United States
Environmental Protection
Agency
Office of Water
Washington, DC 20460
EPA-821-R-23-003
February 28, 2023
<&EPA Benefit and Cost Analysis for
Proposed Supplemental
Effluent Limitations
Guidelines and Standards for
the Steam Electric Power
Generating Point Source
Category
-------
v»EPA
United States
Environmental Protection
Agency
Benefit and Cost Analysis for Proposed
Supplemental Effluent Limitations Guidelines
and Standards for the Steam Electric Power
Generating Point Source Category
EPA-821-R-23-003
February 28, 2023
U.S. Environmental Protection Agency
Office of Water (4303T)
Engineering and Analysis Division
1200 Pennsylvania Avenue, NW
Washington, DC 20460
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Acknowledgements and Disclaimer
This report was prepared by the U.S. Environmental Protection Agency. Neither the United States
Government nor any of its employees, contractors, subcontractors, or their employees make any warranty,
expressed or implied, or assume any legal liability or responsibility for any third party's use of or the results
of such use of any information, apparatus, product, or process discussed in this report, or represents that its
use by such party would not infringe on privately owned rights.
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BCAfor Proposed Supplemental the Steam Electric Power Generating ELGs
Table of Contents
Table of Contents
Table of Contents i
List of Figures v
List of Tables vi
Abbreviations ix
Executive Summary 1
1 Introduction 1-1
1.1 Steam Electric Power Plants 1-2
1.2 Baseline and Regulatory Options Analyzed 1-2
1.3 Analytic Framework 1-4
1.3.1 Constant Prices 1-5
1.3.2 Discount Rate and Year 1-5
1.3.3 Period of Analysis 1-5
1.3.4 Timing of Technology Installation and Loading Reductions 1-6
1.3.5 Annualization of future costs and benefits 1-6
1.3.6 Population and Income Growth 1-6
1.4 Organization of the Benefit and Cost Analysis Report 1-7
2 Benefits Overview 2-1
2.1 Human Health Impacts Associated with Changes in Surface Water Quality 2-4
2.1.1 Drinking Water 2-4
2.1.2 Fish Consumption 2-5
2.1.3 Complementary Measure of Human Health Impacts 2-7
2.2 Ecological and Recreational Impacts Associated with Changes in Surface Water Quality 2-8
2.2.1 Changes in Surface Water Quality 2-9
2.2.2 Impacts on Threatened and Endangered Species 2-10
2.2.3 Changes in Sediment Contamination 2-10
2.3 Economic Productivity 2-11
2.3.1 Water Supply and Use 2-11
2.3.2 Reservoir Capacity 2-13
2.3.3 Sedimentation Changes in Navigational Waterways 2-14
2.3.4 Commercial Fisheries 2-14
2.3.5 Tourism 2-15
2.3.6 Property Values 2-15
2.4 Changes in Air Pollution 2-16
2.5 Summary of Benefits Categories 2-17
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BCAfor Proposed Supplemental the Steam Electric Power Generating ELGs
Table of Contents
3 Water Quality Effects of Regulatory Options 3-20
3.1 Waters Affected by Steam Electric Power Plant Discharges 3-20
3.2 Changes in Pollutant Loadings 3-21
3.2.1 Implementation Timing 3-21
3.2.2 Results 3-22
3.3 Water Quality Downstream from Steam Electric Power Plants 3-25
3.4 Overall Water Quality Changes 3-26
3.4.1 WQI Data Sources 3-26
3.4.2 WQI Calculation 3-28
3.4.3 Baseline WQI 3-29
3.4.4 Estimated Changes in Water Quality (AWQI) from the Regulatory Options 3-30
3.5 Limitations and Uncertainty 3-30
4 Human Health Benefits from Changes in Pollutant Exposure via the Drinking Water Pathway 4-1
4.1 Background 4-1
4.2 Overview of the Analysis 4-2
4.3 Estimates of Changes in Halogen Concentrations in Source Water 4-4
4.3.1 Step 1: Modeling Bromide Concentrations in Surface Water 4-4
4.3.2 Step 2: Modeling Changes in Trihalomethanes in Treated Water Supplies 4-4
4.3.3 Step 3: Quantifying Population Exposure and Health Effects 4-11
4.3.4 Quantifying the Monetary Value of Benefits 4-17
4.4 Results of Analysis of Human Health Benefits from Estimated Changes in Bromide Discharges
Analysis 4-18
4.5 Additional Measures of Human Health Effects from Exposure to Steam Electric Pollutants via
Drinking Water Pathway 4-22
4.6 Limitations and Uncertainties 4-24
5 Human Health Effects from Changes in Pollutant Exposure via the Fish Ingestion Pathway .... 5-1
5.1 Population in Scope of the Analysis 5-2
5.2 Pollutant Exposure from Fish Consumption 5-4
5.2.1 Fish Tissue Pollutant Concentrations 5-4
5.2.2 Average Daily Dose 5-5
5.3 Health Effects in Children from Changes in Lead Exposure 5-6
5.3.1 Methods 5-6
5.3.2 Results 5-9
5.4 Heath Effects in Children from Changes in Mercury Exposure 5-9
5.4.1 Methods 5-10
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Table of Contents
5.4.2 Results 5-11
5.5 Estimated Changes in Cancer Cases from Arsenic Exposure 5-11
5.6 Monetary Values of Estimated Changes in Human Health Effects 5-12
5.7 Additional Measures of Potential Changes in Human Health Effects 5-12
5.8 Limitations and Uncertainties 5-13
6 Nonmarket Benefits from Water Quality Changes 6-1
6.1 Estimated Total WTP for Water Quality Changes 6-1
6.2 Sensitivity Analysis 6-3
6.3 Limitations and Uncertainties 6-4
7 Impacts and Benefits to Threatened and Endangered Species 7-1
7.1 Introduction 7-1
7.2 Baseline Status of Freshwater Fish Species 7-2
7.3 T&E Species Potentially Affected by the Regulatory Options 7-2
7.3.1 Identifying T&E Species Potentially Affected by the Regulatory Options 7-2
7.3.2 Estimating Effects of the Rule on T&E Species 7-3
7.4 Limitations and Uncertainties 7-5
8 Air Quality-Related Benefits 8-1
8.1 Changes in Air Emissions 8-1
8.2 Climate Change Benefits 8-5
8.2.1 Data and Methodology 8-5
8.2.2 Results 8-13
8.3 Human Health Benefits 8-15
8.3.1 Data and Methodology 8-15
8.3.2 Results 8-19
8.4 Annualized Air Quality-Related Benefits of Regulatory Options 8-4
8.5 Limitations and Uncertainties 8-5
9 Estimated Changes in Dredging Costs 9-1
9.1 Methods 9-1
9.1.1 Estimated Changes in Navigational Dredging Costs 9-1
9.1.2 Estimated Changes in Reservoir Dredging Costs 9-2
9.2 Limitation and Uncertainty 9-3
10 Summary of Estimated Total Monetized Benefits 10-1
11 Summary of Total Social Costs 11-1
11.1 Overview of Costs Analysis Framework 11-1
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Table of Contents
11.2 Key Findings for Regulatory Options 11-2
12 Benefits and Social Costs 12-1
12.1 Comparison of Benefits and Costs by Option 12-1
12.2 Analysis of Incremental Benefits and Costs 12-1
13 Cited References 13-1
Appendix A Changes to Benefits Methodology since 2020 Final Rule Analysis 1
Appendix B WQI Calculation and Regional Subindices 1
Appendix C Additional Details on Modeling Change in Bladder Cancer Incidence from Change in
TTHM Exposure 1
Appendix D Derivation of Ambient Water and Fish Tissue Concentrations in Downstream Reaches.1
Appendix E Georeferencing Surface Water Intakes to the Medium-resolution Reach Network 1
Appendix F Sensitivity Analysis for IQ Point-based Human Health Effects 1
Appendix G Methodology for Estimating WTP for Water Quality Changes 1
Appendix H Identification of Threatened and Endangered Species Potentially Affected by the Final
Rule Regulatory Options 1
Appendix I Methodology for Modeling Air Quality Changes for the Proposed Rule 1
iv
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BCAfor Revisions to the Steam Electric Power Generating ELGs
List of Figures
List of Figures
Figure 2-1: Summary of Estimated Benefits Resulting from the Proposed Regulatory Options 2-3
Figure 4-1: Overview of Analysis of Estimated Human Health Benefits of Reducing Bromide Discharges. 4-3
Figure 4-2: Modeled Relationship between Changes in Bromide Concentration and Changes in TTHM
Concentrations based on Median Values in Regli et cil. (2015) 4-10
Figure 4-3: Estimated Number of Bladder Cancer Cases Avoided under the Regulatory Options 4-19
Figure 4-4: Estimated Number of Cancer Deaths Avoided under the Regulatory Options 4-20
Figure 4-5: Contributions of Individual Steam Electric Power Plants to Total Annualized Benefits of Changes
in Bromide Discharges under the Regulatory Options (3 Percent Discount Rate) 4-21
Figure 8-1: Frequency Distribution of Interim SC-CO2 Estimates for 2030 (in 2021$ per Metric Ton C02)8-12
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Tables
List of Tables
Table 1-1: Regulatory Options Analyzed for the Proposed Rule 1-3
Table 2-1: Estimated Annual Pollutant Loadings and Changes in Loadings for Baseline and Regulatory
Options Under Technology Implementation 2-1
Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam Electric
FGD Wastewater, BA Transport Water and CRL Discharges 2-4
Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power Plants 2-
18
Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2
(2030-2049) for Selected Pollutants in Steam Electric Power Plant Discharges, Compared to Baseline
(lb/year) 3-23
Table 3-2: Estimated Exceedances of National Recommended Water Quality Criteria under the Baseline and
Regulatory Options 3-27
Table 3-3: Water Quality Data used in Calculating WQI for the Baseline and Regulatory Options 3-28
Table 3-4: Estimated Percentage of Potentially Affected Reach Miles by WQI Classification: Baseline
Scenario 3-29
Table 3-5: Ranges of Estimated Water Quality Changes for Regulatory Options, Compared to Baseline ... 3-30
Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options 3-31
Table 4-1: Estimated Reaches, Surface Water Intakes, Public Water Systems, and Populations Potentially
Affected by Steam Electric Power Plant Discharges 4-6
Table 4-2: Estimated Distribution of Changes in Source Water and PWS-Level Bromide Concentrations by
Period and Regulatory Option, Compared to Baseline 4-8
Table 4-3: Estimated Increments of Change in TTHM Levels (j^ig/L) as a Function of Change in Bromide
Levels ((ig/L) 4-9
Table 4-4: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served 4-10
Table 4-5: Summary of Data Sources Used in Lifetime Health Risk Model 4-14
Table 4-6: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates 4-16
Table 4-7: Estimated Bromide-related Bladder Cancer Mortality and Morbidity Monetized Benefits 4-20
Table 4-8: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and Thallium
Concentrations by Period and Regulatory Option, Compared to Baseline 4-23
Table 4-9: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway 4-24
Table 5-1: Summary of Population Potentially Exposed to Contaminated Fish Living within 50 Miles of
Affected Reaches (as of 2019) 5-4
Table 5-2: Summary of Group-specific Consumption Rates for Fish Tissue Consumption Risk Analysis .... 5-5
Table 5-3: Value of an IQ Point (2021$) based on Expected Reductions in Lifetime Earnings 5-8
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Tables
Table 5-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead under the Regulatory
Options, Compared to Baseline 5-9
Table 5-5: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline 5-11
Table 5-6: Estimated Benefits of Changes in Human Health Outcomes Associated with Fish Consumption
under the Regulatory Options, Compared to Baseline (Millions of 2021$) 5-12
Table 5-7: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric Pollutants 5-13
Table 5-8: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish Ingestion
Pathway 5-14
Table 6-1: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Improvements
under the Regulatory Options, Compared to Baseline (Main Estimates) 6-3
Table 6-2: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Changes under
the Regulatory Options, Compared to Baseline (Sensitivity Analysis) 6-4
Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits 6-5
Table 7-1: Number of T&E Species with Habitat Range Intersecting Reaches Immediately Receiving or
Downstream of Steam Electric Power Plant Discharges, by Group 7-3
Table 7-2: Higher Vulnerability T&E Species Whose Habitat May be Affected by the Regulatory Options
Compared to Baseline 7-5
Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits 7-6
Table 8-1: IPM Run Years 8-2
Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at Steam
Electric Power Plants 2025-2049, Compared to Baseline 8-2
Table 8-3: Estimated Changes in Air Pollutant Emissions Due to Increase in Trucking at Steam Electric
Power Plants 2025-2049, Compared to Baseline 8-3
Table 8-4: Estimated Changes in Annual CO2, NOx, SO2, and Primary PM2 5 Emissions Due to Changes in
Electricity Generation Profile, Compared to Baseline 8-4
Table 8-5: Estimated Net Changes in Air Pollutant Emissions Due to Changes in Power Requirements,
Trucking, and Electricity Generation Profile, Compared to Baseline 8-5
Table 8-6: Interim Estimates of the Social Cost of Carbon, 2025 - 2049 (2021$/Metric Tonne CO2) 8-10
Table 8-7: Estimated Undiscounted and Total Present Value of Climate Benefits from Changes in CO2
Emissions under the Proposed Rule by SC-CO2 Estimates, Compared to Baseline (Millions of 2021$). 8-
14
Table 8-8: Estimated Total Annualized Climate Benefits from Changes in CO2 Emissions under the Proposed
Rule during the Period of 2025-2049 by Categories of Air Emissions and SC-CO2 Estimates, Compared
to Baseline (Millions of 2021$) 8-15
Table 8-9: Human Health Effects of Ambient Ozone and PM2 5 8-17
Table 8-10: Estimated Avoided PM2 5 and Ozone-Related Premature Deaths and Illnesses by Year for Option
3 of the Proposed Rule, Compared to Baseline (95% Confidence Interval) 8-1
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Tables
Table 8-11: Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for Option 3 of the Proposed Rule (95% Confidence Interval; millions of 2021$)8-3
Table 8-12: Total Annualized Air Quality-Related Benefits of Proposed Rule (Option 3), Compared to the
Baseline, 2025-2049 (Millions of 2021$) 8-4
Table 8-12: Total Annualized Air Quality-Related Benefits of Regulatory Options Based on Extrapolation
from Option 3, Compared to the Baseline, 2025-2049 (Millions of 2021$) 8-5
Table 8-13: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits 8-5
Table 9-1-: Estimated Annualized Navigational Dredging Costs at Affected Reaches Based on Historical
Averages (Millions of 2021$) 9-2
Table 9-2: Estimated Annualized Changes in Navigational Dredging Costs under the Regulatory Options,
Compared to Baseline 9-2
Table 9-3-: Estimated Annualized Reservoir Dredging Volume and Costs based on Historical Averages 9-3
Table 9-4: Estimated Total Annualized Changes in Reservoir Dredging Volume and Costs under the
Regulatory Options, Compared to Baseline 9-3
Table 9-5: Limitations and Uncertainties in Analysis of Changes in Dredging Costs 9-4
Table 10-1: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to
Baseline, at 3 Percent (Millions of 2021$) 10-2
Table 10-2: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to
Baseline, at 7 Percent (Millions of 2021$) 10-3
Table 11-1: Summary of Estimated Incremental Annualized Costs for Regulatory Options (Millions of 2021$)
11-3
Table 11-2: Time Profile of Costs to Society (Millions of 2021$) 11-3
Table 12-1: Total Estimated Annualized Benefits and Costs by Regulatory Option and Discount Rate,
Compared to Baseline (Millions of 2021$) 12-1
Table 12-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options, Compared to Baseline
and to Other Regulatory Options (Millions of 2021$) 12-2
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
Abbreviations
ACS American Community Survey
ADD Average daily dose
As Arsenic
ATSDR Agency for Toxic Substances and Disease Registry
BA Bottom ash
BAT Best available technology economically achievable
BCA Benefit-cost analysis
BEA Bureau of Economic Analysis
BenMAP-CE Environmental Benefits Mapping and Analysis Program—Community Edition
BLS Bureau of Labor Statistics
BMP Best management practices
BOD Biochemical oxygen demand
BW Body weight
CAMx Comprehensive Air Quality Model with Extensions
CBG Census Block Group
CCI Construction Cost Index
CCME Canadian Council of Ministers of the Environment
CCR Coal combustion residuals
CDC Center for Disease Control
CFR Code of Federal Regulations
CO2 Carbon dioxide
COD Chemical oxygen demand
COI Cost-of-illness
COPD Chronic obstructive pulmonary disease
CPI Consumer Price Index
CWA Clean Water Act
D-FATE Downstream Fate and Transport Equations
DBP Disinfection byproduct
DBPR Disinfectants and Disinfection Byproduct Rule
DCN Document Control Number
DICE Dynamic Integrated Climate and Economy
DO Dissolved oxygen
E2RF1 Enhanced River File 1
EA Environmental Assessment
EC Elemental carbon
ECI Employment Cost Index
ECOS Environmental Conservation Online System
EGU Electricity generating unit
EJ Environmental justice
ELGs Effluent limitations guidelines and standards
EO Executive Order
EPA United States Environmental Protection Agency
EROM Enhanced Runoff Method
ESA Endangered Species Act
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
FC
Fecal coliform
FCA
Fish consumption advisories
FGD
Flue gas desulfurization
FUND
Climate Framework for Uncertainty, Negotiation, and Distribution
FR
Federal Register
GDP
Gross Domestic Product
GHG
Greenhouse gas
GIS
Geographic Information System
HAP
Hazardous air pollutant
HC1
Hydrogen chloride
Hg
Mercury
HRTR
High Residence Time Reduction
HUC
Hydrologic unit code
IAM
Integrated assessment model
IBI
Index of biotic integrity
IEUBK
Integrated Exposure, Uptake, and Biokinetics
IPCC
Intergovernmental Panel on Climate Change
IPM
Integrated Planning Model
ISA
Integrated science assessment
IRIS
Integrated Risk Information System
IQ
Intelligence quotient
LADD
Lifetime average daily dose
LML
Lowest measured level
LRTR
Low Residence Time Reduction
MATS
Mercury and Air Toxics Standards
MCL
Maximum contaminant level
MCLG
Maximum contaminant level goal
MDA1
Maximum daily 1-hour average
MDA8
Maximum daily 8-hour average
MGD
Million gallons per day
MRM
Meta-regression model
NAAQS
National Ambient Air Quality Standards
NEI
National Emissions Inventory
NERC
North American Electric Reliability Corporation
NHD
National Hydrography Dataset
NLCD
National Land Cover Dataset
NLFA
National Listing Fish Advisory
NO A A
National Oceanic and Atmospheric Administration
NOx
Nitrogen oxides
NPDES
National Pollutant Discharge Elimination System
NRWQC
National Recommended Water Quality Criteria
NWIS
National Water Information System
03
Ozone
03V
Ozone formed in VOC-limited chemical regimes
03N
Ozone formed in NOx-limited chemical regimes
OA
Organic aerosol
x
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
O&M Operation and maintenance
OMB Office of Management and Budget
OSAT/APCA Ozone Source Apportionment Technique/Anthropogenic Precursor Culpability Assessment
OWTP Willingness-to-pay for a one-point WQI improvement (one-point WTP)
PACE Policy Analysis of the Greenhouse Gas Effect
Pb Lead
PbB Blood lead concentration
PM2 5 Particulate matter (fine inhalable particles with diameters 2.5 |a,m and smaller)
PMio Particulate matter (inhalable particles with diameters 10 |a,m and smaller)
ppm parts per million
PSAT Particulate Source Apportionment Technique
PSES Pretreatment Standards for Existing Sources
PV Present value
PWS Public water system
QA Quality assurance
QC Quality control
RIA Regulatory Impact Analysis
SAB-HES Science Advisory Board Health Effect Subcommittee
SBREFA Small Business Regulatory Enforcement Fairness Act
SC-CO2 Social cost of carbon
SDWIS Safe Drinking Water Information System
Se Selenium
SO2 Sulfur dioxide
SPARROW SPAtially Referenced Regressions On Watershed attributes
SSC Suspended solids concentration
SWFSC Southwest Fisheries Science Center
T&E Threatened and endangered
TDD Technical Development Document
TDS Total dissolved solids
TEC Threshold effect concentration
TN Total nitrogen
TP Total phosphorus
TRI Toxics Release Inventory
TSD Technical support document
TSS Total suspended solids
TTHM Total trihalomethanes
TWTP Total willingness-to-pay
U.S. FWS United States Fish and Wildlife Service
USGS United States Geological Survey
VIP Voluntary Incentive Program
VOC Volatile organic compounds
VSL Value of a statistical life
WBD Watershed Boundary Dataset
WQ Water quality
WQI Water quality index
WQI-BL Baseline water quality index
xi
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WQI-PC Post-technology implementation water quality index
WQL Water quality ladder
WTP Willingness-to-pay
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BCAfor Proposed Supplemental Steam Electric Power Generating ELGs
Executive Summary
Executive Summary
The U.S. Environmental Protection Agency (EPA) is proposing revisions to the technology-based effluent
limitations guidelines and standards (ELGs) for the steam electric power generating point source category,
40 Code of Federal Regulations (CFR) part 423, which EPA promulgated in October 2020 (85 FR 64650).
The proposed rule revises certain best available technology economically achievable (BAT) effluent
limitations and pretreatment standards for existing sources (PSES) for three wastestreams: flue gas
desulfurization (FGD) wastewater, bottom ash (BA) transport water, and combustion residual leachate (CRL).
Regulatory Options
EPA analyzed four regulatory options, summarized in Table ES-1. The options are labeled Option 1 through
Option 4 according to increasing stringency. All options include the same technology basis for CRL
(chemical precipitation) while incrementally increasing controls on FGD wastewater, BA transport water, or
both. EPA identifies one preferred option in the proposed rule, Option 3.
The baseline for the benefit and social cost analyses reflects existing ELG requirements in absence of this
proposed EPA action, i.e., the 2020 ELG. As detailed in this report, EPA calculated the difference between
the baseline and regulatory Options 1 through 4 to determine the net incremental effect of the regulatory
options. In general, the proposed regulatory options are estimated to result in smaller pollutant loads,
improved environmental conditions, and net benefits.
Benefits of Regulatory Options
EPA estimated the potential social welfare effects of the regulatory options and, where possible, quantified
and monetized the benefits (see Chapters 3 through 0 for details of the methodology and results). Table ES-2
and Table ES-3 summarize the benefits that EPA quantified and monetized using 3 percent and 7 percent
discounts, respectively.
EPA quantified but did not monetize other welfare effects of the regulatory options and discusses other effects
only qualitatively. Chapter 2 presents additional information on these welfare effects
ES-1
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BCAfor Proposed Supplemental Steam Electric Power Generating ELGs
Executive Summary
Table ES-1: Regulatory Options Analyzed for the Proposed Rule
Wastestream
Subcategory
Technology Basis for BAT/PSES Regulatory Options3
2020 Rule
(Baseline)
Option 1
Option 2
Option 3
Option 4
FGD
Wastewater
NA (default unless in
subcategory)15
CP + Bio
CP + Bio
CP + Membrane
CP + Membrane
CP + Membrane
Boilers permanently
ceasing the combustion of
coal by 2028
SI
SI
SI
SI
SI
Early adopters or boilers
permanently ceasing the
combustion of coal by 2032
NS
NS
CP + Bio
CP + Bio
NS
High FGD Flow Facilities or
Low Utilization Boilers
CP
CP + Bio
CP + Membrane
CP + Membrane
CP + Membrane
BA Transport
Water
NA (default unless in
subcategory)15
HRR
HRR
HRR
ZLD
ZLD
Boilers permanently
ceasing the combustion of
coal by 2028
SI
SI
SI
SI
SI
Early adopters or boilers
permanently ceasing the
combustion of coal by 2032
NS
NS
NS
HRR
NS
Low Utilization Boilers
BMP Plan
HRR
HRR
ZLD
ZLD
CRL
NA (default)15
BPJ
CP
CP
CP
CP
Abbreviations: BMP = Best Management Practice; CP = Chemical Precipitation; HRR = High Recycle Rate Systems; SI = Surface Impoundment; ZLD = Zero Liquid Discharge; NS = Not
subcategorized (default technology basis applies); NA = Not applicable
a. See TDD for a description of these technologies (U.S. EPA, 2023d).
b. The table does not present existing subcategories included in the 2015 and 2020 rules as EPA did not reopen the existing subcategorization of oil-fired units or units with a
nameplate capacity of 50 MW or less.
Source: U.S. EPA Analysis, 2022
ES-2
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BCAfor Proposed Supplemental Steam Electric Power Generating ELGs
Executive Summary
Table ES-2: Summary of Total Annualized Benefits for Regulatory Options, Compared to Baseline, at 3 Percent (Millions of 2021$)
Benefit Category
Option 1
Option 2
Option 3
Option 4
Human Health
$3.39
$12.36
$12.72
$15.81
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
$0.01
$0.01
Changes in IQ losses in children from exposure to
mercury
$2.94
$2.99
$3.11
$3.11
Changes in cancer risk from disinfection by-products in
drinking water
$0.45
$9.37
$9.61
$12.70
Ecological Conditions and Recreational Uses Changes
$3.02
$3.82
$4.09
$4.27
Use and nonuse values for water quality changes'5
$3.02
$3.82
$4.09
$4.27
Market and Productivity Effects3
<$0.01
<$0.01
<$0.01
<$0.01
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
<$0.01
Air Quality-Related Effects
$690
$1,320
$1,540
$1,650
Climate change effects from changes in C02 emissions0
$190
$370
$440
$450
Human health effects from changes in NOx, S02, and
PM2.5 emissionsd
$500
$950
$1,100
$1,200
Total®
$696
$1,336
$1,557
$1,670
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Value reflects the main willingness-to-pay estimates. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air quality-
related benefits for Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
d. Values for air-quality related effects are rounded to two significant figures. The range reflects the lower and upper bound estimates of human health effects from changes in PM2.5
and ozone levels. See Chapter 8 for details.
e. Values for individual benefit categories may not sum to the total due to independent rounding. Range is based on the air quality-related effects.
Source: U.S. EPA Analysis, 2022
ES-3
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BCAfor Supplemental Steam Electric Power Generating ELGs
Executive Summary
Table ES-3: Summary of Total Annualized Benefits for Regulatory Options, Compared to Baseline, at 7 Percent (Millions of 2021$)
Benefit Category
Option 1
Option 2
Option 3
Option 4
Human Health
$0.82
$6.64
$6.82
$8.84
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
<$0.01
<$0.01
Changes in IQ losses in children from exposure to
mercury
$0.54
$0.55
$0.58
$0.58
Changes in cancer risk from disinfection by-products in
drinking water
$0.28
$6.09
$6.24
$8.26
Ecological Conditions and Recreational Uses Changes
$2.64
$3.32
$3.56
$3.73
Use and nonuse values for water quality changes'5
$2.64
$3.32
$3.56
$3.73
Market and Productivity Effectsd
<$0.01
<$0.01
<$0.01
<$0.01
Changes in dredging costsd
<$0.01
<$0.01
<$0.01
<$0.01
Air Quality-Related Effects
$570
$1,070
$1,280
$1,320
Climate change effects from changes in C02 emissions0
$190
$370
$440
$450
Human health effects from changes in NOx, S02, and
PM2.5 emissionsd
$380
$700
$840
$870
Total®
$573
$1,080
$1,290
$1,333
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Value reflects the main willingness-to-pay estimates. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air quality-
related benefits for Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
d. Values for air-quality related effects are rounded to two significant figures. The range reflects the lower and upper bound estimates of human health effects from changes in PM2.5
and ozone levels. See Chapter 8 for details.
e. Values for individual benefit categories may not sum to the total due to independent rounding. Range is based on the air quality-related effects.
