (U.S. EPA,
2024e). Evidence for associations between PFOS exposure and BP in children and adolescents
was limited and did not suggest an association with elevated BP (U.S. EPA, 2024e). However,
exposure duration was a limitation in these studies, and evidence of an association between
PFOS and increased risk of hypertension, specifically, was limited and inconsistent (U.S. EPA,
2024e). Evidence of associations between BP and PFOS in animal toxicological studies was
mixed (U.S. EPA, 2024e). For additional details on the BP studies and their individual outcomes,
see Chapter 3.4.3 (Cardiovascular) in U.S. EPA (2024e) and U.S. EPA (2024f).
Given the breadth of evidence linking PFOA and PFOS exposure to effects on TC and BP in
general adult populations, the EPA quantified public health impacts of changes in these well-
established CVD risk biomarkers (D'Agostino et al., 2008; Goff et al., 2014; Lloyd-Jones et al.,
2017) by estimating changes in incidence of several CVD events. Specifically, the EPA assumed
that PFOA/PFOS-related changes in TC and BP had the same effect on the CVD risk as the
changes unrelated to chemical exposure and used the Pooled Cohort Atherosclerotic
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Cardiovascular Disease (ASCVD) model (Goff et al., 2014) to evaluate their impacts on the
incidence of MI, IS, and cardiovascular mortality occurring in populations without prior CVD
event experience (see Section 6.5). The EPA observed that the direct evidence of associations
between PFOA/PFOS exposure and CVD risk was limited (U.S. EPA, 2024e; U.S. EPA, 2024f),
with mixed findings reported by one high-quality longitudinal epidemiology study (Mattsson et
al., 2015) and four medium-quality cross-sectional epidemiology studies (Huang et al., 2018;
Shankar et al., 2012; Hutcheson et al., 2019; Fry & Power, 2017). However, inconclusive
evidence of the direct association between PFOA/PFOS exposure and CVD effects from a
limited collection of studies does not imply the absence of such an association. Future analyses
of CVD effects using large longitudinal studies, such as the ones used to develop the ASCVD
model (Goff et al., 2014), could help elucidate whether there is a consistent direct association
between PFOA/PFOS and CVD risk. The EPA notes that the SAB review also supported this
approach in consideration of impact of PFAS on CVD risk (U.S. EPA, 2022i). See Section 6.5
for EPA's analysis of reduced CVD impacts as a result of reduced PFOA and PFOS exposure
from the final rule.
6.2.2.1.3 Cancer Effects
Data on the association between PFOA exposure and kidney cancer (i.e., RCC), particularly
from epidemiological studies, indicate a positive association between exposure and increased
risk of RCC (CalEPA, 2021; U.S. EPA, 2016f; AT SDR, 2021; U.S. EPA, 2024f). PFOA
exposure effects on RCC were shown in two occupational population studies (Raleigh et al.,
2014; Steenland & Woskie, 2012) and two high-exposure community studies (Vieira et al., 2013;
Barry et al., 2013). A recent study of the relationship between PFOA and RCC in the U.S.
general population found strong evidence of a positive association between exposure to PFOA
and RCC in humans (Shearer et al., 2021). A meta-analysis of epidemiological literature also
concluded that there was an increased risk of kidney cancer associated with increased PFOA
serum concentrations (Bartell & Vieira, 2021). In the EPA's Final Human Health Toxicity
Assessment for PFOA, the agency reviewed the weight of the evidence and determined that
PFOA is Likely to Be Carcinogenic to Humans, as "the evidence is adequate to demonstrate
carcinogenic potential to humans but does not reach the weight of evidence for the descriptor
Carcinogenic to Humans" (U.S. EPA, 2005c; U.S. EPA, 2024f).30 This determination is based on
the evidence of kidney and testicular cancer in humans and Ley dig cell tumors (LCTs),
pancreatic acinar cell tumors (PACTs), and hepatocellular tumors in rats (U.S. EPA, 2024f). See
Section 6.6 for the EPA's analysis of the benefits of reduced RCC as a result of reduced PFOA
exposures from the final rule.
Evidence of the association between PFOS exposure and kidney cancer was inconclusive; the
small number and limited scope of studies were inadequate to make definitive conclusions (U.S.
EPA, 2016e; U.S. EPA, 2024e). One recent study observed an association between PFOS and an
increased risk of RCC in the highest exposed quartile and per doubling of PFOS concentration
(Shearer et al., 2021; U.S. EPA, 2024e). However, the association was no longer statistically
30 This determination is comparable to the International Agency for Research on Cancer (IARC) determination, which classified
PFOA as "carcinogenic to humans" based on "sufficient" evidence for cancer in the toxicology literature and "strong"
mechanistic evidence in the epidemiology literature. The IARC also determined that PFOS was classified as "possibly
carcinogenic to humans" based on "strong" mechanistic evidence (Zahm et al., 2024).
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significant after adjusting for other PFAS (Shearer et al., 2021). The EPA did not report any
PFOA or PFOS toxicology studies specifically relating to RCC, although there was evidence of
other cancer types in rodent models treated with PFOA or PFOS (U.S. EPA, 2024e; U.S. EPA,
2024f). The EPA did not quantify benefits associated with PFOS and RCC and the agency notes
that the national quantifiable benefits analysis includes results for PFOA effects on RCC only.
The EPA's benefits analysis for avoided RCC cases from reduced PFOA exposure is detailed in
Section 6.6.
The EPA found evidence of a positive association between PFOS exposure and hepatocellular
tumors in animal studies. Butenhoff et al. (2012)/Thomford (2002) reported a statistically
significant increase in combined hepatocellular adenomas and carcinomas tumor incidence in
female Sprague-Dawley rats exposed to PFOS. There was also a statistically significant increase
in hepatocellular adenomas in males from the highest dose group. The study reported a
statistically significant trend of increased incidence with increasing PFOS concentrations across
dose groups in both sexes. Additionally, recently published studies reporting associations
between PFOS exposure and hepatocellular carcinoma in humans (Goodrich et al., 2022; Cao et
al., 2022) further strengthen these findings in rats and support the cancer classification of Likely
to be Carcinogenic to Humans for PFOS (U.S. EPA, 2024e). Thomford (2002) also reported a
statistically significant trend of increased incidence of pancreatic islet cell carcinomas with
increasing PFOS doses. The EPA reviewed the weight of the evidence and determined that PFOS
Is Likely to Be Carcinogenic to Humans, as "the evidence is adequate to demonstrate
carcinogenic potential to humans but does not reach the weight of evidence for the descriptor
Carcinogenic to Humans" (U.S. EPA, 2005c; U.S. EPA, 2024e). The EPA evaluated the effects
of the final rule on liver cancer using relationships between PFOS exposure and liver cancer in
female rats in Appendix O.
For additional details on cancer studies and their individual outcomes, see Chapter 3.5 (Cancer)
in U.S. EPA (2024e) and U.S. EPA (2024f).
6.2.2.2 Nonquantifiable Benefits of PFOA and PFOS Exposure Reduction
In this section, the EPA qualitatively discusses the potential health benefits resulting from
reduced exposure to PFOA and PFOS in drinking water. These nonquantifiable benefits are
expected to be realized as avoided adverse health effects as a result of the final NPDWR, in
addition to the benefits that the EPA has quantified. The EPA anticipates additional benefits
associated with developmental, cardiovascular, liver, immune, endocrine, metabolic,
reproductive, musculoskeletal, and carcinogenic effects beyond those benefits that the EPA has
quantified. The evidence for these adverse health effects is briefly summarized below.
6.2.2.2.1 Developmental Effects
In addition to the infant birth weight impacts that the EPA has quantified (see Section 6.4), small
for gestational age (SGA) is a developmental health outcome of interest when studying potential
effects of PFOA/PFOS exposure, because infants who are SGA face increased health risks
during pregnancy and delivery as well as post-delivery (Osuchukwu & Reed, 2022). The
majority of epidemiology studies indicated increased risk of SGA with PFOA/PFOS exposure,
although some studies reported null results (U.S. EPA, 2024e; U.S. EPA, 2024f). For instance,
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some studies suggested a potentially positive association between PFOA exposure and SGA
(Govarts et al., 2018; Lauritzen et al., 2017; Y. Wang et al., 2016; Souza et al., 2020; Wikstrom
et al., 2020; Chang et al., 2022; U.S. EPA, 2024f). In addition to decreases in offspring weight,
toxicology studies on PFOA and PFOS exposures in rodents demonstrated relationships with
multiple other developmental endpoints including increased offspring mortality, decreased
maternal body weight and body weight change, skeletal and soft tissue effects, and delayed eye-
opening (U.S. EPA, 2024e; U.S. EPA, 2024f). For additional details on developmental studies
and their individual outcomes, see Chapter 3.4.4 (Developmental) in U.S. EPA (2024e) and U.S.
EPA (2024f).
6.2.2.2.2 Cardiovascular Effects
In addition to the CVD effects that the EPA quantified associated with changes in TC and BP
from exposure to PFOA and PFOS (see Section 6.5), available evidence suggests an association
between exposure to PFOA and PFOS and increased LDLC (ATSDR, 2021; U.S. EPA, 2024e;
U.S. EPA, 2024f). High levels of LDLC are known as the "bad" cholesterol because it can lead
to the buildup of cholesterol in the arteries, which can raise the risk of heart disease and stroke.
Epidemiology studies showed a positive association between PFOA and PFOS exposure and
LDLC levels in adults and children (U.S. EPA, 2024e; U.S. EPA, 2024f). In particular, the
evidence suggested positive associations between serum PFOA and PFOS levels and LDLC
levels in adolescents ages 12-18, while positive associations between serum levels and LDLC
levels in younger children were observed only for PFOA (ATSDR, 2021). Additionally,
available evidence supports a relatively consistent positive association between PFOA or PFOS
and LDLC in adults, especially those who are obese or prediabetic. Associations with other
lipoprotein cholesterol known to increase cardiovascular risks were also positive, which
increased confidence in the findings for LDLC. Available evidence regarding the impact of
PFOA and PFOS exposure on pregnant women was too limited for the EPA to determine an
association (U.S. EPA, 2024e; U.S. EPA, 2024f). Toxicology studies generally reported
alterations in serum lipid levels in mice and rats following oral exposure to PFOA (U.S. EPA,
2024f) or PFOS (U.S. EPA, 2024e), indicating a disruption in lipid metabolism, which is
coherent with effects observed in humans. For additional details on LDLC studies and their
individual outcomes, see Chapter 3.4.3 (Cardiovascular) in U.S. EPA (2024e) and U.S. EPA
(2024f).
6.2.2.2.3 Hepatic Effects
Several biomarkers can be used clinically to diagnose liver diseases, including alanine
aminotransferase (ALT). Serum ALT measures are considered a reliable indicator of impaired
liver function because increased serum ALT is indicative of leakage of ALT from damaged
hepatocytes (Boone et al., 2005; Z. Liu et al., 2014; U.S. EPA, 2002). Additionally, evidence
from both human epidemiological and animal toxicological studies indicates that increased
serum ALT is associated with liver disease (Ioannou, Boyko, & Lee, 2006; Ioannou, Weiss, et
al., 2006; Kwo et al., 2017; Roth et al., 2021). Human epidemiological studies have
demonstrated that even low magnitude increases in serum ALT can be clinically significant
(Mathiesen et al., 1999; J. H. Park et al., 2019). Additionally, numerous studies have
demonstrated an association between elevated ALT and liver-related mortality (reviewed by
Kwo et al., 2017). Furthermore, the American Association for the Study of Liver Diseases
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(AASLD) recognizes serum ALT as an indicator of overall human health and mortality (W. R.
Kim et al., 2008). Epidemiology data provides consistent evidence of a positive association
between PFOS/PFOA exposure and ALT levels in adults (ATSDR, 2021; U.S. EPA, 2024e; U.S.
EPA, 2024f). Studies of adults showed consistent evidence of a positive association between
PFOA exposure and elevated ALT levels at both high exposure levels and exposure levels
typical of the general population (U.S. EPA, 2024f). There is also consistent epidemiology
evidence of associations between PFOS and elevated ALT levels. A limited number of studies
reported inconsistent evidence on whether PFOA/PFOS exposure is associated with increased
risk of liver disease (U.S. EPA, 2024e). It is also important to note that while evaluation of direct
liver damage is possible in animal studies, it is difficult to obtain biopsy-confirmed histological
data in humans. Therefore, liver injury is typically assessed using serum biomarkers of
hepatotoxicity (Costello et al., 2022). Associations between PFOS/PFOA exposure and ALT
levels in children were less consistent than in adults (U.S. EPA, 2024e; U.S. EPA, 2024f).
PFOA toxicology studies showed increases in ALT and other serum liver enzymes across
multiple species, sexes, and exposure paradigms (U.S. EPA, 2024f). Toxicology studies on the
impact of PFOS exposure on ALT also reported increases in ALT and other serum liver enzyme
levels in rodents (U.S. EPA, 2024e). Several studies in animals also reported increases in the
incidence of liver lesions or cellular alterations, such as hepatocellular cell death (U.S. EPA,
2024e; U.S. EPA, 2024f). For additional details on the ALT studies and their individual
outcomes, see Chapter 3.4.1 (Hepatic) in U.S. EPA (2024e) and U.S. EPA (2024f).
6.2.2.2.4 Immune Effects
Proper antibody response helps maintain the immune system by recognizing and responding to
antigens. The available evidence indicates a relationship between PFOA exposure and
immunosuppression; epidemiology studies showed suppression of at least one measure of the
antibody response for tetanus and diphtheria among people with higher prenatal and childhood
serum concentrations of PFOA (ATSDR, 2021; U.S. EPA, 2024f). Data reporting on
associations between PFOA exposure and antibody response to vaccinations other than tetanus
and diphtheria (i.e., rubella and hand, foot, and mouth disease) are limited but supportive of
associations between PFOA and decreased immune response in children (U.S. EPA, 2024f).
Available studies supported an association between PFOS exposure and immunosuppression in
children, where increased PFOS serum levels were associated with decreased antibody
production in response to tetanus, diphtheria, and rubella vaccinations (U.S. EPA, 2024e).
Studies reporting associations between PFOA or PFOS and immunosuppression in adults are less
consistent, though this may be due to a lack of high confidence data (U.S. EPA, 2024e; U.S.
EPA, 2024f). Toxicology evidence suggested that PFOA and PFOS exposure results in effects
similarly indicating immune suppression, such as reduced response of immune cells to
challenges (e.g., reduced natural killer cell activity and immunoglobulin production) (U.S. EPA,
2024e; U.S. EPA, 2024f). For additional details on immune studies and their individual
outcomes, see Chapter 3.4.2 (Immune) in U.S. EPA (2024e) and U.S. EPA (2024f).
Because evidence indicates that PFOA or PFOS exposure results in immune effects, the EPA
expects those effects to potentially impact immune response to other diseases. For instance, the
coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) rapidly evolved into a global pandemic after its first report in
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Wuhan, China, in December 2019. A few recent studies have considered the association between
PFOA and PFOS exposure and COVID-19 infection, severity, or mortality (Catelan et al., 2021;
Grandjean et al., 2020; Ji et al., 2021).
A case-control study in China (Ji et al., 2021) showed increased risks for COVID-19 infection
with high urinary PFOS, PFOA, and total PFASs after adjusting for potential confounding
factors including age, gender, diabetes, cardiovascular disease, and urine albumin-to-creatinine
ratio. Adjusted odds ratios (ORs) were 1.94 (95% CI: 1.39, 2.96) for PFOS and 2.73 (95% CI:
1.71, 4.55) for PFOA. Using metabolome-wide association analysis, Ji et al. (2021) found that
PFOA and PFOS exposure in COVID-19 patients was associated with metabolic disturbances in
biochemical pathways involved in mitochondria stress signaling and the regulation of immune
function, including fatty acid oxidation, tricarboxylic acid cycle, eicosanoid, and kynurenine
pathways. One cross-sectional study in Denmark (Grandjean et al., 2020) observed no
association between PFOA or PFOS concentrations and severity of COVID-19 development.31 In
a spatial ecological analysis, Catelan et al. (2021) showed higher mortality risk for COVID-19 in
a population heavily exposed to PFAS (including PFOA, PFOS, PFHxS, PFBS, PFBA, PFPeA,
PFHxA, and PFHpA) via drinking water in Veneto, Italy.
Although these studies provide a suggestion of possible associations, the body of evidence does
not permit any conclusions about the relationship between COVID-19 and exposures to PFAS.
6.2.2.2.5 Endocrine Effects
Elevated circulating thyroid hormone levels can accelerate metabolism and cause irregular
heartbeat; low levels of thyroid hormones can cause neurodevelopmental effects, tiredness,
weight gain, and increased susceptibility to the common cold. There is suggestive evidence of a
positive association between PFOA/PFOS exposure and thyroid hormone disruption (ATSDR,
2021; U.S. EPA, 2024e; U.S. EPA, 2024f). Epidemiology studies reported inconsistent evidence
regarding associations between PFOA and PFOS exposure and general endocrine outcomes, such
as thyroid disease, hypothyroidism, and hypothyroxinemia (U.S. EPA, 2024e; U.S. EPA, 2024f).
However, for PFOA, epidemiological studies reported suggestive evidence of positive
associations for serum levels of thyroid stimulating hormone (TSH) and the thyroid hormone
triiodothyronine (T3) in adults, and the thyroid hormone thyroxine (T4) in children (U.S. EPA,
2024e; U.S. EPA, 2024f). For PFOS, epidemiological studies reported suggestive evidence of
positive associations for TSH in adults, positive associations for T3 in children, and inverse
associations for T4 in children (U.S. EPA, 2024e). Toxicology studies indicated that PFOA and
PFOS exposure leads to decreases in serum thyroid hormone levels32 and adverse effects to the
endocrine system (ATSDR, 2021; U.S. EPA, 2024b; U.S. EPA, 2024e; U.S. EPA, 2024f).
Overall, changes in serum thyroid hormone levels in animals indicate PFOS and PFOA toxicity
potentially relevant to humans (U.S. EPA, 2024e; U.S. EPA, 2024f). For additional details on
endocrine effects studies and their individual outcomes, see Appendix C.2 (Endocrine) in U.S.
EPA (2024a) and U.S. EPA (2024b).
31 Note that the authors found that PFBA exposure was associated with increasing severity of COVID-19.
32 Decreased thyroid hormone levels are associated with effects such as changes in thyroid and adrenal gland weight, hormone
fluctuations, and organ histopathology, as well as adverse neurodevelopmental outcomes (ATSDR, 2021; U.S. EPA, 2024e).
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6.2.2.2.6 Metabolic Effects
Leptin is a hormone that, along with adiponectin, can be a marker of adipose tissue dysfunction.
Chronic high levels of leptin lead to leptin resistance that mirrors many of the characteristics
associated with diet-induced obesity, including reduced leptin receptors and diminished
signaling. Therefore, high leptin levels are associated with higher body fat mass, a larger size of
individual fat cells, overeating, and inflammation (e.g., of adipose tissue, the hypothalamus,
blood vessels, and other areas). Evidence suggests an association between PFOA exposure and
leptin levels in the general adult population (ATSDR, 2021; U.S. EPA, 2024f). Based on a
review of human epidemiology studies, evidence of associations between PFOS and metabolic
outcomes appears inconsistent, but in some studies, positive associations were observed between
PFOS exposure and leptin levels (U.S. EPA, 2024e). Studies examining newborn leptin levels
did not find associations with maternal PFOA levels (ATSDR, 2021). Maternal PFOS levels
were also not associated with alterations in leptin levels (ATSDR, 2021). For additional details
on metabolic effect studies and their individual outcomes, see Appendix C.3
(Metabolic/Systemic) in U.S. EPA (2024a) and U.S. EPA (2024b).
6.2.2.2.7 Reproductive Effects
Studies of the reproductive effects from PFOA/PFOS exposure have focused on associations
between exposure to these contaminants and increased risk of gestational hypertension and
preeclampsia in pregnant women (ATSDR, 2021; U.S. EPA, 2024e; U.S. EPA, 2024f).
Gestational hypertension (high BP during pregnancy) can lead to fetal problems such as poor
growth and stillbirth. Preeclampsia—instances of gestational hypertension where the mother also
has increased levels of protein in her urine—can similarly pose significant risks to both the fetus
and mother. Risks to the fetus include impaired fetal growth due to the lack of oxygen and
nutrients, stillbirth, preterm birth, and infant death (National Institutes of Health, 2017). Even if
born full term, the infant may be at risk for later problems such as diabetes, high blood pressure,
and congestive heart failure. Effects of preeclampsia on the mother may include kidney and liver
damage, blood clotting problems, brain injury, fluid on the lungs, seizures, and mortality
(National Institutes of Health, 2018). The epidemiology evidence yields mixed (positive and
null) associations, with some suggestive evidence supporting positive associations between
PFOA/PFOS exposure and both preeclampsia and gestational hypertension (ATSDR, 2021; U.S.
EPA, 2024e; U.S. EPA, 2024f). For additional details on reproductive effects studies and their
individual outcomes, see Appendix C.l (Reproductive) in U.S. EPA (2024a) and U.S. EPA
(2024b).
6.2.2.2.8 Musculoskeletal Effects
Adverse musculoskeletal effects such as osteoarthritis and decreased bone mineral density
impact bone integrity and cause bones to become brittle and more prone to fracture. The
available epidemiology evidence suggests that PFOA exposure may be linked to decreased bone
mineral density, bone mineral density relative to bone area, height in adolescence, osteoporosis,
and osteoarthritis (ATSDR, 2021; U.S. EPA, 2024f). Some studies found that PFOA/PFOS
exposure was linked to osteoarthritis, in particular among women under 50 years of age
(ATSDR, 2021). There is limited evidence from studies pointing to effects of PFOS on skeletal
size (height), lean body mass, and osteoarthritis (U.S. EPA, 2024e). Evidence from some studies
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suggests that PFOS exposure has a harmful effect on bone health, particularly measures of bone
mineral density, with more statistically significant effects occurring among females (U.S. EPA,
2024e). However, other reviews reported mixed findings on the effects of PFOS exposure
including decreased risk of osteoarthritis, increased risk for some demographic subgroups, or no
association (ATSDR, 2021). For additional details on musculoskeletal effects studies and their
individual outcomes, see Appendix C.8 (Musculoskeletal) in U.S. EPA (2024a) and U.S. EPA
(2024b).
6.2.2.2.9 Cancer Effects
In the EPA's Final Human Health Toxicity Assessment for PFOA (U.S. EPA, 2024d), the
agency evaluates the evidence for carcinogenicity of PFOA that has been documented in both
epidemiological and animal toxicity studies. The evidence in epidemiological studies is primarily
based on the incidence of kidney and testicular cancer, as well as potential incidence of breast
cancer in genetically susceptible subpopulations or for particular breast cancer types. Other
cancer types have been observed in humans, although the evidence for these is generally limited
to low confidence studies. The evidence of carcinogenicity in animal models is provided in three
chronic oral animal bioassays in Sprague-Dawley rats which identified neoplastic lesions of the
liver, pancreas, and testes (U.S. EPA, 2024f). For more information on the EPA's cancer
determination for PFOA, see Section 6.2.2.1.3.
In the EPA's Final Human Health Toxicity Assessment for PFOS (U.S. EPA, 2024e), the agency
evaluates the evidence for carcinogenicity of PFOS and found that several epidemiological
studies and a chronic cancer bioassay comprise the evidence database for the carcinogenicity of
PFOS (U.S. EPA, 2024e). The available epidemiology studies report elevated risk of liver
cancer, consistent with increased incidence of liver tumors reported in male and female rats.
There is also mixed but plausible evidence of bladder, prostate, kidney, and breast cancers in
humans. The animal chronic cancer bioassay study also provides evidence of increased incidence
of pancreatic islet cell tumors in male rats. For more information on the EPA's cancer
determination for PFOS, see Section 6.2.2.1.3.
The EPA anticipates there are additional nonquantifiable benefits related to potential testicular,
bladder, prostate, and breast cancer effects summarized above. Benefits associated with avoiding
cancer cases not quantified in the EPA's analysis could be substantial. For example, a study by
Obsekov et al. (2023) reports the number of breast cancer cases attributable to PFAS exposure
ranges from 421 to 3,095 annually, with an estimated direct cost of 6-month treatment ranging
from $27.1 to $198.4 million per year ($2022). This study also finds that approximately 5
(0.076%) annual testicular cancer cases are attributable to PFOA exposure with an estimated
direct cost of treatment of $173,450 per year ($2022). Although the methods used by Obsekov et
al. (2023) differ from those used to support the national quantified benefits of the rule, the
information provided in the study is helpful in portraying the costs of cancers that are associated
with PFAS exposures. For additional details on cancer studies and their individual outcomes, see
Chapter 3.5 (Cancer) in U.S. EPA (2024e) and U.S. EPA (2024f).
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6.2.3 Summary of Health Information Considered in the
Economic Analysis
After assessing available health and economic information, the EPA was unable to quantify the
benefits of avoided health effects discussed in Section 6.2.2.2 above. The agency prioritized
health endpoints with the strongest weight of evidence conclusions and readily available data for
monetization, namely cardiovascular effects, developmental effects, and carcinogenic effects.
Several other health endpoints that had indicative or suggestive evidence of associations with
exposure to PFOA and PFOS have not been selected for the economic analysis:
• While immune effects had indicative evidence of associations with exposure to PFOA
and PFOS, the EPA did not identify the necessary information to connect the measured
biomarker responses (i.e., decrease in antibodies) to a disease that could be valued in the
economic analysis;
• Evidence indicates associations between PFOA and PFOS exposure and hepatic effects,
such as increases in ALT. While increased ALT is considered an adverse effect, ALT can
be one of several contributors to a variety of diseases, including liver disease, and it is
difficult to therefore quantify the relationship between this biomarker and a disease that
can be monetized. Similar challenges with the biomarkers representing metabolic effects
(i.e., leptin) and musculoskeletal effects (i.e., bone density) prevented economic analysis
of these endpoints;
• There is evidence of association between exposure to PFOA and testicular cancer in
human and animal studies; however, the available slope factor in rats implied small
changes in the risk of this endpoint. Because testicular cancer is rarely fatal and the Value
of Statistical Life is the driver of economic benefits evaluated in the EA, the benefit of
decreased testicular cancer expected with this rule was smaller in comparison and not
quantified;
• There is evidence of association between exposure to PFOS and hepatic carcinogenicity
in human and animal studies. The EPA quantified benefits associated with reduced liver
cancer cases and deaths as part of a sensitivity analysis for the final rule in response to
public comments received on the proposed rule requesting that the EPA quantify
additional health benefits (see Appendix O);
• Finally, other health endpoints, such as small for gestational age and LDLC effects, were
not modeled in the EA because they overlap with effects that the EPA did model. More
specifically, SGA infants are often born with decreased birth weight or receive similar
care to infants born with decreased birth weight. LDLC is a component of total
cholesterol and could not be modeled separately as the EPA used total cholesterol as an
input to the ASCVD model to estimate CVD outcomes.
6.2.4 Nonquantifiable Benefits of PFAS in Final Rule and PFAS
Expected to be Co-Removed
The EPA also qualitatively summarized the potential health benefits resulting from reduced
exposure to PFAS other than PFOA and PFOS in drinking water. The final rule and all
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regulatory alternatives are expected to result in additional benefits that have not been quantified.
The final rule will reduce exposure to PFHxS, HFPO-DA, and PFNA to below their individual
MCLs. It will also reduce exposure to mixtures of two or more of PFHxS, PFNA, HFPO-DA,
and PFBS to below the HI MCLG and MCL of 1. Benefits from avoided cases of the adverse
health effects discussed below are expected from the final rule due to co-occurrence of these
contaminants in source waters containing PFOA and/or PFOS, as documented in detail in the
Per- and Polyfluoroalkyl Substances (PFAS) Occurrence & Contaminant Background Support
Document (U.S. EPA, 2024g). In addition, PFAS, including PFHxS, HFPO-DA, PFNA, and
PFBS and their mixtures affect common target organs, tissues, or systems to produce dose-
additive effects from their co-exposures with each other, as well as PFOA and PFOS (U.S. EPA,
2024d). The EPA expects that compliance actions taken under the final rule will remove
additional unregulated co-occurring PFAS contaminants where present because the best available
technologies have been demonstrated to co-remove additional PFAS. Treatment responses
implemented to reduce PFOA and PFOS exposure under the final rule and Options la-c are
likely to remove some amount of additional PFAS contaminants where they co-occur.
IX and GAC are effective at removing PFAS; there is generally a linear relationship between
PFAS chain length and removal efficiency, shifted by functional group (McCleaf et al., 2017;
Sorengard, 2020). Perfluoroalkyl sulfonates (PFSAs), such as PFOS, are removed with greater
efficiency than corresponding perfluoroalkyl carboxylates (PFCAs), such as PFOA, of the same
carbon backbone length (Appleman et al., 2014; Du, 2014; Eschauzier et al., 2012; Ochoa-
Herrera & Sierra-Alvarez, 2008; Zaggia et al., 2016). Generally, for a given water type and
concentration, PFSAs are removed approximately as effectively as PFCAs, which have two
additional fully perfluorinated carbons in the carbon backbone. For example, PFHxS (i.e.,
sulfonic acid with a six-carbon backbone) is removed approximately as well as PFOA (i.e.,
carboxylic acid with an eight-carbon backbone) and PFHxA (i.e., carboxylic acid with a six-
carbon backbone) is removed approximately as well as PFBS (i.e., sulfonic acid with a four-
carbon backbone). Further, PFAS compounds with longer carbon chains display lower
percentage decreases in average removal efficiency over time (McCleaf et al., 2017).
In cases where the six PFAS included in the final rule occur at concentrations above their
respective regulatory standards, there is also an increased probability of co-occurrence of
additional unregulated PFAS. Further, as the same technologies also remove other long-chain
and higher carbon/higher molecular weight PFAS, the EPA expects that treatment will provide
additional public health protection and benefits due to co-removal of unregulated PFAS that may
have adverse health effects. While the EPA has not quantified these additional benefits, the
agency expects that these important co-removal benefits will further enhance public health
protection.
The EPA identified a wide range of potential health effects associated with exposure to PFAS
other than PFOA and PFOS using documents that summarize the recent literature on exposure
and associated health impacts: the ATSDR's Toxicological Profile for Perfluoroalkyls (ATSDR,
2021); the EPA's toxicity assessment of HFPO-DA (U.S. EPA, 2021c); publicly available IRIS
assessments for PFBA and PFHxA (U.S. EPA, 2022e; U.S. EPA, 2023d); EPA's toxicity
assessment of PFBS (U.S. EPA, 2021d); and the recent National Academies of Sciences,
Engineering, and Medicine Guidance on PFAS Exposure, Testing, and Clinical Follow-up
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(NASEM, 2022). Note that the determinations of associations between PFAS and associated
health effects are based on information available as of September 2023.
Developmental effects: Toxicology and/or epidemiology studies observed evidence of
associations between birth weight and/or other developmental effects and exposure to PFBA,
PFDA, PFHxS, PFHxA, HFPO-DA, PFNA, PFUnA, and PFBS. Specifically, data from
toxicology studies support this association for PFBS, PFBA, PFHxA, and HFPO-DA, while both
toxicology and epidemiology studies support this association for PFHxS, PFDA, PFUnA, and
PFNA (ATSDR, 2021; U.S. EPA, 2021c; U.S. EPA, 2022e; Wright et al., 2023). In general,
epidemiological studies did not find associations between exposure and adverse pregnancy
outcomes (miscarriage, preterm birth, or gestational age) for PFNA, PFUnA, and PFHxS
(ATSDR, 2021; NASEM, 2022). Epidemiological studies support an association between PFNA,
PFHxS or PFDA exposure and developmental effects such as decreases in infant birth weight
and birth length, small for gestational age and increased risk of low birth weight (Valvi et al.,
2017; C.C. Bach et al., 2016; Louis et al., 2018; Wright et al., 2023; Manzano-Salgado et al.,
2017; Starling et al., 2017). Few epidemiologic studies also indicate that PFDA exposure is
associated with developmental effects (Wikstrom et al., 2020; Valvi et al., 2017; Luo et al.,
2021; Yao et al., 2021). The EPA has determined that evidence indicates that exposure to PFBA
or PFHxA likely causes developmental effects, based on moderate evidence from animal studies
and indeterminate evidence from human studies (U.S. EPA, 2022e; U.S. EPA, 2023d).
Cardiovascular effects: Epidemiology and/or toxicology studies observed evidence of
associations between PFNA, PFDA, and PFHxS exposures and effects on total cholesterol,
LDLC, and HDLC. Epidemiological studies report consistent associations between PFHxS and
total cholesterol in adults (Cakmak et al., 2022; Dunder et al., 2022; Canova et al., 2020; Lin et
al., 2019; G. Liu et al., 2020; Fisher et al., 2013). In an analysis based on studies published
before 2018, evidence for associations between PFNA exposure and serum lipid levels in
epidemiology studies was mixed; associations have been observed between serum PFNA levels
and total cholesterol in general populations of adults but not in pregnant women, and evidence in
children is inconsistent (ATSDR, 2021). Most epidemiology studies did not observe associations
between PFNA and LDLC or HDLC. Epidemiological studies report consistent associations
between PFDA and effects on total cholesterol in adults (Cakmak et al., 2022; Dunder et al.,
2022; G. Liu et al., 2020; Dong et al., 2019). Positive associations between PFDA and other
serum lipids, adiposity, cardiovascular disease, and atherosclerosis were observed in some
epidemiology studies, but findings were inconsistent (Huang et al., 2018; Mattsson et al., 2015;
Christensen et al., 2016). A single animal study observed decreases in cholesterol and
triglyceride levels in rats at PFDA doses above 1.25 mg/kg/d for 28 days (National Toxicology
Program, 2018b). There was no association between PFBA and serum lipids in a single
epidemiology study and no animal studies on PFBA evaluated cardiovascular endpoints (U.S.
EPA, 2022e). Other PFAS for which lipid outcomes were examined in toxicology or
epidemiology studies showed limited to no evidence of associations. Studies have examined
possible associations between various PFAS and blood pressure in humans or heart
histopathology in animals. Epidemiological studies report positive associations between PFHxS
and hypertension in adolescents and young adults (Averina et al., 2021; N. Li et al., 2021; Pitter
et al., 2020), but not in other adults (P.-I. D. Lin et al., 2020; A. Chen et al., 2019; Christensen et
al., 2018; G. Liu et al., 2018; Bao et al., 2017 ; Christensen et al., 2016) or children
(Papadopoulou et al., 2021; Khalil et al., 2018; Manzano-Salgado et al., 2017). No evidence was
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observed of associations between PFHxS and cardiovascular diseases (Huang et al., 2018;
Mattsson et al., 2015). Overall, studies did not find likely evidence of cardiovascular effects for
other PFAS except for PFOS and PFOA (U.S. EPA, 2024e; U.S. EPA, 2024f).
Hepatic effects: Toxicology and/or epidemiology studies have reported associations between
exposure to PFAS (PFBA, PFDA, PFUnA, PFDoDA, PFHxA, PFHxS, HFPO-DA, and PFBS)
and hepatotoxicity. The results of the animal toxicology studies provide strong evidence that the
liver is a sensitive target of PFHxS, PFNA, PFDA, PFUnA, PFBS, PFBA, PFDoDA, HFPO-DA
and PFHxA toxicity. Observed effects in rodents include increases in liver weight, hepatocellular
hypertrophy, hyperplasia, and necrosis (ATSDR, 2021; U.S. EPA, 2021c; U.S. EPA, 2022e; U.S.
EPA, 2023d). Increases in serum enzymes (such as ALT) and decreases in serum bilirubin were
observed in several epidemiological studies of PFNA and PFDA (Nian et al., 2019; Jain &
Ducatman, 2019b; J.-J. Liu et al., 2022; Cakmak et al., 2022). Associations between exposure to
PFHxS and effects on serum hepatic enzymes are less consistent (Cakmak et al., 2022; J.-J. Liu
et al., 2022; Jain & Ducatman, 2019b; Salihovic et al., 2018; Gleason et al., 2015 ). Mixed
effects were observed for serum liver enzymes in epidemiological studies for PFNA (ATSDR,
2021).
Immune effects: Epidemiology studies have reported evidence of associations between PFDA
or PFHxS exposure and antibody response to tetanus or diphtheria (Grandjean et al., 2012;
Grandjean, Heilmann, Nielsen, et al., 2017; Grandjean, Heilmann, Weihe, et al., 2017; Budtz-
J0rgensen & Grandjean, 2018). There is also some limited evidence for decreased antibody
response for PFNA, PFUnA, and PFDoDA, although there were notable inconsistencies across
studies examining associations for these compounds (ATSDR, 2021). There is limited evidence
for associations between PFHxS, PFNA, PFDA, PFBS, and PFDoDA and increased risk of
asthma due to the small number of studies evaluating the outcome and/or inconsistent study
results (ATSDR, 2021). The small number of studies investigating immunotoxicity in humans
following exposure to PFHpA and PFHxA did not find associations (ATSDR, 2021; U.S. EPA,
2023d, NASEM, 2022). Toxicology studies have reported evidence of associations between
HFPO-DA exposure and effects on various immune-related endpoints in animals (ATSDR, 2021;
U.S. EPA, 2021c). No laboratory animal studies were identified for PFUnA, PFHpA, PFDoDA,
or FOSA. A small number of toxicology studies evaluated the immunotoxicity of other
perfluoroalkyls and most did not evaluate immune function. No alterations in spleen or thymus
organ weights or morphology were observed in studies on PFHxS and PFBA. A study on PFNA
found decreases in spleen and thymus weights and alterations in splenic lymphocyte phenotypes
(ATSDR, 2021). Changes in spleen and thymus weights were reported in female mice and
male/female rats in two 28-day gavage studies of PFDA, although the direction and dose-
dependency of these changes in rats was inconsistent across studies (Frawley et al., 2018 ;
National Toxicology Program, 2018b).
COVID-19: A cross-sectional study in Denmark (Grandjean et al., 2020) showed that PFBA
exposure was associated with increasing severity of COVID-19, with an OR of 1.77 (95% CI:
1.09, 2.87) after adjustment for age, sex, sampling site, and interval between blood sampling and
diagnosis. A case-control study showed increased risk of COVID-19 infection with high urinary
PFAS (including PFOA, PFOS, PFHxA, PFHpA, PFHxS, PFNA, PFBS, PFDA, PFUnA,
PFDoA, PFTrDA, PFTeDA) levels (Ji et al., 2021). Adjusted odds ratios were 1.94 (95% CI:
1.39, 2.96) for PFOS, 2.73 (95% CI: 1.71, 4.55) for PFOA, and 2.82 (95% CI: 1.97-3.51) for
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total PFAS (sum of 12 PFAS), while other PFAS were not significantly associated with COVID-
19 susceptibility after adjusting for confounders. In a spatial ecological analysis, Catelan et al.
(2021) showed higher mortality risk for COVID-19 in a population heavily exposed to PFAS
(including PFOA, PFOS, PFHxS, PFBS, PFBA, PFPeA, PFHxA, and PFHpA) via drinking
water. Overall, results suggested a general immunosuppressive effect of PFAS and/or increased
COVID-19 respiratory toxicity due to a concentration of PFBA in the lungs. Although these
studies provide a suggestion of possible associations, the body of evidence does not permit
conclusions about the relationship between COVID-19 infection, severity, or mortality, and
exposures to PFAS. In addition to the adverse health effects listed above, there was little or no
evidence that exposure to the various PFAS is associated with the additional health effects
summarized below.
Endocrine effects: Epidemiology studies have observed associations between serum PFHxS,
PFNA, PFDA, and PFUnA and effects on thyroid stimulating hormone (TSH), triiodothyronine
(T3), or thyroxine (T4) levels in serum or thyroid disease; however, there are notable
inconsistencies across the studies identified in the available reports (ATSDR, 2021; NASEM,
2022). Toxicology studies have reported consistent associations between exposure to PFHxS,
PFBA, PFHxA, and PFBS and effects on thyroid hormones, thyroid organ weight, and thyroid
histopathology in animals; the endocrine system was a notable target of PFBS and PFHxS
toxicity (ATSDR, 2021; U.S. EPA, 2021d; U.S. EPA, 2022e; U.S. EPA, 2023d; National
Toxicology Program, 2018a; Ramh0j et al., 2018; Ramh0j et al., 2020; Butenhoff et al., 2009).
Metabolic effects: Epidemiology and toxicology studies have examined possible associations
between various PFAS and metabolic effects, including leptin, body weight, or body fat in
humans or animals (ATSDR, 2021). Exposure to PFDA has been associated with an increase in
adiposity in adults (Blake et al., 2018; Christensen et al., 2018; G. Liu et al., 2018). However,
evidence of associations was not suggestive or likely for any PFAS in this summary except for
PFOA and PFOS (U.S. EPA, 2024a; U.S. EPA, 2024b; U.S. EPA, 2024e; U.S. EPA, 2024f).
Evidence for changes such as maternal body weight gain, pup body weight, or other
developmentally focused weight outcomes is strong but is considered under the Developmental
effects category (ATSDR, 2021; NASEM, 2022).
Renal effects: A small number of epidemiology studies with inconsistent results evaluated
possible associations between PFHxS, PFNA, PFDA, PFBS, PFDoDA, or PFHxA and renal
function (including estimated glomerular filtration rate and increases in uric acid levels)
(ATSDR, 2021; NASEM, 2022; U.S. EPA, 2023d). Toxicology studies have not observed
impaired renal function or morphological damage following exposure to PFHxS, PFDA, PFUnA,
PFBS, PFBA, PFDoDA, or PFHxA (ATSDR, 2021). Associations with kidney weight in animals
were observed for PFBS and HFPO-DA and was a notable target for PFBS toxicity (ATSDR,
2021; U.S. EPA, 2021c; U.S. EPA, 2021d).
Reproductive effects: A small number of epidemiology studies with inconsistent results
evaluated possible associations between reproductive hormone levels and PFHxS, PFNA, PFDA,
PFUnA, PFDoDA, or PFHxA. Some associations between PFAS (PFHxS, PFHxA, PFNA,
PFDA) exposures and sperm parameters have been observed, but often only one sperm
parameter was altered. While there is suggestive evidence of an association between PFHxS or
PFNA exposure and an increased risk of early menopause, this may be due to reverse causation
since an earlier onset of menopause would result in a decrease in the removal of PFAS in
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menstrual blood. Epidemiological studies provide mixed evidence of impaired fertility (increased
risks of longer time to pregnancy and infertility), with some evidence for PFHxS, PFNA,
PFHpA, and PFBS but the results are inconsistent across studies or were only based on one study
(ATSDR, 2021; Carlsen Bach et al., 2018; Velez et al., 2015). Toxicology studies have evaluated
the potential histological alterations in reproductive tissues, alterations in reproductive hormones,
and impaired reproductive functions. No effect on fertility was observed for PFBS and PFDoDA,
and no histological alterations were observed for PFBS and PFBA. One study found alterations
in sperm parameters and decreases in fertility in mice exposed to PFNA, and one study for
PFDoDA observed ultrastructural alterations in the testes (ATSDR, 2021). Decreased uterine
weights, changes in hormone levels, and increased time spent in diestrus were observed in
studies of PFDA or PFHxS exposures (National Toxicology Program, 2018b; Yin et al., 2021).
Musculoskeletal effects: Epidemiology studies observed evidence of associations between
PFNA and PFHxS and musculoskeletal effects including osteoarthritis and bone mineral density,
but data are limited to two studies (ATSDR, 2021; Khalil et al., 2016; Khalil et al., 2018).
Toxicology studies reported no morphological alterations in bone or skeletal muscle in animals
exposed to PFBA, PFDA, PFHxA, PFHxS, or PFBS, but evidence is based on a very small
number of studies (NTP, 2018; ATSDR, 2021; U.S. EPA, 2022e; U.S. EPA, 2023d).
Hematological effects: A single uninformative epidemiological study reported on blood counts
in pregnant women exposed to PFHxA (U.S. EPA, 2024e). Epidemiological data were not
identified for the other PFAS (ATSDR, 2021). A limited number of toxicology studies observed
alterations in hematological indices following exposure to relatively high doses of PFHxS,
PFDA, PFUnA, PFBS, PFBA, or PFDoDA (ATSDR, 2021; U.S. EPA, 2022e; National
Toxicology Program, 2018b; 3M Company, 2000; Frawley et al., 2018). Toxicology studies
observed robust evidence of association between PFHxA or HFPO-DA exposure and
hematological effects, including decreases in red blood cell (RBC) number, hemoglobin, and
percentage of RBCs in the blood (U.S. EPA, 2021c; U.S. EPA, 2023d). A small number of
toxicology studies observed slight evidence of associations between exposure to PFHxS, PFDA,
or PFBA and decreases in multiple red blood cell parameters and in prothrombin time; however,
effects were not consistent (U.S. EPA, 2022e; Butenhoff et al., 2009).
Other non-cancer effects: A limited number of epidemiology and toxicology studies have
examined possible associations between various PFAS and dermal, ocular, and other non-cancer
effects. However, the evidence does not support associations for any PFAS in this summary
except for PFOA and PFOS (ATSDR, 2021; U.S. EPA, 2021d; U.S. EPA, 2022e; U.S. EPA,
2023d).
Cancer effects: A small number of epidemiology studies reported limited associations between
multiple PFAS (i.e., PFHxS, PFDA, PFUnA, and FOSA) and cancer effects. No consistent
associations were observed for breast cancer risk for PFHxS, PFHxA, PFNA, PFHpA, or
PFDoDA; increased breast cancer risks were observed for PFDA and FOSA, but this was based
on a single study (Bonefeld-Jorgensen et al., 2014), and one study observed non-significant
increased risk for breast cancer risk and PFDA (Tsai et al., 2020). Exposure to PFHxS was
associated with increased breast cancer risk in one study and with decreased breast cancer risk in
two related studies (Bonefeld-Jorgensen et al., 2014; Ghisari et al., 2017; Tsai et al., 2020). No
associations between PFHxS, PFNA, PFDA, or PFUnA and prostate cancer risk were observed.
However, among men with a first-degree relative with prostate cancer, associations were
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observed for PFHxS, PFDA (Hardell et al., 2014), and PFUnA, but not for PFNA (ATSDR,
2021; U.S. EPA, 2022e; U.S. EPA, 2023d). A decreased risk of thyroid cancer was associated
with exposure to PFHxS and PFDA in a single study (M. Liu et al., 2021). Epidemiological
studies examining potential cancer effects were not identified for PFBS or PFBA (ATSDR,
2021; U.S. EPA, 2022e). No animal studies examined carcinogenicity of PFHxS or PFBA. Aside
from a study that suggested an increased incidence of liver tumors in rats exposed to high doses
of HFPO-DA, the limited number of available toxicology studies reported no evidence of
associations between exposure to other PFAS (i.e., PFDA and PFHxA) and risk of cancer
(ATSDR, 2021; U.S. EPA, 2021c; U.S. EPA, 2023d). At this time, there is inadequate
information to assess carcinogenic potential for PFAS other than PFOA, PFOS, and HFPO-DA.
6.2.5 Sensitive Populations
SDWA Section 1412(b)(3)(C) establishes requirements for the EPA to develop a HRRCA that
presents both quantifiable and nonquantifiable benefits and costs likely to occur as a result of
compliance with the NPDWR. In developing this HRRCA, the EPA considered adverse health
effects to sensitive populations and subpopulations.
Adverse health effects of PFAS such as cancer, developmental, hepatic, immune, and serum lipid
effects (see Sections 6.2.2 and 6.2.4) have been observed in the general population, including
women of reproductive age. Effects have been observed in vulnerable populations of groups who
have relatively high exposures, for example workers and their families who worked at and/or
lived near facilities that used PFOA (such as the C8 Health Project33 populations). However, data
for the elucidation of differential susceptibility dependent on life stage (e.g., developing
embryo/fetus, or pregnant women) are very limited or not available. Children are frequently
more vulnerable to contaminants than the average adult because of the differences in their
behaviors and biology. These differences can result in greater exposure and/or unique windows
of developmental susceptibility during the prenatal and postnatal periods for both the pregnant
mother and the developing fetus.
When evaluating NPDWRs for any unregulated contaminant, the EPA considers the adverse
health risks to infants/children, pregnant women, the elderly, individuals with a history of serious
illness, and any subpopulation that are identifiable as being at greater risk due to exposure to
contaminants in drinking water than the general population to ensure that the most sensitive
population groups are protected. SDWA Section 1412(b)(3)(C)(i)(V). In conducting risk
analyses and assessments, the EPA and other agencies and organizations consider subpopulations
that may be sensitive to PFAS exposure to be pregnant women, infants/children, individuals who
are immunologically compromised, and the elderly (U.S. EPA, 2024e; U.S. EPA, 2024f;
ATSDR, 2021; CalEPA, 2021; Minnesota Pollution Control Agency, 2021). CalEPA (2021) and
the Minnesota Pollution Control Agency (2021) also identify the timing of exposure to PFAS to
be critical in the development of adverse health effects. There is evidence of associations with
birth weight effects and exposure to PFDA, PFHxS, PFNA, PFOA, PFOS, or PFUnA (see
Sections 6.2.2 and 6.2.4). There is some sex-specific variation in the toxicokinetics of PFOA in
33 The C8 Health Project studied over 60,000 individuals who had lived, worked, or attended school for more than one year in
one of six water districts contaminated by PFOA between 1950 and 2004 (Frisbee et al., 2010).
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humans and rodents, with females generally excreting PFOA faster than males (U.S. EPA,
2024f).
Overall, given that evidence of exposure and adverse health effects of PFAS is mostly reported
in studies of the general population, not all potentially sensitive populations are quantified in
developing this HRRCA. However, the modeled endpoints, including decreases in infant birth
weight (Section 6.4), CVD (Section 6.5), and RCC (Section 6.6), are prevalent in sensitive
populations (i.e., infants and the elderly).
6.2.6 Co-Removal of Additional Contaminants
Additional co-removal benefits can occur with the advanced treatment options for PFAS
removal. Advanced treatment technologies including GAC, IX, as well as high-pressure
membranes such as nanofiltration (NF) and reverse osmosis (RO) can remove many
contaminants in addition to those specifically targeted by the final PFAS rule, including other
contaminants that the EPA may regulate in the future (Chowdhury et al., 2013; de Abreu
Domingos & da Fonseca, 2018; McNamara et al., 2018; Pramanik et al., 2015; Yu et al., 2012).
For example, membrane technology (depending on pore size) can be used to lower DBP
formation by the removal of organic carbon, and can also remove many microbial contaminants
(e.g., bacteria and protozoans) of public health concern (S. K. Park et al., 2019).
Organic matter can also be removed by IX and GAC (Crittenden et al., 1993; W. H. Kim et al.,
1997; Yapsakli & £e9en, 2010; Dickenson & Higgins, 2016; Yuan et al., 2022). Removing TOC,
which functions as a DBP precursor, may also help address DBP issues, including regulated and
nonregulated DBPs. Epidemiological studies have shown that increased exposure to chlorinated
DBPs is associated with higher risk of bladder cancer and other adverse health outcomes (Cantor
et al., 1998; Freeman et al., 2017). Weisman et al. (2022) found that approximately 8,000 of the
79,000 annual bladder cancer cases in the U.S. were potentially attributable to chlorinated DBPs
in drinking water systems.
In addition, TOC removal lowers disinfectant demand and could lower disinfectant dose
requirements (Hooper & Allgeier, 2002). Membrane technology, IX, and GAC lower nutrient
availability for bacterial growth, produce a more biologically stable finished water, and facilitate
management of water quality in the distribution system. Lower organic matter concentration is
also associated with lower assimilable organic carbon (AOC) and nutrient availability for biofilm
growth, helping to maintain disinfectant residual in the distribution system and to reduce
microbial risk (U.S. EPA, 2005b).
A major concern for drinking water systems is biofilm control in reducing microbial risk. One
opportunistic pathogen of concern is Legionella, which can grow and multiply in amoeba that
live in biofilms and sediments (National Academies of Sciences, 2020). Certain conditions in the
distribution and plumbing systems can also support its proliferation, including low disinfectant
residual (U.S. EPA, 2016i; LeChevallier, 2020). Legionella exposure can lead to legionellosis,
Pontiac fever, or a form of pneumonia called Legionnaires' disease (National Academies of
Sciences, 2020). Collier et al. (2021) estimated that in 2014 there were 11,000 cases of
Legionnaires' disease due to waterborne exposure in the U.S., with an estimated one in 10 cases
leading to death.
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Since membrane technology and GAC also remove SOCs, these advanced treatment options
provide additional protection from exposure to chemicals associated with accidental spills or
environmental runoff. The EPA has previously used the term SOC to include volatile organic
carbons, herbicides, pesticides, and other anthropogenic organic compounds (U.S. EPA, 1998d).
One example of a volatile organic carbon that can be co-removed by GAC is dichloromethane
(also known as methylene chloride), which has been linked to liver, neurological, and blood cell
damage in addition to various cancers (U.S. EPA, 2014). The EPA also identified alachlor as a
herbicide that can be removed by GAC and has been linked to liver, kidneys, and spleen damage
(U.S. EPA, 1998a). Another SOC example that can be removed by GAC treatment is atrazine, a
pesticide that targets the endocrine system and has been associated with adverse developmental
reproductive effects (U.S. EPA, 2007a). Removal of any contaminants that may face current
and/or future regulation could result in additional public health protection and cost savings to a
water system. As public water systems move to advanced treatment, other non-health benefits
are also anticipated including better-tasting and smelling water.
6.3 Blood Serum Concentration Modeling for PFAS
6.3.1 Introduction
The U.S. EPA implemented PK models to evaluate blood serum PFOA and PFOS levels in
adults resulting from exposure to PFAS via drinking water. This section discusses the application
of the PFOA and PFOS PK models in the context of the benefits analysis.
63.2 Application of PK Models to Benefits Analyses
The EPA used baseline and regulatory alternative PFOA/PFOS drinking water concentrations as
inputs to its PK models to estimate blood serum PFOA/PFOS concentrations for adult males and
females. In this analysis, the agency implemented the final PFOA/PFOS PK model version in
SafeWater MCBC.34 See the EPA's Final Human Health Toxicity Assessments for PFOA and
PFOS for further information on the model (U.S. EPA, 2024e; U.S. EPA, 2024f) and EPA's
Github repository for pharmacokinetic modeling.35 The PK models require total PFOA/PFOS
dose in mg/kg of body weight per day to be provided as an input. The EPA multiplied
PFOA/PFOS drinking water concentrations in mg/L by a water intake of 0.013 L/kg of body
weight per day based on the EPA's Exposure Factors Handbook (U.S. EPA, 201 lb) in order to
compute the PFOA/PFOS dose from drinking water sources.
The EPA acknowledges that sources or pathways of exposure other than drinking water
consumption may contribute to an individual's total PFOA/PFOS exposure (see Section 6.3.3 for
discussion of contributions from other sources). However, the assumed baseline exposure from
drinking water sources does not affect the estimated changes in serum PFOA/PFOS, which is the
key quantity of interest to the benefits estimation. For the PK model in humans, the EPA selected
a "linear" approach in which the rates in the model are all proportional to concentration. In this
34 SafeWater MCBC was programmed for maximal computational efficiency. The implementation is mathematically consistent
with what is described in the SAB documentation and associated R code, however, SafeWater performs a series of pre-
calculations to reduce model runtime.
35 https://github.com/USEPA/OW-PFOS-PFOA-MCLG-support-PK-models
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type of model, predicted serum concentration is proportional to the dose, with a proportionality
constant that is dependent on time, but not dose. Given the same model parameters, such as
sampling age and exposure duration, doubling the dose will double the predicted serum
concentration. Note that each simulation models an individual from birth through to the sampling
age, with a default exposure scenario of constant lifetime exposure beginning at birth.36 This
implies that the change in predicted serum concentration is dependent only on the change in
drinking water dose and independent of the dose from non-drinking water sources. The EPA
additionally assumed that non-drinking water exposure is independent of the drinking water
PFOA/PFOS concentration and estimated the total regulatory alternative dose as the sum of the
baseline non-drinking water dose and the regulatory alternative drinking water dose.37
The EPA used the PK models to evaluate the following PWS EP-specific exposure scenarios in
male and female subpopulations:
• Lifetime baseline exposure scenario: Lifetime exposure to baseline PFOA/PFOS
drinking-water dose for cohorts of all ages alive at the start of the evaluation period in
2024 and cohorts born after 2024;
• Lifetime regulatory alternative exposure scenario: Lifetime exposure to regulatory
alternative PFOA/PFOS drinking-water dose for cohorts born during or after 2029 (i.e.,
the year of full regulatory alternative implementation);
• Partial lifetime treatment exposure scenario: Exposure to baseline PFOA/PFOS
drinking-water dose until age A-l years and regulatory alternative PFOA/PFOS dose
thereafter for cohorts aged A > 0 years in 2029.
The EPA selected the annual midpoint (the value on June 1 of each year) of the PK-modeled
serum PFOA/PFOS concentration time series to represent the annual average serum
PFOA/PFOS concentrations under the baseline and regulatory options. The EPA estimated
changes in annual average serum PFOA/PFOS concentrations under the regulatory alternatives
by subtracting baseline cohort-specific serum PFOA/PFOS concentrations from either full or
partial lifetime cohort-specific serum PFOA/PFOS concentrations (as appropriate) under the
regulatory alternatives. The EPA applied the PFOA/PFOS blood serum concentration time series
estimated using the PK models to all benefits analyses that considered changes in PFOA/PFOS
drinking water concentrations.
36 Specifically, let C = a ¦ Dt, where C is serum concentration, a is a proportionality constant, and Dt is the total dose. This can
be expanded to C = a ¦ Dt = a ¦ (Ddw + D0 ), where the total dose is the sum of the dose from drinking water, Ddw, and from
other sources, D0. The change in concentration due to a change in dose from drinking water is then AC = a ¦ ADdw + a ¦ AD0 =
a ¦ ADdw, given that the dose from other sources is constant, AD0 = 0.
37 The EPA used the fraction of exposure from drinking water under baseline conditions to estimate the total daily dose of
PFOA/PFOS and the exposure from sources other than drinking water, which did not change upon implementation of the
treatment scenario. While the total change in exposure is independent of the amount of exposure from other sources, the relative
change in exposure does depend on the relative amount of exposure from non-drinking water sources. A greater fraction of
exposure from drinking water sources will result in a greater relative change in total exposure upon implementation of the
treatment scenario. The EPA also notes that, in reality, some portion of the non-drinking water exposure will be related to
drinking water concentration (e.g., water used for cooking). This portion is difficult to estimate, and, depending on the
relationship, there may be a time lag between the decrease in drinking water concentration and the decrease in the non-drinking
water exposure.
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The birth weight analysis focuses only on women of childbearing age defined by the Centers for
Disease Control and Prevention (CDC) as those aged 15 to 44 (Ellington et al., 2020) and thus
considers only maternal serum PFOA/PFOS levels. As described above, the PK models provide
estimates of changes in serum PFOA/PFOS levels by PWS EP, age, and sex for each year during
the period of analysis (2024 to 2105). The birth weight analysis requires a single estimate of
change in maternal serum levels for each PFAS compound per year and location to evaluate
potential changes in birth weight resulting from the regulatory alternatives. Therefore, the EPA
used the race/ethnicity-specific distribution of populations of women of childbearing age during
the period of analysis to estimate average annual race/ethnicity-specific change in PFOA/PFOS
levels at each PWS EP and for each year. The EPA relied on the average age of race/ethni city -
specific women of childbearing age when determining PFOA/PFOS serum levels to reflect
differences in maternal age across these groups. The population of women of childbearing age
per PWS, race/ethni city, age, and sex are based on population estimates for women aged 15 to 44
using county-level data from the U.S. Census (U.S. Census Bureau, 2020a; see Appendix B).38
6.3.3 Contributions from Other Sources
The regulatory alternatives considered in this economic analysis are based on potential
reductions in PFOA/PFOS levels in drinking water. However, human exposures to PFOA and
PFOS may also result from sources other than drinking water, including diet, ambient and indoor
air, incidental soil/dust ingestion, consumer products, and others (U.S. EPA, 2024a; U.S. EPA,
2024b).
Following a systematic review of the PFOA and PFOS source contribution literature, the EPA
identified ingestion of food as the dominant source of both PFOA and PFOS exposures in adults
from the general population (U.S. EPA, 2024a; U.S. EPA, 2024b). This pathway is particularly
dominant due to bioaccumulation of PFOA and PFOS in food from environmental emissions,
large amounts of foods being consumed, and high gastrointestinal uptake. PFOA and PFOS may
be present in food due to contact with non-stick cookware or grease-proofing agents in food
packaging. PFOA and PFOS have also been shown to bioaccumulate in fish and shellfish.
Consumer products, including certain cosmetics, textiles, and other household goods, are also a
source of PFOA and PFOS exposure. While PFAS have been detected in ambient air globally,
concentrations vary widely depending on location. PFAS have been detected in soils and dust
from carpets and upholstered furniture. Incidental exposures from soils and dust are particularly
important exposure routes for small children, who have a higher level of hand-to-mouth behavior
compared to adults. PFAS levels in soils and surface water can also impact PFAS levels found in
air particulates, fish, dairy products, meat/poultry, and produce (ATSDR, 2021; U.S. EPA,
2024a; U.S. EPA, 2024b).
6.4 Developmental Effects
Exposure to PFOA and PFOS is linked to developmental effects, including decreased infant birth
weight (Steenland et al., 2018; Dzierlenga et al., 2020; Verner et al., 2015; Negri et al., 2017;
ATSDR, 2018; ATSDR, 2021; Waterfield et al., 2020; U.S. EPA, 2016e; U.S. EPA, 2016f; U.S.
38 County-level population estimates are linked to PWSs based on the "counties served" field provided by the SDWIS/Fed 2021
Q4 database.
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EPA, 2024e; U.S. EPA, 2024f). The route through which infants are exposed prenatally to PFOA
and PFOS is maternal blood via the placenta. Most studies of the association between maternal
serum PFOA/PFOS and birth weight report inverse relationships (Verner et al., 2015; Negri et
al., 2017; Steenland et al., 2018; Dzierlenga et al., 2020).39 This chapter outlines the overall
methodology, assumptions, and data used for estimating changes in birth weight among infants
whose mothers were exposed to PFOA and PFOS in drinking water during or prior to
pregnancy.40
The EPA also considered the potential benefits from reduced exposure to PFNA that may be
realized as a direct result of the final rule. The agency explored the birth weight impacts of
PFNA in a sensitivity analysis based on epidemiological studies published before 2018 cited in
the best available final human health analysis of PFNA (ATSDR, 2021), as well as a recently
published meta-analysis of mean birth weight that indicates the birth weight results for PFNA are
robust and consistent, even if associations in some studies may be small in magnitude (Wright et
al., 2023). The EPA used a unit PFNA reduction scenario (i.e., 1 ppt change) and the PFAS
serum calculator developed by Lu and Bartell (2020) to estimate PFNA blood serum levels
resulting from PFNA exposures in drinking water. To estimate blood serum PFNA based on its
drinking water concentration, the EPA used a first-order single-compartment model whose
behavior was previously demonstrated to be consistent with PFOA pharmacokinetics in humans
(Bartell et al., 2010). In addition to the PFOA-birth weight and PFOS-birth weight effects
analyzed in the EA, the EPA examined the effect of inclusion of PFNA-birth weight effects
using estimates from two studies (Lenters et al., 2016; Valvi et al., 2017). The EPA found that
inclusion of a 1 ppt PFNA reduction could increase annualized birth weight benefits by a factor
of 5.6 to 7.8, relative to the scenario that quantifies a 1 ppt reduction in PFOA and a 1 ppt
reduction in PFOS only. The range of estimated PFNA-related increases in benefits is driven by
the exposure-response, with smaller estimates produced using the slope factors from Lenters et
al. (2016), followed by Valvi et al. (2017). The EPA notes that the PFNA slope factor estimates
are orders of magnitude larger than the slope factor estimates used to evaluate the impacts of
PFOA/PFOS reductions. The EPA also notes that the PFNA slope factor estimates in this
analysis are not precise, with 95 percent CIs covering wide ranges that include zero (i.e., serum
PFNA slope factor estimates are not statistically significant at 5 percent level). Caution should be
exercised in making judgements about the potential magnitude of change in the national benefits
estimates based on the results of these sensitivity analyses, although conclusions about the
directionality of these effects can be inferred. The EPA did not include PFNA effects in the
national benefits estimates for the final rulemaking because there was insufficient data above the
UCMR 3 MRL to reasonably fit model parameters for PFNA (U.S. EPA, 2024g). For the EPA's
PFNA sensitivity analysis, see Appendix K.
6.4.1 Overview of the Birth Weight Risk Reduction Analysis
Figure 6-1 provides an overview of the approach used to quantify and value the changes in birth
weight-related risks associated with reductions in exposure to PFOA and PFOS via drinking
water. Section 4.4 and Section 6.3 detail the PWS EP-specific PFOA/PFOS drinking water
39 Note that recent evidence indicates that relationships between maternal serum PFOA/PFOS and birth weight may be impacted
by changes in pregnancy hemodynamics (Sagiv et al., 2018; Steenland et al., 2018).
40 The PK model assumes that mothers were exposed to PFOA/PFOS from birth to the year in which pregnancy occurred.
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occurrence estimation and modeling of serum PFOA/PFOS concentrations, respectively. EP-
specific time series of the differences between serum PFOA/PFOS concentrations under baseline
and regulatory alternatives are inputs into this analysis. For each EP, evaluation of the changes in
birth weight impacts involves the following key steps:
1. Estimating the changes in birth weight based on modeled changes in serum PFOA/PFOS
levels and exposure-response functions for the effect of serum PFOA/PFOS on birth
weight;
2. Estimating the difference in infant mortality probability between the baseline41 and
regulatory alternatives based on changes in birth weight under the regulatory alternatives
and the association between birth weight and mortality;
3. Identifying the infant population affected by reduced exposure to PFOA/PFOS in
drinking water under the regulatory alternatives;
4. Estimating the changes in the expected number of infant deaths under the regulatory
alternatives based on the difference in infant mortality rates and the population of
surviving infants affected by increases in birth weight due to reduced PFOA/PFOS
exposure; and
5. Estimating the economic value of reducing infant mortality based on the Value of
Statistical Life and infant morbidity based on reductions in medical costs associated with
changes in birth weight for the surviving infants based on the cost of illness.
Section 6.4.2 discusses the exposure-response modeling for birth weight. Section 6.4.3 describes
estimation of birth weight-related mortality and morbidity impacts in the affected population.
Section 6.4.4 discusses the EPA's valuation methodology for reductions in birth weight-related
mortality and morbidity. Section 6.4.5 presents the results of the analysis.
41 Based on mortality rates per state and 500 g birth weight increment from the Centers for Disease Control and Prevention
(CDC) from 2012 to 2018.
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Legend:
Cost of illness
function per BW
change
Change in BW between
baseline and treatment
scenario
Change in infant \
mortality rates between
baseline and treatment
Change in the number
of BW morbidity cases'
Serum PFOA and PFOS
concentration difference
between baseline and
regulatory alternative
CDC data3
"X
Affected infant
population
Result of upstream
analysis
Data/Inputs
Model
ff Analysis step
Valuation endpoint
SDWIS
population
served"
Census Bureau
population data
Change in BW-related
mortality
Value of reduced
BW-related infant
mortality
Value of a
statistical life
Total value of changes in BW
Notes:
SDWIS - Safe drinking water information system, CDC - Centers for Disease Control, BW - birth weight
includes baseline state-level birth rate and average BW (varies by 100-gm BW increment) and infant mortality rate
(varies by 500-gm BW increment) data distributed based on national-level race/ethnicity-specific data.
Baseline infant mortality rates, along with the BW-infant mortality relationship, are used to determine the change in
infant mortality rate between the baseline and policy scenario. Birth rate and average BW data describe the
affected population of infants.
includes both large and small surface water and ground water systems.
'Morbidity cases refer to the total affected population minus infant mortality cases under the regulatory
alternatives.
Figure 6-1: Overview of Analysis of Birth Weight-Related Benefits
6.4.2 Estimation of Birth Weight Changes Between Baseline and
Regulatory Alternatives
To estimate changes in birth weight resulting from reduced exposure to PFOA and PFOS under
the regulatory alternatives, the EPA relied on the estimated time series of changes in serum
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PFOA/PFOS concentrations specific to women of childbearing age and serum-birth weight
exposure-response functions provided in recently published meta-analyses. The estimation of the
time series of changes in serum PFOA/PFOS concentrations is explained in Section 6.3.2. The
EPA reviewed five recent meta-analyses of PFAS-birth weight relationships in detail. As
described in Table 6-8, two of the analyses used well-documented systematic review and risk of
bias procedures to identify relevant studies in the literature (Johnson et al., 2014; Negri et al.,
2017). The three other studies did not document risk of bias protocols and study quality
evaluation criteria, however, the EPA evaluated most of the studies used in these meta-analyses
for study quality (Verner et al., 2015; Dzierlenga et al., 2020; Steenland et al., 2018; U.S. EPA,
2024e; U.S. EPA, 2024f). As discussed below, there was extensive overlap in the studies used in
the various meta-analyses. Two of the meta-analyses included exposure-response modeling for
both PFOS and PFOA (Verner et al., 2015; Negri et al., 2017), while one addressed only PFOS
(Dzierlenga et al., 2020) and the remaining two addressed only PFOA (Johnson et al., 2014;
Steenland et al., 2018).
Table 6-8: Summary of Studies Relating PFOA or PFOS to Birth Weight
Author
PFOA
PFOS
Documented Risk of
Bias Protocols
Johnson et al. (2014)
X
X
Verner et al. (2015)
X
X
Negri et al. (2017)
X
X
X
Steenland et al. (2018)
X
Dzierlenga et al. (2020)
X
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctane sulfonic acid.
The EPA evaluated the applicability of these studies for use in the evaluation of birth weight
changes resulting from reduced PFOS and PFOA exposure based on the following criteria:
number of studies, homogeneity among studies, and sensitivity analyses. Based on these
considerations, the agency selected results from Steenland et al. (2018) as the birth weight
exposure-response function for PFOA and results from Dzierlenga et al. (2020) as the birth
weight exposure-response function for PFOS.
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Steenland et al. (2018) conducted a random effects meta-analysis based on 24 studies. The
authors estimated a slope of-10.5 g birth weight per ng PFOA/mL with significant
heterogeneity (I2 = 63%)42 (p-value for heterogeneity <0.0001). The agency chose the results
from this study for use in the risk assessment from exposure to PFOA and benefits analysis of
reducing PFOA in drinking water because it is the most recent meta-analysis on PFOA-birth
weight, and it included a large number of studies.
Dzierlenga et al. (2020) conducted a random effects meta-analysis based on 32 results from 29
studies. An EPA reanalysis of this study43 estimated a slope of-3.0 g birth weight per ng
PFOS/mL with significant heterogeneity (I2 = 58%) (p-value for heterogeneity <0.001). The
agency chose the results from this study for use in the risk assessment from exposure to PFOS
and benefits analysis of reducing PFOS in drinking water because it is the most recent meta-
analysis on PFOS-birth weight and includes a large number of the most recent studies. While
sensitivity analyses suggested that results may be sensitive to the timing of blood draw, the
authors observed consistent inverse associations with birth weight among those with blood
measurements in early pregnancy and in later pregnancy.