Source: U.S. EPA Analysis, 2022
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Executive Summary
Social Costs of Regulatory Options
Table ES-4 (below) presents the incremental social costs attributable to the regulatory options, calculated as
the difference between each option and the baseline. The regulatory options generally result in additional
costs across regulatory options and discount rates. Chapter 12 describes the social cost analysis. The
compliance costs of the regulatory options are detailed in the Regulatory Impact Analysis (RIA) (U.S. EPA,
2023c).
Comparison of Benefits and Social Costs of Regulatory Options
In accordance with the requirements of Executive Order 12866: Regulator}! Planning and Review and
Executive Order 13563: Improving Regulation and Regulatory Review, EPA compared the benefits and costs
of each regulatory option. Table ES-4 presents the monetized benefits and social costs attributable to the
regulatory options, calculated as the difference between each option and the baseline.
Table ES-4: Total Annualized Benefits and Social Costs by Regulatory
Option and Discount Rate (Millions of 2021$)
Regulatory Option
Total Monetized Benefits3
Total Social Costs
3% Discount Rate
Option 1
$696
$88.4
Option 2
$1,336
$167.0
Option 3
$1,557
$200.3
Option 4
$1,670
$207.2
7% Discount Rate
Option 1
$573
$96.6
Option 2
$1,080
$180.4
Option 3
$1,290
$216.5
Option 4
$1,333
$224.1
a. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air
quality-related benefits for Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM
outputs. The range of benefits reflects the lower and upper bound estimates of human health
effects from changes in PM2.5 and ozone levels. See Chapter 8 for details.
Source: U.S. EPA Analysis, 2022.
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1: Introduction
1 Introduction
EPA is proposing to revise the technology-based ELGs for the steam electric power generating point source
category, 40 CFR part 423, which EPA promulgated in October 2020 (85 FR 64650). The proposed rule
would revise certain effluent limitations based on BAT and pretreatment standards for existing sources for
three wastestreams: flue gas desulphurization (FGD) wastewater, bottom ash (BA) transport water, and
combustion residual leachate (CRL).1
This document presents an analysis of the benefits and social costs of the regulatory options and complements
other analyses EPA conducted in support of this proposal, described in separate documents:
• Environmental Assessment for Proposed Supplemental Effluent Guidelines and Standards for the
Steam Electric Power Generating Point Source Category (EA; U.S. EPA, 2023a). The EA
summarizes the potential environmental and human health impacts that are estimated to result from
the proposed regulatory options, if implemented.
• Technical Development Document for Proposed Supplemental Effluent Guidelines and Standards for
the Steam Electric Power Generating Point Source Category (TDD; U.S. EPA, 2023d). The TDD
summarizes the technical and engineering analyses supporting the proposed rule. The TDD presents
EPA's updated analyses supporting the revisions to limitations and standards applicable to discharges
of FGD wastewater, BA transport water, and leachate. These updates include additional data
collection that has occurred since publication of the 2020 rule, updates to the industry (e.g.,
retirements, treatment updates), cost methodologies, pollutant removal estimates, and explanations for
the calculation of the effluent limitations and standards.
• Regulatory Impact Analysis for Proposed Supplemental Revisions to the Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source Category (RIA;
U.S. EPA, 2023c). The RIA describes EPA's analysis of the costs and economic impacts of the
regulatory options. This analysis provides the basis for social cost estimates presented in Chapter 11
of this document. The RIA also provides information pertinent to meeting several legislative and
administrative requirements, including the Regulatory Flexibility Act of 1980 (as amended by the
Small Business Regulatory Enforcement Fairness Act [SBREFA] of 1996), the Unfunded Mandates
Reform Act of 1995, Executive Order 13211 on Actions Concerning Regulations That Significantly
Affect Energy Supply, Distribution, or Use, and others.
• Environmental Justice Analysis for Proposed Supplemental Revisions to the Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source Category (EJA;
U.S. Environmental Protection Agency, 2023b). This report presents a profile of the communities and
populations potentially impacted by this proposal, analysis of the distribution of impacts in the
baseline and proposed changes, and summary of input from potentially impacted communities that
EPA met with prior to the proposal.
The proposed rule also solicits comment on BAT for legacy wastewater but does not include BAT or PSES for that wastewater.
Thus, for purposes of estimating benefits and costs, this report does not discuss legacy wastewater further.
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1: Introduction
The rest of this chapter discusses aspects of the regulatory options that are salient to EPA's analysis of the
benefits and social costs of the proposed rule and summarizes key analytic inputs used throughout this
document.
The analyses of the regulatory options are based on data generated or obtained in accordance with EPA's
Quality Policy and Information Quality Guidelines. EPA's quality assurance (QA) and quality control (QC)
activities for this rulemaking include the development, approval and implementation of Quality Assurance
Project Plans for the use of environmental data generated or collected from all sampling and analyses, existing
databases and literature searches, and for the development of any models which used environmental data.
Unless otherwise stated within this document, the data used and associated data analyses were evaluated as
described in these quality assurance documents to ensure they are of known and documented quality, meet
EPA's requirements for objectivity, integrity and utility, and are appropriate for the intended use.
1.1 Steam Electric Power Plants
The ELGs for the Steam Electric Power Generating Point Source Category apply to a subset of the electric
power industry, namely those plants "with discharges resulting from the operation of a generating unit by an
establishment whose generation of electricity is the predominant source of revenue or principal reason for
operation, and whose generation of electricity results primarily from a process utilizing fossil-type fuel (coal,
oil, or gas), fuel derived from fossil fuel (e.g., petroleum coke, synthesis gas), or nuclear fuel in conjunction
with a thermal cycle employing the steam water system as the thermodynamic medium" (40 Code of Federal
Regulations [CFR] 423.10).
As described in the RIA, of the 870 steam electric power plants in the universe identified by EPA, only those
coal-fired power plants that discharge FGD wastewater, BA transport water or CRL may incur compliance
costs under the proposed regulatory options. After accounting for planned retirements and fuel conversions,
EPA estimated that 163 coal-fired power plants will be operating after December 31, 2028, and of those, an
estimated 93 steam electric power plants generate the relevant wastestreams and may incur costs to meet the
effluent limits under one or more regulatory options. See TDD and RIA for details (U.S. EPA, 2023c, 2023d).
1.2 Baseline and Regulatory Options Analyzed
EPA presents four regulatory options (see Table 1-1). These options differ in the stringency of controls and
applicability of these controls to generating units or plants based on generation capacity utilization, retirement
or repowering status, technology adoption status, and scrubber purge flow (see TDD for a detailed discussion
of the options and the associated treatment technology bases).
The baseline for this analysis reflects applicable requirements (in absence of the proposed rule). The baseline
includes the 2020 rule (85 FR 64650). As discussed further in Section 2.2.2 of the RIA, the baseline for this
analysis also includes the effects of the 2020 CCR Part A rule.
The Agency estimated and presents in this report the water quality and other environmental effects of FGD
wastewater, BA transport water, and leachate discharges under both the 2020 rule baseline and regulatory
options 1 through 4 presented in Table 1-1. The Agency calculated the difference between the baseline and
the regulatory options to determine the net effect of each regulatory option. EPA is proposing Option 3 as the
preferred regulatory option.
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Table 1-1: Regulatory Options Analyzed for the Proposed Rule
Wastestream
Subcategory
Technology Basis for BAT/PSES Regulatory Options3
Baseline
Option 1
Option 2
Option 3
Option 4
FGD
Wastewater
NA (default unless in
subcategory)15
CP + Bio
CP + Bio
CP + Membrane
CP + Membrane
CP + Membrane
Boilers permanently
ceasing the combustion of
coal by 2028
SI
SI
SI
SI
SI
Early adopters or boilers
permanently ceasing the
combustion of coal by 2032
NS
NS
CP + Bio
CP + Bio
NS
High FGD Flow Facilities or
Low Utilization Boilers
CP
CP + Bio
CP + Membrane
CP + Membrane
CP + Membrane
BA Transport
Water
Boilers permanently
ceasing the combustion of
coal by 2028
SI
SI
SI
SI
SI
Early adopters or boilers
permanently ceasing the
combustion of coal by 2032
NS
NS
NS
HRR
NS
Low Utilization Boilers
BMP Plan
HRR
HRR
ZLD
ZLD
NA (default)15
BPJ
CP
CP
CP
CP
CRL
NA (default unless in
subcategory)15
CP + Bio
CP + Bio
CP + Membrane
CP + Membrane
CP + Membrane
Abbreviations: BMP = Best Management Practice; CP = Chemical Precipitation; HRR = High Recycle Rate Systems; SI = Surface Impoundment; ZLD = Zero Liquid Discharge; NS = Not
subcategorized (default technology basis applies); NA = Not applicable
a. See TDD for a description of these technologies (U.S. EPA, 2023d).
b. The table does not present existing subcategories included in the 2015 and 2020 rules as EPA did not reopen the existing subcategorization of oil-fired units or units with a
nameplate capacity of 50 MW or less.
Source: U.S. EPA Analysis, 2022
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1.3 Analytic Framework
The analytic framework of this benefit-cost analysis (BCA) includes basic components used consistently
throughout the analysis of benefits and social costs2 of the regulatory options:
1. All values are presented in 2021 dollars;
2. Future benefits and costs are discounted using rates of 3 percent and 7 percent back to 2024, which is
the expected year for the final rule publication;
3. Benefits and costs are analyzed over a 25-year period (2025 to 2049) which covers the years when
plants implement wastewater treatment technologies to meet the revised ELGs (2025-2029) and the
subsequent life of these technologies (20 years);
4. Technology installation and the resulting pollutant loading changes occur at the end of the estimated
wastewater treatment technology implementation year;
5. Benefits and costs are annualized;
6. Positive values represent net benefits (e.g., improvements in environmental conditions or social
welfare) compared to baseline; and
7. Future values account for annual U.S. population and income growth, unless noted otherwise.
These components are discussed in the sections below.
EPA's analysis of the regulatory options generally follows the methodology the Agency used previously to
analyze the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b). In analyzing the regulatory options, however,
EPA made several changes relative to the analysis of the 2020 rule:
• EPA used revised inputs that reflect the costs and loads estimated for each of the four regulatory
options (see TDD and RIA for details; U.S. EPA, 2023c, 2023d). Like the analysis of the 2020 rule,
EPA estimated loading reductions for two periods (2025-2029 and 2030-2049) during the overall
period of analysis (2025-2049) to account for transitional conditions when different plants are in the
process of installing technologies to meet the proposed requirements.
• EPA updated the baseline industry information to incorporate changes in the universe and operational
characteristics of steam electric power plants such as electricity generating unit retirements and fuel
conversions since the analysis of the 2020 final rule. EPA also incorporated updated information on
the technologies and other controls that plants employ. See the TDD for details on the changes (U.S.
EPA, 2023d).
• Finally, EPA made certain changes to the methodologies to be consistent with approaches used by the
Agency for other rules and/or incorporate recent advances in environmental assessment, health risk,
and resource valuation research.
These changes are described in the relevant sections of this document, and summarized in Appendix A.
Unless otherwise noted, costs represented in this document are social costs.
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1.3.1 Constant Prices
This BCA applies a year 2021 constant price level to all future monetary values of benefits and costs. Some
monetary values of benefits and costs are based on actual past market price data for goods or services, while
others are based on other measures of values, such as household willingness-to-pay (WTP) surveys used to
monetize ecological changes resulting from surface water quality changes. This BCA updates market and
non-market prices using the Consumer Price Index (CPI), Gross Domestic Product (GDP) implicit price
deflator, or Construction Cost Index (CCI).3
1.3.2 Discount Rate and Year
This BCA generally estimates the annualized value of future benefits using two discount rates: 3 percent and
7 percent. The 3 percent discount rate reflects society's valuation of differences in the timing of consumption;
the 7 percent discount rate reflects the opportunity cost of capital to society. In Circular A-4, the Office of
Management and Budget (OMB) recommends that 3 percent be used when a regulation affects private
consumption, and 7 percent in evaluating a regulation that would mainly displace or alter the use of capital in
the private sector (OMB, 2003; updated 2009). The same discount rates are used for both benefits and costs.
One exception to this practice is discounting of the benefits of avoided greenhouse gas emissions for which
EPA uses values of the social cost of carbon dioxide (SC-CO2) developed by the Interagency Working Group
on the Social Cost of Greenhouse Gases (IWG) using discount rates of 2.5 percent, 3 percent, and 5 percent.
Because greenhouse gases are long-lived and subsequent damages of current emissions can occur over a long
time, the approach to discounting greatly influences the present value of future damages. The IWG published
a set of four SC-CO2 values for use in benefit-cost analyses (IWG, 2021): an average value resulting from
integrated assessment model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus
a fourth value, selected as the 95 th percentile of estimates based on a 3 percent discount rate.4 Section 8.2
provides additional details on climate change-related benefits estimated using these different discount rates.
When summarizing total annualized benefits, EPA includes climate-related benefits estimated using the 3-
percent average SC-CO2 values even when other costs and benefits are discounted at 7 percent.
All future cost and benefit values are discounted back to 2024.5
1.3.3 Period of Analysis
Benefits are projected to begin accruing when each plant implements the control technologies needed to
comply with any applicable BAT effluent limitations or pretreatment standards. As described in greater detail
in the NPRM, EPA is establishing availability timing for BAT limitations that is "as soon as possible" after
the effective date of any final rule but "no later than" five years from the effective date (i.e.. a 2029 deadline).
As discussed in the RIA (in Chapter 3), for the purpose of the economic impact and benefit analysis, EPA
generally estimates that plants will implement control technologies to meet the applicable rule limitations and
3 To update the value of a Statistical Life (VSL), EPA used the GDP deflator and the elasticity of VSL with respect to income of
0.4, as recommended in EPA's Guidelines for preparing Economic Analysis (U.S. EPA, 2010a). EPA used the GDP deflator to
update the value of an IQ point, CPI to update the WTP for surface water quality improvements, cost of illness (COI) estimates,
and the price of water purchase, and the CCI to update the cost of dredging navigational waterways and reservoirs.
4 The IWG included the fourth value to provide information on potentially higher-than-expected economic impacts from climate
change, conditional on the 3 percent estimate of the discount rate (IWG, 2021).
5 In its analysis of the 2015 rule, EPA presented benefits in 2013 dollars and discounted these benefits and costs to 2015 (see U.S.
EPA, 2015a), whereas the analysis of the 2020 rule and used 2018 dollars and discounted benefits and costs to 2020 (see U.S.
Environmental Protection Agency, 2020b).
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1: Introduction
standards as their permits are renewed, and no later than December 31, 2029. This schedule recognizes that
control technology implementation is likely to be staggered over time across the universe of steam electric
power plants.
The period of analysis extends to 2049 to capture the estimated life of the compliance technology at any
steam electric power plant (20 or more years), starting from the year of technology implementation, which
can be as late as 2029.
The different compliance years between options, wastestreams, and plants means that environmental changes
may occur in a staggered fashion over the analysis period as plants implement control technologies to meet
applicable limits under each option. To analyze environmental changes from the baseline and resulting
benefits, EPA used the annual average of loadings or other environmental changes (e.g., air emissions, water
withdrawals) projected during two distinct periods (2025-2029 and 2030-2049) within the overall analysis
period (2025-2049). Section 3.2 provides further details on the breakout of the analysis periods.
1.3.4 Timing of Technology Installation and Loading Reductions
For the purpose of the analysis of benefits and social costs, EPA estimates that plants meet revised applicable
limitations and standards by the end of their estimated technology implementation year and that any resulting
changes in loadings will be in effect at the start of the following year.
1.3.5 Annualization of future costs and benefits
Consistent with the timing of technology installation and loading reductions described above, EPA uses the
following equation to annualize the future stream of costs and benefits:
Equation 1-1.
r(PV)
AV = —
(1 + r)[l - (1 +r)~n]
Where A V is the annualized value, PVis the present value, r is the discount rate (3 percent or 7 percent), and n
is the number of years (25 years).
1.3.6 Population and Income Growth
To account for future population growth or decline, EPA used Woods & Poole population forecasts for the
United States (U.S. Census Bureau, 2017; Woods & Poole Economics Inc., 2021). EPA used the growth
projections for each year to adjust affected population estimates for future years (i.e.. from 2025 to 2049).
Because WTP is expected to increase as income increases, EPA accounted for income growth for estimating
the value of avoided premature mortality based on the value of a statistical life (VSL) and WTP for water
quality improvements. To develop income adjustment factors, EPA calculated income growth factors using
historical and projected "real disposable personal income" estimates (U.S. Energy Information
Administration, 2021). For the VSL calculations, EPA used the VSL value in 1990 dollars ($4.8 million) and
multiplied the value by the income growth rate (relative to 1990) for the applicable analysis year and an
income elasticity of 0.4 (U.S. EPA, 2010a). For the WTP for water quality improvements, EPA multiplied
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1: Introduction
income estimates by the income growth rate, relative to 2019, for the applicable analysis period year (i.e..
from 2025 to 2049).6
1.4 Organization of the Benefit and Cost Analysis Report
This BCA report presents EPA's analysis of the benefits of the regulatory options, assessment of the total
social costs, and comparison of the social costs and monetized benefits.
The remainder of this report is organized as follows:
• Chapter 2 provides an overview of the main benefits expected to result from the implementation of
the four regulatory options analyzed for this proposal.
• Chapter 3 describes EPA's estimates of the environmental changes resulting from the regulatory
options, including water quality modeling that underlays the Agency's estimates of several categories
of benefits.
• Chapters 4 and 5 details the methods and results of EPA's analysis of human health benefits from
changes in pollutant exposure via the drinking water and fish ingestion pathways, respectively.
• Chapter 6 discusses EPA's analysis of the nonmarket benefits of changes in surface water quality
resulting from the regulatory options.
• Chapter 7 discusses EPA's analysis of benefits to threatened and endangered (T&E) species.
• Chapter 8 describes EPA's analysis of benefits associated with changes in emissions of air pollutants
associated with energy use, transportation, and the profile of electricity generation for the regulatory
options.
• Chapter 9 describes benefits from changes in maintenance dredging of navigational channels and
reservoirs.
• Chapter 10 summarizes monetized benefits across benefit categories.
• Chapter 11 summarizes the social costs of the regulatory options.
• Chapter 12 addresses the requirements of Executive Orders that EPA is required to satisfy for the
final rule, notably Executive Order (EO) 12866, which requires EPA to compare the benefits and
social costs of its actions.
• Chapter 13 provides references cited in the text.
Several appendices provide additional details on selected aspects of analyses described in the main text of the
report.
There is a relatively strong consensus in economic literature that income elasticities of approximately "1" are appropriate for
adjusting WTP for water quality improvements in future years (Johnston etal., 2019; Tyllianakis & Skuras, 2016). Therefore,
EPA used an income elasticity of "1" in this analysis.
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2: Benefits Overview
2 Benefits Overview
This chapter provides an overview of the estimated welfare effects to society resulting from changes in
pollutant loadings due to implementation of the main regulatory options analyzed for the proposed rule. EPA
expects the regulatory options to change discharge loads of various categories of pollutants when fully
implemented. The categories of pollutants include conventional (such as suspended solids, biochemical
oxygen demand (BOD), and oil and grease), priority (such as mercury [Hg], arsenic [As], and selenium [Se]),
and non-conventional pollutants (such as total nitrogen [TN], total phosphorus [TP], chemical oxygen
demand [COD] and total dissolved solids [TDS]).
Table 2-1 presents estimated annual pollutant loads under full implementation of the effluent limitations and
standards for the baseline and the regulatory options. The TDD provides further detail on the loading changes
(U.S. EPA, 2023d). As described in Section 3.2, EPA anticipates a transition period and estimated loadings
during interim years before all plants have implemented control technologies to meet the applicable rule
limitations and standards under the proposed regulatory options may differ from these values.
Table 2-1: Estimated Annual Pollutant Loadings and Changes in Loadings for Baseline and
Regulatory Options Under Technology Implementation
Estimated Total Industry Pollutant
Estimated Changes in Pollutant
Regulatory Option
Loadings3
Loadings3 from Baseline
(pounds per year)
(pounds per year)
Baseline
1,126,905,000
NA
Option 1
1,080,844,000
46,061,000
Option 2
216,584,000
910,322,000
Option 3
200,460,000
926,445,000
Option 4
114,668,000
1,012,237,000
NA: Not applicable to the baseline
Note: Pollutant loadings and removals are rounded to three significant figures, so changes may match differences in the values
shown due to independent rounding. See TDD for details (U.S. EPA, 2023d).
a. Industry-wide pollutant loadings reflect full implementation of effluent limitations and include bromide loadings in FGD
wastewater under the maximum scenario (as well as bromide loadings in BA transport water). Values shown in this table do not
account for generating unit retirements or conversions during the period of analysis which are estimated to reduce total industry
loadings under the baseline and regulatory options.
Source: U.S. EPA Analysis, 2022
In addition to water quality changes, effects of the regulatory options in comparison to the 2020 rule also
include other effects of the implementation of control technologies and changes in plant operations, such as
changes in emissions of air pollutants (e.g., carbon dioxide [CO2], fine particulate matter [PM2.5], nitrogen
oxides [NOx], and sulfur dioxide [SO2]) which result in benefits to society in the form of changes in
morbidity and mortality and CO2 impacts on environmental quality and economic activities.
This chapter also provides a brief discussion of the effects of pollutants found in FGD wastewater, BA
transport water, and CRL and addressed by the regulatory options on human health and ecosystem services,
and a framework for understanding the benefits expected to be achieved by these options. For a more detailed
description of steam electric wastewater pollutants, their fate, transport, and impacts on human health and
environment, see the EA (U.S. EPA, 2023a).
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2: Benefits Overview
Figure 2-1 summarizes the potential effects of the regulatory options, the expected environmental changes,
and categories of social welfare effects as well as EPA's approach to analyzing those welfare effects. EPA
was not able to bring the same depth of analysis to all categories of social welfare effects because of imperfect
understanding of the link between discharge changes or other environmental effects of the regulatory options
and welfare effect categories, and how society values some of these effects. EPA was able to quantify and
monetize some welfare effects, quantify but not monetize other welfare effects, and assess still other welfare
effects only qualitatively. The remainder of this chapter provides a qualitative discussion of the social welfare
effects applicable to the proposed rule, including human health effects, ecological effects, economic
productivity, and changes in air pollution. Some estimates of the monetary value of social welfare changes
presented in this document rely on models with a variety of limitations and uncertainties, as discussed in more
detail in Chapters 3 through 0 for the relevant benefit categories.
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Figure 2-1: Summary of Estimated Benefits Resulting from the Proposed Regulatory Options.
DBF = Disinfection byproducts; WTP = Willingness to Pay; VSL=Value of Statistical Life; COI = Cost of illness
Source: U.S. EPA Analysis, 2022.
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2.1 Human Health Impacts Associated with Changes in Surface Water Quality
Pollutants present in steam electric power plant wastewater discharges can cause a variety of adverse human
health effects. Chapter 3 describes the approach EPA used to estimate changes in pollutant levels in waters.
More details on the fate, transport, and exposure risks of steam electric pollutants are provided in the EA
(U.S. EPA, 2023a).
Human health effects are typically analyzed by estimating the change in the expected number of adverse
human health events in the exposed population resulting from changes in effluent discharges. While some
health effects (e.g., cancer) are relatively well understood and can be quantified in a benefits analysis, others
are less well characterized and cannot be assessed with the same rigor, or at all.
The regulatory options affect human health risk by changing exposure to pollutants in water via two principal
exposure pathways discussed below: (1) treated water sourced from surface waters affected by steam electric
power plant discharges and (2) fish and shellfish taken from waterways affected by steam electric power plant
discharges. The regulatory options also affect human health risk by changing air emissions of pollutants via
shifts in the profile of electricity generation, changes in auxiliary electricity use, and transportation; these
effects are discussed separately in Section 2.4.
2.1.1 Drinking Water
Pollutants discharged by steam electric power plants to surface waters may affect the quality of water used for
public drinking supplies. People may then be exposed to harmful constituents in treated water through
ingestion, as well as inhalation and dermal absorption (e.g., showering, bathing). The pollutants may not be
removed adequately during treatment at a drinking water treatment plant, or constituents found in steam
electric power plant discharges may interact with drinking water treatment processes and contribute to the
formation of disinfection byproducts (DBPs).
Public drinking water supplies are subject to legally enforceable maximum contaminant levels (MCLs)
established by EPA (U.S. EPA, 2018b). As the term implies, an MCL for drinking water specifies the highest
level of a contaminant that is allowed in drinking water. The MCL is based on the MCL Goal (MCLG), which
is the level of a contaminant in drinking water below which there is no known or expected risk to human
health. EPA sets the MCL as close to the MCLG as possible, with consideration for the best available
treatment technologies and costs. Table 2-2 shows the MCL and MCLG for selected constituents or
constituent derivatives of steam electric power plant effluent.
Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam
Electric FGD Wastewater, BA Transport Water and CRL Discharges
Pollutant
MCL
MCLG
(mg/L)
(mg/L)
Antimony
0.006
0.006
Arsenic
0.01
0
Barium
2.0
2.0
Beryllium
0.004
0.004
Bromate
0.010
0
Cadmium
0.005
0.005
Chromium (total)
0.1
0.1
Copper3
1.3
1.3
Cyanide (free cyanide)
0.2
0.2
Lead3
0.015
0
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Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam
Electric FGD Wastewater, BA Transport Water and CRL Discharges
Pollutant
MCL
MCLG
(mg/L)
(mg/L)
Mercury
0.002
0.002
Nitrate-Nitrite as N
10 (Nitrate); 1 (Nitrite)
10 (Nitrate); 1 (Nitrite)
Selenium
0.05
0.05
Thallium
0.002
0.0005
Total trihalomethanes15
0.080
Not applicable
bromodichloromethane
Not applicable
0
bromoform
Not applicable
0
dibromochloromethane
Not applicable
0.06
chloroform
Not applicable
0.07
a. MCL value is based on action level.
b. Bromide, a constituent found in steam electric power plant effluent, is a precursor for Total Trihalomethanes and three of its
subcomponents. Additional trihalomethanes may also be formed in the presence of iodine, a constituent also found in steam
electric power plant wastewater discharges.
Source: 40 CFR 141.53 as summarized in U.S. EPA (2018b): National Primary Drinking Water Regulation, EPA 816-F-09-004
Pursuant to MCLs, public drinking water supplies are tested and treated for pollutants that pose human health
risks. For the purpose of analyzing the human health benefits of the regulatory options, EPA assumes that
treated water meets applicable MCLs in the baseline. Table 2-2 shows that for arsenic, bromate, lead, and
certain trihalomethanes, the MCLG is zero. For these pollutants and for those that have an MCL above the
MCLG (thallium), there may be incremental benefits from reducing concentrations even where they are below
the MCL.
EPA used a mass balance approach to estimate the changes in halogen (bromide) levels in surface waters
downstream from steam electric power plant outfalls. Halogens can be precursors for halogenated disinfection
byproduct formation in treated drinking water, including trihalomethanes addressed by the total
trihalomethanes (TTHM) MCL. The occurrence of TTHM and other halogenated disinfection byproducts in
downstream drinking water depends on a number of environmental factors and site-specific processes at
drinking water treatment plants. There is some evidence of associations between adverse human health
effects, including bladder cancer, and exposure to sufficient levels of halogenated disinfection byproducts in
drinking water. For additional information on these topics, see the EA (U.S. EPA, 2023a). For the proposed
rule, EPA quantitatively estimated the marginal effect of changes in surface water bromide levels on drinking
water TTHM levels and bladder cancer incidence in exposed populations. EPA also monetized associated
changes in human mortality and morbidity.
To assess potential for changes in health risk from exposure to arsenic, lead, and thallium in drinking water,
EPA estimated changes in pollutant levels in source waters downstream from steam electric power plants
under each regulatory option. This analysis is discussed in Section 4.3.2.3. EPA did not quantify or monetize
benefits from reduced exposure to arsenic, lead, and thallium via drinking water due to the relatively small
concentration changes in source waters downstream from steam electric plants. EPA however notes that coal
ash effluents can make water more corrosive by increasing the conductivity of source waters used by
downstream water systems and, as a result, increase lead leaching from water distribution infrastructure.
2.1.2 Fish Consumption
Recreational and subsistence fishers (and their household members) who consume fish caught in the reaches
downstream of steam electric power plants may be affected by changes in pollutant concentrations in fish
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tissue. EPA analyzed the following direct measures of change in risk to human health from exposure to
contaminated fish tissue:
• Neurological effects to children ages 0 to 7 from exposure to lead;
• Neurological effects to infants from in-utero exposure to mercury;
• Incidence of skin cancer from exposure to arsenic7; and
• Reduced risk of other cancer and non-cancer toxic effects.
The Agency evaluated potential changes in intellectual impairment, or intelligence quotient (IQ), resulting
from changes in childhood and in-utero exposures to lead and mercury. EPA also translated changes in the
incidence of skin cancer into changes in the number of skin cancer cases.
For constituents with human health ambient water quality criteria, the change in the risk of other cancer and
non-cancer toxic effects from fish consumption is addressed indirectly in EPA's assessment of changes in
exceedances of these criteria (see Section 5.1).
EPA used a cost-of-illness (COI) approach to estimate the value of changes in the incidence of skin cancer,
which are generally non-fatal (see Section 5.5). The COI approach allows valuation of a particular type of
non-fatal illness by placing monetary values on measures, such as lost productivity and the cost of health care
and medications that can be monetized. Some health effects of changes in exposure to steam electric
pollutants, such as neurological effects to children and infants exposed to lead and mercury, are measured
based on avoided IQ losses. Changes in IQ cannot be valued based on WTP approaches because the available
economic research provides little empirical data on society's WTP to avoid IQ losses. Instead, EPA calculated
monetary values for changes in neurological and cognitive damages based on the impact of an additional IQ
point on an individual's future earnings and the cost of compensatory education for children with learning
disabilities. These estimates represent only one component of society's WTP to avoid adverse neurological
effects and therefore produce a partial measure of the monetary value from changes in exposure to lead and
mercury. Employed alone, these monetary values would underestimate society's WTP to avoid adverse
neurological effects. See Sections 5.3 and 5.4 for applications of this method to valuing health effects in
children and infants from changes in exposure to lead and mercury. This is the same approach EPA used in its
analysis of the 2019 Proposed Lead and Copper Rule (U.S. Environmental Protection Agency, 2019d).
During the 2020 rulemaking, EPA received comments that it did not evaluate potential health impacts via the
fish consumption pathway arising from changes in discharges of other steam electric pollutants, such as
aluminum, boron, cadmium, hexavalent chromium, manganese, selenium, thallium, and zinc (U.S. EPA,
2020f). Analyses of these health effects require data and information on the relationships between ingestion
rate and potential adverse health effects and on the economic value of potential adverse health effects. Thus,
due to data limitations and uncertainty in these quantitative relationships, EPA again did not quantify, nor was
it able to monetize, changes in health effects associated with exposure to these pollutants under the regulatory
options. Despite numerous studies conducted by EPA and other researchers, dose-response functions are
available for only a subset of health endpoints associated with steam electric wastewater pollutants. In
EPA is currently revising its cancer assessment of arsenic to reflect new data on internal cancers including bladder and lung
cancers associated with arsenic exposure via ingestion (U.S. EPA, 2010b). Because cancer slope factors for internal organs have
not been finalized, the Agency did not consider these effects in the analysis of the final rule.
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addition, the available research does not always allow complete economic evaluation, even for quantifiable
health effects. For example, sufficient data are not available to evaluate and monetize the following potential
health effects from fish consumption: low birth weight and neonatal mortality from in-utero exposure to lead
and other impacts to children from exposure to lead, such as decreased postnatal growth in children ages one
to 16, delayed puberty, immunological effects, and decreased hearing and motor function (Cleveland el al.,
2008; NTP, 2012; U.S. EPA, 2013d; 2019d); effects to adults from exposure to lead such as cardiovascular
diseases8, decreased kidney function, reproductive effects, immunological effects, cancer and nervous system
disorders (Aoki et al., 2016; Chowdhury et al.. 2018; Clay el al.. 2021; Grossman & Slusky, 2019 Lanphear
et al., 2018; Navas-Acien, 2021; NTP, 2012; U.S. EPA, 2013d; 2019d;); neurological effects to children from
exposure to mercury after birth (Grandjean et al., 2014); effects to adults from exposure to mercury, including
vision defects, hand-eye coordination, hearing loss, tremors, cerebellar changes, premature mortality, and
others (Hollingsworth & Rudik, 2021 Mergler et al., 2007; Center for Disease Control and Prevention (CDC),
2009;); and other cancer and non-cancer effects from exposure to other steam electric pollutants (e.g., kidney,
liver, and lung damage from exposure to cadmium,9 reproductive and developmental effects from exposure to
arsenic, boron, and thallium, liver and blood effects from exposure to hexavalent chromium, and neurological
effects from exposure to manganese) (California EPA, 2011; Oulhote et al., 2014; Roels el al., 2012; U.S.
Department of Health and Human Services, 2012; U.S. EPA, 2020f; Ginsberg, 2012).
EPA recognizes that there may be cumulative or synergistic effects of pollutants that share the same toxicity
mechanism, affect the same body organ or system, or result in the same health endpoint. For example,
exposure to several pollutants discharged by steam electric plants (i.e., lead, mercury, manganese, and
aluminum) is associated with adverse neurological effects, in particular in fetuses and small children (Agency
for Toxic Substances and Disease Registry (ATSDR), 2009; Grandjean et al., 2014; NTP, 2012; Oulhote et
al., 2014; U.S. EPA, 2013d). However, data and resource limitations preclude a full analysis of such
cumulative or synergistic effects. A weight of evidence approach is typically used in qualitatively evaluating
the cumulative effect of a chemical mixture. Cumulative effects often depend on exposure doses as well as
potential threshold effects (ATSDR, 2004; 2009). While there are no existing methods to fully analyze and
monetize these effects, EPA quantified some of these effects in the EA (U.S. Environmental Protection
Agency, 2023a).
Due to these limitations, the total monetary value of changes in human health effects included in this analysis
represent only a subset of the potential health benefits that are expected to result from the regulatory options.
2.1.3 Complementary Measure of Human Health Impacts
EPA quantified, but did not monetize, changes in pollutant concentrations in excess of human health-based
national recommended water quality criteria (NRWQC). This analysis provides an approximate indication of
the change in cancer and non-cancer health risk by comparing the number of receiving reaches exceeding
health-based NRWQC for steam electric pollutants in the baseline to the number exceeding NRWQC under
the regulatory options (Section 5.7).
Several systematic reviews of epidemiological studies found that lead exposure was positively associated with clinical
cardiovascular outcomes, including cardiovascular mortality (Navas-Acien, 2021). However, the estimated changes in lead
loadings and fish tissue concentrations are relatively small and thus unlikely to result in tangible benefits to adults. As shown in
Section 2.1.2, the expected changes in blood lead levels are small even in sensitive populations (i.e., children ages 0 to 7).
EPA is reviewing and evaluating new research on the relationship between cadmium exposure and kidney damage. Depending on
the outcome of this evaluation, EPA may add a quantitative analysis for cadmium exposure changes to the final rule analysis.
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Because the NRWQC in this analysis are set at levels to protect human health through ingestion of water and
aquatic organisms, changes in the frequency at which human health-based NRWQC are exceeded could
translate into changes in risk to human health. This analysis should be viewed as an indirect indicator of
changes in risk to human health because it does not reflect the magnitude of human health risk changes or the
population over which those changes would occur.
2.2 Ecological and Recreational Impacts Associated with Changes in Surface Water Quality
The regulatory options may affect the value of ecosystem services provided by surface waters through
changes in the habitats or ecosystems (aquatic and terrestrial) that receive steam electric power plant
discharges.
The composition of steam electric power plant wastewater depends on a variety of factors, such as fuel
properties, air pollution control technologies, and wastewater management techniques. Wastewater often
contains toxic pollutants such as aluminum, arsenic, boron, cadmium, chromium, copper, iron, lead,
manganese, mercury, nickel, selenium, thallium, vanadium, molybdenum, and zinc (U.S. EPA, 2023a).
Discharges of these pollutants to surface water can have a wide variety of environmental effects, including
fish kills, reduction in the survival and growth of aquatic organisms, behavioral and physiological effects in
wildlife, and degradation of aquatic habitat in the vicinity of steam electric power plant discharges (U.S. EPA,
2023a). The adverse effects associated with releases of steam electric pollutants depend on many factors such
as the chemical-specific properties of the effluent, the mechanism, medium, and timing of releases, and site-
specific environmental conditions. The modeled changes in environmental impacts are small relative to the
changes estimated for the 2015 rule. Still, EPA expects the ecological impacts from the regulatory options
could include improved habitat conditions for fresh- and saltwater plants, invertebrates, fish, and amphibians,
as well as terrestrial wildlife and birds that prey on aquatic organisms exposed to steam electric pollutants.
The change in pollutant loadings has the potential to enhance ecosystem productivity in waterways and the
health of resident species, including T&E species. Loading reductions projected under the regulatory options
have the potential to impact the general health of fish and invertebrate populations, their propagation to
waters, and fisheries for both commercial and recreational purposes. Water quality improvements also have
the potential to enhance recreational activities such as swimming, boating, fishing, and water skiing. Finally,
the proposed rule has the potential to impact nonuse values (e.g., option, existence, and bequest values) of the
waters that receive steam electric power plant discharges.
Society values changes in ecosystem services by a number of mechanisms, including increased frequency of
use and improved quality of the habitat for recreational activities (e.g., fishing, swimming, and boating).
Individuals also value the protection of habitats and species that may reside in waters that receive FGD
wastewater, BA transport water and CRL discharges, even when those individuals do not use or anticipate
future use of such waters for recreational or other purposes, resulting in nonuse values. The sections below
discuss selected categories of benefits associated with changes in ecosystem services (additional economic
productivity benefits associated with changes in ecosystem services are discussed in section 2.3).
EPA's analysis is intended to isolate possible effects of the regulatory options on aquatic ecosystems and
organisms, including T&E species; however, it does not account for the fact that the National Pollutant
Discharge Elimination System (NPDES) permit for each steam electric power plant, like all NPDES permits,
is required to have limits more stringent than the technology-based limits established by an ELG, wherever
necessary to protect water quality standards. In cases where a NPDES permit would already provide for more
stringent limits in the baseline than those that would be required under the proposed ELG, the improvements
attributable to the proposed rule will be less than estimated in this analysis.
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2.2.1 Changes in Surface Water Quality
EPA quantified potential environmental impacts from the regulatory options by estimating in-waterway
concentrations of FGD wastewater, BA transport water and CRL pollutants and translating water quality
estimates into a single numerical indicator, a water quality index (WQI). EPA used the estimated change in
WQI as a quantitative estimate of changes in aquatic ecosystem conditions for this regulatory analysis.
Section 3.4 of this report provides details on the parameters used in formulating the WQI and the WQI
methodology and calculations. In addition to estimating changes using the WQI, EPA compared estimated
pollutant concentrations to freshwater NRWQC for aquatic life (see Section 3.4.1.1). The EA details
comparisons of the estimated concentrations in immediate receiving and downstream reaches to the
freshwater acute and chronic NRWQC for aquatic life for individual pollutants (U.S. EPA, 2023a).
A variety of primary methods exist for estimating recreational use values, including both revealed and stated
preference methods (Freeman III, 2003). Where appropriate data are available or can be collected, revealed
preference methods can represent a preferred set of methods for estimating use values. Revealed preference
methods use observed behavior to infer users' values for environmental goods and services. Examples of
revealed preference methods include travel cost, hedonic pricing, and random utility (or site choice) models.
In contrast to direct use values, nonuse values are considered more difficult to estimate. Stated preference
methods, or benefit transfer based on stated preference studies, are the generally accepted techniques for
estimating these values (U.S. EPA, 2010a; OMB, 2003; Johnston, Boyle, et al., 2017). Stated preference
methods rely on carefully designed surveys, which either (1) ask people about their WTP for particular
environmental improvements, such as increased protection of aquatic species or habitats with particular
attributes, or (2) ask people to choose between competing hypothetical "packages" of environmental
improvements and household cost (Bateman et al., 2006; Johnston, Boyle, et al., 2017). In either case, values
are estimated by statistical analysis of survey responses.
Although the use of primary research to estimate values is generally preferred because it affords the
opportunity for the valuation questions to closely match the policy scenario, the realities of the regulatory
process often dictate that benefit transfer is the only option for assessing certain types of non-market values
(Rosenberger and Johnston, 2007; Johnston et al., 2021). Benefit transfer is described as the "practice of
taking and adapting value estimates from past research ... and using them ... to assess the value of a similar,
but separate, change in a different resource" (Smith et al., 2002, p. 134). It involves adapting research
conducted for another purpose to estimate values within a particular policy context (Bergstrom & De Civita,
1999; Johnston et al., 2021). Among benefit transfer methods, meta-analyses are often more accurate
compared to other types of transfer approaches due to the data synthesis from multiple source studies
(Rosenberger and Phipps, 2007; Johnston et al., 2021). However, EPA acknowledges that there is still a
potential for transfer errors (Shrestha et al., 2007) and no transfer method is always superior (Johnston et al.,
2021).
EPA followed the same methodology used in analyzing the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b)
and relied on a benefit transfer approach based on an updated meta-analysis of surface water valuation studies
to estimate the use and non-use benefits of improved surface water quality under the regulatory options. The
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updates consisted of incorporating WTP estimates from more recent peer reviewed studies into EPA's
existing econometric model.10 This analysis is presented in Chapter 6.
2.2.2 Impacts on Threatened and Endangered Species
For T&E species, even minor changes to reproductive rates and small mortality levels may represent a
substantial portion of annual population growth. By reducing discharges of steam electric pollutants to aquatic
habitats, the regulatory options have the potential to impact the survivability of some T&E species living in
these habitats. These T&E species may have both use and nonuse values. However, given the protected nature
of T&E species and the fact that use activities, such as fishing or hunting, generally constitute "take" which is
illegal unless permitted, the majority of the economic value for T&E species comes from nonuse values.11
EPA quantified but did not monetize the potential effects of the regulatory options on T&E species. EPA
constructed databases to determine which species have habitat ranges that intersect waters downstream from
steam electric power plants. EPA then queried these databases to identify "affected areas" of those habitats
where 1) receiving waters do not meet aquatic life-based NRWQC under the baseline conditions; and
2) receiving waters do meet aquatic life-based NRWQC under regulatory options, or vice versa. Because
NRWQC are set at levels to protect aquatic organisms, reducing the frequency at which aquatic life-based
NRWQC are exceeded should translate into reduced effects to T&E species and potential improvement in
species populations.
EPA was unable to monetize the proposed rule's effects on T&E species due to challenges in quantifying the
response of T&E populations to changes in water quality. Although a relatively large number of economic
studies have estimated WTP for T&E protection, these studies focused on estimating WTP to avoid species
loss/extinction, increase in the probability of survival, or an increase in species population levels (Subroy et
al., 2019; L. Richardson & Loomis, 2009). These studies, as summarized in Subroy et al. (2019), suggest that
people attach economic value to protection of T&E species ranging from $15.5 per household (in 2021$) for
Colorado pikeminnow to $152.8 (in 2021$) for lake sturgeon (both fish species).12 In addition, T&E species
may serve as a focus for eco-tourism and provide substantive economic benefit to local communities. For
example, Solomon et al. (2004) estimate that manatee viewing provides a net benefit (tourism revenue minus
the cost of manatee protection) of $12.5 million to $13.8 million (in 2021$) per year for Citrus County,
Florida.13 EPA's analysis does not account for the potential for the NPDES permit issuance process to
establish more stringent site-specific controls to meet applicable water quality standards (i.e.. water quality-
based effluent limits issued under Section 301(b)(1)(C)). The analysis may therefore overestimate any
potential impacts to T&E species and associated benefits.
2.2.3 Changes in Sediment Contamination
Effluent discharges from steam electric power plants can also contaminate waterbody sediments. For
example, sediment adsorption of arsenic, selenium, and other pollutants found in FGD wastewater, BA
transport water and CRL discharges can result in accumulation of contaminated sediment on stream and lake
beds (Ruhl et al.. 2012), posing a particular threat to benthic (i.e.. bottom-dwelling) organisms. These
10 See ICF (2022) for additional detail on updating the meta-analysis.
11 The U.S. Endangered Species Act (ESA) defines "take" to mean "to harass, harm, pursue, hunt, shoot, wound, kill, trap, capture,
or collect, or to attempt to engage in any such conduct." 16 U.S. Code § 1532
12 Values adjusted from $8.32 and $138 per household per year (in 2006$), respectively, using the CPI.
13 Range adjusted from $8.2 million to $9 million (in 2001 $), using the CPI.
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pollutants can later be re-released into the water column and enter organisms at different trophic levels.
Concentrations of selenium and other pollutants in fish tissue of organisms of lower trophic levels can bio-
magnify through higher trophic levels, posing a threat to the food chain at large (Ruhl el al., 2012).
In waters receiving direct discharges from steam electric power plants, EPA examined potential exposures of
ecological receptors (/'. e., sediment biota) to pollutants in contaminated sediment. Benthic organisms can be
affected by pollutant discharges such as mercury, nickel, selenium, and cadmium (U.S. EPA, 2023a). The
pollutants in steam electric power plant discharges may accumulate in living benthic organisms that obtain
their food from sediments and pose a threat to both the organism and humans consuming the organism. As
discussed in the EA, EPA modeled sediment pollutant concentrations in immediate receiving waters and
compared those concentrations to threshold effect concentrations (TECs) for sediment biota (U.S. EPA,
2023a). In 2015, EPA also evaluated potential risks to fish and waterfowl that feed on aquatic organisms with
elevated selenium levels and found that steam electric power plant selenium discharges elevated the risk of
adverse reproduction impacts among fish and mallards in immediate receiving waters (U.S. EPA, 2015b).
By reducing discharges of pollutants to receiving reaches, the proposed rule may reduce the contamination of
waterbody sediments, impacts to benthic organisms, and the probability that pollutants could later be released
into the water column and affect surface water quality and the waterbody food chain. Due to data limitations,
EPA did not quantify or monetize the associated benefits.
2.3 Economic Productivity
The regulatory options may have economic productivity effects stemming from changes in the quality of
public drinking water supplies and irrigation water; changes in sediment deposition in reservoirs and
navigational waterways; and changes in tourism, commercial fish harvests, and property values.14 EPA
estimated the changes in sediment deposition in reservoirs and navigational waterways. Chapter 9 discusses
the associated benefits. Other benefit categories (e.g., effects on drinking water treatment costs) are discussed
qualitatively in the following sections.
2.3.1 Water Supply and Use
The regulatory options are projected to reduce loadings of steam electric pollutants to surface waters relative
to the baseline, and thus may affect the uses of these waters for drinking water supply and agriculture. EPA
expects the effects to be relatively small, but the Agency is nevertheless considering engineering or treatment
cost elasticity approaches to quantify avoided treatment costs from reduced halogens to inform understanding
of these effects. Stakeholders with interest in this analysis are encouraged to provide additional information
via public comments to EPA on how treatment costs vary with source water characteristics affected by coal
ash effluents.
2.3.1.1 Drinking Water Treatment Costs
The regulatory options have the potential to affect drinking water treatment costs (e.g., for filtration and
chemical treatment) by changing eutrophication levels and pollutant concentrations in source waters.
Eutrophication, which is most commonly caused by an overabundance of nitrogen and phosphorus, is one of
14 EPA estimated changes in the marketability of coal combustion ash as a benefit of the 2015 rule (U.S. EPA, 2015a). However,
based on the baseline for this proposed rule which already requires ash to be handled dry, EPA does not expect incremental
changes in the amount of ash handled dry vs. wet and benefits from increased marketing of coal combustion ash under any of the
regulatory options.
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the main causes of taste and odor impairment in drinking water and can have a major negative impact on
public perceptions of drinking water safety. Additional treatment to address foul tastes and odors potentially
increases the cost of public water supply.
The Agency conducted a screening-level assessment to evaluate the potential for changes in costs incurred by
public drinking water systems and concluded that such changes, while they may exist, are likely to be
negligible. The assessment involved identifying the pollutants for which treatment costs may vary depending
on source water quality, estimating changes in downstream concentrations of these pollutants at the location
of drinking water intakes, and determining whether modeled water quality changes have the potential to affect
drinking water treatment costs. Based on this analysis, EPA determined that there are no drinking water
systems drawing water at levels that exceed an MCL for metals and other toxics15 listed in Table 2-2 such as
selenium and cyanide under either the baseline or the regulatory options (see Section 4.3.2.3 for details). EPA
estimated no changes in MCL exceedances under the regulatory options. Treatment system operations do not
generally respond to small incremental changes in source water quality for one pollutant or a small subset of
pollutants. Accordingly, EPA did not conduct an analysis of changes in treatment costs incurred by public
water systems (PWS) for this proposal given the relatively small changes in source water quality expected
under the proposed rule and data gaps regarding effects on treatment system operations; however the Agency
is considering possible approaches to calculate potential avoided drinking water treatment costs for the final
rule.
Potential effects of the estimated changes in the levels of halogens downstream from steam electric power
plant outfalls on drinking water treatment costs are currently uncertain in part because there are other
environmental sources of halogens. In addition, existing treatment technologies in the majority of PWS are
not designed to remove halogens from raw surface waters. Halogens found in source water can react during
routine drinking water treatment to generate harmful DBPs at levels that vary with site-specific conditions
(Good & VanBriesen, 2017, 2019; Regli et al, 2015; U.S. EPA, 2016c). EPA estimated the costs of
controlling DBP levels to the MCL in treated water as part of the Stage 2 Disinfectants and Disinfection
Byproduct Rule (DBPR). These costs include treatment technology changes as well as non-treatment costs
such as routine monitoring and operational evaluations. PWS may adjust their operations to control DBP
levels, such as changing disinfectant dosage, moving the chlorination point, or enhancing coagulation and
softening. These changes carry "negligible costs" (U.S. EPA, 2005b, pages 7-19). Where low-cost changes
are insufficient to meet the MCL, PWS may need to incur irreversible capital costs to upgrade their treatment
process to use alternative disinfection technologies such as ozone, ultraviolet light, or chloride dioxide; switch
to chloramines for residual disinfection; or add a pre-treatment stage to remove DBP precursors (e.g.,
microfiltration, ultrafiltration, aeration, or increased chlorine levels and contact time). Some drinking water
treatment facilities have already upgraded their treatment systems as a direct result of halogen discharges
from steam electric power plants (United States of America v. Duke Energy, 2015; Rivin, 2015). However,
not all treatment technologies remove sufficient organic matter to control DBP formation to required levels
(Watson et al., 2012). Thus, increased halogens levels in raw source water could translate into permanently
higher drinking water treatment costs at some plants, in addition to posing increased human health risk.
Conversely, reducing halogen levels in source waters can reduce the health risk, even where treatment
changes have already occurred.16 In some cases, operation and maintenance (O&M) costs may also be
15 Modeled drinking water concentrations reflect discharged pollutant loads from steam electric plants and from other facilities
reporting to the Toxics Resources Inventory (TRI).
16 Regli et al. (2015) estimated benefits of reducing bromide across various types of water treatment systems.
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reduced. EPA did not have information on drinking water treatment costs at affected water systems or
estimates of how costs of drinking water treatment for specific technologies vary with changes in halogen
concentrations in source water. EPA is evaluating the application of engineering models or a halogen
treatment cost elasticity approach to quantify avoided treatment costs from reduced source water halogens.
Stakeholders are encouraged to provide information to help quantification of avoided drinking water
treatment costs under the proposed rule. Aside from avoided treatment costs, the Agency assessed the changes
in levels of halogens downstream from steam electric power plant outfalls and estimated health outcomes
(avoided bladder cancer cases) associated with reduced DBP formation at downstream PWS (see Section
2.1.1 for a discussion of this benefit category and Chapter 4 for a discussion of the analysis).17
2.3.1.2 Irrigation and Other Agricultural Uses
Irrigation accounts for 42 percent of the total U.S. freshwater withdrawals and approximately 80 percent of
the Nation's consumptive water use. Irrigated agriculture provides important contributions to the U.S.
economy accounting for approximately 40 percent of the total farm sales (Hellerstein etal., 2019). Pollutants
in steam electric power plant discharges can affect the quality of water used for irrigation and livestock
watering. Although elevated nutrient concentrations in irrigation water would not adversely affect its
usefulness for plants, other steam electric pollutants, such as arsenic, mercury, lead, cadmium, and selenium
have the potential to affect soil fertility and enter the food chain (National Research Council, 1993; Zhang et
al., 2018). For example, the same heavy metals found in oilfield produced waters (including barium, lead, and
chromium) have been shown to accumulate in soil, plants, and oranges (Zhang etal., 2018). Additionally,
nutrients can increase eutrophication, promoting cyanobacteria blooms that can kill livestock and wildlife that
drink the contaminated surface water. TDS can impair the utility of water for both irrigation and livestock use.
EPA did not quantify or monetize effects of quality changes in agricultural water sources arising from the
regulatory options due to data limitations on how costs vary with relatively small estimated changes in water
quality.
2.3.2 Reservoir Capacity
Reservoirs serve many functions, including storage of drinking and irrigation water supplies, flood control,
hydropower supply, and recreation. Streams can carry sediment into reservoirs, where it can settle and build
up over time, reducing reservoir capacity and the useful life of reservoirs (Graf etal, 2010; Palinkas & Russ,
2019; Rahmani et al, 2018). Reservoir capacity has been diminishing overtime. At a national scale, Randle
etal. (2021) found that total reservoir storage capacity has dropped from apeak of 850 Gm3 to 810 Gm3. At a
state scale, Rahmani et al. (2018) found that all 24 federally operated reservoirs in Kansas have collectively
lost 17 percent of their original capacity with the highest single-reservoir loss of 45 percent. Dredging and
other sediment management strategies can be used to reclaim capacity (Hargrove et al., 2010; Miranda, 2017;
Morris, 2020; Randle etal., 2021; Winkelman. M.O. et al., 2019).18 EPA expects that changes in suspended
solids discharges under the regulatory options could affect reservoir maintenance costs by changing the
frequency or volume of dredging activity. Changes in sediment loads could result in a modest decrease in
dredging costs in reservoirs under all regulatory options. See Chapter 9 for details.
17 Note that EPA's separate proposed rulemaking to regulate discharges of per- and polyfluoroalkyl substances in drinking water
could result in implementation of drinking water treatment technologies that would reduce DBP levels during the analysis period.
18 Other sedimentation management strategies may be used instead of, or in combination with, dredging. This includes reducing
sediment yield through watershed management practices and routing sediments through or around reservoirs (Morris, 2020;
Randle et al., 2021).