Changes in serum PFOA and PFOS concentrations are calculated for each PWS EP during each
year in the analysis period. The EPA assumes that, given the long half-lives of PFOS and PFOA
(with median half-lives of 2.7 and 3.5 years, respectively; Y. Li et al., 2018), any one-time
measurement during or near pregnancy is reflective of a critical exposure window and not
subject to considerable error. In other words, blood serum concentrations in a single year are
expected to correlate with past exposures and are reflective of maternal exposures regardless of
the timing of pregnancy. The mean change in birth weight per increment in long-term PFOA and
PFOS exposure is calculated by multiplying each annual change in PFOA and PFOS serum
concentration (ng/mL serum) by the PFOA and PFOS serum-birth weight exposure-response
slope factors (g birth weight per ng/mL serum) provided in Table 6-9, respectively. The mean
annual change in birth weight attributable to changes in both PFOA and PFOS exposure is the
sum of the annual PFOA- and PFOS-birth weight change estimates. Appendix D provides
additional details on the derivation of the exposure-response functions. Appendix K presents an
analysis of birth weight risk reduction considering slope factors specific to the first trimester.
Table 6-9: Serum Exposure-Birth Weight Response Estimates
Compound
g Birth Weight/ng/mL Serum (95% CI)
PFOAa
-10.5 (-16.7, -4.4)
PFOSb
-3.0 (-4.9,-1.1)
Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctane sulfonic acid; g - gram.
Notes:
aThe serum-birth weight slope factor for PFOA is based on the main random effects estimate from Steenland et al. (2018).
bThe serum-birth weight slope factor for PFOS is based on an EPA reanalysis of Dzierlenga et al. (2020).
The EPA places a cap on estimated birth weight changes in excess of 200 g based on existing
studies that found that changes to environmental exposures result in relatively modest birth
4212 represents the proportion of total variance in the estimated model due to inter-study variation.
43 In the original Dzierlenga et al. (2020) estimate, the authors duplicated an estimate from M. H. Chen et al. (2017) in the
pooled estimate. The EPA reran the analysis excluding the duplicated estimate.
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weight changes (Windham & Fenster, 2008; Klein & Lynch, 2018; Kamai et al., 2019).44'45
Modest changes in birth weight even as a result of large changes in PFOA/PFOS serum
concentrations may be due to potential bias from studies only including live births (Liew et al.,
2015). Additionally, the magnitude of birth weight changes may be correlated with other
developmental outcomes such as preterm birth, gestational duration, fetal loss, birth defects, and
developmental delays. As described in Section 6.2, these developmental outcomes have limited
epidemiology evidence showing associations with PFOA/PFOS exposure and due to this
uncertainty, these outcomes were not further assessed.
6.4.3 Estimation of Birth Weight impacts
LBW is linked to a number of health effects that may be a source of economic burden to society
in the form of medical costs, infant mortality, parental and caregiver costs, labor market
productivity loss, and education costs (Chaikind & Corman, 1991; J. R. Behrman & Rosenzweig,
2004; R. E. Behrman & Butler, 2007; Joyce et al., 2012; Kowlessar et al., 2013; Colaizy et al.,
2016; Nicoletti et al., 2018; Klein & Lynch, 2018). Recent literature also linked LBW to
educational attainment and required remediation to improve student outcomes, childhood
disability, and future earnings (Jelenkovic et al., 2018; Temple et al., 2010; Elder et al., 2020;
Hines et al., 2020; Chatterji et al., 2014; Dobson et al., 2018). The EPA's analysis focuses on
two categories of birth weight impacts that are amenable to monetization associated with
incremental changes in birth weight: (1) medical costs associated with changes in infant birth
weight and (2) the value of avoiding infant mortality at various birth weights.
The birth weight literature related to other sources of economic burden to society (e.g., parental
and caregiver costs and productivity losses) is limited in geographic coverage, population size,
and range of birth weights evaluated and therefore cannot be used in the economic analysis of
birth weight effects from exposure to PFOA/PFOS in drinking water (ICF, 2021). The following
sections summarize the relationship between infant mortality and birth weight as well as methods
used to estimate changes in the number of infant deaths and the number of surviving infants
whose birth weight is affected by reduced PFOA/PFOS exposures.
6.4.3.1 Impacts of Birth Weight on Infant Mortality
Infant mortality is defined as the deaths among infants who were delivered alive but passed
before their first birthday. Birth weight is a significant factor in infant survival (Jacob, 2016).
Epidemiology studies in the U.S. have reported relationships between birth weight and mortality.
Most of these studies typically evaluate relationships between infant mortality and birth weight
above or below various birth weight thresholds (e.g., Mclntire et al., 1999; Lau et al., 2013).
However, even small changes in birth weight could result in substantial avoided mortality
benefits.
44 Klein et al. (2018) indicate that birth weight changes in response to reduced environmental exposures are likely to be small and
simulated changes in birth weight up to 100 g. Kamai et al. (2019) found maximum changes in birth weight in response to
reduced exposures to cigarette smoke of 150 g, while Windham et al. (2008) found a maximum decrement in mean birth weight
of 200 g for infants of smokers.
45 Under the final rule, the EPA estimates that the 200 g birth weight cap is triggered in 0.01 percent of affected infants.
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Two studies showed statistically significant relationships between incremental changes in birth
weight and infant mortality: Almond et al. (2005) and Ma and Finch (2010). Ma and Finch
(2010) used 2001 National Center for Health Statistics (NCHS) linked birth/infant death data for
singleton and multiple birth infants among subpopulations defined by sex and race/ethnicity to
estimate a regression model assessing the associations between 14 key birth outcome measures,
including birth weight, and infant mortality. They found notable variation in the relationship
between birth weight and mortality across race/ethnicity subpopulations, with odds ratios for
best-fit birth weight-mortality models ranging from 0.8-1 (per 100 g birth weight change).
Almond et al. (2005) used 1989-1991 NCHS linked birth/infant death data for multiple birth
infants to analyze relationships between birth weight and infant mortality within birth weight
increment ranges. For their preferred model, they reported coefficients in deaths per 1,000 births
per 1 g increase in birth weight that range from -0.420 to -0.002. However, the data used in
these studies (Almond et al., 2005 and Ma & Finch, 2010) are outdated (1989-1991 and 2001,
respectively). Given the significant decline in infant mortality over the last 30 years (ICF, 2020)
and other maternal and birth characteristics that are likely to influence infant mortality (e.g.,
average maternal age and rates of maternal smoking), the birth weight-mortality relationship
estimates from Almond et al. (2005) and Ma and Finch (2010) are likely to overestimate the
benefits of birth weight changes.
Considering the discernible changes in infant mortality over the last 30 years, the EPA developed
a regression analysis to estimate the relationship between birth weight and infant mortality using
the Period/Cohort Linked Birth-Infant Death Data Files published by NCHS from the 2017
period/2016 cohort and the 2018 period/2017 cohort (CDC, 2017, 2018). These data provide
information on infants who are delivered alive and receive a birth certificate.46 The EPA selected
variables of interest for the regression analysis, including maternal demographic and
socioeconomic characteristics, maternal risk and risk mitigation factors (e.g., number of prenatal
care visits, smoker status), and infant birth characteristics. The EPA included several variables
used in Ma and Finch (2010) (maternal age, maternal education, marital status, and others - see
Appendix E for the complete list) as well as additional variables to augment the set of covariates
included in the analyses. In addition, the EPA developed separate models for different
race/ethnicity categories (non-Hispanic Black, non-Hispanic White, and Hispanic) and interacted
birth weight with categories of gestational age, similar to Ma and Finch (2010).47 Appendix E
provides details on model development and regression results.
Table 6-10 presents the resulting odds ratios and marginal effects (in terms of deaths per 1,000
births for every 1 g increase in birth weight) estimated for changes in birth weight among
different gestational age categories in the mortality regression models for non-Hispanic Black,
non-Hispanic White, and Hispanic race/ethnicity subpopulations. Marginal effects for birth
weight among different gestational age categories indicate the change in the incidence of infant
46 These data do not include information on miscarriages or stillbirths.
47 Note that Ma and Finch (2010) developed a model for infants with Mexican heritage, rather than the Hispanic population, and
interacted birth weight with gestational age as a continuous interaction variable, rather than developing different birth weight
variables per gestational age category. Ma and Finch (2010) did not consider the Hispanic paradox, a term for the
epidemiological finding that Hispanic and Latino Americans often have lower risk of poor health outcomes compared to
race/ethnicity groups with higher income and education levels..
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mortality per 1 g increase in birth weight.48 Marginal effects for birth weight among gestational
age categories vary across different race/ethnicity subpopulations. As shown in Figure 6-2, the
marginal effects for birth weight among different gestational age categories are higher in the
non-Hispanic Black model than in the non-Hispanic White and Hispanic models, particularly for
extremely and very preterm infants, indicating that LBW increases the probability of mortality
within the first year more so among non-Hispanic Black infants than among non-Hispanic White
and Hispanic infants.
The EPA relies on odds ratios estimated using the birth weight-mortality regression model to
assess mortality outcomes of reduced exposures to PFOA/PFOS in drinking water under the
regulatory alternatives. To obtain odds ratios specific to each race/ethnicity and 100 g birth
weight increment considered in the birth weight benefits model,49 the EPA averaged the
estimated odds ratios for 1 g increase in birth weight over the gestational age categories using the
number of infants (both singleton and multiple birth) that fall into each gestational age category
as weights. Separate gestational age category weights were computed for each 100 g birth weight
increment and race/ethnicity subpopulation within the 2017 period/2016 cohort and 2018
period/2017 cohort Linked Birth-Infant Death Data Files. The weighted birth weight odds ratios
are then used in conjunction with the estimated change in birth weight and baseline infant
mortality rates to determine the probability of infant death under the regulatory alternatives, as
described further in Section 6.4.3.1.
48 All marginal effect values for birth weight among different gestational age categories are negative and decrease in magnitude
with each higher gestational age category, indicating that the probability of mortality decreases as gestational age and birth
weight increase. For example, using marginal effects from the non-Hispanic Black model, for extremely preterm infants a 100 g
birth weight increase on average would translate to 20 fewer infant deaths per 1,000 births in this gestational age category or a
2% decrease in the probability of mortality within one year of birth. The same birth weight increase at a higher gestational age
would still decrease mortality risk but to a lesser extent.
49 The birth weight risk reduction model evaluates changes in birth weight in response to PFOA/PFOS drinking water level
reductions for infants who fall into 100 g birth weight increments (e.g., birth weight 0-99 g, 100-199 g, 200-299 g... 8,000-8,099
g, 8,100-8,165 g).
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Non-Hispanic Black —a—Non-Hispanic White -•-Hispanic
o.oo
-0.05
o
o
o
§ -0.10
CD
c
CD
i/>
03
CD
¥ -0.15
-0.20
-0.25
Extremely Preterm Very Preterm Moderately Preterm Term
Gestational Age Category
Figure 6-2: Comparison of Change in Incidence of Infant Death per 1 g Increase in Birth
Weight by Gestational Age Category and Race/Ethnicity (Deaths per 1,000 Births)
Notes: Gestational age categories defined as extremely preterm (<=28 weeks), very preterm (>28 weeks and <=32 weeks),
moderately preterm (>32 weeks and <=37 weeks), and term (>37 weeks). Data based on the 2016/17 and 2017/18 CDC Period
Cohort Linked Birth-Infant Death Data Files obtained from NCHS/NVSS. Marginal effects and odds ratios are estimated using a
regression model that also includes covariates representative of infant birth characteristics in addition to birth weight, maternal
demographic characteristics, and maternal risk factors. Details are included in Appendix E.
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Table 6-10: Race/Ethnicity- and Gestational Age-Specific Birth Weight Marginal Effects
and Odds Ratios from the Mortality Regression Models
Gestational Age Marginal Effect per
Category*1 1,000 births (95% CI) ( '
Non-Hispanic Black
Extremely Preterm
-0.20400
(-0.21910, -0.18890)
0.99817
(0.99802, 0.99832)
Very Preterm
-0.04580
(-0.04820, -0.04340)
0.99816
(0.99804, 0.99827)
Moderately Preterm
-0.01030
(-0.01080, -0.009850)
0.99852
(0.99846, 0.99857)
Term
-0.00453
0.99856
(-0.00472, -0.00434)
(0.99851,0.9986)
Non-Hispanic White
Extremely Preterm
-0.12160
(-0.13080, -0.11240)
0.99866
(0.99855, 0.99878)
Very Preterm
-0.03290
(-0.03430, -0.03140)
0.9985
(0.99842, 0.99858)
Moderately Preterm
-0.00677
(-0.00702, -0.00652)
0.99867
(0.99863, 0.99872)
Term
-0.00228
0.99865
(-0.00236, -0.00221)
(0.99861, 0.99868)
Hispanic
Extremely Preterm
-0.15260
(-0.16770, -0.13750)
0.99835
(0.99817, 0.99853)
Very Preterm
-0.03290
(-0.03510, -0.03070)
0.99846
(0.99835, 0.99858)
Moderately Preterm
-0.00626
(-0.00659, -0.00592)
0.99856
(0.99849, 0.99862)
Term
-0.00219
0.99849
(-0.00229, -0.00208)
(0.99844, 0.99855)
Notes:
aData based on the 2016/17 and 2017/18 CDC Period Cohort Linked Birth-Infant Death Data Files obtained from
NCHS/NVSS. Marginal effects and odds ratios are estimated using a regression model that also includes covariates
representative of infant birth characteristics in addition to birth weight, maternal demographic characteristics, and maternal
risk factors. All effects were statistically significant at the 5% level. Additional details are included in Appendix E.
bGestational age categories defined as extremely preterm (<=28 weeks), very preterm (>28 weeks and <=32 weeks),
moderately preterm (>32 weeks and <=37 weeks), and term (>37 weeks).
The EPA weighted the race/ethnicity-specific mortality odds ratios in Table 6-10 by the
proportions of the infant populations who fell into each gestational age within a 100 g birth
weight increment, based on the 2016/17 and 2017/18 period cohort data, to obtain a weighted
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mortality odds ratio estimate for each modeled race/ethnicity subpopulation and 100 g birth
weight increment. The weighted mortality odds ratios are shown in Figure 6-3.50
50 Note that weighted mortality odds ratios for the Hispanic population at larger birth weight increments fluctuate between
0.99849 and 0.99856. Due to the small sample size of the Hispanic infant population within these birth weight increments, 100
percent of infants in a specific birth weight increment is associated with either moderately preterm or term gestational age
categories. For instance, all Hispanic infants included in the analysis who were between 7,800 and 7,899 g were full-term, while
all Hispanic infants who were between 7,900 and 7,999 g were moderately preterm. Therefore, the weighted mortality odds ratio
for Hispanic infants between 7,800 and 7,899 g is equal to the full-term mortality odds ratio estimated for the Hispanic infant
population, while the weighted mortality odds ratio for Hispanic infants between 7,900 and 7,999 g is equal to the moderately
preterm mortality odds ratio estimated for the Hispanic infant population.
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Non-Hispanic Black -----. Non-Hispanic White/Other Hispanic
o
LnoLOOLOOLOOLnoLnOLOOLOO
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Note that the EPA did not model the relationship between birth weight and infant mortality for
other race/ethnicity subpopulations because doing so for each individual race/ethnicity or
combination of all "other" races/ethnicities is precluded by very low sample sizes (i.e., imprecise
coefficients and imprecise marginal effects). The EPA applies the weighted mortality odds ratios
estimated for the non-Hispanic White subpopulation to the "other" race/ethnicity subpopulation
because of similarities in infant death rates from 2016 to 2018 among non-Hispanic White
infants (4.75 deaths per 1,000) and non-Hispanic other infants (4.45 deaths per 1,000).
6.4.3.2 Estimating the Number of Infants Affected by Birth Weight
Changes and Changes in Infant Mortality
Based on reduced serum PFOA/PFOS exposures under the regulatory alternatives and the
estimated relationship between birth weight and infant mortality, the EPA estimates the
subsequent change in birth weight for those infants affected by decreases in PFOA/PFOS and
changes in the number of infant deaths. The EPA evaluates these changes at each PWS EP
affected by the regulatory alternatives and the calculations are performed for each race/ethnicity
group, 100 g birth weight category, and year of the analysis.
6.4.3.2.IChanges in Birth Weight
The EPA combined estimated average annual changes in PFOA and PFOS serum levels for
women of childbearing age (15 to 44 years old) by analysis year, race/ethnicity group, and PWS
EP (see Section 6.3.2) with the serum PFOA/PFOS-birth weight exposure-response slope factors
(see Table 6-9) to compute average annual changes in birth weight per newborn as follows:
Equation 6:
ABWy r p max (^CAP,SFBy^ppQj^ ¦ APFOA_SeTU7Yiyrp + 1 APFOSserumy r p)
Where ABW is the change in birth weight under the regulatory alternatives, y is the analysis
year, r is the race/ethnicity group, p is the PWS EP analyzed; APFOA_Serum is the change in
PFOA serum for women of childbearing age under the regulatory alternatives; APFOS_Serum is
the change in PFOS serum for women of childbearing age under the regulatory alternatives;
SFbw,pfoa ar|d SFbwpfos are the serum-birth weight exposure-response slope factors for PFOA
and PFOS, respectively; and CAP is the 200 g cap placed on the birth weight changes.
6.4.3.2.2Changes in Infant Death Rate
The EPA used average annual changes in birth weight under the regulatory alternatives
(Equation 6) to estimate the associated infant mortality odds ratios, ORy i r p.
Equation 7:
0Ry,i,r,p = exp(ABWyX:P ¦ ln(0%))
Where y is the analysis year, i is the 100 g birth weight increment, r is the race/ethnicity group,
p is the PWS EP analyzed, and ORi r is the weighted odds ratio for a 1 g birth weight increase
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associated with each 100 g birth weight increment for a given race/ethnicity category (see
Section 6.4.3).
The EPA combined the result of Equation 7 with the baseline infant death rate to estimate the
infant death rate under the regulatory alternatives, DRRegulatory Alternativeyiirv.
Equation 8:
ORy,i,r,p ' DRBaseline,y,i,r,p
DRRegulatory Alternative,y,i,r,p
1 ORy,i,r,p ' DRBaseline,y,i,r,j.
Where DRBaseUneyirp is the baseline death rate per birth computed from 2012-2018 death rates
per 500 g birth weight increment (CDC, 2020a),51 y is the analysis year, i is 100 g birth weight
increment, r is the race/ethnicity group, p is the PWS EP analyzed, and ORyirp is the mortality
odds ratio associated with the annual change in birth weight under the regulatory alternatives.
6.4.3.2.3 Affected Infant Population Size
The annual race/ethnicity- and PWS EP-specific number of infants affected by changes in
PFOA/PFOS drinking water levels is based on the 2021 retail population served at each PWS
from the SDWIS/Fed and 2021 race/ethnicity-specific population estimates from the U.S. Census
(U.S. Census Bureau, 2020a; see Appendix B). Because birth rates per race/ethni city group and
100 g birth weight increment are often suppressed due to lack of data, the EPA multiplied state-
level birth rates per race/ethni city group from the Centers for Disease Control and Prevention
(CDC) Linked Birth/Infant Death records from 2012 to 2018 (CDC, 2020a) by the ratio of
infants falling within each 100 g birth weight increment per state (not specific to race/ethni city)
to the total number of infants per state to distribute the number of affected infants in each state.
The EPA imputed state-level data that was missing from the 2012-2018 CDC Linked
Birth/Infant Death records with data at the census region level. The EPA used the same approach
to assign average birth weights per race/ethni city group over the 100 g birth weight increments
for use in COI data matching (See Section 6.4.4). Using the 2012-2018 imputed state-level birth
rate data, the EPA computed the share of births that correspond to each 100 g birth weight
increment (i), race/ethni city (r), and PWS EP (p) as the ratio of race/ethni city- and state-specific
(s) birth rates52 in a particular birth weight increment to the sum of birth rates associated with all
birth weight increments:
Equation 9:
£ „¦ -l2012-2018,i,r,sj
Share of Birthsi r„ =
SUm (Si?201Z—2018,i.r.s)
Next, the EPA assumed that the share of births within each 100 g birth weight increment (from
Equation 9) would remain constant throughout the period of analysis and estimated the annual
51 The EPA assumed that the same death rate applies to infants in all 100 g birth weight increments falling in the 500 g birth
weight range.
52 In this analysis, the EPA applies state-specific birth rates that correspond to the state for which each PWS EP is located.
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affected infant population size for each future analysis year (y), 100 g birth weight increment (i),
race/ethnicity group (r), and PWS EP (p), Birthsyirp as follows:
Equation 10:
Birthsy i r p = BirthSy r v ¦ Share of Birthsirp
6.4.3.2.4 Infant Deaths Avoided and the Number of Surviving Infants
The EPA used the estimated annual infant population size, Birthsy i r p, along with infant death
rates, DR^asenne y i r p and DR^egU[ai-ory Alternative ,y ,i,r ,p-: to compute the annual number of
deaths expected at baseline (Equation 11) and the annual number of deaths expected under the
regulatory alternatives (Equation 12):
Equation 11:
DeathsBaseiiney i r p BirthSyir p ¦ DRBasenney i r p
Equation 12:
DeathsReguiatoryAiternativey i r p — BirthSy irp ¦ DRRegUiat:ory Alternative,y,,i,r,P
The EPA estimated the annual number of avoided infant deaths, Avoided Deathsy i r p, as:
Equation 13:
Avoided DeathSy i r p DeathSgaseiine y i r p DeQ,thSRegUiai:ory Alternative,y,i,r,P
The EPA computed the population of surviving infants whose birth weight would be affected by
changes in PFOA/PFOS exposure {SurvivorsReguiatory Alternative,y,i,r,p) as the number of births
less the number of deaths under the regulatory alternatives. The EPA estimated the annual
number of avoided infant deaths, Avoided Deathsy i r p, as:
Equation 14:
SurvivorsReguiatory Alternatively,iir,p — BirthSy i r p 1 (1 — DRRegUiatory Alternative,y,i,r,p)
6.4.4 Valuation of Reduced Birth Weight Impacts
The EPA uses the Value of Statistical Life to estimate the benefits of reducing infant mortality
and COI to estimate the economic value of increasing birth weight in the population of surviving
infants born to mothers exposed to PFOA and PFOS in drinking water. Value of Statistical Life
updating information is provided in Section 2.2.
The EPA's approach to monetizing benefits associated with incremental increases in birth weight
resulting from reductions in drinking water PFOA/PFOS levels relies on avoided medical costs
associated with various ranges of birth weight. Although the economic burden of treating infants
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at various birth weights also includes non-medical costs, very few studies to date have quantified
such costs (Klein & Lynch, 2018; ICF, 2021). The EPA selected the medical cost function from
Klein and Lynch (2018) to monetize benefits associated with the estimated changes in infant
birth weight resulting from reduced maternal exposure to PFOA/PFOS.53 The EPA selected the
cost function from Klein and Lynch (2018) because it is based on recent data on birth weight,
healthcare utilization, and healthcare costs that encompass a longer time period and a larger
population than data used in other studies (e.g., Almond et al., 2005). Additional studies that the
EPA reviewed provided only an incremental cost for LBW infants compared to normal birth
weight infants (greater or equal to 2,500 g; e.g., Almond et al., 2010 and Malits et al., 2018).
Klein and Lynch (2018), on the other hand, estimated incremental medical costs as a function of
birth weight over the range from 900 to 4,500 g and used a continuous spline function (Figure
6-4), rather than allowing for a discontinuity at the very low birth weight level (i.e., < 1,500
grams). Table 6-11 summarizes the incremental cost changes associated with birth weight
increases from Klein and Lynch (2018).
$80,000
o" $70,000
o
r\i
$60,000
L_
ro
>. $50,000
¦P
1/5
S $40,000
15 $30,000
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Table 6-11: Simulated Cost Changes for Birth Weight Increases ($2022) (Based on Klein
and Lynch, 2018 Table 8)
Simulated Cost Changes for Birth Weight Increases, Dollars per Gram
Birth Weight3 ($2022)b
+0.04 lb (+18 g)
+0.11 lb (+50 g)
+0.22 lb (+100 g)
2 lb (907 g)
-$131.66
-$117.44
-$113.82
2.5 lb (1,134 g)
-$98.72
-$88.07
-$85.35
3 lb (1,361 g)
-$74.03
-$66.04
-$64.00
3.3 lb (1,497 g)
-$62.29
-$55.56
-$53.85
4 lb (1,814 g)
-$41.63
-$37.13
-$35.99
4.5 lb (2,041 g)
-$31.21
-$27.84
-$26.98
5 lb (2,268 g)
-$23.41
-$20.88
-$20.23
5.5 lb (2,495 g)
-$0.97
-$0.88
-$0.87
6 lb (2,722 g)
-$0.95
-$0.86
-$0.86
7 lb (3,175 g)
-$0.92
-$0.83
-$0.83
8 lb (3,629 g)
-$0.89
-$0.81
-$0.80
9 lb (4,082 g)
$3.28
$2.99
$3.01
10 lb (4,536 g)
$3.69
$3.37
$3.39
Notes:
aNote that simulated medical costs increase, rather than decrease, in response to increased birth weight changes among high
birth weight infants (those greater than 8 lb). Among high birth weight infants, there is a higher risk of birth trauma, metabolic
issues, and other health problems (Klein & Lynch, 2018).
bValues scaled from $2010 to $2022 using the medical care Consumer Price Index (U.S. Bureau of Labor Statistics, 2022a).
Using the incremental cost changes from Klein and Lynch (2018), the EPA calculates the change
in medical costs resulting from changes in birth weight among infants in the affected population
who survived the first year following birth. To do so, the EPA linearly interpolates between the
birth weight and cost values presented in Klein and Lynch (2018) to obtain a cost value for every
1 g birth weight increment, as shown in Figure 6-5. The EPA then matches this interpolated birth
weight value to the nearest baseline average birth weight value in each 100 g birth weight
increment to obtain the simulated cost change for birth weight increases that are estimated to be
between zero and 18 g, between 19 and 50 g, and between 51 and 100 g or more.54
54 Note that the EPA caps birth weight changes at 200 g, as described in earlier sections. The EPA assumes that the cost of illness
estimates for birth weight increases between 51 and 100 g apply to birth weight increases greater than 100 g.
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$10
fN*
r\i
I "$i°
QJ
CO
j,
£ -$30
u
_c
CU)
§ -$50
T—1
o
m -$70
o
ld
M
oo -$90
0)
Q_
8 -$no
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6.4.5 Results
Table 6-12 to Table 6-15 provide the health effects avoided and valuation associated with birth
weight impacts.
Table 6-12: National Birth Weight Benefits, Final Rule (PFOA and PFOS MCLs of 4.0
ppt each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million
$2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Increase in Birth Weight
129.6
216.8
304.1
(millions of grams)
Number of Birth Weight-
781.9
1.301.7
1,823.6
Related Deaths Avoided
Total Annualized Birth
$124.85
$209.00
$292.78
Weight Benefits (Million
$2022)b
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. Quantifiable benefits are increased under final rule table results relative to the other options presented because
of modeled PFHxS occurrence, which results in additional quantified benefits from co-removed PFOA and PFOS.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-13: National Birth Weight Benefits, Option la (PFOA and PFOS MCLs of 4.0
ppt) (Million $2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Increase in Birth Weight
128.8
215.6
302.1
(millions of grams)
Number of Birth Weight-
777.4
1.294.4
1,812.9
Related Deaths Avoided
Total Annualized Birth
$124.82
$207.82
$291.00
Weight Benefits (Million
$2022)b
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
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Table 6-14: National Birth Weight Benefits, Option lb (PFOA and PFOS MCLs of 5.0
ppt) (Million $2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Increase in Birth Weight
111.3
185.6
260.3
(millions of grams)
Number of Birth Weight-
668.9
1.114.7
1,561.2
Related Deaths Avoided
Total Annualized Birth
$107.34
SI 78.97
$250.00
Weight Benefits (Million
$2022)b
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-15: National Birth Weight Benefits, Option lc (PFOA and PFOS MCLs of 10.0
ppt) (Million $2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Increase in Birth Weight
62.1
102.0
142.4
(millions of grams)
Number of Birth Weight-
375.8
616.6
859.1
Related Deaths Avoided
Total Annualized Birth
$60.24
$98.97
$137.75
Weight Benefits (Million
$2022)b
Note: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
6.5 Cardiovascular Disease
6.5.1 Overview of the Cardiovascular Disease Risk Analysis
Figure 6-6 provides an overview of the approach used to quantify and value the changes in CVD
risk associated with reductions in exposure to PFOA and PFOS via drinking water. Section 4.4
details the PWS EP-specific PFOA/PFOS drinking water occurrence estimation and Section 6.3
describes modeling of serum PFOA/PFOS concentrations. EP-specific time series of the
differences between serum PFOA/PFOS concentrations under baseline and regulatory
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alternatives are inputs into this analysis. For each EP, evaluation of the changes in CVD risk
involves the following key steps:
1. Estimation of annual changes in TC55 and BP levels using exposure-response functions
for the potential effects of serum PFOA/PFOS on these biomarkers;
2. Estimation of the annual incidence of fatal and non-fatal first hard CVD events,56 defined
as fatal and non-fatal myocardial infarction (MI; i.e., heart attack), fatal and non-fatal IS,
or other coronary heart disease (CHD) death occurring in populations without prior CVD
event experience (D'Agostino et al., 2008; Goff et al., 2014; Lloyd-Jones et al., 2017),
and post-acute CVD mortality corresponding to baseline and regulatory alternative TC
and BP levels in all populations alive during or born after the start of the evaluation
period; and
3. Estimation of the economic value of reducing CVD mortality and morbidity from
baseline to regulatory alternative levels, using the Value of Statistical Life and COI
measures, respectively.
Section 6.5.2 discusses the exposure-response models for TC and BP. Section 6.5.3 details the
estimated CVD risk reductions using the Pooled Cohort ASCVD risk model (Goff et al., 2014)
and the life table approach. Section 6.5.4 discusses the EPA's valuation methodology for fatal
and non-fatal CVD events. Section 6.5.5 presents the results of the analysis.
55 The EPA discusses the relationship between PFOA/PFOS exposure and other forms of cholesterol in Appendix F.
56 Hard CVD events include fatal and non-fatal myocardial infarction, fatal and non-fatal stroke, and other coronary heart disease
mortality.
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Legend:
Result of
upstream analysis
Data/Inputs
# Analysis step
Serum PFOA and PFOS
concentration difference
between baseline and
regulatory alternative
TC and BP difference
between baseline and
treatment scenario
Life table CVD model
Location-specific
population size
Prevalence and
incidence of CVD
events
Annual cause-specific
mortality rates and life
table information'
Baseline total
cholesterol
Blood pressure level
and treatment status
Smoking and
diabetes status
Medical costs of
CVD treatment
Change in incidence
of non-fatal CVDb
Value of reduced
CVD incidence
Change in incidence
of fatal CVDc
\
1
r
Value of avoided
excess mortality
Value of a
statistical life
Total value of reduced CVD
Abbreviations: PFOA - perfluorooctanoic acid, PFOS - perfluorooctanesulfonic acid, TC - total cholesterol, BP -
blood pressure, CVD - cardiovascular disease, ASCVD - atherosclerotic cardiovascular disease, MI - myocardial
infarction, IS - ischemic stroke, CFTD - coronary heart disease
Notes:
:'Data from the Centers for Disease Control (CDC) and Prevention.
^Non-fatal CVD includes non-fatal first Ml and non-fatal first IS
cFatal CVD includes fatal first MI, fatal first IS, other fatal first CUD events, and post acute CVD mortality among
survivors of the first MI and the first IS
Figure 6-6: Overview of the CVD Risk Model
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6.5.2 Cardiovascular Disease Exposure-Response Analyses
6.5.2.1 Estimation of Cholesterol Changes
The ASCVD model includes TC as a predictor of first hard CVD events. The EPA did not
identify any readily available relationships for PFOA or PFOS and TC that were specifically
relevant to the age group of interest (40-89 years, the years for which the ASCVD model
estimates the probability of a first hard CVD event). Therefore, the agency developed a meta-
analysis of studies reporting associations between serum PFOA or PFOS and TC in general
populations (e.g., populations that are not a subset of workers or pregnant women). Statistical
analyses that combine the results of multiple studies, such as meta-analyses, are widely applied
to investigate the associations between contaminant levels and associated health effects. Such
analyses are suitable for economic assessments because they can improve precision and
statistical power (Engels et al., 2000; Deeks, 2002; Riicker et al., 2009). Appendix F provides
details on the studies selection criteria, meta-data development, meta-analysis results, and
discussion of the uncertainty and limitations inherent in the EPA's exposure-response analysis.
The EPA identified studies for inclusion in the meta-analysis using data from literature reviews,
including those performed by the ATSDR in the development of their Toxicological Review
Public Comment Draft (ATSDR, 2018), which included literature through mid-2017, and those
performed for developing the EPA's Final Human Health Toxicity Assessment for PFOA and
PFOS (U.S. EPA, 2024e; U.S. EPA, 2024f), which included studies published from 2016
through September 2020. The EPA included studies in the meta-analysis if they reported
quantitative estimates (e.g., regression coefficients) and measures of uncertainty (e.g., standard
errors, confidence intervals) of associations between serum PFOA or PFOS and TC or HDLC in
general population adults aged 20 years and older. The EPA included a total of 14 studies in the
meta-analysis. Of these, 12 studies were used to develop exposure-response relationships for
serum PFOA or PFOS and TC (i.e., not all relevant studies report the effects for both PFOA and
PFOS). The unit in the meta-analysis was the change in TC (or HDLC) in mg/dL per increases in
serum PFOA or PFOS.
Table 6-16 summarizes the 14 studies that the EPA identified from literature reviews and used to
derive slope estimates for PFOA and PFOS associations with serum TC levels.57 Six of the
studies that the EPA retained for use in the meta-analysis were based on serum PFAS and serum
TC measurements from the U.S. general population (National Health and Nutrition Examination
Survey [NHANES]) (Dong et al., 2019; Fan et al., 2020; He et al., 2018; Jain & Ducatman,
2019a; H.-S. Liu et al., 2018; Nelson et al., 2010); there were also general population studies
from Canada (Fisher et al., 2013), Sweden (Y. Li et al., 2020), Taiwan (Yang et al., 2018; C. Y.
Lin et al., 2020), and Henan Province, China (Fu et al., 2014). Chateau-Degat et al. (2010)
reported on the association between PFOS and TC in a Canadian Inuit population. The EPA also
retained the results from a study of a highly exposed population in the U.S. (the C8 cohort)
(Steenland et al., 2009) and from a study using participants in a U.S. diabetes prevention
program (Lin et al., 2019). The EPA retained results from Steenland et al. (2009) because serum
levels in the examined cohort were only modestly elevated compared to less exposed populations
(e.g., the median serum PFOA concentration in this cohort was 27 ng/mL, with an interquartile
57 For this effort, the EPA focused on PFOA and PFOS, since these are by far the most well-studied perfluorinated compounds.
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range of 13.1 to 67 ng/mL). The EPA retained results from Lin et al. (2019) because the
examined cohort included pre-diabetic adults enrolled in a diabetes prevention program; thus,
this cohort was representative of a large portion of the U.S. adult population.
Table 6-16: Studies Selected for Inclusion in the Meta-Analyses
TC and Serum PFAS
Relationship Evaluated in
Author and Year Title Study
PFOA
PFOS
Steenland et al.,
2009a-d
Association of Perfluorooctanoic Acid and Perfluorooctane
Sulfonate With Serum Lipids Among Adults Living Near a
X
X
Chemical Plant
Chateau-Degat et
al., 2010ad
Effects of Perfluorooctanesulfonate Exposure on Plasma
Lipid Levels in the Inuit Population of Nunavik (Northern
X
Quebec)
Nelson et al.,
2010a-d
Exposure to Polyfluoroalkyl Chemicals and Cholesterol,
Body Weight, and Insulin Resistance in the General U.S.
X
X
Population
Fisher etal., 2013
a,d
Do Perfluoroalkyl Substances Affect Metabolic Function and
Plasma Lipids? —Analysis of the 2007-2009, Canadian
X
X
Health Measures Survey (CHMS) Cycle 1
Associations Between Serum Concentrations of
Fu et al., 2014a d
Perfluoroalkyl Acids and Serum Lipid Levels in a Chinese
Population
PFOA is Associated with Diabetes and Metabolic Alteration
X
X
He et al., 2018c
in US Men: National Health and Nutrition Examination
Survey 2003-2012
Association Among Total Serum Isomers of Perfluorinated
X
X
Liu et al., 2018c
Chemicals, Glucose Homeostasis, Lipid Profiles, Serum
X
X
Protein and Metabolic Syndrome in Adults: NHANES,
2013-2014
Using 2003-2014 U.S. NHANES Data to Determine the
Dong et al., 2019b
Associations Between Per- and Polyfluoroalkyl Substances
and Cholesterol: Trend and Implications
X
X
Jain et al., 2019b
Roles of Gender and Obesity in Defining Correlations
X
X
Between Perfluoroalkyl Substances and Lipid/Lipoproteins
P.-I. D. Lin et al.,
2019b
Per- and Polyfluoroalkyl Substances and Blood Lipid Levels
in Pre-Diabetic Adults—Longitudinal Analysis of the
X
X
Diabetes Prevention Program Outcomes Study
Fan et al., 2020b
Serum Albumin Mediates the Effect of Multiple Per- and
X
X
Polyfluoroalkyl Substances on Serum Lipid Levels
Associations Between Perfluoroalkyl Substances and Serum
Y. Li et al., 2020b
Lipids in a Swedish Adult Population With Contaminated
Drinking Water
X
X
Abbreviations: TC - total cholesterol; PFOS - perfluorooctane sulfonic acid; PFOA - perfluorooctanoic acid; PFAS - per-and
polyfluoroalkyl substances.
Notes:
aStudies identified based on ATSDR literature review.
bStudies identified based on the EPA's literature review.
cStudies available in both assessments.
dStudies available in PFOA and/or PFOS health effects support documents (U.S. EPA, 2016e; U.S. EPA, 2016f).
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The EPA developed exposure-response relationships between serum PFOA/PFOS and TC for
use in the CVD analysis using the meta-analyses restricted to studies of adults in the general
population reporting similar models. The EPA used untransformed serum PFOA/PFOS to reduce
bias due to back-transformations of effect estimates. For studies that provided results only for
log-transformed serum PFOA/PFOS (five studies) or log-transformed outcomes (two studies), or
both log-transformed serum PFOA/PFOS and outcomes (two studies), the EPA approximated the
results for an untransformed analysis using the approach outlined by Rodriguez-Barranco et al.
(2017) and Dzierlenga et al. (2020). When using studies reporting linear associations between
TC and serum PFOA or PFOS, the EPA estimated a positive increase in TC of 1.57 (95% CI:
0.02, 3.13) mg/dL per ng/mL serum PFOA (p-value = 0.048), and of 0.08 (95% CI: -0.01, 0.16)
mg/dL per ng/mL serum PFOS (p-value = 0.064). The EPA selected the pooled slope estimate
based on the studies using linear models to ease interpretability and to reduce bias due to back-
transformations of effect estimates with log-transformed outcomes or exposures (see Appendix F
for details). While the association for PFOS and TC is not significant at the 0.05 confidence
level, it is significant at the 0.10 confidence level (p-value = 0.064). Furthermore, the literature
provides sufficient support of a positive association (e.g., Chateau-Degat et al., 2010; Dong et
al., 2019; U.S. EPA, 2024e; U.S. EPA, 2024f). The studies are large with more than 700 and
8,900 participants, respectively (Chateau-Degat et al., 2010; Dong et al., 2019) and have low risk
of bias. In addition, the estimated values are supported by sensitivity analyses and by the
estimates from potential candidate studies from exposure-response modeling for ongoing agency
efforts (Dong et al., 2019). Based on the systematic literature review of epidemiologic studies
published through February 2023 for developing the EPA's Final Human Health Toxicity
Assessments for PFOA and PFOS, the available evidence supports a positive association
between PFOS and TC in the general population (U.S. EPA, 2024e; U.S. EPA, 2024f). For more
information on the systematic review and results, see the EPA's Final Human Health Toxicity
Assessments for PFOA and PFOS (U.S. EPA, 2024e; U.S. EPA, 2024f).
Note that the EPA sought comments from the EPA SAB on the cardiovascular disease exposure-
response approach (U.S. EPA, 2022i). The SAB recommended that the EPA evaluate how the
inclusion of HDLC effects would influence results. The EPA evaluated the inclusion of HDLC
effects in a sensitivity analysis, described in Appendix K.
6.5.2.2 Estimation of BP Changes
PFOS exposure has been linked to other cardiovascular outcomes, such as systolic BP and
hypertension (Liao et al., 2020; U.S. EPA, 2024e). Because systolic BP is another predictor used
by the ASCVD model, the EPA included the estimated changes in BP from reduced exposure to
PFOS in the CVD analysis. The EPA selected the slope from the Liao et al. (2020) study — a
high confidence study conducted based on U.S. general population data from NHANES cycles
2003-2012. Liao et al. (2020) estimated an increase of 1.35 (95% CI: 0.18, 2.53) in mmHg
systolic BP per logl0(ng/mL) PFOS among those not using antihypertensive medications. For
the purposes of this analysis, the EPA converted this slope to 0.044 (95% CI: 0.006, 0.083)
mmHg per ng/mL. The evidence on the associations between PFOA and BP is not as consistent
as for PFOS (see Section 6.2.2.1.2). Therefore, the EPA is not including effect estimates for the
serum PFOA-BP associations in the CVD analysis.
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6.5.3 Estimation of Cardiovascular Disease Risk Reductions
The EPA relies on the life table-based approach to estimate CVD risk reductions because
(1) changes in serum PFOA/PFOS in response to changes in drinking water PFOA/PFOS occur
over multiple years, (2) CVD risk, relying on the ASCVD model, can be modeled only for those
older than 40 years without prior CVD history, and (3) individuals who have experienced non-
fatal CVD events have elevated mortality implications immediately and within at least five years
of the first occurrence.58 Recurrent life table calculations are used to estimate a PWS EP-specific
annual time series of CVD event incidence for a population cohort characterized by sex,
race/ethnicity, birth year, age at the start of the PFOA/PFOS evaluation period (i.e., 2024), and
age- and sex-specific time series of changes in TC and BP levels obtained by combining serum
PFOA/PFOS concentration time series (Section 6.3) with exposure-response information
(Section 6.5.3). Baseline and regulatory alternatives are evaluated separately, with regulatory
alternative TC and BP levels estimated using baseline information on these biomarkers from
external statistical data sources and modeled changes in TC and BP due to conditions under the
regulatory alternatives (see Appendix G for detailed information on data sources used in CVD
modeling).
The EPA estimated the incidence of first hard CVD events based on TC serum and BP levels
using the ASCVD model (Goff et al., 2014), which predicts the 10-year probability of a hard
CVD event to be experienced by a person without a prior CVD history (see Section 6.5.3.2).59
The EPA adjusted the modeled population cohort to exclude individuals with pre-existing
conditions, as the ASCVD risk model does not apply to these individuals. For BP effects
estimation, the EPA further restricts the modeled population to those not using antihypertensive
medications for consistency with the exposure-response relationship (see Section 6.5.3.2 for
detail). Modeled first hard CVD events include fatal and non-fatal MI, fatal and non-fatal IS, and
other CHD mortality. The EPA has also estimated the incidence of post-acute CVD mortality
among survivors of the first MI or IS within 6 years of the initial event (Section 6.5.3.3).
The estimated CVD risk reduction resulting from reducing serum PFOA and serum PFOS
concentrations is the difference in annual incidence of CVD events (i.e., mortality and morbidity
associated with first-time CVD events and post-acute CVD mortality) under the baseline and
regulatory alternatives. Appendix G provides detailed information on all CVD model
components, computations, and sources of data used in modeling.
6.5.3.1 Life Table Calculations
The CVD model integrates the ASCVD model predictions and post-acute CVD mortality rates in
the series of recurrent calculations that produce a life table estimate for the affected population
cohort (e.g., non-Hispanic White females aged 70 years at the beginning of the evaluation
period). For each PWS EP, the EPA evaluates population cohorts defined by a combination of
birth year, age, sex (males and females), and race/ethnicity (non-Hispanic White, non-Hispanic
58 The EPA notes that elevated mortality for hard CVD event survivors may persist beyond five years of the initial event.
However, the EPA did not identify U.S. based studies with sufficiently long follow-up to quantify mortality impacts beyond five
years of the initial event.
59 The EPA did not identify studies that found statistically significant associations between the modeled biomarkers (TC, BP) and
CVD events in populations with prior CVD history. Discussion of the relevant literature is provided in Appendix G.
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Black, Hispanic, Other). In addition to the key standard life table components (i.e., the number of
persons surviving to a specific age and the number of all-cause deaths occurring at a given age)
for ages 40 years or older, the CVD model estimates the number of surviving persons with and
without a history of hard CVD events, the number of persons experiencing hard CVD events at a
given age, and the deaths from CVD and non-CVD causes at a given age.
Figure 6-7 summarizes the CVD model calculations for a population cohort age 0 at the start of
the evaluation period.60 The CVD model calculations are identical across race/ethnicity and sex
demographic subgroups but use subgroup-specific parameters.61 For cohorts born prior to or in
2024, the CVD model is initialized using the PWS-specific number of persons estimated to be
alive at the beginning of 2024. For cohorts born after 2024 (i.e., 2025-2105), the CVD model is
initialized using the PWS EP-, race/ethnicity-, sex, and scenario-specific number of persons who
died in the previous calendar year of the analysis, thereby ensuring that the size of the modeled
population remains constant throughout the analysis period. Additional PWS EP- and sex,
race/ethnicity, and age-specific population estimation assumptions are provided in Section 2.2;
additional details are included in Appendix B.
Once the model is initialized, the following types of calculations occur for each year within the
simulation period:62
• Recurrent standard life table calculations that rely on the all-cause, age-specific annual
mortality rates to evaluate the number of deaths among persons of a specific integer age
and the number of survivors to the beginning of the next integer age.63 These calculations
are executed whenever the current cohort age is in the 0-39 range. They are represented
by the navy-blue segment of the timeline shown in Figure 6-7.
• Recurrent life table calculations that separately track subpopulations with and without a
history of hard CVD events, including estimation of the number of annual CVD and non-
CVD deaths (in either subpopulation), as well as the number of annual post-acute CVD
deaths experienced by survivors of the first hard CVD events that occurred, at most, 5
years ago. These calculations are executed whenever the current cohort age is 40 years or
older.64 These calculations are represented by the blue segment of the timeline. Figure
6-7 and Figure 6-8 further illustrates the year-specific calculations required for explicit
tracking of subpopulations with and without a hard CVD event history.
60 This initial population cohort age is chosen because it allows for illustration of the full set of calculation types used in the CVD
model.
61 There are different ASCVD model coefficients for non-Hispanic White and non-Hispanic Black males and females. The figure
shows the generalized approach of the CVD model.
62 The EPA notes that the simulation period is the lifespan of individuals relevant to the analysis. The simulation period is distinct
from the period of analysis in that some parts of the simulation period may fall outside the period of analysis. For example, for a
person aged 40 years at the start of the analysis period, the period of analysis will not capture the first 40 years of simulation
results.
63 Life table calculations are based on the present-day information about life expectancy, disease, environmental exposure, and
other factors.
64 People 85 years or older, are treated as a single cohort in the model. The mortality rate for this cohort is assumed to be the
average mortality rate for those age 85-100 years. This cohort also used serum PFOA/PFOS values at age 85.
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Model initialization
with estimated
location- and
race/ethnicity-specific
male/female age 0
population Recurrent standard life table
calculations using age-,
race/ethnicity-, and
sex-specific all-cause
annual mortality rates
A
Modeled population is
split into CVD and non-CVD
subpopulations using
age-, race/ethnicity-, and sex-specific
CVD prevalence data
r
Recurrent life table calculations
with explicit treatment of CVD population,
using age- and sex-specific CVD prevalence,
cause-specific annual mortality rates,
ASCVD model-based CVD incidence,
and post-acute CVD event mortality
Figure 6-7: Overview of Life Table Calculations in the CVD Model
Note: The figure illustrates the model for population cohort age 0 years at the beginning of the evaluation period (i.e., calendar year 2024). Hie model is initialized using the age 0
PWS EP-specific population (see Appendix B for PWS population estimation details).
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Figure 6-8 provides additional information on the post-acute CVD mortality estimation. Each
person included in the surviving current age-specific incident CVD subpopulation65
(corresponding to the group F result in Figure 6-8) is tracked for 5 additional years to estimate
the number of CVD deaths occurring in that timeframe. The recurrent estimates rely on age-
specific non-CVD mortality rates, estimated based on the CDC's life table data and annual CVD
mortality rates, and on post-acute CVD mortality rates, estimated based on Thom et al. (2001)
and S. Li et al. (2019).
Further details of the life table calculations are provided in Appendix G. The outputs of the life
table calculations and application of the ASCVD model are the PWS EP-specific estimates of the
annual number of persons experiencing their first non-fatal MI or IS event and the number of
deaths among those who have experienced their first hard CVD event, at most, 6 years ago. Note
that the ASCVD model does not predict risks separately by type of first hard CVD event (i.e.,
non-fatal MI, non-fatal IS, and fatal CVD). The distribution of these events by type is estimated
using data publicly available on CVD prevalence, incidence, and hospital mortality statistics as
described in Section 6.5.3.2 and integrated into the overall CVD impacts modeling.
65 For example, persons who experienced their first non-fatal MI or IS at age 70 and survived through the first post-event year.
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Non-CVD population estimated to be alive at the start of integer age (A), applicable to ages 40-89.
Non CVD deaths (B)
-
^^^on-CVD population (A) is adjusted for non CVD deaths (B) ancTusec^^^
^^^^basis for estimating first hard CVD event incidence
~
Non-CVD population eligible for
the first hard CVD event in the
following year (A-B-C-D).
First hard CVD event deaths (C)
First hard CVD event survivors (D)
CVD event survivor population (D) is adjusted for post-acute
excess mortality in year 0 since the initial event (E)*
Post-acute excess deaths
among CVD survivors in
first post-event year (E)
First hard CVD event
survivors at the end of
first post-event year (F)
¦ Living subpopulation without prior history Note:
of CVD events. * Estimated number of CVD events is an input to
the monetization step.
Deaths occurring at the current age
¦ Living subpopulation that experienced first
hard CVD at the current integer age
• Calculations occurring in years 1-5
following the first hard CVD event
Figure 6-8: CVD Model Calculations for Ages 40+ Tracking CVD
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6.5.3.2 Risk and Distribution of First Hard Cardiovascular Disease Event
The first hard CVD event incidence estimates are generated by the Pooled Cohort ASCVD
model (Goff et al., 2014). The ASCVD model is commonly used in clinical practice to estimate
CVD risk for those between ages 40 and 80, as well as for overall population risk management
(Lloyd-Jones et al., 2017). The ASCVD model predicts the 10-year probability of a hard CVD
event—fatal and non-fatal MI, fatal and non-fatal IS, or CHD death—to be experienced by a
person without a prior history of MI, IS, congestive heart failure, percutaneous coronary
intervention, coronary bypass surgery, or atrial fibrillation. The ASCVD model is a survival
model that links predictor levels at the start of the 10-year follow-up period to the first hard CVD
event incidence during the follow-up period; the modeling does not account for changes in CVD
risk predictors over time.
Four large longitudinal community-based epidemiologic cohort studies were combined to
develop a geographically and racially diverse dataset used for the ASCVD model estimation.66
The predictors of the ASCVD model include age, TC and HDLC concentrations, systolic BP,
current smoking, diagnosed diabetes, and whether the participant is undergoing treatment for
high BP. The model was fit separately to four population subgroups: non-Hispanic White
females, non-Hispanic Black females, non-Hispanic White males, and non-Hispanic Black
males.
Several studies assessed predictive performance of the ASCVD risk model in racial and ethnic
groups other than other non-Hispanic White and non-Hispanic Black populations, as well as in
various sociodemographic subgroups in the U.S. Two studies concluded that the ASCVD risk
model overestimated CVD risk among Asian and Hispanic groups, while noting that these
groups were not included in the development and validation of the ASCVD model (Mongraw-
Chaffin et al., 2018; Rodriguez et al., 2019). Five studies acknowledged limitations for the
ASCVD risk model in terms of performance among individuals with high levels of CVD risk,
diabetes, older adults with frailty and multimorbidity, smokers, and women (Muntner et al.,
2014; Leigh et al., 2019; Mora et al., 2018; Q. D. Nguyen et al., 2020; Raghavan et al., 2020).
Overall, the literature across different sociodemographic subgroups concluded that the ASCVD
risk model tended to overestimate risk but suggested the model may improve through additional
input variables and recalibration given contemporary ASCVD prevalence, especially if the
prevalence differs significantly across geographic locations to which the model is applied (Mora
et al., 2018; (Muntner et al., 2014). Extended discussion of ASCVD risk model performance and
availability of alternative CVD risk prediction models for national analysis is provided in ICF
(2022a).
In light of these findings, the EPA does not follow the Goff et al. (2014) recommendation that
the ASVCD risk model for non-Hispanic White populations be used for other race/ethnicity
groups. In the development and parameterization of the CVD model for Hispanic, Asian
American, and American Indian/Alaska Native populations, the EPA applies the model for non-
Hispanic Black populations based on the ASCVD model validation relative to reported CVD
66 These studies include the Atherosclerosis Risk in Communities (ARIC) study (Williams, 1989) and the Cardiovascular Health
Study (Fried et al., 1991), along with applicable data from the Coronary Artery Risk Development in Young Adults (CARDIA)
study (Friedman et al., 1988) and the Framingham Original and Offspring cohort data (D'Agostino et al., 2008).
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prevalence and mortality statistics (the EPA analysis based on Medical Expenditure Panel
Surveys from 2010-2017), as described in Appendix G. The results of this validation exercise
showed that the ASCVD model coefficients for the non-Hispanic Black model are more
consistent with data on CVD prevalence and mortality for Hispanic and non-Hispanic other race
subpopulations than the ASCVD model coefficients for the non-Hispanic White model. The all-
cause and CVD mortality was obtained from CDC's National Vital Statistics System, whereas
CVD prevalence was estimated using agency for Healthcare Research and Quality survey data
(see Appendix G for details). As explained in Appendix G, race/ethnicity and sex-specific CVD
incidence consistent with these reported statistics was compared with the incidence estimated
using the ASCVD model, where the baseline race/ethnicity- and sex-specific values for the
ASCVD model predictors were obtained from CDC's public health surveys (see Appendix G for
details).
The ASCVD model generates predictions of the 10-year probability of the first hard CVD event
without differentiation across CVD event types. The specifics of annual first hard CVD event
probability derivation, which is needed for the life table calculations in Section 6.5.3.1, are
provided in Appendix G. As is also detailed in Appendix G, the EPA combined the Medical
Expenditure Panel Survey (MEPS) 2010-2017 data and the Healthcare Cost and Utilization
Project (HCUP) 2017 data to derive the ASCVD event distribution over the following event
types: non-fatal MI, non-fatal IS, and fatal CVD events. The fatal CVD events include fatal MI,
fatal IS, and other fatal CHD events. The EPA used the MEPS data to identify the subpopulation
of persons without a prior CVD event history and estimate the rate of new CVD events by type
(i.e., MI, IS, and other CHD) in this subpopulation. The probabilities of in-hospital death for MI,
IS, and other CHD were obtained from HCUP.
Table 6-17 shows the derived race/ethnicity-, sex-, and age group-specific shares of first hard
CVD events for the following event types: non-fatal MI, fatal MI, non-fatal IS, fatal IS, other
non-fatal CHD, and other fatal CHD. For males, looking across race/ethnicity and age categories,
the share of non-fatal MI events is 4.9 percent to 28 percent, the share of non-fatal IS events is
9.4 percent to 38 percent, and the share of other non-fatal CHD events is 44 percent to 78
percent. For females, across race/ethnicity and age categories, the share of non-fatal MI events is
6.4 percent to 19 percent, the share of non-fatal IS events is 8.7 percent to 29 percent, and the
share of other non-fatal CHD events is 51 percent to 76 percent. For both sexes, shares of all
fatal events increase with age. The share of fatal CVD events is largest for Hispanic and non-
Hispanic other race subpopulations of both sexes. Table 6-17 also shows derived race/ethnicity-,
sex-, and age group-specific shares of first hard CVD events over ASCVD event types (i.e., non-
fatal MI, non-fatal IS, and fatal CVD). Note that these shares were re-normalized to sum to 100
percent after exclusion of other non-fatal CHD not predicted by the ASCVD model. The CVD
model relies on the re-normalized shares to allocate the total number of first hard CVD events
predicted by the ASCVD model.
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Table 6-17: Estimated Shares of Fatal and Non-Fatal First Hard CVD Events Based on
MEPS and HCUP Data
Non-Fatal CVD (%)
Fatal CVD (%)
Sex
Age (in years)
Race/
Ethnicity
Non-Fatal
Non-Fatal
Other Non-
Fatal CHD
Fatal MI
Fatal IS
Other
Fatal
MI (%)
IS (%)
(%)
(%)
(%)
CHD (%)
Shares of First Hard CVD Events
Males
18-44
NH White
14
9.4
77
0.19
0.17
0
45-64
NH White
16
15
69
0.39
0.34
0.44
65-84
NH White
13
20
64
0.71
0.75
0.76
85 or older
NH White
13
20
63
1.3
1.4
1.9
18-44
NH Black
4.9
17
78
0.067
0.31
0
45-64
NH Black
11
38
50
0.28
0.88
0.32
65-84
NH Black
8.9
22
67
0.48
0.8
0.79
85 or older
NH Black
8.5
21
66
0.87
1.5
2
18-44
Hispanic
23
17
59
0.31
0.31
0
45-64
Hispanic
19
29
51
0.48
0.67
0.32
65-84
Hispanic
20
17
60
1.1
0.65
0.71
85 or older
Hispanic
19
17
59
2
1.2
1.8
18-44
NH Other
26
30
44
0.35
0.54
0
45-64
NH Other
28
19
52
0.71
0.43
0.33
65-84
NH Other
13
25
60
0.71
0.92
0.71
85 or older
NH Other
12
24
59
1.3
1.7
1.8
Females
18-44
NH White
8.1
19
72
0.13
0.41
0
45-64
NH White
6.9
20
72
0.2
0.55
0.54
65-84
NH White
11
28
58
0.68
1.2
0.82
85 or older
NH White
10
27
57
1.2
2.3
2.1
18-44
NH Black
15
8.7
76
0.23
0.18
0
45-64
NH Black
10
27
61
0.29
0.74
0.46
65-84
NH Black
6.7
29
62
0.42
1.2
0.87
85 or older
NH Black
6.4
28
61
0.76
2.3
2.2
18-44
Hispanic
8.8
18
73
0.14
0.38
0
45-64
Hispanic
13
27
59
0.37
0.73
0.45
65-84
Hispanic
19
26
52
1.2
1.1
0.73
85 or older
Hispanic
18
25
51
2.1
2.1
1.9
18-44
NH Other
11
13
75
0.17
0.27
0
45-64
NH Other
14
29
55
0.42
0.78
0.42
65-84
NH Other
12
28
58
0.74
1.2
0.81
85 or older
NH Other
11
27
56
1.3
2.3
2.1
Shares of First Hard CVD Event Categories Predicted by the ASCVD Model3
Males
18-44
NH White
58
40
-
1.5
45-64
NH White
50
47
-
3.7
65-84
NH White
37
57
-
6.2
85 or older
NH White
34
53
-
13
18-44
NH Black
22
77
-
1.7
45-64
NH Black
22
75
-
2.9
65-84
NH Black
27
66
-
6.4
85 or older
NH Black
25
62
-
13
18-44
Hispanic
56
42
-
1.5
45-64
Hispanic
38
59
-
3.0
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Table 6-17: Estimated Shares of Fatal and Non-Fatal First Hard CVD Events Based on
MEPS and HCUP Data
Sex
Age (in years)
Race/
Ethnicity
Non-Fatal CVD (%)
Fatal CVD (%)
Non-Fatal
MI (%)
Non-Fatal
IS (%)
Other Non-
Fatal CHD
(%)
Fatal MI Fatal IS
(%) (%)
Other
Fatal
CHD (%)
65-84
Hispanic
50
44
-
6.1
85 or older
Hispanic
47
41
-
12
18-44
NH Other
46
53
-
1.6
45-64
NH Other
58
39
-
3.1
65-84
NH Other
33
62
-
5.8
85 or older
NH Other
30
58
-
12
Females
18-44
NH White
29
69
-
1.9
45-64
NH White
24
71
-
4.6
65-84
NH White
26
67
-
6.5
85 or older
NH White
24
63
-
13
18-44
NH Black
62
36
-
1.7
45-64
NH Black
26
70
-
3.9
65-84
NH Black
18
76
-
6.7
85 or older
NH Black
16
70
-
14
18-44
Hispanic
32
66
-
1.9
45-64
Hispanic
31
65
-
3.8
65-84
Hispanic
40
54
-
6.4
85 or older
Hispanic
37
51
-
12
18-44
NH Other
45
53
-
1.8
45-64
NH Other
32
64
-
3.6
65-84
NH Other
28
66
-
6.5
85 or older
NH Other
26
61
-
13
Abbreviations: CVD - cardiovascular disease; CHD - coronary heart disease; fatal CVD - includes fatal MI, fatal IS, and fatal
other coronary heart disease events; HCUP - Healthcare Cost and Utilization Project; IS - ischemic stroke; MEPS - Medical
Expenditure Panel Survey; MI - myocardial infarction; NH - non-Hispanic.
Note:
aThe distribution is derived by (1) excluding the other non-fatal CHD category; (2) aggregating fatal MI, fatal IS, and other fatal
CHD categories into the fatal CVD category; and (3) re-normalizing the data to sum to 100%.
6.5.3.3 Risk of Post-Acute Cardiovascular Disease Mortality
Persons who have experienced non-fatal MI and non-fatal IS have an elevated risk of post-acute
CVD mortality and morbidity (Roger et al., 2012). Studies focusing on secondary hard CVD
events point to an elevated risk of these events among survivors of the first hard CVD event
(e.g., Beatty et al., 2015; S. Li et al., 2019; Thorn et al., 2001), but do not support the link
between these risks and TC/BP levels (Beatty et al., 2015). (See Appendix G for details.)
Therefore, the CVD model evaluates post-acute CVD mortality among survivors of the initial
MI/IS event under baseline and regulatory alternatives using the baseline post-acute mortality
rates that do not depend on the levels of modeled biomarkers. The CVD model does not
explicitly evaluate secondary CVD morbidity because available first non-fatal MI/IS valuation
measures (e.g., O'Sullivan et al., 2011) incorporate incidence of these secondary events.
For survivors of the first hard CVD event at ages 40-65, the EPA uses estimates of sex- and
race/ethnicity-specific all-cause post-acute mortality for MI survivors at 1- and 5-year follow-up
from Thom et al. (2001). Because Thom et al. (2001) reports all-cause post-acute mortality rates,
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the EPA adjusted these rates to exclude deaths from non-CVD causes. To this end, the EPA used
general population integer age- and sex-specific all-cause mortality from U.S. Life Tables, 2017
(Arias & Xu, 2019), U.S. CVD mortality rates (CDC, 2020b), and U.S. Life Tables Eliminating
Certain Causes of Death, 1999-2000 (Arias et al., 2013). Appendix G provides additional
estimation details. Although the EPA was unable to identify comparable post-acute mortality
statistics for non-fatal IS, an analysis of the Medicare population by S. Li et al. (2019) suggests
that post-acute MI mortality is a reasonable approximation for post-acute IS mortality.67 Table
6-18 shows estimated post-acute CVD mortality rates for survivors of the first MI or IS at ages
40-65 that are used to parameterize the CVD model.
For survivors of the first hard CVD event at ages 66 or older, the EPA uses the results from S. Li
et al. (2019) to estimate the number of post-acute CVD deaths within 6 years of the initial event.
Because S. Li et al. (2019) reports only all-cause post-acute mortality rates, the EPA adjusted
these rates to exclude deaths from non-CVD causes. Integer age- and sex-specific probability of
death from non-CVD causes was derived from U.S. Life Tables, 2017 (Arias & Xu, 2019), U.S.
CVD mortality rates (CDC, 2020b), and U.S. Life Tables Eliminating Certain Causes of Death,
1999-2000 (Arias et al., 2013). The sex-specific probabilities of death from non-CVD causes
were average using the demographic information for the cohorts analyzed by S. Li et al. (2019).
See Appendix G for additional estimation details. Table 6-18 shows estimated post-acute CVD
mortality rates for survivors of the first MI and survivors of the first IS at ages 66 years or older
that are used to parameterize the CVD model.68
67 For those age 65 or older, S. Li et al. (2019) have estimated the probability of death within 1 year after non-fatal IS to be 32.07
percent and the probability of death within 1 year after non-fatal MI to be 32.09 percent.
68 These rates are applied to all those aged 66 or older in the SafeWater MCBC implementation of the model.
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Table 6-18: Estimated Risk of Post-Acute CVD Mortality Following the First Non-Fatal
Hard CVD Event
Post-Acute CVD Mortality Rate per 100,000 by Integer Year
„ . „ Since the First Non-Fatal Hard CVD Event
Demographic Group
0 1 2 3 4 5
Source: Thom et al. (2001)
Non-Hispanic Whiteb males aged
4,500
910
860
820
760
45-65 years
Non-Hispanic Black males aged
12,000
1,200
1,100
1,100
1,000
A/TT TCa
45-65 years
Non-Hispanic Whiteb females
8,600
1,900
1,900
1,900
1,800
aged 45-65 years
Non-Hispanic Black females aged
7,700
4,300
4,200
4,100
4,100
45-65 years
Source: S. Li et al. (2019)
MI
Persons aged 66 years or older
27,000
11,000
9,600
9,040
8,600
8,040
IS
Persons aged 66 years or older
28,000
9,900
10,000
9,800
8,900
8,030
Abbreviations: CVD - cardiovascular disease; IS - ischemic stroke (International Classification of Disease Ninth Revision
[ICD9] = 433,434; International Classification of Disease Tenth Revision [ICD10] = 163), MI - myocardial infarction (ICD9 =
410; ICD10 = 121).
Notes:
aThom et al. (2001) reported data for the first MI survivors only for aged 45-64 years. The CVD model applies these rates to
both the first MI and first IS survivors.
bEstimates for non-Hispanic White populations are applied to other race/ethnicity-specific populations.
6.5.4 Valuation of Cardiovascular Disease Risk Reductions
The EPA uses the Value of Statistical Life to estimate the benefits of reducing mortality
associated with hard CVD events in the population exposed to PFOA and PFOS in drinking
water. Value of Statistical Life updating information is provided in Section 2.2. The EPA relies
on COI-based valuation that represents the medical costs of treating or mitigating non-fatal first
hard CVD events (MI, IS) during the three years following an event among those without prior
CVD history, adjusted for post-acute mortality.
The annual medical expenditure estimates for MI and IS are based on O'Sullivan et al. (2011).
The estimated expenditures do not include long-term institutional and home health care. For non-
fatal MI, O'Sullivan et al. (2011) estimated medical expenditures are $53,246 ($2022)69 for the
initial event and then $33,162, $14,635, $13,078 annually within 1, 2, and 3 years after the initial
event, respectively. For non-fatal IS, O'Sullivan et al. (2011) estimated medical expenditures are
$16,503 ($2022) for the initial event and then $11,988, $788, $1,868 annually within 1, 2, and 3
years after the initial event, respectively. Annual estimates within 1, 2, and 3 years after the
initial event include the incidence of secondary CVD events among survivors of first MI and IS
events.
To estimate the present discounted value of medical expenditures within 3 years of the initial
non-fatal MI, the EPA combined O'Sullivan et al. (2011) Mi-specific estimates with post-acute
69 Original values from the source were inflated to $2022 using the medical care Consumer Price Index (U.S. Bureau of Labor
Statistics, 2021).
Type of First
Non-Fatal
Hard CVD
Event
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survival probabilities based on Thom et al. (2001) (for MI survivors aged 40-64) and S. Li et al.
(2019) (for MI survivors aged 65 or older). To estimate the present discounted value of medical
expenditures within 3 years of the initial non-fatal IS, the EPA combined O'Sullivan et al. (2011)
IS-specific estimates with post-acute survival probabilities based on Thom et al. (2001) (for IS
survivors aged 40-64, assuming post-acute MI survival probabilities reasonably approximate
post-acute IS survival probabilities) and S. Li et al. (2019) (for IS survivors aged 65 or older).
The EPA did not identify post-acute IS mortality information in this age group, but instead
applied post-acute MI mortality estimates for IS valuation.70 Table 6-19 presents the resulting MI
and IS unit values.
Table 6-19: Cost of Illness of Non-Fatal First CVD Event Used in Modeling
Type of First Non-fatal . „
Hard CVD Event Age Group
Present Discounted Value of 3-Year Medical
Expenditures ($2022,2% Discount Rate)a b
Adjusted for Post-Acute Mortality0
MI 40-64 years
$110,040
65 years or older
$96,626
IS 40-64 years
$30,373
65 years or older
$27,954
Abbreviations: CVD - cardiovascular disease; MI - myocardial infarction (ICD9 = 410; ICD10 = 121, IS - ischemic stroke
(ICD9 = 433,434; ICD10 = 163).
Notes:
^Estimates of annual medical expenditures are from O'Sullivan et al. (2011).
bOriginal values from O'Sullivan et al. (2011) were inflated to $2022 using the medical care Consumer Price Index (U.S.
Bureau of Labor Statistics, 2022a).
cPost-acute MI mortality data for those aged 40-64 years is from Thom et al. (2001); probabilities to survive 1 year, 2 years, and
3 years after the initial event are 0.93, 0.92, and 0.90, respectively. The EPA applies these mortality values to derive the IS value
in this age group. Post-acute MI mortality data and post-acute IS mortality data for persons aged 65 years and older are from S.
Li et al. (2019). For MI, probabilities to survive 1 year, 2 years, and 3 years after the initial event are 0.68, 0.57, and 0.49,
respectively. For IS, probabilities to survive 1 year, 2 years, and 3 years after the initial event are 0.67, 0.57, and 0.48,
respectively.
70 Post-acute mortality estimates for IS and MI were very close in the Medicare population (S. Li et al., 2019). For those ages 65
years or older, S. Li et al. (2019) have estimated probability of death within 1 year after non-fatal IS to be 32.07 percent and
probability of death within 1 year after non-fatal MI to be 32.09 percent. Therefore, reliance on the post-acute mortality for MI to
approximate the same for stroke is reasonable.
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6.5.5 Results
Table 6-20 to Table 6-23 provide the health effects avoided and valuation associated with
cardiovascular disease.
Table 6-20: National CVD Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million $2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile"
Number of Non-Fatal
1,407.7
6,333.1
11,189.0
MI Cases Avoided
Number of Non-Fatal
2,074.8
9,247.6
16,279.0
IS Cases Avoided
Number of CVD
845.5
3,715.8
6,555.6
Deaths Avoided
Total Annualized
$140.66
$606.09
$1,069.40
CVD Benefits
(Million $2022)b
Abbreviations: CVD - cardiovascular disease, MI - myocardial infarction, IS - Ischemic Stroke.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates. Quantifiable benefits are increased under final rule table results relative to the other options presented
because of modeled PFHxS occurrence, which results in additional quantified benefits from co-removed PFOA and PFOS.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-21: National CVD Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal
1,400.8
6,296.0
11,115.0
MI Cases Avoided
Number of Non-Fatal
2,065.0
9,194.8
16,203.0
IS Cases Avoided
Number of CVD
839.9
3,695.1
6,484.4
Deaths Avoided
Total Annualized
$140.12
$602.72
$1,059.60
CVD Benefits
(Million $2022)b
Abbreviations: CVD - cardiovascular disease, MI - myocardial infarction, IS - Ischemic Stroke.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
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Table 6-22: National CVD Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile"
Number of Non-Fatal
1,209.2
5,352.0
9,417.5
MI Cases Avoided
Number of Non-Fatal
1,778.3
7,826.9
13,778.0
IS Cases Avoided
Number of CVD
733.1
3,146.8
5,518.0
Deaths Avoided
Total Annualized
$119.18
$513.27
$900.13
CVD Benefits
(Million $2022)b
Abbreviations: CVD - cardiovascular disease, MI - myocardial infarction, IS - Ischemic Stroke.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable costs, and the potential direction of impact these costs would have on the
estimated monetized total annualized costs in this table.
Table 6-23: National CVD Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile
a
Expected Value
95th Percentile3
Number of Non-Fatal
MI Cases Avoided
673.7
2,776.5
4,872.8
Number of Non-Fatal
IS Cases Avoided
987.0
4,079.2
7,145.6
Number of CVD
Deaths Avoided
Total Annualized
CVD Benefits
(Million $2022)b
411.6
$66.97
1,640.9
$267.56
2,878.1
$469.05
Abbreviations: CVD - cardiovascular disease, MI -
- myocardial infarction, IS - Ischemic Stroke.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
6.6 Renal Cell Carcinoma
6.6.1 Overview of the RCC Risk Reduction Analysis
Figure 6-9 illustrates the approach used to quantify and value the changes in RCC risk associated
with lowered serum PFOA levels from reductions in drinking water PFOA concentrations under
the regulatory alternatives. Section 4.4 and Section 6.3 detail the PWS EP-specific PFOA
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drinking water occurrence estimation and modeling of serum PFOA concentrations, respectively.
PWS EP-specific time series of the differences between serum PFOA concentrations under
baseline and regulatory alternatives are inputs into this analysis. For each PWS EP, evaluation of
the changes in RCC impacts involves the following key steps:
1. Estimating the changes in RCC risk based on modeled changes in serum PFOA levels
and the exposure-response function for the effect of serum PFOA on RCC;
2. Estimating the annual incidence of RCC cases and excess mortality among those with
RCC in all populations corresponding to baseline and regulatory alternative RCC risk
levels, as well as estimating the regulatory alternative-specific reduction in cases relative
to the baseline; and
3. Estimating the economic value of reducing RCC mortality from baseline to regulatory
alternative levels, using the Value of Statistical Life and COI measures, respectively.
Section 6.6.2 discusses the exposure-response modeling for RCC. Section 6.6.3 summarizes the
life table-based approach for estimation of RCC risk reductions. Section 6.6.4 discusses the
EPA's valuation methodology for RCC mortality and morbidity. Section 6.6.5 presents the
results of the analysis.
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Legend: Serum PFOA
Abbreviations:
PFOA - perfluorooctanoic acid, RCC - renal cell carcinoma, SEER - Surveillance, Epidemiology, and End Results program
Notes:
(a) Data from the Centers for Disease Control (CDC) and Prevention.
Figure 6-9: Overview of Analysis of Reduced RCC Risk
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6.6.2 RCC Exposure-Response Modeling
To identify an exposure-response function, the EPA reviewed studies highlighted in the HESD
for PFOA (U.S. EPA, 2016f) and a recent study discussed in both the California Environmental
Protection Agency's Office of Environmental Health Hazard Assessment (OEHHA) PFOA
Public Health Goals report (CalEPA, 2021) and the EPA's Final Human Health Toxicity
Assessment for PFOA (U.S. EPA, 2024f). Steenland and Woskie (2012) observed an increase in
kidney cancer deaths among workers with high exposures to PFOA. Vieira et al. (2013) found
that kidney cancer was positively associated with "high" and "very high" PFOA exposures. Barry
et al. (2013) found a slight trend in cumulative PFOA serum exposures and kidney cancer among
the C8 Health Project population.71 In a large case-control general population study of the
relationship between PFOA and kidney cancer in 10 locations across the U.S., Shearer et al.
(2021) found evidence that exposure to PFOA is associated with RCC, the most common form of
kidney cancer, in humans.
To evaluate changes between baseline and regulatory alternative RCC risk resulting from
reduced exposure to PFOA, the EPA relied on the estimated time series of changes in serum
PFOA concentrations (Section 6.3) and the serum-RCC exposure-response function provided by
Shearer et al. (2021): 0.00178 (95% CI: 0.00005, 0.00352) per ng/mL. The analysis reported in
Shearer et al. (2021) was designed as a case-control study with population controls based on 10
sites within the U.S. population. Shearer et al. (2021) accounted for age, sex, race, ethnicity,
study center, year of blood draw, smoking, and hypertension in modeling the association
between PFOA and RCC. Results showed a strong and statistically significant association
between PFOA and RCC. The EPA selected the exposure-response relationship from Shearer et
al. (2021) because it included exposure levels typical in the general population and the study was
found to have a low risk of bias when assessed in the EPA's Final Human Health Toxicity
Assessment for PFOA (U.S. EPA, 2024f).
The linear slope factor developed by the agency (see Section 4.2 of U.S. EPA, 2024f) based on
Shearer et al. (2021) enables estimation of the changes in the lifetime RCC risk associated with
reduced lifetime serum PFOA levels:
Equation 15:
LR(x) = LR(z) + 0.00178 ¦ (x - z)
Where LR(x) is the probability of lifetime RCC incidence for an individual exposed to a lifetime
average serum PFOA concentration of x ng/mL, and LR(z) is the probability of lifetime RCC at
the baseline lifetime average serum PFOA concentration of z ng/mL.
Because baseline RCC incidence statistics are not readily available from the National Cancer
Institute public use data, the EPA used kidney cancer statistics in conjunction with an
assumption that RCC comprises 90 percent of all kidney cancer cases to estimate baseline
lifetime probability of RCC (U.S. EPA, 2024f American Cancer Society, 2020). The EPA
estimated the baseline lifetime RCC incidence for males at 1.89 percent and the baseline lifetime
71 The C8 Health Project collected data to ascertain the amount of C8 (otherwise known as PFOA) in blood among Mid-Ohio
Valley communities from 2005-2013. Mean PFOA at enrollment was 24 ng/mL.
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RCC incidence for females at 1.05 percent. Details of these calculations are provided in
Appendix H. Because the Shearer et al. (2021) slope factor is not sex-specific, the EPA averaged
sex-specific baseline lifetime RCC estimates to obtain LR(z) = 0.0147 for use in the estimation
of annual RCC risk changes.
To enable annual RCC risk estimation, the EPA further assumed that the relative risk
relationship implied by Equation 15, i.e., RR(x,z) = LR(x)/LR(z) = 1 + 0.00178 ¦
(x — z)/LR(z) = 1 + 0.00178 ¦ (x — z)/0.0147, also holds for the cumulative RCC risk and
cumulative average exposure to serum PFOA from birth to a specific age.
A person's cumulative serum PFOA exposure by age a—denoted by xa—is defined as:
The EPA estimated the relative risk of RCC by a particular age from a change in average serum
PFOA experienced by this age as follows:
Where RR(xa, za) is the relative cumulative risk of RCC by age a associated with a change from
baseline cumulative exposure za to treatment cumulative exposure xa and PAF is the
environmental exposure-related population attributable fraction of RCC incidence set at 0.0394.
As such, this equation implies that the EPA caps the magnitude of PFOA-related cumulative
RCC risk reduction at the PAF of 3.94 percent to ensure plausibility of the estimated RCC
benefits size. The EPA developed this PAF estimate based on its review of literature on
environmental contaminant-attributable risk estimates for cancers (ICF, 2022b). In calculations
of the annual RCC risk changes, the EPA continued to assume that RCC comprises 90 percent of
annual kidney cancer incidence.
The EPA relies on the life table approach to estimate RCC risk reductions because:
• Changes in serum PFOA in response to changes in drinking water PFOA occur over
multiple years;
• Annual risk of new RCC should be quantified only among those not already experiencing
this chronic condition;
• RCC has elevated mortality implications.
The EPA used recurrent life table calculations to estimate PWS EP-specific time series of RCC
incidence for a population cohort characterized by sex, race/ethnicity, birth year, and age at the
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Equation 16:
Equation 17:
6.6.3 Estimation of RCC Risk Reductions
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beginning of the evaluation period (i.e., 2024) under the baseline scenario and the regulatory
alternatives. The life table analysis accounts for the gradual changes in lifetime exposures to
PFOA following implementation of treatment under the regulatory alternatives compared to the
baseline.72 Details of the life table calculations are provided in Appendix H. The outputs of the
life table calculations are the PWS EP-specific estimates of the annual change in the number of
RCC cases and the annual change in RCC population mortality.
Although the change in PFOA exposure likely affects the risk of developing RCC beyond the
end of the analysis period (the majority of RCC cases manifest during the latter half of the
average individual lifespan; see Appendix H), the EPA does not capture effects after the end of
the period of analysis, 2105. Individuals alive after the end of the period of analysis likely benefit
from lower lifetime exposure to PFOA. Lifetime health risk model data sources include
SDWIS/Fed; age-, sex-, and race/ethnicity-specific population estimates from the U.S. Census
Bureau (U.S. Census Bureau, 2020a); the Surveillance, Epidemiology, and End Results (SEER)
program database (National Cancer Institute),73 and the CDC NCHS.74 Appendix H provides
additional detail on the data sources and information used in this analysis as well as baseline
kidney cancer statistics. Appendix B describes estimation of the affected population.
6.6.4 Valuation of RCC Risk Reductions
The EPA uses the Value of Statistical Life to estimate the benefits of reducing mortality
associated with RCC in the population exposed to PFOA in drinking water. Section 2.2 provides
information on updating Value of Statistical Life for inflation and income growth. The EPA uses
the COI-based valuation to estimate the benefits of reducing morbidity associated with RCC.
The EPA used the medical cost information from a recent RCC cost-effectiveness study by
Ambavane et al. (2020) to develop COI estimates for RCC morbidity. Ambavane et al. (2020)
used a discrete event simulation model to estimate the lifetime treatment costs of several RCC
treatment sequences, which included first and second line treatment75 medication costs,
medication administration costs, adverse effect management costs, and disease management
costs on- and off-treatment. To this end, the authors combined RCC cohort data from a
CheckMate 214 clinical trial and recent US-based healthcare cost information assembled from
multiple sources (see supplementary information from Ambavane et al. (2020)).
The EPA received public comments on the economic analysis for the proposed rule related to the
EPA's use of cost of illness information for morbidity valuation. Specifically, some commenters
recommended that the EPA use willingness to pay information (instead of cost of illness
information) when valuing the costs associated with non-fatal illnesses, stating that willingness
to pay information better accounts for lost opportunity costs (e.g., lost productivity and pain and
suffering) associated with non-fatal illnesses. To better account for these opportunity costs, the
72 As described above, the EPA models PFAS changes under the regulatory alternatives as being in effect for the years 2024
through 2105, with nonzero PFAS changes first occurring in 2029, the year when all PWSs are assumed to comply with PFAS
treatment requirements.
73 For cancer incidence and stage distribution data, the EPA relies on SEER 21 (2009-2018); for cancer survival data, the EPA
relies on SEER 18 (2000-2017).
74 CDC WONDER data on 1999-2019 all-cause and kidney cancer mortality by age and sex.
75 Second line cancer treatment is a treatment implemented after the failure of the initial treatment (i.e., first line treatment). The
first line treatment may fail because it stops working or has side effects that are not tolerated.
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EPA used recently available willingness to pay values in a sensitivity analysis for morbidity
associated with RCC. The sensitivity analysis results show that when willingness to pay values
are used in RCC benefits analysis, morbidity benefits are increased by 2.0 percent. See Appendix
O for full details and results on the willingness to pay sensitivity analyses.
Table 6-24 summarizes RCC morbidity COI estimates derived by the EPA using Ambavane et
al. (2020)-reported disease management costs on- and off-treatment along with medication,
administration, and adverse effect management costs for the first line treatment that initiated the
most cost-effective treatment sequences as identified by Ambavane et al. (2020), i.e., the
nivolumab and ipilimumab drug combination. This is a forward-looking valuation approach in
that it assumes that the clinical practice would follow the treatment recommendations in
Ambavane et al. (2020) and other recent studies cited therein. The EPA notes that the second line
treatment costs are not reflected in the EPA's COI estimates, because Ambavane et al. (2020) did
not report information on the expected durations of the treatment-free interval (between the first
line treatment discontinuation and the second line treatment initiation) and the second line
treatment phase, conditional on survival beyond discontinuation of the second line treatment. As
such, the EPA valued RCC morbidity at $261,175 ($2022) during year 1 of the diagnosis,
$198,705 ($2022) during year 2 of the diagnosis, and $1,661 ($2022) starting from year 3 of the
diagnosis. Additionally, the EPA assumed that for individuals with RCC who die during the
specific year, the entire year-specific cancer treatment regimen is applied prior to the death
event. This may overestimate benefits if a person does not survive the entire year.
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Table 6-24: RCC Morbidity Valuation
APRIL 2024
S 90 -n "S
.2 -h aj « fl
¦a ° £ S g
^ ^ as s s
• flj ^
S3— ^ W) ef sf
5:23 3® a S?h Total Total
Time Interval ^ ® +- "a « ®
I g I a .S I 8 ^ £ ($2018) ($2022)d
s s ¦ ¦ ya-
u
si
la
w
.9 E Jg g
Monthly cost, month 1-3 32,485 516 78 73 33,152 37,382
from diagnosis3 6
Monthly cost, month 4-24 13 88? 64? ?8 ?3 u ^ l6 55g
from diagnosis"1
Monthly cost, month 25+ ^3 ^3 139
from diagnosis8
Annual cost, year 1 from 222,438 7,371 934 878 231,621 261,175
diagnosis
Annual cost, year 2 from 166,644 7,764 934 878 176,220 198,705
diagnosis
Annual cost, year 3+ from
diagnosis
1,473 1,473 1,661
Abbreviations: RCC - renal cell carcinoma.
Notes:
aAmbavane et al. (2020) Table 1;
bAmbavane et al. (2020) p. 41, a maximum treatment duration assumption of 2 years;
cThe adverse effect management costs of $1,868 in Ambavane et al. (2020) Table 1 were reported for the treatment duration.
The EPA used the treatment duration of 24 months (i.e., 2 years) to derive monthly costs of $77.83.
dTo adjust for inflation, the EPA used U.S. Bureau of Labor Statistics Consumer Price Index for All Urban Consumers:
Medical Care Services in U.S. (City Average).
eFirst line treatment induction
fFirst line treatment maintenance
gTreatment-free interval
6.6.5 Results
Table 6-25 to Table 6-28 provide the health effects avoided and valuation associated with renal
cell carcinoma.
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Table 6-25: National RCC Benefits, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each,
PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million $2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal RCC
Cases Avoided
1,091.5
6.964.2
17,937.0
Number of RCC-Related
Deaths Avoided
320.4
2.028.8
5,206.5
Total Annualized RCC
Benefits (Million $2022)b c
$61.33
$353.90
$883.55
Abbreviations: RCC - renal cell carcinoma.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates. Quantifiable benefits are increased under final rule table results relative to the other options presented
because of modeled PFHxS occurrence, which results in additional quantified benefits from co-removed PFOA and PFOS.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
cWhen using willingness to pay metrics to monetize morbidity benefits, total annualized RCC benefits are increased by $7.1
million (see Appendix O).
Table 6-26: National RCC Benefits, Option la (PFOA and PFOS MCLs of 4.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3 Expected Value 95th Percentile3
Number of Non-Fatal RCC
1,082.0
6.922.4
17,870.0
Cases Avoided
Number of RCC-Related
319.1
2.016.7
5,190.9
Deaths Avoided
Total Annualized RCC
$60.90
$351.79
$877.47
Benefits (Million $2022)b
Abbreviations: RCC - renal cell carcinoma.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
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Table 6-27: National RCC Benefits, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal RCC
Cases Avoided
851.9
5.6%. 1
14,906.0
Number of RCC-Related
Deaths Avoided
251.6
1.663.8
4,328.4
Total Annualized RCC
Benefits (Million $2022)b
$48.41
$290.72
$730.99
Abbreviations: RCC - renal cell carcinoma.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-28: National RCC Benefits, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3 Expected Value 95th Percentile3
Number of Non-Fatal RCC
372.1
2.648.1
6,967.4
Cases Avoided
Number of RCC-Related
111.5
782.8
2,057.3
Deaths Avoided
Total Annualized RCC
$21.20
$137.30
$352.07
Benefits (Million $2022)b
Abbreviations: RCC - renal cell carcinoma.
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
6.7 Benefits from Co-Removal of Disinfection Byproducts
As part of its health risk reduction and cost analysis, the EPA is directed by SDWA to evaluate
quantifiable and nonquantifiable health risk reduction benefits for which there is a factual basis
in the rulemaking record to conclude that such benefits are likely to occur from reductions in co-
occurring contaminants that may be attributed solely to compliance with the maximum
contaminant level (SDWA 1412(b)(3)(C)(II)). These co-occurring contaminants are expected to
include additional PFAS contaminants not directly regulated by the final PFAS NPDWR, co-
occurring chemical contaminants such as SOCs, VOCs, and DBP precursors. In this section, the
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EPA presents a quantified estimate of the reductions in DBP formation potential that are likely to
occur as a result of compliance with the final PFAS NPDWR.76
6.7.1 Overview of Reduced Disinfection Byproduct Formation
DBPs are formed when disinfectants react with naturally occurring materials in water. Under the
Stage 2 Disinfectants and Disinfection Byproducts Rule (Stage 2 DBP Rule, U.S. EPA, 2006b),
the EPA regulates 11 individual DBPs from three subgroups: four trihalomethanes, five
haloacetic acids, and two inorganic compounds (bromate and chlorite). Under the Stage 2 DBP
Rule, compliance is based on a locational running annual average (LRAA) calculation, where the
annual average at each sampling location in the distribution system is used to determine
compliance with the MCL of 0.08 mg/L for THM4 (regulated as TTHM, bromodichloromethane,
bromoform, chloroform, and dibromochloromethane). There is a substantial body of literature on
DBP precursor occurrence and THM4 formation mechanisms in drinking water treatment. The
formation of THM4 in a particular drinking water treatment plant is a function of several factors
including disinfectant type, disinfectant dose, bromide concentration, organic material type and
concentration, temperature, pH, and system residence times. Epidemiology studies have shown
that THM4 exposure, a surrogate for chlorinated drinking water, is associated with an increased
risk of bladder cancer, among other diseases (Cantor et al., 1998; Cantor et al., 2010; Costet et
al., 2011; Freeman et al., 2017; King & Marrett, 1996; Regli et al., 2015; Villanueva et al., 2004;
Villanueva et al., 2006; U.S. EPA, 2019d). These studies considered THM4 as surrogate
measures for DBPs formed from the use of chlorination that may co-occur. Reductions in
exposure to THM4 is expected to yield significant public health benefits (Regli et al., 2015). In
what Richardson (2022) describes as the "largest risk assessment of DBPs in the U.S. to date,
focusing on bladder cancer cases associated with chlorinated drinking water", Weisman et al.
(2022) estimated that 8,000 of 79,000 national cases of bladder cancer are attributable to DBPs
in drinking water.
The EPA used the following data sources for the DBP co-removal analysis (see Table 6-29).
76 The methodology detailed in Section 6.7.1 on estimated DBP reductions was externally peer reviewed by three experts in GAC
treatment for PFAS removal and DBP formation potential. The external peer reviewers supported the EPA's approach and edits
based on their recommendations for clarity and completeness are reflected in the following analysis and discussion. Please see
"Response to Letter of Peer Review for Disinfectant Byproduct Reduction " (U.S. EPA, 2023b) for discussion of the peer review
and the EPA's responses to peer reviewed comments.
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Table 6-29: Data Sources and How the Information Derived from each Source is Used in
the DBP Co-Removal Analysis
Data Source
Acronym
How Specific Data were Used in Analysis
Consumer Confidence Reports CCR
DBP Information Collection
Rule Treatment Study
Database
DBP ICR Aux 1 (1998)
Six-Year Review 3,
Information Collection Rule
(2011)
Six-Year Review 4,
Information Collection Rule
(2019)
Unregulated Contaminant
Monitoring Rule 3
Unregulated Contaminant
Monitoring Rule 4
DBP ICR TSD
Aux 1
SYR3 ICR
SYR4 ICR
UCMR3
UCMR4
Identify GAC treatment start date/year.
Identify intended purpose for GAC
treatment.
Estimate baseline THM4 (four regulated
trihalomethanes) concentrations at systems
when SYR4 data were unavailable.
Calculate THM4 reduction at systems when
SYR4 data were unavailable.
Estimate changes in THM4 levels based on
implementing GAC treatment.
Evaluate changes in DBP precursor
occurrence over time by comparing TOC
data to SYR3 TOC data.
Evaluate raw water TOC data.
• Evaluate raw water TOC data.
• Estimate baseline THM4 concentrations.
• Calculate THM4 reductions.
• Inform a Bayesian occurrence model to
identify PWSs expected to implement
treatment under the NPDWR.
• Identify PWSIDs that had a detectable level
of PFOA and/or PFOS to identify systems
used in trihalomethane reduction
comparison.
• Identify plants that indicated GAC
treatment.
Inform disinfectant type.
Abbreviations: THM4 - four regulated trihalomethanes; DBP - disinfection byproduct; NPDWR - National Primary Drinking
Water Regulation; PWS - public water system; PWSID - public water system identifier; SYR - Six-Year Review; GAC -
granular activated carbon; TOC - total organic carbon; PFOS - perfluorooctane sulfonic acid; PFOA - perfluorooctanoic acid.
6.7.1.1 Overview of PFAS Treatment with Disinfection Byproduct
Reduction
GAC adsorption has been used to remove synthetic organic chemicals, taste and odor
compounds, and natural organic matter (NOM) during drinking water treatment (Chowdhury et
al., 2013). Recently, many water utilities have installed or are considering installing GAC and/or
other advanced technologies as a protective or mitigation measure to remove various
contaminants of emerging concern, such as PFAS (Dickenson & Higgins, 2016). Because NOM
often exists in a much higher concentration (in mg/L) than trace organics (in [j,g/L or ng/L) in
water, NOM, often measured as TOC, can interfere with the adsorption of trace organics by
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outcompeting the contaminants for adsorption sites and by general fouling (blockage of
adsorption pores) of the GAC.
NOM and inorganic matter are precursors for the formation of THMs and other DBPs when
water is disinfected using chlorine and other disinfectants to control microbial contaminants in
finished drinking water. Removal of DBP precursors through adsorption onto GAC has been
included as a treatment technology for compliance with the existing DBP Rules and is a BAT for
the Stage 2 DBP Rule. Dissolved organic matter can be removed by GAC through adsorption
and biodegradation (Crittenden et al., 1993; W. H. Kim et al., 1997; Yapsakli & £e9en, 2010).
Upon startup, the initial removal is via adsorption of the DBP precursors; GAC is well-
established for removal of THM and HAA precursors (Dastgheib et al., 2004; Cheng et al., 2005;
Iriarte-Velasco et al., 2008; Summers et al., 2013; Cuthbertson et al., 2019; L. Wang et al.,
2019). However, biodegradation becomes the predominant mechanism over time as adsorption
capacity is exhausted and microbial growth within the GAC column establishes itself (Speitel Jr
et al., 1989; Velten et al., 2007). In addition to removal of organic DBPs, GAC also exhibits
some capacity for removal of inorganic DBPs such as bromate and chlorite (Kirisits et al., 2000;
Sorlini & Collivignarelli, 2005) and removal of preformed organic DBPs via adsorption and
biodegradation (Jiang et al., 2017; Terry & R.S., 2018). Further, GAC may offer limited removal
of dissolved organic nitrogen (Chili et al., 2012).
Based on an extensive review of published literature in sampling studies where both contaminant
groups (PFAS and DBPs) were sampled, there is limited information about PFAS removal and
co-occurring reductions in DBPs, specifically THMs. To help inform its economic analysis, the
EPA relied on the DBP Information Collection Rule Treatment Study Database and DBP
formation studies to estimate reductions in THM4 (ATHM4) that may occur when GAC is used
to remove PFAS. Subsequently, these results were compared to THM4 data from PWSs that
have detected PFAS and have indicated use of GAC.
The objective of the co-removal benefits analysis is to determine the reduction in bladder cancer
cases associated with the decrease of regulated THM4 in treatment plants due to the installation
of GAC for PFAS removal. Figure 6-10 illustrates the EPA's approach for quantifying the
human health benefits of reducing THM4 levels in drinking water. The analysis entails:
1. Estimating the number of systems expected to install GAC treatment in compliance with
the final PFAS NPDWR and affected population size;
2. Estimating changes in THM4 levels that may occur when GAC is installed for PFAS
removal based on influent TOC levels;
3. Estimating changes in the cumulative risk of bladder cancer using an exposure-response
function linking lifetime risk of bladder cancer to THM4 concentrations in residential
water supply (Regli et al., 2015);
4. Estimating annual changes in the number of bladder cancer cases and mortality in the
bladder cancer population corresponding to changes in THM4 levels under the final rule
and regulatory alternatives in all populations alive during or born after the start of the
evaluation period;
5. Estimating the economic value of reducing bladder cancer morbidity and mortality from
baseline to the final rule and regulatory alternative levels, using COI measures and the
Value of Statistical Life, respectively.
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legend;
Analysis
component
Data/Inputs
Model
# nalysis step
1 Public drinking
—— water treatment
systems*
Reduction in THM4
concentration"
cancer exposure-
response functioa
Location-specific
population size
Change in cumulative
bladder cancer risk
Life table cancer
model
Raw water TOC
levels'3
National Cancer
Institute SEER
program data
Centers for
Disease Control
health statistics
Change in the number
of bladder cancer cases
Change in excess
bladder cancer
population mortality
Medical cost of
bladder cancer
treatment
Value of a statistical
life
Abbreviations: THM4 = Four Regulated Species of Trihalomethanes; SEER = Surveillance, Epidemiology,
and End Results; TOC = Total Organic Carbon
Notes:
"Systems expected to be triggered info PFAS treatment using either granular activated carbon (GAC) or ion
exchange (IX) treatment technologies,
''Based on median raw water TOC annual system-means for non-purchased water s\ stems
"Based on THM4 ieductions due to GAC installation from the disinfection byproduct (DBP) information
collection rule (ICR) treatment studies. Reductions dependent on empty bed contact time (EBCT) and
source w ater t) pe (surface water or groundwater).
Figure 6-10: Overview of Analysis of Co-Removal Benefits
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6.7.1.2 Baseline Information on DBP Precursors and Trihalomethane
Formation
DBP precursors are the chemical constituents that are reactants or intermediates in the formation
of DBPs. Precursors can be characterized by their origin and the nature of their chemistry
(inorganic vs. organic). Precursors include NOM and anthropogenic organic matter (i.e.,
wastewater) from watersheds, organic matter contaminants within treatment processes, and
biofilm growth within the distribution system. Additional precursors include inorganic matter
present in source water from anthropogenic and natural sources, or chemical additives introduced
during treatment. The presence of DBP precursors is site-specific and dependent on many factors
such as, but not limited to, environment, location, watershed, and treatment.
The EPA evaluated raw water TOC data included in the SYR3 and SYR4 ICR datasets (U.S.
EPA, 2016j; U.S. EPA, 2022f). The fourth Unregulated Contaminant Monitoring Rule (UCMR
4) TOC data were not used since that dataset did not include THM4 information. In addition, the
EPA compared the DBP ICR Aux 1 TOC data (pre-Stage 1 DBP Rule77) to the SYR3 ICR TOC
data to evaluate changes in DBP precursor occurrence over time. PWSs (specifically subpart H
systems78) are required to achieve a certain percentage of TOC removal; occurrence estimates for
TOC are typically evaluated at the plant-level. The SYR3 ICR dataset contains TOC data for 33
states and systems of all sizes. The SYR4 ICR dataset contains TOC data for 49 states/tribes and
systems of all sizes. To be consistent with SYR3 and SYR4 data management protocols, non-
detections of TOC were assigned a value of 0.0 mg/L for all plant-mean calculations (U.S. EPA,
2016a).
In U.S. EPA (2005b), the EPA reviewed the raw water TOC levels for ground water plants
included in the DBP ICR Aux 1 data. The results shown in Table 6-30 represent the distribution
of ground water plant-mean data as calculated using ICR Aux 1 monthly data from the year
1998. Only plants with reported data for at least 9 of the 12 months are included in this summary
table. Note that the table does not include results for blended, mixed, or purchased water plants.
Table 6-31 shows the distribution of plant-mean TOC concentrations in raw water for non-
purchased surface water plants. Segmenting the plants with raw water TOC means provides
some indication of the percentage of plants that would be within each THM4 reduction category
outlined in Section 6.7.1.3. The levels in ground water plants tended to be lower compared to
concentrations in surface water plants (Table 6-30 and Table 6-32 compared to Table 6-31 and
Table 6-33). As mentioned above, TOC non-detections were assumed to be zero for plant-mean
calculations.
77 Stage 1 Disinfectants and Disinfection Byproducts Rule was promulgated by the EPA in December 1998 (U.S. EPA, 1998e).
78 Subpart H systems are defined as public water systems using surface water or ground water under the direct influence of
surface water as a source that are subject to the requirements of subpart H of the National Primary Drinking Water Regulations
(U.S. EPA, 2006a).
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Table 6-30: DBP ICR (1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Summary of Raw
Water TOC Annual System Means for Ground Water Systems
Data Source
(Year)3
Source Water
Type
Count of
Systems
Median
(mg/L)
Mean
(mg/L)
90th
Percentile
(mg/L)
Range of
System-
Means1'
DBP ICR (1998)
Ground Water
103
0.19
1.46
3.36
0.0 - 16.1
SYR3 ICR (2011)
Ground Water
68
2.19
3.33
5.85
0.42-17.0
SYR4 ICR (2019)
Ground Water
80
1.50
2.54
7.11
0.0-15.73
Notes:
Abbreviations: DBP - disinfection byproduct; ICR - information collection rule; SYR - Six-Year Review; TOC - total organic
carbon.
aUsing SYR3 cutoff values, values >100 mg/L were excluded from calculations.
bValues below the MRL were converted to 0.0 mg/L to calculate system-means.
Source: ICRAUX1 database; table extracted from Exhibit 3.6 of U.S. EPA (2005b).
Table 6-31: DBP ICR (1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Summary of Raw
Water TOC Annual System Means for Surface Water Systems
Data Source
(Year)3
Source Water
Type
Count of
Systems
Median
(mg/L)
Mean
(mg/L)
90th
Percentile
(mg/L)
Range of
System-
Means1'
DBP ICR (1998)
Surface Water
307
2.71
3.14
5.29
0.0-21.4
SYR3 ICR (2011)
Surface Water
756
2.89
3.45
6.45
0.0-29.3
SYR4 ICR (2019)
Surface Water
802
3.29
3.88
6.93
0.0-38.9
Abbreviations: ICR - information collection rule; SYR - Six-Year Review; TOC - total organic carbon.
Notes:
aUsing SYR3 cutoff values, values >100 mg/L were excluded from calculations.
bValues below the MRL were converted to 0.0 mg/L to calculate system-means.
The EPA reviewed the finished water TOC levels included in SYR3 ICR and SYR4 ICR data.
The results shown in Table 6-32 represent the distribution of TOC concentrations for ground
water plants. Note that ground water plants are not federally required to report finished water
TOC data. In addition, the EPA reviewed finished water TOC levels for surface water plants
included in SYR3 and SYR4 ICR data. Table 6-33 displays the distribution of TOC levels in
finished water for surface water plants. Similar to the raw water comparison, TOC levels tended
to be higher among surface water plants compared to ground water plants.
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Table 6-32: SYR3 ICR (2011) and SYR4 ICR (2019) - Summary of Finished Water TOC
Annual System Means for Ground Water Systems
Data Source Source Water
(Year)3 Type
Count of
Systems
Median
(mg/L)
Mean 90th Percentile Range of
(mg/L) (mg/L) System-Meansb
SYR3 ICR (2011) Ground Water
78
1.86
2.30 4.53 0.0- 11.4
SYR4 ICR (2019) Ground Water
113
0.73
2.77 3.63 0.0-93.0
Abbreviations: ICR - information collection rule; SYR - Six-Year Review; TOC
Notes:
aUsing SYR3 cutoff values, values >100 mg/L were excluded from calculations.
bValues below the MRL were converted to 0.0 mg/L to calculate system-means.
- total organic carbon.
Table 6-33: SYR3 ICR (2011) and SYR4 ICR (2019) - Summary of Finished Water TOC
Annual System Means for Surface Water Systems
Data Source Source Water
(Year)3 Type
Count of
Systems
Median
(mg/L)
Mean 90th Percentile Ran§e of
(mg/L) (mg/L, ^e™b"
SYR3 ICR (2011) Surface Water
756
1.93
2.32 3.99 0.0-25.1
SYR4 ICR (2019) Surface Water
802
1.89
2.24 3.90 0.0-74.4
Abbreviations: ICR - information collection rule; SYR - Six-Year Review; TOC - total organic carbon.
Notes:
aUsing SYR3 cutoff values, values >100 mg/L were excluded from calculations.
bValues below the MRL were converted to 0.0 mg/L to calculate system-means.
The EPA compared the levels of raw water TOC between the DBP ICR and SYR3 ICR to
evaluate the changes in TOC occurrence over time (U.S. EPA, 2016g). The EPA used 1998 data
from the DBP ICR Aux 1 database and 2011 data from the SYR3 ICR dataset and included only
the data from systems that were found in both datasets (referred to as "common systems"). The
evaluation of TOC changes over time was limited to large surface water systems (>100,000
population served) because the DBP ICR only covered large systems.
Table 6-34 below presents plant-level summary statistics for finished water TOC from common
systems in the Aux 1 database and SYR3 ICR. The common systems were distributed across 14
states (Alabama, Alaska, Illinois, Indiana, Iowa, Kentucky, Nevada, New Jersey, North Carolina,
Oklahoma, Pennsylvania, South Carolina, Virginia, and West Virginia). The comparison of data
for large surface water supplies between 1998 and 2011 shows a small decrease in treated water
TOC levels. The median finished water TOC concentrations at large systems were 1.76, 1.75,
and 1.51 mg/L in the Aux 1 database, SYR3 ICR dataset, and SYR4 ICR dataset, respectively.
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Table 6-34: DBP ICR (Aux 1; 1998), SYR3 ICR (2011), and SYR4 ICR (2019) - Finished
Water Annual System Mean TOC; Common Surface Water Systems
Data Source
Count of
Median
Mean
90th Percentile
95th Percentile
%
Means >
2 mg/L
%
Means >
3 mg/L
(Year)
Systems3
(mg/L)
(mg/L)
(mg/L)
(mg/L)
DBP ICR
(1998)
SYR3 ICR
(2011)
1.76
1.77
2.90
3.23
34%
8%
80
1.75
1.74
2.78
3.24
30%
8%
SYR4 ICR
(2019)
80
1.51
1.49
2.44
2.81
21%
5%
Abbreviations: DBP
- disinfection byproduct; ICR - information collection rule; SYR - Six-Year Review; TOC -
total
organic carbon.
Note:
aSome systems included data for multiple plants.
Source: Table extracted from Exhibit 6.11 of U.S. EPA (2016g)
Table 6-35 summarizes THM4 baselines under DBP ICR, which represents pre-Stage 1 and
Stage 2 DBP Rules. Prior to evaluating the SYR4 ICR THM4 data, the EPA removed values
greater than 10 times the MCL (800 |ig/L) due to potential data entry errors. Additionally, the
EPA converted values below the MRL (10 |ig/L) to 0 |ig/L, which is consistent with previous
SYR data analysis (U.S. EPA, 2016a). Average THM4 values were higher for surface water
plants compared to ground water plants across the two datasets. Within the DBP ICR dataset,
representing PWSs serving populations >100,000, 82 ground water plants had a median THM4
concentration of 6.8 |ig/L with a range of 0-123 |ig/L. For the 213 surface water plants in the
DBP ICR, the median THM4 concentration was 40 |ig/L with a range of 0 to 117 |ig/L. In
comparison, post-Stage 1 and 2 DBP Rules SYR4 ICR data show median THM4 concentrations
of 5.0 |ig/L and 41.4 |ig/L and mean THM4 concentrations of 13.4 |ig/L and 41.1 |ig/L in ground
water and surface water, respectively. Plant means ranged from 0 to 371.4 |ig/L and from 0 to
263.8 |ig/L for ground water and surface water, respectively. Note that the SYR4 dataset was
from voluntary submissions and includes data from systems of all sizes. The SYR4 ICR reduced
dataset, limited to PWSs serving populations >100,000, shows median THM4 concentrations of
24.4 and 36.1 |ig/L and mean THM4 concentrations of 25.0 and 35.1 |ig/L for ground water and
surface water, respectively. Plant means ranged from 0 to 66.6 |ig/L and from 0 to 62.0 |ig/L for
ground water and surface water, respectively.
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Table 6-35: Summary of THM4 Baseline Comparing DBP ICR and SYR4 ICR
Source Water
Data Source Type
Count of
Systems0
THM4
Median
(Mg/L)
THM4
Mean
(Mg/L)
90th
Percentile
(Mg/L)
Range of
System-Means'1
DBP ICR (1998)' Groundwater
82
6.8
15.4
37
0-123
DBP ICR (1998)1' Surface Water
213
40
42
70
0-117
SYR4 ICR
84
24.4
25.0
53.1
0-66.6
Reduced (2012- Groundwater
2019)b'e,f
SYR4 ICR
291
36.1
35.1
50.2
0-62.0
Reduced (2012- Surface Water
2019)b-e'f
SYR4 ICR (2012- „ ,... +
2oi9)b,e Groundwater
26,243
5.0
13.4
38.5
0-371.4
SYR4 ICR (2012- c , ... t
2019)1, : Surface Water
9,618
41.4
41.1
64.1
0-263.8
Abbreviations: DBP - disinfection byproduct; ICR - information collection rule; SYR - Six-Year Review; THM4 - four regulated
trihalomethanes.
Notes:
aStage 2 DBP Rule Economic Analysis (U.S. EPA, 2005b), screened data from Exhibit 3.15 and 3.20
bUsing SYR3 cutoff values, values >10 times the MCL were excluded from calculations.
CNA values and blanks were removed prior to calculations.
dValues below the MRL were converted to 0.0 (ig/L to calculate system-means.
eSYR4 data collected from 2012 to 2019. All years were included in calculations.
fSYR4 reduced dataset included only PWSs serving populations >100,000
In the Economic Analysis for the Stage 2 DBP Rule, the EPA estimated a combined average
THM4 reduction for all systems of 7.8 percent, with surface water systems ranging from 9.2
percent (systems serving >10,000) to 7.2 percent (systems serving <10,000), and ground water
systems ranging from 1.4 percent (systems serving >10,000) to 2.0 percent (systems serving
<10,000) (U.S. EPA, 2005b). Comparisons of the DBP ICR THM4 baseline data and the SYR4
data that reflects Stage 1 and Stage 2 DBP Rule changes indicate that the Stage 2 EA slightly
overestimated the ATHM4 for surface water systems (40 to 41.4 |ig/L, 3.5% increase) and
underestimated the ATHM4 for ground water systems (6.8 to 5.0 |ig/L, 26.5% reduction).
Comparing all systems (surface water and ground water) serving >100,000, no statistically
significant difference (p-value = 0.2) was observed between the DBP ICR and SYR4 dataset
means. Comparing ground water systems in the DBP ICR dataset to those in the reduced SYR4
dataset showed a statistically significant difference (p-value = 0.0003) in THM4 means, with
THM4 increasing in the more recent years (SYR4). Comparing surface water systems in the
DBP ICR dataset to those in the reduced SYR4 dataset showed no statistically significant
difference (p-value = 0.3) in THM4 means. The lack of statistically significant differences in
THM4 means between the DBP ICR and SYR4 datasets for surface water systems indicates that
TOC and THM4 trends support the use of the DBP ICR dataset to predict ATHM4 resulting from
GAC treatment. For large ground water systems (populations >100,000), reductions in THM4
mean concentrations may be underestimated due to the increase in THM4 baseline
concentrations observed from data reported in the DBP ICR to the SYR4 ICR. Based on the
TOC and THM4 trends over time and the percent differences observed between the DBP ICR
and SYR4 dataset means, the EPA determined that using the DBP ICR Treatment Study
Database results for ATHM4 to predict future ATHM4 resulting from GAC treatment was
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justified and reasonable. Additionally, with this focus on GAC treatment and the reduction of
THM4, it is important to note that the DBP ICR treatment study required systems to conduct
DBP precursor removal studies (Treatment Study Database), which contains the most extensive
amount of data on GAC treatment and DBP formation potentials (U.S. EPA, 1996; L. Wang et
al., 2019).
Larger datasets, such as SYR ICRs, do not include data on both disinfectant type and DBP
formation. The DBP ICR collected this information in addition to other source and water quality
parameters. Table 6-36 shows mean THM4 concentrations in the DBP ICR per disinfectant type
and source water type.
Table 6-36: DBP ICR (Aux 1) Summary of THM4 Concentrations Based on Disinfectant
and Source Water Type
Disinfectant Type Source Water Type Count of Plants / Facilities Mean THM4 concentration (jig/L)
Ground Water
15
29.2
Chloramine
77
Surface Water
43.2
Ground Water
34
21.3
Free Chlorine
164
Surface Water
45.0
Free Chlorine +
Ground Water
1
18.7
Chloramine (DS)
Surface Water
20
53.2
Abbreviations: DBP - disinfection byproduct; THM4 - four regulated trihalomethanes; DS - distribution system.
Despite the significant public health improvements provided by the EPA's Stage 2 DBP Rule,
DBPs are still estimated to cause approximately 8,000 cases of drinking water-attributable
bladder cancer cases every year (Weisman et al., 2022). Hence, there are still public health
benefits to be realized when DBPs are reduced when feasible. Where systems install activated
carbon, the PFAS rule will, for many systems, further reduce DBP concentrations because of
precursor removal. While the Stage 1 and Stage 2 DBP Rules were effective at reducing THM4,
there are remaining risks associated with DBP exposure that could be further reduced as shown
in the baseline analysis above. The Stage 2 DBP Rule was promulgated in 2006 and since the
rule implementation there have been numerous peer-reviewed studies that have shown an
increased weight of evidence supporting an association between chlorination DBPs and bladder
cancer with updated estimates on attributable cases (Weisman et al., 2022; Regli et al., 2015).
Additionally, there is an increased understanding of the role of genetically susceptible
populations and exposure routes for THMs (i.e., oral, inhalation, and dermal) that impact risk
assessments. This comparison between the SYR4 ICR (2019) and DBP ICR (1998) showed that
the DBP ICR THM4 data were still relevant for the post Stage 2 DBP Rule baselines for both
TOC (i.e., DBP precursors) and THM4. Because the baseline was pre-Stage 1 (DBP ICR), the
EPA took the low-end estimate for THM4 reduction to reduce possible overestimation. Further
reduction in TOC concentrations in finished water could be achieved if additional treatment is
added (i.e., PFAS removal using GAC treatment).
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6.7.1.3 Estimation of Trihalomethane Reduction using Treatment
Models
6.7.1.3.1 DBP Information Collection Rule Treatment Study Database
The Information Collection Rule Treatment Study Database (ICR TSD) contains results of the
most extensive GAC study conducted on a national scale. The ICR TSD contains treatment study
data submitted by systems required to conduct DBP precursor removal studies under the DBP
ICR (U.S. EPA, 1996). The systems included in the ICR TSD were considered "challenged" in
their ability to achieve compliance with potential Stage 2 DBP rule revision MCLs. The
participating systems included surface water systems (and ground water systems under the direct
influence of surface water) serving 100,000 or more people and having > 4 mg/L of TOC in
source water, and ground water systems serving 50,000 or more people and having > 2 mg/L of
TOC in finished water. Both free chlorine and chloramine systems were included in the
treatment study (U.S. EPA, 1996; L. Wang et al., 2019).
Data from the ICR TSD study from these "challenged systems" can be used to identify
conservative estimates of TOC reduction and associated ATHM4. Due to upstream pollution,
drought, and/or climate change, individual drinking water sources may be as challenged as when
the ICR TSD data were collected (Hashempour et al., 2020; McDonough et al., 2020). While the
GAC treatment dataset dates are from 1998, the physical/chemical relationships observed have
only improved with the current application of GAC being at least as effective for THM4 as was
observed in the ICR TSD (Yuan et al., 2022). While source water parameters and treatments at
individual plants have changed over time, as seen in the baseline characterization in Section
6.7.1.2, the EPA determined the ICR TSD was still appropriate to inform estimates of ATHM4
formation potential given the lack of available data to directly inform ATHM4 from PFAS
adsorption studies and the low percent difference in TOC changes on a national scale between
the DBP ICR and SYR4 collection efforts.
From the 63 GAC systems included in the ICR TSD, a total of 182 pilot/rapid small-scale
column test (RSSCT) studies were conducted to develop breakthrough curves of TOC and DBP
formation changes. Two EBCTs, 10 and 20 min, were evaluated for treated water that had passed
through any full-scale treatment processes previously in place at each drinking water treatment
plant to remove precursors (i.e., coagulation/flocculation, sedimentation, filtration) but before
any disinfectant was added. To determine the effect of GAC treatment on DBP formation, these
studies evaluated TOC removal and THM4 formation potential for the treated water before and
after GAC treatment. Uniform formation condition procedures were standardized across each
study, with a reaction time of 24 ± 1 hours, temperature of 20.0 ± 1.0°C, buffered pH at 8.0 ±
0.2, and 24-hr chlorine residual of 1.0 ± 0.4 mg/L as C12 (U.S. EPA, 1996; Summers et al.,
1996).
The pilot/RSSCT studies were timed to account for seasonal changes and an "averaging"
approach was used to remove temporal variations. This approach was consistent with analysis
used to characterize different GAC options for compliance with the MCLs under the Stage 2
DBP Rule (Hooper & Allgeier, 2002). Additional details on the GAC study design specifications
under the ICR TSD are available in the "ICR Manual for Bench and Pilot Scale Treatment
Studies" (U.S. EPA, 1996).
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For drinking water systems in the ICR TSD that used chloramines (n = 123 pilots/RSSCTs) in
their distribution system, free chlorine was still used in the DBP formation tests, therefore the
pilot and RSSCT systems were not compared based on disinfectant type used by the individual
treatment system. For reference, a summary of the THM4 estimates by disinfectant type is
provided in Appendix I Table 1-1. Additionally, if the comparison categories were further parsed
by source water type, disinfectant type, and TOC concentrations, then the number of systems in
each bin would not provide sufficient studies to compare the ATHM4 estimates. Therefore, the
EPA analyzed the THM4 reductions based on raw-water TOC.
The TOC and THM4 formation potential reductions data from the ICR TSD were modeled with
a logistic equation using results from 182 pilot plant/RSSCT studies. The EPA fit the logistic
function parameters for each EBCT and did not consider feed water quality parameters. Results
were categorized by TOC level and source water type. Further subdivision of these or additional
categories would have resulted in very small numbers of systems in bins and some bins not being
filled (see Appendix I Table 1-1 for example of "disinfection type" added as a category). The
model calculated individual system TOC removal for the EBCT and results were averaged for
each subset of systems for the GAC replacement interval. The model was not intended to
simulate the dynamics of TOC removal by GAC or the formation of THM4, but it simulated the
TOC ranges within the pilot/RSSCT studies and the changes in THM4 due to the reduction in
TOC observed in the ICR TSD. The EPA used Python to individually fit data from each pilot or
RSSCT study in the ICR TSD to a logistic equation and the performance was then averaged.
Additional details on the data model are included in Appendix I.
To conservatively estimate national scale THM4 reduction due to GAC treatment to reduce
levels of PFAS compounds, the EPA chose a 2-year GAC replacement time. The EPA assumes
that this is the longest amount of time before replacement would be required and percent
removals are approximately at their long-term removal level with minimal further changes. The
PFAS NPDWR will likely result in some systems replacing GAC media more frequently than 2
years, which the EPA expects would result in a greater average TOC reduction since TOC
removal decreases over time with GAC treatment (see Figure 6-11 and Figure 6-12 for ground
water and surface water respectively). The overall trends seen in Figure 6-11 and Figure 6-12
show greatest TOC removal in the first 200 days of use, after which the predicted TOC removal
becomes consistent for ground water with 26.9 percent (EBCT 20 min) and surface water with
37.5 percent (EBCT 20 min).
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100
Water Type: ground
>
o
E
<1J
CCL
U
o
80-
60
40 ¦
20
—i 1 1 1 1—
100 200 300 400 500
Replacement Interval (days)
? round • TOC • 10 mm EBCT
J itl J\ removed
around • TOC - 20 m*n E8CT
26.9211.3 % removed
600
700
Abbreviations: TOC - total organic carbon; GAC - granular activated carbon; I 'liC'T - empty bed contact times.
Notes:
Pink shaded area represents ±1 standard deviation for ground water TOC with a GAC EBCT of 10 min
Gray shaded area represents ±1 standard deviation for ground water TOC with a GAC EBCT of 20 min
Figure 6-11: Estimated TOC Percent Removal in Ground Water Using GAC Based on
Logistic Equation Model
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100
80
E 60
01
cc
Water Type: surface
\
—
vurface • TOC • 10 mm C8CT
28 lit 7 % removed
wf«ce TOC • 20 min E8CT
37.5x13.3 % removed
\
\ s
\ "V
w *
* ^ ^
— — —
£
u
o
40-
20
100 200 300 400 500
Replacement Interval (days)
600
700
Abbreviations: TOC - total organic carbon; GAC - granular activated carbon; EBCT - empty bed contact time.
Notes;
Pink shaded area represents ±1 standard deviation for surface water TOC with a GAC EBCT of 10 min
Gray shaded area represents ±1 standard deviation for surface water TOC with a GAC EBCT of 20 min
Figure 6-12: Estimated TOC Percent Removal in Surface Water Using GAC Based on
Logistic Equation Model
To estimate the TOC reduction, the ICR TSD pilot/RSSCT studies (n = 182) were partitioned
into five potential bins based on TOC concentrations in raw water (Very Low <1 mg/L, Low >1
to <2 mg/L TOC, Mid >2 to <3.5mg/L, High-Mid >3.5 to <5mg/L, High TOC >5mg/L TOC).
There were no systems in the ICR TSD that fell into very low TOC bin. Based on the logistic
equation for TOC reduction, higher raw water TOC concentrations yield greater TOC reductions
(in absolute value) following GAC treatment. Table 6-37 shows the TOC reduction for all waters
(both ground water and surface water) for a 20 min EBCT.
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Table 6-37: TOC Reduction for All Waters (Both Surface Water and Ground Water)
with GAC EBCT of 20 Min and a 2-year Replacement Time
TOC Bin
Number of Studies in
GAC Treatment Dataset
TOC Reduction ± 1-
Standard Deviation (%)
TOC Reduction (mg/L)
TOC 1-2 mg/L
20
41.9 ±23.2
0.75 ±0.39
TOC 2-3.5 mg/L
103
37.1 ± 11.6
1.06 ±0.36
TOC 3.5-5 mg/L
44
32.0 ±9.6
1.31 ± 0.39
TOC above 5mg/L
15
26.3 ± 14.2
1.83 ±0.91
Abbreviations: TOC - total organic carbon; GAC - granular activated carbon; EBCT - empty bed contact time.
Note: The model calculated individual system TOC removal and results were averaged for each influent TOC bin for a two-
year GAC replacement interval and the standard deviation was calculated for each subset average.
Using the same raw water TOC bins, the EPA estimated national scale ATHM4 values resulting
from GAC treatment. The selected ATHM4 estimate was based on a conservative approach
(mean concentration minus one standard deviation), since the DBP ICR systems included in the
treatment studies were "challenged systems" (i.e., systems that had difficulty meeting regulatory
compliance requirements) that may experience increased TOC reduction due to GAC
installation.
The analysis here assumes operation of GAC with a replacement interval of 730 days (2 years).
Although some systems will operate with longer replacement intervals, after 730 days, the
modeled TOC reduction due to GAC shows consistent removal when further extending the
replacement interval (Figure 6-11 and Figure 6-12). While systems may replace GAC at shorter
time intervals, the 2-year replacement assumption also approximates blended systems (i.e.,
multiple GAC treatment trains in parallel with varying replacement intervals) and TOC long
term removal by adsorption since GAC treatment for PFAS uses adsorption rather than
biodegradation (Kempisty et al., 2022). Therefore, the estimated TOC reduction at 730 days
should also be representative for systems with longer replacement intervals or systems with
intermittent use. If GAC replacement occurred more frequently due to PFAS treatment needs,
higher average TOC removal would occur, resulting in greater THM4 reduction as shown in the
6-month GAC replacement time-steps (1/2, 1, 1 V2, and 2 years) shown in Appendix I (Tables I-
2,1-3,1-4, and 1-5). Using the longer replacement time of 2 years is consistent with the EPA's
conservative approach to estimating ATHM4 even when the presence of PFAS compounds and
other source water conditions may affect GAC replacement frequency.
Based on common treatment designs, the EPA expects GAC treatment parameters for PFAS
removal to be 20 min EBCTs (U.S. EPA, 2024i). Table 6-38 and Table 6-39 provide estimates of
THM4 reductions in the modeled 182 pilot/RSSCT systems considering a 20 min EBCT broken
out by surface water versus ground water. The number of GAC systems included in each TOC
bin is provided along with the average ATHM4 and the "conservative" ATHM4 (defined as
average ATHM4 minus 1-standard deviation) with a GAC replacement time of 2 years.
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Table 6-38: Estimation of ATHM4 in Surface Water with a 20 Min EBCT, and a 2-year
GAC Replacement Time
Raw Water TOC Bin
Number of Studies in GAC
Treatment Dataset
Average ATHM4 ± 1-
Standard Deviation
(Mg/L)
Conservative ATHM4
(Mg/L)
TOC 1-2 mg/L
12
14.23 ±7.34
6.9
TOC 2-3.5 mg/L
89
31.54 ±24.02
7.5
TOC 3.5-5 mg/L
37
48.55 ±31.81
16.7
TOC above 5mg/L
7
67.2 ± 18.3
48.9
Abbreviations: EBCT - empty bed contact time; TOC - total organic carbon; THM4 - four regulated trihalomethanes; GAC -
granular activated carbon.
Table 6-39: Estimation of ATHM4 in Ground Water with a 20 Min EBCT, and a 2-year
GAC Replacement Time
Raw Water TOC Bin
Number of Studies in GAC Average ATHM4 ± 1-
Treatment Dataset Standard Deviation (ju.g/L)
Conservative ATHM4
(Mg/L)
TOC 1-2 mg/L
8 15.14 ±8.98
6.2
TOC 2-3.5 mg/L
14 22.02 ±17.48
4.5
TOC 3.5-5 mg/L
7 27.46 ±8.33
19.1
TOC above 5 mg/L
8 56.66 ±38.69
18.0
Abbreviations: EBCT - empty bed contact time; TOC - total organic carbon; THM4 - four regulated trihalomethanes; GAC -
granular activated carbon.
For the Low (1-2 mg/L), Mid (2-3.5 mg/L), and High-Mid (3.5-5 mg/L) TOC bins, the
conservative ATHM4 estimates were reasonable compared to the mean concentrations reported
into the SYR4 baseline occurrence data. Conservative ATHM4 estimates in the High TOC bin
(>5 mg/L) were higher due to the greater reduction in TOC. For the THM4 reduction observed in
the High TOC bin (>5 mg/L), the conservative THM4 reduction estimates were higher due to the
greater reduction in TOC and may not be plausible based on the baseline occurrence information
and if it is assumed that all systems are currently in compliance (THM4 <80 |ig/L). However,
based on SDWIS/Fed violations, not all systems are currently in compliance with the Stage 2
DBP Rule. The EPA assumes that these larger ATHM4 estimates would be observed only in the
90th percentile of TOC data. Ground water systems in the High TOC bin may also be
mischaracterized during the ICR TSD and should be more accurately described as GWUDI of
surface water (Brunke & Gonser, 1997; Chin & Qi, 2000). The GWUDI provisions of the 1989
Surface Water Treatment Rule instituted the concept of ground water that is so closely connected
to surface water that public water supply wells should be regulated as surface water rather than
as ground water (U.S. EPA, 1989). If one or more ground water systems were mischaracterized,
then this could overestimate the ATHM4 estimate since these systems make act more like a
surface water system in terms of TOC removal.
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Since these conservative ATHM4 estimates were based on the longest assumed time period for
GAC use (i.e., GAC replacement time) in the current regulatory options, the EPA assumes that
the estimated ATHM4 values are conservative, considering that shorter replacement times would
increase the average TOC removal during that operation time.
Since these surface water and ground water systems have already been identified as "challenged"
in the ICR TSD (pre-Stage 1 and Stage 2 DBP Rules), this indicates the specific advantages of
using GAC to reduce THM4 precursors in comparison to conventional treatment (i.e.,
coagulation/flocculation followed by sedimentation and filtration). When GAC treatment is used
for additional contaminant removal such as PFAS, TOC reduction benefits will also be observed.
Since there is a lack of PFAS and TOC co-removal data, the ICR TSD can provide the largest
dataset on TOC reduction and THM4 formation changes in drinking water to provide a national
estimate of ATHM4.
The limitations and uncertainties for using this method to quantify ATHM4 due to GAC
treatment for PFAS are listed in Section 6.8. One major limitation of using the ICR TSD was that
this dataset only used chlorine as a disinfectant and does not capture THM reduction in
chloraminating systems. This limitation may lead to an overestimate of THMs formed in systems
that used chloramines in the distribution system since THM4 can continue to form within the
distribution system and formation tends to be lower when chloramines are used in comparison to
free chlorine (Hua & Reckhow, 2008). Most chlorinating systems use free chlorine as a primary
disinfectant followed by the addition of ammonia to form chloramines for the secondary
disinfectant. Of the 9,838 ground water EPs to distribution systems included in UCMR 3,
chlorine disinfection was used 8.8 times more often than chloramine (n = 7,881 for chlorine
exclusively and n = 896 for chloramines or both chlorine and chloramines) (U.S. EPA, 2016g).
For the 3,179 surface water EPs to distribution systems in UCMR 3, chlorine was used 1.9 times
more than chloramine (n = 1,648 for chlorine exclusively and n = 879 for chloramines or both
chlorine and chloramines) (U.S. EPA, 2016g).
By assuming the use of free chlorine only, the estimates of ATHM4 from pilot/RSSCTs studies
may provide an overestimation when factoring in use of both free chlorine and chloramines.
Thus, using the conservative free chlorine THM4 formation potential (average ATHM4 minus 1-
standard deviation) rather than the average ATHM4, the EPA attempted to address the
overestimation and provide a reasonable national estimate of ATHM4.
In a separate DBP formation study under the ICR TSD, individual DBP formation conditions
were selected to represent simulated distribution systems for each individual plant that accounted
for the disinfectant differences (i.e., chlorine versus chloramine) by using only chlorine as the
disinfectant and varying the reaction times. The simulated distribution system studies were not
included in the estimated ATHM4 provided in this document since including them would have
further increased the uncertainty error for systems using chloramine due to the longer reaction
times.
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6.7.1.3.2 Trihalomethane Reduction Comparison to Fourth Six-Year Review PFAS
Plants with GAC Treatment
The EPA compared ATHM4 estimates from the ICR TSD to the SYR4 data for PFAS-associated
plants that have installed GAC. The objective of this analysis was to compare the ICR TSD
modeled predictions of ATHM4 to the observed ATHM4 concentrations from PWSs that
installed GAC for PFAS treatment.
The EPA identified systems that had detectable levels of PFOA and/or PFOS in UCMR 3.
Subsequently, the EPA used UCMR 4 data to identify which systems indicated use of GAC
treatment. Finally, the EPA used CCRs for all systems that detected PFAS and specified GAC
treatment for PFAS to approximate the year that GAC treatment was installed and the purpose
for installation. While this approach limited the number of systems available for comparison (n =
7), it allowed the EPA to pinpoint, approximately, which samples were taken before and after
GAC installation. The EPA obtained THM4 compliance monitoring data through the SYR4 ICR,
based on data collected between 2012 and 2019. The EPA calculated the ATHM4 values based
on observed THM4 levels before and after GAC installation.
The EPA identified plants using the following criteria (see Table 6-40):
1. Detectable level of PFAS in UCMR 3 (i.e., detections of PFOA and/or PFOS above their
respective MRL values).
2. GAC installed as indicated in UCMR 4 and confirmed for PFAS treatment by using CCR
information.
3. Ability to identify the year GAC was installed using CCR information.
4. THM4 data available from SYR4 (CCR THM4 data were used as an alternative when
SYR4 data were unavailable).
Table 6-40: Selected Distribution Systems from SYR4 Based on Outlined Criteria
PWSID
Source Water Type
Disinfectant Type
Year GAC Began
AL0000577
Surface Water
Free Chlorine3
2018
AL0001092
Surface Water
Free Chlorine, Chlorine Dioxide
2016
AZ0407046
Ground Water
Free Chlorine
2017
MI0005370
Surface Water
Free Chlorine
2018
NY3503549
Surface Water
Free Chlorine
2018
OH2903412
Ground Water
Free Chlorine
2017
PA1090069
Ground Water
Free Chlorine
2017
Abbreviations: PWSID - public water system identifier; SYR - Six-Year Review; GAC - granular activated carbon.
Note:
Tree chlorine includes gaseous chlorine, offsite generated hypochlorite, or onsite generated hypochlorite.
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The EPA chose sampling years to represent conditions before and after GAC treatment based on
the following criteria:
• If source water type was surface water, used one year before and one year after the year
in which GAC treatment began.
• If source water type was ground water, used two years before and two years after the year
in which GAC treatment began. Since ground water plants have fewer samples, this was
done to offset the lower sample number. (Note that ground water quality typically has
fewer fluctuations than surface water quality, so the EPA expects fewer changes in year-
to-year data for ground water systems.)
The EPA extracted and matched sampling point IDs for the years that represent before and after
GAC treatment (see Appendix I). Only sampling point IDs with the same number of samples for
before and after GAC treatment were used to determine THM4 averages. The seasonality and
quantity of samples were considered, and the EPA found that samples were taken consistently
and remained at the same frequency throughout the years selected to represent before and after
GAC treatment.
The EPA calculated ATHM4 concentrations for each system at matched sampling point locations
using THM4 data collected before and after GAC installation. The EPA also estimated ATHM4
concentrations at the broader plant level by aggregating all THM4 locational sampling data
collected before and after GAC installation (see Table 6-41).
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Table 6-41: Information on Selected Distribution System and Corresponding ATHM4 Values
PWSID
Source
Water Type
Disinfectant Type
Sampling Point IDa
Average THM4
(Before) (ju.g/L)
Average THM4
(After) (jig/L)
ATHM4 (jig/L)b
Average
ATHM4
(Mg/L)c
AL0000577
Surface Water
Free Chlorine
12975
16.5
10.9
5.7
9.8
AL0001092
Surface Water
Free Chlorine,
Chlorine Dioxide
23592
16.6
6.4
10.2
15.7
AZ0407046
Ground Water
Free Chlorine
33997
28.8
21.6
7.3
4.8
MI0005370
Surface Water
Free Chlorine
CCR
84.9
66.4
18.5
18.5
NY3503549
Surface Water
Free Chlorine
334940
39.1
7.6
31.5
31.5
OH2903412
Ground Water
Free Chlorine
541452
8.9
7.0
1.9
-4.1
PA1090069
Ground Water
Free Chlorine
892902
21.0
21.3
-0.3
-10.7
Abbreviations: THM4 - four regulated trihalomethanes.
Notes:
Sampling point IDs that have a sampling point type of EP were used when available. When unavailable, the first listed sampling point ID was used.
bATHM4 = THM4 Average (Before) - THM4 Average (After).
cAverage delta of pairwise changes in THM4 for each location in the entire distribution system.
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Based on available data, the EBCT for the seven plants from SYR4 is unknown. The EPA used
TOC values from SYR4 when available and used CCR TOC data as an alternative when TOC
data were missing from SYR4. TOC values for SYR4 ground water plants were missing from the
SYR4 dataset and corresponding CCRs, and due to this limitation, the EPA did not use raw water
TOC bins, but instead used a range of ATHM4 values for comparison between SYR4 and ICR
TSD.
The EPA compared ATHM4 values from the SYR4 to the ICR TSD dataset conservative
approach (see Table 6-42). Among SYR4 ground water plants, ATHM4 values ranged from -
10.7 |ig/L to 4.8 |ig/L. ICR TSD ground water ATHM4 values ranged from 3.5 |ig/L to 67.2
|ig/L. SYR4 ground water averages were between -7.2 |ig/L to 62.4 |ig/L lower than ICR TSD
surface water averages.
Table 6-42: Comparison Between ICR TSD Conservative ATHM4 and SYR4 ATHM4 for
Surface Water Systems
Raw Water
TOC Bin3
Surface Water
ICR TSD Conservative ATHM4
(Mg/L)
PWSID
SYR4 ATHM4 (jig/L)b
TOC 0-1 mg/L
No available data
AL0000577,
AL0001092
5.7,
15.7
TOC 1-2 mg/L
6.9
NY3503549
31.5
TOC 2-3.5 mg/L
7.5
MI0005370
18.5
TOC 3.5-5 mg/L
16.7
No available data
No available data
TOC >5 mg/L
48.9
No available data
No available data
Abbreviations: TOC - total organic carbon; THM4 - four regulated trihalomethanes; ICR - information collection rule; TSD -
treatment study database.
Notes:
aThree of the seven surface water PWSs had no TOC measurements.b20 min EBCT was used to determine best-case and
conservative ATHM4 values.
Two of the three ground water systems showed increased THM4 formation after the installation
of GAC. Possible reasons for increased formation may include source water changes (i.e.,
increased sediment runoff or spore concentration fluctuations in ground water), operational
challenges of the GAC treatment, changes to other treatments within the PWS, or changes in
retention time within the distribution system. The four surface water systems had ATHM4 values
ranging from 5.7 to 31.5 |ig/L.
Three out of the seven plants had no available TOC data in SYR4 or CCRs. TOC data for the
SYR4 THM4 analysis were only available for surface water plants. SYR4 surface water plants
with influent TOC concentrations between 1-2 mg/L had an average ATHM4 of 31.5 |ig/L
compared to the ICR TSD conservative ATHM4 estimate of 6.9 |ig/L. For SYR4 surface water
plants with influent TOC concentrations between 2-3.5 mg/L, the EPA observed an average
ATHM4 of 18.5 |ig/L compared to the ICR TSD conservative ATHM4 estimate of 7.5 |ig/L.
Both comparisons of TOC bins for surface water show that the conservative estimates for THM4
reduction are plausible. Note that this finding is based on a small subset of systems (n = 4) and
may not be representative of systems nationally.
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Due to lack of TOC data for SYR4 ground water plants, the EPA compared ground water plants
to the lowest TOC bin (1-2 mg/L) with ICR TSD data available. SYR4 ground water plants had
an average ATHM4 between ICR TSD ground water plants with influent TOC concentrations
between 1-2 mg/L had an average change in THM4 between -10.7 |ig/L to 4.8 |ig/L compared to
the ICR TSD conservative THM4 reduction estimate of 4.5 |ig/L. Limitations on the comparison
between the ICR TSD ATHM4 estimates and the SYR4 THM changes are described in Section
6.8.
6.7.2 Estimation of Bladder Cancer Risk Reductions
Evaluation of the expected reductions in bladder cancer risk resulting from treatment of PFAS in
drinking water involves five steps listed in Section 6.7.1.1. Section 6.7.1.3.2 provides details on
the estimation of changes in THM4, while Section 6.7.2.1 provides details on selecting the
changes in THM4 specific to the modeled scenarios.79
6.7.2.1 Application of Changes in THM 4 to PFAS PWSs
The EPA expects PWSs that exceed the PFAS regulatory threshold to consider both treatment
and nontreatment options to achieve compliance with the drinking water standard. The EPA
assumes that the populations served by systems with EPs expected to install GAC based on the
compliance forecast detailed in Section 5.3 will receive the DBP exposure reduction benefits.
The EPA notes that other compliance actions included in the compliance forecast could result in
DBP exposure reductions, including installation of RO. However, these compliance actions are
not included in the DBP benefits analysis because this DBP exposure reduction function is
specific to GAC. Switching water sources may or may not result in DBP exposure reductions,
therefore the EPA assumed no additional DBP benefits for an estimated percentage of systems
that elect this compliance option. Also, the EPA assumed no change in DBP exposure at water
systems that install IX, as that treatment technology is not expected to remove a substantial
amount of DBP precursors. Finally, the EPA also assumed that PWSs included in this analysis
use chlorine only for disinfection and have conventional treatment in place prior to GAC
installation.
As described in Section 6.7.1.3, the EPA used the relationship between median raw water TOC
levels and changes in THM4 levels estimated in the 1998 DBP ICR to estimate changes in
THM4 concentrations in the finished water of PWSs fitted with GAC treatment. The EPA
applied changes in THM4 levels to PWS treating for PFAS using the following steps:
1. Identifying the PWSs expected to be triggered into PFAS treatment under various
thresholds and the associated PWS populations served by source water type: surface
water and ground water;
79 The benefits analyses described herein relied on methodology implemented in R software (R Core Team, 2021) and differ
slightly from SafeWater MCBC methods. Specifically, SafeWater performs a set of pre-calculations to maximize computational
efficiency and, as such, the order of analytical steps across R and SafeWater models differs. However, results across models are
mathematically consistent.
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2. Estimating the TOC levels associated with each source water type, based on median raw
water TOC data collected among non-purchased surface water and ground water systems
from the 2019 SYR4 dataset; and
3. Identifying the associated THM4 reduction value based on relationships between raw
water TOC levels and changes in THM4 levels estimated in the 1998 DBP ICR.
As shown in the Section 6.7.1.3 tables, the EPA estimated changes in THM4 levels that vary
based on the following characteristics:
• Replacement time: Assumed to be 730 days;
• EBCT:20min;
• Source water type: Surface Water, Ground Water;
• THM4 change scenario: Conservative (mean DBP ICR THM4 reduction minus one
standard deviation per TOC bin).
For the DBP risk reduction modeling, the EPA focused on the following treatment scenario (See
Table 6-38 and Table 6-39):
• PWS treatment threshold: PFOA or PFOS mean concentration exceeds threshold
defined by regulatory alternatives;
• EBCT:20min;
• Source water type: Surface Water, Ground Water;
• THM4 change scenario: Conservative.
As described in Section 2.2.4, the EPA models a scenario where reduced exposures to THM4
begin in 2029. Therefore, the EPA assumed that the population affected by reduced THM4 levels
resulting from implementation of GAC treatment is exposed to baseline THM4 levels prior to
actions to comply with the rule (i.e., prior to 2029) and to reduced THM4 levels from 2029
through 2105.
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6.7.2.2 Affected Population
Information on PWS attributes required for estimating changes in population-level bladder
cancer is obtained from the EPA's 2021 Q4 SDWIS/Fed database (U.S. EPA, 2021h). This
information includes data on PWS primary sources of water (e.g., whether a PWS relies
primarily on ground water or surface water for their source water), operational status, and
population served. Some PWSs have multiple EPs delivering drinking water to the distribution
network. As discussed in Section 6.7.2.1, the analysis assumes that PWSs will reduce PFAS
levels by fitting individual EPs for either GAC or IX treatment and therefore changes in NOM
and THM4 will also be specific to EPs.
Rather than modeling individual locations (e.g., PWS), the EPA evaluates changes in bladder
cancer cases among the aggregate population per treatment scenario and source water type that is
expected to install GAC treatment to reduce PFAS levels. Because of this aggregate modeling
approach, the EPA used national-level population estimates to distribute the SDWIS/Fed
populations based on single-year age and sex and to extrapolate the age- and sex-specific
populations to future years. Section 5.3 describes the decision tree for GAC technology selection.