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2.3.3 Sedimentation Changes in Navigational Waterways
Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the
United States' transportation network (Clark et al.. 1985). Navigable channels are prone to reduced
functionality due to sediment build-up, which can reduce the navigable depth and width of the waterway
(Clark et al., 1985; Marc Ribaudo & Johansson, 2006). For many navigable waters, periodic dredging is
necessary to remove sediment and keep them passable. For example, the U.S. Army Corps of Engineers
(USACE) maintains the Southwest Pass19, the most highly utilized commercial deep-draft waterway in the
country, and its rapid-onset shoaling has led to prolonged periods of draft restrictions for transiting vessels
(e.g., reductions in the amount of cargo that can be transported per voyage). To counteract channel shoaling,
the USACE has dredged an annual average 25 million cubic yards of sediment since 2015 (Hartman et al.,
2022). Dredging of navigable waterways can be costly. Following the previous example, total dredging
expenditures in the Southwest Pass for the 2019 fiscal year amounted to $147.8 million (dredging
expenditures between the 2015 and 2018 fiscal years ranged from $66.0 million to $65.4 million) (Hartman et
al., 2022).
EPA estimated that all regulatory options would reduce sediment loadings to surface waters and reduce
dredging of navigational waterways. EPA quantified and monetized these benefits based on the avoided cost
for projected changes in future dredging volumes. Chapter 0 describes this analysis.
2.3.4 Commercial Fisheries
Pollutants in steam electric power plant discharges can reduce fish populations by inhibiting reproduction and
survival of aquatic species. These changes may negatively affect commercial fishing industries as well as
consumers of fish, shellfish, and fish and seafood products. Estuaries are particularly important breeding and
nursery areas for commercial fish and shellfish species (Alkire et al., 2020; Brame et al., 2019; Beck et al.,
2001). In some cases, excessive pollutant loadings can lead to the closure of shellfish beds, thereby reducing
shellfish harvests and causing economic losses from reduced harvests (Jin et al., 2008; Trainer et al., 2007;
Islam & Masaru, 2004). Improved water quality due to reduced discharges of steam electric pollutants would
enhance aquatic life habitat and, as a result, contribute to reproduction and survival of commercially harvested
species and larger fish and shellfish harvests, which in turn could lead to an increase in producer and
consumer surplus. Conversely, an increase in pollutant loadings could lead to negative impacts on fish and
shellfish harvest.
EPA did not quantify or monetize impacts to commercial fisheries under the regulatory options. EPA
estimated that five steam electric power plants discharge BA transport water, FGD wastewater or CRL
directly to the Great Lakes or to estuaries. Large distances and stream flows greatly reduce the relative impact
of steam electric power plants discharging upstream from these systems. Although estimated decreases in
annual average pollutant loads under the regulatory options may benefit local fish populations and
commercial harvest, the overall effects to commercial fisheries arising from the regulatory options are
difficult to quantify but are likely to be relatively small. Commercial species potentially affected by steam
electric discharges account for approximately 1 percent of total landings value in the U.S.20 Moreover, most
19 This is the entrance channel for a port system which encompasses waters ranging from the Mississippi River in Baton Rouge,
Louisiana to the Gulf of Mexico Project (Hartman et al., 2022).
20 Based on U.S. commercial fisheries landing values in 2019. EPA obtained commercial fisheries landing data for areas that may
be affected by steam electric discharges (Mississippi (Big Lake, connected to Biloxi Bay), Tampa, FL area (closest port to
Hillsborough Bay), Lake Eerie, and Lake Michigan) and compared the potentially affected commercial fisheries landing value to
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species of fish have numerous close substitutes. The economic literature suggests that when there are plentiful
substitute fish products (e.g., chicken is substitute for fish) the measure of consumer welfare (consumer
surplus) is unlikely to change as a result of small changes in fish landings, such as those EPA expects under
the regulatory options.
2.3.5 Tourism
Discharges of pollutants may also affect the tourism and recreation industries (e.g., boat rentals, sales at local
restaurants and hotels) and, as a result, local economies in the areas surrounding affected waters due to
changes in recreational opportunities (U.S. Bureau of Economic Analysis, 2021; Mojica & Fletcher, 2020;
Highfill & Franks, 2019). The effects of water quality on tourism are likely to be highly localized. Moreover,
since substitute tourism locations may be available, increased tourism in one location (e.g., the vicinity of
steam electric power plants) may lead to a reduction in tourism in other locations or vice versa. Due to the
relatively small water quality changes expected from the regulatory options (see Section 3.4 for details) and
availability of substitute sites, the overall effects on tourism and, as a result, social welfare is likely to be
negligible. Therefore, EPA did not quantify or monetize this benefit category.
2.3.6 Property Values
Discharges of pollutants may affect the aesthetic quality of water resources by altering water clarity, odor, and
color in the receiving and downstream reaches. Technologies implemented by steam electric power plants to
comply with the regulatory options remove nutrients and sediments to varying degrees and have varying
effects on water eutrophication, algae production, water turbidity, and other surface water characteristics.
Several studies (e.g., Austin, 2020; Bin & Czajkowski, 2013; K.J. Boyle etal., 1999; Cassidy etal., 2021;
Gibbs etal., 2002; Kuwayama etal., 2022; Leggett & Bockstael, 2000; Liu etal., 2017; M. R. Moore etal.,
2020; Netusil etal, 2014; Tang etal, 2018; Tuttle & Heintzelman, 2014; Patrick J. Walsh etal, 2011; P.J.
Walsh el al., 2017; Wolf el al., 2022) suggest that both waterfront and non-waterfront properties are more
desirable when located near unpolluted water. For example, Austin (2020) finds that, in North Carolina, coal
ash discharges' negative impacts to drinking water led to a 12 to 14 percent decline in sale price for homes
within one mile of a coal ash pond after potential risks were made more salient by a state regulation.
Therefore, the value of properties located in proximity to waters affected by steam electric plant discharges
may increase due to reductions in discharges of FGD wastewater, BA transport water, and CRL.
EPA did not quantify or monetize the potential change in property values associated with the regulatory
options. The magnitude of the effect on property values depends on many factors, including the number of
housing units located in the vicinity of the affected waterbodies,21 community characteristics (e.g., residential
density), housing stock (e.g., single family or multiple family), and the effects of steam electric pollutants on
the aesthetic quality of surface water. Given that changes in the aesthetic quality of surface waters (e.g.,
total U.S. commercial fisheries landing value (marine and Great Lakes). EPA obtained commercial fishery landing value for
Mississippi and the U.S. from NOAA Fisheries (National Oceanic and Atmospheric Administration, 2022), for the Tampa area
from the Florida Fish and Wildlife Conservation Commission (Florida Fish and Wildlife Conservation Commission, 2022), and
for the Great Lakes from the Great Lakes Fishery Commission (Great Lakes Fishery Commission, 2022). EPA assumed that all
fish species in Lake Eerie and Lake Michigan may be affected by steam electric discharges. For commercial fishery landings in
Tampa and Mississippi, EPA removed deep sea fish species (e.g., tuna, sharks, jacks, and octopus) from consideration of fish
potentially affected by steam electric power plant discharges since they are unlikely to use the estuarine areas where discharges
occur.
21 In a review of 36 hedonic studies that focus on the impact of water quality on housing values, Guignet et al. (2021) note that
some studies have detected property value impacts up to a mile away from impacted waterways.
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clarity and odor) that may result from the relatively small changes in pollutant concentrations under the
regulatory options is difficult to quantify, EPA did not estimate impacts of the proposed rule on property
values. In addition, there may be an overlap between shifts in property values and the estimated total WTP for
surface water quality changes discussed in Section 2.2.1.
2.4 Changes in Air Pollution
The proposed rule is expected to affect air pollution through three main mechanisms: 1) changes in energy use
by steam electric power plants to operate wastewater treatment and other systems needed to comply with the
final rule; 2) changes in transportation-related emissions due to changes in trucking of CCR and other waste
to on-site or off-site landfills; and 3) the change in the profile of electricity generation due to relatively higher
cost to generate electricity at plants incurring compliance costs. The three mechanisms can produce changes
in different directions. For example, increased energy use by power plant tend to increase air emissions
associated with power generation, but those changes are relatively small when compared to the changes
resulting from shifts in the electricity generation mix away from coal-fired generation and toward sources
with lower emission factors. These shifts in generation mix result tend to reduce overall emissions at the
national level, although the localized changes in air pollutant emissions may be positive or negative
depending on which electricity generating units produce more or less electricity as a result of these shifts.
As described in Chapter 5 of the RIA, EPA used the Integrated Planning Model (IPM®), a comprehensive
electricity market optimization model that can evaluate impacts within the context of regional and national
electricity markets, to analyze impacts of the proposed rule (i.e., Option 3). Electricity market analyses using
IPM project that the proposed rule (Option 3) will expand on the baseline trend by shifting away from coal
fired electric power generation toward generation from other energy sources, such as natural gas and
renewables. Relative to the baseline, IPM projects coal-fired generation to decline as a result of the proposed
rule. These changes are offset in part by an increase in natural gas generation, nuclear generation, and
generation by renewables. Differences in emissions factors across energy sources generally results in net
reductions in air emissions from electricity generating units across all modeled pollutants at the national level
(CO2, SO2, NOx, direct PM2 5, PM10, Hg, and hydrogen chloride (HC1)). Overall for the three mechanisms
(auxiliary services, transportation, and market-level generation), EPA estimates net reductions in CO2, SO2,
and NOx emissions as compared to the baseline at the national level. However, the distribution of the changes
may result in localized increases even as the overall changes nationwide are decreases, and air emissions of
some pollutants may increase in some years and decrease in others. See the RIA for details (U.S. EPA,
2023c).
CO2 is the most prevalent of the greenhouse gases, which are air pollutants that EPA has determined endanger
public health and welfare through their contribution to climate change. EPA used estimates of the social cost
of carbon (SC-CO2) to monetize the benefits of changes in CO2 emissions as a result of the proposed rule The
SC-C02 is a metric that estimates the monetary value of projected impacts associated with marginal changes
in CO2 emissions in a given year. It includes a wide range of anticipated climate impacts, such as net changes
in agricultural productivity and human health, property damage from increased flood risk, and changes in
energy system costs, such as reduced costs for heating and increased costs for air conditioning. Chapter 8
details this analysis.
NOx, and SO2 are known precursors to PM2 5, a criteria air pollutant that has been associated with a variety of
adverse health effects, including premature mortality and hospitalization for cardiovascular and respiratory
diseases (e.g., asthma, chronic obstructive pulmonary disease [COPD], and shortness of breath). EPA
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quantified changes in direct PM2.5 emissions and in emissions of PM2 5 and ozone22 precursors NOx and SO2
and assessed impacts of those emission changes on air quality changes across the country using the
Comprehensive Air Quality Model with Extensions (CAMx) (Ramboll Environ International Corporation,
2016). EPA then used spatial fields of baseline and post-compliance air pollutant concentrations as input to
Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to estimate incremental human
health effects (including the potential for premature mortality and morbidity) from changes in ambient air
pollutant concentrations (U.S. EPA, 2018a). Chapter 8 details this analysis.
The proposed rule may also affect air quality through changes in electricity generation units emissions of
larger particulate matter (PM10) and hazardous air pollutants (HAP) including mercury and hydrogen chloride.
The health effects of mercury are detailed in the EA (U.S. EPA, 2023a). Hydrogen chloride is a corrosive gas
that can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. For more
information about the impacts of mercury and hydrogen chloride emissions, see the Final Mercury and Air
Toxics Standards (MATS) for Power Plants,23 including 2020 revisions to the 2012 Coal- and Oil-Fired
Electric Utility Steam Generating Units National Emission Standards for Hazardous Air Pollutants (85 FR
31286).
The proposed rule may also affect air quality if steam electric power plants alter their coal storing and
handling practices, since Jha and Muller (2018) found that a 10 percent increase in coal stockpiles held by
U.S. power plants results in a 0.09% increase in average PM2.5 concentration levels within 25 miles of these
plants. In addition to health effects from air emissions, air pollution can create a haze that affects visibility.
Reduced visibility could impact views in national parks by softening the textures, fading colors, and
obscuring distant features and therefore reduce the value of recreational activities (e.g., K. J. Boyle etal.,
2016; Pudoudyal etal., 2013). A number of studies (e.g., Bayer etal., 2006; Beron etal., 2001; Chay &
Greenstone, 1998) also found that reduced air quality and visibility can negatively affect residential property
values.
2.5 Summary of Benefits Categories
Table 2-3 summarizes the potential social welfare effects of the regulatory options analyzed for the proposed
rule and the level of analysis applied to each category. As indicated in the table, only a subset of potential
effects can be quantified and monetized. The monetized welfare effects include reductions in some human
health risks, use and non-use values from surface water quality improvements, reduced costs for dredging
reservoirs and navigational waterways, and changes in air emissions. Other welfare effect categories,
including changes in waters exceeding NRWQC, were quantified but not monetized. Although EPA was not
able to quantify or monetize other welfare effects, including some other human health risks and impacts to
commercial fisheries, those unquantified benefits may be relatively small compared to other monetized
benefits.24 EPA evaluated these effects qualitatively as discussed above in Sections 2.1 through 2.4.
22 Emissions of nitrogen oxides (NOx) lead to formation of both ozone andPM2 .5 while SO2 emissions lead to formation of PM2 .5
only.
23 See https://www.epa.gov/mats/regulatorv-actions-final-mercurv-and-air-toxics-standards-mats-power-plants.
24 The 2015 and 2020 rules, which are included in the baseline for this analysis, significantly reduced toxic pollutant and nutrient
loadings, making additional reductions estimated for this proposed rule smaller, particularly when compared to the benefits that
can be quantified and monetized.
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Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power
Plants
Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Human Health Benefits from Surface Water Quality Improvements
Changes in human health
Changes in exposure to halogenated
VSLand COI (Chapter
effects (e.g., bladder
DBPs in drinking water
2)
cancer) associated with
V
V
halogenated DBP
exposure via drinking
water
IQ losses to children ages
Changes in childhood exposure to lead
V
V
IQ point valuation
0 to 7
from consumption of self-caught fisha
(Chapter 5)
Need for specialized
Changes in childhood exposure to lead
V
V
Qualitative discussion
education
from consumption of self-caught fisha
(Chapter 5)
Incidence of
Changes in exposure to lead from
Qualitative discussion
cardiovascular disease
consumption of self-caught fisha
(Chapter 2)
IQ losses in infants
Changes in in-utero mercury exposure
IQ point valuation
from maternal consumption of self-
V
V
(Chapter 5)
caught fisha
Incidence of cancer
Changes in exposure to arsenic from
COI (Chapter 5);
consumption of self-caught fisha
V
V
Qualitative discussion
(Chapter 2)
Other adverse health
Changes in exposure to toxic pollutants
Human health criteria
effects (cancer and non-
(lead, cadmium, thallium, etc.) via fish
V
exceedances (Chapter
cancer)
consumption or drinking water
5); Qualitative
discussion (Chapter 2)
Reduced adverse health
Changes in exposure to pollutants from
Qualitative discussion
effects
recreational water uses
(Chapter 2)
Ecological Condition and Recreational Use Effects from Surface Water Quality Changes
Aquatic and wildlife
Changes in ambient water quality in
habitatb
receiving reaches
Water-based recreation15
Changes in swimming, fishing, boating,
and near-water activities from water
quality changes
Benefit transfer
Aesthetics15
Changes in aesthetics from shifts in
V
V
(Chapter 6);
water clarity, color, odor, including
Qualitative discussion
nearby site amenities for residing,
(Chapter 2)
working, and traveling
Non-use values'5
Changes in existence, option, and
bequest values from improved
ecosystem health
Protection of T&E
Changes in T&E species habitat and
Habitat range
species
potential effects on T&E species
intersecting with
populations
V
reaches with NRWQC
exceedances (Chapter
7); Qualitative
discussion (Chapter 2)
Sediment contamination
Changes in deposition of toxic pollutants
to sediment
Qualitative discussion
(Chapter 2)
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Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power
Plants
Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Market and Productivity Effects
Dredging costs
Changes in costs for maintaining
navigational waterways and reservoir
capacity
V
V
Cost of dredging
(Chapter 0);
Qualitative discussion
(Chapter 2)
Water treatment costs
for drinking water
Changes in quality of source water used
for drinking
Qualitative discussion
(Chapter 2)
Water treatment costs
for irrigation and other
agricultural uses
Changes in quality of source water used
for irrigation and other agricultural uses
Qualitative discussion
(Chapter 2)
Commercial fisheries
Changes in fisheries yield and harvest
quality due to aquatic habitat changes
Qualitative discussion
(Chapter 2)
Tourism industries
Changes in participation in water-based
recreation
Qualitative discussion
(Chapter 2)
Property values
Changes in property values from
changes in water quality
Qualitative discussion
(Chapter 2)
Air Quality-Related Effects
Air emissions of PM2.5,
NOx and S02
Changes in mortality and morbidity from
exposure to particulate matter (PM2.5)
emitted directly or linked to changes in
NOx and S02 emissions (precursors to
PM2.5 and ozone)
V
V
VSL and COI (Chapter
8); Qualitative
discussion (Chapter 2)
Air quality effects of coal
stockpiles
Air quality effects of storing and
handling coal at steam electric power
plants
Qualitative discussion
(Chapter 2)
Air emissions of NOx and
S02
Changes in ecosystem effects; visibility
impairment; and human health effects
from direct exposure to N02, S02, and
hazardous air pollutants.
Qualitative discussion
(Chapters 2 and 8)
Air emissions of C02
Changes in climate change effects
V
V
Social cost of carbon
(SC-CO2) (Chapter 8)
a. Reductions in discharges of lead, mercury, and other toxic pollutants may reduce concentrations of these pollutants in open seas,
thus reducing levels of pollutants in high trophic level fish harvested commercially. There are unquantified benefits associated with
all of these end points for those who consume commercially harvested fish, but these benefits are very difficult to estimate.
b. These values are implicit in the total WTP for water quality improvements.
Source: U.S. EPA Analysis, 2022
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3 Water Quality Effects of Regulatory Options
Changes in the quality of surface waters, aquatic habitats and ecological functions under the regulatory
options depend on a number of factors, including the operational characteristics of steam electric power
plants, treatment technologies implemented to control pollutant levels, the timing of treatment technology
implementation, and the hydrography of reaches receiving steam electric pollutant discharges, among others.
This chapter describes the surface water quality changes projected under the regulatory options. EPA modeled
water quality based on loadings estimated for the baseline and for each of the regulatory options (Option 1
through Option 4). The differences in concentrations between the baseline and option scenarios represent the
changes attributable to the regulatory options. These changes inform the analysis of several of the benefits
described in Chapter 2 and detailed in later chapters of this report.
The analyses use pollutant loading estimates detailed in the TDD (U.S. EPA, 2023d) and expand upon the
analysis of immediate receiving waters described in the EA (U.S. EPA, 2023a) by estimating changes in both
receiving and downstream reaches. The EA provides additional information on the effects of steam electric
power plant discharges on surface waters and how they may change under the regulatory options.
3.1 Waters Affected by Steam Electric Power Plant Discharges
EPA estimates the regulatory options potentially affect 163 steam electric power plants. EPA used the United
States Geological Survey (USGS) medium-resolution National Hydrography Dataset (NHD) (USGS, 2018) to
represent and identify waters affected by steam electric power plant discharges, and used additional attributes
provided in version 2 of the NHDPlus dataset (U.S. EPA, 2019f) to characterize these waters.
Of the plants represented in the analysis, EPA estimated that 91 plants have non-zero pollutant discharges
under the baseline or the regulatory options for any of the modeled wastestreams (FGD wastewater, BA
transport water, or CRL). In the aggregate, the 91 plants discharge to 101 waterbodies (as categorized in
NHDPlus), including lakes, rivers, and estuaries.25 Receiving reaches that lack NHD classification for both
waterbody area type and stream order generally correspond to reaches that do not have valid flow paths26 for
analysis of the fate and transport of steam electric power plant discharges (see Section 3.31.1). While six
steam electric power plants discharge FGD wastewater, BA transport water or CRL to tidal reaches or the
Great Lakes,27 EPA did not assess pollutant loadings and water quality changes associated with these
waterbodies because of the lack of a defined flow path in NHDPlus, the complexity of flow patterns, and the
relatively small changes in concentrations expected.28 EPA did not quantify the water quality changes and
25 Ten plants discharge waste streams to multiple (two or three) different receiving waters and one reach receives discharges from
two separate plants.
20 In NHDPlus, the flow path represents the distance traveled as one moves downstream from the reach to the terminus of the
stream network. An invalid flow path suggests that a reach is disconnected from the stream network.
27 Three plants (Elm Road, JH Campbell, and Oak Creek) discharge non-zero loads to Lake Michigan, one plant (Monroe)
discharges to Lake Erie, one plant (Big Bend) discharges to Hillsborough Bay, and one plant (Jack Watson) discharges via a
canal to Big Lake, which is connected to Biloxi Bay. Because Great Lakes are complex waterbodies accurately modeling water
quality impacts to the Great Lakes would require the application of complex models that was not feasible within this rulemaking.
28 EPA looked at the changes in pollutant loadings and impacts to these systems in selected case studies as part of the analysis of
the 2015 rule (see 2015 EA for details; U.S. EPA, 2015b).
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resulting benefits to these systems. Thus, EPA estimated changes in water quality downstream from 85 steam
electric plants associated with a total of 96 receiving reaches.29
3.2 Changes in Pollutant Loadings
EPA estimated post-technology implementation pollutant loadings for each plant under the baseline and the
regulatory options. The TDD details the methodology (U.S. EPA, 2023d). The sections below discuss the
approach EPA used to develop a profile of loading changes over time under the baseline and each regulatory
option and summarize the results.
3.2.1 Implementation Timing
Benefits analyses account for the temporal profile of environmental changes as the public values changes
occurring in the future less than those that are more immediate (OMB, 2003). As discussed in Section 1.3.3,
for the purpose of the economic impact and benefit analysis, EPA generally estimates that plants will
implement control technologies to meet the applicable rule limitations and standards as their permits are
renewed, and no later than December 31, 2029. This schedule recognizes that control technology
implementation is likely to be staggered over time across the universe of steam electric power plants. This in
turn can translate into variations in pollutant loads to waters overtime.
To estimate the benefits of the regulatory options, EPA first developed a time profile of loadings for each
scenario (i.e., baseline and each regulatory option), electricity generating unit (EGU), wastestream, and
pollutant that reflects the baseline loadings, the estimated loadings under the applicable technology basis, the
estimated technology implementation year for the plant, and the timing of any retirements or repowerings.
Specifically, EPA used baseline loadings starting in 2025 through the applicable technology implementation
year, applicable technology-based loadings corresponding to the analyzed scenario (baseline or regulatory
option) for all years following a plant's modeled implementation year, and zero loadings following a unit's
retirement or repowering (where applicable).
EPA then used this year-explicit time profile to calculate the annual average loadings discharged by each
plant for two distinct periods within the overall period of analysis of 2025 through 2049:
• Period 1, which extends from 2025 through 2029, when the universe of plants would transition from
current (baseline) treatment practices to practices that achieve the revised limits, and
• Period 2, which extends from 2030 through 2049 and is the post-transition period during which the
full universe of plants is projected to employ treatment practices that achieve the revised limits.
The analysis accounts for each plant's technology implementation year(s) and for announced unit retirements
or repowerings. Using average annual values for two distinct periods instead of a single average over the
entire period of analysis enables EPA to better represent the rule implementation and capture the transitional
effects of the regulatory options. While using an annual average does not show the differences between the
baseline and regulatory options for individual years within Period 1, EPA considers that the average provides
29 EPA analyzed a total of 163 plants that generate the wastestreams within the scope of the proposed rule. Not all these plants have
costs and/or loads under the baseline or regulatory options, so while the modeling scope is all 163 plants, as discussed in this
section, some plants have zero loads whereas others discharge to waters that lack a valid flow path (e.g., Great Lakes and
estuaries), leaving 85 plants for which EPA analyzed changes in downstream water quality.
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a reasonable measure of the transitional effects of the regulatory options given the categories of benefits that
EPA is analyzing, which generally result from changes in multi-year processes.
As discussed in the RIA (U.S. EPA, 2023c), there is uncertainty in the exact timing of when individual steam
electric power plants would be implementing technologies to meet the proposed rule or the other regulatory
options. This benefits analysis uses the same plant- and wastestream-specific technology installation years
used in the cost and economic impact analyses. To the extent that technologies are implemented earlier or
later, the annualized loading values presented in this section may under- or overstate the annual loads during
the analysis period.
3.2.2 Results
Differences in the stringency of effluent limits and pretreatment standards and the timing of their applicability
to steam electric power plants (and the resulting treatment technology implementation) mean that changes in
pollutant loads between the regulatory options and the baseline vary over the period of analysis. Within the
period of analysis, the years 2025-2029 represent a period of transition as plants implement treatment
technologies to meet the revised limits under the regulatory options, whereas years 2030 through 2049 have
steady state loadings that reflect implementation of technologies across all plants.30
Table 3-1 summarizes the average annual reductions during Period 1 and Period 2 in FGD wastewater, BA
transport water, CRL, and total loads for selected pollutants that inform EPA's analysis of the benefits
discussed in Chapters 4 through 7 and in Chapter 10. The regulatory options are estimated to result in either
no change or in reductions in pollutant loadings under an option as compared to the baseline, with the
reductions generally increasing as one progresses from Option 1 to Option 4. Further, loading reductions are
largest during Period 2 when all steam electric plants have implemented the treatment technologies associated
with the limits, as compared to the transition period represented by Period 1.
30 This steady state reflects unit retirements and repowerings. EPA accounted for unit retirements and repowerings by zeroing out
the loadings starting in the year following the change in status.