Appendix B provides additional details on estimation of the affected population.
6.7.2.3 Bladder Cancer Exposure-Response Modeling
The relationship between exposure to DBPs, specifically trihalomethanes and other halogenated
compounds resulting from water chlorination, and bladder cancer has been the subject of
multiple epidemiology studies (Cantor et al., 2010; U.S. EPA, 2016g; NTP, 2018c), meta-
analyses (Villanueva et al., 2003; Costet et al., 2011), and a pooled analysis (Villanueva et al.,
2004). The EPA used the relationship between THM4 levels and bladder cancer in the
Villanueva et al. (2004) study to support the benefits analysis for the Stage 2 DBP Rule80 which
specifically aimed to reduce the potential health risks from DBPs (U.S. EPA, 2005b).
Regli et al. (2015) analyzed the potential lifetime bladder cancer risks associated with increased
bromide levels in surface source water resulting in increased THM4 levels in finished water.81
To account for variable levels of uncertainty across the range of THM4 exposures from the
pooled analysis of Villanueva et al. (2004), they derived a weighted mean slope factor from the
odds ratios reported in Villanueva et al. (2004). They showed that, while the original analysis
deviated from linearity, particularly at low concentrations, the overall pooled exposure-response
relationship for THM4 could be well-approximated by a linear slope factor that predicted an
incremental lifetime cancer risk of 1 in ten thousand exposed individuals (10"4) per 1 |ig/L
increase in THM4. The linear slope factor developed by Regli et al. (2015) is 0.00427 per 1
|ig/L. Using a fixed effects meta-analysis model assumed by Regli et al. (2015), the EPA
estimated a 95% confidence interval for the estimated slope of 0.00331-0.00522 per 1 |ig/L. This
80 See DBP Rule documentation at https://www.epa.gov/dwreginfo/stage-l-and-stage-2-disinfectants-and-disinfection-
byproducts-rules
81 The Regli et al. (2015) slope factor was utilized in the recently peer-reviewed Weisman et al. (2022) study, which estimates
that 8,000 of 79,000 US bladder cancer cases are attributable to bladder cancer. Among other things, the authors found that there
is a stronger weight of evidence linking DBPs and bladder cancer since the promulgation of the 2006 Stage 2 DBP regulations
and even since publication of Regli et al. (2015).
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slope enables estimation of the changes in the lifetime bladder cancer risk associated with
lifetime exposures to reduced THM4 levels:
Equation 18:
Odds(x) = Odds(0) ¦ exp(0.00427 * x)
Where Odds(x) are the odds of lifetime bladder cancer incidence for an individual exposed to a
lifetime average THM4 concentration in residential water supply of x |ig/L and Odds(0) are the
odds of lifetime bladder cancer in the absence of exposure to THM4 in residential water supply.
The relationship (Equation 18) has the advantage of being independent from the baseline THM4
exposure level, which is highly uncertain for most affected individuals due to lack of historical
data.
To enable annual bladder cancer risk estimation, the EPA assumed that the relationship
(Equation 18) also holds for the cumulative bladder cancer risk and cumulative average exposure
to residential water THM4 from birth to a specific age. A person's cumulative THM4 exposure
from drinking water by age a—denoted by xa—is defined as:
Equation 19:
1 v1"-1
*0 - -> THM4j, x0 = 0
a •£—»£=o
The EPA estimated the relative risk of bladder cancer by a particular age from a change in
average THM4 experienced by this age as follows:
Equation 20:
exp(0.00427 * [xa — za])
RR(xa,za) exp(0.00427 * [xa - za]) * LR{za) + 1 - LR{za)
Where RR(xa, za) is the relative cumulative risk of bladder cancer associated with a change
from baseline cumulative exposure za to treatment cumulative exposure xa. This calculation
requires an estimate of baseline cumulative bladder cancer risk LR(za) which is described in
Appendix H.
6.7.2.4 Estimation of Bladder Cancer Risk Reductions
The EPA estimated changes in annual bladder cancer cases and annual mortality in the bladder
cancer population due to estimated reductions in lifetime THM4 exposure using a life table-
based approach. This approach was used because (1) annual risk of new bladder cancer should be
quantified only among those not already experiencing this chronic condition, and (2) bladder
cancer has elevated mortality implications.
The EPA used recurrent life table calculations to estimate a water source type-specific time
series of bladder cancer incidence for a population cohort characterized by sex, birth year, and
age at the beginning of the PFOA/PFOS evaluation period under the baseline scenario and the
GAC regulatory alternative described in Section 6.7.2.1. The estimated risk reduction from lower
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exposure to DBPs in drinking water is calculated based on changes in THM4 levels used as
inputs to the Regli et al. (2015)-based health impact function, as shown in Section 6.7.2.3. The
life table analysis accounts for the gradual changes in lifetime exposures to THM4 following
implementation of GAC treatment under the regulatory alternative compared to the baseline.82
Details of the life table calculations are provided in Appendix H. The outputs of the life table
calculations are the water source type-specific estimates of the annual change in the number of
bladder cancer cases and the annual change in bladder cancer population mortality.
Although the change in THM4 exposure likely affects the risk of developing bladder cancer
beyond the end of the analysis period (the majority of cancer cases manifest during the latter half
of the average individual life span; Hrudey et al., 2015), the EPA does not capture effects after
the end of the period of analysis, 2105. Individuals alive after the end of the period of analysis
likely benefit from lower lifetime exposure to THM4. Lifetime health risk model data sources
include; the SDWIS/Fed; age- and sex-specific population estimates from the U.S. Census
Bureau (U.S. Census Bureau, 2020a); the SEER program database (National Cancer Institute),83
and the CDC National Center for Health Statistics.84 Appendix H provides additional detail on
the data sources and information used in this analysis as well as baseline bladder cancer
statistics. Appendix B provides additional details on the estimation of the affected population.
6.7.2.5 Valuation of Bladder Cancer Risk Reductions
The EPA uses the Value of Statistical Life to estimate the benefits of reducing mortality
associated with bladder cancer in the affected population. Section 2.2 provides information on
updating Value of Statistical Life for inflation and income growth. The EPA uses COI-based
valuation to estimate the benefits of reducing morbidity associated with bladder cancer.
Specifically, the EPA used bladder cancer treatment-related medical care and opportunity cost85
estimates from Greco et al. (2019). Table 6-43 shows the original COI estimates from Greco et
al. (2019) which were reported in $2010, along with the values updated to $2022 used in this
analysis. The EPA further notes that the estimates for non-invasive bladder cancer subtype were
used to value local, regional, and unstaged bladder cancer morbidity reductions, while the
estimates for the invasive bladder cancer subtype were used to value distant bladder cancer
morbidity reductions.86
The EPA received public comments on the economic analysis for the proposed rule related to the
EPA's use of cost of illness information for morbidity valuation. Specifically, a couple
commenters recommended that the EPA use willingness to pay information (instead of cost of
illness information) when valuing the costs associated with non-fatal illnesses, stating that
82 As described above, the EPA models THM4 changes under the treatment scenario as being in effect for the years 2024 through
2105, with nonzero THM4 changes first occurring in 2029, the year when all PWS are assumed to comply with PFAS treatment
requirements.
83 For cancer incidence and stage distribution data, the EPA relies on SEER 21 (2009-2018); for cancer survival data, the EPA
relies on SEER 18 (2000-2017).
84 CDC Wonder data on 1999-2019 all-cause and bladder cancer mortality by age and sex.
85 Opportunity (or indirect) costs modeled by this study were represented by the value of time needed to undergo the cancer
treatment, which could otherwise have been dedicated to work or leisure activities.
86 Local cancer is a malignant cancer confined entirely to the organ where the cancer began. Remote cancer refers to cancer that
has grown beyond the original (primary) tumor to nearby lymph nodes or organs and tissues. Distant cancer refers to cancer that
has spread from the original (primary) tumor to distant organs or distant lymph nodes; it is also called a distant metastasis.
Unstaged cancer is a cancer whose subtype is unknown.
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willingness to pay information better accounts for lost opportunity costs (e.g., lost productivity
and pain and suffering) associated with non-fatal illnesses. To better account for these
opportunity costs, the EPA used recently available willingness to pay values in a sensitivity
analysis for morbidity associated with bladder cancer. The sensitivity analysis results show that
when willingness to pay values are used in bladder cancer benefits analysis, morbidity benefits
are increased by 19.9 percent. See Appendix O for full details and results on the willingness to
pay sensitivity analyses.
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Table 6-43: Bladder Cancer Morbidity Valuation
Bladder Cancer
Subtype3
Type of Cost
Cost in First Year
($2010)b
Cost in Subsequent Years
($2010)b
Cost in First Year ($2022)°
Cost in Subsequent Years
($2022)c
Medical care
$9,133
$916
$12,851
$1,289
Non-invasive
Opportunity cost
$4,572
$24
$6,212
$33
Total cost
$13,705
$941
$19,062
$1,321
Medical care
$26,951
$2,455
$37,922
$3,454
Invasive
Opportunity cost
$10,513
$77
$14,283
$105
Total cost
$37,463
$2,532
$52,205
$3,559
Notes:
aThe estimates for non-invasive bladder cancer subtype were used to value local, regional, and unstaged bladder cancer morbidity reductions, while the estimates for the
invasive bladder cancer subtype were used to value distant bladder cancer morbidity reductions.
bThe estimates come from Greco et al. (2019).
cTo adjust for inflation, the EPA used the U.S. Bureau of Labor Statistics Consumer Price Index for All Urban Consumers: Medical Care Services in U.S. (City Average).
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6.7.3 Results
Table 6-44 to Table 6-47 provide the health effects avoided and valuation associated with
bladder cancer.
Table 6-44: National Bladder Cancer Benefits, Final Rule (PFOA and PFOS MCLs of 4.0
ppt each, PFHxS, PFNA, and HFPO-DA MCLs of 10 ppt each and HI of 1) (Million
$2022)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal
5,781.0
7.313.0
8,912.7
Bladder Cancer Cases
Avoided
Number of Bladder Cancer-
2,029.6
2.567.8
3,129.9
Related Deaths Avoided
Total Annualized Bladder
$300.64
$380.41
$463.74
Cancer Benefits (Million
$2022)b'c
Notes: See Appendix P for results presented at 3 and 7 percent discount rates. Quantifiable benefits are increased under final
rule table results relative to the other options presented because of modeled PFHxS occurrence, which results in additional
quantified benefits from co-removed PFOA and PFOS.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
cWhen using willingness to pay metrics to monetize morbidity benefits, total annualized bladder cancer benefits are increased
by $75.87 million (see Appendix O).
Table 6-45: National Bladder Cancer Benefits, Option la (PFOA and PFOS MCLs of 4.0
ppt)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal
5,789.3
7.312.9
8,896.0
Bladder Cancer Cases
Avoided
Number of Bladder Cancer-
2,032.5
2.567.8
3,123.2
Related Deaths Avoided
Total Annualized Bladder
$301.06
$380.41
$462.73
Cancer Benefits (Million
$2022)b
Notes: See Appendix P for results presented at 3 and 7 percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
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Table 6-46: National Bladder Cancer Benefits, Option lb (PFOA and PFOS MCLs of 5.0
ppt)
2% Discount Rate
Benefits Category
5th Percentile3 Expected Value 95th Percentile3
Number of Non-Fatal
4,739.4
6,034.0
7,367.1
Bladder Cancer Cases
Avoided
Number of Bladder Cancer-
1,664.0
2,118.7
2,587.1
Related Deaths Avoided
Total Annualized Bladder
$246.48
$313.88
$383.32
Cancer Benefits (Million
$2022)b
Notes: See Appendix P for results presented at 3 and 7 percent discount rates.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized benefits in this table.
Table 6-47: National Bladder Cancer Benefits, Option lc (PFOA and PFOS MCLs of
10.0 ppt)
2% Discount Rate
Benefits Category
5th Percentile3
Expected Value
95th Percentile3
Number of Non-Fatal
2,326.9
3,087.9
3,885.3
Bladder Cancer Cases
Avoided
Number of Bladder Cancer-
816.8
1,084.3
1,364.3
Related Deaths Avoided
Total Annualized Bladder
$120.97
$160.62
$202.14
Cancer Benefits (Million
$2022)b
Notes: See Appendix P for results presented at 3 and 7 percent discount rates.
aThe 5tli and 95th percentile range is based on modeled variability and uncertainty. This range does not include the uncertainty
described in Table 6-48.
bSee Table 7-6 for a list of the nonquantifiable benefits, and the potential direction of impact these benefits would have on the
estimated monetized total annualized costs in this table.
6.8 Limitations and Uncertainties of the Benefits Analysis
This section describes limitations of the quantified benefits analysis, along with uncertainties that
could not be modeled quantitatively as part of the national benefits analysis. The sources of
uncertainty characterized quantitatively are presented in Section 6.1.2. In the tables below, the
EPA summarizes limitations and uncertainties that apply to:
• All quantitative benefits analyses implemented for the final PFAS rule (Table 6-48);
• Application of PK models for blood serum PFAS concentration estimation (Table 6-49);
• Developmental effects (i.e., infant birth weight) modeling (Table 6-50);
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• CVD impacts modeling (Table 6-51);
• RCC impacts modeling (Table 6-52); and
• Modeling of bladder cancer impacts from GAC treatment related THM4 reductions
(Table 6-53).
The EPA notes that in most cases it is not possible to judge the extent to which a particular
limitation or uncertainty could affect the magnitude of the estimated benefits. Therefore, in each
table below, the EPA notes the potential direction of the impact on the quantified benefits (e.g., a
source of uncertainty that tends to underestimate quantified benefits indicates expectation for
larger quantified benefits) but does not prioritize the entries with respect to the impact
magnitude.
Table 6-48: Limitations and Uncertainties that Apply to Benefits Analyses Considered for
the Final PFAS Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The EPA has quantified
benefits for three health
endpoints for PFOA (birth
weight, CVD, and RCC)
and two health endpoints
for PFOS (birth weight and
CVD).
Underestimate
For various reasons, the EPA has not quantified the benefit
of removing PFOA and PFOS from drinking water for
most of the health endpoints PFOA and PFOS are expected
to impact. See discussion in Section 6.2.2 for more
information about these nonquantifiable benefits.
The EPA has quantified
benefits for one co-
removed contaminant
group (THM4).
Underestimate
Treatment technologies that remove PFAS can also remove
numerous other contaminants, including some other PFAS
compounds, additional regulated and unregulated DBPs,
heavy metals, organic contaminants, pesticides, among
others. These co-removal benefits may be significant,
depending on co-occurrence, how many facilities install
treatment and which treatment option they select.
The EPA has not quantified
national benefits for any
health endpoint for the
PFAS that make up the
Hazard Index (PFHxS,
PFNA, PFBS, and HFPO-
DA).
Underestimate
PFHxS, PFNA, PFBS, and HFPO-DA each have
substantial health impacts on multiple health endpoints.
However, the effects of PFNA on birth weight are
evaluated as part of a sensitivity analysis in Appendix K.
The analysis does not
explicitly consider changes
in PFOA/PFOS and THM4
concentrations for systems
that purchase their drinking
water from other PWSs.
Uncertain
Many PWSs purchase their primary source water from
PWSs that are likely to implement treatment under the
rule. The SDWIS/Fed inventory of PWSs includes these
systems with their retail populations instead of allocating
those populations to the wholesale systems. The MCMC
occurrence analysis outputs for the wholesale system and
purchasing system may vary from one another, resulting in
either an under- or over-estimate of affected population in
any iteration. The net effect on total benefits is uncertain.
The analysis does not
account for populations that
consume bottled water as
their primary drinking
water source.
Uncertain
Studies indicate that between 13 percent and 33 percent of
the U.S. population consumes bottled water as their
primary drinking water source (Z. Hu et al., 2011;
Rosinger et al., 2018; Vieux et al., 2020). The benefits
models do not consider these populations. This could result
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Table 6-48: Limitations and Uncertainties that Apply to Benefits Analyses Considered for
the Final PFAS Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
in an overestimate of avoided cases of health effects and
associated benefits. However, bottled water consumers can
also be CWS customers and may still be exposed to PFAS
by using water for cooking etc., and therefore, would
benefit from PFAS removal. (U.S. FDA, 2022; Aquafina,
2022). Finally, the benefits may also be underestimated
because those using bottled water as a primary drinking
water source may switch to CWS supply as a result of the
final NPDWR; the EPA did not model this behavioral
response and hence the benefits do not account for the
potential cost savings to those consuming bottled water at
baseline.
The analysis considers
PFOA/PFOS
concentrations from
NTNCWSs.
Overestimate
SDWIS/Fed population served estimates for NTNCWSs
represent both the population that has regular exposure to
the NTNCWS' drinking water (e.g., the employees at a
location) and the peak day transient population (e.g.,
customers) who have infrequent exposure to the
NTNCWS' drinking water. Estimating the demographic
distribution and the share of daily drinking water
consumption for these two types of NTNCWS populations
would be difficult across many of the industries which
operate NTNCWSs. The inclusion of NTNCWS results is
an overestimate of benefits because daily drinking water
consumption for these populations is also modeled at their
residential CWS.
The EPA assumes that the
effects of PFOA and PFOS
exposures are
independent.
Uncertain
The exposure-response functions used in benefits analyses
assume that the effects of serum PFOA/PFOS on the health
outcomes considered are independent and therefore
additive. This assumption is consistent with the
Framework for Estimating Noncancer Health Risks
Associated with Mixtures of Per- and Polyfluoroalkyl
Substances (PFAS) (U.S. EPA, 2024d). Due to limited
evidence, the EPA does not consider synergies or
antagonisms in PFOA/PFOS exposure-response.
The derivation of
PFOA/PFOS exposure-
response functions for the
relationship between
PFOA/PFOS serum and
associated health outcomes
assumes that there are no
threshold serum
concentrations below which
effects do not occur.
Overestimate
The EPA's Final Human Health Toxicity Assessments
indicate that the levels at which adverse health effects
could occur are much lower than previously understood
when the EPA issued the 2016 health advisories for PFOA
and PFOS (70 ppt) - including near zero for certain health
effects. Therefore, the exposure-response functions used in
benefits analyses assume that there are no threshold serum
concentrations below which effects do not occur. This
could result in a slight overestimate of benefits for
noncancer health endpoints.
Causality is assumed for all
health effects for which
exposure-response
functions are used to
estimate risk.
Overestimate
Analyses evaluating the evidence on the associations
between PFAS exposure and health outcomes are ongoing
and the EPA has not conclusively determined causality. As
described in Section 6.2, the EPA modeled health risks
from PFOA/PFOS exposure for endpoints for which the
evidence of association was found to be likely. These
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Table 6-48: Limitations and Uncertainties that Apply to Benefits Analyses Considered for
the Final PFAS Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
endpoints include birth weight, TC, and RCC. While the
evidence supporting causality between DBP exposure and
bladder cancer has increased since the EPA's Stage 2 DBP
Rule (NTP, 2021; Weisman et al., 2022), causality has not
yet been conclusively determined (Regli et al., 2015).
The analysis assumes that
quantified benefits
categories are additive.
Uncertain
The EPA did not model birth weight, CVD, RCC, and
bladder cancer benefits jointly, in a competing risk
framework. Therefore, reductions in health risk in a
specific benefits category do not influence health risk
reductions in another benefits category. For example,
lower risk of CVD and associated mortality implies a
larger population that could benefit from cancer risk
reductions, because cancer incidence grows considerably
later in life (see Tables G-3 through G-6 in Appendix G).
The scope of the analysis
does not include intra- or
international migration
throughout the evaluation
period.
Uncertain
Throughout the analysis period people may migrate from
one place to another. If persons migrate to locations with
larger decreases in PFOA/PFOS under the regulatory
alternative, the EPA would be underestimating the impacts.
The opposite is true if persons migrate to locations with
smaller decreases in PFOA/PFOS under the regulatory
alternative.
The analysis does not take
into account population
growth and other changes
in long-term trends.
Underestimate
The benefits analysis does not reflect the effects of
growing population that may benefit from reduction in
PFOA/PFOS exposure, which is expected to result in
underestimated benefits. The EPA uses present-day
information on life expectancy, disease, environmental
exposure, and other factors, which are likely to change in
the future.
The analysis does not
include the impacts of
COVID-19 on future
population health and
economic growth.
Uncertain
Impacts of the COVID-19 pandemic have had resulting
effects on conception, pregnancy, and birth rates (Aassve
et al., 2021; McLaren Jr et al., 2021; Ullah et al., 2020).
Some studies suggest that the economic recession caused
by the COVID-19 pandemic may impose long-term
impacts on fertility rates (McLaren Jr et al., 2021; Ullah et
al., 2020). Such impacts are not accounted for in this
benefits analysis.
For PWSs with multiple
EPs, the analysis assumes a
uniform population
distribution across the EPs.
Uncertain
Data on the populations served by each EP are not
available and the EPA therefore uniformly distributes
system population across EPs. Effects of the regulatory
alternative may be greater or smaller than estimated,
depending on actual populations served by affected EPs.
For one large system serving more than one million
customers the EPA has sufficient data on EP flow to
proportionally assign effected populations.
Valuation of mortality risk
reductions assumes that per
capita income will grow at
the constant rate.
Uncertain
The EPA uses Value of Statistical Life adjusted for income
growth to estimate economic value of the premature
mortality avoided in the future. Per capita income growth
projections were available through 2050. The EPA
estimated the compound annual growth rate in per capita
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Table 6-48: Limitations and Uncertainties that Apply to Benefits Analyses Considered for
the Final PFAS Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
income during 2023-2050 and applied it to project Value
of Statistical Life over the analysis period 2024-2105.
The EPA does not
characterize uncertainty
associated with the Value
of Statistical Life reference
value or Value of Statistical
Life elasticity.
Uncertain
The EPA did not quantitatively characterize the uncertainty
for the Value of Statistical Life reference value and income
elasticity. Because the economic value of avoided
premature mortality comprises the majority of the overall
benefits estimate, not considering uncertainty surrounding
the Value of Statistical Life is a limitation.
Process wastes are not
classified as hazardous.
Underestimate
The national economic analysis reflects the assumption
that PFAS-contaminated wastes are not considered RCRA
regulatory or characteristic hazardous wastes. The EPA
acknowledges that if Federal authorities later determine
that PFAS-contaminated wastes require handling as
hazardous wastes, there will be additional benefits to
public health and the environment from reduced exposures
to PFAS that have not been quantified as part of this
analysis.
Abbreviations: COVID-19 - coronavirus disease 2019; CVD - cardiovascular disease; CWS - community water system; DBP -
disinfection byproduct; MCLG - maximum contaminant level goal; PFOA - perfluorooctanoic acid; PFOS - perfluorooctane
sulfonic acid; PWS - public water system; RCC - renal cell carcinoma; RO - reverse osmosis; UCMR - unregulated
contaminant monitoring rule.
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Table 6-49: Limitations and Uncertainties in the PK Model Application
Uncertainty/
Assumption
Effect on Benefits
Estimate
Notes
The benefits analysis
assumes that there are no
reductions in PFOA/PFOS
exposure from other
sources associated with
treatment-related
reductions in PFOA/PFOS
drinking water
concentrations.
Underestimate
Some portion of the non-drinking water PFOA/PFOS
exposure could be related to drinking water concentration
(e.g., food affected by water contamination). This portion
is difficult to estimate, and, depending on the relationship,
there may be a time lag between the decrease in drinking
water concentration and the decrease in the non-drinking
water exposure.
The birth weight analysis
uses the adult PK model
to estimate changes in
female serum
PFOA/PFOS from
changes in drinking water
PFOA/PFOS.
Overestimate
Evidence from epidemiology studies connects birth weight
to serum PFOA/PFOS levels throughout pregnancy:
The serum PFOS-birth weight slope factor in the birth
weight benefits module comes from the meta-analysis of
29 studies by Dzierlenga et al. (2020). Table 1 in
Dzierlenga et al. (2020) summarizes the timing of the
serum samples for the contributing studies, including pre-
pregnancy (2 studies), first trimester (6 studies), second
trimester (5 studies), third trimester (5 studies), and cord
blood samples/delivery (11 studies).3
The serum PFOA-birth weight slope factor comes from the
meta-analysis of 24 studies by Steenland et al. (2018).
Steenland et al. (2018) summarizes the timing of the serum
samples for the contributing studies, including pre-
pregnancy (2 studies), first trimester (4 studies), straddling
first and second trimester (1 study), second trimester (2
studies), straddling second and third trimester (2 studies),
third trimester (4 studies), and cord blood samples/delivery
(9 studies).13
Because the slope factors included epidemiological
evidence throughout pregnancy, a developmental version
of the PK model may be a more appropriate choice. A
developmental PK model would allow the observed
decrease in serum levels that occurs during pregnancy to
be captured by accounting for maternal physiological
changes. For example, Glynn et al. (2012) found a mean
decrease of 16 percent for PFOA and 11 percent for PFOS
between serial measurements taken in the 1st trimester and
3rd trimester of pregnancy. This decrease is associated
with increases in maternal plasma volume and transfer of
the chemicals to the placenta and fetus. The EPA expects
that the use of the adult PK model overestimates the
additive difference in serum concentrations between
baseline and regulatory alternative (and, therefore, the
birth weight benefits of the regulatory alternative) because
of the expected larger volume of distribution for pregnant
females and, therefore, proportionally lower serum
concentrations.
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Abbreviations: PFOA - perfluorooctanoic acid; PFOS - perfluorooctane sulfonic acid; PK - pharmacokinetic.
Notes:
Tor PFOS, the EPA used 4 high confidence studies (Chu et al., 2020; Sagiv et al., 2018; Starling et al., 2017; and Wikstrom et
al., 2019) with a variety of PFOS exposure measures across the fetal and neonatal window. Sagiv et al. (2018) collected maternal
samples in trimester 1, while Wikstrom et al. (2020) collected them in trimesters 1 and 2. The samples from Starling et al. (2017)
were from trimesters 2 and 3, while Chu et al. (2020) collected exclusively in trimester 3. Of these studies, only Sagiv et al.
(2018) and Starling et al. (2017) were part of the Dzierlenga et al. (2020) meta-analysis.
bFor PFOA, the EPA used 5 high confidence studies (Chu et al., 2020; Govarts et al., 2016; Sagiv et al., 2018; Starling et al.,
2017; and Wikstrom et al., 2020) with a variety of PFOA exposure measures across the fetal and neonatal window. Sagiv et al.
(2018) collected maternal samples in trimester 1, while Wikstrom et al. (2020) collected them in trimesters 1 and 2. The samples
from Starling et al. (2017) were from trimesters 2 and 3, while Chu et al. (2020) collected exclusively in trimester 3. The samples
in the Govarts et al. (2016) study were collected from umbilical cords. None of these studies were part of the Negri et al. (2017)
meta-analysis.
Table 6-50: Limitations and Uncertainties in the Analysis of Birth Weight Benefits Under
the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
Characterizing the Exposed Population
The analysis does not
consider the effects of
PFOA/PFOS exposure on
fertility rates.
Uncertain
Studies have shown that exposure to PFAS may lead
to reduced fertility rates among women (Fei et al.,
2009; Waterfield et al., 2020), while the evidence
supporting PFAS effects on the male reproductive
system is inconclusive (Cathrine Carlsen Bach et al.,
2016; U.S. EPA, 2024e; U.S. EPA, 2024f). The birth
weight risk reduction analysis does not account for
any potential differences in birth rates among the
baseline and treatment scenario due to PFAS-related
changes in fertility.
The EPA uses state-
specific birth rate data,
distributed based on census
region-level race/ethnicity-
specific birth rates, to
determine the share of
infants born to women of
childbearing age at each
PWS and within each 100
g birth weight increment.
Uncertain
County-level birth rates from CDC by 100 g birth
weight increment are often tagged as "unreliable" by
CDC in cases where there are low infant counts per
birth weight increment. State-specific 100 g
increment-specific birth rates may not reflect the
number of infants born in each 100 g birth weight
increment in PWS service area that is affected by
PFOA/PFOS through the pregnant mother's
ingestion of drinking water. Using state-specific birth
rates may over- or underestimate the number of
infants falling into each 100 g birth weight increment
born to mothers who experience PWS specific
changes in drinking water PFOA/PFOS levels. This
in turn may over- or underestimate benefits
associated with changes in PFOA/PFOS levels.
The EPA uses state-
specific death rate data,
distributed based on
national-level
race/ethnicity -specific
infant mortality rates, as
the baseline infant
mortality rate (i.e., number
of deaths per 1,000 births)
Uncertain
State-specific death rates may not reflect the baseline
number of infants who die in each PWS that is
affected by PFOA/PFOS in mother's drinking water.
Using state-specific baseline death rates may over- or
underestimate the post-regulation death rates
determined using the birth weight-mortality
relationship and changes in birth weight, and result
in an over- or underestimate of benefits associated
with changes in PFOA/PFOS levels.
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Table 6-50: Limitations and Uncertainties in the Analysis of Birth Weight Benefits Under
the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
of infants born to women
of childbearing age at each
PWS.
Baseline infant death rates
per location are held
constant throughout the
years of the analysis.
Uncertain
Although changes in infant death rates may not be
consistent across race/ethnicity and location in the
US, medical advances in infant care will likely
reduce infant mortality in future years.
The EPA uses county-
specific percentages of the
population that fall within
four race/ethnicity
categories (non-Hispanic
White, non-Hispanic
Black, Hispanic, and other)
to separate total PWS-
specific populations into
race categories for
application of the birth
weight-mortality marginal
effects estimates.
Uncertain
County-specific population percentages may not
accurately represent the race/ethnicity makeup of
PWS-level populations served. PWS populations
served may span multiple counties or may represent
a portion of a single county.
Modeling Changes in Health Risks
The analysis does not
model variability in
pregnancy stage-specific
serum PFOA/PFOS
concentrations and
exposure-response
relationships.
The studies estimating the link between maternal
serum PFOA/PFOS and infant birth weight use
serum PFOA/PFOS measurements from various
stages of pregnancy. The EPA used a constant, adult
PK model-based estimate of serum PFOA/PFOS
concentration to represent exposure during
pregnancy, which is more consistent with early
pregnancy exposures and likely overestimates the
reduction in serum PFOA/PFOS exposure later in
pregnancy. In a sensitivity analysis (Appendix K),
the EPA estimated birth weight benefits using
exposure-response functions that evaluated the
association between early pregnancy serum
PFOA/PFOS and birth weight. The EPA found that
using an early pregnancy-based exposure-response
function would result in approximately a 60 percent
reduction in birth weight benefits.
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Table 6-50: Limitations and Uncertainties in the Analysis of Birth Weight Benefits Under
the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The analysis assumes that
birth weight changes
resulting from changes in
PFAS serum levels will not
exceed 200 g.
Underestimate
The EPA places a cap on estimated birth weight
changes in excess of 200 g based on existing studies
that found that changes to environmental exposures
result in relatively modest birth weight changes
(Windham & Fenster, 2008; Klein & Lynch, 2018;
Kamai et al., 2019). Under the final rule, this birth
weight threshold is exceeded in only 0.01 percent of
affected infants.
Economic Valuation of Changes in Health Risk
Some possible benefits
from increased birth weight
in infants are omitted from
the analysis.
Underestimate
Omitted benefit categories include reduction in IQ
loss, special education costs, early intervention costs,
and labor market productivity losses associated with
specific developmental diseases, among others
(National Academies of Sciences, 2023). The EPA's
analysis omitted these categories because the
available studies documenting relationships between
birth weight and non-medical effects either did not
identify methods for determining the associated
economic burden of such effects or had other
limitations such as older (pre-2000s) data, limited
geographical coverage, small sample sizes, small
ranges of birth weight evaluated, performed outside
of the U.S., or lack of statistical significance. See
ICF (2021) for additional details.
The analysis does not
monetize medical treatment
costs for infants who die
within 1 year of birth.
Underestimate
This limitation likely results in an underestimate of
total benefits. The magnitude of this underestimate is
likely to be small because the number of infants who
do not survive represent a small percentage of the
total number of LBW infants. In addition, the
medical cost function is based on estimated treatment
expenses over a two-year period after birth and thus
the EPA would have to scale down medical costs to
account for the distribution of infant death timing
within 1-year (e.g., within 28 days of birth or 3
months). Based on the 2016-2018 NCHS/NVSS data,
approximately 50 percent of LBW infant deaths
occur within the first 28 days of birth. Thus, it is
likely that only a small portion of medical costs from
Klein et al. (2018) is applicable to infants who die
within 1 year of birth.
Simulated medical cost
changes from Klein and
Lynch (2018) do not reflect
Uncertain
Preliminary modeling indicates that reductions in
PFOA/PFOS concentrations based on the regulatory
alternatives may lead to birth weight changes greater
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Table 6-50: Limitations and Uncertainties in the Analysis of Birth Weight Benefits Under
the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
birth weight changes
greater than 100 g.
than 100 g. Although the EPA caps birth weight
change estimates at 200 g, the EPA uses the COI
estimates associated with a 100 g change in birth
weight for all birth weight changes between 100 and
200 g to avoid extrapolation outside of the data
range.
Abbreviations: CDC - Centers for Disease Control and Prevention; COI - cost of illness; g - gram; LBW - low birth weight;
NCHS - National Center for Health Statistics; NTNCWS - non-transient non-community water system; NVSS - National
Vital Statistics System; PFAS - per-and polyfluoroalkyl substances; PFOA - perfluorooctanoic acid; PFOS - perfluorooctane
sulfonic acid; PWS - public water system; SDWIS/Fed - Safe Drinking Water Information System Federal Version.
Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
Characterizing the Exposed Population
The analysis uses national-
level estimates of CVD
prevalence and incidence
rates, life tables, and
ASCVD model inputs (e.g.,
prevalence of treated and
untreated hypertension,
diabetes, smoking).
Uncertain
Using national-level baseline health data may over- or
underestimate the effects of regulatory alternatives on
CVD morbidity and mortality overall and in specific
PWSs.
The effects of statin use on
changes in CVD risk were
not modeled in this
analysis.
Uncertain
Because statin medications lower LDLC, statin use may
impact the relationship between serum PFOA/PFOS
levels and TC and, ultimately, the estimated changes in
CVD risk. The EPA did not model population variability
with respect to this factor for two reasons. First, as
described in Appendix F, not all studies modeling serum
PFOA/PFOS levels and TC consider and/or control for
statin use. Exclusion of persons who rely on statins for
LDLC control from the modeled population would
underestimate CVD benefits if serum PFOA/PFOS-TC
effect represents an average across statin user and non-
user groups. Second, there are challenges in estimating
statin use prevalence. Depending on age, sex,
race/ethnicity, and disease status, approximately 20
percent-40 percent of the U.S. population relies on statins
(Robinson & Booth, 2010). Factors such as overt CVD,
healthcare, and demographics are significantly associated
with statin use (Leino et al., 2020; Electricwala et al.,
2020). While statin therapy is intended to be permanent,
many individuals who are prescribed statins take them
irregularly (Colantonio, 2019; Lewey et al., 2013; Ellis et
al., 2004; Goldstein et al., 2016; Toth et al., 2019); Toth
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Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
et al. (2019) found a <25 percent rate of adherence 5
years after initiation of therapy.
Modeling Changes in Health Risks
The analysis assumes that
there is no lag between
changes in serum
PFOA/PFOS
concentrations and changes
in TC and BP. Likewise,
the analysis assumes that
there is no lag between
changes in TC/BP and
changes in CVD risk.
Overestimate
The studies estimating the link between serum
PFOA/PFOS and TC/BP and the ASCVD model are not
dynamic, and hence do not provide insights into whether
TC/BP may respond gradually to changes in serum
PFOA/PFOS and/or if CVD risk may respond gradually
to changes in TC/BP. The analysis assumes immediate
adjustment, which may overestimate impacts to the
exposed population. Note, however, that reductions in
TC/BP and CVD risk do not instantaneously follow the
reductions in PFOA/PFOS drinking water concentrations,
because the reductions in serum PFOA/PFOS are gradual,
as predicted by the PK model.
The derivation of
PFOA/PFOS exposure-
response functions for the
relationship between
PFOA/PFOS serum and
TC levels assumes that the
studies used in the meta-
analysis represent the
PFOA/PFOS effects on
serum TC levels in general
population adults.
Uncertain
The exposure-response function was developed based on
six general population studies reporting linear serum
PFAS-TC level associations. Four of these studies were
high quality as reflected by the lower risk of bias
evaluations. These studies may not capture all possible
relationships between PFOA/PFOS and serum TC levels.
The analysis excludes
exposure-response
relationships between
serum PFOA/PFOS and
HDLC.
Uncertain
The relationship between serum PFOA/PFOS and HDLC
is uncertain. As shown in Section 6.5.2 and Appendix F,
the meta-analysis-based estimate of the effect of serum
PFOA/PFOS on HDLC concentration is positive but not
statistically significant. Single-study analyses of this
relationship have generated both positive (Dong et al.
(2019) serum PFOS-HDLC relationship) and inverse
(Dong et al. (2019) serum PFOA-HDLC relationship, Lin
et al. (2019) serum PFOA-HDLC and serum PFOS-
HDLC relationship) effect estimates that were not
statistically significant. To better understand the impact
of incorporating HDLC in the CVD risk model, the EPA
has implemented a sensitivity analysis (see details in
Appendix K). The EPA found that, using the meta-
analysis results, inclusion of HDLC would decrease
benefits by approximately 23-25%.
The analysis assumes that
the CVD risk impact of
changes in TC/BP from
reductions in serum
PFOA/PFOS is the same as
the CVD risk impact of
changes in these
Uncertain
While the CVD risk impacts of changes in TC/BP from
behavioral and medical interventions is well documented
(Lloyd-Jones et al., 2017), there is no information on
whether changes in serum PFOS/PFOA leading to
changes in these biomarkers would result in similar
outcomes.
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Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
biomarkers due to other
reasons such as behavioral
changes or medication.
The CVD risk analysis
assumes that person's
TC/BP level history does
not have an impact on
changes in CVD risk due to
changes in the levels of
these biomarkers.
Uncertain
The ASCVD model links TC/BP levels at the start of the
10-year follow-up period to first hard CVD event
incidence during the follow-up period. The modeling
does not account for TC/BP changes over time, which
could have an impact on the CVD event risk.
The AS CVD model was
not recalibrated for the
contemporary CVD
incidence and prevalence.
Overestimate
Assessments of ASCVD risk model performance across
different sociodemographic subgroups (Asian
populations, Hispanic populations, persons with high
levels of CVD risk, diabetes, older adults with frailty and
multimorbidity, smokers, and women) indicated that the
model tended to overestimate risk but suggested that the
model may improve through additional input variables
and recalibration given contemporary CVD incidence and
prevalence (Mora et al., 2018; Muntner et al., 2014).
The analysis uses the
ASCVD model developed
for non-Hispanic Black
populations to assess
potential CVD risks for
race/ethnicity groups other
than non-Hispanic Black
and non-Hispanic White
populations.
Uncertain
The ASCVD model documentation encourages the use of
equations for non-Hispanic White populations for other
race/ethnicity categories, specifying that estimated risks
may be biased upward, especially for Hispanic and Asian
American populations. The EPA's model validation
analysis detailed in Appendix G shows that the non-
Hispanic Black model is a better fit for these
race/ethnicity groups. However, the ultimate impact of
this assumption is uncertain.
The EPA uses the fraction
of the population who
smokes and has diabetes as
inputs into the ASCVD
model.
Underestimate
The ASCVD model uses binary values to indicate
whether a person is a current smoker or has diabetes. The
EPA simplifies calculations by using the fraction of the
population who smokes and has diabetes as inputs to the
ASCVD model. The EPA has implemented a targeted
evaluation of the effect of this assumption and confirmed
that this simplification likely underestimates impacts by
approximately 5 percent to 10 percent, depending on the
age group, due to the non-linearity of the estimated
model.
The analysis assumes that
the threshold for high BP is
a systolic/diastolic
measurement of 140/90.
Underestimate
In November 2017, the threshold defined for high BP was
reduced to 130/80 (Whelton et al., 2018). The analysis
relies on high BP prevalence data and treated, untreated,
and normal BP measurements that are based on
NHANES surveys from 2011 to 2016. Therefore, the
EPA adheres to the pre-2017 threshold. Furthermore, the
ASCVD model was developed prior to the change in high
BP definition. Adhering to the pre-2017 threshold may
affect the number of people sorted into the high BP
population category, potentially underestimating CVD
risk.
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Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The analysis assumes
independence among the
prevalence of high BP,
smoking, and diabetes.
Overestimate
Smoking and high BP are often related, and smoking is a
risk factor for Type 2 diabetes. Assuming independence
among the prevalence of high BP, smoking, and diabetes
may result in overestimated CVD risk impacts.
The analysis assumes that
deaths from causes other
than hard CVD events
occur first.
Underestimate
By assuming that deaths from causes other than hard
CVD events occur first, the EPA underestimates the
eligible population (e.g., population without CVD
history) evaluated for the first hard CVD event
estimation.
The analysis does not
account for survivors of
first hard CVD events that
are neither MI nor IS. The
analysis does not account
for persons who were
younger than 40 years at
the time of their first hard
CVD event.
Underestimate
The ASCVD model captures risk of non-fatal MI, non-
fatal IS, and fatal CVD; however, it does not capture
other non-fatal CHD. The ASCVD model can be used to
predict the annual probability of a first hard CVD event
for persons aged 40-89 years; the EPA applied this model
to populations aged 40 years and older. The prevalence of
CVD history before age 40 is low (<7% based on
estimates from the Medical Expenditure Panel Survey)
and likely includes persons whose CVD arises from
genetic factors (Zhang et al., 2019). Early life PFAS
exposures and TC are inconclusively associated for
PFOA and positively associated forPFOS (U.S. EPA,
2024e; U.S. EPA, 2024f). TC later in life is highly
positively correlated with early TC as seen in Pletcher et
al. (2016) and Zhang et al. (2019). This analysis does not
directly capture effects of early life increases in TC due to
PFAS exposures. The analysis does capture the effects of
early life TC indirectly to the extent that early and later in
life TC levels are correlated.
The analysis does not
capture post-acute CVD
mortality beyond 5 years of
the first MI or IS for those
ages 40-65 at the time of
the initial event nor does it
capture post-acute CVD
mortality beyond 6 years of
the first MI or IS for those
ages 66-89 at the time of
the initial event.
Underestimate
The risk of post-acute CVD mortality was estimated
based on Thom et al. (2001) for those aged 40-65 years
and on S. Li et al. (2019) for those older than 65 years.
Neither study reported post-acute mortality information
for a longer follow-up period. The reported information
does not support complete post-acute mortality risk
elimination beyond the longest follow-up period. The
EPA did not identify U.S. population-based MI/IS
survivor studies that had a longer follow-up time and,
thus, has no reliable quantitative basis to estimate post-
acute mortality impacts beyond 6 years of the initial
event.
The analysis assumes that
post-acute CVD mortality
for survivors of IS at ages
40-65 is the same as post-
acute CVD mortality for
survivors of MI at ages 40-
65.
Uncertain
Post-acute mortality estimates for IS and MI were very
close in the Medicare population (S. Li et al., 2019). For
those aged 65 years or older, S. Li et al. (2019) have
estimated the probability of death within 1 year after non-
fatal IS to be 32.07 percent and the probability of death
within 1 year after non-fatal MI to be 32.09 percent.
Therefore, reliance on the post-acute mortality for MI to
approximate the same for stroke is reasonable.
The analysis models the 85
year or older group jointly
Uncertain
The effect of this modeling approximation on the CVD
benefits is not certain because the integer age-specific
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Table 6-51: Limitations and Uncertainties in the Analysis of CVD Benefits Under the Final
Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
and applies average
mortality rate for those
aged 85 or older in this age
group.
mortality rates may be above or below the average
mortality rate.
The analysis models the 85
year or older group jointly
and uses serum
PFOA/PFOS estimates for
age 85 in initiate
calculations in this age
group.
Underestimate
Because the impacts of changes in PFOA/PFOS drinking
water concentrations on serum PFOA/PFOS levels
increase over time, the use of serum PFOA/PFOS
concentrations at 85 years to model the 85 or older age
group will underestimate the CVD risk impacts in this
group.
The analysis applies the
ASCVD model to those
older than 80 years.
Overestimate
The ASCVD model evaluates first hard CVD event risk
for adults aged 40-80. Applying the predicted hard CVD
event risk for those aged 80 years or older results in an
overestimate of benefits.
The EPA does not
characterize uncertainty
associated with ASCVD
model parameters.
Uncertain
The EPA treats the coefficients of the ASCVD risk model
as certain. However, uncertainty surrounding
race/ethnicity- and sex-specific ASCVD model
parameters could be characterized by multivariate normal
distribution using the ASCVD model coefficient
estimates, and the variance-covariance matrix shared by
the ASCVD model authors. Assuming that ASCVD
model parameters are certain is a limitation of this
analysis.
Economic Valuation of Changes in Health Risk
The analysis monetized
changes in non-fatal first
MI/IS risk using medical
expenditures that do not
cover long-term
institutional or at-home
care. Furthermore, the COI
estimates do not include
lost productivity. Finally,
the COI-based approach
does not account for the
pain and suffering
associated with non-fatal
CVD events.
This analysis likely understates morbidity benefits since
hard CVD events, particularly IS, require a longer
rehabilitation period. According to HCUP 2017 data, 65
percent of IS survivors and 33 percent of MI survivors
are discharged to a long-term care facility or to a home
healthcare setting. Lost productivity impacts are also
likely (Cropper & Krupnick, 2000; Skolarus et al., 2014).
MI/IS survivors also experience significant reductions in
the health-related quality of life (Bach et al., 2011;
Kirchberger et al., 2020; Martino Cinnera et al., 2020;
Mollon & Bhattachaijee, 2017).
Abbreviations: ASCVD - Atherosclerotic cardiovascular disease; BP - blood pressure; CVD - cardiovascular disease; HDLC -
high-density lipoprotein; IS - ischemic stroke (ICD9 = 433, 434; ICD10 = 163); MI - myocardial infarction (ICD9 = 410; ICD10
= 121); NHANES - National Health and Nutrition Examination Survey; PFOA - perfluorooctanoic acid; PFOS -
perfluorooctane sulfonic acid; TC - total cholesterol.
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Table 6-52: Limitations and Uncertainties in the Analysis of RCC Benefits Under the
Final Rule
Uncertainty/Assumption
Effect on Benefits
Estimate
Notes
Characterizing the Exposed Population
The analysis uses national-level
estimates of kidney cancer
incidence, prevalence, stage
distribution, and relative survival
data, as well as national-level life
tables.
Uncertain
Using national-level baseline health data may
over- or underestimate the effects of regulatory
alternatives on RCC morbidity and mortality in
specific PWSs and well as overall.
The EPA assumed that RCC
comprises 90 percent of kidney
cancer incidence.
Uncertain
Because baseline RCC incidence statistics are
not readily available from the National Cancer
Institute public use data, the EPA used kidney
cancer statistics in conjunction with an
assumption that RCC comprises 90 percent of
all kidney cancer cases to estimate baseline
lifetime probability of RCC. This assumption
was used in RCC exposure-response modeling
by U.S. EPA (2024f).
RCC risks are estimated for
populations for which reductions in
PFOA exposures relative to
baseline exposures start at different
ages, including children.
Uncertain
The relative cancer potency of PFOA in children
is unknown, which may bias benefits estimates
either upward or downward. Because RCC
incidence in children is very small, we assess
any bias to be negligible.
Modeling Changes in Health Risks
The analysis assumes that the
magnitude of RCC risk reductions
resulting from reductions in serum
PFOA levels will not exceed a PAF
of 3.94 percent.
Uncertain
The EPA placed a cap of 3.94 percent on the
magnitude of the estimated cumulative RCC risk
reduction resulting from reductions in serum
PFOA levels, based on its analysis of PAF
values found in the literature on environmental
contaminants and cancers (ICF, 2022b). This
review found that changes in environmental
exposures result in relatively modest PAFs
(between 0.2 percent and 17.9%); however, few
of the studies provided PAFs related specifically
to RCC or kidney cancer. The EPA
characterized the uncertainty surrounding this
parameter using a log-uniform distribution with
a minimum of 0.2 percent and a maximum of
17.9 percent. For the central estimate of RCC
benefits, the EPA used a PAF of 3.94 percent,
which is the mean of the PAF uncertainty
distribution. As such, the EPA assumed that
RCC risk reduction estimates in excess of the
PAF are unreasonable even as a result of large
changes in serum PFOA concentrations.
Because this PAF cap is not based on RCC
studies specifically, it is uncertain whether the
RCC impacts are under- or overestimated.
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Table 6-52: Limitations and Uncertainties in the Analysis of RCC Benefits Under the
Final Rule
Uncertainty/Assumption
Effect on Benefits
Estimate
Notes
The analysis assumes that there is
no lag between changes in serum
PFOA concentrations and changes
in RCC incidence.
Overestimate
The studies estimating the link between serum
PFOA and RCC are not dynamic, and hence do
not provide insights into whether RCC incidence
may respond gradually to changes in serum
PFOA. The PK model estimates daily serum
levels, which are averaged annually for the
purposes of modeling gradual serum changes for
the RCC risk reduction analysis. The RCC risk
reduction analysis assumes immediate RCC
incidence adjustment within each year, which
may overestimate impacts to the exposed
population.
The analysis relies on public-access
SEER 18 10-year relative kidney
cancer survival data to model
mortality patterns in the kidney
cancer population.
Uncertain
Reliance on these data generates both a
downward and an upward bias. The downward
bias is due to the short, 10-year excess mortality
follow-up window. Survival rates beyond 10
years following the initial diagnosis are likely to
be lower. The upward bias comes from the
inability to determine how many of the excess
deaths were deaths from kidney cancer.
The analysis assumes that RCC
incidence patterns and survival are
reasonably approximated by the
kidney cancer statistics.
Uncertain
The exposure-response function provides
information on changes in RCC risk, while
detailed race/ethnicity-, sex-, and age-specific
cancer incidence, stage, and survival
information is available for kidney cancer only.
For consistency with the RCC exposure-
response modeling (U.S. EPA, 2024f), the EPA
assumed that RCC comprises 90 percent of
kidney cancer cases. In absence of RCC-specific
detailed information, the model relies on
patterns based on kidney cancer statistics.
The analysis models the 85 years or
older group jointly and applies the
average mortality rate for those
aged 85 or older in this age group.
Uncertain
The effect of this modeling approximation on
the RCC benefits is not certain because integer
age-specific mortality rates may be above or
below the average mortality rate.
The analysis models the 85 years or
older group jointly and uses serum
PFOA estimates for those aged 85
to initiate calculations in this age
group.
Underestimate
Because the impacts of changes in PFOA
drinking water concentrations on serum PFOA
levels increase over time, the use of serum
PFOA concentrations at 85 years to model the
85 or older age group will underestimate the
RCC risk impacts in this group.
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Table 6-52: Limitations and Uncertainties in the Analysis of RCC Benefits Under the
Final Rule
Uncertainty/Assumption
Effect on Benefits
Estimate
Notes
Economic Valuation of Changes in Health Risk
RCC morbidity valuation is based
on medical costs associated with
the first line treatment that resulted
in the most cost-effective treatment
sequences, as reported in
Ambavane et al. (2020).
Uncertain
The valuation is biased downward because it
does not account for (1) the second line
treatments that may also be applied; (2) lost
productivity by the person experiencing RCC
and family caregivers; and (3) the pain and
suffering associated with experiencing RCC
and/or adverse effects of RCC treatment. The
valuation is biased upward because (1) the full
year-specific cancer treatment is assumed to
occur prior to the year-specific cancer
population death; and (2) the first line treatment
may be discontinued prior to the assumed
maximum treatment duration of 2 years. The
effect of using costs associated with the most
cost-effective treatment from Ambavane et al.
(2020) rather than costs for treatments currently
prevalent in clinical practice is uncertain. The
EPA could not assess the impact of this
assumption because the EPA is not aware of
publicly available information on the frequency
of various kidney cancer treatments in the U. S.
population. To assess the impact of using a
willingness to pay based valuation approach, the
EPA performed a sensitivity analysis using
willingness to pay values for non-fatal
unspecified cancer to value reductions in risk of
RCC morbidity (See Appendix O).
Abbreviations: PFOA -
cell carcinoma.
perfluorooctanoic acid; PFOS - perfluorooctane sulfonic acid; PK - pharmacokinetic; RCC - renal
Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
Modeling Reduced THM4 in PWSs
Reductions in THM4 formation
depend only on the relationship
between raw water TOC levels and
THM4 levels as estimated in the
1998 DBP ICR. Other source water
quality parameters were not
modeled.
Uncertain
The EPA assumes that PWSs affected by
implementation of PFAS treatment technologies
have similar characteristics as those evaluated in
the 1998 DBP ICR. Source water parameters
and treatments at individual plants may have
changed over time.
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The EPA uses available TOC data
to estimate reduced THM4
concentration.
Uncertain
Due to the lack of site-specific information on
factors affecting THM4 formation at each
potentially affected drinking water treatment
plant, the EPA uses relationships between TOC
levels and changes in THM4 levels among
GAC-treating systems from the 1998 DBP ICR
and median raw water TOC levels for each
source water type from the 2019 SYR4 dataset.
Actual changes in THM4 concentrations for a
given change in treatment at any specific PWS
could be higher or lower than that estimated
using the EPA's approach.
The EPA assigned TOC values at
the system level based on ground
water or surface water
distributions.
Uncertain
Because the TOC levels for all systems are not
available, the EPA used TOC data provided by
states in response to the fourth Six-Year Review
to derive TOC probability distributions for
influent into a PFAS treatment process; one
distribution for ground water systems and
another for surface water systems. The EPA
randomly assigned values from these
distributions to each ground water or surface
water system, respectively. The actual TOC
values may be higher or lower than the assigned
values. For systems using GAC for PFAS
removal, the corresponding impact would be
under-stating or over-stating costs.
The EPA estimates THM4
reduction based on free chlorine
formation potential but does not
estimate the reduction based on
chloramine use.
Overestimate
The 1998 DBP ICR TSD provided information
for systems that only used free chlorine as a
disinfectant and did not capture THM4
reduction in chloraminating systems. This
limitation likely leads to an overestimate of
THM4 formed in systems that used chloramines
in the distribution system because THM4
formation within the distribution system is lower
when chloramines are used, compared to when
free chlorine is used (Hua & Reckhow, 2008).
Based on SYR3 data, 36 percent of surface
water systems and 4 percent of ground water
systems use chloramination (U.S. EPA, 2016j).
Chloramines may produce greater amounts of
genotoxic and carcinogenic DBPs, but a
reduction in the TOC prior to disinfection will
also yield a reduction in DBP formation
(Cuthbertson et al., 2019).
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
THM4 is assumed to be a surrogate
for other chlorination DBPs, some
of which are more genotoxic and
cytotoxic than THMs.
Uncertain
The EPA's analysis relies on the slope factor
from Regli et al. (2015), which links lifetime
risk of bladder cancer to THM4 concentrations
in finished water. Regli et al. (2015) did not
explicitly account for brominated or nitrogenous
DBPs, but instead used THM4 as a surrogate for
the broad suite of chlorination DBPs. This is
consistent with the approach used in numerous
epidemiolocal studies (Costet et al., 2011;
Freeman et al., 2017) since insufficient data are
available to estimate the co-occurrence and co-
removal of specific genotoxic or cytotoxic
DBPs.
The EPA estimates THM4
reduction based on GAC use but
does not estimate the reduction in
individual THM4 species.
Uncertain
GAC has been shown to shift the speciation
among THM4 and can result in a relatively
larger fraction of brominated species (THM3)
compared to chloroform. However, studies show
that even as speciation shifts, the absolute
concentrations of each species are reduced
(Cuthbertson et al., 2019; L. Wang et al., 2019).
The EPA assumed a GAC
replacement frequency.
Underestimate
A GAC replacement frequency of 730 days was
assumed based on the estimated percent removal
of TOC curves (see Figures 6-11 and 6-12).
After 730 days of GAC use the modeled TOC
removal remained consistent for both ground
water and surface water models. If the GAC was
replaced more frequently based on PFAS
removal needs, then increased average TOC
removal would be observed further reducing
DBP precursors.
The logistic model uses
pilot/RSSCT results to predict
ATHM4.
Overestimate
RSSCTs may overpredict full-scale adsorption
capacity of GAC (Kempisty et al., 2022;
Zachman & Summers, 2010)
SYR4 Comparison
Estimates of reductions in THM4
formation assume that GAC
treatment is the only treatment
change in a distribution system.
Uncertain
Uncertainty exists if other changes (i.e., new
source water, chemical dosing, other treatments
added such as pre-chlorination, existing
treatments changed such as new filter media)
that could have been made in public water
systems beyond GAC treatment could
potentially over- or underestimate THM4
reduction.
The EPA analyzed only systems
that were sampled under UCMR 3
and indicated GAC treatment under
UCMR 4.
Uncertain
Assessing only UCMR GAC systems limited the
sample to PWS serving > 10,000 people.
Therefore, the EPA was unable to compare
THM4 reduction estimates to measured data for
small systems.
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The EPA relied on available CCRs
to estimate the GAC treatment start
date to determine before and after
treatment years.
Uncertain
Available CCRs were used to inform the GAC
start date. When CCRs were unavailable, the
EPA searched the web to identify information
about the timeline of treatment for individual
PWSs. While installation dates were found, the
exact date for when the GAC systems went into
full-scale use was not always specified.
The EPA obtained THM4 values
from multiple data sources.
Uncertain
For PWSs that met criteria outlined in Section
6.7.1.3.2 but had no THM4 data available in
SYR4, the EPA relied on CCR THM4 data.
Reporting on THM4 levels is inconsistent across
CCRs. If a CCR listed "Amount Detected"
instead of the THM4 average, then the EPA
used the "Amount Detected" value to represent
the THM4 average.
Characterizing the Exposed Population
Analysis assumes that systems
implementing IX do not accrue
benefits associated with bladder
cancer risk reductions.
Underestimate
Systems using IX for PFAS removal will also
benefit from some TOC removal, but the
removal will be limited in comparison to GAC
treatment because PFAS-selective IX can show
preferential removal of PFAS over organic
matter (de Abreu Domingos & da Fonseca,
2018).
The analysis does not model
location-specific demographics.
Uncertain
Because the EPA models impacts to aggregate
populations based on systems triggered into
treatment under various scenarios, the EPA
relies on national-level demographic and bladder
cancer data. The impact of this limitation is
uncertain. For instance, populations with a large
portion of elderly or male individuals will be
more sensitive to changes in THM4 levels due
to the high baseline bladder cancer incidence
among elderly and male populations, compared
to younger and female populations.
The analysis does not model
variability by race/ethnicity.
Uncertain
Because the EPA models impacts based on a
national-level distribution of finished water
TOC levels, specific TOC levels at actual PWSs
are not available. Therefore, these impacts were
not included in the EPA's DBP analysis.
Accordingly, the EPA did not pursue
race/ethnicity-specific modeling of health risk
because it would not provide meaningful insight
into distributional effects.
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
Bladder cancer risks are estimated
for populations for which
reductions in THM4 exposures
relative to baseline exposures start
at different ages, including
children.
Uncertain
The relative cancer potency of THM4 in
children is unknown, which may bias estimates
either upward or downward. Past reviews found
no clear evidence that children are at greater risk
of adverse effects from bromoform or
dibromochloromethane exposure (U.S. EPA,
2005a), although certain modes of action and
health effects may be associated with exposure
to THM4 during childhood (U.S. EPA, 2016g).
Because bladder cancer incidence in children is
very small, the EPA assesses any bias to be
negligible.
Modeling Changes in Health Risks
Analysis assumes an immediate
and full reduction in bladder cancer
risk following THM4 exposure
reduction.
Overestimate
The EPA did not model the transitional
dynamics in relative annual risk of bladder
cancer following the THM4 exposure reduction.
The EPA considered age-specific cohort
cumulative exposures to THM4. Therefore,
while drinking water concentrations are assumed
to be reduced upon compliance with the
rulemaking, the changes in cumulative average
exposure are much more gradual. The EPA has
not identified any studies on bladder cancer-
specific risk cessation lag. Regli et al. (2015) do
not provide pertinent information; as such, this
is a cross-sectional analysis quantifying the
relationship between lifetime cancer risk and
lifetime average exposure. Existing cancer risk
cessation lag studies focused on smoking and
arsenic exposure (e.g., Hrubec & McLaughlin,
1997, Hartge et al., 1987, and C. W. Chen &
Gibb, 2003); show that, annual cancer risk drops
within the first 25 years after exposure cessation,
yet it may never reach the annual cancer risk of
persons who were always exposed to the
treatment contaminant levels. In the EPA's
modeling this issue pertains to those alive at the
start of the evaluation period who have been
exposed to the pre-treatment THM4 levels for a
considerable amount of time, such as persons
older than 60 years at the start of the evaluation
period. This subpopulation comprises
approximately 20 percent of the affected
population alive in 2023.
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
The analysis relies on public-access
SEER 18 10-year relative bladder
cancer survival data to model
mortality patterns in the bladder
cancer population.
Uncertain
Reliance on these data generates both a
downward and an upward bias. The downward
bias is due to the short, 10-year excess mortality
follow-up window. Survival rates beyond 10
years following the initial diagnosis are likely to
be lower. The upward bias comes from the
inability to determine how many of the excess
deaths were deaths from bladder cancer.
The relationship from Regli et al.
(2015) is a linear approximation of
the odds ratios reported in
Villanueva et al. (2004).
Uncertain
Given the uncertainty about the historical,
location-specific THM4 baselines, Regli et al.
(2015) provides a reasonable approximation of
the risk. However, depending on the baseline
THM4 exposure level, the impact computed
based on Regli et al. (2015) may be larger or
smaller than the impact computed using the
Villanueva et al. (2004)-reported odds ratios
directly.
The analysis assumes that the
magnitude of DBP risk reductions
resulting from reductions in serum
PFOA levels will not exceed a PAF
of 3.94 percent.
Uncertain
The EPA placed a cap of 3.94 percent on the
magnitude of the estimated cumulative bladder
cancer risk reduction resulting from reductions
in THM4 levels, based on its analysis of PAF
values found in the literature on environmental
contaminants and cancers (ICF, 2022b). This
review found that changes in environmental
exposures result in relatively modest PAFs
(between 0.2 and 17.9 percent); however, few of
the studies provided PAFs related specifically to
bladder cancer. For the estimate of bladder
cancer benefits, the EPA used a PAF of 3.94
percent, which is the mean of the PAF
uncertainty distribution. As such, the EPA did
not quantify bladder cancer risk reduction
estimates in excess of the PAF that are predicted
to occur as a result of changes in THM4
concentrations. Because this PAF cap is not
based on bladder cancer studies specifically, it is
uncertain whether the bladder cancer impacts are
under- or overestimated. Because the PAF is
rarely binding in the bladder cancer analysis, the
influence of PAF uncertainty on the analysis is
likely negligible.
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Table 6-53: Limitations and Uncertainties in the Analysis of DBP Quantified Benefits
Under the Final Rule
Uncertainty/ Assumption
Effect on Benefits
Estimate
Notes
Economic Valuation of Changes in Health Risk
Bladder cancer morbidity valuation
is based on medical costs and
indirect/time costs (by cancer
stage), as reported in Greco et al.
(2019).
Uncertain
The valuation is biased downward because it
does not account for (1) lost productivity by the
family caregivers and volunteers; (2) broader
labor market participation effects for those
experiencing bladder cancer and/or providing
care; and (3) the pain and suffering associated
with experiencing bladder cancer and/or adverse
effects of bladder cancer treatment. The
valuation is biased upward because (1) the full
year-specific cancer treatment is assumed to
occur prior to the year-specific cancer
population death; and (2) the treatment may be
discontinued if it is no longer effective. To
assess the impact of using a willingness to pay
based valuation approach, the EPA performed a
sensitivity analysis using willingness to pay
values for non-fatal bladder cancer to value
reductions in risk of bladder cancer morbidity
(See Appendix O).
Abbreviations: CCR - consumer confidence reports; DBP - disinfection byproduct; GAC - granular activated carbon; ICR -
information collection request; PFAS - per-and polyfluoroalkyl substances; PFOA - perfluorooctanoic acid; PFOS -
perfluorooctane sulfonic acid; PWS - public water system; SYR - Six-Year Review; THM4 - four regulated trihalomethanes;
TOC - total organic carbon; TSD - treatment study database; UCMR - Unregulated Contaminant Monitoring Rule, PAF -
population attributable fraction.
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7 Comparison of Costs to Benefits
This chapter provides a comparison of the incremental costs and benefits of the final rule, as
described in Chapter 5 and Chapter 6.87 The incremental cost is the difference between costs that
will be incurred if the final rule is enacted over current baseline conditions. Incremental benefits
reflect the avoided future adverse health outcomes attributable to PFAS reductions and co-
removal of additional contaminants due to actions undertaken to comply with the final rule. This
chapter also provides benefits and costs for the alternatives to the final rule that the EPA
considered. Results for the final rule precede estimates for the alternatives. The EPA notes that
under SDWA, the EPA must consider whether the costs of the rule are justified by the benefits
based on all statutorily-prescribed costs and benefits, not just the quantified costs and benefits
(see SDWA 1412(b)(3)(c)(i)).
Table 7-1 provides the incremental quantified costs and benefits of the final rule at a 2 percent
discount rate in 2022 dollars. The top row shows total monetized annualized costs including total
PWS costs and primacy agency costs. The second row shows total monetized annualized benefits
including all endpoints that could be quantified and valued. For both, the estimates are the
expected (mean) values and the 5th percentile and 95th percentile quantified estimates from the
uncertainty distribution. These percentile estimates come from the distributions of annualized
quantified costs and annualized quantified benefits generated by the 4,000 iterations of
SafeWater MCBC, as described in Sections 5.1.2 and 6.1.2. Therefore, these distributions reflect
the joint effect of the multiple sources of variability and uncertainty for quantified costs
identified in Section 5.1.2 and for quantified benefits identified in Section 6.1.2 as well as the
baseline uncertainties discussed throughout Chapter 4 such as baseline PFAS occurrence. The
third row shows net quantified benefits (benefits minus costs). The net annual quantified
incremental benefits are $760,000. Because of the variation associated with the use of statistical
models such as SafeWater MCBC, the modeled quantified net benefits are nearly at parity. The
uncertainty range for net quantified benefits is negative $622 million to $725 million. Additional
uncertainties are presented in Table 7-6.
87 The cost-benefit analysis results for each option reflect the variability and uncertainties that could be quantified given the best
available scientific data. There are many factors that the EPA could not quantify because of data limitations. For example,
benefits will be underestimated if the PFOA and PFOS reductions result in avoided adverse health outcomes that cannot be
quantified and valued. Chapters 0 and 0 identify these limitations and the potential effect on the cost or benefit estimates,
respectively.
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Table 7-1: Annualized Quantified National Costs and Benefits, Final Rule (PFOA and
PFOS MCLs of 4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each, and HI of
1) (Million $2022)
2% Discount Rate
5th Percentile3
Mean
95th Percentile"
Total Annualized Rule
$1,435.70
$1,548.64
$1,672.10
Costs
Total Annualized Rule
$920.91
$1,549.40
$2,293.80
Benefits
Total Net Benefitsb c d
-$621.99
$0.76
$725.07
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7 percent
discount rates. Quantifiable benefits are increased under final rule table results relative to the other options presented because of
modeled PFHxS occurrence, which results in additional quantified benefits from co-removed PFOA and PFOS.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1 for
costs and Section 6.1.2 and Table 6-1 for benefits. This range does not include the uncertainty described in Table 5-21 for costs
and Table 6-48 for benefits.
bSee Table 7-6 for a list of the nonquantifiable benefits and costs, and the potential direction of impact these benefits and costs
would have on the estimated monetized total annualized benefits and costs in this table.
The national level cost estimates for PFHxS are reflective of both the total national cost for PFHxS individual MCL
exceedances, and HI MCL exceedances where PFHxS is present above its HBWC while one or more other HI PFAS is also
present in that same mixture. Total quantified national cost values do not include the incremental treatment costs associated
with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional national costs of the HI and
individual MCLs associated with HFPO-DA, PFNA, and PFBS occurrence in a quantified sensitivity analysis; see Appendix N,
Section N.3 for the analysis and more information.
dPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
Table 7-2 to Table 7-4 summarize the monetized total annualized costs and benefits for Options
la, lb, and lc, respectively.
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Table 7-2: Annualized Quantified National Costs and Benefits, Option la (PFOA and
PFOS MCLs of 4.0 ppt) (Million $2022)
2% Discount Rate
5th Percentile3
Mean
95th Percentile"
Total Annualized Rule
Costs
Total Annualized Rule
Benefits
Total Net Benefitsb c
$1,423.60
$913.05
-$613.79
$1,537.07
$1,542.74
$5.67
$1,660.30
$2,280.10
$722.09
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1 for
costs and Section 6.1.2 and Table 6-1 for benefits. This range does not include the uncertainty described in Table 5-21 for
costs and Table 6-48 for benefits.
bSee Table 7-6 for a list of the nonquantifiable benefits and costs, and the potential direction of impact these benefits and costs
would have on the estimated monetized total annualized benefits and costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
Table 7-3: Annualized Quantified National Costs and Benefits, Option lb (PFOA and
PFOS MCLs of 5.0 ppt) (Million $2022)
2% Discount Rate
5th Percentile3 Mean 95th Percentile3
Total Annualized Rule $1,102.60 $1.19213 $1,291.40
Costs
Total Annualized Rule $768.55 $1,296.84 $1,919.30
Benefits
Total Net Benefits'^ -$414.34 $104.71 $710.38
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1 for
costs and Section 6.1.2 and Table 6-1 for benefits. This range does not include the uncertainty described in Table 5-21 for
costs and Table 6-48 for benefits.
bSee Table 7-6 for a list of the nonquantifiable benefits and costs, and the potential direction of impact these benefits and costs
would have on the estimated monetized total annualized benefits and costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
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Table 7-4: Annualized Quantified National Costs and Benefits, Option lc (PFOA and
PFOS MCLs of 10.0 ppt) (Million $2022)
2% Discount Rate
5th Percentile" Mean 95th Percentile"
Total Annualized Rule $462.87 $499.29 $540.68
Costs
Total Annualized Rule $397.28 $664.45 $970.70
Benefits
Total Net Benefitsb'c -$96.42 S I 65.16 $468.54
Notes: Detail may not add exactly to total due to independent rounding. See Appendix P for results presented at 3 and 7
percent discount rates.
aThe 5th and 95th percentile range is based on modeled variability and uncertainty described in Section 5.1.2 and Table 5-1
for costs and Section 6.1.2 and Table 6-1 for benefits. This range does not include the uncertainty described in Table 5-21 for
costs and Table 6-48 for benefits.
bSee Table 7-6 for a list of the nonquantifiable benefits and costs, and the potential direction of impact these benefits and
costs would have on the estimated monetized total annualized benefits and costs in this table.
cPFAS-contaminated wastes are not considered RCRA regulatory or characteristic hazardous wastes at this time and therefore
total costs reported in this table do not include costs associated with hazardous waste disposal of spent filtration materials. To
address stakeholder concerns about potential costs for disposing PFAS-contaminated wastes as hazardous should they be
regulated as such in the future, the EPA conducted a sensitivity analysis with an assumption of hazardous waste disposal for
illustrative purposes only. See Appendix N, Section N.2 for additional detail.
The EPA notes that these quantified benefits are estimated using a COI approach (see Chapter 6
for further discussion). In the sensitivity analysis, the EPA also calculated quantified benefits
using a willingness to pay approach instead of COI information, for non-fatal RCC and bladder
cancer illnesses. In this case, the estimated expected quantified annualized costs are $1,548.64
million and the estimated expected quantified annualized benefits increase to $1,632.34 million
(see Appendix O), resulting in $83.7 million in expected annualized net benefits.
The EPA further notes that the quantified benefit-cost results above are not representative of all
benefits and costs anticipated under the final NPDWR. Due to occurrence, health, and economic
data limitations, there are several adverse health effects associated with PFAS exposure and costs
associated with treatment that the EPA could not estimate quantitatively.
PFAS exposure is associated with a wide range of adverse health effects, including reproductive
effects such as decreased fertility; increased high blood pressure in pregnant women;
developmental effects or delays in children, including low birth weight, accelerated puberty,
bone variations, or behavioral changes; increased risk of some cancers, including prostate,
kidney, and testicular cancers; reduced ability of the body's immune system to fight infections,
including reduced vaccine response; interference with the body's natural hormones; and
increased cholesterol levels and/or risk of obesity. Based on the available data at rule proposal
and submitted by public commenters, the EPA is only able to quantify three PFOA- and PFOS-
related health endpoints (i.e., changes in birth weight, CVD, and RCC) in the national analysis.
The EPA also evaluated the impacts of PFNA on birth weight and PFOS on liver cancer in
quantitative sensitivity analyses (see Appendix K and Appendix O, respectively). Those analyses
demonstrate that there are potentially significant other quantified benefits not included in the
national quantified benefits above. For example, the EPA's quantitative sensitivity analysis for
PFNA (Appendix K) found that inclusion of a 1 ppt PFNA reduction could increase annualized
birth weight benefits 5.6-7.8-fold in a model system serving 100,000 people, relative to a
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scenario that quantifies a 1 ppt reduction in PFOA and a 1 ppt reduction in PFOS only. In the
case of PFOS impacts on liver cancer, the EPA has estimated additional benefits of up to $4.79
million via the reduction in liver cancer cases anticipated to be realized by the final rule. All
regulatory alternatives are expected to produce substantial additional benefits from all the other
adverse health effects avoided, but that cannot be quantified at this time. Treatment responses
implemented to remove PFOA and PFOS under Options la-c are likely to remove some amount
of additional PFAS contaminants where they co-occur. Co-occurrence among PFAS compounds
has been observed frequently as discussed in the PFAS Occurrence & Contaminant Background
Support Document (U.S. EPA, 2024g). The final rule is expected to produce the greatest
reduction in exposure to PFAS compounds as compared to the three regulatory alternative MCLs
because it includes PFHxS, HFPO-DA, PFNA, and PFBS in the regulation. Inclusion of the HI
will trigger more systems to treat (as shown in Section 4.4.4) and provides enhanced public
health protection by ensuring reductions of these additional compounds when present above the
HI of 1. For further discussion of the quantitative and qualitative benefits associated with the
final rule, see Section 6.2.
The EPA also expects that the final rule will result in additional nonquantifiable costs. As noted
above, the HI and individual MCLs are expected to trigger more systems into more frequent
monitoring and treatment. In the national cost analysis, the EPA quantified the national treatment
and monitoring costs associated with the PFHxS individual MCL and the HI associated costs
based on PFHxS occurrence only. Due to occurrence data limitations, cost estimates for PFNA,
PFBS, and HFPO-DA are less precise relative to those for PFOA, PFOS, and PFHxS
compounds, and as such, the EPA performed a quantitative sensitivity analysis of the national
cost impacts associated with HI exceedances resulting from PFNA, PFBS, and HFPO-DA and
the PFNA and HFPO-DA MCLs to understand and consider the potential magnitude of costs
associated with treating these three PFAS. The EPA found that in addition to the costs associated
with PFHxS exceedances, which are included in the national cost estimate, the HI and individual
MCLs for PFNA and HFPO-DA could cost an additional $82.4 million per year. In cases where
these compounds co-occur at locations where PFAS treatment is implemented because of
nationally modeled PFOA, PFOS, and PFHxS occurrence, treatment costs are likely to be
marginally higher as treatment media estimated bed-life is shortened. In instances where
concentrations of HFPO-DA, PFNA, and PFBS are high enough to cause or contribute to an HI
exceedance when the concentrations of PFOA, PFOS, and PFHxS would not have already
otherwise triggered treatment, the national modeled costs may be underestimated. If these PFAS
occur in isolation at levels that affect treatment decisions, or if these PFAS occur in combination
with PFHxS when PFHxS concentrations were otherwise below the its respective HBWC in
isolation (i.e., less than 10 ppt) then the quantified costs underestimate the impacts of the final
rule. See Appendix N.3 for a sensitivity analysis of additional treatment costs at systems with HI
exceedances. See Appendix N.4 for a sensitivity analysis of the marginal costs of HFPO-DA and
PFNA MCLs. For further discussion of how EPA considered the costs of the five individual
MCLs and the HI MCL, see Section XII. A.4 of the preamble for the final rule.
The EPA has proposed designating PFOA and PFOS as CERCLA hazardous substances (U.S.
EPA, 2022b). Stakeholders have expressed concern to the EPA that a hazardous substance
designation for certain PFAS may limit their disposal options for drinking water treatment
residuals (e.g., spent media, concentrated waste streams) and/or potentially increase costs.
Designation of PFOA and PFOS as CERCLA hazardous substances would not require waste
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(e.g., biosolids, treatment residuals, etc.) to be treated in any particular fashion, nor disposed of
at any specific particular type of landfill. The designation also would not restrict, change, or
recommend any specific activity or type of waste at landfills. In its estimated national costs, the
EPA has maintained the assumption that disposal does not have to occur in accordance with
hazardous waste standards thus national costs may be underestimated. The EPA has conducted a
sensitivity analysis that assumes hazardous waste disposal at all systems treating for PFAS to
assess the potential increase in costs (see Appendix N). Table 7-5 summarizes the benefits and
costs that are quantified and nonquantified under the final NPDWR.
Table 7-5: Summary of Quantified and Nonquantified Benefits and Costs in the National
Analysis
Methods (Report
Category Quantified Non-quantified Section where Analysis
is Detailed)
Costs
PWS treatment costs3
V
Section 5.3.1
PWS sampling costs
V
Section 5.3.2.2
PWS implementation and
V
Section 5.3.2.1
administration costs
Primacy agency rule implementation
V
Section 5.3.2
and administration costs
Hazardous waste disposal for treatment
V
Section 5.6
media
POU not in compliance forecast
V
Section 5.6
Benefits
PFOA and PFOS birth weight effects
Section 6.4
PFOA and PFOS cardiovascular effects
V
Section 6.5
PFOA and PFOS renal cell carcinoma
V
Section 6.6
Health effects associated with
disinfection byproducts, specifically
V
Section 6.7
bladder cancer
Other PFOA and PFOS health effects'3
V
Section 6.2.2.2
Health effects associated with HI
compounds HFPO-DA, PFNA, PFBS,
V
Section 6.2
and PFHxS
Health effects associated with other
V
Section 6.2
PFAS
Abbreviations: HFPO-DA - hexafluoropropylene oxide dimer acid; PFAS - per and polyfluoroalkyl substances; PFBS -
perfluorobutanesulfonic acid; PFHxS - perfluorohexane sulfonate; PFNA - perfluorononanoic acid; PFOA -
Perfluorooctanoic Acid; PFOS- Perfluorooctane Sulfonate; POU - point-of-use; PWS- public water system
Notes:
aThe national level cost estimates for PFHxS are reflective of both the total national cost for PFHxS individual MCL
exceedances, and HI MCL exceedances where PFHxS is present above its HBWC while one or more other HI PFAS is also
present in that same mixture. Total quantified national cost values do not include the incremental treatment costs associated
with the co-occurrence of HFPO-DA, PFBS, and PFNA. EPA has considered the additional national costs of the HI and
individual MCLs associated with HFPO-DA, PFNA, and PFBS occurrence in a quantified sensitivity analysis; see Appendix
N, Section N.3 for the analysis and more information.
bEffects of PFOS on liver cancer are summarized as a national-level sensitivity analysis in Appendix O.
Table 7-6 provides a summary of the likely impact of nonquantifiable benefit-cost categories. In
each case, the EPA notes the potential direction of the impact on costs and/or benefits. For
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example, benefits are underestimated if the PFOA and PFOS reductions result in avoided adverse
health outcomes that cannot be quantified and valued. Sections 5.7 and 6.8 identify the key
methodological limitations and the potential effect on the cost or benefit estimates, respectively.
Table 7-6: Potential Impact of Nonquantifiable Benefits and Costs
Source
Final Rule
Option la
Option lb
Option lc
Nonquantifiable PFOA
and PFOS health
endpoints
B: underestimate
B: underestimate
B: underestimate
B: underestimate
Limitations with
nationally representative
HFPO-DA, PFNA, and
PFBS occurrence data
B&C:
underestimate
N/A
N/A
N/A
Nonquantifiable HFPO-
DA, PFNA, PFHxS, and
PFBS health endpoints
B: underestimate
N/A
N/A
N/A
Limitations with
nationally representative
occurrence data for
additional PFAS
B&C:
underestimate
B&C:
underestimate
B&C:
underestimate
B&C:
underestimate
compounds
Removal of co-occurring
non-PFAS contaminants
POU not in compliance
forecast
B&C:
underestimate
C: overestimate
B&C:
underestimate
C: overestimate
B&C:
underestimate
C: overestimate
B&C:
underestimate
C: overestimate
Unknown future
hazardous waste
management
requirements for PFAS
B&C:
underestimate
B&C:
underestimate
B&C:
underestimate
B&C:
underestimate
Abbreviations: B - benefits; C - costs; POU - point-of-use; PFAS - per-and polyfluoroalkyl substances.
Table 7-1 through Table 7-6 summarize the results of this final rule analysis. As indicated in
Section 2.2.2 of this EA, the EPA discounted the estimated monetized cost and benefit values
using a 2 percent discount rate, consistent with OMB Circular A-4 (OMB, 2003; OMB, 2023)
guidance. The U.S. White House and OMB recently finalized and re-issued the A-4 and A-94
benefit-cost analysis guidance (see OMB Circular A-4, 2023), and the update includes new
guidance to use a social discount rate of 2 percent. The updated OMB Circular A-4 states that the
discount rate should equal the real (inflation-adjusted) rate of return on long-term U.S.
government debt, which provides an approximation of the social rate of time preference. This
rate for the past 30 years has averaged around 2.0 percent per year in real terms on a pre-tax
basis. OMB arrived at the 2 percent discount rate figure by considering the 30-year average of
the yield on 10-year Treasury marketable securities, and the approach taken by OMB produces a
real rate of 1.7 percent per year, to which OMB added a 0.3 percent per-year rate to reflect
inflation as measured by the personal consumption expenditure (PCE) inflation index. The OMB
guidance states that Agencies must begin using the 2 percent discount rate for draft final rules
that are formally submitted to OIRA after December 31, 2024. The updated OMB Circular A-4
guidance further states that "to the extent feasible and appropriate, as determined in consultation
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with OMB, agencies should follow this Circular's guidance earlier than these effective dates."
Given the updated default social discount rate prescribed in the OMB Circular A-4 and also
public input received on the discount rates considered by the EPA in the proposed NPDWR, for
this final rule, the EPA estimated national benefits and costs at the 2 percent discount rate for the
final rule and incorporated those results into the final economic analysis. Since the EPA
proposed this NPDWR with the 3 and 7 percent discount rates based on guidance in the previous
version of OMB Circular A-4, the EPA has kept the presentation of results using these discount
rates in Appendix P. The Administrator reaffirms his determination that the benefits of the rule
justify the costs. The EPA's determination is based on its analysis under in SDWA section
1412(b)(3)(C) of the quantifiable benefits and costs at the 2 percent discount rate, in addition to
at the 3 and 7 percent discount rate, as well as the nonquantifiable benefits and costs. The EPA
found that significant nonquantifiable benefits are likely to occur from the final PFAS NPDWR.
The quantified analysis is limited in its characterization of uncertainty. In Table 7-1, the EPA
provides 5th and 95th percentile values for net benefits. These values represent the quantified, or
modeled, potential range in the expected net benefit values associated with the uncertainty
resulting from the following variables: the baseline PFAS occurrence; the affected population
size; the compliance technology unit cost curves, which are selected as a function of baseline
PFAS concentrations and population size, the distribution of feasible treatment technologies, and
the three alternative levels of treatment capital costs; the concentration of total organic carbon in
a system's source water (which impacts GAC O&M costs); the demographic composition of the
system's population; the magnitude of PFAS concentration reductions; the health effect-serum
PFOA and PFOS slope factors that quantify the relationship between changes in PFAS serum
level and health outcomes for birth weight, CVD, and renal cell carcinoma; and the cap placed
on the cumulative renal cell carcinoma risk reductions due to reductions in serum PFOA. These
modeled sources of uncertainty are discussed in more detail in Sections 5.1.2 and 6.1.2. The
quantified 5th and 95th percentile values do not include a number of factors that impact both
costs and benefits but for which the agency did not have sufficient data to include in the
quantification of uncertainty. The factors influencing the final rule cost estimates that are not
quantified in the uncertainty analysis are detailed in Table 5-21. These uncertainty sources
include: the specific design and operating assumptions used in developing treatment unit cost;
the use of national average costs that may differ from the geographic distribution of affected
systems; the possible future deviation from the compliance technology forecast; and the degree
to which actual TOC source water values differ from the EPA's estimated distribution. The EPA
has no information to indicate a directional influence of the estimated costs with regard to these
uncertainty sources. To the degree that uncertainty exists across the remaining factors, it would
most likely influence the estimated 5th and 95th percentile range and not significantly impact the
expected value estimate of costs.
Table 6-48 discusses the sources of uncertainty affecting the estimated benefits not captured in
the estimated 5th and 95th reported values. The modeled values do not capture the uncertainty in:
the exposure that results from daily population changes at NTNCWSs or routine population
shifting between PWSs, for example spending working hours at a NTNCWS or CWS and home
hours at a different CWS; the exposure-response functions used in the benefits analyses assume
that the effects of serum PFOA/PFOS on the health outcomes considered are independent,
additive, and that there are no threshold serum concentrations below which effects
(cardiovascular, developmental, and renal cell carcinoma) do not occur; the distribution of
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population by size and demographics across EPs within modeled systems and future population
size and demographic changes, and the Value of Statistical Life reference value or income
elasticity used to update the VSL. Given information available to the agency, four of the listed
uncertainty sources would not affect the benefits expected value but the dispersion around that
estimate. They are the unmodeled movements of populations between PWSs with potentially
differing PFAS concentrations; the independence and additivity assumptions with regard to the
effects of serum PFOA/PFOS on the health outcomes; the uncertainty in the population and
demographic distributions among EPs within individual systems; and the VSL value and the
income elasticity measures. Two of the areas of uncertainty not captured in the analysis would
tend to indicate that the quantified benefits numbers are overestimates. First, the data available to
the EPA with regard to population size at NTNCWSs, while likely capturing peaks in
populations utilizing the systems, does not account for the variation in use and population and
would tend to overestimate the exposed population. The second source of uncertainty, which
definitionally would indicate overestimates in the quantified benefits values, is the assumption
that there are no threshold serum concentrations below which health effects (cardiovascular,
developmental, and renal cell carcinoma) do not occur. One source of possible underestimation
of benefits not accounted for in the quantified analysis is the impact of general population
growth over the extended period of analysis.
In addition to the quantified cost and benefit expected values, the modeled uncertainty associated
within the 5th and 95th percentile values, and the un-modeled uncertainty associated with a
number of factors listed above, there are also significant nonquantifiable costs and benefits,
which are important to the overall weighing of costs and benefits. Table 7-6 provides a summary
of these nonquantifiable cost and benefit categories along with an indication of the directional
impact each category would have on total costs and benefit. Table 5-21 and Table 6-48 also
provide additional information on a number of these nonquantifiable categories.
For the nonquantifiable costs, the EPA had insufficient nationally representative data to precisely
characterize occurrence of HFPO-DA, PFNA, and PFBS at the national level and therefore could
not include complete treatment costs associated with: the co-occurrence of these PFAS at
systems already required to treat as a result of estimated PFOA, PFOS, or PFHxS levels, which
would shorten the filtration media life and therefore increase operation costs; and the occurrence
of HFPO-DA, PFNA, and/or PFBS at levels high enough to cause systems to exceed the
individual MCLs for PFNA and HFPO-DA or the HI and have to install PFAS treatment. The
EPA expects that the quantified national costs, which do not include HFPO-DA, PFNA, and
PFBS treatment costs are marginally underestimated (on the order of 5%) as a result of this lack
of sufficient nationally representative occurrence data. In an effort to better understand and
consider the costs associated with treatment of the PFNA and HFPO-DA MCLs and potentially
co-occurring HFPO-DA, PFNA, and PFBS at systems both with and without PFOA, PFOS and
PFHxS occurrence in exceedance of the MCLs the EPA performed a quantitative sensitivity
analysis of the national cost impacts associated with HI MCL exceedances resulting from HFPO-
DA, PFNA, and PFBS and/or individual MCL exceedances of PFNA and HFPO-DA. The
analysis is discussed in Section 5.3.1.4 and Appendix N.3. Two additional nonquantifiable cost
impacts stemming from insufficient co-occurrence data could also potentially shorten filtration
media life and increase operation costs. The co-occurrence of other PFAS and other non-PFAS
contaminants not regulated in the final rule could both increase costs to the extent that they
reduce media life. The EPA did not include POU treatment in the compliance technology
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forecast because current POU units are not certified to remove PFAS to the standards required in
the final rule. Once certified, this technology may be a low-cost treatment alternative for some
subset of small systems. Not including POU treatment in this analysis has resulted in a likely
overestimate of costs.
Appendix N.2 contains a sensitivity analysis that estimates possible additional national
annualized costs of $99 million, which would accrue to systems if the waste filtration media
from GAC and IX were handled as RCRA regulatory or characteristic hazardous waste. This
sensitivity analysis includes only disposal costs and does not consider other potential
environmental benefits and costs associated with the disposal of the waste filtration media.
There are significant nonquantifiable sources of benefits that were not captured in the quantified
benefits estimated for the proposed rule. While the EPA was able to monetize some of the PFOA
and PFOS benefits related to cardiovascular disease, infant birth weight, and renal cell carcinoma
effects, the agency was unable to quantify additional reductions in negative health impacts in the
national quantitative analysis. In addition to the national analysis for the final rule, the agency
developed a sensitivity analysis assessing liver cancer impacts, which is detailed in Appendix O.
The EPA did not quantify PFOA and PFOS benefits related to health endpoints including
developmental, cardiovascular, hepatic, immune, endocrine, metabolic, reproductive,
musculoskeletal, and other types of carcinogenic effects. Section 6.2.2 provides additional
information on the nonquantifiable impacts of PFOA and PFOS. Further, the agency did not
quantify any health endpoint benefits associated with the potential reductions in HI PFAS, which
include PFHxS, HFPO-DA, PFNA, and PFBS, or other co-occurring non-regulated PFAS which
would be removed due to the installation of required filtration technology at those systems that
exceed the final MCLs. The nonquantifiable benefits impact categories associated with PFHxS,
HFPO-DA, PFNA, and PFBS include developmental, cardiovascular, immune, hepatic,
endocrine, metabolic, reproductive, musculoskeletal, and carcinogenic effects. In addition, the
EPA did not quantify the potential developmental, cardiovascular, immune, hepatic, endocrine,
metabolic, reproductive, musculoskeletal, and carcinogenic impacts related to the removal of
other co-occurring non-regulated PFAS. See Section 6.2.4 for additional information on the
nonquantifiable impacts of PFHxS, HFPO-DA, PFNA, and PFBS, and other non-regulated co-
occurring PFAS.
The treatment technologies installed to remove PFAS can also remove numerous other non-
PFAS drinking water contaminants which have negative health impacts including additional
regulated and unregulated DBPs (the quantified benefits assessment does estimate benefits
associated with THM4), heavy metals, organic contaminants, and pesticides, among others. The
removal of these co-occurring non-PFAS contaminants could have additional positive health
benefits. In total these nonquantifiable benefits are anticipated to be significant and are discussed
qualitatively in Section 6.2.
To fully weigh the costs and benefits of the action, the agency considered the totality of the
monetized values, the potential impacts of the nonquantifiable uncertainties described above, the
nonquantifiable costs and benefits, and public comments received by the agency related to the
quantification and qualitative assessment of the costs and benefits. In the final rule, the EPA is
reaffirming the Administrator's determination made at proposal that the quantified and
nonquantifiable benefits of the rule justify its quantified and nonquantifiable costs (88 FR
18638).
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8 Environmental Justice Analysis
8.1 Introduction
The EPA defines environmental justice (EJ) as "the fair treatment and meaningful involvement
of all people regardless of race, color, national origin, or income with respect to the
development, implementation, and enforcement of environmental laws, regulations, and
policies" (U.S. EPA, 2016h). The concept of fair treatment includes not just the distribution of
burdens across populations but also the distribution of risk reduction from the EPA's actions. The
EPA reviews potential EJ concerns regarding minority populations, low-income populations,
and/or indigenous peoples (U.S. EPA, 2016h).
The framework used to evaluate the anticipated EJ impacts of the final rule for per- and
polyfluoroalkyl substances (PFAS) comes from the Technical Guidance for Assessing
Environmental Justice in Regulatory Analysis (U.S. EPA, 2016h), which provides the following
guiding questions:
• Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern in the baseline?
• Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern for the regulatory options under
consideration?
• For the regulatory options under consideration, are potential EJ concerns created or
mitigated compared to the baseline?
Contextualizing these questions for the final PFAS rule, the EPA evaluated the following
questions:
• Are population groups of concern (i.e., people of color and low-income populations)
disproportionately exposed to PFAS compounds in drinking water delivered by PWSs?
• Are population groups of concern disproportionately affected by the final rule and
regulatory alternatives under consideration for the final PFAS NPDWR?
• If any disproportionate impacts are identified, do they create or mitigate baseline EJ
concerns?
As part of the proposal process for the PFAS NPDWR, the EPA conducted the EJ analyses in
this chapter to assess the demographic distribution of baseline PFAS drinking water exposure
and impacts that are anticipated to result from the final rule. The EPA conducted two separate
analyses to address the research questions presented above. To inform the first question, the EPA
conducted an analysis using the agency's EJSCREENbatch R package, which utilizes data from
EJScreen, the agency's Environmental Justice Screening and Mapping Tool and from the U.S.
Census Bureau's American Community Survey (ACS) 2015-2019 five-year sample (U.S. EPA,
2019a). To inform the second and third questions above, the EPA conducted an EJ analysis of
the EPA's final regulatory option and regulatory alternatives using SafeWater MCBC.
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Section 8.2 provides an overview of the EPA's EJ literature review. Sections 8.3 and 8.4
describe the EJ analyses the EPA conducted. Section 8.5 presents the conclusions from the
EPA's EJ analyses.
8.2 Literature Review
The EPA conducted a literature review to develop a broad understanding of current research at
the intersection of drinking water quality, PFAS exposure, and communities with related EJ
concerns. The literature covered a range of specific topics including the likelihood of exposure
based on proximity to sites of contamination, sociodemographic characteristics of communities
exposed to PFAS in region-specific studies and understanding the sociodemographic distribution
of health outcomes associated with exposure to PFAS. The EPA's literature review also
examined the relationship between PFAS exposure via drinking water in overburdened
communities and a range of health outcomes.
8.2.1 Methods
The EPA conducted its literature review to evaluate and synthesize findings from studies that
explored associations between PFAS exposure via drinking water in overburdened communities
and associated health outcomes, including those health endpoints the EPA quantified as part of
its benefits analysis: changes in infant birth weight, CVD, and kidney cancer.
The EPA applied a variety of search terms for the literature review, including: CVD; disparities;
disproportionate exposure; disproportionate impact; drinking water quality/contamination;
environmental justice; equity; forever chemicals; inequity; infant birth weight; kidney cancer;
low-income; minority; over-burdened; people of color; PFAS; PFAS interactions; PFC(s);
PFOA; PFOS; race differences in health effects after PFAS exposure; race disparities in health
effects, immune effects, and PFAS exposure; race ethnicity and health effects of PFAS exposure
and interactions; sociodemographic differences in health effects after PFAS exposure; social
justice; and tribal.
From the literature review, the EPA found that there are a limited number of studies that focus on
the association between disproportionate exposure to PFAS via drinking water and health
outcomes for overburdened communities on a national level. The agency excluded studies that
examined exposure routes apart from drinking water and/or did not evaluate race/ethnicity within
their participant demographics. Of the studies that the EPA identified as part of its literature
review, all but two studies were published in peer reviewed journals (with the remaining two
studies appearing in gray literature).
8.2.2 Findings
To contextualize its analysis of EJ impacts related to PFAS in drinking water, the EPA reviewed
studies that evaluate overall EJ concerns related to environmental contamination. In 1987, the
EPA reported in a nationwide study that roughly twice as many people of color resided in
proximity to a commercial hazardous waste facility compared to communities without a facility
(U.S. EPA, 1994). Later research indicated that communities of low socioeconomic status are
more likely to reside in proximity to environmental hazardous facilities, thereby potentially
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facing a disproportionate impact of exposure to toxic chemicals than communities of higher
socioeconomic status (Brown, 1995; Brulle & Pellow, 2006). A 2010 study showed 63 percent of
large polluters in a North Carolina county were operating in census tracts with per capita income
below $21,000, as identified in the EPA's Toxics Release Inventory (TRI) (Banzhaf et al., 2019).
When specifically examining studies related to PFAS in drinking water, available literature
showed associations between PFAS contamination in drinking water and proximity to sites
including those critical for transportation infrastructure, industry, and national defense (Black et
al., 2021; X. C. Hu et al., 2016; Johnston & Cushing, 2020; Sunderland et al., 2019). Researchers
noted that identifiable sources of PFAS are often prevalent at aforementioned locations and are
more frequently located in overburdened communities (Black et al., 2021; X. C. Hu et al., 2016;
Stoiber et al., 2020).
The characteristics of PFAS, such as high aqueous solubility and persistence within the
environment, allow them to travel readily between ecological zones (ATSDR, 2021; X. C. Hu et
al., 2016; Kotlarz et al., 2020). As such, PFAS contamination can negatively impact drinking
water sources downstream from an original contamination site, putting residents in communities
surrounding known sources of PFAS at a disproportionate risk of exposure. A 2019 study in
Michigan by Desikan et al. (2019) evaluated the proportion of low-income households and
households with people of color in communities within five miles of PF AS-contaminated sites
compared to census projections for those areas. The study found that 38,962 more low-income
households and 294,591 more households with people of color reside within five miles of a site
contaminated with PFAS than expected, based on U.S. Census data.
In California, Lee et al. (2021) demonstrated that overburdened communities are more likely to
be served by PWSs with higher levels of PFAS. PFAS data were integrated with results from
CalEnviroScreen 3.0, a statewide EJ screening tool (OEHHA, 2016). Of the 7,896 PWSs in the
state, about 3 percent (n = 248) had been monitored for PFAS, serving 42 percent of California's
total population. Results from the study showed that PFAS was detected in 160 of 248 PWSs, or
roughly 65 percent of systems monitored. Lee, Kar, and Reade (2021) overlaid the upper 25
percent of disadvantaged communities as identified by CalEnviroScreen 3.0 with water systems
experiencing the highest levels of PFAS contamination. Among the communities in the top
quartile for people of color and low-income demographic groups, 69 percent had PFAS detected
in their water system. Further, PWSs in 20 percent of overburdened communities with PFAS
contamination fell within the highest quartile of PFAS concentration levels in the state of
California, suggesting that PFAS occurrence is disproportionately higher in drinking water
serving already overburdened communities. Only 2 of the 10 water systems with the highest
PFAS concentrations fell below the state average for all relevant demographic indicators
included in the study (people of color, education level, unemployment, poverty, and housing
burden).
A 2023 study by Liddie, Schaider, and Sunderland examined sociodemographic disparities in
exposures to PFAS via drinking water based on potential exposure source. Community water
systems were geocoded within 8-digit hydrologic codes where the exact coordinates of water
source regions were unavailable, and the study refers to these areas as watersheds or CWS
watersheds. In examining data from 18 states with the most robust PFAS monitoring and
reporting infrastructure, Liddie et al. (2023) found that watersheds with higher concentrations of
PFAS also had higher concentrations of the potential PFAS exposure sources that the researchers
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examined. Sources of potential exposure (based on correlation of proximity) included industrial
sites, wastewater treatment plants, municipal solid waste landfills, military fire training areas,
and civilian airports. Additionally, watersheds that served Hispanic/Latino communities and non-
Hispanic Black communities were found to have significantly greater odds of containing PFAS
sources. Further, CWS watersheds with PFAS concentrations above 5 ppt or above the lowest
state-level MCL served communities with greater proportions of Hispanic/Latino and non-
Hispanic Black populations than CWS watersheds with concentrations below these limits (Liddie
et al., 2023).
At least two studies identified the use of aqueous film forming foam (AFFF) as a predictor of
PFAS concentrations in U.S. drinking water (Johnston & Cushing, 2020; Sunderland et al.,
2019). Using nationally representative PFAS occurrence data from UCMR 3, a study from X. C.
Hu et al. (2016) found that the presence of a military fire training area using AFFF within a
watershed's eight-digit hydraulic unit code (HUC) increased the frequency of exposure to at least
one PFAS analyte in drinking water from 10.4 percent to 28.2 percent. For each additional
military site within a HUC, drinking water samples with detectable levels of PFAS found a 20
percent increase in PFHxS, a 10 percent increase in both PFHpA and PFOA, and a 35 percent
increase in PFOS.
The EPA also sought to characterize literature that discusses potential pathways of PFAS
exposure for communities in proximity to waste disposal and destruction sites. The EPA is
unaware of any literature which specifically discusses PFAS exposure for communities with
potential environmental justice concerns due to disposal of PFAS-contaminated drinking water
treatment residuals. Therefore, the EPA has reviewed literature which discusses the siting of
waste facilities in general as well as the pathways of exposure for other contaminants. It is also
important to note that there are uncertainties associated with the potential pathways of exposure
for communities with potential environmental justice concerns regarding the destruction and
disposal of PFAS in drinking water. For information related to the destruction and disposal of
PFAS, please see the EPA's Interim Guidance on the Destruction and Disposal of Perfluoroalkyl
and Polyfluoroalkyl Substances and Materials Containing Perfluoroalkyl and Polyfluoroalkyl
Substances, version 2 (U.S. EPA, 2020c.
Waste facilities are often disproportionately located near communities with potential
environmental justice concerns (Martuzzi et al., 2010). Additionally, in a national-level study of
the demographic characteristics of communities collocated with waste management facilities,
racial composition was found to be an independent predictor of waste management facility siting,
controlling for other socioeconomic variables (Mohai & Saha, 2015). As such, communities with
potential EJ concerns may experience adverse health effects that result from these
disproportionate exposures to PFAS due to proximity to waste sites if PFAS are released from
these sites (Desikan et al., 2019).
Martin et al. (2023) found that communities with hazardous waste incinerators that regularly
receive PFAS shipments have demographic characteristics that indicate that potential exposures,
if any, that result from incineration may affect individuals that reside in communities with lower
incomes and less education than the US average. However, there is uncertainty regarding
exposures from PFAS destruction at these hazardous waste incinerators. PFAS residuals from
drinking water are likely carbon, which is likely to be reactivated. As described in the EPA's
Interim Guidance on the Destruction and Disposal of Perfluoroalkyl and Polyfluoroalkyl
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Substances and Materials Containing Perfluoroalkyl and Polyfluoroalkyl Substances, version 2
(U.S. EPA, 2020c), carbon reactivation systems have the potential to remove PFAS from the
reactivated carbon and destroy PFAS. Additionally, Distefano et al. (2022) showed >99.99
percent destruction of measured PFAS at a full-scale commercial reactivation facility. The EPA
believes when proper guidance, such as that from the EPA's Interim Guidance on the Destruction
and Disposal of Perfluoroalkyl and Polyfluoroalkyl Substances and Materials Containing
Perfluoroalkyl and Polyfluoroalkyl Substances, is followed, the destruction and disposal of
drinking water treatment residuals can be suitably managed in a way which can minimize risk.
For more information and further discussion on this topic, please see Section 4 (Considerations
for Potentially Vulnerable Populations Living Near Likely Destruction or Disposal Sites) in
Interim Guidance on the Destruction and Disposal of Perfluoroalkyl and Polyfluoroalkyl
Substances and Materials Containing Perfluoroalkyl and Polyfluoroalkyl Substances, version 2
(U.S. EPA, 2020c).
To remain consistent with the health endpoints associated with PFAS exposure that are
monetized as part of the final PFAS NPDWR's benefits analysis, the health outcomes of focus in
this literature review included CVD, kidney cancer, and impacts on infant birth weight. For more
information on the EPA's quantified benefits analysis, see Chapter 60.
Literature showed that overburdened communities experience relatively higher adverse health
outcomes compared to communities with fewer people of color (Driscoll & Gregory, 2021; Fryar
et al., 2017; Pinheiro et al., 2021). Literature also showed that risk of CVD, kidney cancer, and
changes in infant birth weight are associated with PFAS exposure (Almond et al., 2005; Barry et
al., 2013; Goff et al., 2014; Ma & Finch, 2010; Raleigh et al., 2014; Steenland & Woskie, 2012;
Vieira et al., 2013; U.S. EPA, 2016d; U.S. EPA, 2016h; U.S. EPA, 2021a; U.S. EPA, 2024e;
U.S. EPA, 2024f), discussed in more detail in Chapter 60.
The Centers for Disease Control and Prevention (CDC) identified hypertension (HTN) as a
substantial risk factor for CVD (Fryar et al., 2017). Using the 140/90 mmHg threshold for HTN
diagnosis, the CDC reported that African American adults reported a higher burden of HTN
(40.3%) compared to White (27.8%), Asian (25.0%), or Hispanic (27.8%) adults (Fryar et al.,
2017). Additionally, a comprehensive narrative literature review by Graham (2015) found
disproportionate rates of CVD among minority subpopulations in the U.S., particularly the
African American population. African American subpopulations were found to have higher
incidence of myocardial infarction, heart failure, stroke, among other cardiovascular events and
experience the highest overall death rate from CVD among various minority population groups.
With regards to cancer, a study by Uche et al. (2021) showed statistically significantly greater
cumulative cancer risk was identified in communities in which small and large CWSs serve
higher proportions of Hispanic/Latino and Black/African American residents in Texas and
California.88 In Texas, greater cumulative cancer risk was statistically significantly greater for
small and medium CWSs serving relatively higher proportions of Hispanic/Latino community
members. Additionally, small CWSs serving relatively higher proportions of Black/African
88 A CWS was defined as small if it served 501-3,300 people, medium if it served 3,301-10,000 people, large if it served 10,001-
100,000 people, and very large if it served more than 100,000 people.
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American residents had statistically significantly greater cumulative cancer risk. In California,
cumulative cancer risk was statistically significantly greater for very large CWSs serving
relatively higher proportions of Black/African American community members, followed by
small CWSs serving relatively higher proportions of Hispanic/Latino residents.
Pinheiro et al. (2021) studied kidney cancer rates in White, Black, Asian/Pacific Islander (API),
American Indian, all non-Hispanic, and Hispanic populations of any race by using reported
cancer deaths in California and Florida (2008-2018) and New York (2008-2017). This study's
methodology directly compared results for specific race/ethnicity groups to White populations.
Results indicated that American Indian individuals experience the highest mortality (54%), and
mortality is 53 percent higher for men and women when compared to mortality among White
participants (Pinheiro et al., 2021). Conversely, API populations showed significantly lower
mortality than White populations, with 45 percent lower mortality among males and 43 percent
lower mortality among females. Kidney cancer mortality among Black populations and all-
combined Hispanic populations (i.e., Cuban, Puerto Rican, and Mexican) was also significantly
lower than among White populations, but by smaller margins: mortality was 12 percent and 16
percent lower for Black males and females and 11 percent and 8 percent lower for Hispanic
males and females, respectively.
Additionally, the CDC's National Vital Statistics Reports used the 2020 birth file from the
National Vital Statistic System to display distributions in prepregnancy body mass index (BMI),
including three classes of obesity, by maternal race and Hispanic origin for women who gave
birth in 2020 (Driscoll & Gregory, 2021). Infants born to non-Hispanic Black women had the
highest rate of low birth weight (14.19%), followed by infants of Hispanic women (7.40%).
Infants of non-Hispanic White women had the lowest rate of low birth weight (6.84%) (Driscoll
& Gregory, 2021).
Furthermore, the EPA reviewed studies that examine blood serum levels of PFAS across various
demographic groups. Studies analyzing biomarker data indicate some demographic disparities
that exist in blood serum levels across certain PFAS analytes (Boronow et al., 2019; Calafat et
al., 2007; Eick et al., 2021; C. Y. Lin et al., 2020; Nelson et al., 2012; V. K. Nguyen et al., 2020;
S. K. Park et al., 2019). Specifically, blood serum levels of PFNA and PFOS were found to be
elevated in Black adults (Boronow et al., 2019; Calafat et al., 2007; Eick et al., 2021; C. Y. Lin
et al., 2020; Nelson et al., 2012; S. K. Park et al., 2019). PFNA was also found to be elevated in
Asian American mothers, when compared to all other races (Eick et al., 2021). Additionally,
PFDA was found to be elevated in Asian American women, when compared to non-Hispanic
White populations (V. K. Nguyen et al., 2020). Finally, Me-FOSAA was found to be elevated in
Black women at some but not all study sites analyzed (S. K. Park et al., 2019).
However, many studies indicate lower average blood serum PFAS levels among people of color.
Three studies in particular demonstrated that non-Hispanic White populations had the highest
concentrations of PFAS across all analytes (Barton et al., 2020; Kato et al., 2014; Kingsley et al.,
2018). It should be noted, however, that the study design for Barton et al. (2020), Kato et al.
(2014), and Kingsley et al. (2018) each had majority non-Hispanic White participant
demographics of 75 percent, 63 percent, and 61 percent of study participants, respectively. The
literature also indicates that higher socioeconomic status (e.g., income) is associated with higher
PFAS blood serum levels (Buekers et al., 2018).
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8.2.3 Discussion and Limitations
The EPA's purpose in conducting its literature review was to examine the relationship between
PFAS exposure via drinking water in overburdened communities and health outcomes related to
CVD, changes in infant birth weight, and kidney cancer. Presented studies indicate that higher
percentages of low-income and minority communities reside near a range of PFAS-contaminated
sites. Such contamination is also shown to occur at higher levels in low-income and minority
communities. Further, the EPA's literature review analysis indicates that PFAS contamination
occurs more often and/or at higher levels in overburdened communities.
It should be noted there are substantial gaps in current literature on PFAS exposure and health
outcomes in overburdened communities. One substantial gap in the available literature is a dearth
of studies that examine differential impacts of health outcomes associated with PFAS exposure,
as reported by race or ethnicity. Potential gaps in understanding also relate to determining
whether the rate of developed risk for one or more of the aforementioned health endpoints is
related to exposure to PFAS contamination in drinking water rather than other exposure
pathways.
The blood serum PFAS studies evaluated as part of this literature review have their limitations in
extrapolating to the potential disproportionate impacts of PFAS drinking water exposure given
their focus on overall PFAS exposure across many exposure routes rather than drinking water-
specific exposures. Wilder et al. (2017) note that national average PFAS blood serum levels are
influenced by a variety of major exposure pathways, including diet and consumer products in
addition to exposure via drinking water. As such, this limits conclusions that can be drawn about
the demographic breakdown of PFAS blood serum levels due to drinking water exposure alone.
Additional information on exposure via drinking water alone is necessary to better understand
the impacts of PFAS drinking water contamination on PFAS blood serum levels within
overburdened communities.
Another limitation of these blood serum-based studies is their inequitable representation of study
participants by race. The participant demographic makeup of three published studies that
examined PFAS blood serum levels was highly biased toward the non-Hispanic White
population, resulting in an incomplete understanding of people of color's exposure to PFAS.
Statisticians can adjust the results if certain participant demographic groups are
disproportionately represented. However, these adjustments are based on assumptions about the
underlying demographic makeup of the study population.
8.3 EJ PFAS Exposure Analysis
This section describes the data sources and approach the EPA used to characterize the
demographic distribution of PFAS exposure in drinking water. This analysis is designed to
answer the question posed in the beginning of the chapter: Are population groups of concern
(i.e., people of color and low-income populations) disproportionately exposed to PFAS
compounds in drinking water delivered by PWSs? This analysis estimates anticipated exposure
rates above various PFAS concentrations for four PFAS analytes, where occurrence of these is
used as a proxy for co-occurrence of many other PFAS compounds. In some cases, the
thresholds that the EPA uses in this analysis overlap with regulatory alternatives considered by
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the EPA in the final regulatory action. This analysis does not evaluate the anticipated costs and
benefits of the final rule and regulatory alternatives. The EPA's analysis of the anticipated
demographic distribution of costs and benefits of the final rule and regulatory alternatives can be
found in Section 8.4.
The EPA estimated the sociodemographic characteristics of populations that the EPA anticipates
are exposed to levels higher than various threshold concentrations of four PFAS analytes (PFOA,
PFOS, PFHxS, and PFHpA). For this analysis, the EPA had sufficient information on PFAS
occurrence and PWS service area boundaries in the sample population, which was a subset of
PWSs.89 PWSs were first categorized by available data (Section 8.3.1), using availability of
UCMR 3 sampling data, state sampling data, and availability of service area boundary
information (Table 8-1).
The EPA used PWS service area data in conjunction with the EJSCREENbatch R package to
obtain sociodemographic characteristics of the populations served by PWSs (U.S. EPA, 2022a).
The EJSCREENbatch R package allows analysts to conduct EJ screening analyses for multiple
geographies using environmental and sociodemographic data from EJScreen and the American
Community Survey. The EPA estimated the rate of exposure to PFAS across demographic
groups using PFAS occurrence data and the sociodemographic characteristics of populations
served with designated service area boundaries. The EPA conducted this analysis using several
thresholds: hypothetical trigger levels set above Method 537.1 detection limits and intended to
reflect a water system's margin of safety below the MCL values (also referred to as baseline
occurrence level for this analysis), UCMR 5 MRLs, and 10.0 ppt. This analysis serves as an
estimate of possible exposure to PFAS levels over these thresholds, as the EPA cannot confirm
that these populations consumed the water at the time of elevated PFAS occurrence at each PWS.
8.3.1 Data Sources and Approach
8.3.1.1 Categorization of Public Water Systems
The EPA designated distinct categories for PWSs based on data availability for PFAS occurrence
and estimated PWS service area boundaries. The agency used two types of PFAS occurrence
data sources in this analysis: (1) simulated PFAS occurrence data for PWSs with sampled PFAS
occurrence data under UCMR 3; and (2) state-collected PFAS occurrence data for PWSs not
sampled under UCMR 3 (U.S. EPA, 2017). PWS service area boundary data are distinguished by
three types: (1) those with predelineated PWS service area boundaries, (2) those where zip codes
served by PWSs were used as a proxy to approximate and delineate PWS service area
boundaries, and (3) those with no available PWS service area boundary information. Table 8-1
describes the characteristics of each of the six distinct PWS categories examined in this analysis.
For the EJ exposure analysis, the EPA focused on reporting results for PWSs in categories 1 and
2, which were sampled for PFAS under UCMR 3. The PWSs in categories 4 and 5 include
systems with state PFAS occurrence data, and the EPA has summarized the results for these
categories in Appendix M. The EPA used data from EJScreen (U.S. EPA, 2022a) and the
89 PWS service area boundaries are defined as the spatial extent of the geographic area served by a PWS.
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American Community Survey along with PWS service area boundary data to characterize the
sociodemographic characteristics of PWSs.
PWSs in categories 1 and 2 account for 252.2 million people served (n = 4,743 PWSs), and
PWSs in categories 4 and 5 account for approximately 2 million people served (n = 736 PWSs).
PWSs in categories 3 and 6 were not included in the EJ exposure analysis, as PWS service area
boundaries or zip codes served by the PWS were unavailable.
Table 8-1: Categorizing of PWSs Based on Data Availability for PFAS Occurrence and
PWS Service Area Boundaries
PWS State PFAS Occurrence Data
PWS Included in UCMR 3 Available and Not Included in
UCMR3
PWS Service Areas Available Category 1 Category 4
PWS Service Area Boundary Category 2 Category 5
Estimates from Zip Codes
No PWS Service Area Information Category 3 Category 6
Available
Abbreviations: PWS - public water system; UCMR - Unregulated Contaminant Monitoring Rule.
8.3.1.2 Data Sources
8.3.1.2.1 PFAS Occurrence
The two data source categories used to derive PFAS occurrence estimates for this analysis are
described in more detail below. All PFAS occurrence data are presented in parts per trillion
(PPt)-
Generally, if a system was sampled for PFAS under UCMR 3, the EPA used simulated
occurrence data that were based on system-specific results. For PWSs in categories 1 and 2 (n =
4,743 PWSs), the EPA simulated PFAS occurrence data using a hierarchical Bayesian model that
was optimized with PFAS occurrence data from UCMR 3 and, where available, state data (see
Cadwallader et al., 2022, and Section 4.44.4 for further description). The EPA calculated the
system-level geometric mean occurrence value for each PWS from the simulated water sample
concentrations. All simulated values (i.e., simulated samples for PWSs in categories 1 and 2)
were above zero because the occurrence model assumes a log-normal distribution for water
concentration. The system-level geometric mean occurrence values for the category 1 and 2
PWSs ranged from 0.01 to 254.65 ppt.
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For other systems, the EPA used state sampling data. The EPA used state monitoring data from
12 states90, which generally conducted nontargeted monitoring (i.e., random sampling) of
finished drinking water for one or more of the four PFAS in this analysis. PWSs that had state
sampling data but were not sampled under UCMR 3 fell into categories 4 and 5 (n = 736). The
EPA calculated the system-level geometric means of measured PFAS water sample
concentrations to characterize PFAS occurrence for each PWS. For this dataset, the agency did
not pursue Bayesian estimation of non-detection concentrations due to a limited sample size and
non-standardized sampling regime. Instead, for these data, the EPA set non-detections to a small
constant, 10 percent of the lowest analyte sample value (i.e., 0.02 ppt for each analyte), before
calculating the system-level geometric mean.91
Among the 12 state occurrence datasets used in this analysis to characterize PFAS occurrence for
category 4 and 5 PWS service areas, the EPA noted that different states utilized various
reporting, quantification, and/or detection limits when analyzing and presenting data, and for
some states, no clearly defined limits were publicly provided as part of the dataset. Further, the
limits often varied within the data for each state depending on the specific PFAS analyte. In
some cases, states reported detection, quantification, or reporting limits and/or presented data at
concentrations below the EPA's final rule detection limits and/or practical quantitation limits
provided in the Federal Register Notice for this final regulatory action. In addition to variable
reporting limits and PFAS analytes evaluated, sample collection routines across state datasets
also lacked uniformity. For more information on the collection and analysis of occurrence data,
see U.S. EPA(2024g).
For both simulated occurrence data and state-sampled occurrence data, system-level geometric
means were calculated to represent a typical concentration of a single sample for each PFAS
analyte in a system. The concentrations of samples are log-normally distributed for all four
PFAS analytes (PFOA, PFOS, PFHxS, PFHpA), meaning that while most samples have low
concentrations, some may have much higher concentrations.
8.3.1.2.2 PWS Service Area Boundaries
For CWSs and NTNCWSs that had PFAS occurrence data sampled under UCMR 3 or PFAS
occurrence data collected by states, the EPA acquired or estimated service area boundaries. Since
TNCWSs have changing populations throughout the year, they were not included in this
analysis. Data were categorized by the availability of PWS service areas, those with
predelineated PWS service areas (categories 1 and 4), and those where zip codes served by
PWSs were used to approximate PWS service area boundaries (categories 2 and 5). When
available, predelineated PWS service areas were prioritized over zip code-approximated PWS
service area boundaries. The EPA used the federal version of the SDWIS/Fed to inform the type
of water system (e.g., CWS, NTNCWS), population served, identify Native American-owned
90 States include: Alabama, Colorado, Illinois, Kentucky, Massachusetts, Michigan, New Hampshire, New Jersey, North Dakota,
Ohio, South Carolina, and Vermont.
91 The EPA evaluated the difference between using 10 percent (0.02 ppt) and 50 percent (1 ppt) of the minimum reported sample
concentration for all analytes. The difference in population estimates from this change was less than 0.5 percent for all analytes.
10 percent of the minimum reported value was used in the analysis (0.02 ppt).
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PWSs, and determine activity status for PWSs included in the analysis. Only active systems, as
identified in SDWIS/Fed fourth quarter 2021, were included.
For predelineated PWS service area boundaries, the EPA aggregated spatial data from a variety
of sources spanning multiple file formats into one ESRI file geodatabase.92 Data sources are
provided in Table 8-2.
92 File formats included: ESRI ArcGIS Online (AGOL) layers, shapefiles, and GeoJSON.
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Table 8-2: Data Sources for Predelineated PWS Service Areas
APRIL 2024
Accessed Through State Sources or EPA Correspondence
State
Source Name
Link
Date
CO
State of Colorado - Water District
httDs://data.colorado.20v/Water/Water-District-
Accessed
Boundaries
Boundaries/82ke-a8t2
1/26/2022
CA
State of California - Division of
Drinking Water, California Water
Resources Control Board
littDs://eisDiiblic.waterboards.ca.eov/Dortal/liQiTie/ite
m.html?id=fbba842bfl34497c9d611ad506ec48cc
Accessed
1/31/2022
NJ
EPA correspondence
EPA Office of Ground Water and Drinking Water
Accessed
1/31/2022
httDs://ca talog.newmexicowaterdata.org/dataset/5d06
NM
State of New Mexico - water data
9bbb-lbfe-4c83-bbf7-
3582a42fce6e/resource/037d915d-4a28-4c39-9922-
3556ec492698/download/nm dws areas.zio
Accessed
1/26/2022
NY
State of New York - Department
httDs://water. nv.gov/doh2/aDDlinks/wateraual/assets/
Accessed
of Health
PWS GeoJson3.ison
1/31/2022
htfDs://www.Qwrb.Qk.gov/niaDs/data/lavers/Water%2
OK
State of Oklahoma - Water
OSuddIv/ws system service areas.htm;
Accessed
Resources Board
httDs://owrb. maDs.arcgis.com/aDDs/webaDDviewer/in
dex.html?id=68c5f3fd492a43ee8386f39a80f88afb
1/26/2022
httDs://newdata-DadeD-
State of Pennsylvania -
1.ODendata.arcgis.com/datasets/Diiblic-water-
Accessed
1/12/2022
PA
Department of Environmental
svstems-Diiblic-water-siiDDlier-service-
Protection
a reas/exolo re? local io n=40.917958%2C-
77.621150%2C8.24
RI
EPA correspondence
EPA Office of Ground Water and Drinking Water
Accessed
1/31/2022
Accessed through EPA ArcGIS Online Portal
State
Source
Link
Date
AR
EPA ArcGIS - Portal
AZ
EPA ArcGIS - Portal
CT
EPA ArcGIS - Portal
KS
EPA ArcGIS - Portal
httDs://eDa.maDs.arceis.com/home/item.html?id=59e
Accessed
MO
EPA ArcGIS - Portal
b7810caa044678f1e26e637b4fa7 9
12/7/2021
MS
EPA ArcGIS - Portal
TX
EPA ArcGIS - Portal
UT
EPA ArcGIS - Portal
NC
EPA ArcGIS - Portal
httos://www.nconemaD.eov/search?erouDlds=9eb59a
7bdc8e4bdf8cbe2488c8584552
Accessed
1/10/2021
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Under UCMR 3 and 4, PWSs sampled were asked to report U.S. Postal Service zip code(s) for
all areas being served water by a PWS. As such, when pre-delineated PWS service area
boundaries were unavailable, the EPA used zip codes served by PWSs to delineate approximated
boundaries using the following steps:
• The EPA joined zip codes served—as specified for PWSs in UCMR 3 (U.S. EPA, 2017)
and UCMR 4 (U.S. EPA, 2022c)—to a zip code polygon layer that represented postal
service delivery areas.
• The EPA projected zip codes served by PWSs.
• In cases where zip codes did not have polygons (i.e., zip codes for post offices and large
volume mail customers), to map these zip codes as approximate service areas, the EPA
selected and overlaid zip code points for each service area with zip code polygons to
select the polygon at that location. Then, the EPA merged and dissolved all zip codes
(both point- and polygon-based) to map each service area.
• The EPA aggregated all zip code polygons served by each PWS into one boundary
representative of PWS service area boundaries.
• In instances where one zip code was served by multiple PWSs, the EPA included the zip
code boundary in all corresponding PWS service area boundaries. For example, if one zip
code was served by two PWSs, both PWS service area boundaries would contain the
same zip code region represented in their boundaries. In some cases, this resulted in the
EPA referencing the same population demographic composition for multiple systems;
however, the populations were not double-counted because population-served data were
obtained from SDWIS/Fed and were unique to each PWS.
PWSs with pre-delineated PWS service areas (categories 1 and 4), account for 38.4 percent of all
PWSs included in the analysis. PWSs with zip code delineated boundaries (categories 2 and 5),
account for 61.6 percent of all PWSs included in the analysis.
Because there is greater accuracy with the predelineated PWS service areas, and to reduce
double-counting of affected populations, the EPA removed the portion of the zip code
boundaries that were already accounted for within the predelineated PWS service area
boundaries.
For example, in rural areas, the zip code boundaries can be relatively large and therefore overlap
with predelineated PWS service area boundaries. To avoid redundancy and reduce bias from
potentially counting populations outside a service area in the demographic composition of a
system, the EPA used the following approach:
• The EPA used predelineated PWS service area boundaries (including overlap93) when
available.
93 For PWSs with predelineated PWS service area boundaries, the EPA conducted a sensitivity analysis of the results of the
EPA's EJ exposure analysis to evaluate the impact of retaining PWS boundaries including overlapping areas versus removing
overlapping boundaries. The impact on the results of the EPA's EJ exposure analysis showed very few differences across the two
approaches. As such, the EPA used service area boundaries with overlapping areas included.
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• If predelineated PWS service areas were not available, the EPA used zip code-
approximated PWS service area boundaries (as provided in UCMR 3 and UCMR 4).
The EPA carved out or removed predelineated PWS service area boundaries from the zip code-
approximated PWS service area boundaries to reduce the risk of double-counting the
demographic composition of the populations served.
The EPA used predelineated PWS service area boundaries and zip code-approximated PWS
service area boundaries as inputs to the EJSCREENbatch R package to estimate the
sociodemographic characteristics of PWS service areas included in the analysis (see Section
8.3.1.2.3 for more detail on this process) (U.S. EPA, 2022a). The population served counts were
obtained from SDWIS/Fed for each PWS. Further description of the population-served data and
sociodemographic characteristics of the population served by PWS service areas is provided in
Section 8.3.2.1 and in Appendix M.
8.3.1.2.2.1 Categories 1 and 2
Categories 1 and 2 contained PWSs that had sampled PFAS occurrence data from UCMR 3.
Category 1 (n = 1,707 PWSs) comprised PWSs that had predelineated PWS service area
boundaries, whereas category 2 (n = 3,036 PWSs) comprised PWSs that had zip code-
approximated PWS service area boundaries.
The exposure analysis included service areas for 1,707 category 1 PWSs and 3,036 category 2
PWSs, for a total of 4,743 PWSs. There were 4,920 PWSs that conducted PFAS sampling under
UCMR 3, and categories 1 and 2 PWSs accounted for approximately 96 percent of all PWSs that
participated in UCMR 3. Of the 4,920 PWSs that participated in UCMR 3, 10 PWSs did not have
predelineated PWS service area boundaries or zip code-served data available to approximate
PWS service area boundaries. Systems were excluded from the analysis if they were classified as
"inactive" in SDWIS/Fed (67 PWSs). Additionally, PWSs could not be evaluated if there were
errors processing the EJSCREENbatch R package (100 PWSs). The majority of these systems
are located in US territories.94 In such instances, the EJSCREENbatch R package did not provide
sociodemographic characteristics for a given PWS service area.
Category 1 and 2 PWSs account for 252.2 million people served, or approximately 76 percent of
the U.S. population. However, the subset of category 1 and 2 PWSs captured in the analysis
represented roughly 3 percent of active PWSs.95
8.3.1.2.2.2 Categories 4 and 5
The EPA used state PFAS occurrence data for PWSs in categories 4 and 5 because these systems
did not monitor for PFAS under UCMR 3. Category 4 (n = 440 PWSs) included PWSs that had
predelineated PWS service areas, whereas category 5 (n = 296 PWSs) included PWSs that had
zip code-approximated PWS service area boundaries.
94 These included 69 PWSs in Puerto Rico, 3 PWSs in the Virgin Islands, 2 PWSs in Guam, 1 PWS in American Samoa, and 1
system in the Northern Mariana Islands.
95 The number of active public water systems was retrieved from SDWIS/Fed fourth quarter 2021.
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The EJ exposure analysis includes PWS service areas for 440 category 4 PWSs and 296 category
5 PWSs. Category 4 and 5 PWSs account for approximately 5 percent of all PWSs with state
PFAS sample occurrence data. 1,143 PWSs with state PFAS occurrence data have PFAS
occurrence data available in UCMR 3, and therefore are included in the analysis under categories
1 and 2. In addition, the EPA included PWSs with state PFAS occurrence data in the analysis
only if finished water samples were available for at least one of the four PFAS analytes. The
agency could not include many of the PWSs with state PFAS occurrence data because
predelineated PWS service areas or zip code approximated PWS service area boundaries were
not available.
Category 4 and 5 PWSs account for 2 million people served, or approximately 0.6 percent of the
U.S. population. The EPA summarized the results for these PWSs in Appendix M.
8.3.1.2.2.3 Categories 3 and 6
The EPA did not include category 3 and 6 PWSs in the EJ exposure analysis because
predelineated PWS service areas and information containing zip codes served by PWSs were
both unavailable.
8.3.1.2.3 Sociodemographic Data
The EPA used version 2.0.1 of the agency's EJSCREENbatch R package to characterize the
sociodemographic makeup of populations living in PWS service areas, as described in Section
8.3.1.2.2 (U.S. EPA, 2022a). The EJSCREENbatch R package offers functions to extract and
process Census block group EJScreen data within user-provided geographies. This analysis relies
on 2021 EJScreen data, which corresponds to demographic estimates from the U.S. Census
Bureau's ACS 2015-2019 five-year sample (U.S. EPA, 2022a). EJScreen data are input into a
function that spatially apportions (i.e., using areal apportionment) data to service areas using a 1
km resolution raster population dataset from NASA's Socioeconomic Data and Applications
Center.
The EPA used the following data outputted from the EJSCREENbatch package on the race,
ethnicity, and poverty status of populations served by the PWSs:
• Race: Percent non-Hispanic American Indian or Alaska Native; percent non-Hispanic
Asian; percent non-Hispanic Black or African American; percent non-Hispanic White,
and percent non-Hispanic Pacific Islander.96
• Ethnicity: Percent Hispanic.
• Income: Percent of the population below twice the Federal poverty level; percent of the
population above twice the Federal poverty level.
In addition, the agency identified PWSs that are Native American-owned and within the EPA's
tribal primacy program using SDWIS/Fed data (U.S. EPA, 2021h).
96 In an effort to avoid double counting populations, race/ethnicity categories reported here do not account for people who
selected "some other race alone" or "two or more races" in the ACS.
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Note that sociodemographic information used for the EPA's EJ exposure analysis includes
additional demographic groups from those used in the EPA's benefits analysis, which relies on
SDWIS/Fed and race/ethnicity-specific population estimates from the U.S. Census Bureau
(2020a). Population estimates from the U.S. Census Bureau are available at the county level, but
more granular location-specific population and demographic information was needed for the
EPA's EJ exposure analysis. In particular, this analysis presents non-Hispanic American Indian
or Alaska Native, non-Hispanic Asian, and non-Hispanic Pacific Islander instead of the Other
demographic category employed in Section 8.4. Both analyses include the demographic
categories non-Hispanic Black, Hispanic, and non-Hispanic White. For further information on
the use of U.S. Census Bureau population proportions in the EPA's benefits analysis, see
Appendix B.
Based on public comment provided on this proposed rule, the EPA has disaggregated the Asian
and Pacific Islander demographic group into two separate categories for the final rule analysis;
one representing Asian populations and the other Pacific Islander populations. The EPA
disaggregated these demographic groups to ensure the prior aggregated category for Asian and
Pacific Islander populations would not mask exposures and impacts specific to various ethnic
subpopulations that fall under the broader Asian and Pacific Islander designation. These
subpopulations vary in language, culture, and historic, social, economic, and environmental
experiences; these differences contribute to unique social determinants of health, which could
lead to disparate environmental exposures, impacts, and health outcomes (Look et al, 2020;
Bhakta 2022). An aggregate Asian and Pacific Islander demographic group that encapsulates
these various subpopulations may obscure possible disparities that exist across subpopulations
(Quint et al, 2021). For the final rule analysis, the EPA disaggregated this group to investigate
such possible disparities among the diverse subpopulations.
8.3.1.3 EJ Exposure Analytic Approach
The EPA conducted a baseline analysis of populations served by PWS service areas in categories
1 and 2 to evaluate the demographic characteristics of systems exposed to PFAS concentrations
above a baseline set of thresholds and two hypothetical regulatory thresholds.
For purposes of this baseline analysis, the EPA assumed the following baseline thresholds are
intended to reflect trigger levels of one-half of the MCL values for PFOA, and PFOS. For
consistency, the EPA also applied these baseline thresholds for PFHxS and PFHpA. Note that the
following values are slightly higher than Method 537.1 detection limits (U.S. EPA, 2018):97'98'99
97 There are no detection limits reported for Method 533 (U.S. EPA, 2019b).
98 The EPA used these detection limits solely as baseline thresholds for purposes of its EJ analysis. The EPA has defined the Rule
Detection Limit for purposes of consideration of monitoring data to determine monitoring schedules as 1/2 the MCL for PFOA
and PFOS, or 1.3 ppt. Refer to Sections VI, VIII, and IX of the federal register notice for this proposed regulatory action for
further discussion on the EPA's analytical methods and the determination of practical quantitation limits (PQLs).
99 As noted in Section 8.3.1.2.1, different states utilized various reporting, quantification, and/or detection limits when analyzing
and presenting data, and for some states, no clearly defined limits were publicly provided as part of the dataset. Further, the limits
often varied within the data for each state depending on the specific PFAS analyte. In some cases, states reported detection,
quantification, or reporting limits and/or presented data at concentrations below the EPA's proposed rule detection limits and/or
practical quantitation limits provided in the federal register notice for this proposed regulatory action. For more information on
the collection and analysis of occurrence data, see U.S. EPA (2022h).
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• PFHpA: 2 ppt
• PFHxS: 2 ppt
• PFOS: 2 ppt
• PFOA: 2 ppt
The EPA also evaluated the rate of exposure using two hypothetical regulatory thresholds: (1)
the UCMR 5 MRL values for each PFAS analyte, and (2) 10.0 ppt. For the purpose of this
analysis, these values are assumed to be individual regulatory thresholds for each contaminant.
The EPA notes that while these thresholds are not exactly set at the final or regulatory alternative
MCL values, the EPA began this analysis prior to refinement of those regulatory options. This
analysis is not intended to determine the demographic breakdown of costs and benefits expected
to result from the final rule and alternatives; rather, this analysis determines whether
overburdened communities are disproportionately exposed to PFAS over baseline conditions and
these hypothetical thresholds. The UCMR 5 MRL values for PFOA, PFOS, PFHpA, and PFHxS
are as follows:
• PFHpA: 3 ppt
• PFHxS: 3 ppt
• PFOS: 4 ppt
• PFOA: 4 ppt
The EPA compared the estimated population served in each demographic group anticipated to
experience reductions in PFAS exposure under each hypothetical regulatory threshold to the total
population served across all demographic groups. This analysis seeks to answer the following
question: When PFAS occurs in drinking water over a certain threshold, will overburdened
communities be disproportionately exposed to PFAS compared to the total population that is
exposed to PFAS over the same threshold?
As described above, the EPA's EJ exposure analysis for the final rule uses data from EJScreen
and the American Community Survey to examine anticipated exposure above set baseline and
theoretical regulatory thresholds using system-level mean occurrence data. As the literature
shows, the degree to which a community experiences PFAS exposure above a specific threshold
can vary. As such, the EPA also characterized population-weighted mean concentrations of
PFAS to evaluate the extent to which the levels of potential exposure correlate with community
characteristics.
8.3.2 EJ Exposure Analysis Results
This section describes the demographic characterization of category 1 and 2 PWS service areas
in the baseline as well as the results of the analysis exploring the EJ implications of two
hypothetical regulatory thresholds. The EPA focused on category 1 and 2 PWS service areas due
to the availability of spatial boundaries (from both predelineated PWS service area boundaries
and zip code-approximated PWS service area boundaries) and PFAS occurrence data from
UCMR 3. Results from categories 4 and 5 are reported in Appendix M.
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8.3.2.1 Demographic Profile of PWS Service Areas
Table 8-3 summarizes the breakdown of category 1 and 2 PWS service areas by state and by
size, where small systems are those serving fewer than or equal to 10,000 people. In total, these
PWSs account for roughly 252 million people served, or approximately 76 percent of the U.S.
population. Category 1 and 2 PWSs span all states in the continental U.S. Category 1 and 2
PWSs included in this analysis capture roughly 3 percent of active PWSs. Among the 3 percent
of active PWSs captured by the EPA's analysis (i.e., category 1 and 2 PWSs), there are 26 PWSs
within the EPA's tribal primacy program, serving a population of approximately 306,000 people.
Additionally, approximately 17 percent of the systems are defined as small (serving fewer than
10,000 people), accounting for 1.3 percent of the total population served.
Table 8-4 summarizes the demographic profile for category 1 and 2 PWS service areas and
compares it to the demographic characteristics of the overall U.S. population. There are slight
differences in the demographic characteristics of the population served by PWS service areas
included in the EPA's analysis compared to the overall U.S. population, with percent differences
all being less than +/- 4.1 percent. The population served by these PWSs has slightly higher
percentages of Asian (+0.8%) and Black (+1.5%) populations compared to the overall U.S.
population. The percentage of American Indian or Alaska Native and Pacific Islander
populations is consistent with the percent of these populations across the U.S. The Hispanic
population served by category 1 and 2 PWSs is slightly higher (+2.3%) and the non-Hispanic
White population is lower (-4.1%) than that of the overall U.S. population. When examining
income demographics, Table 8-4 shows that category 1 and 2 PWSs have a slightly higher
percentage of populations with income below twice the Federal poverty level (+1.4%) and a
slightly lower percentage of population with income above twice the Federal poverty level (-
1.4%) compared to the overall U.S. population.
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Table 8-3: Number of Category 1 and 2 PWSs and Populations Served by Size and State
State
Number of Total
Service Areas
Number of
Small Service
Areas
Total Population
Served3
Population Served in
Small Systems3
Population Served in
Medium and Large
Systems
Tribal Service Areas
26
12
305,846
36,235
269,611
Alabama
124
19
4,488,042
86,106
4,401,936
Arizona
75
14
5,897,987
52,559
5,845,428
Arkansas
63
18
1,786,895
81,217
1,705,678
California
451
38
36,995,867
149,032
36,846,835
Colorado
81
13
5,298,922
54,590
5,244,332
Connecticut
42
6
2,457,248
13,799
2,443,449
Delaware
13
3
642,261
13,535
628,726
District of Columbia
3
0
676,068
0
676,068
Florida
259
28
19,366,933
111,293
19,255,640
Georgia
124
20
8,752,508
77,382
8,675,126
Idaho
26
6
991,096
16,854
974,242
Illinois
252
32
9,702,346
120,173
9,582,173
Indiana
101
20
3,792,604
63,428
3,729,176
Iowa
57
15
1,810,021
52,241
1,757,780
Kansas
45
14
1,999,477
50,363
1,949,114
Kentucky
119
26
3,599,670
172,624
3,427,046
Louisiana
88
24
3,363,018
84,804
3,278,214
Maine
16
3
411,385
16,456
394,929
Maryland
39
8
4,980,513
20,084
4,960,429
Massachusetts
171
15
6,236,022
74,117
6,161,905
Michigan
158
25
5,895,618
122,403
5,773,215
Minnesota
98
14
3,478,561
40,952
3,437,609
Mississippi
77
24
1,399,379
86,698
1,312,681
Missouri
86
21
3,879,698
87,393
3,792,305
Montana
15
6
416,576
10,070
406,506
Nebraska
21
7
1,136,091
12,642
1,123,449
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Table 8-3: Number of Category 1 and 2 PWSs and Populations Served by Size and State
State
Number of Total
Service Areas
Number of
Small Service
Areas
Total Population
Served3
Population Served in
Small Systems3
Population Served in
Medium and Large
Systems
Nevada
16
4
2,826,471
10,200
2,816,271
New Hampshire
23
5
570,449
10,907
559,542
New Jersey
173
17
8,123,044
54,089
8,068,955
New Mexico
28
5
1,442,144
7,457
1,434,687
New York
169
32
15,965,142
98,790
15,866,352
North Carolina
147
21
7,307,497
82,447
7,225,050
North Dakota
12
3
425,637
4,903
420,734
Ohio
184
28
8,971,538
113,929
8,857,609
Oklahoma
66
16
2,533,092
57,411
2,475,681
Oregon
65
11
2,875,275
33,730
2,841,545
Pennsylvania
174
34
9,402,219
130,731
9,271,488
Rhode Island
17
2
934,307
12,485
921,822
South Carolina
80
9
3,475,385
46,773
3,428,612
South Dakota
18
5
458,464
17,065
441,399
Tennessee
137
16
6,143,130
86,951
6,056,179
Texas
383
92
21,617,805
370,158
21,247,647
Utah
62
8
2,595,756
32,847
2,562,909
Vermont
12
6
142,888
23,438
119,450
Virginia
80
13
6,263,605
48,692
6,214,913
Washington
132
20
6,304,525
70,712
6,233,813
West Virginia
32
8
844,387
30,705
813,682
Wisconsin
92
18
2,920,851
82,496
2,838,355
Wyoming
11
2
268,828
3,341
265,487
TOTAL
4,743
806
252,173,091
3,137,307
249,035,784
Abbreviations: PWS - public water system.
Note:
Population served by PWSs was obtained from SDWIS/Fed fourth quarter 2021. Small systems include those serving fewer than or equal to 10,000 people. Medium and large
systems serve populations more than 10,000 people.
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Table 8-4: Population Served by Category 1 and 2 PWSs Compared to Percent of U.S. Population by Demographic Group
Race/Ethnicity Income
JN on-
Hispanic
American
Indian or
Alaska
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Native
Population
Served
1,140,247
16,168,317
34,581,847
366,278
51,710,333
141,147,967
78,625,592
173,547,499
252,173,091
Percent of
Total
Population
Served
0.5%
6.4%
13.7%
0.10%
20.50%
56.00%
31.2%
68.8%
100.0%
U.S.
Population
Percent by
Demographic
Group3
0.6%
5.6%
12.2%
0.20%
18.20%
60.10%
29.8%
70.2%
-
Percent
Difference
Between
Population
Served and
U.S.
Population
-0.1%
0.8%
1.5%
-0.10%
2.30%
-4.10%
1.4%
-1.4%
-
Note:
aU.S. population estimates were obtained from the U.S. Census Bureau's American Community Survey 2016-2020 five-year estimates.
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8.3.2.2 Exposure Analysis Results
8.3.2.2.1 Baseline Scenario
To evaluate impacts of the final rule on population groups of concern, the percent of a specific
demographic group with modeled PFAS above baseline thresholds needs to be presented in
relation to another group, typically referred to as a comparison group. The way in which the
comparison group is defined can have important implications for identifying differences in
potential exposure across population groups of concern in an EJ analysis. The agency's
Technical Guidance for Assessing Environmental Justice in Regulatory Analysis notes that the
comparison group can be defined as individuals with similar socioeconomic characteristics
across different areas in the state, region or nation (i.e., within-group comparison) or as affected
individuals with different socioeconomic characteristics (i.e., across-group comparison) (U.S.