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Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2 (2030-2049) for Selected Pollutants in
Steam Electric Power Plant Discharges, Compared to Baseline (lb/year)
Pollutant
Option V
Option 2a
Option 3a
Option 4a
FGD
Si
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
Period 1 (2025-2029)
Antimony
0
47
0
47
45
47
0
92
45
93
0
138
48
95
0
143
Arsenic
0
25
210
235
62
25
210
297
62
50
210
321
65
51
210
326
Barium
0
288
0
288
1,490
288
0
1,780
1,490
569
0
2,060
1,570
584
0
2,160
Beryllium
0
0
0
0
14
0
0
14
14
0
0
14
15
0
0
15
Boron
0
14,400
0
14,400
2,380,000
14,400
0
2,400,000
2,380,000
28,400
0
2,410,000
2,520,000
29,200
0
2,550,000
Bromide
0
13,800
0
13,800
2,950,000
13,800
0
2,960,000
2,950,000
27,300
0
2,970,000
3,210,000
28,000
0
3,240,000
Cadmium
0
2
38
40
45
2
38
85
45
4
38
87
47
4
38
89
Chromium
0
14
13,600
13,600
68
14
13,600
13,700
68
27
13,600
13,700
72
28
13,600
13,700
Copper
0
11
25
35
40
11
25
75
40
21
25
86
42
22
25
89
Cyanide
0
0
0
0
10,100
0
0
10,100
10,100
0
0
10,100
10,600
0
0
10,600
Lead
0
28
0
28
36
28
0
64
36
56
0
92
38
57
0
95
Manganese
0
414
0
414
132,000
414
0
133,000
132,000
818
0
133,000
140,000
840
0
141,000
Mercury
0
0
6
6
1
0
6
7
1
1
6
7
1
1
6
7
Nickel
0
47
241
288
67
47
241
355
67
93
241
401
71
96
241
407
TN
0
7,140
0
7,140
79,500
7,140
0
86,600
79,500
14,100
0
93,600
84,100
14,500
0
98,500
TP
0
600
0
600
3,380
600
0
3,980
3,380
1,190
0
4,570
3,580
1,220
0
4,790
Selenium
0
33
0
33
61
33
0
94
61
66
0
126
64
67
0
131
Thallium
0
3
0
3
104
3
0
107
104
6
0
110
110
6
0
116
TSS
0
36,200
176,000
212,000
91,000
36,200
176,000
303,000
91,000
71,400
176,000
338,000
96,300
73,300
176,000
345,000
Zinc
0
92
1,230
1,320
212
92
1,230
1,530
212
181
1,230
1,620
224
186
1,230
1,640
Period 2 (2030-2049)
Antimony
0
235
0
235
98
235
0
333
98
327
0
426
103
328
0
431
Arsenic
0
126
583
709
135
126
583
844
135
176
583
894
142
176
583
901
Barium
0
1,440
0
1,440
3,240
1,440
0
4,680
3,240
2,010
0
5,250
3,410
2,010
0
5,420
Beryllium
0
0
0
0
31
0
0
31
31
0
0
31
33
0
0
33
Boron
0
71,900
0
71,900
5,190,000
71,900
0
5,260,000
5,190,000
100,000
0
5,290,000
5,460,000
100,000
0
5,560,000
Bromide
0
69,100
0
69,100
7,520,000
69,100
0
7,590,000
7,520,000
96,400
0
7,620,000
8,680,000
96,500
0
8,780,000
Cadmium
0
10
106
115
97
10
106
213
97
14
106
217
102
14
106
222
Chromium
0
69
37,700
37,800
149
69
37,700
38,000
149
96
37,700
38,000
156
96
37,700
38,000
Copper
0
53
68
121
87
53
68
209
87
75
68
230
92
75
68
234
Cyanide
0
0
0
0
21,900
0
0
21,900
21,900
0
0
21,900
23,000
0
0
23,000
Lead
0
141
0
141
78
141
0
219
78
197
0
275
82
197
0
279
Manganese
0
2,070
0
2,070
289,000
2,070
0
291,000
289,000
2,890
0
292,000
303,000
2,890
0
306,000
Mercury
0
1
17
18
1
1
17
19
1
2
17
20
1
2
17
20
Nickel
0
236
669
906
146
236
669
1,050
146
330
669
1,140
153
330
669
1,150
TN
0
35,700
0
35,700
173,000
35,700
0
209,000
173,000
49,800
0
223,000
182,000
49,800
0
232,000
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Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2 (2030-2049) for Selected Pollutants in
Steam Electric Power Plant Discharges, Compared to Baseline (lb/year)
Pollutant
Option V
Option 2a
Option 3a
Option 4a
FGD
Si
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
FGD
SI
<
CO
CRLC
Total"
TP
0
3,000
0
3,000
7,370
3,000
0
10,400
7,370
4,180
0
11,600
7,750
4,190
0
11,900
Selenium
0
166
0
166
132
166
0
298
132
231
0
364
139
232
0
371
Thallium
0
15
0
15
227
15
0
242
227
21
0
248
238
21
0
260
TSS
0
181,000
488,000
669,000
198,000
181,000
488,000
868,000
198,000
252,000
488,000
939,000
209,000
252,000
488,000
949,000
Zinc
0
458
3,410
3,870
461
458
3,410
4,330
461
638
3,410
4,510
485
639
3,410
4,540
TN = Nitrogen, total (as N); TP = Phosphorus, total (as P); TSS = Total suspended solids
a. All numbers presented with three significant figures.
b. EPA did not estimate changes in ammonia, beryllium, and cyanide loadings associated with BA transport water.
c. EPA did not estimate changes in ammonia, beryllium, bromide, cyanide, lead, nitrogen, and phosphorus associated with CRL.
d. FGD, BA, and CRL loadings may not add up to the total due to independent rounding.
Source: U.S. EPA Analysis, 2022.
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3.3 Water Quality Downstream from Steam Electric Power Plants
EPA used the estimated annual average changes in total pollutant loadings for Periods 1 and 2 to estimate
concentrations downstream from each plant. EPA used the same approach as used for the analysis of the 2020
rule and relied on two main models to estimate downstream concentrations from each plant for each period:
• A dilution model to estimate pollutant concentrations downstream from the plants. The approach,
which for the purpose of this analysis is referred to as the D-FATE model (Downstream Fate And
Transport Equations), involves calculating concentrations in each downstream medium-resolution
NHD reach using annual average Enhanced Runoff Method (EROM) flows from NHDPlus v2 and
mass conservation principles.
• USGS's SPAtially Referenced Regressions On Watershed attributes (SPARROW) to estimate flow-
weighted nutrient (TN and TP) and suspended sediment concentrations. The SPARROW models
provide baseline and regulatory option concentrations of TN, TP, and suspended solids concentration
(SSC). For this analysis, EPA used the calibrated regional models published by the USGS (Ator,
2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise etal., 2019). These
models define the stream network using the same medium-resolution NHD reaches used in D-FATE.
The models represent only non-zero discharges to reaches represented in the NHD, which include the vast
majority of plants within the scope of the rule; the models represent 85 plants out of the 91 plants with non-
zero discharges under the baseline or regulatory options. As discussed in Section 3.1, EPA omitted six steam
electric power plants that discharge non-zero loads to the Great Lakes or to estuaries from this analysis.
In the D-FATE model, EPA used stream routing and flow attribute information from the medium-resolution
NHDPlus v2 to track masses of pollutants from steam electric power plant discharges and other pollutant
sources as they travel through the hydrographic network. For each point source discharger, the D-FATE
model estimates pollutant concentrations for the receiving reach and all downstream reaches based on NHD
mean annual flows. In-stream flows are kept constant (i.e.. discharges have no effect on flows). EPA notes
that steam electric power plant discharges frequently constitute a return of flow withdrawn for plant use from
the same surface water. In addition, FGD and BA wastewater discharges generally comprise a very small
fraction of annual mean flows in the NHDPlus v2 dataset.31
Following the approach used in the analysis of the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b) to estimate
pollutant concentrations, EPA also included loadings from major dischargers (in addition to the steam electric
power plants) that reported to the Toxics Release Inventory (TRI). EPA used loadings reported to the TRI in
2019.32 TRI data were available for a subset of toxics: arsenic, barium, chromium, copper, lead, manganese,
mercury, nickel, selenium, thallium, and zinc. EPA summed reach-specific concentrations from TRI
dischargers and concentration estimates resulting from steam electric power plant loadings to represent water
quality impacts from multiple sources. The pollutant concentrations calculated in the D-FATE model are used
to derive fish tissue concentrations used to analyze human health effects from consuming self-caught fish (see
31 Steam electric power plant FGD discharge rates are typically approximately 1 million gallons per day (MGD), whereas the
annual mean stream flows in receiving waters average approximately 15,000 MGD.
32 According to EPA TRI National Analysis, TRI releases to water reported in 2019 were approximately 3 percent higher, in the
aggregate, than releases reported in 2018 (200.6 million pounds versus 194.3 million pounds), although longer trends generally
show declines over time. See https://www.epa.gov/trinationalanalvsis/water-releases for details.
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Chapter 5), analyze nonmarket benefits of water quality improvements (see Chapter 6), and assess potential
impacts to T&E species whose habitat ranges intersect with waters affected by steam electric plant discharges
(see Chapter 7).
3.4 Overall Water Quality Changes
Following the approach used in the analysis of the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b), EPA used
a WQI to link water quality changes from reduced toxics, nutrient and sediment discharges to effects on
human uses and support for aquatic and terrestrial species habitat. The WQI translates water quality
measurements, gathered for multiple parameters (e.g., dissolved oxygen [DO], nutrients) that are indicative of
various aspects of water quality, into a single numerical indicator. The WQI ranges from 10 to 100 with low
values indicating poor quality and high values indicating good water quality.
As detailed in U.S. EPA (2015a), the WQI includes seven parameters: DO, BOD, fecal coliform (FC), TN,
TP, suspended solids, and one aggregate subindex for toxics. The pollutants considered in the aggregate
subindex for toxics are those that are discharged by modeled steam electric power plants or 2019 TRI
dischargers and that have chronic aquatic life-based NRWQC. Pollutants that meet these qualifications
include arsenic, cadmium, hexavalent chromium, copper, lead, mercury, nickel, selenium, and zinc. See the
EA for details on NRWQC (U.S. EPA, 2023a). The subindex curve for toxics assigns the lowest WQI value
of 0 to waters where exceedances are observed for the nine toxics analyzed, and a maximum WQI value of
100 to waters where there are no exceedances. Intermediate values are distributed between 100 and 0 in
proportion to the number of exceedances.
3.4.1 WQI Data Sources
To calculate the WQI, EPA used modeled NRWQC exceedances for toxics (using concentrations from D-
FATE) and modeled concentrations for TN, TP, and SSC from the respective SPARROW regional models.
Following the approach used for the 2020 rule analysis, the USGS National Water Information System
(NWIS) provided concentration data from 2007-2017 for three parameters that are held constant between the
baseline and regulatory options: 1) fecal coliform, 2) dissolved oxygen, and 3) biochemical oxygen demand
(see Section 3.4.1.2).33
3.4.1.1 Exceedances of Water Quality Standards and Criteria
For each regulatory option, EPA identified reaches that do not meet NRWQC for aquatic life in Periods 1 and
2.34 Table 3-2 summarizes the number of reaches with estimated exceedances of NRWQC in the baseline and
under the regulatory options. In Period 2, option 3 is estimated to eliminate all exceedances of chronic criteria
33 USGS's NWIS provides information on the occurrence, quantity, quality, distribution, and movement of surface and underground
waters based on data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, and U.S. territories.
More information on NWIS can be found at http://waterdata.usgs.gov/nwis/.
34 Aquatic life criteria are the highest concentration of pollutants in water that are not expected to pose a significant risk to the
majority of species in a given environment. For most pollutants, aquatic NRWQC are more stringent than human health NRWQC
and thus provide a more conservative estimate of potential water quality impairment. Chronic criteria are derived using longer
term (7-day to greater than 28-day) toxicity tests if available, or an acute-to-chronic ratio procedure where the acute criteria is
derived using short term (48-hour to 96-hour) toxicity tests (U.S. EPA, 2017a). More information on aquatic NRWQC can be
found at https://www.epa.gov/wac/national-recommended-water-qualitv-criteria-aauatic-life-criteria-table and in the EA (U.S.
EPA, 2023a).
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for 5 reaches (of 40 reaches with at least one exceedance), and eliminate all exceedances of acute criteria for
all four reaches with baseline exceedances.
Table 3-2: Estimated Exceedances of National Recommended
Water Quality Criteria under the Baseline and Regulatory Options
Regulatory Option
Number of Reaches with at Least One
NRWQC Exceedance
Chronic
Acute
Period 1 (2025-2029)
Baseline
42
4
Option 1
42
2
Option 2
42
2
Option 3
40
2
Option 4
40
2
Period 2 (2030-2049)
Baseline
40
4
Option 1
40
2
Option 2
35
0
Option 3
35
0
Option 4
35
0
Source: U.S. EPA Analysis, 2022
Refer to the EA for additional discussion of comparisons of receiving and downstream water pollutant
concentrations to acute and chronic aquatic NRWQC (U.S. EPA, 2023a).
3.4.1.2 Sources for Ambient Water Quality Data
Following the approach used for the 2020 rule analysis, EPA used average monitoring values for fecal
coliform, dissolved oxygen, and biochemical oxygen demand for 2007-2017 where available. Where more
recent data were not available, EPA used the same averages as for the 2015 rule analysis. EPA used a
successive average approach to assign average values for the three WQI parameters not explicitly modeled
(i.e., DO, BOD, fecal coliform). The approach, which adapts a common sequential averaging imputation
technique, involves assigning the average of ambient concentrations for a given parameter within a hydrologic
unit to reaches within the same hydrologic unit with missing data, and progressively expanding the
geographical scope of the hydrologic unit (Hydrologic unit code (HUC8, HUC6, HUC4, and HUC2) to fill in
all missing data.35 This approach is based on the assumption that reaches located in the same watershed
generally share similar characteristics. Using this estimation approach, EPA compiled ambient water quality
data and/or estimates for all analyzed NHD reaches. As discussed below, the values of the three WQI
parameters not explicitly modeled are kept constant for the baseline and regulatory policy scenarios. This
35 Hydrologic Unit Codes (HUCs) are cataloguing numbers that uniquely identify hydrologic features such as surface drainage
basins. The HUCs consist of 8 to 14 digits, with each set of 2 digits giving more specific information about the hydrologic
feature. The first pair of values designate the region (of which there are 22), the next pair the subregion (approximately 245), the
third pair the basin or accounting unit (approximately 405), and the fourth pair the subbasin, or cataloguing unit (approximately
2,400) (U.S. Geological Survey, 2007, 2022). Digits after the first eight offer more detailed information at the watershed and
subwatershed levels. In this discussion, a HUC level refers to a set of waters that have that number of HUC digits in common.
For example, the HUC6 level includes all reaches for which the first six digits of their HUC are the same.
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approach has not been peer reviewed, but it has been used by EPA for several prior rules and reviewed by the
public during the associated comment periods.
The water quality analysis included a total of 17,676 medium-resolution NHD reaches that are potentially
affected by steam electric power plants under the baseline. Of these 17,676 NHD reaches, EPA estimated
concentrations for 12,954 reaches affected by non-zero loadings from steam electric power plants. Table 3-3
summarizes the data sources used to estimate baseline and regulatory option values by water quality
parameter.
Table 3-3: Water Quality Data used in Calculating WQI for the Baseline and Regulatory Options
Parameter
Baseline
Regulatory Option
TN
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
TP
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
Suspended
sediment
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
DO
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
BOD
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
Fecal Coliform
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
Toxics
Baseline exceedances calculated using D-FATE
model
Regulatory option exceedances calculated
using D-FATE model
WBD = Watershed Boundary Dataset. The WBD is a companion dataset to the NHD
Source: U.S. EPA Analysis, 2022.
3.4.2 WQI Calculation
EPA used the approach described in the BCA for the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b) to
estimate WQI values for each reach under the baseline and each option, and used the subindex curves for TN,
TP, and SSC used for the 2020 rule36 that reflect data from the most current SPARROW regional models
(Ator, 2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise etal., 2019). Implementing
the WQI methodology involves three key steps: 1) obtaining water quality levels for each of seven parameters
included in the WQI; 2) transforming parameter levels to subindex values expressed on a common scale; and
3) aggregating the individual parameter subindices to obtain an overall WQI value that reflects waterbody
conditions across the seven parameters. These steps are repeated for each reach to calculate the WQI value for
the baseline, and for each analyzed regulatory option. See details of the calculations in Appendix B, including
the subindex curves used to transform levels of individual parameters. The scope of this analysis is the same
30 The 2015 WQI includes a subindex for TSS. For this analysis, EPA used the same curve for SSC used for the 2020 rule based on
more recent SPARROW regional models which estimates SSC rather than TSS concentrations (Ator, 2019; Hoos & Roland Ii,
2019; Robertson & Saad, 2019; Wise, 2019; Wise etal., 2019). This bypasses translation of SSC to TSS values and any
associated uncertainty.
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as that for the analysis of nonmarket benefits of water quality improvements discussed in Chapter 6, which
focuses on reaches within 300 km of a steam electric plant outfall.37
3.4.3 Baseline WQI
The WQI value can be related to suitability for potential uses. Vaughan (1986) developed a water quality
ladder (WQL) that can be used to indicate whether water quality is suitable for various human uses (i.e.,
boating, rough fishing, game fishing, swimming, and drinking without treatment). Vaughan identified
"minimally acceptable parameter concentration levels" for each of the five potential uses. Vaughan used a
scale with a top value of 10 instead of the WQI scale with a top value of 100 to classify water quality based
on its suitability for potential uses. Therefore, the WQI value corresponding to a given water quality use
classification equals the WQL value multiplied by 10.
Based on the estimated WQI value under the baseline scenario (WQI-BL), EPA categorized each of the
9,358 NHD reaches using five WQI ranges (WQI < 25, 25
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3.4.4 Estimated Changes in Water Quality (AWQI) from the Regulatory Options
To estimate the benefits of water quality improvements resulting from the regulatory options, EPA calculated
the change in WQI for each analyzed regulatory option as compared to the baseline. This analysis was done
for each reach and for each of the two Periods. As discussed in Section 1.1, EPA estimated changes in
ambient concentrations of TN, TP and SSC using the USGS's SPARROW models and toxics concentrations
using the D-FATE model. Although the regulatory options would also indirectly affect levels of other WQI
parameters, such as BOD and DO, these other parameters were held constant in this analysis for all regulatory
options, due to methodological and data limitations.
The difference in the WQI between baseline conditions and a given regulatory option (hereafter denoted as
AWQI) is a measure of the change in water quality attributable to the regulatory option. Table 3-5 presents
water quality change ranges for the analyzed regulatory options under each analysis period.
Table 3-5: Ranges of Estimated Water Quality Changes for Regulatory Options, Compared to
Baseline
Options
Minimum
AWQI
Maximum
AWQI
25th
Percentile
AWQI
Median
AWQI
75th
Percentile
AWQI
AWQI
Interquartile
Range
Period 1 (2025-2029)
Option 1
0
0.91
0
3.25xl0"7
1.74xl0"5
1.74xl0"5
Option 2
0
0.91
0
7.13X106
1.59xl0"4
1.59xl0"4
Option 3
0
0.91
5.52xl0"6
5.11xl0"5
5.04xl0"4
4.98xl0"4
Option 4
0
0.91
9.81xl0"6
6.30xl0"5
7.76xl0"4
7.66xl0"4
Period 2 (2030-2049)
Option 1
0
1.17
0
2.78X106
4.50xl0"5
4.50xl0"5
Option 2
0
15.60
3.61xl0"7
3.70xl0"5
5.13xl0"4
5.13xl0"4
Option 3
0
18.77
2.01xl0"5
1.61xl0"4
1.24xl0-3
1.22x10-3
Option 4
0
18.77
2.57xl0"5
1.91xl0"4
2.26xl0-3
2.23x10-3
Source: U.S. EPA Analysis, 2022
3.5 Limitations and Uncertainty
The methodologies and data used in the estimation of the environmental effects of the regulatory options
involve limitations and uncertainties. Table 3-6 summarizes the limitations and uncertainties and indicates the
direction of the potential bias. Uncertainties associated with some of the input data are covered in greater
detail in other documents. Regarding the uncertainties associated with use of the NHDPlus attribute data, see
the NHDPlus v2 documentation (U.S. EPA, 2019f). Regarding the uncertainties associated with estimated
loads, see the TDD (U.S. EPA, 2023d).
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Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options
Uncertainty/Limitation
Effect on Water
Quality Effects
Estimation
Notes
Limited data are available to validate
water quality concentrations
estimated in D-FATE
Uncertain
The modeled concentrations reflect only a subset of
pollutant sources (e.g., steam electric power plant
discharges and TRI releases) whereas monitoring data
also reflect other sources such as bottom sediments,
air deposition, and other point and non-point sources
of pollution. TRI releases are also reported by the
facilities and could potentially suffer from misreporting
or faulty estimation techniques. EPA comparisons of D-
FATE estimates to monitoring data available for
selected locations and parameters (e.g., bromide
concentrations downstream of steam electric power
plant discharges) confirmed that D-FATE provides
reasonable values. Also refer to the 2015 EA for
discussion of model validation for selected case studies
(U.S. EPA, 2015b)
Steam electric power plant
discharges have no effects on reach
annual average or seasonal flows
Overestimate
The degree of overestimation in the estimation of
pollutant concentrations, if any, would be small given
that steam electric power plant discharge flows tend to
be very small as compared to flows in modeled
receiving and downstream reaches. Further, EPA
acknowledges that the effect of steam electric power
plant discharges on reach flows may vary seasonally
due to low- and high-flow periods.
Ambient water toxics concentrations
are based only on loadings from
steam electric power plants and
other TRI discharges.
Uncertain
Concentration estimates do not account for
background concentrations of these pollutants from
other sources, such as legacy pollution in sediments,
non-point sources, point sources that are not required
to report to TRI, air deposition, etc. Not including other
contributors to background toxics concentrations in
the analysis is likely to result in understatement of
baseline concentrations of these pollutants and
therefore of NRWQC exceedances. The effect on WQI
calculations is uncertain.
Annual loadings are estimated based
on EPA's estimated plant-specific
technology implementation years
Uncertain
To the extent that technologies are implemented
earlier or later, the Period 1 annualized loading values
presented in this section may under- or overstate the
annual loads during the analysis period. The effect of
this uncertainty is limited to Period 1 since loads reach
a steady-state level by the technology implementation
deadlines applicable to the regulatory options (e.g., by
the end of 2029)
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Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options
Uncertainty/Limitation
Effect on Water
Quality Effects
Estimation
Notes
Changes in WQI reflect only
reductions in toxics, nutrient, and
suspended sediment concentrations.
Underestimate
The estimated changes in WQI reflect only water
quality changes resulting directly from changes in
toxics, nutrient and sediment concentrations. They do
not include changes in other water quality parameters
(e.g., BOD, dissolved oxygen) that are part of the WQI
and for which EPA used constant values. Because the
omitted water quality parameters are also likely to
respond to changes in pollutant loads (e.g., dissolved
oxygen levels respond to changes in nutrient levels),
the analysis underestimates the water quality changes.
EPA used regional averages of
monitoring data from 2007-2017 for
fecal coliform, dissolved oxygen, and
biochemical oxygen demand, when
location-specific data were not
available. In cases where more
recent data were not available, EPA
used the same averages as used in
the 2015 rule analysis (U.S. EPA,
2015a).
Uncertain
The monitoring values were averaged over
progressively larger hydrologic units to fill in any
missing data. As a result, WQI values may not reflect
certain constituent fluctuations resulting from the
various regulatory options and/or may be limited in
their temporal and spatial relevance. Note that the
analysis keeps these parameters constant under both
the baseline and regulatory options. Modeled changes
due to the regulatory options are not affected by this
uncertainty.
Use of nonlinear subindex curves
Uncertain
The methodology used to translate suspended
sediment and nutrient concentrations into subindex
scores (see Section 3.4.2 and Appendix B) employs
nonlinear transformation curves. Water quality
changes that fall outside of the sensitive part of the
transformation curve (i.e., above/below the
upper/lower bounds, respectively) yield no change in
the analysis and no benefits in the analysis described in
Chapter 6.
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4 Human Health Benefits from Changes in Pollutant Exposure via the
Drinking Water Pathway
EPA expects that the changes in pollutant loadings from the regulatory options relative to the 2020 rule could
affect several aspects of human health by changing bromide and other pollutant discharges to surface waters
and, as a result, pollutant concentrations in the reaches that serve as sources of drinking water. The EA (U.S.
EPA, 2023a) provides details on the health effects of steam electric pollutants.
As described in Section 2.1, human health benefits deriving from changes in pollutant loadings to receiving
waters include those associated with changes in exposure to pollutants via treated drinking water use and fish
consumption. This chapter addresses the first exposure pathway: drinking water. Chapter 5 addresses the fish
consumption pathway.
The changes in pollutant loadings from the regulatory options relative to the 2020 rule could affect human
health by changing halogen and other pollutant discharges to surface waters and, as a result, pollutant
concentrations in the reaches that serve as sources of drinking water. The EA presents background
information regarding the potential impacts of halogen discharges on drinking water quality and human health
(U.S. EPA, 2023a). Section 4.1 provides background information on trihalomethane precursor development.
Sections 4.2 through 4.4 present EPA's analysis of human health effects from changes in bromide discharges.
Section 4.5 summarizes potential impacts on source waters from changes in other pollutant discharges.
Section 4.6 discusses uncertainty and limitations associated with the analysis presented in this chapter.
4.1 Background
FGD wastewater and BA transport water discharges contain variable quantities of bromide due to the natural
presence of bromide in coal feedstock and from additions of halogens, including bromide-containing salts,
and use of brominated activated carbon products to enhance air emissions control (Kolker et al, 2012).
Wastewater treatment technologies employed at steam electric power plants vary widely in their ability to
remove bromide. A number of studies have documented elevated bromide levels in surface water due to steam
electric power plant discharges (e.g., Cornwell et al, 2018; Good & VanBriesen, 2016, 2017; McTigue et al,
2014; Ruhl etal, 2012; States ei al.. 2013; U.S. EPA, 2017c; 2019b) and have attributed measured increases
in bromide levels to the increasing number of installed wet FGD devices at steam electric power plants. FGD
wastewaters have been shown to contain relatively high levels of bromide relative to other industrial
wastewaters. Modeling studies have sought to quantify the potential for drinking water sources to be affected
by FGD wastewater discharges (Good & VanBriesen, 2019).
Bromide does not undergo significant physical (e.g., sorption, volatilization), chemical or biological
transformation in freshwater environments and is commonly used as a tracer in solute transport and mixing
field studies. Surface waters transport bromide discharges to downstream drinking water treatment facility
intakes where they are drawn into the treatment systems.
Although the bromide ion has a low degree of toxicity (World Health Organization, 2009), it can contribute to
the formation of brominated DBPs during drinking water disinfection processes, including chlorination,
chloramination, and ozonation. Bromate, a regulated DBP under the Safe Drinking Water Act (SDWA),
forms when bromine reacts directly with ozone. Chlorine reacts with bromide to produce hypobromite (BrO),
which reacts with organic matter to form brominated and mixed chloro-bromo DBPs, including three of the
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four regulated trihalomethanes39 (THM4, also referred to as total trihalomethanes (TTHM) in this discussion)
and two of the five regulated haloacetic acids40 (HAA5). Additional unregulated brominated DBPs have been
cited as an emerging class of water supply contaminants that can potentially pose health risks to humans (S.
D. Richardson etal., 2007; NTP, 2018; U.S. EPA, 2016c).
There is a substantial body of literature on trihalomethane precursor occurrence, trihalomethane formation
mechanisms in drinking water treatment plants, and relationships between source water bromide levels and
TTHM levels in treated drinking water. The formation of TTHM in a particular drinking water treatment plant
is a function of several factors including chlorine, bromide, organic material, temperature, and pH levels as
well as system residence times. There is also substantial evidence linking TTHM exposure to bladder cancer
incidence (U.S. EPA, 2016c). Bromodichloromethane and bromoform are likely to be carcinogenic to humans
by all exposure routes and there is evidence suggestive of dibromochloromethane's carcinogenicity (NTP,
2018; U.S. EPA, 2016c). The relationships between exposure to DBPs, specifically TTHMs and other
halogenated compounds resulting from water chlorination, and bladder cancer are further discussed in Section
4.3.3.2 and U.S. EPA (2019a).
4.2 Overview of the Analysis
Figure 4-1 illustrates EPA's approach for quantifying and valuing the human health effects of altering
bromide discharges from steam electric power plants. The analysis entails estimating in-stream changes in
bromide levels between conditions under the baseline and each of the four regulatory options (Step 1);
estimating the change in source water bromide levels and corresponding changes in TTHM concentrations in
treated water supplies (Step 2); relating these estimated changes to changes in exposure and the subsequent
changes in the incidence of bladder cancers41 in the exposed population (Step 3); and estimating the
associated monetary value of benefits (Step 4). This approach was implemented in EPA's 2019 proposed rule
(U.S. EPA, 2019a) and relies on findings from a peer-reviewed paper by Regli et al. (2015) that built on the
approach taken in the Stage 2 Disinfectants and Disinfection Byproduct Rule (DBPR) (U.S. EPA, 2005b) to
derive a slope factor to relate changes in lifetime bladder cancer risk to changes in TTHM exposure. This
analysis also incorporates recent National Cancer Institute's Surveillance, Epidemiology, and End Results
(SEER) program data to model incidence of bladder cancers by age and sex, cancer stage, changes in lifetime
cancer risk attributable to the proposed rule options, and survival outcomes. The life-table modeling approach
used by EPA to estimate changes in health outcomes is a widely used method in public health, insurance,
medical research, and other studies and was used for analysis of lead-associated health effects in the 2015
rule. The main advantage of this approach is that it allows for explicitly accounting for age and cancer stage-
specific patterns in cancer outcomes, as well as for other causes of mortality in the affected population.