EPA, 2016h).
For this final regulatory action, the EPA examines individuals served by PWSs with modeled
PFAS occurrence above the baseline concentration threshold or a specific hypothetical
alternative policy threshold. The EPA presents the total affected population as a possible metric
of comparison, noting however that each affected demographic group is reflected also within the
total affected population. It is possible that the EPA understates the magnitude of
disproportionate baseline exposure to PFAS for populations of concern by using the total
affected population as the basis of comparison. For this reason, the EPA also makes comparisons
between affected population groups of concern and the mutually exclusive affected non-Hispanic
White population or the affected population with income above twice the Federal poverty level.
As currently defined, race and ethnicity classifications are presented in a disaggregated form
such that racial categories include individuals who identify as non-Hispanic, while the Hispanic
category includes individuals of any race who identify with Hispanic ethnicity. In aggregate,
those who identify (1) as a race other than White and/or (2) identify with Hispanic ethnicity are
considered "people of color" when considering potential EJ concerns. The EPA has therefore
included the category non-Hispanic White in the analysis, as this category does not include
individuals who identify as a race or ethnicity included within "people of color".
The results of the EPA's analysis of baseline exposure are shown in Table 8-5 and Table 8-6.
Table 8-5 summarizes the population served by category 1 and 2 PWSs with modeled PFAS
occurrence above baseline thresholds based on a trigger level of 2 ppt for each PFAS analyte,
which is slightly above the Method 537.1 detection limits. The second set of rows in Table 8-5
summarizes the percentage of each demographic group with modeled PFAS occurrence above
these baseline thresholds. Table 8-6 shows average population-weighted PFAS concentrations
across demographic groups. In Table 8-5, percentages are bolded and italicized when the
percentage of the population in a specific demographic group with modeled PFAS above the
baseline threshold is greater than the percentage of the total population across all demographic
groups exposed to modeled PFAS above this threshold (right-hand column). In, the highlighted
numbers represent where percentages of the population served in a particular demographic group
are more than 1 percentage point greater than percentages of the total population. In Table 8-6,
highlighted cells represent whether the average concentration for a given demographic group is
higher than the average for the total population served across all demographic groups (right-hand
column). Higher percentages or concentrations indicate higher PFAS exposure for a given
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demographic group compared to the percentage of the population served across all demographic
groups. Between 4.7 percent and 10.8 percent of the total population served by category 1 and 2
PWS service areas, depending on the analyte, are exposed to modeled PFAS occurrence above
baseline thresholds based on a trigger level of 2 ppt for each PFAS analyte.
The following are findings from the EPA's baseline EJ exposure analysis:100
• The percentage of Hispanic populations served with exposure to PFAS above baseline
thresholds is higher across all four PFAS analytes compared to the percentage of the total
population served across all demographic groups with anticipated PFAS exposure above
baseline thresholds. All percentages are more than 1 percentage point greater than
percentages exposed across the total population, ranging from 1.3 - 2.6 percentage points
higher. These percentages are also higher than those of non-Hispanic White populations
by 2.1 - 3.5 percentage points.
• The percentage of non-Hispanic Black populations served with exposure to PFAS above
baseline thresholds is higher across all four PFAS analytes compared to the percentage of
the total population served across all demographic groups. Exposure is at least one
percentage point greater for PFOA and PFOS and less than 1 percentage point greater for
PFHxS and PFHpA, with a range of 0.3 - 1.6 percentage points difference. The
percentage of non-Hispanic Black populations exposed is also greater than the percentage
of non-Hispanic White populations for all four PFAS analytes. The difference in
percentage exposed between Black and non-Hispanic White populations ranges from 0.9
- 2.4 percentage points.
• The percentage of non-Hispanic American Indian or Alaska Native populations served
have greater PFHxS exposure above its baseline threshold compared to the total
population served across all demographic groups, and exposures to PFOS, PFHpA, and
PFOA are similar to or less than the percentages exposed across all demographic groups.
Exposure to PFHxS, PFHpA, and PFOA exposure above the baseline thresholds is higher
for non-Hispanic American Indian or Alaska Native populations in comparison to the
non-Hispanic White population by 0.3 to 1.8 percentage points.
• The percentage of non-Hispanic Asian populations served with exposure above baseline
thresholds is comparable or less than the percentages of the population served across all
demographic groups. When compared to non-Hispanic White populations, the percentage
of non-Hispanic Asian populations served with exposure above baseline thresholds is 1
percentage point higher for PFOS but less than the exposure for non-Hispanic White
populations for PFHxS, PFHpA, and PFOA.
• Other demographic groups, including non-Hispanic Pacific Islanders and those
representing relative income status, are anticipated to experience percentages of PFAS
occurrence above baseline thresholds similar to (within 0.5%) or less than the percentage
of the population served across all demographic groups facing exposure above baseline
thresholds.
100 Although differences in anticipated exposure between a particular demographic group and the entire sample population are
<5%, all results are reported in the EPA's summary of results regardless of magnitude.
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Table 8-6 characterizes population-weighted mean concentrations of PFAS by demographic
group. In addition to having a higher percentage of populations served by PWSs with
concentrations of PFAS above baseline thresholds, Hispanic and non-Hispanic Black populations
are also exposed to higher mean concentrations than is typical for the total population served and
average population-weighted exposures for non-Hispanic White populations. On average,
Hispanic populations are exposed to 0.1-0.2 ppt more of each of the four PFAS analytes
examined than non-Hispanic White populations served. Differences in average exposure between
non-Hispanic Black populations and non-Hispanic White populations are close to or less than 0.1
ppt.
The results also suggest that non-Hispanic American Indian and Alaska Native as well as non-
Hispanic Pacific Islander populations have greater exposure to PFHxS and PFOA in comparison
to the total population served and the non-Hispanic White population. Non-Hispanic Pacific
Islander populations have the highest average exposures to PFHxS and PFOA of any
demographic group, while Hispanic populations are the most highly exposed to PFOS and
PFHpA. The findings of differential population-weighted average exposure for non-Hispanic
American Indian or Alaska Native as well as Pacific Islander populations was not observed in
Table 8-5 except with respect to PFHxS for non-Hispanic American Indian or Alaska Native
populations; this difference in results suggests that these populations may be exposed to higher
average PFHxS and PFOA concentrations when exposure does occur, however these populations
are not always more likely to be served by public water systems with above baseline
concentrations of PFAS.
In addition, low-income populations are exposed to higher average concentrations of PFHxS,
PFHpA, and PFOA in comparison to the total population served and to populations with income
above twice the Federal poverty level, although differences are all less than 0.1 ppt.
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Table 8-5: Baseline Scenario: Population Served by Category 1 and 2 PWS Service Areas Above Baseline Thresholds and as
a Percent of Total Population Served
Race/Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below Twice
the Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Population Served Above Baseline Threshold
PFOS
96,464
1,761,960
4,130,816
25,193
6,263,677
14,156,536
8,035,262
19,075,300
27,110,562
PFHxS
79,130
802,126
2,303,033
20,823
4,458,586
7,184,715
5,095,233
10,132,796
15,228,029
PFHpA
52,579
602,837
1,732,707
12,312
3,459,309
5,729,697
3,670,395
8,188,555
11,858,950
PFOA
88,674
1,069,233
3,423,882
19,851
5,202,088
10,561,078
6,651,205
14,211,140
20,862,345
Population Served Above Baseline Threshold as a Percent of Total Population Served
PFOS
8.5%
10.9%
11.9%
6.9%
12.1%
10.0%
10.2%
11.0%
10.8%
PFHxS
PFHpA
6.9%
4.6%
5.0%
3.7%
6.7%
5.0%
5.7%
3.4%
8.6%
6.7%
5.1%
4.1%
6.5%
4.7%
5.8%
4.7%
6.0%
4.7%
PFOA
7.8%
6.6%
9.9%
5.4%
10.1%
7.5%
8.5%
8.2%
8.3%
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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Table 8-6: Modeled Average PFAS Concentrations (ppt) by Demographic Group in the Baseline, Category 1 and 2 PWS
Service Areas
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
PFOS
0.97
1.01
1.05
0.90
1.15
0.96
1.01
1.02
1.01
PFHxS
0.81
0.58
0.64
0.86
0.75
0.59
0.64
0.62
0.63
PFHpA
0.53
0.50
0.55
0.51
0.64
0.50
0.54
0.53
0.53
PFOA
1.05
0.85
1.03
1.14
1.11
0.89
0.99
0.94
0.96
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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8.3.2.2.2 Hypothetical Regulatory Scenario #1: UCMR 5 MRLs
Table 8-7 and Table 8-8 summarize the results for population served by category 1 and 2 PWSs
with PFAS occurrence above UCMR 5 MRL values. For this hypothetical regulatory scenario,
the EPA assumed that PWSs with PFAS system-level means above the MRL value will reduce
PFAS levels to comply with the final rule. The first set of rows in Table 8-7 summarizes
populations served by category 1 and 2 PWS service areas with modeled PFAS occurrence above
the UCMR 5 MRLs. The second set of rows provides these estimates as a percentage of the total
population served by PWSs included in the EPA's analysis. Table 8-8 summarizes the
population-weighted average reductions in PFAS assuming all PWSs reduce their concentrations
to UCMR 5 MRL levels.
In Table 8-7, percentages are bolded and italicized when the percentage of the population in a
specific demographic group with PFAS occurrence above the MRL value is greater than the
percentage of the total population across all demographic groups with PFAS occurrence above
the MRL (right-hand column). In Table 8-7, the highlighted numbers represent where
percentages of the population served in a particular demographic group are more than 1 percent
greater than percentages of the total population. In Table 8-8, highlighted cells represent whether
the average reduction in PFAS concentrations for a given demographic group is higher than the
average for the total populations served across all demographic groups (right-hand column). The
percentages that are bolded, italicized, or highlighted indicate higher PFAS exposure above the
MRL for a given demographic group; the EPA anticipates that relatively higher reductions in
PFAS exposure will accrue to these demographic groups under this hypothetical regulatory
scenario compared to the percentage of the population across all demographic groups. The EPA
provides additional details on anticipated exposure above UCMR 5 MRL values in Appendix M.
Between 3 percent and 5.4 percent of the population served by category 1 and 2 PWS service
areas, depending on the PFAS analyte, are exposed to modeled PFAS concentrations above the
UCMR 5 MRL values for PFOS, PFOA, PFHpA, and PFHxS. Under this hypothetical regulatory
scenario, where MCLs are assumed to be equal to UCMR 5 MRL values, the EPA expects these
populations to experience reductions in PFAS exposure to below the hypothetical regulatory
thresholds. The EPA's analysis of the demographic distribution of anticipated health benefits and
household costs due to reductions in PFAS exposure resulting from the final PFAS rule and
regulatory alternatives is discussed in Section 8.4.2.
Based on this analysis, non-Hispanic American Indian or Alaska Native, non-Hispanic Black,
Hispanic, and low-income populations are estimated to face higher rates of system-level mean
PFAS exposure above UCMR 5 MRL values compared to rates of exposure over these
thresholds for the total population served across all demographic groups. The differences are
even greater when compared to the rates of exposure over these thresholds for non-Hispanic
White populations. Specifically, non-Hispanic American Indian or Alaska Native populations
served have higher exposure above the UCMR 5 MRL values for PFOA, PFHxS, and PFHpA
compared to the percent of the population served across all demographic groups by 0.5 to 1.1
percentage points. These differences in exposure are 1.1 to 2.1 percentage points greater when
compared to non-Hispanic White populations. Non-Hispanic Black populations served have
higher exposure above the UCMR 5 MRL values for all four PFAS analytes compared to the
percent of the total population served across all demographic groups, although these differences
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are all less than 0.5 percentage points. In comparison to the non-Hispanic White population, non-
Hispanic Black populations have higher exposure above the UCMR 5 MRLs for all PFAS
analytes examined, with a range of 0.7 to 1.2 percentage points greater exposure. Hispanic
populations served have higher exposure above the UCMR 5 MRL values across all four PFAS
analytes compared to the percent of the total population served across all demographic groups.
The percent of Hispanic populations served with exposure above the UCMR 5 MRL values is at
least double the percent of non-Hispanic White populations with exposure above the UCMR 5
MRL values for PFHxS and PFHpA and at least 2 percentage points greater for all PFAS
analytes. This is the most notable difference in exposure for a single demographic group. The
percent differences observed suggest that, in this analysis, Hispanic populations are estimated to
face the highest baseline levels of exposure to all four PFAS analytes compared to the entire
sample population across all demographic groups. As such, these Hispanic populations could
also be expected to experience the greatest reductions in PFAS exposure under this hypothetical
regulatory scenario. Populations served with income less than twice the Federal poverty level
have higher rates of PFAS exposure above the UCMR 5 MRL values across all four PFAS
analytes compared to the percent of the population served across all demographic groups.
Exposure percentages for populations served with income less than twice the Federal poverty
level are also higher than exposure for populations with income above twice the Federal poverty
level for all four PFAS analytes, however differences are generally relatively small at between
0.2 - 0.6 percentage points.
Table 8-8 displays population-weighted reductions in PFAS exposure in a hypothetical
regulatory scenario where system-level means are reduced to UCMR 5 MRLs to comply with the
final rule. Hispanic populations see the greatest reductions in concentrations for PFOS and
PFHpA in this hypothetical regulatory scenario, which is consistent with higher levels of
exposure for this group observed in Table 8-7. However, despite having a lower percentage of
populations affected than Hispanic populations, non-Hispanic Pacific Islander populations see
the greatest reduction in PFOA and PFHxS of any demographic group in this hypothetical
regulatory scenario. Similarly, non-Hispanic American Indian or Alaska Native populations see
relatively large reductions in PFHxS and PFOA in comparison to both the total population served
and non-Hispanic White populations. Non-Hispanic Black populations see higher reductions in
PFOA and PFHpA than the average across the total population served, which is consistent with
the percentage of non-Hispanic Black individuals with exposure above UCMR 5 MRLs being
slightly higher than percentage of the total population served across all demographic groups as
observed in Table 8-7.
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Table 8-7: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above UCMR 5 MRLs and as a Percent of Total Population Served
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total Population
Served
Population Served Above UCMR5 MRL
PFOS
49,319
688,558
1,795,216
11,297
3,575,722
5,792,353
3,911,510
8,272,754
12,184,264
PFHxS
62,140
515,410
1,544,025
14,361
3,706,494
4,703,878
3,667,141
7,131,026
10,798,167
PFHpA
39,582
371,042
1,083,498
7,783
2,656,914
3,326,278
2,587,679
5,071,760
7,659,439
PFOA
67,366
706,541
2,026,879
11,952
3,925,638
6,642,157
4,409,705
9,273,284
13,682,989
Population Served Above UCMR 5 MRL as a Percent of Total Population Served
PFOS
4.3%
4.3%
5.2%
3.1%
6.9%
4.1%
5.0%.
4.8%
4.8%
PFHxS
5.4%
3.2%
4.5%.
3.9%
7.2%
3.3%
4.7%
4.1%
4.3%
PFHpA
3.5%.
2.3%
3.1%
2.1%
5.1%
2.4%
3.3%
2.9%
3.0%
PFOA
5.9%
4.4%
5.9%
3.3%
7.6%
4.7%
5.6%
5.3%
5.4%
Abbreviations: MRL - minimum reporting level; PFHpA - perfluoroheptanoic acid
perfluorooctanesulfonic acid; UCMR - Unregulated Contaminant Monitoring Rule.
[; PFHxS - perlluorohexanesulfonic acid; PFOA -
perfluorooctanoic acid; PFOS -
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Table 8-8: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a Hypothetical Regulatory
Scenario with Maximum Contaminant Level at the UCMR 5 MRLs, Category 1 and 2 PWS Service Areas
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
PFOS
0.18
0.17
0.19
0.16
0.26
0.18
0.19
0.19
0.19
PFHxS
0.29
0.10
0.13
0.37
0.15
0.14
0.15
0.14
0.14
PFHpA
0.03
0.02
0.06
0.05
0.06
0.04
0.05
0.04
0.04
PFOA
0.43
0.22
0.35
0.58
0.40
0.29
0.35
0.30
0.32
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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8.3.2.2.3 Hypothetical Regulatory Scenario #2: 10.0 ppt
Table 8-9 and Table 8-10 summarize the results of the population served by category 1 and 2
PWS service areas with modeled PFAS occurrence above 10.0 ppt. The first set of rows in Table
8-9 summarizes populations served by PWSs with PFAS occurrence above 10.0 ppt. The second
set of rows displays these estimates as a percent of the total population served for PWSs included
in the EPA's analysis. Table 8-10 shows the population-weighted average reduction in PFAS
concentrations assuming all PWSs reduce their concentrations to 10.0 ppt.
In Table 8-9, percentages are bolded and italicized when the percentage of the population in a
specific demographic group with PFAS occurrence above 10.0 ppt is greater than the percentage
of the total population served across all demographic groups with PFAS occurrence above 10.0
ppt (right-hand column). In Table 8-10, highlighted cells represent whether the average reduction
in PFAS concentrations for a given demographic group is higher than the average for the total
populations served across all demographic groups (right-hand column). The percentages that are
bolded, italicized, or highlighted indicate greater PFAS exposure above 10.0 ppt for a given
demographic group compared to the total population served across all demographic groups; the
EPA anticipates potentially relatively higher reductions in PFAS exposure to accrue to these
demographic groups under this hypothetical regulatory scenario compared to the percentage of
population across all demographic groups. Unlike the results from the EPA's exposure analysis
where UCMR 5 MRLs are used as hypothetical MCL values, percentages in particular
demographic groups are less than 1 percent greater than percentages across the total population.
Between 0.1 percent and 1.3 percent of the population served by category 1 and 2 PWS service
areas, depending on the PFAS analyte, is exposed to PFAS occurrence above 10.0 ppt.
The following are findings from the EJ exposure analysis for PFAS occurrence above 10.0 ppt:
• Non-Hispanic American Indian or Alaska Native, non-Hispanic Asian, non-Hispanic
Black, and low-income populations have slightly higher PFAS exposure above 10.0 ppt
for some PFAS analytes compared to the population served across all demographic
groups. These results are essentially unchanged when comparing exposures above 10.0
ppt to non-Hispanic White populations.
• The most notable difference is for PFOA and PFHxS exposure for non-Hispanic
American Indian or Alaska Native populations served. PFOA and PFHxS population
exposure percentages are 2.5 and 1.3 percent, respectively, for non-Hispanic American
Indian or Alaska Native populations served compared to 1.3 and 0.6 percent of the total
population served across all demographic groups.
• Non-Hispanic Asian populations also have elevated exposure to PFOS over 10.0 ppt at
1.3 percent, whereas the percent of the total population served with PFOS above 10.0 ppt
is 0.9 percent.
Table 8-10 characterizes population-weighted average reductions of PFAS by demographic
group in a hypothetical regulatory scenario where system-level means are reduced to 10.0 ppt.
This analysis provides similar evidence with respect to PFOA and PFHxS exposures above 10.0
ppt for non-Hispanic American Indian or Alaska Native populations as was summarized in Table
8-9. Similarly, reductions in PFOA exposure for non-Hispanic Black populations are greater than
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for the total population served. Notably, we observe that non-Hispanic Pacific Islander
populations see greater reductions than the total population served for all PFAS analytes despite
having similar percentages of the population exposed above 10.0 ppt. Reductions of PFHxS and
PFOA for non-Hispanic Pacific Islander populations are at least three times greater than
reductions in these PFAS for both the total population served and non-Hispanic White
populations. Low-income populations see slightly greater reductions in PFOA in comparison to
the total population served as well as those with income above twice the Federal poverty level.
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Table 8-9: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above 10.0 ppt and as a Percent of Total Population Served
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Non-
Non-
Non-
Hispanic
Pacific
Islander
Non-
Below Twice
Above Twice
Total Population
Served
Hispanic
Asian
Hispanic
Black
Hispanic
Hispanic
White
the Poverty
Level
the Poverty
Level
Native
Population Served Above 10.0 ppt
PFOS
6,398
202,496
223,186
2,212
504,833
1,379,440
666,066
1,703,438
2,369,504
PFHxS
14,318
77,674
204,849
2,500
237,828
1,041,439
493,350
1,136,891
1,630,241
PFHpA
1,674
11,466
74,657
722
52,522
218,417
119,836
251,455
371,291
PFOA
28,563
178,353
535,779
5,607
716,412
1,775,254
1,143,134
2,185,557
3,328,691
Population Served Above 10.0 ppt as a Percent of Total Population Served
PFOS
0.6%
1.3%
0.6%
0.6%
1.0%
1.0%
0.8%
1.0%
0.9%
PFHxS
1.3%
0.5%
0.6%
0.7%
0.5%
0.7%
0.6%
0.7%
0.6%
PFHpA
0.1%
0.1%
0.2%
0.2%
0.1%
0.2%
0.2%
0.1%
0.1%
PFOA
2.5%
1.1%
1.5%
1.5%
1.4%
1.3%
1.5%
1.3%
1.3%
Abbreviations: PFHpA - perfluoroheptanoic acid; PFHxS - perfluorohexanesulfonic acid; PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; ppt - parts per
trillion.
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Table 8-10: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a Hypothetical Regulatory
Scenario with Maximum Contaminant Level at 10.0 ppt, Category 1 and 2 PWS Service Areas
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
PFOS
0.04
0.03
0.04
0.07
0.03
0.05
0.04
0.04
0.04
PFHxS
0.09
0.04
0.05
0.30
0.06
0.07
0.06
0.06
0.06
PFHpA
0.01
0.00
0.01
0.03
0.01
0.01
0.01
0.01
0.01
PFOA
0.20
0.09
0.16
0.45
0.13
0.15
0.16
0.14
0.14
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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8.3.2.3 Comparison of Results by PWS Size
8.3.2.3.1 Demographic Profile of PWS Service Areas
Table 8-11 and Table 8-12 summarize the demographic profile for category 1 and 2 PWS service
areas by system size for large and small PWS service areas, respectively. Small systems are
defined as systems serving fewer than or equal to 10,000 people while large systems serve more
than 10,000 people. Table 8-11 and Table 8-12 also provide a comparison to the demographic
characteristics of the overall U.S. population.
Table 8-11 shows that the population served by large category 1 and 2 PWS service areas has
slight differences in demographic characteristics compared to the overall U.S. population, with
percent differences all being less than +/- 4.3 percent. The population served by large category 1
and 2 PWS service areas has lower percentages of non-Hispanic White (-4.3%) populations and
populations with income above twice the Federal poverty level (-1.4%) compared to the overall
U.S. population. Additionally, the population served by large category 1 and 2 PWS service
areas have higher percentages of non-Hispanic Black (+1.6%), Hispanic (+2.4%), and non-
Hispanic Asian populations (+0.9%) populations and populations with income below twice the
Federal poverty level (+1.4%) compared to the overall U.S. population. The percentage of non-
Hispanic American Indian or Alaska Native populations and non-Hispanic Pacific Islander
populations is relatively consistent with the percent of these populations across the U.S.
Table 8-12 shows that the population served by small category 1 and 2 PWS service areas has
greater differences in the demographic characteristics of the population served compared to the
overall U.S. population, with percent differences being generally greater than +/- 2 percent, and
the greatest difference being +12.2 percent. The population served by small category 1 and 2
PWS service areas has lower percentages of non-Hispanic Asian (-3.62%), non-Hispanic Black
(-2.13%)), and Hispanic (-6.58%) populations and populations with income above twice the
Federal poverty level (-4.02%) compared to the overall U.S. population. Additionally, the
population served by small category 1 and 2 PWS service areas has higher percentages of non-
Hispanic American Indian or Alaska Native (+1%), non-Hispanic White (+12.23%) populations,
and populations with income below twice the Federal poverty level (+4.02%) compared to the
overall U.S. population.
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Table 8-11: Population Served by Category 1 and 2 PWSs and Percent of U.S. Population by Demographic Group, Large
Systems
Race and Ethnicity Income
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Population Served
1,089,683
16,106,131
34,265,828
363,866
51,345,649
138,878,798
77,564,529
171,471,255
249,035,784
Percent of Total
Population Served
0.44%
6.47%
13.76%
0.15%
20.62%
55.77%
31.15%
68.85%
100.00%
U.S. Population
Percent
0.6%
5.6%
12.2%
0.2%
18.2%
60.1%
29.8%
70.2%
Percent Difference
Between
Population Served
Percent and U.S.
Percent
-0.16%
0.87%
1.56%
-0.05%
2.42%
-4.33%
1.35%
-1.35%
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Table 8-12: Population Served by Category 1 and 2 PWSs and Percent of U.S. Population by Demographic Group, Small
Systems
Race and Ethnicity Income
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-Hispanic
White
Below Twice
the Poverty
Level
Above Twice
the Poverty
Level
Total
Population
Served
Population Served
50,564
62,185
316,020
2,412
364,684
2,269,169
1,061,063
2,076,244
3,137,307
Percent of Total
Population Served
1.61%
1.98%
10.07%
0.08%
11.62%
72.33%
33.82%
66.18%
100.00%
U.S. Population
Percent
0.6%
5.6%
12.2%
0.2%
18.2%
60.1%
29.8%
70.2%
Percent Difference
between Population
Served Percent and
U.S. Percent
1.01%
-3.62%
-2.13%
-0.12%
-6.58%
12.23%
4.02%
-4.02%
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8.3.2.3.2 Baseline Scenario
Table 8-13 and Table 8-14 summarize the populations served by large and small category 1 and
2 PWS service areas with modeled PFAS occurrence above baseline thresholds based on a
trigger level of 2 ppt for each PFAS analyte, which is slightly above the Method 537.1 detection
limits for each PFAS analyte. The second set of rows in Table 8-13 and Table 8-14 summarize
the percentage of the total population served by demographic group with modeled PFAS
occurrence above baseline thresholds. Percentages are bolded and italicized when the percentage
of the population in a specific demographic group with modeled PFAS above baseline thresholds
is greater than the percentage of the total population across all demographic groups exposed to
modeled PFAS above the baseline thresholds. Additionally, percentages are highlighted when
the percentage of the population in a specific demographic group with modeled PFAS above the
baseline threshold represents greater than a 1 percentage point difference compared to the total
population across all demographic groups. Table 8-15 characterizes population-weighted average
PFAS concentrations across demographic groups in large and small category 1 and 2 PWSs.
Highlighted cells represent whether the average concentration for a given demographic group is
higher than the average concentration for the total population served across all demographic
groups (right-hand column).
Depending on the PFAS analyte, between 4.8 percent and 10.8 percent of the total population
served by large category 1 and 2 PWS service areas are exposed to modeled PFAS occurrence
above baseline thresholds based on a trigger level of 2 ppt. Depending on the PFAS analyte,
between 0.5 percent and 3.7 percent of the total population served by small category 1 and 2
PWS service areas is exposed to modeled PFAS occurrence above baseline thresholds based on
baseline thresholds of 2 ppt.
For large systems, the percentage of Hispanic populations served by category 1 and 2 PWS
service areas is higher across all four PFAS analytes compared to the percentage of the total
population served across all demographic groups with anticipated PFAS exposure above baseline
thresholds. Depending on the PFAS analyte, the percentage of Hispanic populations served by
large systems with exposure above baseline thresholds is 1.3 percent to 2.6 percentage points
higher than percentages of the total population served across all demographic groups, or 2.6 to
3.5 percentage points higher than for non-Hispanic White populations. Non-Hispanic Black
populations are also exposed to all PFAS analytes above baseline levels to a greater extent than
the total population served, with 1.6 and 1.2 percentage point higher exposure levels to PFOA
and PFOS, respectively. In comparison to non-Hispanic White populations, non-Hispanic Black
populations have 1 to 2.4 percentage points greater exposure depending on the PFAS analyte.
The percent of non-Hispanic American Indian or Alaska Native populations served with
exposure above baseline thresholds to PFHxS is 1.2 percentage points greater than the share of
the total population served, or 2.1 percentage points higher than the percent of non-Hispanic
White populations exposed. For large systems, significant differences in exposure to any PFAS
for non-Hispanic Asian, non-Hispanic Pacific Islander, or low-income populations were not
observed.
For small systems, the percent of non-Hispanic Asian populations served by category 1 and 2
PWS service areas with PFAS above baseline thresholds is higher for PFOA and PFOS in
comparison to the total population served, with a range of 1.5 to 2.2 percentage points more of
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the population served. The percentage of non-Hispanic Black populations served by category 1
and 2 PWS service areas with PFAS above baseline thresholds is 0.8 percentage points higher for
PFHxS compared to the percentage of the total population served across all demographic groups
with anticipated PFAS exposure above baseline thresholds. The EPA also observed that non-
Hispanic White populations and those with income above twice the Federal poverty level have
slightly higher percentages of the population exposed across all PFAS analytes examined. Given
the data gaps in occurrence information among small systems, extrapolating these results to
small systems across the country is not possible. For example, the EPA observed only 2,400
individuals who identify as non-Hispanic Pacific Islander.
Table 8-15 provides detail on average concentrations across these demographic groups for large
and small water systems, respectively. The first panel of Table 8-15 supports the previous
findings in Table 8-13 that, for large PWSs, non-Hispanic American Indian or Alaska Native,
non-Hispanic Black, and Hispanic populations served have greater exposure across at least two
PFAS analytes in comparison to exposure for the total population served across all demographic
groups. Hispanic and non-Hispanic Black populations served have greater exposure to all four
PFAS in comparison to the total population served.
In addition, Table 8-15 demonstrates that non-Hispanic Pacific Islander populations are served
by water systems with higher average concentrations of PFHxS and PFOA in comparison to
average concentrations of these PFAS analytes for the total population served by large PWSs,
even though non-Hispanic Pacific Islander populations have similar or even lower share of the
population exposed in comparison to the total population served, as shown in Table 8-13. Non-
Hispanic White and non-Hispanic Asian populations generally have lower average
concentrations, on average, across all four PFAS analytes in comparison to the total population
served. Further, populations with income less than twice the Federal poverty level are on average
served by large PWSs with higher concentrations of three PFAS analytes in comparison to
populations with income above twice the Federal poverty level or the total population served
across all large PWSs.
The second panel of Table 8-15 shows that non-Hispanic Asian, non-Hispanic Black, and non-
Hispanic White populations have greater potential exposure to specific PFAS analytes in
comparison to the total population served across all demographic groups served by small PWSs.
Non-Hispanic Asian populations served by small PWSs have elevated concentrations of PFOS
and PFHpA in comparison to the total population served by small PWSs. Non-Hispanic Black
populations served by small PWSs have greater exposure to PFOS, PFHxS, and PFOA in
comparison to the total population served by small PWSs. Non-Hispanic White populations also
see greater exposure to PFHpA in comparison to the total population served in small PWSs,
although this difference is small (0.01 ppt).
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Table 8-13: Baseline Scenario: Demographic Breakdown of Population Served by Category 1 and 2 PWS Service Areas
Above Baseline Thresholds and as a Percent of Total Population Served, Large Systems
Race and Ethnicity
Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above Baseline Threshold
PFOS
96,211
1,758,287
4,120,087
25,192
6,253,084
14,069,015
8,013,031
18,982,735
26,995,766
339
PFHxS
79,002
802,016
2,296,946
20,822
4,457,379
7,156,640
5,083,638
10,107,999
15,191,637
186
PFHpA
52,475
602,507
1,732,401
12,295
3,458,779
5,714,325
3,666,804
8,175,133
11,841,937
143
PFOA
88,407
1,066,286
3,413,512
19,849
5,195,766
10,480,694
6,630,748
14,129,410
20,760,158
296
Population Served Above Baseline Threshold as a Percentage of Total Population Served
PFOS
8.83%
10.92%
12.02%
6.92%
12.18%
10.13%
10.33%
11.07%
10.84%
-
PFHxS
7.25%
4.98%
6. 70%
5.72%
8.68%
5.15%
6.55%
5.89%
6.10%
-
PFHpA
4.82%
3.74%
5.06%
3.38%
6. 74%
4.11%
4.73%
4.77%
4.76%
-
PFOA
8.11%
6.62%
9.96%
5.46%
10.12%
7.55%
8.55%
8.24%
8.34%
-
Total Population Served in Sampled Population
1,089,683 16,106,131 34,265,828 51,345,649 138,878,798 363,866 77,564,529 171,471,255 249,035,784
Abbreviations: PFHpA - perfluoroheptanoic acid; PFHxS - perlluorohexanesulfonic acid; PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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Table 8-14: Baseline Scenario: Demographic Breakdown of Population Served by Category 1 and 2 PWS Service Areas
Above Baseline Thresholds and as a Percent of Total Population Served, Small Systems
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above Baseline Threshold
PFOS
253
3,672
10,730
1
10,593
87,520
22,231
92,565
114,796
22
PFHxS
128
111
6,087
1
1,207
28,075
11,595
24,797
36,392
9
PFHpA
104
330
306
17
530
15,372
3,591
13,422
17,013
4
PFOA
267
2,947
10,369
1
6,322
80,383
20,456
81,731
102,187
20
Population Served Above Baseline Threshold as a Percentage of Total Population Served
PFOS
0.50%
5.90%
3.40%
0.04%
2.90%
3.86%
2.10%
4.46%
3.66%
-
PFHxS
0.25%
0.18%
1.93%
0.04%
0.33%
1.24%
1.09%
1.19%
1.16%
-
PFHpA
0.21%
0.53%
0.10%
0.70%
0.15%
0.68%
0.34%
0.65%
0.54%
-
PFOA
0.53%
4. 74%
3.28%
0.04%
1.73%
3.54%
1.93%
3.94%
3.26%
-
Total Population Served in Sampled Population
50,564 62,185 316,020 364,684 2,269,169 2,412 1,061,063 2,076,244 3,137,307
Abbreviations: PFHpA - perfluoroheptanoic acid; PFHxS - perlluorohexanesulfonic acid; PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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Table 8-15: Modeled Average PFAS Concentrations (ppt) by Demographic Group and System Size in the Baseline, Category
1 and 2 PWS Service Areas
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-Hispanic
Asian
Non-Hispanic
Black
Non-Hispanic
Pacific
Islander
Hispanic
Non-Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Large Systems
PFOS
0.99
1.02
1.05
0.90
1.16
0.96
1.02
1.02
1.02
PFHxS
0.84
0.58
0.64
0.86
0.75
0.59
0.65
0.63
0.63
PFHpA
0.54
0.50
0.55
0.51
0.64
0.50
0.55
0.53
0.54
PFOA
1.09
0.85
1.04
1.14
1.12
0.90
0.99
0.95
0.96
Small Systems
PFOS
0.46
0.68
0.59
0.44
0.54
0.57
0.50
0.60
0.57
PFHxS
0.20
0.24
0.26
0.19
0.23
0.23
0.23
0.24
0.24
PFHpA
0.22
0.26
0.24
0.24
0.23
0.25
0.23
0.25
0.24
PFOA
0.32
0.45
0.57
0.29
0.38
0.46
0.42
0.48
0.46
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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8.3.2.3.3 Hypothetical Regulatory Scenario #1: UCMR 5 MRLs
Table 8-16, Table 8-17, and Table 8-18 summarize the results for populations served by large
and small category 1 and 2 PWS service areas with PFAS occurrence above UCMR 5 MRL
values, respectively. The EPA assumed that PWS service areas with PFAS system-level means
above the UCMR 5 MRL value will reduce PFAS levels to comply with the final rule.
The first set of rows in Table 8-16 and Table 8-17 summarize populations served by large and
small category 1 and 2 PWSs with modeled PFAS occurrence above the UCMR 5 MRLs,
respectively. The second set of rows provides these estimates as a percentage of the total
population served by PWS service areas included in the EPA's analysis. In Table 8-16 and Table
8-17, percentages are bolded and italicized when the percentage of the population in a specific
demographic group with PFAS occurrence above the MRL value is greater than the percentage
of the total population across all demographic groups with PFAS occurrence above the MRL.
Additionally, percentages are highlighted when the percentage of the population in a specific
demographic group with modeled PFAS above the MRL represents greater than a 1 percentage
point difference compared to the total population across all demographic groups exposed to
modeled PFAS above the MRL value. Table 8-17 characterizes population-weighted average
reductions in PFAS concentrations across demographic groups in large and small category 1 and
2 PWSs. Highlighted cells represent whether the average reduction for a given demographic
group is higher than the average reduction for the total population served across all demographic
groups (right-hand column). The percentages that are bolded, italicized, or highlighted indicate
more PFAS exposure above the MRL for a given demographic group; the EPA anticipates
relatively higher reductions in PFAS exposure will accrue to these demographic groups under
this hypothetical regulatory scenario compared to the percentage of the population across all
demographic groups.
Depending on the PFAS analyte, between 3.1 percent and 5.5 percent of the total population
served by large category 1 and 2 PWS service areas are exposed to at least one of the modeled
four PFAS occurrences above UCMR 5 MRL values. For small category 1 and 2 PWS service
areas, depending on the PFAS analyte, between 0.3 percent and 1.9 percent of the total
population served is exposed to modeled PFAS occurrence above UCMR 5 MRL values.
Findings for large systems are as follows:
• Non-Hispanic American Indian or Alaska Native populations served have higher
exposure above UCMR 5 MRL values for PFOA, PFHpA, and PFHxS compared to the
percent of the population served across all demographic groups. Non-Hispanic American
Indian or Alaska Native populations also have higher exposure than the non-Hispanic
White population across all PFAS analytes. The differences in percent of non-Hispanic
American Indian or Alaska Native populations exposed range from 0.6 - 1.4 percentage
points in comparison to the total population served across all demographic groups.
• Non-Hispanic Black populations served have higher exposure above the UCMR 5 MRL
for all PFAS analytes compared to the percent of the total population served across all
demographic groups, however the differences are all relatively small in magnitude (<0.5
percentage points). Exposure to PFOA, PFOS, and PFHxS for non-Hispanic Black
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populations is at least 1 percentage point higher than percent of the non-Hispanic White
population exposed to these PFAS analytes.
• Hispanic populations served by large PWSs have higher exposure above the UCMR 5
MRL values for all four PFAS analytes compared to the percent of the total population
served across all demographic groups. Differences in percent of Hispanic populations
exposed are consistently at least 2 percentage points higher in comparison to the total
population served across all demographic groups. Hispanic populations have at least
double the exposure above UCMR 5 MRL values in comparison to non-Hispanic White
populations for PFHxS and PFHpA.
• Non-Hispanic Asian and non-Hispanic Pacific Islander populations served have
comparable or lower levels of exposure above UCMR 5 MRL values for all PFAS
analytes compared to both the percent of the total population served across all
demographic groups and the non-Hispanic White population.
• Low-income populations have higher PFAS exposure above UCMR 5 MRL values for all
PFAS analytes compared to the total population served across all demographic groups.
These differences are all relatively small at less than 0.4 percentage points, but disparate
exposures are larger for each PFAS analyte when compared to populations with income
above twice the Federal poverty level.
Findings for small systems are as follows:
• Non-Hispanic Asian populations served have higher exposure above UCMR 5 MRL
values for PFOS and PFHpA compared to the percent of the total population served
across all demographic groups, with PFOS exposure 1 percentage point higher than the
percent of the total population or non-Hispanic White population served that is exposed
to PFOS.
• Non-Hispanic Black populations served have higher exposure above the UCMR 5 MRL
values for PFOS, PFHxS, and PFOA compared to the percent of the total population
served across all demographic groups, with PFOA exposure over 1 percentage point
higher than the percent of the total population or non-Hispanic White population served.
The differences in population exposed range from 0.9 - 1.3 percentage points when
comparing exposure for non-Hispanic Black populations to the total population served.
• Non-Hispanic White populations served experience slightly higher exposure above the
UCMR 5 MRL value for PFHpA compared to the percent of the total population served
across all demographic groups.
• Populations with income above twice the Federal poverty level have higher exposure
above the UCMR 5 MRL values across all PFAS analytes compared to the percent of the
total population served across all demographic groups.
Table 8-18 characterizes population-weighted average reductions in PFAS exposures anticipated
to occur for large and small PWSs in a hypothetical regulatory scenario where system-level
means are reduced to UCMR 5 MRL values. For large systems, as in Table 8-16, Hispanic
populations have higher exposures above UCMR 5 MRL values for all PFAS analytes in
comparison to the total population served among large PWSs. The results also show that non-
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Hispanic Black populations have higher average exposures to PFHpA and PFOA than the total
population served across all demographic groups in large PWSs. On average, non-Hispanic
American Indian or Alaska Native populations also see large reductions in PFHxS and PFOA in
comparison to the total population served, which is consistent with elevated percentages of the
population exposed as shown in Table 8-16. Non-Hispanic Pacific Islander populations served
by large systems see the greatest reductions in PFHxS and PFOA of any demographic group for
large systems, with reductions of each that are roughly twice the average reduction observed for
the total population served by large systems. In large systems, the EPA also observed elevated
population-weighted concentrations of all PFAS analytes for low-income populations in
comparison to the total population served, and low-income populations are also more exposed to
all PFAS analytes in comparison to populations with income above twice the Federal poverty
level. For small systems, non-Hispanic Asian, non-Hispanic Black, and Hispanic populations
have larger reductions in particular PFAS than the total population served across all demographic
groups. In general, however, differences in PFAS reductions across demographic groups are
slight for small systems.
It should be noted that the sample size of small PWS service areas included in categories 1 and 2
with PFAS exposure above UCMR 5 MRL values is limited and could meaningfully impact the
results presented in this analysis. The population served by small category 1 and 2 PWS service
areas included in this analysis captures roughly 1 percent of the total U.S. population. Given that
approximately 20 percent of the U.S. population is served by small systems, this subset of
systems may not be representative of small systems across the U.S. As such, results from this
analysis cannot be extrapolated to be representative of small systems nationwide. Additionally,
the population served by the subset of small systems in categories 1 and 2 is disproportionately
non-Hispanic White, with 12.2 higher percentage point representation compared to the overall
U.S. population. The population served is also less Hispanic, with representation of this group
being 6.58 percentage points lower than the overall U.S. population. Further evaluation is needed
to demonstrate whether the sample population served by small category 1 and 2 PWS service
areas is representative of the demographic breakdown of all small systems nationwide.
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Table 8-16: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above UCMR 5 MRLs and as a Percent of Total Population Served, Large Systems
Race and Ethnicity
Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non- ,T
TT. . Non-
Hispanic ... . ...
Pacific Hispanic Hispanic
T . . White
Islander
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above UCMR 5 MRL
PFOS
49,110
686,780
1,786,279
11,297 3,571,565 5,750,090
3,898,651
8,227,263
12,125,914
190
PFHxS
62,128
515,390
1,539,938
14,361 3,706,392 4,695,697
3,663,223
7,122,201
10,785,424
111
PFHpA
39,572
370,784
1,083,401
7,783 2,656,626 3,318,167
2,586,693
5,063,861
7,650,554
81
PFOA
67,138
705,879
2,017,982
11,950 3,922,241 6,608,160
4,397,699
9,237,285
13,634,984
175
Population Served Above UCMR 5 MRL as a Percentage of Total Population Served
PFOS
4.51%
4.26%
5.21%
3.10% 6.96% 4.14%
5.03%
4.80%
4.87%
-
PFHxS
5.70%
3.20%
4.49%
3.95% 7.22% 3.38%
4. 72%
4.15%
4.33%
-
PFHpA
3.63%
2.30%
3.16%
2.14% 5.17% 2.39%
3.33%
2.95%
3.07%
-
PFOA
6.16%
4.38%
5.89%
3.28% 7.64% 4.76%
5.67%
5.39%
5.48%
-
Total Population Served in Sampled Population
1,089,683
16,106,131
34,265,828
51,345,649 138,878,798 363,866
77,564,529
171,471,255
249,035,784
-
Abbreviations: MRL - minimum reporting level; PFHpA - perfluoroheptanoic acid; PFHxS - perfluorohexanesulfonic acid; PFOA -
perfluorooctanesulfonic acid; UCMR - Unregulated Contaminant Monitoring Rule.
- perfluorooctanoic acid; PFOS -
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Table 8-17: Hypothetical Regulatory Scenario #1: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above UCMR 5 MRLs and as a Percent of Total Population Served, Small Systems
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non-
Hispanic
Pacific
Islander
Hispanic
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above UCMR 5 MRL
PFOS
209
1,778
8,937
0
4,157
42,263
12,859
45,491
58,350
13
PFHxS
12
21
4,087
0
103
8,181
3,918
8,825
12,743
4
PFHpA
9
258
97
0
287
8,111
986
7,899
8,885
2
PFOA
228
663
8,897
1
3,397
33,997
12,006
35,999
48,005
10
Population Served Above UCMR 5 MRL as a Percentage of Total Population Served
PFOS
0.41%
2.86%
2.83%
0.00%
1.14%
1.86%
1.21%
2.19%
1.86%
-
PFHxS
0.02%
0.03%
1.29%
0.00%
0.03%
0.36%
0.37%
0.43%
0.41%
-
PFHpA
0.02%
0.41%
0.03%
0.00%
0.08%
0.36%
0.09%
0.38%
0.28%
-
PFOA
0.45%
1.07%
2.82%
0.04%
0.93%
1.50%
1.13%
1.73%
1.53%
-
Total Population Served in Sampled Population
50,564 62,185 316,020 364,684 2,269,169 2,412 1,061,063 2,076,244 3,137,307
Abbreviations: MRL - minimum reporting level; PFHpA - perfluoroheptanoic acid; PFHxS - perfluorohexanesulfonic acid; PFOA - perfluorooctanoic acid;
PFOS - perfluorooctanesulfonic acid; UCMR - Unregulated Contaminant Monitoring Rule.
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Table 8-18: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a Hypothetical Regulatory
Scenario with Maximum Contaminant Levels at the UCMR 5 MRLs, Category 1 and 2 PWS Service Areas
Race and Ethnicity
Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-Hispanic
Asian
Non-Hispanic
Black
Non-Hispanic
Pacific
Islander
Hispanic
Non-Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Large Systems
PFOS
0.19
0.17
0.19
0.16
0.26
0.18
0.20
0.19
0.19
PFHxS
0.31
0.10
0.14
0.38
0.16
0.15
0.15
0.14
0.15
PFHpA
0.04
0.02
0.06
0.05
0.06
0.04
0.05
0.04
0.04
PFOA
0.44
0.22
0.36
0.58
0.41
0.29
0.35
0.31
0.32
Small Systems
PFOS
0.01
0.09
0.05
0.00
0.03
0.06
0.03
0.07
0.06
PFHxS
0.00
0.02
0.01
0.00
0.03
0.02
0.02
0.02
0.02
PFHpA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PFOA
0.02
0.05
0.17
0.00
0.05
0.10
0.09
0.10
0.10
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
Note:
aThe demographic group people of color includes individuals who identify as Hispanic and/or a race other than White. It is calculated from E.TScreen's percent minority
indicator and is non-duplicative across race and ethnicity categories.
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8.3.2.3.4 Hypothetical Regulatory Scenario #2: 10.0 ppt
Table 8-19 summarizes results for populations served by large category 1 and 2 PWS service
areas with PFAS occurrence above 10.0 ppt. Table 8-20 summarizes results for populations
served by small category 1 and 2 PWS service areas with PFAS occurrence above 10.0 ppt.
Table 8-21 characterizes population-weighted average reductions in PFAS exposures anticipated
to occur for large and small PWSs in a hypothetical regulatory scenario where system-level
means are reduced to 10.0 ppt. The first set of rows in Table 8-19 and Table 8-20 summarizes
populations served by large and small category 1 and 2 PWSs with modeled PFAS occurrence
above the 10.0 ppt, respectively. The second set of rows provides these estimates as a percentage
of the total population served by these PWS service areas.
In Table 8-19 and Table 8-20, percentages are bolded and italicized when the percent of the
population in a specific demographic group with PFAS occurrence above 10.0 ppt is greater than
the percentage of the total population across all demographic groups with PFAS occurrence
above 10.0 ppt. In Table 8-21, highlighted cells represent whether the average reduction for a
given demographic group is higher than the reductions for the total population served across all
demographic groups in large and small PWSs (right-hand column). The percentages that are
bolded, italicized, or highlighted indicate more PFAS exposure above 10.0 ppt for a given
demographic group; the EPA anticipates relatively higher reductions in PFAS exposure will
accrue to these demographic groups under this hypothetical regulatory scenario compared to the
percentage of the population across all demographic groups.
For large systems, a greater percent of non-Hispanic American Indian or Alaska Native
populations experience exposure to PFHxS and PFOA in comparison to the total population
served, with differences ranging from 0.7 to 1.3 percentage points. Non-Hispanic Asian
populations are exposed to PFOS to a greater extent than the total population served, with a
difference of 0.3 percentage points. The percent of the non-Hispanic Black population with
PFHpA and PFOA above 10.0 ppt is also elevated, although differences are relatively small in
comparison to both the total population served or non-Hispanic White populations (<0.2
percentage points). Non-Hispanic Pacific Islander populations served by large systems have
elevated exposure above 10.0 ppt for PFHpA, PFOA and PFHxS compared to the total
population served across all demographic groups, although differences are again relatively small
(<0.2 percentage points). Hispanic populations are slightly more likely to be served by large
PWSs with PFOA and PFOS concentrations above 10.0 ppt (<0.1 percentage points).
Populations with income below twice the Federal poverty level are also slightly more likely to
have PFOS and PFHxS above 10.0 ppt in comparison to the total population served.
For small systems, non-Hispanic Asian populations are slightly more likely to be served by
PWSs with PFOS above 10.0 ppt than the total population served by small systems. Non-
Hispanic Black populations are more likely to be served by small systems with PFOA above 10.0
ppt, with a difference in percent of the population exposed of 0.9 percentage points in
comparison to the total population served or non-Hispanic White populations. Non-Hispanic
White populations have slightly elevated exposure above 10.0 ppt for PFOS compared to the
population served across all demographic groups. The average reductions by demographic group,
shown in Table 8-21, largely confirm the findings of Table 8-19 and Table 8-20, with greater
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average reductions generally accruing to populations with a higher percentage of potentially
exposed individuals for large and small PWSs.
As previously noted, the sample size of small PWS service areas included in categories 1 and 2 is
limited, with population served capturing roughly 1 percent of the total U.S. population.
Additionally, the population served by the subset of small systems in categories 1 and 2 is
disproportionately White and non-Hispanic compared to the overall U.S. population, as
previously discussed. Further evaluation is needed to demonstrate whether the sample population
served by small category 1 and 2 PWS service areas is representative of the demographic
breakdown of all small systems nationwide.
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Table 8-19: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above 10.0 ppt and as a Percent of Total Population Served, Large Systems
Race and Ethnicity
Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non- ,T
TT. . Non-
Hispanic ... . ...
Pacific Hispanic Hispanic
t i j White
Islander
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above 10.0 ppt
PFOS
6,388
202,238
223,089
2,212 504,545 1,371,329
665,080
1,695,539
2,360,619
48
PFHxS
14,317
77,668
204,840
2,500 237,779 1,041,289
493,256
1,136,755
1,630,011
32
PFHpA
1,673
11,460
74,648
722 52,474 218,268
119,742
251,319
371,061
8
PFOA
28,551
178,333
531,691
5,607 716,309 1,767,073
1,139,216
2,176,732
3,315,948
62
Population Served Above 10.0 ppt as a Percentage of Total Population Served
PFOS
0.59%
1.26%
0.65%
0.61% 0.98% 0.99%
0.86%
0.99%
0.95%
-
PFHxS
PFHpA
PFOA
1.31%
0.15%
2.62%
0.48%
0.07%
1.11%
0.60%
0.22%
1.55%
0.69% 0.46% 0.75%
0.20% 0.10% 0.16%
1.54% 1.40% 1.27%
0.64%
0.15%
1.47%
0.66%
0.15%
1.27%
0.65%
0.15%
1.33%
-
Total Population Served in Sampled Population
1,089,683
16,106,131
34,265,828
51,345,649 138,878,798 363,866
77,564,529
171,471,255
249,035,784
-
Abbreviations: MRL - minimum reporting level; PFHpA - perfluoroheptanoic acid; PFHxS - perfluorohexanesulfonic acid; PFOA - perfluorooctanoic acid;
PFOS - perfluorooctanesulfonic acid; UCMR - Unregulated Contaminant Monitoring Rule.
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Table 8-20: Hypothetical Regulatory Scenario #2: Demographic Breakdown of Population Served by Category 1 and 2 PWS
Service Areas Above 10.0 ppt and as a Percent of Total Population Served, Small Systems
Race and Ethnicity Income
Non-
Hispanic
American
Indian or
Alaska
Native
Non-
Hispanic
Asian
Non-
Hispanic
Black
Non- ,T
TT. . Non-
Hispanic ... . ...
Pacific Hispanic Hispanic
t i j White
Islander
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
System
Count
Population Served Above 10.0 ppt
PFOS
9
258
97
0 287 8111
986
7899
8,885
2
PFHxS
1
7
9
0 48 149
94
136
230
1
PFHpA
1
7
9
0 48 149
94
136
230
1
PFOA
12
21
4087
0 103 8181
3918
8825
12,743
3
Population Served Above 10.0 ppt as a Percentage of Total Population Served
PFOS
0.02%
0.41%
0.03%
0.00% 0.08% 0.36%
0.09%
0.38%
0.28%
-
PFHxS
0.00%
0.01%
0.00%
0.00% 0.01% 0.01%
0.01%
0.01%
0.01%
-
PFHpA
0.00%
0.01%
0.00%
0.00% 0.01% 0.01%
0.01%
0.01%
0.01%
-
PFOA
0.02%
0.03%
1.29%
0.00% 0.03% 0.36%
0.37%
0.43%
0.41%
-
Total Population Served in Sampled Population
50,564
62,185
316,020
364,684 2,269,169 2,412
1,061,063
2,076,244
3,137,307
-
Abbreviations: PFHpA - perfluoroheptanoic acid; PFHxS - perfluorohexanesulfonic acid; PFOA - perfluorooctanoic acid; PFOS - perfluorooctanesulfonic acid; ppt - parts per
trillion.
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Table 8-21: Reductions in Average PFAS Concentrations (ppt) by Demographic Group in a Hypothetical Regulatory
Scenario with Maximum Contaminant Levels at 10.0 ppt, Category 1 and 2 PWS Service Areas
Race and Ethnicity Income
PFAS
Non-
Hispanic
American
Indian or
Alaska
Native
Non-Hispanic
Asian
Non-Hispanic
Black
Non-Hispanic
Pacific
Islander
Hispanic
Non-Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Total
Population
Served
Large Systems
PFOS
0.04
0.03
0.04
0.07
0.03
0.05
0.04
0.04
0.04
PFHxS
0.10
0.04
0.05
0.30
0.06
0.07
0.06
0.06
0.06
PFHpA
0.01
0.00
0.01
0.03
0.01
0.01
0.01
0.01
0.01
PFOA
0.21
0.09
0.16
0.45
0.13
0.15
0.16
0.14
0.15
Small Systems
PFOS
0.00
0.02
0.00
0.00
0.01
0.01
0.00
0.01
0.01
PFHxS
0.00
0.02
0.01
0.00
0.03
0.01
0.02
0.01
0.02
PFHpA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PFOA
0.01
0.03
0.06
0.00
0.03
0.05
0.05
0.05
0.05
Abbreviations: PFHpA - periluoroheptanoic acid; PFHxS - periluorohexanesulfonic acid; PFOA - periluorooctanoic acid; PFOS - perfluorooctanesulfonic acid.
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8.4 SafeWater EJ Analysis of Final Rule and Regulatory
Alternatives
8.4.1 Methodology
In addition to analyzing EJ exposure using the EJSCREENbatch R package, the EPA also
conducted an EJ analysis of the final rule and regulatory alternatives using the SafeWater
MCBC. The EPA's final rule sets MCLs of 4.0 ppt for PFOA and PFOS each, MCLs of 10 ppt
each for PFHxS, PFNA, and HFPO-DA and an HI of 1.0 for PFNA, HFPO-DA, PFHxS, and
PFBS. Options la, lb, and lc set MCL values for PFOA and PFOS at 4.0 ppt, 5.0 ppt, and 10.0
ppt, respectively.
SafeWater MCBC was used to analyze the distribution of anticipated health benefits and
household costs associated with the final PFAS NPDWR across race/ethnicity groups and
income level. For more information on SafeWater MCMC and its application in the EPA's
analysis of national quantified benefits and costs associated with the final PFAS NPDWR, see
Section 5.2.
Using SafeWater MCBC, the EPA estimated the quantified health benefits and household costs
expected to accrue to specific race/ethnicity and income level groups for category 1 and 2 PWS
service areas. As previously described in Section 8.3.1, category 1 and 2 PWS service areas
include systems that have sampled PFAS occurrence data from UCMR 3 and have predelineated
service area boundaries or those estimated using zip code served information (n = 4,723). The
subset of category 1 and 2 PWSs captured in the analysis represents roughly 3 percent of active
PWSs.101
Results are presented across four race/ethnicity groups, consistent with the subpopulation
definitions used to estimate the national quantified benefits for the final PFAS NPDWR (see
Section 8.1). These race/ethnicity groups include: non-Hispanic Black, Hispanic, non-Hispanic
White, and Other.102 Race/ethnicity categories examined in the EPA's analysis using SafeWater
MCBC differ from the demographic groups presented in the exposure analysis discussed
previously in this chapter due to the availability of demographic information utilized in the
EPA's quantified benefits analysis. For more information on the selection of data inputs to the
EPA's benefit analysis, see Chapter 6.
The total sample population captured by the EPA's analysis using SafeWater MCBC is roughly
196 million people, with a breakdown by race/ethnicity and income group as follows:
• Non-Hispanic Black: 25.1 million (-13%)
• Hispanic: 32.6 million (-17%)
• Other: 12.2 million (-6%)
101 The number of active PWSs was retrieved from SDWIS/Fed fourth quarter 2021.
102 The "Other" race/ethnicity category includes any race/ethnicity populations that are not non-Hispanic Black, Hispanic, or non-
Hispanic White.
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• Non-Hispanic White: 125.9 million (-64%)
• Income below twice the poverty level: 61.6 million (-32%)
• Income above twice the poverty level: 133.9 million (-68%)
When compared to the breakdown of the total U.S. population by these same race/ethnicity
groups, the makeup of the sample population in the EPA's analysis is generally representative of
the overall U.S. population. Non-Hispanic Black, Hispanic, and Other race/ethnicity groups
(making up -13 percent, -19 percent, and -8 percent of the U.S. population, respectively) are
slightly underrepresented, while the non-Hispanic White race/ethnicity group (making up -60%
of the U.S. population) is slightly overrepresented in the EPA's analysis (U.S. Census Bureau,
2020a).
Because demographic proportion information utilized in the EPA's benefits analysis was
available at the county level, the EPA utilized the following step-by-step approach to identify the
number of people in each race/ethnicity and income group within a given PWS service area.
Specifically, in this order, the EPA utilized the following stepwise approach:
1. Overlayed census block groups with PWS service area boundaries;
2. Calculated the area of each census block group and PWS service area boundary;
3. Calculated the percent of each census block group overlapping each PWS service area
boundary;
4. Multiplied the population of the census block group by the percent of each census block
overlapping each PWS service area boundary;
5. Summed across census block groups to calculate the population in each PWS service area
boundary that lives in each county;
6. Calculated the percent of the population in each county (PWS county weight) for each
PWS; and
7. Estimated the number of people served by a PWS for each subpopulation as follows:
Sub Pop = number of people in each subpopulation served by a PWS
PWS_County_weightc = the percentage of the PWS population in each county (c)
PWS_Pop = Number of people served by PWS from SDWIS/Fed inventory
Subpop_sharec = The share of county (c) population consisting of the subpopulation from the
U.S. Census
Equation 21:
SubPop = PWS county weighty, x PWS Pop x Subpop sharec
C
Where:
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As part of its national analysis of quantified benefits and costs using SafeWater MCBC, the EPA
accounted for states that have enacted enforceable MCLs for PFAS contaminants. For these
states, the EPA assumed that the state MCL is the maximum baseline PFAS occurrence value for
all EPs in the state. For more information on this assumption and on state-enacted MCLs, see
Section 4. The EPA has applied this assumption as part of its EJ analysis conducted in SafeWater
MCBC.
8.4.2 SafeWater EJ Analysis Results
8.4.2.1 Health Benefits
To determine if there are disproportionate health impacts borne by any race/ethnicity
subpopulation or income group under the final rule or regulatory alternatives, the EPA estimated
the annual avoided cases of mortality and morbidity per 100,000 people, as shown in Table 8-22
through Table 8-25.
For the analysis conducted in SafeWater MCBC, the EPA reports the estimated avoided cases of
mortality and morbidity by race/ethnicity and income groups for the following health endpoints:
• CVD: Non-fatal MI, non-fatal IS, CVD deaths
• RCC: Non-fatal RCC cases avoided, fatal RCC cases avoided
• Birth weight: Birth weight gain (total grams), birth weight-related deaths avoided
Baseline incidence associated with these health endpoints varies by demographic group, and
disparities in underlying incidence by demographic group likely influence the distribution of
quantified health benefits expected under the final PFAS NPDWR. For example, non-fatal MI
incidence is generally most prevalent among non-Hispanic White males, while non-fatal IS
incidence is generally most prevalent among non-Hispanic Black males. Additionally, low
income and poverty are linked to higher cancer mortality rates. Survival after a cancer diagnosis
is shorter for people of all races who have a lower socio-economic status (National Cancer
Institute, 2020). The demographic distribution of quantified health benefits presented here
incorporates differing incidence in baseline health outcomes by race/ethnicity. However, the
demographic distribution of quantified health benefits that the EPA reports here have not been
adjusted for income. For a detailed breakdown of incidence associated with the effects of
reduced birth weight on infant mortality, CVD events, and RCC by race/ethnicity, see
Appendices E, G, and H, respectively.
The EPA did not analyze the demographic breakdown of bladder cancer cases avoided that are
expected to result from the co-removal of PFAS and DBP precursors (discussed in Section 6.7).
The EPA models bladder cancer impacts based on a national-level distribution of finished water
TOC levels; because specific TOC levels at actual PWSs are not available, the EPA did not
include these impacts in the portion of its EJ analysis conducted in SafeWater MCBC.
Table 8-22 summarizes the number of avoided cases of morbidity and mortality per 100,000
people per year for all health endpoints evaluated under the EPA's final regulatory option. Table
8-23 through Table 8-25 summarize the number of avoided cases of morbidity and mortality per
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100,000 people per year for all health endpoints evaluated under the EPA's regulatory
alternatives.
For the final rule and all regulatory alternatives, benefits are anticipated to be realized across all
health endpoints and demographic groups (i.e., race/ethnicity and income) evaluated. A summary
of benefits anticipated for each health endpoint is included below. In general, when comparing
benefits under the final rule to those across regulatory alternatives, the distribution of quantified
health benefits for a given demographic group is relatively similar. Variation exists between the
final rule and regulatory alternatives with respect to the total amount of health benefits
anticipated. Additionally, across all health endpoints evaluated and across all race/ethnicity
groups, the greatest benefits are anticipated under the final rule.
Below is a summary of quantified health benefits categorized by endpoint, with results presented
across the final rule and regulatory alternatives and across demographic groups.
Cardiovascular Disease
Non-Fatal MI Cases Avoided- Under the final rule and all alternatives and across all
race/ethnicity and income groups, values range from 1.07 to 3.78 cases avoided per 100,000
people per year. Under the final rule and all alternatives, the EPA anticipates the greatest benefit
to accrue to the Hispanic race/ethnicity group and the lowest benefit to accrue to the non-
Hispanic Black race/ethnicity group. The number of MI cases avoided per 100,000 people per
year is similar across income groups (e.g., for the final rule, 3.09 cases avoided per 100,000
people for populations with income below twice the poverty level vs. 2.99 cases avoided per
100,000 people for populations with income above twice the poverty level).
Non-Fatal IS Cases Avoided - Under the final rule and all alternatives and across all
race/ethnicity and income groups, values range from 1.58 to 7.48 cases avoided per 100,000
people per year. Under the final rule and all alternatives, the EPA anticipates the greatest benefit
to accrue to the non-Hispanic Black race/ethnicity group. Under the final rule, the EPA
anticipates the lowest benefit to accrue to the non-Hispanic White race/ethnicity group, though
this is not the case across all regulatory alternatives evaluated.103 The number of IS cases avoided
per 100,000 people per year is similar across income groups (e.g., for the final rule, 4.68 cases
avoided per 100,000 people for populations with income below twice the poverty level vs. 4.45
cases avoided per 100,000 people for populations with income above twice the poverty level).
CVD Deaths Avoided -Under the final rule and all alternatives and across all race/ethnicity and
income groups, values range from 0.53 to 3.90 deaths avoided per 100,000 people per year.
Under the final rule and all alternatives, the EPA anticipates the greatest benefit to accrue to the
non-Hispanic Black race/ethnicity group. The lowest benefit is anticipated to accrue to the non-
Hispanic White race/ethnicity group under the final rule and Options la and lb, whereas the
Other race/ethnicity group is anticipated to experience the lowest benefit under Option lc. The
number of deaths avoided per 100,000 people per year is similar across income groups (e.g., for
the final rule, 1.72 deaths avoided per 100,000 people for populations with income below twice
103 The non-Hispanic White race/ethnicity group is anticipated to experience the lowest benefit related to non-fatal IS cases
avoided under the final rule and under Options la and lb. Under Option lc, the Other race/ethnicity group is anticipated to
experience the lowest benefit for non-fatal IS cases avoided, (i.e., 1.58 cases avoided per 100,000 people).
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the poverty level vs. 1.62 deaths avoided per 100,000 people for populations with income above
twice the poverty level).
Renal Cell Carcinoma
Non-Fatal RCC Cases Avoided - Under the final rule and all alternatives and across all
race/ethnicity and income groups, values range from 0.98 to 4.04 cases avoided per 100,000
people per year. Under the final rule and all alternatives, the EPA anticipates the greatest benefit
to accrue to Hispanic race/ethnicity groups and the lowest benefit to accrue to the non-Hispanic
White race/ethnicity group. The number of cases avoided per 100,000 people per year is similar
across income groups (e.g., for the final rule, 3.09 cases avoided per 100,000 people for
populations with income below twice the poverty level vs. 3.02 cases avoided per 100,000
people for populations with income above twice the poverty level).
Fatal RCC Cases Avoided - Under the final rule and all alternatives and across all race/ethnicity
and income groups, values range from 0.26 to 1.44 deaths avoided per 100,000 people per year.
Under the final rule and all alternatives, the EPA expects the greatest benefit to accrue to the
Hispanic race/ethnicity group and the lowest benefit to accrue to the non-Hispanic White
race/ethnicity group. The number of deaths avoided per 100,000 people per year is similar across
income groups (e.g., for the final rule, 0.91 deaths avoided per 100,000 people for populations
with income below twice the poverty level vs. 0.88 deaths avoided per 100,000 people for
populations with income above twice the poverty level).
Birth Weight
Birth Weight Gain (total grams) - Under the final rule and all alternatives and across all
race/ethnicity and income groups, values range from 32,431 grams to 167,846 grams of birth
weight gain per 100,000 people per year. Under the final rule and all alternatives, the EPA
expects the largest benefit to accrue to the Hispanic race/ethnicity group and the lowest benefit to
accrue to the non-Hispanic White race/ethnicity group. The EPA also expects slightly larger
benefits to accrue to populations with income below twice the poverty level (100,943 grams of
birth weight gain per 100,000 people per year under the final rule) compared to populations with
income above twice the poverty level (93,366 grams of birth weight gain per 100,000 people per
year under the final rule).
Birth Weight-Related Deaths Avoided -Under the final rule and all alternatives and across all
race/ethnicity and income groups, values range from 0.19 to 1.00 birth weight-related deaths
avoided per 100,000 people per year. Under the final rule and all alternatives, the EPA
anticipates the greatest benefit to accrue to the non-Hispanic Black race/ethnicity group and the
lowest benefit to accrue to the non-Hispanic White race/ethnicity group. The number of birth
weight-related deaths avoided per 100,000 people per year is similar across income groups (e.g.,
for the final rule, 0.62 deaths avoided per 100,000 people for populations with income below
twice the poverty level vs. 0.55 deaths avoided per 100,000 people for populations with income
above twice the poverty level).
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Table 8-22: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Final Rule (PFOA and PFOS MCLs of 4.0 ppt each, PFHxS, PFNA, HFPO-DA
MCLs of 10 ppt each and HI of 1)
Race and Ethnicity Income
Health
Endpoint
Non-
Hispanic
Black
Hispanic
Other
Non-Hispanic
White
Below
Twice
the
Poverty
Level
Above
Twice
the
Poverty
Level
Non-Fatal MI
2.34
3.78
3.52
2.91
3.09
2.99
Cases Avoided
Non-Fatal IS
7.48
5.33
3.87
3.78
4.68
4.45
Cases Avoided
CVD Deaths
3.90
1.57
1.29
1.26
1.72
1.62
Avoided
Non-Fatal RCC
3.31
4.04
3.04
2.73
3.09
3.02
Cases Avoided
Fatal RCC
0.96
1.44
0.86
0.74
0.91
0.88
Cases Avoided
Birth Weight
122,024
167,846
102,190
71,201
100,943
93,366
Gain (total
grams)
Birth Weight-
1.00
0.93
0.47
0.41
0.62
0.55
Related Deaths
Avoided
Abbreviations: CVD - cardiovascular disease; MI - myocardial infarction; IS - ischemic stroke; RCC - renal cell carcinoma.
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Table 8-23: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option la (PFOA and PFOS MCLs of 4.0 ppt)
Race and Ethnicity Income
Health Endpoint
Non-
Hispanic
Black
Hispanic
Other
Non-
Hispanic
White
Below
Twice the
Poverty
Level
Above
Twice the
Poverty
Level
Non-Fatal MI
2.32
3.76
3.50
2.90
3.07
2.97
Cases Avoided
Non-Fatal IS
7.44
5.30
3.85
3.76
4.65
4.42
Cases Avoided
CVD Deaths
3.88
1.56
1.28
1.26
1.71
1.62
Avoided
Non-Fatal RCC
3.29
4.02
3.02
2.72
3.07
3.00
Cases Avoided
Fatal RCC Cases
0.96
1.43
0.85
0.73
0.90
0.87
Avoided
Birth Weight Gain
121,470
166,945
101,591
70,745
100,345
92,829
(total grams)
Birth Weight-
0.99
0.92
0.47
0.41
0.62
0.55
Related Deaths
Avoided
Abbreviations: CVD - cardiovascular disease; MI - myocardial infarction; IS - ischemic stroke; RCC - renal cell carcinoma.
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Table 8-24: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option lb (PFOA and PFOS MCLs of 5.0 ppt)
Race and Ethnicity Income
Health
Endpoint
Non- Hispanic
Black
Hispanic
Other
Non-Hispanic
White
Below
Twice
the
Poverty
Level
Above
Twice
the
Poverty
Level
Non-Fatal MI
2.00
3.30
2.96
2.46
2.64
2.54
Cases Avoided
Non-Fatal IS
6.40
4.66
3.26
3.19
4.01
3.78
Cases Avoided
CVD Deaths
3.34
1.37
1.09
1.07
1.47
1.38
Avoided
Non-Fatal RCC
2.75
3.48
2.50
2.22
2.57
2.49
Cases Avoided
Fatal RCC Cases
0.80
1.23
0.70
0.60
0.76
0.73
Avoided
Birth Weight
105,756
147,990
86,953
60,483
87,588
80,186
Gain (total
grams)
Birth Weight-
0.86
0.82
0.40
0.35
0.54
0.48
Related Deaths
Avoided
Abbreviations: CVD - cardiovascular disease; MI - myocardial infarction; IS - ischemic stroke; RCC - renal cell carcinoma.