39 The four regulated trihalomethanes are bromodichloromethane, bromoform, chloroform, and dibromochloromethane.
40 The five regulated haloacetic acids are dibromoacetic acid, dichloroacetic acid, monobromoacetic acid, monochloroacetic acid,
and trichloroacetic acid.
41 Regli et al. (2015) estimated the additional lifetime risk from a 1 (ig/L increase in TTHM. This relationship holds over the TTHM
range expected for systems in compliance with the Stage 2 Disinfectants and Disinfection Byproduct Rule.
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Figure 4-1: Overview of Analysis of Estimated Human Health Benefits of Reducing Bromide
Discharges.
Legend:
Analysis
component
Data/Inputs
Analysis step
Valuation
endpoint
Source: U.S. EPA Analysis, 2022.
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4.3 Estimates of Changes in Halogen Concentrations in Source Water
For the proposed rule, EPA estimated the change in halogen levels in the source water for PWS that have
intakes downstream from steam electric power plants.42 Halogens such as bromide are precursors for
halogenated disinfection byproduct formation in treated drinking water, including certain trihalomethanes
addressed by the TTHM MCL. Higher halogen levels in PWS source waters have been associated with higher
levels of halogenated DBPs in treated drinking water. The formation of DBPs varies with site-specific factors.
In vitro toxicology studies with bacteria and mammalian cells have documented evidence of genotoxic
(including mutagenic), cytotoxic, tumorigenic, and developmental toxicity properties of iodinated DBPs, but
the available data are insufficient at this time to determine the extent of iodinated DBP's contribution to
adverse human health effects from exposure to treated drinking water. Populations exposed to changes in
halogenated disinfection byproduct levels in their drinking water under the regulatory options could
experience changes in the incidence of adverse health effects, and in turn the total counts of these health
effects.
In this section, the Agency presents the number of PWS with modeled changes in bromide concentration in
their source water, the magnitude and direction of these changes, and the PWS service population estimated to
experience a change in DBP exposure levels due to changes in source water bromide levels.
4.3.1 Step 1: Modeling Bromide Concentrations in Surface Water
EPA estimated steam electric power plant-level bromide loadings associated with FGD wastewater and BA
transport water for the baseline and the regulatory options.43 This chapter presents EPA's best estimate of
changes in bromide loadings under each of the regulatory options.
EPA used the D-FATE model described in Section 3.3 to estimate in-stream bromide concentrations
downstream from 47 steam electric power plants that EPA estimated have non-zero bromide loads (i.e..
discharge FGD wastewater and/or BA transport water) under the baseline or regulatory options. EPA first
estimated the annual average bromide loads in Period 1 and Period 2 (see Section 3.2.1). EPA then estimated
concentrations in the receiving reach and each downstream reach in Period 1 and Period 2, using conservation
of mass principles, until the load reaches the network terminus (e.g., Great Lake, estuary) 44 EPA summed
individual contributions from all plants to estimate total in-stream concentrations under the baseline and the
regulatory options in Period 1 and Period 2. Finally, EPA estimated the change in bromide concentrations in
each reach as the difference between each regulatory option and the baseline. This change is not dependent on
bromide contributions from other sources (e.g., receiving waterbody background levels).
4.3.2 Step 2: Modeling Changes in Trihalomethanes in Treated Water Supplies
4.3.2.1 Affected Public Water Systems
For the proposed rule, EPA updated the universe of PWS potentially affected by steam electric plant
discharges to reflect adjustments to the universe of plants projected to be subject to the rule and their
42 These analyses correspond to steps 1 and 2 of the methodology EPA used for the 2019 proposal (see Chapter 4 in U.S. EPA,
2019a)
43 EPA did not estimate bromide loadings associated with CRL discharges.
44 As discussed in Section 3.1, EPA did not estimate concentration changes in the Great Lakes or estuaries.
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associated downstream reaches. EPA also collected more recent information about the operating
characteristics of the water systems (e.g., population served, facility status, wholesale water purchases).
EPA's Safe Drinking Water Information System (SDWIS) database45 provides the latitude and longitude of
surface water facilities46, including source water intakes for public drinking water treatment systems. To
identify potentially affected PWS, the Agency georeferenced each permanent surface water facility associated
with non-transient community water systems to the NHD medium-resolution stream network used in D-
FATE.47 Appendix E describes the methodology EPA used to identify the NHD water feature for each facility.
The SDWIS database also includes information on PWS primary sources (e.g., whether a PWS relies
primarily on groundwater or surface water for their source water), operational status, and population served,
among other attributes. For this analysis, EPA used the subset of facilities that identify surface water as their
primary water source (specifically surface water intakes and reservoirs) and are categorized as "active" and
"permanent" in SDWIS. This subset of facilities corresponds to PWS that are more likely to be affected by
upstream bromide releases on an ongoing basis, as compared to other systems that may use surface water
sources only sporadically. This approach identifies populations most likely to experience changes in long-
term halogenated DBP exposures and associated health effects due to the regulatory options.
PWS can be either directly or indirectly affected by steam electric power plant discharges. Directly affected
PWS are systems with surface water intakes drawing directly from reaches downstream from steam electric
power plants discharging bromide.48 Other PWS are indirectly affected because they purchase their source
water from another PWS via a "consecutive connection" instead of withdrawing directly from a surface water
or groundwater source. For these systems, SDWIS provides information on the PWS that supplies the
purchased water. EPA used SDWIS data to identify PWS that may be indirectly affected by steam electric
power plant discharges because they purchase water from a directly affected PWS. The total potentially
exposed population consists of the people served by either directly or indirectly affected systems.
Table 4-1 summarizes the intakes, PWS, and populations potentially affected by steam electric power plant
discharges.49 In this analysis, the average distance from the steam electric power plant discharge point to the
drinking water treatment plant intake is 392 miles and approximately 17 percent of the intakes are located
within 50 miles of a steam electric power plant outfall. A subset of these PWS is downstream of FGD
wastewater and BA transport water discharges containing bromide,5" specifically 485 reaches have intakes
used by 722 PWS serving a total of 27.8 million people.
45 EPA used intake locations and PWS data as of April 2021, which reflects the first quarter report for 2021. Intake location data are
protected from disclosure due to security concerns. SDWIS public data records are available from the Federal Reporting Services
system at https://ofmpub.epa. gov/apex/sfdw/.
40 Surface water facilities include any part of a PWS that aids in obtaining, treating, and distributing drinking water. Facilities in the
SDWIS database may include groundwater wells, consecutive connections between buyer and seller PWS, pump stations,
reservoirs, and intakes, among others.
47 This analysis does not include intakes that draw from the Great Lakes or other water bodies not analyzed in the D-FATE model.
48 To identify potentially affected PWS, EPA looked at all downstream reaches starting from the immediate reach receiving the
steam electric power plant discharge to the reach identified as the terminus of the stream network.
49 Four PWS may be both directly and indirectly affected.
50 Note that when plants retire, bromide may still be present in CRL. The present analysis considers bromide discharges from FGD
wastewater and BA transport water only.
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Table 4-1: Estimated Reaches, Surface Water Intakes, Public Water Systems, and Populations
Potentially Affected by Steam Electric Power Plant Discharges
PWS Impact Category
Number of Reaches
with Drinking Water
Intakes
Number of Intakes
Downstream of
Steam Electric Power
Plants
Number of PWS
Total Population
Served (Million
People)
Direct3
244
370
262
16.4
Indirect
Not applicable
Not applicable
690
25.6
Total
244
370
952
42.0
a. Includes four systems with intakes downstream of steam electric power plant discharges and that purchase water from other
systems with intakes downstream of steam electric power plant discharges.
Source: U.S. EPA analysis, 2022
4.3.2.2 System-Level Changes in Bromide Concentrations in Source Water
EPA estimated the change in bromide concentrations in the source water for each PWS that could result from
the regulatory options. In this discussion, the term "system" refers to PWS and their associated drinking water
treatment operations, whereas the term "facility" refers to the intake that is drawing untreated water from a
source reach for treatment at the PWS level.
To estimate changes in bromide concentrations at the PWS level, EPA obtained the number of active
permanent surface water sources used by each PWS based on SDWIS data. SDWIS does not provide
information on respective source flow contributions from surface water and groundwater facilities for a given
PWS. For drinking water treatment systems that have both surface water and groundwater facilities, EPA
assessed changes from surface water sources only. This approach is reasonable given that the analysis is
limited to the PWS for which SDWIS identifies surface water as primary source.
For intakes located on reaches modeled in D-FATE, EPA calculated the reach-level change in bromide
concentration as the difference between the regulatory option and the baseline conditions. Some PWS rely on
a single intake facility for their source water supply. If the source water reach associated with this single
intake is affected by steam electric power plant bromide discharges, the system-level changes in bromide
concentration at the PWS would equal the estimated change in bromide concentration of the source water
reach. Other PWS rely on multiple intake facilities that may be located along different source water reaches.
System-level changes in bromide concentrations at these PWS are an average of the estimated changes in
bromide concentrations associated with each source water reach. For any additional intakes not located on the
modeled reaches and for intakes relying on groundwater sources, EPA estimated zero change in bromide
concentration. Because SDWIS does not provide information on source flows contributed by intake facilities
used by a given PWS, EPA calculated the system-level change in bromide concentration assuming each active
permanent source facility contributes equally to the total volume of water treated by the PWS. For example,
the PWS-level change in bromide concentration for a PWS with three intakes, of which one intake is directly
affected by steam electric power plant discharges, is estimated as one third of the modeled reach
concentration change ([ABr + 0 + 0]/3).
EPA addressed water purchases similarly, but with the change in bromide concentration associated with the
consecutive connection set equal to the PWS-level change estimated for the seller PWS instead of a reach-
level change. For facilities affected only indirectly by steam electric power plant discharges, EPA assumed
zero change in bromide concentrations for any other unaffected source facility associated with the buyer. EPA
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also assumed that each permanent source facility contributes an equal share of the total volume of water
distributed by the buyer. For the four PWS classified as both directly and indirectly affected by steam electric
power plant bromide discharges, EPA assessed the total change in bromide concentration as a blended
average of the change in concentration from both directly-drawn and purchased water.
Table 4-2 summarizes the distribution of changes in bromide concentrations under the regulatory options for
the two analysis periods. The direction of the changes depends on the Period, option, source water reach, and
PWS but is generally consistent with the changes in bromide loadings associated with FGD and bottom ash
transport wastewaters under each regulatory option (see Table 3-1). During Periods 1 and 2, all options show
either reductions or no changes in bromide concentrations for all source waters and PWS. For all options, the
magnitude and scope (the number of reaches, PWS, and population served) of the bromide reductions are
larger during Period 2 than during Period 1.
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Table 4-2: Estimated Distribution of Changes in Source Water and PWS-Level Bromide Concentrations by Period and Regulatory Option,
Compared to Baseline
ABr Range (|ig/L)
Number of Source Water Reaches
Number of PWSa
Population Served by PWS
Reduction ABr
No ABr (ABr = 0)
Reduction ABr
No ABr (ABr = 0)
Reduction ABr
No ABr (ABr = 0)
Period 1 (2025-2029)
Option 1
Oto 10
4
217
11
780
445,998
37,906,912
Option 2
Oto 10
86
135
311
480
7,152,912
31,199,998
Option 3
Oto 10
140
81
565
226
25,187,987
13,164,923
Option 4
Oto 10
156
64
606
183
26,964,720
11,303,045
10 to 30
1
0
2
0
85,145
0
Period 2 (2030-2049)
Option 1
Oto 10
4
217
11
780
445,998
37,906,912
Option 2
Oto 10
87
129
278
463
6,425,440
30,953,816
10 to 30
4
0
43
0
820,436
0
30 to 50
1
0
7
0
153,218
0
>50
0
0
0
0
0
0
Option 3
Oto 10
148
68
541
197
29,454,222
7,909,016
10 to 30
4
0
46
0
836,454
0
30 to 50
1
0
7
0
153,218
0
>50
0
0
0
0
0
0
Option 4
Oto 10
163
51
580
154
31,227,763
6,047,138
10 to 30
5
0
48
0
839,646
0
30 to 50
1
0
7
0
153,218
0
50 to 75
0
0
0
0
0
0
>75
1
0
2
0
85,145
0
a. Includes systems potentially directly and/or indirectly affected by steam electric power plant discharges.
Source: U.S. EPA Analysis, 2022.
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4.3.2.3 Changes in TTHM Concentration in Treated Water Supplies
The prior step provides the estimated PWS-level change in bromide concentration in the blend of source
waters used by a given system. The step described in this section provides the estimated PWS-level change in
TTHM concentration associated with this change in bromide concentration.
Regli etcil. (2015) applied the Surface Water Analytical Tool (SWAT) version 1.1, which models TTHM
concentrations in drinking water treatment plants as a function of precursor levels, source water quality (e.g.,
bromide and organic material levels), water temperature, treatment processes (e.g., pH, residence time), and
disinfectant dose (e.g., chlorine levels) to predict the distribution of changes in TTHM concentrations in
finished water associated with defined increments of changes in bromide concentration in source waters. That
study estimated the distribution of increments of change in TTHM concentration for a subset of the
population of PWS characterized in the 1997-1998 Information Collection Rule (ICR) dataset. Table 4-3
summarizes the results from the Regli et al. (2015) analysis.
Table 4-3: Estimated Increments of Change in TTHM Levels (ng/L) as a Function of Change in
Bromide Levels (ng/L)
Change in bromide
Change in TTHM concentration (|ig/L)
concentration
Minimum
5th
Median
Mean
LO
(Ti
Maximum
(Hg/L)
Percentile
Percentile
10
0.0
0.1
1.1
1.3
3.4
10.1
30
0.0
0.3
2.6
3.2
8.3
23.7
50
0.0
0.5
3.7
4.6
11.6
33.2
75
0.0
0.6
4.9
6.0
14.8
42.1
100
0.0
0.8
5.8
7.1
17.5
49.3
Source: Regli et al. (2015), Table 2.
For this analysis, EPA used the results from Regli et al. (2015) to predict TTHM concentration changes for
each water treatment plant with changes in bromide concentrations in their source water due to the regulatory
options. Figure 4-2 shows the relationship (dashed line) between the change in bromide concentration and the
change in TTHM concentration based on fitting a polynomial curve through the median estimates from Table
4-3 (circular markers). EPA used the equation of the best-fit curve51 to estimate changes in TTHM
concentration as a function of changes in bromide concentration within the bromide concentration range
presented in Regli et al. (2015) (0 to 100 (ig/L). Estimates of TTHM concentration changes presented in the
remainder of this section reflect median changes from Regli et al. (2015).52 EPA evaluated the sensitivity of
benefits estimates to the relationship between changes in bromide and changes in TTHM using the 5th and 95th
percentile estimates in Table 4-3 in the 2019 proposed rule (U.S. EPA, 2019a).
51 The polynomial curve fits observations in Table 4-3 with residuals of zero over the range of observations.
52 While Regli et al. (2015) show similar mean and median changes in TTHM concentrations across the range of changes in
bromide concentrations, EPA used the median to minimize potential influence of outlier values or skew in the distribution. Mean
changes in TTHM for changes in bromide levels of 10, 30, 50, 75, and 100 jug/L were 1.3, 3.2, 4.6, 6.0 and 7.1 jug/L,
respectively. Median changes in TTHM for changes in bromide levels of 10, 30, 50, 75, and 100 jug/L were 1.1, 2.6, 3.7, 4.9, and
5.8 jug/L, respectively.
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Figure 4-2: Modeled Relationship between Changes in Bromide Concentration and Changes in TTHM
Concentrations based on Median Values in Regli eta/. (2015).
If A Br >100 ug/L:
ATTHMsoto = 5.80 + 0.022 (ABr -100)
if A Br<100ng/L:
ATTHMm = -8.30x10s ABr4 + 1.96xl05 ABr3 -1.81xl03 ABR2 + 1.26x1a1 ABr
/
0 20 40 60 80 100 120 140 160
ABr (ug/L)
Source: U.S. EPA Analysis, 2022, based on Regli et al. (2015).
Table 4-4 shows the distribution of modeled absolute changes in TTHM concentrations and the potentially
exposed populations under each of the regulatory options. As shown in the table, the magnitude of estimated
bromide concentration changes is generally less than 10 |a,g/L, corresponding to estimated changes in TTHM
concentrations of less than 1.1 |ag/L. Compared to the baseline, all options are estimated to reduce TTHM
concentrations in treated water.
Table 4-4: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served
Absolute ABr range3
(Hg/L)
Absolute ATTHM range3
(Hg/L)
Number of PWSb
Total population served
(million people)c
Period 1 (2025-2029)
Option 1
>0 to 10
0.10 to 0.14
11
0.45
Option 2
>0 to 10
0.01 to 0.89
311
7.15
Option 3
>0 to 10
0.00 to 0.88
565
25.19
Option 4
>0 to 10
0.00 to 0.89
606
26.97
10 to 30
1.62 to 1.62
2
0.09
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Table 4-4: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served
Absolute ABr range3
(Hg/L)
Absolute ATTHM range3
(Hg/L)
Number of PWSb
Total population served
(million people)c
Period 2 (2030-2049)
Option 1
>0 to 10
0.49 to 0.66
11
0.45
Option 2
>0 to 10
0.02 to 1.09
278
6.43
10 to 30
1.10 to 1.90
43
0.82
30 to 50
3.06 to 3.06
7
0.15
Option 3
>0 to 10
0.00 to 1.08
541
29.45
10 to 30
1.10 to 1.91
46
0.84
30 to 50
3.06 to 3.06
7
0.15
Option 4
>0 to 10
0.00 to 1.08
580
31.23
10 to 30
1.10 to 1.91
48
0.84
30 to 50
3.06 to 3.06
7
0.15
50 to 75
-
-
-
>75
5.12 to 5.12
2
0.09
No data (i.e., there are no observations within the specified ABr range)
Source: U.S. EPA Analysis, 2022.
4.3.3 Step 3: Quantifying Population Exposure and Health Effects
EPA used the following steps to quantify changes in human health resulting from changes in TTHM levels in
drinking water supplies:
• Characterize the exposed populations;
• Estimate changes in individual health risk; and
• Quantify the changes in adverse health outcomes.
4.3.3.1 Exposed Populations
The exposed populations consist of people served by each affected PWS. SDWIS provides the total
population served by each PWS and identifies the ZIP codes constituting the PWS service area. EPA used ZIP
codes information to determine the demographic characteristics of the population served.53 Some PWS-ZIP
code assignments are absent from the SDWIS 2021 Quarter 3 dataset (U.S. EPA, 2021c). In these cases, EPA
relied on ZIP code assignments from the fourth Unregulated Contaminant Monitoring database (U.S. EPA,
2016a) to supplement PWS-ZIP code assignments.
53 EPA used ZIP codes instead of counties for the 2019 proposed rule and 2020 final rule analyses to enable a more accurate
characterization of the demographic and socioeconomic characteristics of the service areas.
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EPA used ZIP code-level data from the 2019 American Community Survey (U.S. Census Bureau, 2019) to
distribute the total population served by each PWS by age group to model health effects as described in
Section 4.3.3.3.
EPA assumed that all individuals served by a given PWS are exposed to the same modeled changes in TTHM
levels for the PWS, i.e., there are no differences in TTHM concentrations in different parts of the water
distribution system.
4.3.3.2 Health Impact Function
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 epidemiological studies
(Cantor etal., 2010; U.S. EPA, 2005b; NTP, 2018), a meta-analysis (Villanueva etal., 2003; Costet etal.,
2011), and pooled analysis (Villanueva etal., 2004). The relationship between trihalomethane levels and
bladder cancer in the Villanueva et al. (2004) study was used to support the benefits analysis for EPA's Stage
2 DBP Rule54 which specifically aimed to reduce the potential health risks from DBPs (U.S. EPA, 2005b).
Regli et al. (2015) conducted an analysis of potential bladder cancer risks associated with increased bromide
levels in surface source water. To estimate risks associated with modeled TTHM levels, they built on the
approach taken in EPA's Stage 2 DBP Rule, i.e., deriving a slope factor from the pooled analysis of
Villanueva et al. (2004). They showed that the overall pooled exposure-response relationship for TTHM is
linear over a range of relevant doses. The linear relationship predicted an incremental lifetime cancer risk of 1
in ten thousand exposed individuals (10 4) per 1 (ig/L increase in TTHM. The linear model proposed by Regli
et al. (2015) provides a basis for estimating the dose-response relationship associated with changes in TTHM
levels estimated for the regulatory options. The linear slope factor enables estimates of the total number of
cancer cases associated with lifetime exposures to different TTHM levels.
EPA used the relationship estimated by Regli et al. (2015) to model the impact of changes in TTHM
concentration in treated water on the lifetime bladder cancer risk:
Equation 4-1. 0(x) = 0(0) ¦ exp (0.00427x),
where 0 (x) are the odds of lifetime bladder cancer incidence for an individual exposed to a lifetime average
TTHM concentration in residential water supply of x (ig/L and 0(0) are the odds of lifetime bladder cancer in
the absence of exposure to TTHM in residential water supply. The log-linear relationship (Equation 4-1) has
the advantage of being independent from the baseline TTHM exposure level, which is highly uncertain for
most affected individuals due to lack of historical data.
4.3.3.3 Health Risk Model and Data Sources
EPA estimated changes in lifetime bladder cancer cases due to estimated changes in lifetime TTHM exposure
using a dynamic microsimulation model that estimates affected population life tables under different exposure
conditions. Life table approaches are standard among practitioners in demography and risk sciences and
provide a flexible method for estimating the probability and timing of health impacts during a defined period
54 See DBP Rule documentation at https://www.epa.gov/dwreginfo/stage-l-and-stage-2-disinfectants-and-disinfection-bvproducts-
rules
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(Miller & Hurley, 2003; Rockett, 2010).55 In this application, the life table approach estimates age-specific
changes in bladder cancer probability and models subsequent bladder cancer mortality, which is highly
dependent on the age at the time of diagnosis. This age-specific cancer probability addresses variability in
age-specific life expectancy across the population alive at the time the change occurs. This model allows for
quantification of relatively complex policy scenarios, including those that involve variable contaminant level
changes over time.
For this analysis, EPA assumed that the population affected by estimated changes in bromide discharges from
steam electric power plants is exposed to baseline TTHM levels prior to implementation of the regulatory
options - i.e., prior to 2025 - and to alternative TTHM levels from 2025 through 2049. As described in
Section 1.3.3, the period of analysis is based on the approximate life span of the longest-lived compliance
technology for any steam electric power plant (20 or more years) and the final year of implementation (2029).
The change in TTHM exposure affects the risk of developing bladder cancer beyond this period, however,
because the majority of cancer cases manifest during the latter half of the average individual life span (Hrudey
et al., 2015). To capture these effects while being consistent with the framework of evaluating costs and
benefits incurred from 2025-2049, EPA modeled changes in health outcomes resulting from changes in
exposure in 2025-2049. Since changes in cancer incidence occur long after exposure, EPA modeled
associated changes in cancer incidence through 2125, though only for the changes attributable to changed
exposures in the 2025-2049 timeframe.
Lifetime health risk model data sources, detailed in Table 4-5 (next page), include EPA SDWIS and UCMR
4, ACS 2019 (U.S. Census Bureau, 2019), the Surveillance, Epidemiology, and End Results (SEER) program
database (National Cancer Institute), and the Center for Disease Control (CDC) National Center for Health
Statistics.
55 The EPA has used life table approaches to estimate health risks associated with radon in homes, formaldehyde exposure, and
Superfund and RCRA site chemicals exposure, among others (Pawel & Puskin, 2004; Munns & Mitro, 2006; National Research
Council, 2011).
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Table 4-5: Summary of Data Sources Used in Lifetime Health Risk Model
Data element
Modeled variability
Data source
Notes
Number of persons in the
affected population in 2025
Age: 1-year groups (ages 0 to
100)
Sex: males, females
Location: zip code for PWS
service area from SDWISa and the
fourth Unregulated Contaminant
Monitoring Rule (UCMR4)
database15
2019 American Community Survey
(ACS) (data on age- and sex-specific zip
code-level population [U.S. Census
Bureau, 2019 and age- and sex-specific
population projections from Woods &
Poole Economics Inc. (2021).
ACS data were in 5-year age groups. EPA assumed
uniform distribution within each age interval to
represent data as 1-year age groups. EPA then grew
the age- and sex-specific zip code-level population
data to the beginning of the analysis period (2025)
using corresponding county-specific growth rates
calculated using the Woods & Poole Economics Inc.
(2021) complete demographic database. EPA then
computed relevant age- and sex- population shares
and used them to distribute location-specific affected
population within each zip code
Bladder cancer incidence
rate (IR) per 100,000
persons
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
SEER 21 (Surveillance Research
Program - National Cancer Institute,
2020b)c
Distinct SEER 21 IR data were available for ages 0,1-4,
5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-
84, 85+. EPA assumed that the same IR applies to all
ages within each age group. EPA assumed that non-
Hispanic Black IRs can be approximated by Black IRs.
EPA assumed that non-Hispanic Other IRs can be
approximated by all race IRs.
General population
mortality rate
Age: 1-year groups (ages 0 to
100)
Sex: males, females
Center for Disease Control
(CDC)/National Center for Health
Statistics (NCHS) United States Life
Tables, 2017
EPA used race/ethnicity-, age- and sex-specific
probabilities of dying within the integer age intervals.
EPA assumed that non-Hispanic Other data can be
approximated by all race data.
Share of bladder cancer
incidence at specific cancer
stage
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Cancer stage: localized, regional,
distant, unstaged
SEER 18 distribution of bladder cancer
incidence over stages by age and sex at
diagnosis
Distinct SEER 18 data were available for ages 0-44, 45-
54, 55-64, 65-74, 75+. EPA assumed that the same
cancer incidence shares by stage apply to all ages
within each age group.
Share of cancer deaths
among all-cause deaths
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Cancer type: Kidney Cancer;
Urinary Bladder (Invasive & In
Situ) Cancer
Underlying Cause of Death, 1999-2019
on CDC WONDER Online Database
(Centers for Disease Control and
Prevention, 2020)
EPA calculated share of cancer deaths among all-
cause deaths by race/ethnicity, age and sex by
dividing the number of cancer deaths during 1999-
2019 with the number of all-cause deaths during
1999-2019.
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Table 4-5: Summary of Data Sources Used in Lifetime Health Risk Model
Data element
Modeled variability
Data source
Notes
Race/ethnicity: All, non-Hispanic
White, non-Hispanic Black,
Hispanic, non-Hispanic Other
Relative bladder cancer
survival by cancer stage
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Duration: 1-year groups
(durations 0 to 100 years)
Cancer stage: localized, regional,
distant, unstaged
Cancer type: Urinary Bladder
(Invasive & In Situ) Cancer
SEER 18 relative bladder cancer
survival by age at diagnosis, sex, cancer
stage and duration with diagnosis for
2000-2017 (Surveillance Research
Program - National Cancer Institute,
2020a)
Distinct SEER 18 data were available for ages at
diagnosis 0-14, 15-39, 40-64, 65-74, 75+. EPA
assumed that the same cancer relative survival
patterns apply to all ages within each age group.
SEER 18 contained data on relative survival among
persons that had bladder cancer for 0,1, 2, 3, 4, 5, 6,
7, 8, 9, and 10 years. For disease durations longer
than 10 years EPA applied 10-year relative survival
rates.
a EPA's Safe Drinking Water Information System SDWIS: https://www3.epa.gov/enviro/facts/sdwis/search.html
b ICF matched zip-code level populations from the 2019 ACS data (U.S. Census Bureau, 2019) to zip codes associated with each PWS in the SDWIS 2021 Q1 dataset (U.S. EPA, 2021c)
or the UCMR 4 dataset (U.S. EPA, 2016a). The SDWIS dataset often contains a one-to-many relationship between PWS and zip codes served, whereas the UCMR 4 dataset provides a
one-to-one relationship between PWS and zip codes.
c SEER program, National Cancer Institute, National Institute of Health
Source: U.S. EPA Analysis, 2022.
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Table 4-6 summarizes sex- and age group-specific general population mortality rates and bladder cancer
incidence rates used in the model simulations, as well as the sex-specific share of the affected population for
each age group. Appendix C summarize sex- and age group-specific distribution of bladder cancer cases over
four analyzed stages as well as the age of onset-specific relative survival probability for each stage.
Using available data on cancer incidence and mortality, EPA calculated changes in bladder cancer cases
resulting from the regulatory options using the relationship between the change in TTHM concentrations and
the change in lifetime bladder cancer risk estimated by Regli et cil. (2015) (see Section 4.3.3.2). The analysis
accounts for the gradual changes in lifetime exposures to TTHM following estimated changes in annual
average bromide discharges and associated TTHM exposure under the regulatory options compared to the
baseline.
Table 4-6: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates
Sex
Age group
Sex-specific share of the
affected population3
General population
mortality rate
(per 100,000)b
General population
bladder cancer incidence
rate
(per 100,000)bc
Female
<1
0.011
537
0.000
Female
1-4
0.044
36
0.000
Female
5-9
0.058
12
0.000
Female
10-14
0.060
10
0.000
Female
15-19
0.061
19
0.000
Female
20-24
0.063
40
0.009
Female
25-29
0.068
54
0.017
Female
30-34
0.070
73
0.034
Female
35-39
0.066
98
0.140
Female
40-44
0.063
135
0.310
Female
45-49
0.060
203
0.640
Female
50-54
0.063
317
1.300
Female
55-59
0.064
470
2.200
Female
60-64
0.064
675
4.000
Female
65-69
0.056
987
6.500
Female
70-74
0.048
1,533
12.000
Female
75-79
0.033
2,481
22.000
Female
80-84
0.022
4,171
36.000
Female
85+
0.025
-
-
Male
<1
0.065
646
0.009
Male
1-4
0.065
44
0.000
Male
5-9
0.048
15
0.000
Male
10-14
0.012
12
0.009
Male
15-19
0.068
34
0.000
Male
20-24
0.073
112
0.012
Male
25-29
0.075
142
0.020
Male
30-34
0.069
159
0.046
Male
35-39
0.064
185
0.190
Male
40-44
0.060
229
0.520
Male
45-49
0.063
323
1.400
Male
50-54
0.063
508
3.100
Male
55-59
0.063
784
7.100
Male
60-64
0.061
1136
12.000
Male
65-69
0.051
1593
22.000
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Table 4-6: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates
General population
General population
bladder cancer incidence
Sex-specific share of the
mortality rate
rate
Sex
Age group
affected population3
(per 100,000)b
(per 100,000)bc
Male
70-74
0.042
2304
37.000
Male
75-79
0.027
3577
70.000
Male
80-84
0.017
5770
123.000
Male
85+
0.015
-
-
a Shares calculated for the total population served by potentially affected PWS, based on county-level data.
b Based on the general population of the United States.
cSingle age-specific rates were aggregated up to the age groups reported in the table using the individual age-specific number of
affected persons as weights.
Source: U.S. EPA analysis (2022) of 2019 ACS data (U.S. Census Bureau, 2019).
4.3.3.4 Model Implementation
EPA analyzed effects of the regulatory options using the dynamic microsimulation model and data sources
described in Section 4.3.3.3. As described above, EPA models TTHM changes (ATTHM) due to the
regulatory options as being in effect for the years 2025 through 2049. After 2049, EPA does not attribute
costs or changes in bromide loadings to the rule, and therefore does not model incremental changes in
exposures to TTHM.56
To estimate changes in bladder cancer incidence, EPA defined and quantified a set of 110,898 unique
combinations57 of the following parameters:
• Location and TTHM changes: 549 PWS groups;58
• Age: age of the population at the start of the evaluation period (2025), ranging from 0 to 100;
• Sex: population sex (male or female).
4.3.4 Quantifying the Monetary Value of Benefits
EPA estimated total monetized benefits from avoided morbidity and mortality (also referred to as avoided
cancer cases and avoided cancer deaths, respectively, in this discussion) from estimated changes in bromide
discharges, and estimated changes in TTHM exposure and the resulting estimated bladder cancer incidence
rate using 3 percent and 7 percent discount rates for each of the four regulatory options.59
• Morbidity: To value changes in the economic burden associated with cancer morbidity EPA used
estimates of annual medical expenses for bladder cancer treatment from Greco etal. (2019) and the
50 In other words, costs after 2049 = $0 and Abromide after 2049 is zero (hence ATTHM after 2049 is zero).
57 The set of 110,898 combinations was determined by multiplying the number of PWS groups by the number of ages and sexes
considered (549 x 101 x 2).
58 The PWS groups represent unique combinations of location (comity) and ATTHM values and typically consist of a directly
affected PWS and other PWSs serving populations located in the same comity and purchasing water from the directly affected
PWS. The number of PWS in each PWS group ranges from 1 to 11.
59 In some cases, benefits are derived from a delay in cancer morbidity and mortality.
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estimated life years with cancer morbidity (differentiating between first and subsequent years after
cancer diagnosis). For invasive cancer, the medical treatment costs are $50,061 and $3,420 per case
for the first and subsequent years respectively. For non-invasive cancer, medical treatment costs are
$18,272 and $1,270 per case for the first and subsequent years, respectively.
• Mortality: To value changes in excess mortality from bladder cancer EPA extrapolated the default
central tendency of the VSL distribution recommended for use in EPA's regulatory impact analyses,
$4.8 million (1990 USD, 1990 income year), to future years, ranging from $11.69 million per death in
2021 to $14.01 million per death in 2049 (U.S. EPA, 2010b). The product of VSL and the estimated
aggregate reduction in risk of death in a given year represents the affected population's aggregate
WTP to reduce its probability of death in one year.
4.4 Results of Analysis of Human Health Benefits from Estimated Changes in Bromide
Discharges Analysis
Using the data EPA assembled on cancer incidence and mortality, the Agency estimated changes in bladder
cancer cases for the regulatory options using the relationship between TTHM concentrations and the lifetime
bladder cancer risk estimated by Regli et cil. (2015). Figure 4-3 and Figure 4-4 show the estimated number of
bladder cancer cases and premature deaths avoided, respectively, under the four regulatory options by decade.
In each decade, the estimated number of bladder cancer cases is never in excess of 35 cases and the estimated
number of premature deaths avoided is never in excess of nine deaths avoided.
Consistent with the relatively small decrease in bromide loadings for Option 1 in Table 3-1, this option would
result in a relatively small decrease in cancer incidence and mortality as compared to the baseline. Options 2,
3, and 4 generally show larger decreases in cancer incidence and mortality over the period of analysis. More
than 50 percent of the modeled avoided bladder cancer incidence associated with Options 1, 2, 3, and 4 occurs
between 2025 and 2054. This pattern is consistent with existing cancer cessation lag models (e.g., Hrubec &
McLaughlin, 1997, Hartge etal, 1987, and Chen & Gibb, 2003) that show between 61 and 94 percent
reduction in cancer risk in the first 25 years after exposure cessation (see Appendix C for detail). After 2054,
the benefits attributable to exposures incurred under the regulatory options in 2025-2049 decline due to
comparably fewer people surviving to mature ages.6" In the years after 2085, the avoided cases decline
considerably and in the last decade considered in the analysis, the cancer incidences increase relative to
baseline incidences.61
00 In the period between 2055 and 2084, the estimated avoided cases decline slowly as the living people exposed to the estimated
changes in TTHM levels reach 70 years (the age at which the highest annual incidence of bladder cancer is observed). According
to American Cancer Society, about 9 out of 10 people diagnosed with bladder cancer are over the age of 55. The average age at
the time of diagnosis is 73 (U.S. Census Bureau, 2019).
01 The increase in cancer cases in the last decade is due to the connection between survival and cancer incidence. Lower estimated
TTHM exposure due to reductions in bromide loadings under certain regulatory options reduces the estimated number of people
developing bladder cancer during the earlier years of the analysis and increases overall survival rates. Higher estimated rates of
survival lead to longer life spans and more people developing cancer later in life. This effect becomes more apparent closer to the
end of the evaluation period, at which point there are fewer people estimated to be alive in the baseline population compared to
the estimated number of people alive under certain regulatory option scenarios.
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Figure 4-3: Estimated Number of Bladder Cancer Cases Avoided under the Regulatory Options.
35
30
25
T3
CD
¦g
o
>
<
20
15
U
10
ll...
2025-2034 2035-2044 2045-2054 2055-2064 2065-2074 2075-2084 2085-2094 2095-2104 2105-2114 2115-2124
Option 1 I Option 2 ¦ Option 3 ¦ Option 4
Source: U.S. EPA Analysis, 2022.
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Figure 4-4: Estimated Number of Cancer Deaths Avoided under the Regulatory Options.
10
9
T3
-5 6
o
>
< r-
ro
Q 4
I—
OJ
o
re 3
1
¦
¦
¦
¦
III
U
2025-2034 2035-2044 2045-2054 2055-2064 2065-2074 2075-2084 2085-2094 2095-2104 2105-2114 2115-2124
Option 1 I Option 2 ¦ Option 3 ¦ Option 4
Source: U.S. EPA Analysis, 2022.
Table 4-7 summarizes the estimated changes in the incidence of bladder cancer from exposure to TTHM due
to the regulatory options and the value of benefits from avoided cancer cases, including avoided mortality and
morbidity.
Table 4-7: Estimated Bromide-related Bladder Cancer Mortality and Morbidity Monetized Benefits
Regulatory
Option
Changes in cancer cases from
changes in TTHM exposure
2025-20493
Benefits (million 2021$, discounted to 2024)
Total bladder
cancer cases
avoided
Total cancer
deaths
avoided
Annualized13
benefits from
avoided mortality
Annualized13
benefits from
avoided morbidity
Total annualized13
benefits
3%
7%
3%
7%
3%
7%
1
5
2
$0.45
$0.13
$0.00
$0.00
$0.45
$0.28
2
110
31
$9.29
$6.04
$0.08
$0.05
$9.37
$6.09
3
112
32
$9.53
$6.19
$0.08
$0.05
$9.61
$6.24
4
149
42
$12.60
$8.19
$0.10
$0.07
$12.70
$8.26
aThe analysis accounts for the persisting health effects (up until 2125) from changes in TTHM exposure during the period of
analysis (2025-2049).
b Benefits are annualized over 25 years.
Source: U.S. EPA Analysis, 2022
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These estimated total benefits are not uniformly distributed across plants that discharge bromide. For
example, out of the 86 steam electric power plants included in this analysis, under Option 2 more than
85 percent of total benefits are attributable to discharge changes at only seven steam electric power plants.
Similarly, approximately 85 percent of the benefits of Option 3 come from seven steam electric power plants
and approximately 85 percent of the benefits of Option 4 come from changes at nine steam electric power
plants. Figure 4-5 illustrates the plant-level contributions to total annualized benefits for Options 2, 3, and 4
during Period 2. Only a single plant contributes to total annualized benefits for Option 1, so it is not shown in
Figure 4-5.
Figure 4-5: Contributions of Individual Steam Electric Power Plants to Total Annualized Benefits of
Changes in Bromide Discharges under the Regulatory Options (3 Percent Discount Rate)
Option 2
$14
O $12
$10
2 5
~ o
CO (J
1/1
tu Q $8
§ SS
m rn
" I
-T, ~ 56 ¦"
xs ro
J -¦
ro (N
3 O
c rsi
<
$0
I
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Cumulative Number of Steam Electric Plants
Option 3
dJ LJ
C so
m on
cq rn
-M
T3 ru
<1)
M
$14
J „ $12
— 4—1
| $io
CO (J
CO
03 fN
3 O
d rsi
cz
<
$8
$6
$4
$2
$0
I
I
7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Cumulative Numberof Steam Electric Plants
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Figure 4-5: Contributions of Individual Steam Electric Power Plants to Total Annualized Benefits of
Changes in Bromide Discharges under the Regulatory Options (3 Percent Discount Rate)
Option 4
$14
$12
c
O
4—1
c
^3
o
co
(J
CO
m—
0)
Q
c
Q)
O
CO
m
4—'
"a
03
M
-t/v
T—1
TO
r\i
O
C
rsi
C
<
$8 _¦
$6
$0
I
I
$2
I
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Cumulative Number of Steam Electric Plants
4.5 Additional Measures of Human Health Effects from Exposure to Steam Electric Pollutants
via Drinking Water Pathway
The regulatory options may result in relatively small changes to source water quality for additional parameters
that can adversely affect human health (see Section 2.1.1). Many pollutants in steam electric power plant
discharges have MCLs that set allowable levels in treated water. For some pollutants that have an MCL above
the MCLG, there may be incremental benefits from reducing concentrations below the MCL. In addition to
certain brominated DBPs discussed in the previous sections, there are no "safe levels" for lead and arsenic
and therefore any reduction in exposure to these pollutants is expected to yield benefits.62
Estimated concentrations of arsenic and lead in drinking water source reaches downstream of steam electric
facilities do not exceed typical detection limits for these contaminants. The results show thallium
concentrations in source waters that exceed levels detectable by standard methods (0.005 j^ig/L) in one source
water reach but are below 0.005 (ig/L in all other modeled source waters. Relative to baseline concentrations,
the changes in arsenic, lead, and thallium concentrations are small (e.g., less than 0.02 (ig/L in Period 1 and
less than 0.004 (ig/L in Period 2 in source waters). Table 4-8 summarizes the direction of changes in arsenic,
lead, and thallium concentrations under the regulatory options for the two analysis periods. The magnitude of
the changes depends on the Period, regulatory option, source water reach, and PWS but is generally consistent
with the changes in halogen loadings associated with FGD wastewater and bottom ash transport water under
each analyzed regulatory option (see Table 3-1). During Period 1, all Options show either reductions or no
changes in arsenic, lead, and thallium concentrations for all source waters and PWS. During Period 2, the four
02 Even in cases where the MCLG is equal to the MCL, there may be incremental health-related benefits associated with changes in
concentrations arising from the regulatory options since detection of the pollutants is subject to imperfect monitoring and
treatment may not remove all contaminants from the drinking water supplies, as evidenced by reported MCL violations for
inorganic and other contaminants at community water systems (U.S. EPA, 2013b).
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Options also show estimated reductions in arsenic, lead, and thallium concentrations with both the magnitude
and scope (the number of reaches, PWS, and population served) of the reductions larger than during Period 1.
To assess potential additional drinking water-related health benefits, EPA estimated the changes in the
number of receiving reaches with drinking water intakes that have modeled pollutant concentrations
exceeding MCLs or MCLGs. EPA did this analysis for all of the pollutants listed in Table 2-2, except bromate
and TTHM.63 This analysis showed no changes in the number of MCL or MCLG exceedances under the
regulatory options, when compared to the baseline. In addition, EPA found no reaches with drinking water
intakes that had modeled lead, arsenic, or thallium concentrations in excess of MCLs or MCLGs under either
the baseline or the regulatory options, even where concentrations increased as summarized in Table 4-8.64 The
Agency concluded, based on these screening analyses, that any additional benefits from changes in exposure
to the pollutants examined in this analysis via the drinking water pathway would be relatively small.
Table 4-8: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and
Thallium Concentrations by Period and Regulatory Option, Compared to Baseline
Regulatory Option
Number of Source Water
Reaches
Number of PWSa
Population Served by PWS
(Millions)
Reduction
No Change
Reduction
No Change
Reduction
No Change
Period 1 (2025-2029)
Arsenic
Option 1
117
102
378
497
15.0
25.9
Option 2
144
75
534
341
15.9
25.0
Option 3
171
48
677
198
30.6
10.4
Option 4
172
47
679
196
30.6
10.3
Lead
Option 1
4
166
11
626
0.4
31.9
Option 2
86
84
311
326
7.2
25.2
Option 3
140
30
565
72
25.2
7.1
Option 4
157
13
608
29
27.0
5.3
Thallium
Option 1
4
215
11
864
0.4
40.5
Option 2
86
133
311
564
7.2
33.8
Option 3
140
79
565
310
25.2
15.7
Option 4
157
62
608
267
27.0
13.9
Period 2 (2030-2049)
Arsenic
Option 1
166
53
585
290
25.4
15.5
Option 2
192
27
737
138
26.3
14.6
Option 3
218
1
873
2
40.8
0.1
Option 4
219
0
875
0
40.9
0.0
Lead
Option 1
4
166
11
626
0.4
31.9
Option 2
92
78
328
309
7.4
24.9
03 EPA did not consider MCL or MCLG exceedances for bromate and TTHM because the background data on these contaminants
in source waters is not readily available (e.g., these contaminants are not included in the TRI dataset). Additionally, modeled
discharges of bromate from steam electric plant effluent do not exceed EPA's MCL of 0.01 mg/L, but all exceed the MCLG of
zero.
04 EPA also found that there are no reaches with drinking water intakes that have pollutant concentrations exceeding human health
ambient water quality criteria for either the consumption of water and organism or the consumption of organism only.
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4: Human Health Benefits via Drinking Water
Table 4-8: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and
Thallium Concentrations by Period and Regulatory Option, Compared to Baseline
Regulatory Option
Number of Source Water
Reaches
Number of PWSa
Population Served by PWS
(Millions)
Reduction
No Change
Reduction
No Change
Reduction
No Change
Option 3
153
17
594
43
30.4
1.9
Option 4
170
0
637
0
32.3
0.0
Thallium
Option 1
4
215
11
864
0.4
40.5
Option 2
92
127
328
547
7.4
33.5
Option 3
153
66
594
281
30.4
10.5
Option 4
170
49
637
238
32.3
8.6
a. Includes systems potentially directly and/or indirectly affected by steam electric power plant discharges.
Source: U.S. EPA Analysis, 2022.
4.6 Limitations and Uncertainties
Table 4-9 summarizes principal limitations and sources of uncertainties associated with the estimated changes
in pollutant levels in source waters downstream from steam electric power plant discharges. Additional
limitations and uncertainties are associated with the estimation of pollutant loadings (see U.S. EPA, U.S.
EPA, 2020f). Note that the effect on benefits estimates indicated in the second column of the table refers to
the magnitude of the benefits rather than the direction (i.e., a source of uncertainty that tends to underestimate
benefits indicates expectation for either larger forgone benefits or larger realized benefits).
Table 4-9: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
Analysis does not account
for births within the
exposed population.
Underestimate
The analysis does not account for people born after
2025. This likely leads to an underestimate of benefits.
Analysis does not account
for migration within the
exposed population.
Uncertain
The analysis does not account for people leaving or
moving into the service area. The overall effect of this
factor on the estimated benefits is uncertain.
Bladder cancer risks are
estimated for populations
for which changes in
TTHM exposures relative
to baseline exposures
start at different ages,
including children.
Uncertain
The relative cancer potency of TTHM in children is
unknown, which may bias benefits 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 TTHM during childhood (U.S. EPA, 2016c).
Because bladder cancer incidence in children is very
small, EPA assesses any bias to be negligible.
For PWS with multiple
sources of water, the
analysis uses equal
contributions from each
source.
Uncertain
Data on the flow rates of individual source facilities are
not available and EPA therefore estimated that all
permanent active sources contribute equally to a PWS's
total supply. Effects of the regulatory option may be
greater or smaller than estimated, depending on actual
supply shares.
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Table 4-9: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
Changes in bromide
concentrations are
analyzed for active
permanent surface water
intakes and reservoirs
only.
Underestimate
The analysis includes only permanent active surface
water facilities associated with non-transient PWS
classified as "community water systems" that use
surface water as primary source. To the extent that
PWS using surface waters as secondary source or other
non-permanent surface water facilities are affected,
this approach understates the effects of the regulatory
options.
Changes in TTHM
formation depends only
on changes in bromide
levels.
Uncertain
The regulatory options are expected to affect bromide
levels in source water. Other factors such as
disinfection method, pH, temperature, and organic
content affect TTHM formation. EPA assumes that PWS
and source waters affected by steam electric power
plant discharges have similar characteristics as those
modeled in Regli etal. (2015).
Use of a national
relationship from Regli et
al. (2015) to relate
changes in bromide
concentration to changes
in TTHM concentration.
Uncertain
EPA did not collect site-specific information on factors
affecting TTHM formation at each potentially affected
drinking water treatment plant, but instead used the
median from a sample population of approximately 200
drinking water treatment systems. Use of the national
relationship from Regli et al. (2015) could either
understate or overstate actual changes in TTHM
concentrations for a given change in bromide
concentrations at any specific drinking water treatment
system.
Change in risk is based on
changes in exposure to
TTHMs rather than to
brominated
trihalomethanes
specifically.
Underestimate
Brominated species play a prominent role in the overall
toxicity of DBP exposure. Given that the regulatory
options predominantly affect the formation of
brominated DBPs, the estimated changes in cancer risk
resulting from regulatory options could be biased
downward. EPA report provides additional information
about health effects of DBPs (U.S. EPA, 2016c).
The analysis relies on
public-access SEER 18 5-
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,
5-year excess mortality follow-up window. Survival
rates beyond 5 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 dose-response
function used to estimate
risk assumes causality of
bladder cancer from
exposure to disinfected
drinking water.
Overestimate
While the evidence supporting causality has increased
since EPA's Stage 2 DBP Rule, the weight of evidence is
still not definitive (see Regli et al. (2015)).
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Table 4-9: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
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 TTHM baselines, Regli et al. (2015) provides a
reasonable approximation of the risk. However,
depending on the baseline TTHM 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 does not
account for the
relationship between
TTHM exposure and
bladder cancer within
certain subpopulations.
Overestimate
Epidemiological literature suggests that TTHM effects
could be greatest for the smoker population, whose
members are already at higher risk for bladder cancer.
Smoking prevalence has declined in the United States
and relationships estimated with data from the 1980s
and 1990s may overestimate future bladder cancer
impact. Robust synthesis estimates of the relationship
between TTHM and bladder cancer in the smoker
population are lacking, limiting EPA's ability to account
for smoking when modeling health effects.
The change in risk for a
given change in TTHM is
uncertain for changes in
TTHM concentrations that
are less than 1 ng/L.
Uncertain
EPA notes that the majority of the regulatory options
benefits are associated with PWS for which predicted
changes in TTHM concentration are greater than 1
Hg/L Although there is greater uncertainty in the
estimated changes in health risk associated with
changes in TTHM concentrations less than 1 ng/L, EPA
included these changes in the estimated benefits.
Benefits from the regulatory options may be greater or
smaller than estimated, depending on actual risk
changes. EPA
Health effects associated
with DBP exposure other
than bladder cancer are
not quantified in this
analysis.
Uncertain
An EPA report discusses potential linkages between
DBP exposures and other health endpoints, e.g.,
developmental effects (with a short-term exposure)
and cancers other than bladder cancers (with a long-
term exposure), but there is insufficient data to fully
evaluate these endpoints (U.S. EPA, 2016c).
Discharge monitoring data
for bromide from steam
electric power plants are
limited and demonstrate
significant variability
based on site-specific
factors.
Uncertain
Limited bromide monitoring data are available to assess
bromide source water concentration estimates.
The analysis does not
consider pollutant sources
beyond those associated
with steam electric power
plants orTRI dischargers.
Underestimate
The analysis of other pollutants does not account for
natural background and anthropogenic sources that do
not report to TRI. This results in a potential
underestimate of the number of waters exceeding the
MCLor MCLG.
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Table 4-9: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
The analysis does not
account for populations
that consume bottled
water as their primary
drinking water source or
populations that practice
averting behaviors such as
purchasing bottled water
and filters in response to
drinking water violations.
Uncertain
Studies indicate that between 13% and 33% of the U.S.
population consumes bottled water as their primary
drinking water source (Hu et al., 2011; Rosinger et a!.,
2018; Vieux et al., 2020). Recent research also
documents a relationship between sales of bottled
water and violations of the SDWA (Allaire et al., 2019).
The benefits models do not consider populations who
consume bottled water as their primary drinking water
source or populations that practice averting behaviors
in response to poor drinking water quality. The overall
effect of not considering these populations on the
estimated benefits is uncertain.
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5: Human Health Benefits via Fish Ingestion
5 Human Health Effects from Changes in Pollutant Exposure via the Fish
Ingestion Pathway
EPA expects the regulatory options to affect human health risk by changing effluent discharges to surface
waters and, as a result, ambient pollutant concentrations in the receiving reaches. The EA (U.S. EPA, 2023a)
provides details on the health effects of steam electric pollutants. Recreational and subsistence fishers (and
their household members) who consume fish caught65 in the reaches receiving steam electric power plant
discharges could benefit from reduced pollutant concentrations in fish tissue. This chapter presents EPA's
analysis of human health effects resulting from changes in exposure to pollutants in bottom ash transport
water, FGD wastewater and CRL via the fish consumption pathway. The analyzed health effects include:
• Changes in exposure to lead: This includes changes in neurological and cognitive damages in children
(ages 0-7) based on the impact of an additional IQ point on an individual's future earnings and the
cost of compensatory education for children with learning delays.
• Changes in exposure to mercury: Changes in neurological and cognitive damages in infants from
exposure to mercury in-ntero based on the impact of an additional IQ point on an individual's future
earnings.
• Changes in exposure to arsenic: Changes in incidence of cancer cases and the COI associated with
treating skin cancer.
The total quantified human health effects included in this analysis represent only a subset of the potential
health effects estimated to result from the regulatory options. While additional adverse health effects are
associated with pollutants in bottom ash transport water and FGD wastewater (such as kidney damage from
cadmium or selenium exposure, gastrointestinal problems from zinc, thallium, or boron exposure, and others),
the lack of data on dose-response relationships66 between ingestion rates and these effects precluded EPA
from quantifying the associated health effects.
EPA's analysis of the monetary value of human health effects utilizes data and methodologies described in
Chapter 3 and in the EA (U.S. EPA, 2023a). The relevant data include the set of immediate and downstream
reaches that receive steam electric power plant discharges (i.e.. affected reaches), as defined by the NHD
COMID,67 the estimated ambient pollutant concentrations in receiving reaches, and estimated fish
consumption rates among different age and ethnic cohorts for affected recreational and subsistence fishers.
Section 5.1 describes how EPA identified the population potentially exposed to pollutants from steam electric
power plant discharges via fish consumption. Section 5.2 describes the methods for estimating fish tissue
pollutant concentrations and potential exposure via fish consumption in the affected population. Sections 5.3
to 5.5 describe EPA's analysis of various human health endpoints potentially affected by the regulatory
05 As detailed in Sections 5.2 and 5.8, for the subset of recreational and subsistence fishers who consume catch from affected
reaches (i.e., do not practice catch-and-release), EPA assumed that all fish consumed consists of self-caught fish. EPA assumed
no exposure via fish consumption for all other households, including recreational and subsistence fishers who consume catch
from other reaches.