Table 8-25: Annualized Cases Avoided per 100,000 People by Race/Ethnicity and Income
Group, Option lc (PFOA and PFOS MCLs of 10.0 ppt)
Race and Ethnicity Income
Health
Endpoint
Non- Hispanic
Black
Hispanic
Other
Non-Hispanic
White
Below
Twice
the
Poverty
Level
Above
Twice
the
Poverty
Level
Non-Fatal MI
1.07
1.93
1.43
1.26
1.45
1.32
Cases Avoided
Non-Fatal IS
3.41
2.73
1.58
1.64
2.21
1.97
Cases Avoided
CVD Deaths
1.78
0.81
0.53
0.55
0.81
0.72
Avoided
Non-Fatal RCC
1.35
1.99
1.13
0.98
1.28
1.17
Cases Avoided
Fatal RCC Cases
0.39
0.71
0.32
0.26
0.38
0.35
Avoided
Birth Weight
59,981
89,583
44,661
32,431
50,809
44,150
Gain (total
grams)
Birth Weight-
0.49
0.49
0.21
0.19
0.32
0.26
Related Deaths
Avoided
Abbreviations: CVD - cardiovascular disease; MI - myocardial infarction; IS - ischemic stroke; RCC - renal cell carcinoma.
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8.4.2.2 Household Costs
For category 1 and 2 PWS service areas, the EPA used SafeWater MCBC to estimate the
distribution of annualized incremental household costs across race/ethnicity and income groups.
The results are provided by system size category in Table 8-26 through Table 8-29. In addition to
presenting annualized incremental household costs for each race/ethnicity group in Table 8-26
and Table 8-28, the EPA also presents household costs across "All" race/ethnicity groups to
provide a basis for comparison. Table 8-28 and Table 8-29 present annualized incremental
household costs by income group.
In estimating annualized incremental household costs of the final PFAS NPDWR, SafeWater
MCBC first divided each PWS's total compliance costs by the PWS's average daily flow to
determine the cost of compliance per 1,000 gallons of daily flow. Next, this cost was multiplied
by the average household consumption from the Community Water System Survey (CWSS) to
calculate the average household cost of compliance for the PWS. To calculate the average
household cost for each race/ethnicity group by PWS system size strata, for each PWS included
in the subset of systems in the EPA's EJ analysis, the EPA calculated a weighted average
household cost by using the number of people in each race/ethnicity or income group served by
each PWS as the weight. In addition to estimating the demographic breakdown of annualized
incremental household costs of the final PFAS NPDWR for all systems included in the EPA's EJ
analysis, the EPA also estimated the demographic breakdown of annualized incremental
household costs for just the subset of PWSs that are anticipated to install treatment to comply
with the rule.104
Below is a summary of the demographic distribution of incremental household costs, categorized
by system size, for the final rule and regulatory alternatives. Results are presented both for the
entire subset of PWSs included in the EPA's EJ analysis and just those anticipated to install
treatment under the rule. Note that an analysis of household costs served by systems serving
fewer than 3,300 people could not be completed due to limited sample size. In general, across all
demographic groups and system size categories, the final rule is anticipated to have the highest
associated costs and Option lc is anticipated to have the lowest associated costs.
8.4.2.2.1IncrementaI Household Costs for All PWSs
System size 3,300 to 10,000 - Annualized incremental household costs range from $5.88 to
$29.26 per year across the final rule and regulatory alternatives and across race/ethnicity groups.
When comparing household costs borne by particular race/ethnicity groups to those borne by the
overall population served by systems in this size category, the non-Hispanic Black and Other
race/ethnicity groups bear minimally elevated household costs under the final rule and all
regulatory alternatives. Additionally, the Hispanic race/ethnicity group bears minimally elevated
household costs under the final rule and Options la and lb. The magnitude of household cost
differences between each of these race/ethnicity groups and the overall population is small,
104 For additional detail on treatment technology selection among systems anticipated to install treatment under the proposed
rule, see Section 5.3.1.1.
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ranging from $0.03 to $4.04 per year across race/ethnicity groups and across the final rule and
regulatory alternatives. The Other race/ethnicity group bears the highest household costs under
the final rule and Options la and lb, whereas the non-Hispanic Black race/ethnicity group bears
the highest household costs under Option lc. The non-Hispanic White race/ethnicity group bears
the lowest household costs under the final rule and Options la and lb, whereas the Hispanic
race/ethnicity groups bears the lowest household costs under Option lc. When comparing
incremental household costs across income groups, costs range from $4.99 to $26.93 per year
across the final rule and regulatory alternatives. Populations with income above twice the
poverty level bear higher incremental household costs compared to populations with income
below twice the poverty level, with the cost difference ranging from $1.76 to $5.00.
System size 10,000 to 50,000 - Annualized incremental household costs range from $4.34 to
$16.41 per year across the final rule and regulatory alternatives and across race/ethnicity groups.
When comparing household costs borne by particular race/ethnicity groups to those borne by the
overall population served by systems in this size category, the Other race/ethnicity group bears
minimally elevated household costs under the final rule and all regulatory alternatives. The
Hispanic race/ethnicity group also bears minimally elevated household costs under Option lc.
The magnitude of household cost differences between each of these race/ethnicity groups and the
overall population is very small, ranging from $0.02 to $1.74 per year across race/ethnicity
groups and across the final rule and regulatory alternatives. Under the final rule and all
regulatory alternatives, the Other race/ethnicity group bears the highest household costs, whereas
the non-Hispanic Black race/ethnicity group bears the lowest household costs. Under Option lb,
both the non-Hispanic Black and non-Hispanic White race/ethnicity groups bear the lowest
household costs. When comparing incremental household costs across income groups, costs
range from $4.13 to $15.07 per year across the final rule and regulatory alternatives. Populations
with income above twice the poverty level bear slightly higher incremental household costs
compared to populations with income below twice the poverty level, with the cost difference
ranging from $0.45 to $1.32.
System size 50,000 to 100,000 - Annualized incremental household costs range from $3.29 to
$13.67 per year across the final rule and regulatory alternatives and across race/ethnicity groups.
When comparing household costs borne by particular race/ethnicity groups to those borne by the
overall population served by systems in this size category, the Hispanic and Other race/ethnicity
groups bear minimally elevated household costs under the final rule and all regulatory
alternatives. Additionally, the non-Hispanic Black race/ethnicity group bears minimally elevated
household costs under Option lc. The magnitude of household cost differences between each of
these race/ethnicity groups and the overall population is very small, ranging from $0.10 to $1.00
per year across race/ethnicity groups and across the final rule and regulatory alternatives. The
Other race/ethnicity group bears the highest household costs under the final rule and Options la
and lb, whereas the Hispanic race/ethnicity group bears the highest household cost under Option
lc. The non-Hispanic Black race/ethnicity group bears the lowest household costs under the final
rule and Option la, whereas the non-Hispanic White race/ethnicity group bears the lowest
household costs under Options lb and lc. When comparing incremental household costs across
income groups, costs range from $3.42 to $12.87 per year across the final rule and regulatory
alternatives. Populations with income below twice the poverty level bear slightly higher
incremental household costs compared to populations with income above twice the poverty level.
However, the magnitude of these cost differences is small, ranging from $0.14 to $0.31.
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System size 100,000 to 1,000,000 - Annualized incremental household costs range from $3.59 to
$13.46 per year across the final rule and regulatory alternatives and across race/ethnicity groups.
When comparing household costs borne by particular race/ethnicity groups to those borne by the
overall population served by systems in this size category, the non-Hispanic Black, Hispanic, and
Other race/ethnicity groups bear minimally elevated household costs under the final rule and all
regulatory alternatives. As in other system size categories, the magnitude of household cost
differences between each of these race/ethnicity groups and the overall population is small,
ranging from $0.11 to $1.18 per year across race/ethnicity groups and across the final rule and
regulatory alternatives. The Hispanic race/ethnicity group bears the highest household costs
under the final rule and Options la and lb, whereas the Other race/ethnicity group bears the
highest household costs under Option lc. The non-Hispanic White race/ethnicity group bears the
lowest household costs. When comparing incremental household costs across income groups,
costs range from $3.73 to $12.44 per year across the final rule and regulatory alternatives.
Populations with income below twice the poverty level bear slightly higher incremental
household costs compared to populations with income above twice the poverty level. However,
the magnitude of these cost differences is very small, ranging from $0.22 to $0.31.
The EPA's comparison of annualized incremental household costs across system size categories
reveals that, in general, as system size increases, incremental household costs decrease under the
final rule and all regulatory alternatives and across all demographic groups. One exception to this
trend is Option lc among systems serving 100,000 to 1,000,000 people, where costs are
marginally higher than costs for systems serving 50,000 to 100,000 people.
The highest incremental household costs under the final rule and all regulatory alternatives are
realized for the smallest systems (i.e., systems serving 3,300 to 10,000 people). Across
race/ethnicity groups examined, the range of household costs within this system size category is
$5.88 to $29.26 per year, and the EPA anticipates the highest cost ($29.26 per year) under the
final rule for the Other race/ethnicity group. When comparing costs across income groups,
populations with income above twice the poverty level bear the highest costs ($26.93) within this
system size category under the final rule. The lowest incremental household costs under the final
rule and all regulatory alternatives are realized for systems serving 50,000 to 100,000 people.
Across race/ethnicity groups examined, the range of household costs within this system size
category is $3.29 to $13.67, with the non-Hispanic White race/ethnicity group having the lowest
cost of $3.29 under Option lc. When comparing costs across income groups within this system
size category, populations with income above twice the poverty level bear the lowest costs
($3.42) within this system size category under Option lc.
Comparing the magnitude of household costs anticipated across system size categories illustrates
the role that system size plays in household costs anticipated under the final PFAS rule. This is
an expected result due to economies of scale and the impact that a smaller customer and tax base
has on costs per household for funding and financing capital and operational infrastructure
investments. Further, this analysis includes the estimated household costs for all systems
impacted by the rule, not just the systems expected to install and operate treatment after
exceeding the final MCLs. Households served by water systems triggered into treatment are
expected to face greater cost increases than those presented here. The EPA presents the
demographic breakdown of estimated household costs for those systems anticipated to install
treatment under the final rule below in Section 8.4.2.2.2. Additionally, the EPA assesses the
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impact of treatment technology costs specifically on small system households in the small
system affordability analysis. For more information, see the EPA's assessment of small system
affordability in Section 9.13.
Table 8-26: Annualized Population Weighted Household Cost by PWS Size Category and
Race/Ethnicity Group ($2022)
System Size3
Race/Ethnicity
Group
Final Ruleb
Option lac
Option lbd
Option lce
3,300 to 10,000
All
$25.22
$25.15
$18.78
$6.15
3,300 to 10,000
Non-Hispanic Black
$28.24
$28.17
$21.49
$7.89
3,300 to 10,000
Hispanic
$27.20
$27.15
$20.38
$5.88
3,300 to 10,000
Other
$29.26
$29.17
$21.69
$6.18
3,300 to 10,000
Non-Hispanic White
$24.30
$24.23
$18.01
$5.94
10,000 to 50,000
All
$14.67
$14.59
$11.32
$4.44
10,000 to 50,000
Non-Hispanic Black
$14.55
$14.48
$11.23
$4.34
10,000 to 50,000
Hispanic
$14.64
$14.57
$11.29
$4.46
10,000 to 50,000
Other
$16.41
$16.33
$12.90
$5.28
10,000 to 50,000
Non-Hispanic White
$14.57
$14.49
$11.23
$4.40
50,000 to 100,000
All
$12.67
$12.56
$9.51
$3.46
50,000 to 100,000
Non-Hispanic Black
$12.30
$12.22
$9.41
$3.66
50,000 to 100,000
Hispanic
$12.93
$12.80
$9.78
$3.84
50,000 to 100,000
Other
$13.67
$13.51
$10.27
$3.81
50,000 to 100,000
Non-Hispanic White
$12.55
$12.46
$9.38
$3.29
100,000 to 1,000,000
All
$12.28
$12.13
$9.41
$3.83
100,000 to 1,000,000
Non-Hispanic Black
$12.39
$12.24
$9.63
$4.01
100,000 to 1,000,000
Hispanic
$13.46
$13.27
$10.26
$4.27
100,000 to 1,000,000
Other
$13.25
$13.11
$10.24
$4.28
100,000 to 1,000,000
Non-Hispanic White
$11.77
$11.63
$8.98
$3.59
Notes:
aThe number of systems serving fewer than 3,300 people represented in the UMCR 3 occurrence data is too limited to accurately
estimate average population-weighted household costs by subpopulation. Therefore, results for these small systems are omitted.
Also, household costs in this exhibit are population-weighted and will not match average household costs by size category
shown in other exhibits in the economic analysis document that are not population-weighted.
bThe final rule sets PFOA and PFOS MCLs of 4.0 ppt each, MCLs of 10 ppt for HFPO-DA, PFHxS, and PFNA each, and an HI
of 1.
cOption la sets PFOA and PFOS MCLs of 4.0 ppt.
Option lb sets PFOA and PFOS MCLs of 5.0 ppt.
eOption lc sets PFOA and PFOS MCLs of 10.0 ppt.
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Table 8-27: Annualized Population Weighted Household Cost by PWS Size Category and
Income Level ($2022)
System Size"
Income
Final Ruleb
Option lac
Option lbd
Option lce
3,300 to 10,000
Below twice the
$21.93
$21.87
$15.95
$4.99
3,300 to 10,000
poverty level
Above twice the
$26.93
$26.87
$20.25
$6.75
10,000 to 50,000
poverty level
Below twice the
$13.75
$13.68
$10.55
$4.13
10,000 to 50,000
poverty level
Above twice the
$15.07
$15.00
$11.66
$4.58
50,000 to 100,000
poverty level
Below twice the
$12.87
$12.78
$9.73
$3.56
50,000 to 100,000
poverty level
Above twice the
$12.58
$12.47
$9.42
$3.42
100,000 to 1,000,000
poverty level
Below twice the
$12.44
$12.28
$9.56
$4.04
100,000 to 1,000,000
poverty level
Above twice the
poverty level
$12.21
$12.06
$9.33
$3.73
Notes:
aThe number of systems serving fewer than 3,300 people represented in the UMCR 3 occurrence data is too limited to
accurately estimate average population-weighted household costs by subpopulation. Therefore, results for these small systems
are omitted. Also, household costs in this exhibit are population-weighted and will not match average household costs by size
category shown in other exhibits in the economic analysis document that are not population-weighted.
bThe final rule sets PFOA and PFOS MCLs of 4.0 ppt each, MCLs of 10 ppt for HFPO-DA, PFHxS, and PFNA each, and an
HI of 1.
cOption la sets PFOA and PFOS MCLs of 4.0 ppt.
Option lb sets PFOA and PFOS MCLs of 5.0 ppt.
eOption lc sets PFOA and PFOS MCLs of 10.0 ppt.
8.4.2.2.2 Incremental Household Costs for Treating PWSs
System size 3,300 to 10,000 - Annualized incremental household costs for systems anticipated to
install treatment range from $120.94 to $181.78 per year across the final rule and regulatory
alternatives and across race/ethnicity and income groups. When comparing household costs
borne by particular race/ethnicity groups to those borne by the overall population served by
systems in this system size category, the Hispanic and Other race/ethnicity group bears
minimally elevated household costs under the final rule and Option la. Additionally, the non-
Hispanic Black race/ethnicity group bears minimally elevated household costs under Options lb
and lc and the non-Hispanic White race/ethnicity group bears minimally elevated household
costs under Option lc. The magnitude of household cost differences between each of these
race/ethnicity groups and the overall population ranges from $0.21 to $30.12 per year across
race/ethnicity groups and across the final rule and regulatory alternatives. The Other
race/ethnicity group bears the highest household costs under the final rule and Option la,
whereas the non-Hispanic Black race/ethnicity group bears the highest household costs under
Options lb and lc. The non-Hispanic Black race/ethnicity group bears the lowest household
costs under the final rule and Option la, whereas the Hispanic race/ethnicity group bears the
lowest household costs under Options lb and lc. Populations with income below twice the
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poverty level bear lower incremental household costs compared to populations with income
above twice the poverty level under the final rule and all regulatory alternatives. The magnitude
of household cost differences between the two income groups ranges from $0.65 to $15.96 per
year across the final rule and regulatory alternatives.
System size 10,000 to 50,000 - Annualized incremental household costs for systems anticipated
to install treatment range from $39.05 to $51.82 per year across the final rule and regulatory
alternatives and across race/ethnicity and income groups. When comparing household costs
borne by particular race/ethnicity groups to those borne by the overall population served by
systems in this system size category, the Other and non-Hispanic White race/ethnicity groups
bear minimally elevated household costs under the final rule and all regulatory alternatives.
Additionally, the non-Hispanic Black race/ethnicity group bears minimally elevated household
costs under Option lc. The magnitude of household cost differences between each race/ethnicity
group and the overall population is small, ranging from $0.05 to $2.55 per year across
race/ethnicity groups and across the final rule and regulatory alternatives. The Other
race/ethnicity group bears the highest household costs under the final rule and Options la and lb,
whereas the non-Hispanic Black race/ethnicity group bears the highest household costs under
Option lc. The Hispanic race/ethnicity group bears the lowest household costs under the final
rule and all regulatory alternatives. Populations with income below twice the poverty level bear
slightly lower incremental household costs compared to populations with income above twice the
poverty level under the final rule and Options la and lb; populations with income above twice
the poverty level bear slightly lower incremental household costs compared to populations with
income below twice the poverty level under Option lc. The magnitude of household cost
differences between the two income groups is very small, ranging from $0.22 to $1.31 per year
across the final rule and regulatory alternatives.
System size 50,000 to 100,000 - Annualized incremental household costs for systems anticipated
to install treatment range from $31.53 to $43.84 per year across the final rule and regulatory
alternatives and across race/ethnicity and income groups. When comparing household costs
borne by particular race/ethnicity groups to those borne by the overall population served by
systems in this system size category, the non-Hispanic Black and non-Hispanic White
race/ethnicity group bears minimally elevated costs under the final rule and all regulatory
alternatives. Additionally, the Other race/ethnicity group bears minimally elevated household
costs under the final rule and Options la and lb. The magnitude of household cost differences
between each of these race/ethnicity groups and the overall population is very small, ranging
from $0.10 to $2.04 per year across race/ethnicity groups and across the final rule and regulatory
alternatives. The non-Hispanic Black race/ethnicity group bears the highest household costs
under the final rule and all regulatory alternatives. The Hispanic race/ethnicity group bears the
lowest household costs under the final rule and all regulatory alternatives. Populations with
income below twice the poverty level bear slightly lower incremental household costs compared
to populations with income above twice the poverty level under the final rule and all regulatory
alternatives. The magnitude of household cost differences between the two income groups is
very small, ranging from $0.24 to $0.98 per year across the final rule and all regulatory
alternatives.
System 100,000 to 1,000,000 - Annualized incremental household costs for systems anticipated
to install treatment range from $21.63 to $32.92 per year across the final rule and regulatory
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alternatives and across race/ethnicity and income groups. When comparing household costs
borne by particular race/ethnicity groups to those borne by the overall population served by
systems in this size category, the non-Hispanic Black race/ethnicity group bears minimally
elevated costs under the final rule and all regulatory alternatives. Additionally, the Hispanic
race/ethnicity group bears minimally elevated costs under the final rule and Option la, whereas
the non-Hispanic White race/ethnicity group bears minimally elevated costs under Options lb
and lc. The magnitude of household cost differences between each of these race/ethnicity groups
and the overall population is small, ranging from $0.02 to $1.73 per year across race/ethnicity
groups and across the final rule and regulatory alternatives. The non-Hispanic Black
race/ethnicity group bears the highest household costs under the final rule and all regulatory
alternatives. The Other race/ethnicity group bears the lowest household costs under the final rule,
Option la, and Option lb, whereas the Hispanic race/ethnicity group bears the lowest household
costs under Option lc. Populations with income below twice the poverty level bear slightly
higher incremental household costs compared to populations with income above twice the
poverty level under the final rule and all regulatory alternatives. The magnitude of household
cost differences between the two income groups is very small, ranging from $1.01 to $1.74 per
year across the final rule and all regulatory alternatives.
Consistent with the EPA's findings for incremental household costs across all systems, the
EPA's comparison of incremental household costs across system size categories for just treating
systems reveals that, in general, as system size increases, annualized incremental household costs
decrease under the final rule and all regulatory alternatives and across all race/ethnicity and
income groups.
The highest annualized incremental household costs for treating systems under the final rule and
all regulatory alternatives are realized for the smallest systems, with the range of incremental
household costs for systems serving 3,300 to 10,000 people ranging from $120.94 to $181.78 per
year across race/ethnicity groups examined. The EPA anticipates the highest cost ($181.78 per
year) under Option la for the Other race/ethnicity group. Among the two income groups, the
EPA anticipates the highest cost ($180.70) under the final rule for populations with income
above twice the poverty level. Systems serving 100,000 to 1,000,000 people bear the lowest
annualized incremental household costs for treating systems under the final rule and all options.
This analysis provides an opportunity to understand the demographic breakdown of incremental
household costs anticipated to be incurred due to treatment installation needed to comply with
the final PFAS NPDWR. Annualized incremental household costs for systems required to install
treatment are higher for all size categories and across all demographic groups compared to
incremental household costs across all systems. These differences are expected, as treatment
installation costs are higher than other compliance costs (i.e., monitoring and reporting). In some
cases, such as for communities served by the smallest systems (i.e., systems serving 3,300 to
10,000 people), the annual incremental household costs isolated among only systems anticipated
to install treatment are over $100 higher than annual incremental household costs averaged
across all systems.
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Table 8-28: Annualized Population-Weighted Household Cost for Treating PWSs by Size
Category and Race/Ethnicity Group
System Size3
Race/Ethnicity Group
Final Ruleb
Option
lac
Option
lbd
Option
lce
3,300 to 10,000
All
$175.66
$175.56
$167.04
$151.06
3,300 to 10,000
Non-Hispanic Black
$174.17
$173.99
$169.33
$163.10
3,300 to 10,000
Hispanic
$177.67
$177.49
$166.11
$120.94
3,300 to 10,000
Other
$181.64
$181.78
$166.46
$123.78
3,300 to 10,000
Non-Hispanic White
$175.25
$175.17
$166.83
$156.09
10,000 to 50,000
All
$50.92
$50.69
$48.07
$41.03
10,000 to 50,000
Non-Hispanic Black
$50.78
$50.56
$48.02
$41.42
10,000 to 50,000
Hispanic
$48.37
$48.16
$45.57
$39.05
10,000 to 50,000
Other
$51.82
$51.60
$48.92
$41.38
10,000 to 50,000
Non-Hispanic White
$51.39
$51.16
$48.53
$41.36
50,000 to 100,000
All
$43.08
$42.74
$40.51
$32.81
50,000 to 100,000
Non-Hispanic Black
$43.84
$43.58
$41.86
$34.85
50,000 to 100,000
Hispanic
$41.34
$40.96
$38.71
$31.53
50,000 to 100,000
Other
$43.50
$43.04
$40.61
$31.72
50,000 to 100,000
Non-Hispanic White
$43.43
$43.11
$40.82
$33.01
100,000 to 1,000,000
All
$32.62
$32.24
$29.63
$23.34
100,000 to 1,000,000
Non-Hispanic Black
$32.92
$32.54
$30.34
$25.07
100,000 to 1,000,000
Hispanic
$32.77
$32.31
$29.29
$21.63
100,000 to 1,000,000
Other
$32.07
$31.76
$28.95
$22.48
100,000 to 1,000,000
Non-Hispanic White
$32.56
$32.20
$29.65
$23.69
Notes:
aThe number of systems serving fewer than 3,300 people represented in the UMCR 3 occurrence data is too limited to
accurately estimate average population-weighted household costs by subpopulation. Therefore, results for these small systems
are omitted. Also, household costs in this exhibit are population-weighted and will not match average household costs by size
category shown in other exhibits in the economic analysis document that are not population-weighted.
bThe final rule sets PFOA and PFOS MCLs of 4.0 ppt each, MCLs of 10 ppt for HFPO-DA, PFHxS, and PFNA each, and an
HI of 1.
cOption la sets PFOA and PFOS MCLs of 4.0 ppt.
Option lb sets PFOA and PFOS MCLs of 5.0 ppt.
eOption lc sets PFOA and PFOS MCLs of 10.0 ppt.
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Table 8-29: Annualized Population Weighted Household Cost for Treating PWSs by
PWS Size Category and Income Level ($2022)
System Size"
Income
Final Ruleb
Option lac
Option lbd
Option lce
3,300 to 10,000
Below twice the
$164.78
$164.65
$158.28
$150.58
3,300 to 10,000
poverty level
Above twice the
$180.70
$180.61
$170.88
$151.23
10,000 to 50,000
poverty level
Below twice the
$49.99
$49.77
$47.38
$41.19
10,000 to 50,000
poverty level
Above twice the
$51.30
$51.07
$48.35
$40.97
50,000 to 100,000
poverty level
Below twice the
$42.86
$42.58
$40.23
$32.15
50,000 to 100,000
poverty level
Above twice the
$43.18
$42.82
$40.64
$33.13
100,000 to 1,000,000
poverty level
Below twice the
$33.33
$32.92
$30.45
$24.51
100,000 to 1,000,000
poverty level
Above twice the
poverty level
$32.28
$31.91
$29.24
$22.77
Notes:
aThe number of systems serving fewer than 3,300 people represented in the UMCR 3 occurrence data is too limited to
accurately estimate average population-weighted household costs by subpopulation. Therefore, results for these small systems
are omitted. Also, household costs in this exhibit are population-weighted and will not match average household costs by size
category shown in other exhibits in the economic analysis document that are not population-weighted.
bThe final rule sets PFOA and PFOS MCLs of 4.0 ppt each, MCLs of 10 ppt for HFPO-DA, PFHxS, and PFNA each, and an
HI of 1.
cOption la sets PFOA and PFOS MCLs of 4.0 ppt.
Option lb sets PFOA and PFOS MCLs of 5.0 ppt.
eOption lc sets PFOA and PFOS MCLs of 10.0 ppt.
8.5 Conclusions
This section provides a summary of the EJ analyses for estimating the demographic distribution
of baseline PFAS exposure and exposure over several thresholds as well as the cost and benefits
of the final PFAS NPDWR.
8.5.1 EJ PFAS Exposure Analysis
The EPA's analysis of demographic groups with PFAS exposure over baseline thresholds based
on trigger levels of 2 ppt demonstrates that certain communities of color experience elevated
baseline PFAS drinking water exposures compared to the entire sample population. For example,
the percentage of non-Hispanic Black and Hispanic populations with PFAS drinking water
exposure above baseline thresholds is greater than the percentage of the total populations served
across all PFAS analytes considered in this analysis. Similarly, when these results are further
filtered by system size, for large systems, non-Hispanic Black and Hispanic populations have
higher baseline PFAS drinking water exposure compared to the percentage of the total
population served across all demographic groups. For small systems, non-Hispanic Asian and
non-Hispanic Black populations served have higher baseline PFAS drinking water exposure
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compared to the percentage of the total population served across all demographic groups for
particular PFAS analytes.
Across all hypothetical regulatory thresholds, elevated exposure—and thus expected reductions
in exposure under the hypothetical regulatory scenarios—is anticipated to occur in communities
of color and/or low-income populations. The EPA estimates the most notable differences in
anticipated reductions in exposure are for Hispanic populations, specifically when using UCMR
5 MRL values as hypothetical regulatory thresholds in the analysis. The results from the EPA's
analysis indicate that Hispanic populations are estimated to experience at least two percentage
points higher rates of exposure to all PFAS analytes examined in this analysis. Hispanic
populations are therefore also anticipated to experience greater reductions in exposure compared
to the entire sample population. In addition, under hypothetical regulatory thresholds set at the
UCMR 5 MRL values, the EPA anticipates some of the largest reductions in exposure to PFOA
and PFHxS to occur for non-Hispanic Native American or Alaska Native and non-Hispanic
Pacific Islander populations due to relatively high concentration levels when these PFAS are
detected at PWSs serving these groups.
These findings are supported by literature that indicates that communities of lower
socioeconomic status are more likely to live near environmentally hazardous facilities and face
disproportionate impacts of exposure to toxic chemicals than communities of relatively higher
socioeconomic status (Brown, 1995; Brulle & Pellow, 2006; Banzhaf et al., 2019; U.S. EPA,
1994). The literature also indicates that people of color and low-income populations are more
likely to be served by water systems with higher PFAS occurrence or reside in proximity to a
PFAS contamination site, thereby increasing baseline exposure (Black et al., 2021; Lee, Kang, et
al., 2021; Desikan et al., 2019).
8.5.2 Safe Water EJ Analysis of Regulatory Options
The EPA's analysis of the demographic distribution of health benefits and household costs
anticipated to result from the final PFAS NPDWR and regulatory alternatives evaluated
demonstrates that, in general, across demographic groups, the EPA's final rule offers the greatest
quantified benefits when compared to benefits anticipated to result under the regulatory
alternatives. Additionally, in general, when compared to regulatory alternatives evaluated, the
EPA's final rule will result in the highest household costs.
Under the final rule, across all health endpoints evaluated, communities of color (i.e., Hispanic,
non-Hispanic Black, and/or Other race/ethnicity groups) are anticipated to experience the
greatest reductions in adverse health effects associated with PFAS exposure, resulting in the
greatest quantified benefits associated with the final rule. For instance, non-Hispanic Black
populations are expected to experience 7.48 avoided non-fatal IS cases and 3.90 avoided CVD
deaths per 100,000 people per year, as compared to 3.78 avoided non-fatal IS cases and 1.26
avoided CVD deaths per 100,000 people per year for non-Hispanic White populations.
Additionally, under the final rule, while in most cases the difference in cases of illnesses and
deaths avoided across income groups is small, quantified health benefits are higher for low-
income communities (i.e., populations with income below twice the poverty level) across all
health endpoints evaluated, compared to populations with income above twice the poverty level.
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The EPA's findings could be driven by disparities in baseline exposure to PFAS and underlying
disparities in death and/or disease incidence by race/ethnicity. This potential explanation is
supported by literature demonstrating that overburdened communities continue to experience
elevated rates of morbidity and mortality (Uche et al., 2021; Driscoll & Gregory, 2021; Fryar et
al., 2017). Additionally, evidence in the literature indicates that people of color and low-income
populations are more likely to be served by water systems with higher PFAS occurrence or
reside in close proximity to a PFAS contamination site, which also supports this finding (Black
et al., 2021; Lee, Kang, et al., 2021; Desikan et al., 2019).
When examining costs anticipated to result from the final rule, the EPA found that cost
differences across demographic groups are typically small, with no clear unidirectional trend in
cost differences based on demographic group. In some cases, the EPA found that communities of
color and low-income communities are anticipated to bear minimally increased costs but in other
cases, costs to communities of color and low-income communities are anticipated to be lower
than those across all race/ethnicity groups or populations with income above twice the poverty
level, respectively.
Additionally, incremental household costs to all race/ethnicity and income groups generally
decrease as system size increases, which is expected due to economies of scale. This is especially
true if systems serving these communities are required to install treatment to comply with the
PFAS NPDWR. For example, systems serving 3,300 to 10,000 people that will be required to
install treatment to comply with the final rule have substantially higher costs than systems in all
larger size categories, irrespective of demographic group.
8.5.3 Overall Environmental Justice Conclusion
The EPA conducted the EJ analyses presented in this chapter on populations served by a subset
of PWSs to assess the demographic distribution of exposure to PFAS and the EJ impacts that are
anticipated to result from the final PFAS NPDWR. The EPA conducted two separate analyses to
address the following questions:
1. Are population groups of concern (i.e., people of color and low-income populations)
disproportionately exposed to PFAS compounds in drinking water delivered by PWSs?
2. Are population groups of concern disproportionately affected by the final rule?
3. If any disproportionate impacts are identified, do they create or mitigate baseline EJ
concerns?
When examined collectively, results from these analyses identify communities of color and low-
income communities as being disproportionately exposed to PFAS in drinking water under
baseline conditions. In one hypothetical regulatory scenario, non-Hispanic American Indian or
Alaska Native, non-Hispanic Black, and Hispanic populations face elevated exposure across
nearly all PFAS analytes examined when compared to the total population served. In some cases,
these communities experience twice the rate of PFAS exposure in drinking water in comparison
to non-Hispanic White populations. When quantifying the race/ethnicity distribution of
quantified health benefits anticipated to result from the final PFAS NPDWR, the EPA found that
of the race/ethnicity groups evaluated, communities of color are anticipated to experience the
greatest health benefits under the final rule and all regulatory alternatives.
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When comparing benefits across the final rule and regulatory alternatives, quantified health
benefits were generally the highest for communities of color under the final rule. This finding
could be influenced by the fact that elevated baseline exposure rates for these populations
translate to higher benefits associated with the final rule, as greater reductions in exposure are
anticipated to occur as a result of implementing the final PFAS NPDWR.
To alleviate potential cost disparities identified by the EPA's analysis, there may be an
opportunity for many communities to utilize BIL (P.L. 117-58) funding to provide financial
assistance for addressing emerging contaminants. BIL funding has specific allocations for both
disadvantaged and/or small communities and emerging contaminants, including PFAS.
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9 Statutory and Administrative Requirements
As part of the rulemaking process, the EPA is required to address the burden that the final rule
may place on certain types of governments, businesses, and populations. This chapter presents
analyses performed by the EPA in accordance with the following federal mandates and statutory
requirements:
1. Executive Order 12866: Regulatory Planning and Review and Executive Order 13563
(2011): Modernizing Regulatory Review.
2. Paperwork Reduction Act (PRA) (U.S. EPA, 2010b).
3. The Regulatory Flexibility Act (RFA) of 1980, as amended by the Small Business
Regulatory Enforcement Fairness Act (SBREFA) of 1996.
4. Unfunded Mandates Reform Act (UMRA) of 1995.
5. Executive Order 13132: Federalism.
6. Executive Order 13175: Consultation and Coordination with Indian Tribal Governments.
7. Executive Order 13045: Protection of Children from Environmental Health and Safety
Risks.
8. Executive Order 13211: Actions That Significantly Affect Energy Supply, Distribution,
or Use.
9. National Technology Transfer and Advancement Act of 1995 (NTTAA).
10. Executive Order 12898: Federal Action to Address Environmental Justice in Minority
Populations and Low-Income Populations and Executive Order 14096: Revitalizing our
Nation's Commitment to Environmental Justice for All.
11. Consultations with the Science Advisory Board (SAB), National Drinking Water
Advisory Council (NDWAC), and the Department of Health and Human Services.
12. SDWA Section 1412(b)(4)(E) National Small System Affordability Determination.
Many of the statutory requirements and executive orders listed above call for an explanation of
why the final requirements are necessary, the statutory authority for the final requirements, and
the primary objectives that the final requirements are intended to achieve (see Chapter 3 for
additional information regarding the need for the final rule). Others are designed to assess the
financial and health effects of the final regulatory requirements on sensitive, low-income, and
tribal populations as well as on small systems and governments.
9.1 Executive Order 12866: Regulatory Planning and Review
and Executive Order 14094: Modernizing Regulatory
Review
Executive Order 12866, 1993 (58 FR 51735, October 4, 1993), as amended by Executive Order
14094 (88 FR 21879, April 6, 2023) gives OMB the authority to review regulatory actions that
are categorized as "significant" under section 3(f) of Executive Order 12866. The Order defines
"significant regulatory action" as one that is likely to result in a rule that may:
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1. Have an annual effect on the economy of $200 million or more (adjusted every 3 years by
the Administrator of OIRA for changes in gross domestic product); or adversely affect in
a material way the economy, a sector of the economy, productivity, competition, jobs, the
environment, public health or safety, or state, local, or tribal governments or
communities;
2. Create a serious inconsistency or otherwise interfere with an action taken or planned by
another agency;
3. Materially alter the budgetary impact of entitlements, grants, user fees, or loan programs
or the rights and obligations of recipients thereof; or
4. Raise legal or policy issues for which centralized review would meaningfully further the
President's priorities or the principles set forth in this Executive order, as specifically
authorized in a timely manner by the Administrator of OIRA in each case.
This action is an economically significant regulatory action that was submitted to the OMB for
review. Any changes made in response to OMB recommendations have been documented in the
docket. The analysis in Chapter 7 compares the annual estimated incremental costs and the
annual incremental benefits of the final rule. In addition to the monetized costs and benefits of
the final regulation, a number of non-monetized impacts exist. See Sections 5.7, 6.2.2, and 6.2.3
of this EA for greater detail on the non-monetized impacts of the final regulation.
9.2 Additional Analysis Pursuant to EO 12866
The EPA is committed to understanding and addressing climate change impacts in carrying out
the agency's mission of protecting human health and the environment. Pursuant to EO 12866, the
EPA has estimated the carbon dioxide (CO2) emissions associated with the operation of the best
available treatment technologies the EPA expects will be used to comply with the PFAS
NPDWR.
The EPA estimated the climate disbenefits of changes in CO2 emissions expected from the final
PFAS rule using estimates of the social cost of carbon (SC-CO2) that reflect recent advances in
the scientific literature on climate change and its economic impacts and incorporate
recommendations made by the National Academies of Science, Engineering, and Medicine
(National Academies, 2017). The EPA presented these estimates in the regulatory impact
analysis (RIA) of the EPA's December 2023 Final Rulemaking, "Standards of Performance for
New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil
and Natural Gas Sector Climate Review". The EPA solicited public comment on the
methodology and use of these estimates in the RIA for the agency's December 2022
supplemental proposed Oil and Gas rulemaking, and has conducted an external peer review of
these estimates, as described further below.
The SC-CO2 is the monetary value of the net harm to society from emitting a metric ton of CO2
into the atmosphere in a given year, or the benefit of avoiding that increase. In principle, SC-CO2
is a comprehensive metric that includes the value of all future climate change impacts (both
negative and positive), including changes in net agricultural productivity, human health effects,
property damage from increased flood risk, changes in the frequency and severity of natural
disasters, disruption of energy systems, risk of conflict, environmental migration, and the value
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of ecosystem services. The SC-CO2, therefore, reflects the societal value of reducing CO2
emissions by one metric ton and is the theoretically appropriate value to use in conducting
benefit-cost analyses of policies that affect CO2 emissions. In practice, data and modeling
limitations restrain the ability of SC-CO2 estimates to include all physical, ecological, and
economic impacts of climate change, implicitly assigning a value of zero to the omitted climate
damages. The estimates are, therefore, a partial accounting of climate change impacts and likely
underestimate the marginal benefits of abatement (and marginal damages from emissions).
Since 2008, the EPA has used estimates of the social cost of various greenhouse gases (i.e.,
social cost of carbon (SC-CO2), social cost of methane (SC-CH4), and social cost of nitrous
oxide (SC-N2O)), collectively referred to as the "social cost of greenhouse gases" (SC-GHG), in
analyses of actions that affect GHG emissions. The values used by the EPA from 2009 to 2016,
and since 2021 have been consistent with those developed and recommended by the Interagency
Working Group (IWG) on the SC-GHG; and the values used from 2017 to 2020 were consistent
with those required by E.O. 13783, which disbanded the IWG. During 2015-2017, the National
Academies conducted a comprehensive review of the SC-CO2 and issued a final report in 2017
recommending specific criteria for future updates to the SC-CO2 estimates, a modeling
framework to satisfy the specified criteria, and both near-term updates and longer-term research
needs pertaining to various components of the estimation process (National Academies, 2017).
The IWG was reconstituted in 2021 and E.O. 13990 directed it to develop a comprehensive
update of its SC-GHG estimates, recommendations regarding areas of decision-making to which
SC-GHG should be applied, and a standardized review and updating process to ensure that the
recommended estimates continue to be based on the best available economics and science going
forward.
The EPA is a member of the IWG and is participating in the IWG's work under E.O. 13990.
While that process continues, as noted in previous EPA RIAs, the EPA is continuously reviewing
developments in the scientific literature on the SC-GHG, including more robust methodologies
for estimating damages from emissions, and looking for opportunities to further improve SC-
GHG estimation going forward.105 In the December 2022 RIA for the Standards of Performance
for New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources:
Oil and Natural Gas Sector Climate Review, the agency included a sensitivity analysis of the
climate benefits of the Supplemental Proposal using a new set of SC-GHG estimates that
incorporates recent research addressing recommendations of the National Academies (2017) in
addition to using the interim SC-GHG estimates presented in the Technical Support Document:
Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order
13990 (IWG, 2021) that the IWG recommended for use until updated estimates that address the
National Academies' recommendations are available.
The EPA solicited public comment on the sensitivity analysis and the accompanying draft
technical report, EPA Report on the Social Cost of Greenhouse Gases: Estimates Incorporating
Recent Scientific Advances, which explains the methodology underlying the new set of estimates,
in the December 2022 Supplemental Proposal.106 Please see the response to comments document
105 EPA strives to base its analyses on the best available science and economics, consistent with its responsibilities, for example,
under the Information Quality Act.
106 See https://www.epa.gov/environmental-economics/scghg for a copy of the final report and other related materials.
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for that rulemaking for summaries and responses to public comments. The response to comments
document can be found in the docket for the Standards of Performance for New, Reconstructed,
and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas
Sector Climate Review.
To ensure that the methodological updates adopted in the technical report are consistent with
economic theory and reflect the latest science, the EPA also initiated an external peer review
panel to conduct a high-quality review of the technical report, completed in May 2023. The peer
reviewers commended the agency on its development of the draft update, calling it a much-
needed improvement in estimating the SC-GHG and a significant step towards addressing the
National Academies' recommendations with defensible modeling choices based on current
science. The peer reviewers provided numerous recommendations for refining the presentation
and for future modeling improvements, especially with respect to climate change impacts and
associated damages that are not currently included in the analysis. Additional discussion of
omitted impacts and other updates have been incorporated in the technical report to address peer
reviewer recommendations. Complete information about the external peer review, including the
peer reviewer selection process, the final report with individual recommendations from peer
reviewers, and the EPA's response to each recommendation is available on the EPA's website.107
For an overview of the methodological updates incorporated into the SC-GHG estimates applied
in the EA for the final PFAS NPDWR, see Section 3.2 of the RIA for the Standards of
Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for
Existing Sources: Oil and Natural Gas Sector Climate Review (U.S. EPA, 2023g). A more
detailed explanation of each input and the modeling process is provided in the technical report,
Supplementary Material for the RIA: EPA Report on the Social Cost of Greenhouse Gases:
Estimates Incorporating Recent Scientific Advances (U.S. EPA, 2023h), included in the docket
for the Standards of Performance for New, Reconstructed, and Modified Sources and Emissions
Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review, and included in the
docket for this action.
Table 9-1 summarizes the resulting averaged certainty-equivalent SC-CO2 estimates under each
near-term discount rate that are used to estimate the climate disbenefits of the changes in CO2
emissions expected to result from the final PFAS rule. These estimates are reported in 2020
dollars and are identical to those presented in U.S. EPA (2023h). The SC-CO2 increases over
time within the models — i.e., the societal harm from one metric ton emitted in 2030 is higher
than the harm caused by one metric ton emitted in 2025 — because future emissions produce
larger incremental damages as physical and economic systems become more stressed in response
to greater climatic change, and because GDP is growing over time and many damage categories
are modeled as proportional to GDP. The full results generated from the updated methodology
for carbon dioxide and other greenhouse gases (SC-C02, SC-CH4, and SC-N20) for emissions
years 2020 through 2080 are provided in U.S. EPA (2023h).
107 https://www.epa.gov/environmental-economics/scghg-tsd-peer-review
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Table 9-1: Estimates of the Social Cost of CO2, 2020-2080 (2020$ per metric ton CO2)
Near-Term Ramsey Discount Rate
Year
2.50%
2.00%
1.50%
2020
120
190
340
2021
120
200
340
2022
120
200
350
2023
130
200
350
2024
130
210
360
2025
130
210
370
2026
130
220
370
2027
140
220
370
2028
140
220
380
2029
140
230
380
2030
140
230
380
2031
150
230
390
2032
150
240
390
2033
150
240
400
2034
160
250
400
2035
160
250
410
2036
160
250
410
2037
160
260
420
2038
170
260
420
2039
170
260
430
2040
170
270
430
2041
180
270
440
2042
180
280
440
2043
180
280
450
2044
190
280
450
2045
190
290
460
2046
190
290
460
2047
200
300
470
2048
200
300
470
2049
200
300
480
2050
210
310
480
2051
210
310
490
2052
210
320
490
2053
210
320
500
2054
220
320
500
2055
220
330
510
2056
220
330
510
2057
230
330
510
2058
230
340
520
2059
230
340
520
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Table 9-1: Estimates of the Social Cost of CO2, 2020-2080 (2020$ per metric ton CO2)
Near-Term Ramsey Discount Rate
Year
2.50% 2.00% 1.50%
2060
230
350
530
2061
240
350
530
2062
240
350
540
2063
240
350
540
2064
240
360
540
2065
250
360
550
2066
250
360
550
2067
250
370
550
2068
250
370
560
2069
260
370
560
2070
260
380
570
2071
260
380
570
2072
260
380
570
2073
270
390
580
2074
270
390
580
2075
270
390
580
2076
270
390
590
2077
280
400
590
2078
280
400
590
2079
280
400
600
2080
280
410
600
Note: This table displays the values rounded to two significant figures. The annual unrounded values used in the
calculations in this EA are available in Appendix A.5 of U.S. EPA (2023g) and at: www.epa.gov/environmental-
eeonomies/segfag
The methodological updates described in U.S. EPA (2023h) represent a major step forward in
bringing SC-GHG estimation closer to the frontier of climate science and economics and address
many of the National Academies' (2017) near-term recommendations. Nevertheless, the
resulting SC-GHG estimates, including the SC-CO2 estimates presented in Table 9-1, still have
several limitations, as would be expected for any modeling exercise that covers such a broad
scope of scientific and economic issues across a complex global landscape. There are still many
categories of climate impacts and associated damages that are only partially or not reflected yet
in these estimates and sources of uncertainty that have not been fully characterized due to data
and modeling limitations. Please see Section 3.2 of U.S. EPA (2023h) for further discussion.
All of the EPA's peer reviewed WBS models include the consumption of purchased electricity.
The EPA has used the WBS models to estimate the electricity consumed annually by operating
each technology at the entry-point level. For more information on WBS estimation of energy
usage, see the EPA's Work Breakdown Structure-Based Cost Models documents for GAC, IX,
and RO, specifically Appendix E General Assumptions for Operating and Maintenance Costs.
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Table 9-2 below provides a summary of the electricity consumption at the entry-point level by
system size and treatment technology.
Table 9-2: Entry Point Level Electricity Consumption Range by System Size and
Technology (MWh/year)
Treatment Technology
Minimum Electricity Use Maximum Electricity Use
(MWh/year) (MWh/year)
GAC
<100 to 3,300
3,301 to 10,000
10,000 to 100,000
100,000 and above
IX
<100 to 3,300
3,301 to 10,000
10,000 to 100,000
100,000 and above
0 1
6 7
8 233
33 653
0 0
2 2
2 4
7 14
The EPA uses the WBS estimates of MWh by system size and source and the estimates of the
number of water systems anticipated to select each technology based on the decision tree
(presented in Chapter 5 of this document) to estimate the total electricity used nationally to
operate treatment technologies to comply with the rule. Table 9-3 below shows the annual
national electricity use anticipated by system size and technology used. The EPA estimates the
total national annual electricity use to be 229,179 MWh per year.
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Table 9-3: National Electricity Use (MWh/year) by Technology and System Size.
Treatment Technology Total Electricity Use (MWh/year)
GAC
<100 to 3,300 839
3,301 to 10,000 3,216
10,000 to 100,000 55,341
100,000 and above 163,214
IX
<100 to 3,300 38
3,301 to 10,000 398
10,000 to 100,000 2,806
100,000 and above 3,326
Total 229,179
To convert this estimated increase in electricity use nationally into national CO2 emissions
through 2080, the EPA used the latest reference case from the EPA's peer-reviewed Integrated
Planning Model (IPM). The IPM is a multi-regional, dynamic, deterministic linear programming
model of the U.S. electric power sector. It provides projections of least-cost capacity expansion,
electricity dispatch, and emission control strategies for meeting energy demand and
environmental, transmission, dispatch, and reliability constraints (U.S. EPA, 2023e). The EPA
uses the IPM to analyze the projected impact of environmental policies on the electric power
sector, and it also provides projections of CO2 emissions from the power sector through 2055.
The latest reference case, "Post-IRA 2022 reference case" was published in April of 2023 and
reflects the impacts of the Inflation Reduction Act (IRA).
Although the U.S. electricity grid continues to decrease its reliance on coal combustion in favor
of natural gas and renewable alternatives, electricity consumption continues to be associated with
GHG emissions across the entire system of production and delivery. Combustion of fossil fuels
releases CO2, CH4, and N2O; sulfur hexafluoride (SF6) and perfluorocarbons (PFCs) are used in
electricity transmission and distribution equipment; and additional GHG emissions are associated
with the manufacture and installation of equipment as well the extraction and delivery of fossil
fuels (U.S. EPA, 2023c). An exact accounting of all these emissions categories would yield the
most precise estimate of electricity sector climate-related impacts. However, CO2 emissions from
fossil fuel combustion comprise the vast majority of the electricity sector GHG emissions.
Therefore, accounting for combustion emissions of CO2 is sufficient for the purposes of
estimating the approximate magnitude of the climate-related disbenefits of increased electricity
consumption. For example, in 2021, the EPA estimates total electricity sector emissions of 1,584
million metric ton (MMT) of C02-equivalent GHGs from fossil fuel combustion, waste
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incineration, process emissions, and electricity transmission and distribution. Over 97 percent of
this total consists of CO2 emissions from fossil fuel combustion.108 Even accounting for
upstream coal mining and natural gas systems, the share of electricity sector GHG emissions that
are from fossil fuel combustion release of CO2 is still at least 90 percent.109'110 Note that the non-
GHG emissions impacts associated with changes in electricity consumption are not accounted for
in this analysis. For a more complete description of non-GHG impacts from the electricity sector,
including ozone- and PM2.5-attributable premature mortality and illness as well as discussion of
various unquantified health and welfare impacts, see recent the EPA regulatory impact analyses
for air pollution regulations and the utilities sector in particular (U.S. EPA, 2023f).
From IPM reference case summary outputs, the EPA calculated projections of annual national-
average CO2 emissions per MWh of electricity generation over the model time horizon. The EPA
mapped non-model years to calendar years following the IPM documentation guidance (U.S.
EPA, 2023a).111 After calendar year 2059, through the end of the period of analysis (2080), the
EPA assumes that national-average electricity emission factors remain constant, which may lead
to overstating the disbenefits of this rule. Table 9-4 below shows the IPM summary outputs and
implied national-average CO2 emissions factors for each IPM model year.
108 Ibid. See Table 2-11. (1,540.9 MMT CO2 from fossil fuel combustion)/(l,584.1 MMT CO2 eq. total) = 97.3 percent in 2021.
109 92 percent = share of coal consumed by electricity sector in 2022. See U.S. Energy Information Administration, Monthly
Energy Review, Table 6.2.
38 percent = share of natural gas consumed by electricity sector in 2022. See U.S. Energy Information Administration, Monthly
Energy Review, Table 4.3.
90.1 percent = (1,540.9 MMT CO2 from fossil fuel combustion in EPA GHGI Table 2-11)/[(1,584.1 MMT CO2 eq. total in EPA
GHGI Table 2-11) + (217.5 MMT CO2 eq. from natural gas systems in EPA GHGI Table 3-65)*(38 percent) + (44.7 MMT CH4
in CO2 eq. from coal mining in EPA GHGI Table 3-34)*(92 percent) + (2.5 MMT CO2 in CO2 eq. from coal mining in EPA
GHGI Table 3-36)*(92 percent)].
110 Coal and especially gas are inputs to other sectors of the economy, so decreasing electricity sector demand for these fuels does
not necessarily preclude their extraction and use elsewhere.
mThe EPA mapped the calendar year 2028 to model run year 2028, calendar years 2029-31 to run year 2030, calendar years
2032-37 to run year 2035, calendar years 2038-42 to run year 2040, calendar years 2043-47 to run year 2045, calendar years
2048-52 to run year 2050, and calendar years 2053-80 to run year 2055.
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Table 9-4: CO2 Emissions per MWh Calculated from Post-IRA 2022 IPM Reference
Case
IPM Model Year
CO2 Emissions (Million
Metric Tons/year)3
Grand Total Electricity
Generated (Billions
MWh/year)a
CO2 Emissions
(mt/MWh/year)
2028
1,222
4.409
0.28
2030
972
4.545
0.21
2035
608
4.891
0.12
2040
481
5.265
0.09
2045
406
5.628
0.07
2050
357
6.071
0.06
2055
391
6.454
0.06
aSource: Post IRA Reference Case SSR.xlsx available at: https://www.epa.jK)v/pQwer-sectQr-mQdeling/pQst-ira-2022-
reference-case.
The EPA estimates the CO2 emissions per model year associated with PFAS compliance
emissions by multiplying the total annual electricity use associated with the rule per year by the
annual national-average CO2 emissions per MWh from Table 9-4 above. This methodology
using national-average emission factors assumes that the geographic locations of these
technologies and timing of their operations is similar to average U.S. electricity demand. The
EPA believes that these assumptions are a reasonable approximation in this analysis where the
treatment technologies are geographically widespread with fairly continuous operations. At this
time, the EPA does not have sufficient information about the exact locations of units and timing
of operations for a more refined methodology but expects this would have a minimal impact on
the quantified results.
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Table 9-5: CO2 emissions per Year from Operating Treatment Technologies to Comply
with the PFAS NPDWR
I I'M Model Year
Period of Analysis Year3
CO2 Emission (mt/year)
2028
2028
0
2030
2029-2031
49,004
2035
2032-2037
28,505
2040
2038-2042
20,954
2045
2043-2047
16,520
2050
2048-2052
13,466
2055
2053-2080
13,897
Note:
aThe EPA's analysis assumes the rule is promulgated in 2024 and per the final rule requirements, systems must be in
compliance with the final NPDWR by 2029. Therefore, the EPA models emissions associated with electricity use to operate
treatment technologies beginning in 2029. Please see Chapter 1 for additional discussion on compliance timelines.
Table 9-6 presents the monetized climate disbenefits associated with operation of PFAS removal
treatment technologies under the final PFAS NPDWR. The EPA multiplied the projected CO2
emissions each year (shown in Table 9-5) by the SC-CO2 estimate for that year (from Table 9-1)
and annualized these results over the 2024-2080 analysis period. Monetized climate effects are
presented under a 1.5 percent, 2 percent, and 2.5 percent near-term Ramsey discount rate,
consistent with the EPA's updated estimates of the SC-CO2. As described in U.S. EPA (2023h),
the SC-CO2 estimates rely on a dynamic discounting approach that provides over the constant
discount rate framework used for SC-GHG estimation in EPA RIAs to date. Specifically, it
provides internal consistency within the modeling and a more complete accounting of
uncertainty consistent with economic theory and the National Academies' (2017)
recommendation to employ a more structural, Ramsey-like approach to discounting that
explicitly recognizes the relationship between economic growth and discounting uncertainty.
This approach is also consistent with the National Academies' (2017) recommendation to use
three sets of Ramsey parameters that reflect a range of near-term certainty-equivalent discount
rates and are consistent with theory and empirical evidence on consumption rate uncertainty. See
U.S. EPA (2023h) for a more detailed discussion of the entire discounting module and
methodology used to value risk aversion in the SC-GHG estimates. The results presented in
Table 9-6 are not directly comparable to the economic analyses prepared in the HRRCA analysis
presented in Chapters 1-7 of this EA because climate disbenefits were assessed over a shorter
period of analysis112 and at different discount rates113. The EPA estimates a range of climate
disbenefits associated with this rule from $8.8 million dollars per year (at a 1.5 percent discount
112 The final rule analysis evaluates costs and benefits under the final rule for the period of analysis from 2024 through 2105. For
more information see Chapter 2.2.1.
113 The final rule analysis estimates the annualized value of future benefits and costs using a 2 percent discount. For more
information see Chapter 2.2.2.
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rate) to $3.6 million dollars per year (at a 2.5 percent discount rate), which constitute less than
0.6% of the quantified benefits at a 2 percent discount rate.
Table 9-6: Annualized Monetized Climate Disbenefits Associated with Operating
Treatment Technologies to Comply with the Final PFAS NPDWR ($2022)
Ramsey Near Term Discount Rate
Annualized Value ($2022)a
2.5 percent
$3,600,000
2 percent
$5,516,000
1.5 percent
$8,771,000
Note:
^Results were annualized over the 2024-2080 period of analysis.
9.3 Paperwork Reduction Act
The information collection requirements for the final rule will be submitted for approval to OMB
under the Paperwork Reduction Act (PRA), 44 U.S.C. 3501 et seq. The ICR supporting
statement prepared by the EPA has been assigned the EPA ICR number 2732.01 and is available
in the docket at https://www.regulations.gov/docket/EPA-HQ-OW-2022-0114.
The PRA requires the EPA to estimate the burden, as defined in 5 CFR 1320.3(b), on PWSs and
primacy agencies of complying with the rule. The information collected as a result of the final
rule should allow primacy agencies and the EPA to determine appropriate requirements for
specific systems and evaluate compliance with the final rule. Burden is defined at 5 CFR
1320.3(b) and means the total time, effort, and financial resources required to generate, maintain,
retain, disclose, or provide information to or for a federal agency. The burden includes the time
needed to conduct primacy agency and system activities during the first three years after
promulgation, as described below.
9.3.1 Primacy Agency Activities
The EPA anticipates primacy agencies will be involved in the following activities for the first
three years after publication of the final rule:
• Startup activities - read and understand the rule, adopt regulatory change, and provide
internal and system staff with training and technical assistance;
• Review the initial monitoring event results, including confirmation sample results for
MCL exceedances; and
• Review the results of standard monitoring from systems.
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9.3.2 Public Water System Activities
The EPA anticipates systems will be involved in the following activities for the first three years
after publication of the final rule:
• Startup activities - read and understand the rule and attend initial training from the
primacy agency;
• Conduct initial monitoring including confirmation sampling for MCL exceedances; and
• Conduct standard monitoring, as needed; the EPA assumed that sampling for annual and
triennial monitoring would not occur until after the three-year ICR period.
For the first three years after publication of the rule in the Federal Register, information
requirements apply to an average of 33,594 respondents annually, including 33,538 PWSs and 56
primacy agencies. The burden associated with the final rule over the three years covered by the
ICR is 2.1 million hours, for an average of 684,119 hours per year. The total cost over the three-
year period is $176.8 million, for an average of $58.9 million per year (simple average over three
years). The average burden per response (i.e., the amount of time needed for each activity that
requires a collection of information) is 2.6 hours for PWSs and 2.6 hours for primacy agencies;
the average cost per response is $247 for PWSs and $154 for primacy agencies. The collection
requirements are mandatory under SDWA (42 U.S.C. 300g-7). Details on the calculation of the
final rule information collection burden and costs can be found in the ICR for the final rule and
Chapter 5 of this EA. A summary of the average annual burden and costs of the collection is
presented in Table 9-7. The burdens and costs reflect labor and laboratory analysis costs.
Table 9-7: Average Annual Burden, Costs, and Responses for the Final Rule Information
Collection Request
Item
Burden (Hours in
Thousands)3
Costs (Million
$2022)a
Responses
Systems
506
$48.3
195,739
Primacy agencies
178
$10.6
69,056
Totalb
684
$59.0
264,795
Average per response - systems (hours or
dollars)
2.6
$247.0
Not applicable
Average per response - primacy agencies
(hours or dollars)
2.6
$154.0
Not applicable
Notes:
^Different units indicated for the estimates of burden and cost average per response.
bDetail may not add to totals because of independent rounding.
Source: ICR Supporting Statement, available in the docket at https://www.regulations. gov/docket/EPA-HQ-OW-2022-0114.
The estimates of total responses, burden, and cost for system and primacy agency startup
activities are provided in Table 9-8.
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Table 9-8: Total Burden, Costs, and Responses for Each Required Activity
Item
Burden
(Thousand
Hours)
Costs (Million
$2022)
Responses
System startup activities
1,312
$48.5
133,060
Systems collect initial samples
207
$96.5
454,158
System subtotal
1,519
$145.0
587,218
Primacy agency startup activities
326
$19.5
112
Primacy agency review initial monitoring data
207
$12.4
207,056
Primacy agency subtotal"
533
$31.8
207,168
Combined systems and primacy agency3
2,052
$176.8
794,386
Note:
^Detail may not add to totals because of independent rounding.
Source: ICR Supporting Statement, available in the docket at https://www.regulations. gov/docket/EPA-HQ-OW-2022-0114.
An agency may not conduct or sponsor, and a person is not required to respond to, a collection of
information unless it displays a currently valid OMB control number. The OMB control numbers
for the EPA's regulations in 40 CFR are listed in 40 CFR part 9. The control number for this
action is ICR OMB Control No. 2040-0307.
The information collection activities in this final rule have been submitted for approval to the
OMB under the PRA. The ICR document that the EPA prepared has been assigned the EPA ICR
number 2732.01. You can find a copy of the ICR in the docket for this rule at
https://www.reeiilations.eov/docket/EPA-HQ-OW-202 . When OMB approves this ICR,
the agency will announce that approval in the Federal Register and publish a technical
amendment to 40 CFR part 9 to display the OMB control number for the approved information
collection activities contained in this final rule.
9.4 The Final Regulatory Flexibility Analysis
The RFA of 1980, amended by the SBREFA of 1996, requires regulators to assess the effects of
regulations on small entities including businesses, nonprofit organizations, and governments.
RFA/SBREFA generally requires an agency to prepare an initial regulatory flexibility analysis
(IRFA) of any rule subject to notice and comment rulemaking requirements under the
Administrative Procedure Act or any other statute unless the agency certifies that the rule will
not have a significant economic impact on a substantial number of small entities (SISNOSE).
Small entities include small businesses, small organizations, and small governmental
jurisdictions. Under the RFA, the Final Regulatory Flexibility Analysis (FRFA) must include:
1. A statement of the need for, and objectives of, the rule;
2. A statement of the significant issues raised by the public comments in response to the
initial regulatory flexibility analysis, a statement of the assessment of the agency of such
issues, and a statement of any changes made in the proposed rule as a result of such
comments;
3. The response of the agency to any comments filed by the Chief Counsel for Advocacy of
the Small Business Administration in response to the proposed rule, and a detailed
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statement of any change made to the proposed rule in the final rule as a result of the
comments;
4. A description of and an estimate of the number of small entities to which the rule will
apply or an explanation of why no such estimate is available;
5. A description of the projected reporting, recordkeeping and other compliance
requirements of the rule, including an estimate of the classes of small entities which will
be subject to the requirement and the type of professional skills necessary for preparation
of the report or record;
6. A description of the steps the agency has taken to minimize the significant economic
impact on small entities consistent with the stated objectives of applicable statutes,
including a statement of the factual, policy, and legal reasons for selecting the alternative
adopted in the final rule and why each one of the other significant alternatives to the rule
considered by the agency which affect the impact on small entities was rejected.
The RFA provides default definitions for each type of small entity. Small entities are defined as:
(1) a small business as defined by the Small Business Administration's (SBA) regulations at 13
CFR 121.201; (2) a small governmental jurisdiction that is a government of a city, county, town,
school district, or special district with a population of less than 50,000; and (3) a small
organization that is any "not-for-profit enterprise which is independently owned and operated
and is not dominant in its field." The RFA also authorizes an agency to use alternative
definitions for each category of small entity, "which are appropriate to the activities of the
agency" after proposing the alternative definition(s) in the Federal Register and taking comment
(5 USC 601(3)-(5)). In addition, to establish an alternative small business definition, agencies
must consult with SBA's Chief Counsel for Advocacy.
For purposes of assessing the impacts of the final rule on small entities, the EPA considered
small entities to be systems serving 10,000 people or fewer. This is the threshold specified by
Congress in the SDWA 1996 Amendments for small system flexibility provisions. As required
by the RFA, the EPA proposed using this alternative definition in the Federal Register (FR) (63
FR 7620, February 13, 1998), requested public comment, consulted with the SBA, and finalized
the alternative definition in the agency's Consumer Confidence Reports regulation (U.S. EPA,
1998c, 63 FR 44524, August 19, 1998). As stated in that final rule, the alternative definition
would be applied to all future drinking water regulations.
The EPA notes that the Infrastructure Investment and Jobs Act (also known as the BIL, P.L. 117-
58) invests over $11.7 billion in the DWSRF General Supplemental fund; $4 billion in the
DWSRF Emerging Contaminants fund; and $5 billion in the EC-SDC grants program. Together,
these funds will reduce people's exposure to PFAS and other emerging contaminants through
their drinking water. The BIL funding will prioritize investment in local communities that are on
the frontlines of PFAS contamination and that have few options to finance solutions through
traditional programs and help them meet their obligations under this regulation.
9.4.1 Need for, Objectives, and Legal Basis of the Rule
The need for the rule, the objectives of the rulemaking, the stakeholder outreach conducted, and
the statutory authority the EPA is utilizing to finalize the rule are described in detail in Chapter 3.
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See Section 3.1 for detailed information on the need for the rule, Chapter 9 for information on
stakeholder outreach during the rulemaking process, and Section 3.2 for additional detail on the
statutory authority for the promulgation of the PFAS regulation. In summary, SDWA authorizes
the EPA to establish NPDWRs for contaminants that may have an adverse public health effect,
that are known to occur or that present a substantial likelihood of occurring in PWSs at a
frequency and level of public health concern, and that present a meaningful opportunity for
health risk reduction for persons served by PWSs. As a result, the EPA is finalizing an NPDWR
for six PFAS including PFOA, PFOS, PFNA, PFHxS, HFPO-DA, and PFBS. Additionally,
under the SDWA, the EPA Administrator is authorized to establish monitoring, recordkeeping,
and reporting regulations that the Administrator can use to establish regulations under the
SDWA, determine compliance with SDWA, and advise the public of the risks of unregulated
contaminants.
The EPA is also addressing PFAS through several of its statutory authorities other than SDWA,
including the CERCLA, RCRA, Toxic Substances Control Act (TSCA), Clean Water Act, Clean
Air Act, and Emergency Planning and Community Right-to-Know Act. For example, as part of
the EPA PFAS Strategic Roadmap, in 2022, the EPA has proposed to designate PFOA and PFOS
as CERCLA hazardous substances to require reporting of PFOA and PFOS releases, enhance the
availability of data, and ensure agencies can recover cleanup costs. The EPA recognizes that
future actions under some of these statutes may have direct or indirect impacts for drinking water
treatment facilities and could impact the compliance requirements related to disposal of PFAS
treatment residuals that are generated by water systems. The EPA has also committed to restrict
PFAS discharges from industrial sources through a multi-faceted Effluent Limitations Guidelines
program to proactively establish national technology-based regulatory limits. Additionally, the
EPA is seeking to proactively use National Pollutant Discharge Elimination System (NPDES)
authorities to reduce discharges of PFAS at the source and obtain more comprehensive
information through monitoring on the sources of PFAS discharges and quantity of PFAS
discharged by these sources. The EPA notes that these actions may prevent or reduce PFAS
entering into sources of drinking water in the future. More information on these statutory
authorities and PFAS-related EPA activities can be found in the PFAS Strategic Roadmap.
9.4.2 Summary of the SBAR Comments and Recommendations
A Small Business Advocacy Review Panel (SBAR Panel or Panel) was convened to review the
planned proposed rulemaking on the Proposed PFAS NPDWR. In addition to the EPA's Small
Business Advocacy Chairperson, the Panel consists of the Director of the Standards and Risk
Management Division of the EPA OGWDW, the Administrator of the Office of Information and
Regulatory Affairs within the OMB, and the Chief Counsel for Advocacy of the Small Business
Administration. The panel consulted with and reported on the comments of small entity
representatives (SERs) and made findings on issues related to elements of an IRFA under
Section 603 of the RFA. The SERs were presented with information related to PFAS background
(such as health and occurrence, the SDWA regulatory development process and the EPA's
actions to address PFAS in drinking water potential monitoring and reporting rule compliance
considerations, treatment and feasibility considerations, potential public notification and
education rule compliance considerations, and preliminary economic impacts to small systems.
The EPA also provided to SERs that the agency's final regulatory determination for PFOA and
PFOS outlined avenues that the agency considered to further evaluate additional PFAS
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chemicals, other than PFOA and PFOS, and consider groups of PFAS as supported by use of the
best available science. Additionally, as part of the EPA's PFAS Strategic Roadmap, the EPA
reaffirmed its commitment to evaluate additional PFAS and consider regulatory actions to
address additional PFAS or groups of PFAS as it develops the NDPWR. Further, the EPA
provided to SERs that as the EPA considers whether to include additional PFAS as part of this
regulation, the agency would consider several factors, including whether the same treatment
approaches co-remove certain PFAS contaminants and how different PFAS are anticipated to be
removed as part of the treatment process, the likelihood that the PFAS co-occur, the similarity of
health effects and chemical structures, the environmental persistence characteristics, and the
availability of accepted and approved analytical methods or indicators with comparable costs to
those currently identified by the EPA to evaluate PFAS removal from drinking water, among
other considerations.
In light of the SERs' comments, the Panel considered the regulatory flexibility issues and
elements of the IRFA specified by RFA/SBREFA and developed the findings and discussion
summarized in the SBAR report. For example, the SBAR Panel recommended several
flexibilities in monitoring requirements for small systems, including the use of existing
monitoring data (such as the UCMR 5) for initial monitoring purposes, as well as reduced initial
monitoring requirements specifically for small ground water systems. Regarding public comment
requests, the Panel recommended that the EPA request this for a few areas, such as laboratory
capacity for monitoring, additional treatment technologies other than those identified in the
proposed rule that have been shown to reduce levels of PFAS to the proposed regulatory
standards, additional monitoring flexibilities, and PFAS disposal considerations. Moreover,
specific to PFAS disposal, the Panel recommended that the EPA continue to evaluate the
potential impacts related to the disposal of PFAS treatment residuals and potential implications
from other EPA statutory authorities. This recommendation included presenting the costs of both
non-hazardous and hazardous waste disposal of treatment residuals as a part of the proposed rule.
To address stakeholder concerns, including those raised during the SBREFA process, the EPA
conducted a sensitivity analysis with an assumption of hazardous waste disposal for illustrative
purposes only. As part of this analysis, the EPA generated a second full set of unit cost curves
that are identical to the curves used for the national cost analysis with the exception that spent
GAC and spent IX resin are considered hazardous. The EPA acknowledges that if federal
authorities later determine that PFAS-contaminated wastes require handling as hazardous wastes,
the residuals management costs are expected to be higher. The EPA incorporated all Panel
recommendations, as well as others, in the proposed and final rule.
The Panel also recommended the EPA to consider rule implementation delays for potential
laboratory capacity-related challenges if those challenges potentially impact the ability of water
systems to monitor for PFAS and reasonably comply with the NPDWR. As described in the
proposed rule preamble (Section XII.D.), in accordance with SDWA 1412(b)(10), a state or the
EPA may grant an extension of up to two additional years to comply with an NPDWR's MCL if
the state or the EPA determines a system needs additional time for capital improvements. In the
rule proposal, the EPA indicated that the agency did not intend to provide a two-year extension
nationwide. However, the EPA noted in the proposal that under SDWA 1412(b)(10) or 1416
States may provide such extension on an individual system basis which may address compliance
issues associated with treatment, laboratory, and disposal capacity. Additionally, the EPA notes
that in the proposed rule preamble (Section IX.F) the agency sought public comment on the
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proposed initial monitoring timeframe, particularly for NTNCWS or all systems serving 3,300 or
fewer.
The report includes a number of other observations and recommendations to meet the statutory
obligations for achieving small-system compliance through flexible regulatory compliance
options. The report was finalized on August 1, 2022 and transmitted to the EPA Administrator
for consideration. Detailed information on the overall panel process, including the
comprehensive comments of the SERs and full description of Panel recommendations, can be
found in the panel report titled, Final Report of the Small Business Advocacy Review Panel on
the EPA's Planned Proposed Rule Per- and Polyfluoroalkyl Substances National Primary
Drinking Water Regulation and can be found in the rulemaking docket at:
https://www.regulations.gOv/document/EPA-HQ-OW-2022-0114-0048.
9.4.3 Summary of the Final Rule and Public Comments on the
Impacts to Small Entities
The EPA is regulating six PFAS in finished drinking water: (1) PFOS, (2) PFOA, (3) PFNA, (4)
HFPO-DA and its ammonium salt (also known as GenX chemicals)), (5) PFHxS, and (6) PFBS.
The final regulation utilizes compound-specific MCLs for PFOA, PFOS, PFNA, HFPO-DA and
PFHxS and an MCL based on a HI for combinations of PFNA, HFPO-DA, PFHxS, and PFBS in
mixtures. With this action, the EPA finalizes monitoring, reporting, public notification, and
Consumer Confidence Report requirements for PWSs and primacy agencies to comply with the
NPDWR.
In the proposal, the EPA evaluated three significant alternatives to minimize significant
economic impacts on small PWSs that serve 10,000 or fewer people. The proposed and final rule
would also allow water systems to select the most financially and technologically viable strategy
that is effective in reducing PFAS in drinking water. The EPA evaluated the following
significant alternatives for the proposed rule: 1) use of previously collected monitoring data, 2) a
provision for small ground water systems to collect two, rather than four, quarterly samples over
a one-year period for initial monitoring, or 3) installation and maintenance of POU treatment
devices.
In response to the IRFA included as part of the proposal, the EPA received one comment
specifically on the analytical approach used in the IRFA. The commenter states that "[d]etailed
analysis on the impacts to NTNCWSs should be conducted to inform the cost/benefit analysis.
For example, treating PFAS with GAC at the low levels proposed is much more costly than
current treatment for currently regulated contaminants, and a 2008 study is not a reliable
indicator of future costs. Lack of both actual data on occurrence in these systems and reliable
information on cost of compliance makes finalizing the MCL as to NTNCWSs too uncertain."
The EPA disagrees that the agency has not analyzed the impacts of the PFAS NPDWR on
NTNCWS. The EPA has used both actual data on occurrence at NTNCWSs from UCMR3 and
state data, as well as reliable information on costs to NTNCWSs using the WBS treatment cost
models to assess the impact of the rule on NTNCWSs. As the EPA stated in the proposal, the
EPA lacks information on the revenues of NTNCWS, therefore the agency does not take the
same approach used for CWSs in the SISNOSE screening analysis where costs are compared to
1 and 3% of revenues. Instead, the EPA used the best available data, the EPA's Assessment of
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the Vulnerability of Noncommunity Water Systems to SDWA Cost Increases (SAIC, 1998) to
find that NTNCWSs are less vulnerable to SDWA related increases than a typical CWS. The
EPA proceeded with the SBAR Panel process, as detailed in this chapter.
Additionally, the EPA received many comments, including from the SBA Office of Advocacy,
specific to various small system and IRFA-related topics including lack of funding availability
for small water systems, the EPA's estimation of the impacts of the rule on small systems, the
EPA's estimation and characterization on federal funding to defray compliance costs for small
water systems, and "other factors that will further deter timely compliance" such as personnel
shortages, supply chain disruptions, limited lab and disposal capacity, and availability of
treatment technologies. For the EPA's response to SBA and other comments on funding
availability, please see Section I of the preamble. For the EPA's response to SBA and other
comments on the estimated costs to small water systems, please see Section XII of the preamble.
For the EPA's response to SBA and other comments on lab capacity, see Section V and VIII of
the preamble. For the EPA's response to SBA and other comments on technology and disposal
capacity, see Section X of the preamble. For responses to SBA's and other commenters'
recommendations to the EPA to provide burden-reducing flexibilities for small water systems,
including finalizing one of the regulatory alternatives and phasing in the MCL, as well as
providing additional time for compliance see Section V of the preamble. For response to SBA
and other commenters concerned about the EPA's concurrent preliminary determination and
proposed regulation for four PFAS, see Section III of the preamble.
9.4.4 Number and Description of Small Entities Affected
The EPA used SDWIS/Fed data from the fourth quarter of 2021 to identify 62,048 small PWSs,
which represent 93% of all systems that may be impacted by the final PFAS regulation. A small
PWS serves between 25 and 10,000 people. These water systems include 44,753 CWSs that
serve year-round residents and 17,295 NTNCWSs that serve the same persons over six months
per year (e.g., a PWS that is an office park or church). The final NPDWR will not affect
TNCWSs as those systems will not be subject to the rule requirements. Additional information
on the characteristics of these small drinking water systems along with a discussion of
uncertainty in the dataset used to derive the estimated number of small systems impacted by the
final PFAS regulation can be found in Section 4.3.1.
Table 9-9 and Table 9-10 show the number of affected small CWSS and NTNCWs respectively.
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Table 9-9: Inventory of Small CWSs
CWSsa
System Size (Population
Served)
Ground Water
Surface Water
Total
A
B
C = A + B
< 100
10,654
739
11,393
101-500
13,037
2,042
15,079
501-1,000
4,132
1,179
5,311
1,001-3,300
5,503
2,460
7,963
3,301-10,000
2,784
2,223
5,007
TOTAL
36,110
6,601
44,753
Abbreviations: CWS - community water systems.
Note:
includes 23 CWSs serving 10,000 or fewer people for which no primary source water type was reported to SDWIS/Fed.
The EPA assigned these systems to the source type of Ground Water.
Source: SDWIS/Fed fourth quarter 2021 "frozen" dataset that contains information reported through January 14,2022.
Includes all active CWSs.
Table 9-10: Inventory of Small NTNCWSs
NTNCWSs3
System Size (Population
Served)
Ground Water
SW
Total
A
B
C = A+B
< 100
8,084
252
8,336
101-500
6,111
257
6,368
501-1,000
1,476
91
1,567
1,001-3,300
743
121
864
3,301-10,000
97
63
160
TOTAL
16,551
784
17,295
Abbreviations: NTNCWS - non-transient non-community water systems.
Note:
includes 11 NTNCWSs serving 3,300 or fewer people for which no primary source type was reported to SDWIS/Fed. The
EPA assigned these systems to the source water type of Ground Water.
Sources: SDWIS/Fed fourth quarter 2021 "frozen" dataset that contains information reported through January 14,2022.
Includes all active NTNCWSs.
9.4.5 Description of Compliance Requirements of the Final Rule
For a detailed description of the regulatory requirements under the final PFAS regulation see
Section 2.1. The final rule requires PWSs subject to the rule to conduct initial monitoring.
Related to this initial monitoring requirement, the final NPDWR includes a provision, made
available to PWSs of all sizes, including CWSs and NTNCWs serving 10,000 or fewer people, to
use qualified previously collected monitoring data to demonstrate levels of regulated PFAS in
their water system to satisfy the initial monitoring requirement. The EPA assessed the extent to
which this significant alternative minimizes the economic impact on small PWSs specifically in
Section 9.4.7.1 below. Additionally, the EPA has included a provision in the final NPDWR
where ground water systems serving a population of 10,000 or fewer may collect two quarterly
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samples over a one-year period for the purpose of initial monitoring, rather than collecting four
quarterly samples. The EPA assessed the extent to which this regulatory flexibility minimizes the
economic impact on small PWSs in Section 9.4.7.2 below.
Based on initial monitoring results, systems will be required to conduct ongoing monitoring at
least every three years or as often as four times per year. Details on the monitoring frequency
requirements of the final NPDWR can be found in Section VIII of the Federal Register Notice
for the final rule.
PWSs that exceed the drinking water standard are required to choose between treatment and
nontreatment compliance options. The EPA identified the following Small System Compliance
Technologies (SSCTs) GAC, Anion Exchange (AIX), and High-pressure Membranes (RO and
NF). POU RO is not currently listed as a compliance option because the final rule requires
treatment to concentrations below the current National Sanitation Foundation (NSF)
International/American National Standards Institute (NSF/ANSI) certification standard for POU
device removal of PFAS. However, POU treatment is reasonably anticipated to become a
compliance option for small systems in the future if NSF/ANSI or other independent third-party
certification organizations develop a new certification standard that mirrors the EPA's proposed
regulatory standard. Details on SSCTs and costs can be found in Section 5.3.1 and Best Available
Technologies and Small System Compliance Technologies for Per- and Polyfluoroalkyl
Substances (PFAS) in Drinking Water (U.S. EPA, 2024c).
9.4.6 Analysis of impact of Regulatory Options on Small System
Costs
The EPA limited the quantitative cost impact analysis to small CWSs because small NTNCWSs
operate in numerous industries and the EPA does not have information on NTNCWSs' revenues.
The EPA's decision to limit its cost impact analysis to CWSs is supported by the EPA's
Assessment of the Vulnerability of Noncommunity Water Systems to SDWA Cost Increases
(1998). In this study, the EPA examined the burden of SDWA rule costs in comparison to the
average revenues of various categories of NTNCWSs. All the NTNCWS categories reviewed
were less vulnerable to SDWA-related increases than a typical CWS. The report notes that in
some categories of businesses, costs are more easily passed on to the customer base than in
others. In each NTNCWS category, however, total expenditures on water were found to be a
relatively small percentage of total revenues. Water expenditures (including expenditures for
sewer service and miscellaneous other utilities) totaled less than one percent of total revenues in
nearly all cases and were not more than 1.3 percent of total revenues for any category. The
implication is that an increase in water costs would similarly be less than one percent of revenue.
This report included several caveats such as one that considered the potential for underestimating
the impact to golf courses, which were grouped in with other recreational entities whose use of
water was less significant to the core business than the golf courses. The EPA notes, however,
that irrigation water for golf courses would not need to meet the final rule; only water used for
human consumption would need to be treated. Despite the significant caveats listed, the report
strongly suggested that NTNCWSs should not be considered particularly vulnerable to operating
cost increases resulting from SDWA rulemakings.
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To indicate the potential economic impact on small CWSs, the EPA divided annual costs by
annual revenues and converted the decimal values to percentages and identified the number and
percent of CWSs for which the impact percentages exceeded thresholds of one percent and three
percent. For each system, the EPA estimated annual revenue using each system's average daily
flow and the average revenue per thousand gallons delivered from the CWSS (U.S. EPA, 2009).
For annual costs, the EPA estimated annual average monitoring costs based on system size and
baseline PFAS occurrence. Annual costs also included annual treatment costs when baseline
PFAS concentrations exceeded the PFAS limits of the final rule or options. Annual treatment
costs are the sum of annual operating and maintenance costs and annualized capital costs.
Table 9-11 shows the number and proportion of CWSs incurring annual costs that exceed 1
percent and 3 percent of annual revenue at the commercial rate of capital for the final rule. Under
the final rule, 16,542 small CWSs (37 percent of small CWSs) could incur annual costs greater
than 1 percent of annual revenue and 8,199 small CWSs (18 percent of small CWSs) could incur
annual costs greater than 3 percent of annual revenue. These potential impacts are high enough to
preclude a finding of no SISNOSE. Details on treatment costs curves can be found in Section
5.3.1 and Best Available Technologies and Small System Compliance Technologies for Per- and
Polyfluoroalkyl Substances (PFAS) in Drinking Water (U.S. EPA, 2024c). For the EPA's
estimates of treatment costs by system size, see Appendix C.l. For information on federal
financial assistance available to small systems for the installation of PFAS treatment technology,
see Section 9.13.2.2.
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Table 9-11: Cost-Revenue Ratio for Small CWSs, Final Rule (PFOA and PFOS MCLs of
4.0 ppt each, PFHxS, PFNA, HFPO-DA MCLs of 10 ppt each and HI of 1) (Commercial
Cost of Capital)
Number of
Number of
Percent of
Percent of
CWSs
CWSs
CWS with
CWS with
Source
Population
Number of
with Cost
with Cost
Cost
Cost
Ownership
Water
Served Size
CWSs
Revenue
Revenue
Revenue
Revenue
Category
Ratio >
Ratio >
Ratio >
Ratio >
1%
3%
1%
3%
Private
Ground
Less than 100
9,260
9,260
4,967
100%
54%
Private
Ground
100 to 500
8,225
2,892
890
35%
11%
Private
Ground
500 to 1,000
1,313
110
88
8%
7%
Private
Ground
1,000 to 3,300
1,048
80
77
8%
7%
Private
Ground
3,300 to 10,000
347
29
29
8%
8%
Private
Surface
Less than 100
399
398
196
100%
49%
Private
Surface
100 to 500
770
206
67
27%
9%
Private
Surface
500 to 1,000
244
19
15
8%
6%
Private
Surface
1,000 to 3,300
278
18
18
6%
6%
Private
Surface
3,300 to 10,000
184
15
14
8%
8%
Public
Ground
Less than 100
1,394
745
213
53%
15%
Public
Ground
100 to 500
4,812
1,153
395
24%
8%
Public
Ground
500 to 1,000
2,819
245
186
9%
7%
Public
Ground
1,000 to 3,300
4,455
341
326
8%
7%
Public
Ground
3,300 to 10,000
2,437
222
221
9%
9%
Public
Surface
Less than 100
340
184
51
54%
15%
Public
Surface
100 to 500
1,272
255
93
20%
7%
Public
Surface
500 to 1,000
935
72
58
8%
6%
Public
Surface
1,000 to 3,300
2,182
143
140
7%
6%
Public
Surface
3,300 to 10,000
2,039
155
155
8%
8%
Total
44,753
16,542
8,199
37%
18%
Abbreviations: CWS - community water system
Note:
The commercial cost of capital is the weighted average cost for PWSs to raise capital or borrow to pay for compliance
activities. Please see Section 4.3.5 for additional details on how the cost of capital for different CWSs was calculated. The
CWS compliance costs were annualized using the cost of capital and then compared to the average revenue of the CWS size
and ownership category.
9.4.7 The EPA's Steps to Minimize the Significant Economic
impact of the Final Rule on Small Systems
Significant alternatives are described below. The EPA evaluated the minimized economic impact
for small systems for each of these alternatives. In the final rule, the EPA elected to allow use of
previously collected PFAS monitoring data to satisfy initial monitoring requirements and retain
the provision to allow for reduced initial monitoring for small ground water systems serving a
population of 10,000 or fewer. After considering public comments on the proposal, the EPA has
included a provision in the final rule to allow for an annual compliance monitoring frequency,
raised the trigger level which determine when more frequency monitoring is required, and is also
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exercising its authority under SDWA Section 1412(b)(10) to implement a nationwide two-year
capital improvement extension to comply with MCL. Finally, the EPA notes that should POU
devices become certified to meet the final NPDWR standard, this could minimize the economic
impact of the final regulation on small PWSs, particularly on water systems in the smallest size
category (e.g., those serving between 25 and 500 people).
9.4.7.1 Use of Previously Collected PFAS Monitoring Data
The EPA has included a provision in the final NPDWR where PWSs of all sizes may use
previously collected monitoring data if it meets stated criteria to satisfy the initial monitoring
requirement. This significant alternative is expected to offer substantial cost savings to small
PWSs, particularly those serving a population between 3,301 and 10,000 that participate in
UCMR 5. For the national cost analysis, the EPA assumes that systems with either UCMR 5 data
or monitoring data in the State PFAS Database (U.S. EPA, 2024g) will not need to conduct the
initial year of monitoring. As a simplifying assumption for the cost analysis, the EPA assumes all
systems serving a population of greater than 3,300 have UCMR 5 data and those serving 3,300 or
less do not. The EPA notes that this assumption is conservative and will likely overestimate costs
for systems serving a population less than 3,300 as many state monitoring programs and other
efforts will have collected monitoring data that can be used as initial monitoring data for these
systems, thus offsetting those costs. Under these assumptions, the EPA estimates that this
provision will reduce the economic burden on small systems nationally by $7 million dollars per
year for three years.
9.4.7.2 Reduced Monitoring for Small Ground Water Systems
The EPA has included a provision in the final NPDWR where ground water systems serving a
population of 10,000 or fewer may collect two quarterly samples over a one-year period for the
purpose of initial monitoring, rather than collecting four quarterly samples. The EPA estimates
that this provision will reduce the economic burden on small systems nationally by $21 million
per year for three years.
9.4.7.3 Annual Monitoring for Systems "Reliably and Consistently"
below the MCLs
Upon consideration of information submitted by commenters, the EPA has included a provision
in the final rule to allow for annual compliance monitoring for all sized systems that are deemed
to be "reliably and consistently"114 below the MCLs, but still above the trigger levels. These
systems would not be required to remain on quarterly monitoring, as proposed, and would
instead be allowed to monitor annually once meeting the requirements of being deemed "reliably
and consistently" below the MCLs. The introduction of annual monitoring has the potential to
significantly reduce monitoring burden for water systems, including small systems, from taking 4
samples per year to taking 1 sample per year per EP. As most small systems have one EP, this
114 The definition of reliably and consistently below the MCL means that each of the quarterly samples contains regulated PFAS
concentrations below the applicable MCLs. For the PFAS NPDWR, this demonstration of reliably and consistently below the
MCL would include consideration of at least four quarterly samples at an EP below the MCL, but states will make their own
determination as to whether the detected concentrations are reliably and consistently below the MCL.
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requirement would save small water systems that are deemed "reliably and consistently" below
the MCL a minimum approximately $930 per year.115
The EPA estimates that approximately 4,300 to 7,000 small PWSs may have regulated PFAS
occurrence between the trigger levels and the MCLs, and therefore may be eligible for annual
monitoring following four consecutive quarterly samples demonstrating they are "reliably and
consistently" below the MCLs. Further, the EPA believes that most systems treating their water
for regulated PFAS would likely be eligible for this compliance monitoring tier. Therefore, the
EPA estimates that 2,900 to 5,400 small water systems in addition to the 4,300 to 7,000 small
water systems above, may be eligible for annual monitoring, instead of quarterly monitoring,
after taking action to comply with the rule.
9.4.7.4 Increased Trigger Levels
Upon consideration of information submitted by commenters, the EPA is finalizing higher
trigger levels for the rule. In the proposal the EPA included trigger levels at 1/3 the MCLs: 1.3
ppt for PFOA and PFOS, 0.5 for the HI. For the final rule, the EPA has set trigger levels at 1/2 of
the MCLs: 2.0 ppt for PFOA and PFOS, 5 ppt for PFHxS, GenX and PFNA, and 0.5 for the HI.
As the trigger levels determine when more frequent monitoring is required, an increase in these
levels will result in a burden reduction compared to the proposed rule for all water systems,
including small water systems with compliance monitoring results between 1/2 and 1/3 of the
MCLs.
9.4.7.5 MCL Compliance Period Extension
Upon consideration of information submitted by commenters, the EPA is exercising its authority
under SDWA § 1412(b)(10) to implement a nationwide capital improvement extension to
comply with MCL. All systems have 5 years to achieve compliance with the MCLs under the
final rule. However, all systems must comply with the initial monitoring requirements by three
years following rule promulgation, and all other requirements of the NPDWR, other than the
MCL, starting three years following rule promulgation (e.g., compliance monitoring, reporting,
and recordkeeping).
The agency notes that SDWA § 1416(a) and (b)(2)(C) describe how primacy agencies may also
grant an exemption for systems meeting specified criteria that provides an additional period for
compliance. PWSs that meet the minimum criteria outlined in the SDWA§ 1416 may be eligible
for an exemption of up to three years. Exemptions for smaller water systems (<3,300
population), meeting certain specified criteria may be renewed for one or more 2-year periods,
but not to exceed six years. States exercising primacy enforcement responsibility must have
adopted the 1998 Variance and Exemption Regulation for a water system to be eligible for an
exemption in that State.
The EPA anticipates this will significantly reduce the burden of the final rule on all water
systems, including small water systems. For more information see Section XI of the FRN.
115 The laboratory analysis cost per sample for EPA Method 537.1 is $309 ($2022). The cost of three avoided samples equals
$927.
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9.4.7.6 Point-of-Use (POU) Technologies as Small System Compliance
Technologies (SSCTs)
In the Best Available Technologies and Small System Compliance Technologies for Per- and
Polyfluoroalkyl Substances (PFAS) in Drinking Water (U.S. EPA, 2024c), the EPA discusses
POUs technologies and notes that the current certification standard is 70 ppt, which would not
ensure these devices are able to meet the MCLs of the final rule. The EPA notes that based on
the technologies used in many POU devices (e.g., RO), the agency anticipates devices are or will
be capable of meeting the MCLs in this final rulemaking. If POU certifications are updated and
do meet the SSCT criteria in the final NPDWR, this could minimize the economic impact of the
final regulation on small PWSs, particularly on water systems in the smallest size category (e.g.,
those serving between 25 and 500 people). In particular, NTNCWS that control all of their
potable taps (e.g., schools, gas stations, churches) may find the use of POU devices to be a
particularly attractive option. The EPA has not estimated the potential national economic burden
reduction because the current certification prevents POU devices from meeting the SSCT criteria
for the final NPDWR. However, the EPA notes there is a potential for significant burden
reduction particularly for very small water systems if POU certifications are updated and POU
devices meet the SSCT criteria for the final NPDWR in the future.
9.5 Unfunded Mandates Reform Act
The UMRA (1995) seeks to protect state, local, and tribal governments from the imposition of
unfunded federal mandates. In addition, the Act seeks to strengthen the partnership among the
federal government and state, local, and tribal governments.
Title II of UMRA establishes requirements for federal agencies to assess the effects of their
regulatory actions on state, local, and tribal governments, and the private sector. Under Section
202 of UMRA, the EPA generally must prepare a written statement, including a cost-benefit
analysis, for proposed and final rules with "federal mandates" that may result in expenditures by
state, local, and tribal governments, in the aggregate, or by the private sector, of $100 million or
more in any one year, adjusted for inflation. The EPA has calculated the cost of the rule in 2022
dollars, therefore, the UMRA requirements are triggered if expenditures exceed $168 million in
one year (escalation based on GDP deflator).
Section 205 of UMRA generally requires the EPA to identify and consider a reasonable number
of regulatory alternatives and adopt the least costly, most cost-effective, or least burdensome
option that achieves the objectives of the rule. The provisions of Section 205 do not apply when
they are inconsistent with applicable law. Note that in the case of NPDWRs, the UMRA Section
205 requirement to adopt the least costly, most cost-effective or least burdensome option is
inconsistent with SDWA regulatory development requirements. SDWA section 1412(b)(4)(B)
states that each national primary drinking water regulation for a contaminant for which a
maximum contaminant level goal is established under this subsection shall specify a maximum
contaminant level for such contaminant which is as close to the maximum contaminant level goal
as is feasible, with feasible defined in section 1412(b)(4)(B)(5) as "feasible with the use of the
best technology, treatment techniques and other means which the Administrator finds, after
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examination for efficacy under field conditions and not solely under laboratory conditions, are
available (taking cost into consideration)." Moreover, Section 205 allows the EPA to adopt an
alternative other than the least costly, most cost-effective, or least burdensome alternative if the
Administrator publishes with the rule an explanation of why that alternative was not adopted.
The EPA's analysis of regulatory alternatives (Options la through lc) found that they are less
costly and lower burden options compared to the final rule. However, these options do not meet
EPA's statutory requirement to, as stated above, set the MCL for a contaminant(s) as close to the
MCLG as is feasible, taking costs into consideration. EPA has determined that the final rule is
feasible, taking costs into consideration; see discussion in Section V of the preamble. Finally, as
detailed in Chapter 7, the Administrator has reaffirmed the SDWA required (Section
1412(b)(4)(C)) determination made at proposal that the quantifiable and nonquantifiable benefits
of the rule justify the quantifiable and nonquantifiable costs, which provides further justification
for why EPA did not select the least burdensome option.
Before the EPA establishes any regulatory requirements that may significantly or uniquely affect
small governments, including tribal governments, it must have developed under Section 203 of
UMRA a small government agency plan. The plan must provide for notifying potentially
affected small governments, enabling officials of affected small governments to have meaningful
and timely input in the development of the EPA regulatory proposals with significant federal
intergovernmental mandates, and informing, educating, and advising small governments on
compliance with the regulatory requirements. Section 204 of UMRA requires EPA, to the extent
permitted by law, develop an effective process to permit elected officials of state, local, and
tribal governments to provide meaningful and timely input in the development of regulatory
proposals containing significant Federal intergovernmental mandates. Options being considered
for the proposed rule also met the consultation requirements of Federalism, therefore the EPA
elected to engage the UMRA (Sections 203 and 204) and Federalism stakeholders in the same
consultation as there are overlapping interests, and a discussion of potential options for the
development of the proposed rule was more effectively communicated simultaneously. For more
information on the consultation, refer to the Summary Report on Federalism and Unfunded
Mandates Reform Act Consultation for the Development of the Proposed PFAS NPDWR in the
public docket at https://www.regulations.gov/document/EPA-HQ-OW-2022-0114-0706.
The final rule contains a federal mandate that may result in expenditures to state, local, and tribal
governments, in the aggregate, or to the private sector, of $168 million or more in any one year.
For the final rule, the highest annual incremental cost over the analysis period occurs in the 6th
year after rule promulgation. In this year PWSs are expected to have undiscounted incremental
costs of $15.5 billion and Primacy Agencies will have undiscounted incremental costs of $5
million. Therefore, the final rule has costs in a single year of $15.5 billion and, therefore, is
subject to the requirements of Sections 202 and 205 of UMRA. As discussed in Section HE of
the preamble for the final rule, the EPA anticipates that significant federal funding available
through BIL and other sources will assist many disadvantaged communities, small systems, and
others with the costs of addressing emerging contaminants, like PFAS.
The annualized costs of the final rule, that are borne by public, private, and tribal PWSs are
provided in Table 9-12. As the exhibit shows, public entities bear most of the costs (but may pass
them on to consumers). As discussed in Chapter 2, in addition to these PWS costs, primacy
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agencies will incur annualized incremental administrative costs of $4.7 million under the final
rule.
Table 9-12: Annual Costs by PWS Size and Ownership, Final Rule (Million $2022)
(Commercial Cost of Capital)
Public Water All Public Water
Systems Serving < Systems
10,000 People
Publicly-Owned Public Water Systems $189.6 $1,284.6
Privately-Owned Public Water Systems $161.3 $247.0
Tribal-Owned Public Water Systems $4.4 $9.0
Abbreviations: PWS - public water system; PFAS - per- and polyfluoroalkyl substances; PFOS - perfluorooctanesulfonic
acid; MCL - maximum contaminant level; HI - hazard index.
9.6 Executive Order 13132: Federalism
Executive Order 13132 (1999), entitled "Federalism" (64 FR 43255, August 10, 1999), requires
the EPA to develop an accountable process to ensure "meaningful and timely input by state and
local officials in the development of regulatory policies that have federalism implications."
"Policies that have federalism implications" are defined in the Executive Order to include
regulations that have "substantial direct effects on the states, on the relationship between the
national government and the states, or on the distribution of power and responsibilities among
the various levels of government."
To fulfill requirements of Executive Order 13132 Section 6, the EPA held a Federalism
consultation with state and local government officials as well as their representative associations
to solicit input on key areas to inform the development of the proposed rule. Options considered
for the proposed rule also met the consultation requirements of UMRA, therefore the EPA
elected to engage the UMRA stakeholders in the same consultation because there are
overlapping interests, and a discussion of potential options for the development of the proposed
rule was more effectively communicated simultaneously. For more information on the
consultation, refer to the Summary Report on Federalism and Unfunded Mandates Reform Act
Consultation for the Development of the Proposed PFAS NPDWR in the public docket at
https://www.regulations.gov/document/EPA-HQ-OW-2022-0114-0706. The EPA also received
public comments from some of these organizations during the public comment period following
the rule proposal. These individual organization comments are available in the Docket. The EPA
considered all comments provided by individual states and state organizations provided during
the public comment period and used these comments to inform the final rule.
This action has federalism implications due to the substantial direct compliance costs on state or
local governments. The net change in annualized primacy agency related cost for state, local, and
tribal governments in the aggregate is estimated to be $4.7 million. Also see Table 9-12 for
annual costs to publicly-owned water systems, which are estimated to be $1,284.6 million.
Please see Section XIII.E of the preamble for the final rule for the EPA's federalism summary
impact statement.
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9.7 Executive Order 13175: Consultation and Coordination with
Indian Tribal Governments
Executive Order 13175 (2000), entitled "Consultation and Coordination with Indian Tribal
Governments" (65 FR 67249, November 9, 2000), requires the EPA to develop an accountable
process to ensure "meaningful and timely input by tribal officials in the development of
regulatory policies that have tribal implications." The Executive Order defines "policies that
have tribal implications to include regulations that have "substantial direct effects on one or more
Indian tribes, on the relationship between the federal government and the Indian tribes, or on the
distribution of power and responsibilities between the federal government and Indian tribes."
Under Executive Order 13175, the EPA may not issue a regulation that has tribal implications,
that imposes substantial direct compliance costs, and that is not required by statute, unless the
federal government provides the funds necessary to pay the direct compliance costs incurred by
tribal governments, or the EPA consults with tribal officials early in the process of developing
the proposed regulation and develops a tribal summary impact statement.
The EPA has identified 998 public water systems serving tribal communities, 84 of which are
federally owned. The EPA estimates that tribal governments will incur public water system
compliance costs of $9.0 million per year attributable to monitoring, treatment or nontreatment
actions to reduce PFAS in drinking water, and administrative costs, and that these estimated
impacts will not fall evenly across all tribal systems. The final PFAS NPDWR does offer
regulatory relief by providing flexibilities for all water systems to potentially utilize pre-existing
monitoring data in lieu of initial monitoring requirements and for ground water CWSs and
NTNCWSs serving 10,000 or fewer people to reduce initial monitoring from quarterly
monitoring during a consecutive 12-month period to only monitoring twice during a consecutive
12-month period. These flexibilities may result in implementation cost savings for many tribal
systems since 98 percent of tribal CWSs and 94 percent of NTNCWs serve 10,000 or fewer
people.
The EPA has concluded that the final rule has Tribal implications, because it will impose direct
compliance costs on Tribal governments, and the federal government will not provide funds
necessary to pay those direct compliance costs. However, the EPA notes that the federal
government will provide a potential source of funds necessary to offset some of those direct
compliance costs. The Infrastructure Investment and Jobs Act (also known as the BIL, P.L. 117-
58) invests over $11.7 billion in the DWSRF General Supplemental fund; $4 billion in the
DWSRF Emerging Contaminants fund; and $5 billion in the EC-SDC grants program. The EPA
has reserved a portion of the EC-SDC program for EPA Regions to provide direct support to
Tribes, similar to support under the Small, Underserved, and Disadvantaged Communities Tribal
program that was enacted under the WIIN Act. Together, these funds will reduce people's
exposure to PFAS and other emerging contaminants through their drinking water. Additionally,
the EPA partners closely with the Indian Health Service (IHS) Areas to identify infrastructure
needs and to implement drinking water infrastructure projects. Additionally, the EPA partners
with IHS to provide technical assistance to support compliance with regulatory requirements.
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Consistent with the EPA's Policy on Consultation and Coordination with Indian Tribes (May 4,
2011), the EPA consulted with tribal officials early in the process of developing the proposed
regulation to gain an understanding of tribal views on key areas of the proposed PFAS NPDWR
and provide tribal officials an opportunity to have meaningful and timely input on its
development. For more information on the consultation with tribes, refer to the Summary Report
on Tribal Consultation: Development of the Proposed PFAS NPDWR in the public docket at
https://www.regulations.gOv/document/EPA-HQ-OW-2022-0114-0704.
9.8 Executive Order 13045: Protection of Children from
Environmental Health and Safety Risks
Executive Order 13045 (1997), entitled "Protection of Children from Environmental Health and
Safety Risks" (62 FR 19885; April 23, 1997) applies to any rule initiated after April 21, 1998,
that (1) is determined to be "economically significant" as defined under Executive Order 12866;
and (2) concerns an environmental, health, or safety risk that the EPA has reason to believe may
have a disproportionate effect on children. If the regulatory action meets both criteria, the EPA
must evaluate the environmental, health, or safety effects of the planned rule on children, and
explain why the planned regulation is preferable to other potentially effective and reasonably
feasible options considered by the EPA.
The final rule is subject to Executive Order 13045 because it is economically significant as
defined in Executive Order 12866. This action's health and risk assessments are contained in
Section 6.2.2, and the associated appendices. The EPA expects that the final rule would provide
additional protection to both children and adults who consume drinking water supplied by the
affected systems. The EPA also expects that the benefits of the final rule, including reduced
health risk, will provide significant benefits to infants and children. As detailed in the Final
Human Health Toxicity Assessments for PFOA and PFOS (U.S. EPA, 2024e; U.S. EPA, 2024f),
the ATSDR Toxicological Profile for Perfluoroalkyls (ATSDR, 2021), and the toxicity
assessments for HFPO-DA and PFBS (U.S. EPA, 2021c; U.S. EPA, 2021d), there is evidence for
adverse effects of PFAS for several developmental and reproductive endpoints, as well as
evidence for adverse endocrine, and immune effects in infants or children. The EPA discusses
the qualitative benefits from avoided adverse health effects of PFOA, PFOS, and other PFAS,
including effects on infants and children in Section 6.2.2.2. In Section 6.2.2.2.1, the EPA
quantifies the avoided morbidity and mortality associated with reductions in infant birth weight
from reduced maternal PFOA and PFOS exposure in drinking water. The EPA also assesses the
potential benefits of reduced PFNA on infant birth weight in a sensitivity analysis found in
Appendix K.
This rulemaking finalizes the MCLGs for PFOA and PFOS as zero based on cancer effects. This
MCLG is protective of the adverse effects observed in infants and children (e.g., decreased birth
weight). This rulemaking also finalizes individual MCLGs for HFPO-DA, PFNA, and PFHxS, as
well as the HI MCLG for mixtures of PFBS, HFPO-DA, PFNA, and PFHxS. The chronic
toxicity values (i.e., chronic oral reference dose and equivalents) used to develop these MCLGs
U.S. EPA, 2024e; U.S. EPA, 2024fprovide an estimate of a daily oral exposure to the human
population (including sensitive subpopulations) that is likely to be without an appreciable risk of
deleterious non-cancer effects during a lifetime.
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9.9 Executive Order 13211: Actions That Significantly Affect
Energy Supply, Distribution, or Use
Executive Order 13211 (2001), "Actions Concerning Regulations That Significantly Affect
Energy Supply Distribution, or Use," provides that agencies shall prepare and submit to the
Administrator of the Office of Information and Regulatory Affairs, OMB, a Statement of Energy
Effects for certain actions identified as "significant energy actions." Section 4(b) of Executive
Order 13211 defines "significant energy actions" as "any action by an agency (normally
published in the Federal Register) that promulgates or is expected to lead to the promulgation of
a final rule or regulation, including notices of inquiry, advance notices of proposed rulemaking,
and notices of proposed rulemaking: (l)(i) that is a significant regulatory action under Executive
Order 12866 or any successor order, and (ii) is likely to have a significant adverse effect on the
supply, distribution, or use of energy; or (2) that is designated by the Administrator of the Office
of Information and Regulatory Affairs as a significant energy action."
The final rule is not a "significant energy action" as defined in Executive Order 13211. The EPA
estimates that the PFAS NPDWR will result in an increased electricity use of approximately 229
GWh per year, for more information see Section 9.2. Total U.S. electricity consumption in 2022
was about 4.05 million GWh (U.S. EIA, 2023). The electricity consumed as a result of the PFAS
NPDWR represents approximately 0.005% of total U.S. electricity consumption. This rule is a
significant regulatory action under Executive Order 12866; however, it is not likely to have a
significant adverse effect on the supply, distribution, or use of energy, for the reasons described
as follows.
9.9.1 Energy Supply
The final rule does not regulate power generation, either directly or indirectly, and public and
private systems subject to the proposed rule do not, as a general rule, generate power. Further,
the energy cost increases borne by customers of systems as a result of the final rule is a low
percentage of the total cost of water. Therefore, power generation utilities that purchase water as
part of their operations are unlikely to face any significant effects as a result of the final rule.
9.9.2 Energy Distribution
The final rule does not regulate any aspect of energy distribution and systems that are regulated
by the proposed rule already have electrical service. The rule is not expected to increase peak
electricity demand for systems because of the small amount of electricity used (see above).
Therefore, the EPA assumes that the existing connections are adequate and that the final rule has
no discernible adverse effect on energy distribution.
9.9.3 Energy Use
The EPA has determined that the incremental energy used to implement water treatment at
drinking water systems in response to the final regulatory requirements is minimal. Therefore,
the EPA does not expect any noticeable effect on the national levels of power generation in terms
of average and peak loads.
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9.10 National Technology Transfer and Advancement Act
Section 12(d) of the NTTAA of 1995 directs the EPA to use voluntary consensus standards in its
regulatory activities unless to do so would be inconsistent with applicable law or otherwise
impractical. Voluntary consensus standards are technical standards (e.g., materials specifications,
test methods, sampling procedures, and business practices) that are developed or adopted by
voluntary consensus standards bodies. NTTAA directs the EPA to provide Congress, through
OMB, explanations when the EPA decides not to use available and applicable voluntary
consensus standards.
The EPA's approved monitoring and sampling protocols generally include voluntary consensus
standards developed by agencies such as the American National Standards Institute (ANSI) and
other such bodies wherever the EPA deems these methodologies appropriate for compliance
monitoring.
9.11 Executive Order 12898: Federal Actions to Address
Environmental Justice in Minority Populations and Low-
Income Populations, Executive Order 14096: Revitalizing
our Nation's Commitment to Environmental Justice for AH
Executive Order 12898 (1994), "Federal Actions to Address Environmental Justice in Minority
Populations and Low-Income Populations" (59 FR 7629, February 16, 1994) established federal
executive policy on environmental justice. Its main provision directs federal agencies, to the
greatest extent practicable and permitted by law, to make environmental justice part of their
mission. Agencies must do this by identifying and addressing as appropriate any
disproportionately high and adverse human health or environmental effects of their programs,
policies, and activities on minority populations and low-income populations in the U.S.
Executive Order 14096 (2023), "Revitalizing our Nation's Commitment to Environmental
Justice for All" (88 FR 25251, April 21, 2023) builds upon and strengthens its commitment to
environmental justice outlined in Executive Order 12898, directing the federal government to
identify, analyze, and address disproportionate and adverse human health or environmental
effects of agency actions on communities with environmental justice concerns. For information
on the EPA's Environmental Justice Analysis, see Chapter 8.
On March 2, 2022 and April 5, 2022, the EPA held public stakeholder meetings related to EJ and
the development of the proposed NPDWR. The meetings provided an opportunity for the EPA to
share information and for communities to offer input on EJ considerations related to the
development of the proposed rule. The EPA received public comment on topics including
establishing an MCL for PFAS and regulating PFAS as a class, affordability of PFAS abatement
options and responsibility for remediation, limiting industrial discharge of PFAS, and the EPA's
relationship with community groups. For more information on the EJ stakeholder meetings, refer
to the EJ Considerations for the Development of the Proposed PFAS Drinking Water Regulation
Public Meeting Summaries in the public docket at https://www.regulations.gov/documemt/EPA-
HQ-OW-20; and https://vvvvvv.regulations.gov/document/EPA-HQ-OW-2022-01 14-
0026. Additionally, the written public comments from this pre-rule proposal engagement are
included within the public docket.
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9.12 Consultations with the Science Advisory Board, National
Drinking Water Council, and the Secretary of Health and
Human Services
9.12.1 Science Advisory Board
As required by Section 1412(e) of the SDWA, in 2021-2022, the EPA asked SAB to evaluate the
current scientific data on the following: The EPA's Proposed Approaches to the Derivation of a
Draft Maximum Contaminant Level Goal for PFOA and PFOS in Drinking Water (U.S. EPA,
2021f; U.S. EPA, 2021g); a draft framework for estimated noncancer health risks associated with
mixtures of PFAS; and the EPA's methodology for evaluating reduced cardiovascular disease
risks. The EPA sought SAB comment on whether the analyses provided in these documents are
scientifically supported, clearly described, and informative toward supporting the EPA's
proposed National Primary Drinking Water Rulemaking effort (U.S. EPA, 2022j). The SAB
PFAS Review Panel deliberated and sought input from public meetings held in December 2021,
January 2022, and May 2022. The SAB Chartered Body conducted a quality review of the draft
panel report July 2022. The SAB's final report, titled "EPA's Analyses to Support EPA's
National Primary Drinking Water Rulemaking for PFAS" was transmitted to the EPA
Administrator on August 22, 2022. See SAB website at for more information on the SAB
review.116 For information on the EPA responses to SAB's review, see U.S. EPA (2022i).
9.12.2 National Drinking Water Advisory Council
In accordance with Section 1412 (d) of the SDWA, the EPA consulted with NDWAC, on the
proposed rule. The EPA consulted with NDWAC in a public meeting on April 19, 2022, on key
areas of the proposed rule including monitoring, treatment, public notification, and PFAS
mixtures. For more information on the consultation with the NDWAC, refer to the NDWAC
Virtual Public Meeting Summary in the public docket at
https://www.reeiilations.eov/dociiment/EPA-HQ-OW-202z )5.
On August 8, 2023, the EPA consulted with the NDWAC prior to the final rule during a virtual
meeting where the EPA presented the proposed PFAS NPDWR, including the proposed MCLs,
monitoring and public notification requirements, and treatment and economic considerations.
The EPA reiterated that the PFAS NPDWR was developed with extensive consultation from
state, local and tribal partners to identify avenues that would reduce PFAS in drinking water and
reaffirmed its commitment to working with these partners on rule implementation. The EPA
carefully considered the information provided by the NDWAC during the development of a final
PFAS NPDWR. A summary of the NDWAC input from that meeting is available in the National
Drinking Water Advisory Council Meeting Summary Report (NDWAC, 2023).
116 https://sab.epa.gov/ords/sab/f?p=100:18:10311539418988:::18:P18_ID:2601#charge
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9.12.3 Secretary of Health and Human Services
In accordance with Section 1412 (d) of the SDWA, on September 28, 2022, the EPA consulted
with the Department of Health and Human Services (HHS). The EPA provided information to
HHS officials on the draft proposed NPDWR and considered HHS input as part of the
interagency review. A summary of this meeting is available in the docket at EPA-HQ-OW-2022-
0114 at www.reeulations.eov.
On November 2nd, 2022, the EPA consulted with HHS on the final rule. Like with the proposed
rule, the EPA provided information to HHS officials on the final NPDWR and considered HHS
input as part of the interagency review. A summary of this meeting is available in the docket at
EPA-HQ-OW-2022-0114 at www.regulations.gov.
9.13 Affordability Analyses
The SDWA, as amended in 1996, requires that the EPA list technologies for small systems
[Section 1412(b)(4)(E)(ii)]:
The Administrator shall include in the list any technology, treatment technique, or other
means that is affordable, as determined by the Administrator in consultation with the
States, for small public water systems serving -
(I) a population of 10,000 or fewer but more than 3,300;
(II) a population of 3,300 or fewer but more than 500; and
(III) a population of 500 or fewer but more than 25;
and that achieves compliance with the MCL or treatment technique, including packaged
or modular systems and point-of-entry or POU treatment units.
The EPA's long-standing methodology for determining whether there are affordable compliance
technologies for a new drinking water standard for small systems compares the cumulative cost
of providing drinking water that complies with the new standard to an affordability threshold
equal to 2.5 percent of median household income (63 FR 42032). Should the EPA determine
there are no affordable SSCTs, the SDWA Section 1412(b)(15)(B) requires the EPA to identify
variance technologies that may not achieve compliance with the drinking water standard but
achieve the maximum reduction or inactivation efficiency that is affordable considering the size
of the system and the quality of the source water.
In addition to the required analysis for small system affordability, the EPA is using alternative
expenditure margins and other changes to the national level affordability methodology to better
understand the cost impacts of new standards on low income and disadvantaged households
served by small drinking water systems. As part of this analysis, the EPA is utilizing a number of
recommendations from the SAB, NDWAC, and other stakeholders such as the AWWA. The
agency conducted supplemental affordability analyses using alternative metrics suggested to the
EPA by these advisory bodies and stakeholders to demonstrate the potential affordability
implications of the proposed NPDWR on the determination of affordable technologies for small
systems at the national level of analysis.
The EPA's national small system affordability determination can be found in Section 9.13.1. The
EPA's supplementary affordability analyses can be found in Section 9.13.2.
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9.13.1 National Small System Affordability Determination
The EPA determined that there are several affordable treatment technologies for small systems.
The determination, documented in Best Available Technologies and Small System Compliance
Technologies for Per- and Polyfluoroalkyl Substances (PFAS) in Drinking Water (U.S. EPA,
2024c), compared the estimated incremental treatment costs per household with a baseline
expenditure margin that equals 2.5 percent of median household income minus baseline drinking
water utility cost per household. Table 9-13 shows which technologies satisfy the affordability
criterion for three small system size categories. Where the EPA does not consider the substantial
financial assistance for capital costs available as part of the Bipartisan Infrastructure Law and
other mechanisms, for systems serving between 25-500 people IX and POU devices are
affordable technologies, GAC is affordable in some cases, and centralized RO is not. In this
scenario, for the smallest system size category, upper bound estimated annual household
treatment costs for GAC exceed the expenditure margin. This exceedance is primarily driven by
capital costs and attributable to the use of high-cost materials (e.g., stainless steel) in the upper
bound estimates. Systems using low-cost materials, but with source water characteristics
otherwise set to the upper bound (e.g., influent PFAS at approximately 7,000 ppt, influent TOC
at 2 mg/L), would fall below the expenditure margin. As discussed in Section 9.13.2.2 below,
where available financial assistance for capital costs is considered, GAC, IX, and POU devices
are affordable technologies (see Section 9.13.2.2 below). For systems serving 501-3,300 people,
where The EPA does not consider financial assistance, GAC, IX, and POU devices are
affordable technologies, and RO is affordable in some cases. For systems serving 3,301-10,000
people GAC, IX and centralized RO are affordable technologies, and POU treatment is not
applicable to systems of that size category.117
117 Note, the results shown in Table 9-13 and discussed in this section are dependent on the estimated annual household
technology costs reported in Table 9-15 which assumes costs associated with standard waste management of spent GAC and
spent IX resin using current typical management practices (reactivation for GAC and incineration for resin). Future changes to
regulations might result in classification of spent GAC or spent IX resin as hazardous waste. The EPA estimated annual cost per
household if systems are required to dispose of these residuals as hazardous waste and conducted the same national level
affordability analysis using the higher hazardous waste handling treatment costs. The agency found the increased treatment costs
for both GAC and IX did not change the affordability conclusions. See Table 9-16 for annualized cost per household assuming
hazardous waste disposal and U.S. EPA (2024c) for the complete analysis.
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A technology must be both effective and affordable to be designated as an SSCT. Technologies
that meet the effectiveness criterion include those designated as BATs for the final rule are:
GAC, IX, and RO. This section also presents preliminary affordability results for POU devices.
POU devices are not currently evaluated as a compliance option because the regulatory options
under consideration require treatment to concentrations below the current certification standard
for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification
entity develop a new certification standard that mirrors the EPA's regulatory standard. More
information is available in Section XI of the preamble the EPA does not anticipate additional
costs for water systems associated with the certification updating process. To evaluate
affordability, the EPA compared incremental costs per household for each technology against an
expenditure margin. Table 9-14 shows the expenditure margins for each system size category. It
also shows how the EPA derived the expenditure margins, beginning with estimates of median
household income (MHI), which vary by system size category. The annual affordability
threshold for household expenditures on drinking water is 2.5 percent of MHI. The EPA
deducted estimates of baseline or current water bills from the affordability threshold to obtain the
expenditure margin estimates.
Table 9-13: SSCT Affordability Analysis Results - Technologies that Meet Effectiveness
Criterion
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
In some casesb
Yes
No
Yes
501 to 3,300
Yes
Yes
Nob
Yes
3,301 to 10,000
Yes
Yes
Yes
Data
Unavailable0
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; RO - reverse osmosis;
SSCT - small system compliance technology.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is in Section XI of the preamble for the final rule.
bUpper bound estimates of annual household treatment costs exceed expenditure margin. Lower bound estimates of annual
household treatment costs do not exceed the expenditure margin. This exceedance is primarily driven by capital costs and
attributable to the use of high-cost materials (e.g., stainless steel) in the upper bound estimates. Systems using low-cost
materials, but with source water characteristics otherwise set to the upper bound (e.g., influent PFAS at approximately 7,000
ppt, influent TOC at 2 mg/L), would fall below the expenditure margin.
Tor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
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Table 9-14: Expenditure Margins for SSCT Affordability Analysis
System Size
(Population
Median Household
Income3
Affordability
Thresholdb
Baseline Water
Cost0
Expenditure
Margin
Served)
A
B = 2.5% x A
C
D = B - C
25 to 500
$62,950
$1,574
$551
$1,022
501 to 3,300
$60,926
$1,523
$638
$885
3,301 to 10,000
$66,746
$1,669
$666
$1,002
Abbreviations: SSCT - small system compliance technology.
Notes:
aMHI based on U.S. Census Bureau's American Community Survey five-year estimates (U.S. Census Bureau, 2010) stated in
2010 dollars, adjusted to 2022 dollars using the CPI (for all items) for areas under 2.5 million persons.
bAffordability threshold equals 2.5 percent of MHI.
cHousehold water costs derived from 2006 Community Water System Survey (U.S. EPA, 2009), based on residential revenue
per connection within each size category, adjusted to 2022 dollars based on the CPI for All Urban Consumers: Water and Sewer
and Trash Collection Services in U.S. City Average.
Table 9-15 provides ranges of per-household costs for each technology and system size category.
The ranges indicate minimum and maximum costs, for further information on SSCT costs, see
U.S. EPA (2024c).
Table 9-15: Total Annual Cost per Household for Candidate Technologies
System Size (Population
Served)
GAC
IX
RO
POUa
25 to 500
$607 to $1,241
$563 to $990
$4,332 to $5,224
$345 to $357
501 to 3,300
$203 to $484
$171 to $351
$721 to $1,324
$327 to $327
3,301 to 10,000
$178 to $417
$145 to $284
$388 to $544
Data unavailable15
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; RO - reverse osmosis;
SSCT - small system compliance technology.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. Costs
presented here should be considered preliminary estimates because they reflect the costs of devices certified under the current
testing standard, not a future standard. More information is in Section XI of the preamble for the final rule.
bFor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
The results discussed above assume management of spent GAC and spent IX resin using current
typical management practices (reactivation for GAC and incineration for resin). The EPA has
proposed some PFAS be designated as hazardous substances under CERCLA and is in the
process of proposing some PFAS be listed as hazardous constituents under the RCRA. If
finalized, neither of these actions would result in new requirements as to how PFAS containing
waste, including spent GAC or resin, is required to be managed. However, waste management
facilities may, at their own discretion, refuse to accept PFAS-containing materials or drinking
water treatment operations may choose to send spent GAC and resin containing PFAS to
facilities permitted to treat and/or dispose of hazardous wastes. To consider the implications of
this possibility, the EPA has developed an assessment of the current unit costs for disposing
spent treatment materials and the costs associated with their disposal as hazardous waste. Table
9-16 shows the resulting cost per household if systems dispose of these residuals as hazardous
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waste. For the smallest system size category not considering available financial assistance, upper
bound estimated annual household treatment costs for GAC and IX exceed the expenditure
margin in Table 9-8. This exceedance is primarily driven by capital costs and attributable to the
use of high-cost materials (e.g., stainless steel) in the upper bound estimates. Systems using low-
cost materials, but with source water characteristics otherwise set to the upper bound (e.g.,
influent PFAS at approximately 7,000 ppt, influent TOC at 2 mg/L), would fall below the
expenditure margin, even under a hazardous waste scenario. Technologies are affordable for all
small systems when the technologies do not use high-cost materials. Technologies that do not
use high-cost materials are available for small systems. Although costs increase in this scenario,
the increases are not significant enough to change the conclusions about affordability.
Table 9-16: Total Annual Cost per Household Assuming Hazardous Waste Disposal
System Size (Population Served)
GAC
IX
25 to 500
$630 to $1,369
$586 to $1,027
501 to 3,300
$211 to $520
$176 to $360
3,301 to 10,000
$185 to $438
$148 to $289
Abbreviations: GAC - granular activated carbon; IX - ion exchange.
9.13.2 Supplemental Affordability Analyses
In 2002, Congress required the EPA to re-evaluate small system variance policy because of the
concern with the high cost of arsenic treatment in small communities. In response, in 2003, the
EPA consulted with NDWAC and SAB. The SAB and NDWAC made a number of
recommendations regarding the method by which the EPA evaluates the affordability of
compliance with drinking water standards.
Some key recommendations made by both the SAB and the NDWAC include:
• The EPA should consider the household cost of each new regulation on an incremental
basis rather than a total cost of all water treatment regulations, and
• The EPA should consider reducing the current affordability threshold, and
• Financial assistance should be incorporated in the affordability calculations if the
financial support is generally available to all systems (nationwide).
In addition to the SAB and NDWAC recommendations, several additional reports by
stakeholders have offered recommendations on the improvement of the EPA's affordability
methodology, including:
• The National Academy of Public Administration (NAPA) report, Developing a New
Framework for Community Affordability of Clean Water Services (NAPA, 2017),
• The National Association of Clean Water Agencies, American Water Works Association,
and Water Environment Federation report, Developing a New Framework for Household
Affordability and Financial Capability Assessment in the Water Sector (Raucher et al.,
2019), and
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• The American Water Works Association expert panel report, Improving the Evaluation of
Household-Level Affordability in SDWA Rulemaking: New Approaches (AWWA,
2021).
The recommendations in these reports point to the need to further assess the impacts of new
regulatory costs across income groups with a particular focus on low income and disadvantaged
communities and individuals within water systems. In particular, the American Water Works
Association (2021) expert panel report stressed that the agency also assess the affordability
impacts to low-income households by setting the per household expenditure margin based on the
lowest quintile (20th percentile) of the income distribution.
The EPA has estimated the impact of some potential changes to National Level Affordability
Criteria and analysis based on suggested changed from the SAB, NDWAC, and AWWA's expert
panel. In the following subsections, the EPA estimated small system affordability based on; (1)
an incremental approach with expenditure margins of 1.0 percent of annual MHI and 2.5 percent
of the lowest quintile of annual household income, and no additional adjustment for total current
annual water expenditures, and (2) taking into account nationally available financial assistance
when assessing affordability.
9.13.2.1 Small System Affordability Analysis with Potential Additional
Expenditure Margins
As part of the EPA's consideration of additional annual expenditure margins to improve the
assessment of affordability impacts to low income and disadvantaged communities, two
incremental cost analyses are conducted utilizing alternative potential expenditure margins.
Given the recommendations from the NDWAC, the first expenditure margin threshold is based
on 1.0 percent of annual MHI. The second expenditure margin threshold is set equal to 2.5
percent of the lowest quintile of annual household income and is based on the American Water
Works Association (2021) expert panel report. These expenditure margins are estimated for each
of the small system size categories: 25 to 500, 501 to 3,300, and 3,301 to 10,000 people served.
As this is an incremental analysis no additional adjustments are made to the values to account for
current annual drinking water cost. Table 9-17 shows the calculated annual expenditure margins
by system size.
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Table 9-17: Potential Annual Expenditure Margins for SSCT Affordability Analysis
1.0% of Median Household 2.5% of Lowest Quintile
System Size (Population Served) Income3 Incomeb
A B
25 to 500
$629
$731
501 to 3,300
$609
$714
3,301 to 10,000
$667
$774
Abbreviations: SSCT - small system compliance technology.
Notes:
aMHI is based on U.S. Census Bureau's American Community Survey five-year estimates (U.S. Census Bureau, 2010) stated
in 2010 dollars, adjusted to 2022 dollars using the CPI (for all items) for areas under 2.5 million persons.
bLowest quintile (20th percentile) household income is based on U.S. Census 2010 American Community Survey 5-year
estimates (U.S. Census Bureau, 2010) stated in 2010 dollars, adjusted to 2022 dollars using the CPI (for all items) for areas
under 2.5 million persons.
Given these alternative annual expenditure margins the remainder of the assessment process is
the same as the EPA's current small system affordability methodology. The estimated total
annual household costs for each of the deemed efficient treatment technologies presented in
Table 9-15 are compared against the estimated annual expenditure margin thresholds from Table
9-17 for each system size category. Table 9-18 presents the affordability results using the 1.0
percent of annual MHI expenditure margin and Table 9-19 provides the information when the
2.5 percent of the lowest quintile of annual household income is used as the threshold.
Table 9-18: Affordability Analysis Results Using a 1.0% of Annual Median Household
Income Expenditure Margin
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
In some casesb
In some casesb
No
Yes
501 to 3,300
Yes
Yes
No
Yes
3,301 to 10,000
Yes
Yes
Yes
Data unavailable0
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU- point-of-use treatment; and RO - reverse osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's final regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is in Section XI of the preamble for the final rule.
bUpper bound estimates of annual household treatment costs exceed expenditure margin. Lower bound estimates of annual
household treatment costs do not exceed the expenditure margin. This exceedance is primarily driven by capital costs and
attributable to the use of high-cost materials (e.g., stainless steel) in the upper bound estimates. Systems using low-cost
materials, but with source water characteristics otherwise set to the upper bound (e.g., influent PFAS at approximately 7,000
ppt, influent TOC at 2 mg/L), would fall below the expenditure margin.
Tor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
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Table 9-19: Affordability Analysis Results Using a 2.5% of Lowest Quintile of Annual
Household Income Expenditure Margin
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
In some casesb
In some casesb
No
Yes
501 to 3,300
Yes
Yes
No
Yes
3,301 to 10,000
Yes
Yes
Yes
Data unavailable0
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; and RO - reverse
osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is in Section XI of the preamble for the final rule.
bUpper bound estimated annual household treatment costs exceed expenditure margin. Lower bound estimated annual
household treatment costs do not exceed the expenditure margin. This exceedance is primarily driven by capital costs and
attributable to the use of high-cost materials (e.g., stainless steel) in the upper bound estimates. Systems using low-cost
materials, but with source water characteristics otherwise set to the upper bound (e.g., influent PFAS at approximately 7,000
ppt, influent TOC at 2 mg/L), would fall below the expenditure margin.
Tor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
The results in both Table 9-18 and Table 9-19, which utilize the supplemental expenditure
margins, of 1.0 percent of annual MHI and 2.5 percent of the lowest quintile of annual household
income, and the results of the EPA's national level affordability analysis in Table 9-9, which
utilizes a household expenditure margin estimated by adjusting 2.5 percent of median household
income minus baseline median annual drinking water costs, differ in the case of IX for systems
serving 25 to 500 people. As indicated by the "In some cases" reported in Table 9-18 and Table
9-19 for GAC and IX the upper bound annual household treatment cost for both these
technologies exceed both the 1.0 percent of annual MHI and 2.5 percent of the lowest quintile of
annual household income expenditure margins, however, the estimated lower bound annual
household treatment costs do not exceed the expenditure margins. The alternative expenditure
margins also changed the affordability results for RO in the 501-3,300 system size category. In
the national affordability analysis using the 2.5 percent of MHI with baseline adjustment upper
bound RO annual household cost estimates exceed the expenditure margin but the lower bound
costs do not. When using both the 1.0 percent of annual MHI and 2.5 percent of the lowest
quintile of annual household income potential criteria both the high and low bound estimated
annual household treatment costs exceed the expenditure margins.118
118 Note, the results shown in Table 9-18 and Table 9-19 and discussed in this section are dependent on the estimated annual
household technology costs reported in Table 9-15 which assumes costs associated with standard waste management of spent
GAC and spent IX resin using current typical management practices (reactivation for GAC and incineration for resin). Future
changes to regulations might result in classification of spent GAC or spent IX resin as hazardous waste. The EPA estimated
annual cost per household if systems are required to dispose of these residuals as hazardous waste and conducted the same
national level affordability analyses with the 1.0 percent of MHI and 2.5 percent of the lowest quintile of annual household
income expenditure margins and using the higher hazardous waste handling treatment costs. The agency found the increased
treatment costs for both GAC and IX did not change the affordability conclusions.
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9.13.2.2 Small System Affordability Analysis When Accounting for
Financial Assistance
The SAB and NDWAC recommended to the EPA that the national level affordability analysis
should include the impact of financial assistance if the financial support is generally available to
all systems (nationwide). The recommendations themselves indicate a two-step process; (1)
determine if and how much financial assistance is available to small systems on a national level
for compliance with a specific rule, in this case the PFAS drinking water rule, and (2) calculate
the potential impact of the financial assistance on the estimated per household treatment costs for
each of the small system size categories.
On the national level, significant financial assistance is available to small systems for the
installation of PFAS treatment technology. One critical and long-established source of this
assistance is available through the EPA's DWSRF Program that was authorized by Congress as
part of the 1996 Amendments to the SDWA. The DWSRF's purpose is to provide a source of
financial assistance to water systems and states to help them achieve the public health protection
objectives of SDWA. A unique feature of the DWSRF Program is that it is state based. The EPA
awards capitalization grants to states who provide a 20 percent match, creating a dedicated fund
from which loans are made to water systems and into which the loan repayments (and interest)
are deposited so they can be loaned out again. Within some broad statutory constraints contained
in SDWA, the states have considerable flexibility to tailor the DWSRF Program to their own
unique needs and circumstances.
The SDWA established three criteria at the core of the process used by states in ranking projects
in priority order to receive funding. States are required, to the maximum extent practicable, to
give priority for the use of DWSRF funds to projects that:
1. Address the most serious risk to human health;
2. Are necessary to ensure compliance with SDWA requirements; and
3. Assist systems most in need on a per household basis according to state affordability
criteria.
Thus, system level affordability, according to state affordability criteria, is a central
consideration in ranking projects eligible to receive DWSRF assistance. Each state has
developed, and the EPA has approved, a project priority ranking procedure. The specific weight
given to affordability considerations vis-a-vis public health and SDWA compliance
considerations varies from state to state. States are required to include their project priority
ranking system as part of the Intended Use Plan they are required to develop in support of their
application for each capitalization grant. The Intended Use Plan must contain both the project
priority ranking system and the priority list of projects eligible for DWSRF assistance. The state
must provide notice and opportunity for public comment on the priority list of projects.
Under the core DWSRF Program, the state may establish an interest rate between zero percent
and the market rate. The lower the interest rate, the greater the subsidy provided to the borrower.
SDWA requires states to establish a Disadvantaged Communities Program within their DWSRF
under which communities considered disadvantaged according to state developed affordability
criteria could receive additional subsidies beyond a zero percent loan. These additional subsidies
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often take the form of principal forgiveness (i.e., loan forgiveness) or grants. There is no limit to
the amount of additional subsidy that can be provided to a particular project except for an overall
limit on the total amount of additional subsidy of 35 percent of the state's annual capitalization
grant.
This additional subsidization could be directed entirely to a few projects, essentially making the
assistance those projects receive equivalent to a 100 percent grant; or the additional subsidization
could be distributed among a larger number of projects and combined with zero or low-interest
loans. States may also offer communities they consider disadvantaged119 a loan term of 40 years
rather than the base period of 20 to 30 years. Notably, the loan term cannot extend beyond the
design life of the capital improvement constructed via the DWSRF loan.
The SDWA provided the EPA with the authority to publish information to assist states in
establishing affordability criteria for purposes of a disadvantaged community program. The
agency worked with a group of expert stakeholders and published "Information for States on
Developing Affordability Criteria for Drinking Water" (document number 816-R-98-002) in
February 1998 (U.S. EPA, 1998b). The agency provided additional information to assist states'
affordability criteria development in the "Implementation of the Clean Water and Drinking
Water State Revolving Fund Provisions of the Bipartisan Infrastructure Law" memorandum in
March 2022 (U.S. EPA, 2022d).
PFAS drinking water treatment loans and grants have been and will continue to be available to
systems of all sizes under the traditional DWSRF program funding and allocation structure. In
addition to these funding sources, on November 15, 2021, the Infrastructure Investment and Jobs
Act (IDA), often referred to as the Bipartisan Infrastructure Law or BIL (P.L. 117-58),
appropriated $4 billion over 5 years ($800,000,000 per year) for projects that are DWSRF
eligible whose primary purpose must be to address emerging contaminants, with a focus on
PFAS. The EPA expects to establish a NPDWR for PFOA and PFOS. The agency is also
evaluating additional PFAS and groups of PFAS. Given stated Congressional intent of this
appropriation, PFAS-focused projects will be eligible for funding under this appropriation
regardless of whether the EPA has established a NPDWR for that particular PFAS or group of
PFAS. These BIL funds must be distributed to communities entirely as forgivable loans or
grants, and states are not required to provide matching funds as with most DWSRF projects. 25
percent of this BIL funding is targeted toward disadvantaged communities and/or communities
fewer than or equal to 25,000 people.
In addition to the DWSRF BIL funds, as part of a government-wide effort to confront PFAS
pollution, the BIL authorizes $5 billion as part of the EC-SDC grants program that can be used to
reduce PFAS in drinking water in communities facing disproportionate impacts. The goal of the
EC-SDC grants program is for states to provide grants to public water systems in small or
disadvantaged communities to address emerging contaminants, including PFAS. Funding will be
provided to participating states and territories to benefit small or disadvantaged communities in
scoping, planning, testing, and remediating emerging contaminants in drinking and source water.
119 Disadvantaged community is defined as the service area of a public water system that meets affordability criteria established
after public review and comment by the State in which the public water system is located.
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These funds can be used in small or disadvantaged communities to address emerging
contaminants like PFAS in drinking water through actions such as technical assistance, water
quality testing, contractor training, and installation of centralized treatment technologies and
systems. On June 15, 2022, the EPA announced that it is making $1 billion available in FY2022
of a total of $5 billion for fiscal years 2022-2026.
Given the BIL emerging contaminant funding being made available through the DWSRF and the
EC-SDC grants program, the EPA expects that most small systems will have access to financial
assistance for PFAS related capital expenditures. The EPA estimates that the total amount of
initial capital treatment technology expenditures for small systems nationally ranges between
approximately $1.8 and $3.5 billion. The EPA expects funding from BIL to be more than
sufficient to cover the capital costs for small systems. Hence, it seems reasonable to consider
these funds for the purposes of illustrating the potential impact of including financial assistance
in the calculation of the national level affordability assessment for small system compliance
technologies. Because BIL funds are limited to providing grants and loan forgiveness associated
with PFAS drinking water treatment capital expenditures, the EPA in this example zeroed out
only the capital cost of the candidate effective technologies. The annual per household treatment
cost ranges presented in Table 9-15 represent operations and maintenance costs for the
technologies by small system size category. Comparing the cost ranges in Table 9-20 with
unadjusted cost ranges in Table 9-15 demonstrates the potential large decrease in technology cost
when financial assistance is considered. The decreases across technologies and system size
categories range from 52 percent to 84 percent for the centralized technologies, and
approximately 30 percent for POU technologies.
Table 9-20: Annual Cost per Household for Candidate Technologies Assuming 100%
Financial Assistance for Technology Capital Costs
System Size (Population
Served)
GAC
IX
RO
POUa
25 to 500
$134 to $230
$140 to $161
$1,160 to $1,242
$244 to $256
501 to 3,300
$57 to $141
$58 to $78
$281 to $338
$228 to $228
3,301 to 10,000
$66 to $147
$60 to $86
$186 to $219
Data unavailable15
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; and RO - reverse
osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is in Section XI of the preamble for the final rule.
bFor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
Table 9-21, Table 9-22, and Table 9-23 below show the affordability results utilizing the 2.5
percent of annual MHI minus the baseline median annual drinking water cost, the incremental
1.0 percent of annual MHI, and using the 2.5 percent of the lowest quintile of annual household
income expenditure margins, respectively. Given the significant reduction in estimated per
household annual treatment costs for GAC and IX, the technologies were found to satisfy the
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national level affordability criterion for the three statutorily mandated small system size
categories.120 Centralized RO with high per household operations and maintenance costs, of
$1,081 to $1,153, in the system size category of 25-500 people served was found to be
unaffordable in that system size category across all alternative expenditure margins, but
economies of scale reduce per household costs in systems serving between 501 and 10,000
people sufficiently to approve the technology as affordable under the three alternative
expenditure margins. POU treatment was also found to be affordable at the national level of
analysis for systems serving 25 to 500 and 501 to 3,300 people across the three presented
expenditure margins. POU treatment is not applicable to systems serving more than 3,300 people
given the increasing complexity of managing POU programs at such large scales.
Table 9-21: Affordability Analysis Results Using a 2.5% of Annual Median Household
Income Minus the Baseline Median Annual Drinking Water Cost Expenditure Margin
and Assuming 100% Financial Assistance for Technology Capital Costs
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
Yes
Yes
No
Yes
501 to 3,300
Yes
Yes
Yes
Yes
3,301 to 10,000
Yes
Yes
Yes
Data unavailable15
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; and RO - reverse
osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is available in Section XI of the preamble for the final rule.
bFor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
120 Note, the results shown in Table 9-21, Table 9-22, and Table 9-23 and discussed in this section are dependent on the estimated
annual household technology costs reported in Table 9-20 which assumes operations and maintenance costs associated with
standard waste management of spent GAC and spent IX resin using current typical management practices (reactivation for GAC
and incineration for resin). Future changes to regulations might result in classification of spent GAC or spent IX resin as
hazardous waste. The EPA estimated annual operations and maintenance cost per household if systems are required to dispose of
these residuals as hazardous waste and conducted the same national level affordability analyses using the three alternative
expenditure margins using the higher hazardous waste handling treatment costs. The agency found the increased treatment costs
for both GAC and IX did not change the affordability conclusions.
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Table 9-22: Affordability Analysis Results Using a 1.0% of Annual Median Household
Income Expenditure Margin and Assuming 100% Financial Assistance for Technology
Capital Costs
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
Yes
Yes
No
Yes
501 to 3,300
Yes
Yes
Yes
Yes
3,301 to 10,000
Yes
Yes
Yes
Data unavailable15
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU- point-of-use treatment; and RO - reverse osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is available in Section XI of the preamble for the final rule.
bFor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
Table 9-23: Affordability Analysis Results Using a 2.5% of Lowest Quintile of Annual
Household Income Expenditure Margin and Assuming 100% Financial Assistance for
Technology Capital Costs
System Size (Population Served)
GAC
IX
RO
POUa
25 to 500
Yes
Yes
No
Yes
501 to 3,300
Yes
Yes
Yes
Yes
3,301 to 10,000
Yes
Yes
Yes
Data unavailable15
Abbreviations: GAC - granular activated carbon; IX - ion exchange; POU - point-of-use treatment; and RO - reverse
osmosis.
Notes:
aPOU devices are not currently a compliance option because the final rule requires treatment to concentrations below the
current certification standard for POU devices. However, POU treatment is anticipated to become a compliance option for
small systems in the future should NSF/ANSI or another accredited third-party certification entity develop a new certification
standard that mirrors (or is demonstrated to treat to concentrations lower than) the EPA's proposed regulatory standard. The
affordability conclusions presented here should be considered preliminary because they reflect the costs of devices certified
under the current standard, not a future standard. More information is available in Section XI of the final rule.
bFor evaluating costs for this PFAS rulemaking, the EPA's WBS model for POU treatment does not cover systems serving
more than 3,300 people (greater than 1 MGD design flow).
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