00 A dose response relationship is an increase in incidences of an adverse health outcome per unit increase in exposure to a toxin.
07 A COMID is a unique numeric identifier for a given waterbody (reach), assigned by a joint effort of the United States Geological
Survey and EPA.
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5: Human Health Benefits via Fish Ingestion
options, which are then summarized in Section 5.6. Section 5.7 provides additional measures of human health
benefits. Section 5.8 describes limitations and uncertainties.
5.1 Population in Scope of the Analysis
The population in scope of the analysis (/'. e., individuals potentially exposed to steam electric pollutants via
consumption of contaminated fish tissue) includes recreational and subsistence fishers who fish reaches
affected by steam electric power plant discharges (including receiving and downstream reaches), as well as
their household members.68 EPA estimated the number of people who are likely to fish affected reaches based
on typical travel distances to a fishing site and presence of substitute fishing locations. EPA notes that the
universe of sites potentially visited by recreational and subsistence fishers includes reaches subject to fish
consumption advisories (FCA).69 EPA expects that recreational fishers' responses to FCA presence are
reflected in their catch and release practices, as discussed below.
Since fish consumption rates vary across different age, racial and ethnic groups, and fishing mode
(recreational versus subsistence fishing), EPA estimated potential health effects separately for a number of
age-, ethnicity-, and mode-specific cohorts. For each Census Block Group (CBG) within 50 miles of an
affected reach, EPA assembled 2019 American Community Survey data on the number of people in 7 age
categories (0 to 1, 2, 3 to 5, 6 to 10, 11 to 15, 16 to 21, and 21 years or higher), and then subdivided each
group according to 7 racial/ethnic categories:70 1) White non-Hispanic; 2) African-American non-Hispanic; 3)
Tribal/Native Alaskan non-Hispanic; 4) Asian/Pacific Islander non-Hispanic; 5) Other non-Hispanic
(including multiple races); 6) Mexican Hispanic; and 7) Other Hispanic.71 Within each racial/ethnic group,
EPA further subdivided the population according to recreational and subsistence fisher groups. The Agency
assumed that the 95th percentile of the general population fish consumption rate is representative of the
subsistence fisher consumption rate. Accordingly, the Agency assumed that 5 percent of the total fishers
population practices subsistence fishing.72 EPA also subdivided the affected population by income into
poverty and non-poverty groups, based on the share of people below the federal poverty line.73 After
subdividing population groups by age, race, fishing mode, and poverty indicator, each CBG has 196 unique
68 The in-scope population excludes recreational and subsistence fishers who fish other reaches or certain affected waterbodies not
covered by the water quality models (i.e., Great Lakes and estuaries).
69 Based on EPA's review of studies documenting fishers' awareness of FCA and their behavioral responses to FCA, 57.0 percent
to 61.2 percent of fishers are aware of FCA, and 71.6 percent to 76.1 percent of those who are aware ignore FCA (Burger, 2004,
Jakus et al., 1997; Jakus etal., 2002; R. L. Williams etal., 2000). Therefore, only 17.4 percent of fishers may adjust their
behavior in response to FCA (U.S. EPA, 2015a). The analysis reflects EPA's expectations that fishers responses to FCA are
reflected in their catch and release practices.
70 The racial/ethnic categories are based on available fish consumption data as well as the breakout of ethnic/racial populations in
Census data, which distinguishes racial groups within Flispanic and non-Flispanic categories.
71 The Mexican Flispanic and Flispanic block group populations were calculated by applying the Census tract percent Mexican
Flispanic and Flispanic to the underlying block-group populations, since these data were not available at the block-group level.
72 Data are not available on the share of the fishing population that practices subsistence fishing. EPA assumed that 5 percent of
people who fish practice subsistence fishing, based on the assumed 95th percentile fish consumption rate for this population in
EPA's Exposure Factors Flandbook (see U.S. Environmental Protection Agency, 2011).
73 Poverty status is based on data from the Census Bureau's American Community Survey which determines poverty status by
comparing annual income to a set of dollar values called poverty thresholds that vary by family size, number of children, and the
age of the householder.
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5: Human Health Benefits via Fish Ingestion
population cohorts (7 age groups x 7 ethnic/racial groups x 2 fishing modes [recreational versus subsistence
fishing] x 2 poverty status designations).
EPA distinguished the exposed population by racial/ethnic group and poverty status to support analysis of
potential environmental justice (EJ) considerations from baseline exposure to pollutants in steam electric
power plant discharges, and to allow evaluation of the effects of the regulatory options on mitigating any EJ
concerns. See EJA document for details of the EJ analysis. As noted below, distinguishing the exposed
population in this manner allows the Agency to account for differences in exposure among demographic
groups, where supported by available data.
Equation 5-1 shows how EPA estimated the population potentially exposed to steam electric pollutants,
ExPop(i)(s)(c), for CBG i in state 5 for cohort c.
Equation 5-1. ExPop(i)(s)(c) = Pop(i)(c)x %Fish(s) x CaR(c)
Where:
Pop(i)(c) = Total CBG population in cohort c. Age and racial/ethnicity-specific populations in each
CBG are based on data from the 2019 American Community Survey, which provides
population numbers for each CBG broken out by age and racial/ethnic group. To
estimate the population in each age- and ethnicity/race-specific group, EPA calculated
the share of the population in each racial/ethnic group and applied those percentages to
the population in each age group.
%Fish(s) = Fraction of people who live in households with fishers. To estimate what percentage of the
total population participates in fishing, EPA used region-specific U.S. Fish and Wildlife
Service (U.S. FWS, 2018) estimates of the population 16 and older who fish.74 EPA
assumed that the share of households that includes fishers is equal to the fraction of
people over 16 who participate in recreational fishing.
CaR(c) = Adjustment for catch-and-release practices. According to U.S. FWS (U.S. FWS, 2006) data,
approximately 23.3 percent of recreational fishers release all the fish they catch ("catch-
and-release" fishers). Fishers practicing "catch-and-release" would not be exposed to
steam electric pollutants via consumption of contaminated fish. For all recreational
fishers, EPA reduced the affected population by 23.3 percent. EPA assumed that
subsistence fishers do not practice "catch-and-release" fishing.
Table 5-1 summarizes the population living within 50 miles of reaches affected by steam electric power plant
discharges (see Section 5.2.1 for a discussion of this distance buffer) and EPA's estimate of the population
potentially exposed to the pollutants via consumption of subsistence- and recreationally-caught fish (based on
2019 population data and not adjusted for population growth during the analysis period). Of the total
population, 16 percent live within 50 miles of an affected reach and participate in recreational and/or
subsistence fishing, and 12 percent are potentially exposed to fish contaminated by steam electric pollutants in
bottom ash transport water, FGD wastewater, and CRL discharges.
74 The share of the population who fishes ranges from 8 percent in the Pacific region to 20 percent in the East South Central region.
Other regions include the Middle Atlantic (10 percent), New England (11 percent), South Atlantic (15 percent), Mountain
(15 percent), West South Central (17 percent), East North Central (17 percent), and West North Central (18 percent).
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Table 5-1: Summary of Population Potentially Exposed to Contaminated Fish Living within 50 Miles
of Affected Reaches (as of 2019)
Total population
121,117,555
Total fishers population3
19,063,667
Population potentially exposed to contaminated fishb c
14,843,924
a. Total population living within 50 miles of an affected reach multiplied by the state-specific share of the population who fishes
based on U.S. FWS (2018; between 8 percent and 20 percent, depending on the state).
b. Total fishers population adjusted to remove fishers practicing catch-and-release and who therefore do not consume self-caught
fish.
c. Analysis accounts for projected population growth so that the average population in scope of the analysis over the period of
2025 through 2049 is 12.1 percent higher than the population in 2019 presented in the table, or 16.6 million people. The analysis
estimates that the fraction of the U.S. population engaged in recreational and subsistence fishing remains constant from 2025
through 2049.
Source: U.S. EPA Analysis, 2022
5.2 Pollutant Exposure from Fish Consumption
EPA calculated an average fish tissue concentration for each pollutant for each CBG based on a length-
weighted average concentration for all reaches within 50 miles. For each combination of pollutant, cohort and
CBG, EPA calculated the average daily dose (ADD) and lifetime average daily dose (LADD) consumed via
the fish consumption pathway.
5.2.1 Fish Tissue Pollutant Concentrations
The set of reaches that may represent a source of contaminated fish for recreational and subsistence fishers in
each CBG depends on the typical distance fishers travel to fish. EPA assumed that fishers typically travel up
to 50 miles to fish,75 and used this distance to estimate the relevant fishing sites for the population of fishers in
each CBG.
Fishers may have several fishable sites to choose from within 50 miles of travel. To account for the effect of
substitute sites, EPA assumed that fishing efforts are uniformly distributed among all the available fishing
sites within 50 miles from the CBG (travel zone). For each CBG, EPA identified all fishable reaches within
50 miles (where distance was determined based on the Euclidean distance between the centroid of the CBG
and the midpoint of the reach) and the reach length in miles.
EPA then calculated, for each CBG within the 50-mile buffer of a fishable reach, the fish tissue concentration
of As, Hg, and lead (Pb). Appendix E in U.S. EPA (2020b describes the approach used to calculate fish tissue
concentrations of steam electric pollutants in the baseline and under each of the regulatory options.
For each CBG, EPA then calculated the reach length (Lengthweighted fish fillet concentration (C Fish Fuiet
(CBG)) based on all fishable reaches within the 50-mile radius according to Equation 5-2. See Appendix 0 for
additional details about the derivation of fish tissue concentration values.
75 Studies of fishers behavior and practices have made similar observations (e.g., Sohngen et al., 2015 and Sea Grant - Illinois-
Indiana, 2018).
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5: Human Health Benefits via Fish Ingestion
_ Zi=icFishpm^Lengthi
Equation 5-2. CpishptUete(CBG) - zliLmgthi
5.2.2 Average Daily Dose
Exposure to steam electric pollutants via fish consumption depends on the cohort-specific fish consumption
rates. Table 5-2 summarizes the average fish consumption rates, expressed in daily grams per kilogram of
body weight (BW), according to the race/ethnicity and fishing mode. The rates reflect recommended values
for consumer-only intake of finfish in the general population from all sources, based on EPA's Exposure
Factors Handbook (U.S. EPA, 2011). For more details on these fish consumption rates, see the EA (U.S.
EPA, 2023a) and the uncertainty discussion in Section 5.8.
Table 5-2: Summary of Group-specific Consumption Rates for Fish Tissue Consumption Risk
Analysis
Race/ Ethnicity3
EA Cohort Nameb
Consumption Rate (g/kg BW/day)
Recreational
Subsistence
White (non-Hispanic)
Non-Hispanic White
0.67
1.9
African American (non-Hispanic)
Non-Hispanic Black
0.77
2.1
Asian/Pacific Islander (non-Hispanic)
Other, including Multiple Races
0.96
3.6
Tribal/Native Alaskan (non-Hispanic)
Other, including Multiple Races
0.96
3.6
Other non-Hispanic
Other, including Multiple Races
0.96
3.6
Mexican Hispanic
Mexican Hispanic
0.93
2.8
Other Hispanic
Other Hispanic
0.82
2.7
a. Each group is also subdivided into seven age groups (0-1, 2, 3-5, 6-10,11-15,16-20, Adult [21 or higher] and two income groups
[above and below the poverty threshold]).
b. See EA for details (U.S. EPA, 2023a).
Source: U.S. EPA Analysis, 2022
Equation 5-3 and Equation 5-4 show the cohort- and CBG-specific ADD and LADD calculations based on
fish tissue concentrations, consumption rates, and exposure duration and averaging periods from U.S. EPA
(2023a).
Equation 5-3. ADD(c)(i) = cf^jiiie^mlshMxFFlsh
Where:
ADD(c)(i) = average daily dose of pollutant from fish consumption for cohort c in CBG i
(milligrams [mg] per kilogram [kg] body weight [BW] per day)
Cfishjuet (j) = average fish fillet pollutant concentration consumed by humans for CBG /' (mg per kg)
CRflSb{c) = consumption rate of fish for cohort c (grams per kg BW per day); see Table 5-2
Ffsh = fraction of fish from reaches within the analyzed distance from the CBG (percent; estimated value
of 100%)
x- i- a » \f\ ADD(c)(i)xED(c)xEF
Equation 5-4. LADD(c)(i) = ——
^ v J ATX365
Where:
LADD (c)(j) = lifetime average daily dose (mg per kg BW per day) for cohort c in CBG /'
ADD (c)(j) = average daily dose (mg per kg BW per day) for cohort c in CBG /'
ED(c) = exposure duration (years) for cohort c
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h.I'' = exposure frequency (days; set to 350)
AT = averaging time (years; set to 70)
EPA used the doses of steam electric pollutants as calculated above from fish caught through recreational and
subsistence fishing in its analysis of benefits associated with the various human health endpoints described
below.
5.3 Health Effects in Children from Changes in Lead Exposure
Lead is a highly toxic pollutant that can cause a variety of adverse health effects in children of all ages. In
particular, elevated lead exposure may induce a number of adverse neurological effects in children, including
decline in cognitive function, conduct disorders, attentional difficulties, internalizing behavior,76 and motor
skill deficits (see NTP, 2012; U.S. EPA, 2013d, 2019d, and 2020f). Elevated blood lead (PbB) concentrations
in children may also slow postnatal growth in children ages one to 16, delay puberty in 8- to 17-year-olds, and
decrease hearing and motor function (NTP, 2012; U.S. EPA, 2019d). Lead exposure is also associated with
adverse health outcomes related to the immune system, including atopic and inflammatory responses (e.g.,
allergy and asthma) and reduced resistance to bacterial infections. Studies have also found a relationship
between lead exposure in expectant mothers and lower birth weight in newborns (NTP, 2012; U.S. EPA,
2019d; Zhu el a I.. 2010). Because of data limitations, EPA estimated only the effects of changes in
neurological and cognitive damages to pre-school (ages 0 to 7) children using the dose-response relationship
for IQ decrements (Crump et al., 2013).
EPA estimated health effects from changes in exposure to lead to preschool children using PbB as a
biomarker of lead exposure. EPA modeled PbB under the baseline and regulatory option scenarios, and then
used a concentration-response relationship between PbB and IQ loss to estimate changes in IQ losses in the
affected population of children and changes in incidences of extremely low IQ scores (less than 70, or two
standard deviations below the mean). EPA calculated the monetary value of changes in children's health
effects based on the impact of an additional IQ point on an individual's future earnings and the cost of
compensatory education for children with learning disabilities (including children with IQ less than 70 and
PbB levels above 20 |a,g/dL).
EPA used the methodology described in Section 5.1 to estimate the population of children from birth to age
seven who live in recreational fisher and subsistence fisher households and are potentially exposed to lead via
consumption of contaminated fish tissue. EPA notes that fish tissue is not the only route of exposure to lead
among children. Other routes of exposure may include drinking water, dust, and other food. EPA used
reference exposure values for these other routes of lead exposures and held these values constant for the
baseline and regulatory options scenarios. Since this health effect applies to children up to the seventh
birthday only, EPA restricted the analysis to the relevant age cohorts of fisher household members.
5.3.1 Methods
This analysis considers children who are born after implementation of the regulatory options and live in
recreational fisher and subsistence fisher households. It relies on EPA's Integrated Exposure, Uptake, and
Biokinetics (IEUBK) Model for Lead in Children (U.S. EPA, 2021a), which uses lead concentrations in a
variety of media - including soil, dust, air, water, and diet - to estimate total exposure to lead for children in
76 Behavioral difficulties in children may include both externalizing behavior (e.g., inattention, impulsivity, conduct disorders), and
internalizing behaviors (e.g., withdrawn behaviors, symptoms of depression, fearfulness, and anxiety).
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seven one-year age cohorts from birth through the seventh birthday. Based on the estimated total exposure,
the model generates a predicted geometric mean PbB for a population of children exposed to similar lead
levels. See the 2013 BCA report (U.S. EPA, 2013a) for details.
For each CBG, EPA used the cohort-specific ADD based on Equation 5-3. EPA then multiplied the cohort-
specific ADD by the average body weight for each age group77 to calculate the "alternative source" input for
the IEUBK model. Lead bioavailability and uptake after consumption vary for different chemical forms.
Many factors complicate the estimation of bioavailability, including nutritional status and timing of meals
relative to lead intake. For this analysis, EPA used the default media-specific bioavailability factor for the
"alternative source" provided in the IEUBK model, which is 50 percent for oral ingestion.
EPA used the IEUBK model to generate the geometric mean PbB for each cohort in each CBG under the
baseline and post-technology implementation scenarios. The IEUBK model processes daily intake to two
decimal places (fig/day). For this analysis, this means that some of the change between the baseline and
regulatory options is not accounted for by using the model (i.e.. IEUBK does not capture very small changes),
since the estimated changes in health effects are driven by small changes across large populations. This aspect
of the model contributes to potential underestimation of the lead-related health effects in children arising from
the regulatory options.
5.3.1.1 Estimating Changes in IQ Point Losses
EPA used the Crump et al. (2013) dose-response function to estimate changes in IQ losses between the
baseline and regulatory options. Comparing the baseline and regulatory option results provides the changes in
IQ loss per child. Crump et al. (2013) concluded that there was statistical evidence that the exposure-response
is non-linear over the full range of PbB. Equation 5-5 shows an exposure-response function that represents
this non-linearity:
Equation 5-5. A IQ = p1 x ln(PbB + 1)
Where:
/?! = -3.315 (log-linear regression coefficient on the lifetime blood lead level78)
Multiplying the result by the number of affected pre-school children yields the total change in the number of
IQ points for the affected population of children for the baseline and each regulatory option.
The IEUBK model estimates the mean of the PbB distribution in children, assuming a continuous exposure
pattern for children from birth through the seventh birthday. The 2019 American Community Survey
indicates that children ages 0 to 7 are approximately evenly distributed by age. To get an annual estimate of
the number of children that would benefit from implementation of the regulatory options, EPA divided the
estimated number of affected pre-school children by 7. This division adjusts the equation to apply only to
children age 0 to 1. The estimated changes in IQ loss represent an annual value (i.e.. it would apply to the
77 The average body weight values are 11.4 kg for ages 0 to 2,13.8 kg for ages 2 to less than 3, 18.6 kg for ages 3 to less than 6, and
31.8 kg for ages 6 to 7.
78 The lifetime blood lead level in children ages 0 to 7 is defined as a mean from six months of age to present (Crump etal., 2013).
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cohort of children born each year after implementation).79 Equation 5-6 shows this calculation for the annual
increase in total IQ points.
Equation 5-6. A/Q(i)(c) = (ln(AGM(i)(c)) x CRF x
Where:
AIO(i)(c) = the difference in total IQ points between the baseline and regulatory option scenarios for
cohort c in CBG i
Ln(AGM(i)(c)) = the log-linear change in the average PbB in affected population of children ((ig/dL) for
cohort c in CBG i
CRF = -3.315, the log-linear regression coefficient from Crump et cd. (2013)
ExCh(i)(c) = the number of affected children aged 0 to 7 for cohort c in CBG i
The available economic literature provides little empirical data on society's overall WTP to avoid a decrease
in children's IQ. To estimate the value of avoided IQ losses, EPA used estimates of the changes in a child's
future expected lifetime earnings per one IQ point reduction and the cost of compensatory education for
children with learning disabilities.
EPA estimated the value of an IQ point using the methodology presented in Salkever (1995) but with more
recent data from the 1997 National Longitudinal Survey ofYouth (U.S. EPA, 2019c). Updated results based
on Salkever (1995) indicate that a one-point IQ reduction reduces expected lifetime earnings by 2.63 percent.
Table 5-3 summarizes the estimated values of an IQ point based on the updated Salkever (1995) analysis
using 3 percent and 7 percent discount rates. These values are discounted to the third year of life to represent
the midpoint of the exposed children population. EPA also used an alternative value of an IQ point from Lin
et cd. (2018) in a sensitivity analysis (see Appendix 0).
Table 5-3: Value of an IQ Point (2021$) based on Expected
Reductions in Lifetime Earnings
Discount Rate
Value of an IQ Pointa,b (2021$)
3 percent
$22,381
7 percent
$4,875
a. Values are adjusted for the cost of education.
b. EPA adjusted the value of an IQ point to 2021 dollars using the GDP
deflator.
Source: U.S. EPA, 2019c re-analysis of data from Salkever (1995)
5.3.1.2 Reduced Expenditures on Compensatory Education
Children whose PbB exceeds 20 |ag/dL are more likely to have IQs less than 70, which means that they would
require compensatory education tailored to their specific needs. Costs of compensatory education and special
education are not reflected in the IQ point dollar value. Reducing exposure to lead at an early age is expected
79 Dividing by seven undercounts overall benefits. Children from ages 1 to 7 (i.e., bom prior to the base year of the analysis) are not
accounted for in the analysis, although they are also affected by changes in lead exposure.
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to reduce the incidence of children requiring compensatory and/or special education, which would in turn
lower associated costs. Though these costs are not a substantial component of the overall benefits, they do
represent a potential benefit of changes in lead exposure. EPA quantitatively assessed this benefit category
using the methodology from the 2015 BCA (U.S. EPA, 2015a). The estimated cost savings from the estimated
changes in the need for compensatory education are negligible and are not included in the total monetized
benefits.
5.3.2 Results
Table 5-4 shows the benefits associated with changes in IQ losses from lead exposure via consumption of
self-caught fish. Avoided IQ point losses over the entire in-scope population of children with changes in lead
exposure ranges from 1 point (Option 1) to 6 points (Options 3 and 4). Estimated annualized benefits from
avoided IQ losses are $0.01 million for Options 3 and 4 using a 3 percent discount rate. Otherwise, the
estimated annualized benefits are less than $0.01 million.
Table 5-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Children 0
to 7 in Scope of the
Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049 in
All Children 0 to 7 in
Scope of the Analysisc
Annualized Value of Avoided IQ Point
Losses3 (Millions 2021$)
3% Discount Rate
7% Discount Rate
Option 1
1,427,107
1
<$0.01
<$0.01
Option 2
1,427,107
2
<$0.01
<$0.01
Option 3
1,427,107
6
$0.01
<$0.01
Option 4
1,427,107
6
$0.01
<$0.01
a. Based on estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings, following updated
Salkever (1995) values from U.S. EPA (2019c).
b. The number of children in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the
children included in this count see no changes in exposure under some options.
c. EPA notes that the IQ point losses are very small. EPA further notes that the IEUBK model does not analyze blood lead level
changes beyond two decimal points.
Source: U.S. EPA Analysis, 2022
5.4 Heath Effects in Children from Changes in Mercury Exposure
Mercury can have a variety of adverse health effects on adults and children (U.S. EPA, 2023a). The
regulatory options may change the discharge of mercury to surface waters by steam electric power plants and
therefore affect a range of human health outcomes. Due to data limitations, however, EPA estimated only the
monetary value of the changes in IQ losses among children exposed to mercury in-ntero as a result of
maternal consumption of contaminated fish.
EPA identified the population of children exposed in-ntero starting from the CBG-specific population in
scope of the analysis described in Section 5.1. Therefore, this analysis only reflects health effects from
consumption of self-caught fish by households. Also, because this analysis focuses only on infants born after
implementation of the regulatory options, EPA further limited the analyzed population by estimating the
number of women between the ages of 15 and 44 potentially exposed to contaminated fish caught in the
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affected waterbodies and multiplying the result by ethnicity-specific average fertility rates.80 This yields the
cohort-specific annual number of births for each CBG.
The U.S. Department of Health and Human Services provides fertility rates by race for 2019 in the National
Vital Statistics Report (Martin et al.. 2021). The fertility rate measures the number of births occurring per
1,000 women between the ages of 15 and 44 in a particular year. Fertility rates were highest for Hispanic
women at 65.3, followed by African Americans at 64.4, other race/ethnicities at 58.3, Native Americans at
56.2, and Caucasians and Asians at 55.3.
5.4.1 Methods
EPA used the ethnicity- and mode-specific consumption rates shown in Table 5-2 and calculated the CBG-
and cohort-specific mercury ADD based on Equation 5-3. As EPA is not aware of consumption rates specific
to pregnant women, the analysis uses the same consumption rates as in the general population within each
analyzed cohort.
In this analysis, EPA used a linear dose-response relationship between maternal mercury hair content and
subsequent childhood IQ loss from Axelrad et al. (2007). Axelrad et al. (2007) developed a dose-response
function based on data from three epidemiological studies in the Faroe Islands, New Zealand, and Seychelle
Islands. According to their results, there is a 0.18-point IQ loss for each 1 part-per-million (ppm) increase in
maternal hair mercury.
To estimate maternal hair mercury concentrations based on the daily intake (see Section 5.2.2), EPA used the
median conversion factor derived by Swartout and Rice (2000), who estimated that a 0.08 j^ig/kg body weight
increase in daily mercury dose is associated with a 1 ppm increase in hair concentration. Equation 5-7 shows
EPA's calculation of the total annual IQ changes for a given receiving reach.
IQL(i)(c) = IQ changes associated with in-utero exposure to mercury from maternal consumption of fish
contaminated with mercury for cohort c in CBG i
InExPop(i)(cj = population of infants in scope of the analysis for cohort c in CBG /' (the number of
births)
MADD(i)(c) = maternal ADD for cohort c in CBG i (j^ig/kg BW/day)
Conv = conversion factor for hair mercury concentration based on maternal mercury exposure
(0.08 (ig/kg BW/day per 1 ppm increase in hair mercury)
1)111'' = dose response function for IQ decrement based on marginal increase in maternal hair mercury
(0.18-point IQ decrement per 1 ppm increase in hair mercury)
Summing estimated IQ changes across all analyzed CBGs yields the total changes in the number of IQ points
due to in-utero mercury exposure from maternal fish consumption under each analyzed regulatory option. The
EPA acknowledges that fertility rates vary by age. However, the use of a single average fertility rate for all ages is not expected
to bias results because the average fertility rate reflects the underlying distribution of fertility rates by age.
Equation 5-7.
Where:
IQL(i){c) = InExPop(i){c) * MADD(i){c) * * DRF
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benefits of the regulatory options are calculated as the change in IQ points between the baseline and modeled
post-technology implementation conditions under each of the regulatory options.
The available economic literature provides little empirical data on society's overall WTP to avoid a decrease
in children's IQ. To estimate the value of avoided IQ losses, EPA used estimates of the changes in a child's
future expected lifetime earnings per one IQ point reduction. The values of an IQ point presented in Section
5.3.1 are discounted to the third year of life to represent the midpoint of the exposed children population of
interest for that analysis. EPA further discounted the present value of lifetime income differentials three
additional years to reflect the value of an IQ point at birth and better align the benefits of reducing exposure to
mercury with in-utero exposure (U.S. EPA, 2019e). The IQ values discounted to birth range from $3,980 to
$20,482. EPA also used an alternative value of an IQ point from Lin et cil. (2018) in a sensitivity analysis (see
Appendix 0.
5.4.2 Results
Table 5-5 shows the estimated changes in IQ point losses for infants exposed to mercury in-utero and the
corresponding monetary values, using 3 percent and 7 percent discount rates. Avoided IQ point losses over
the entire in-scope population of infants with changes in mercury exposure ranges from 3,712 points (Option
1) to 3,923 points (Option 4). Using a 3 percent discount rate, the annualized benefits of avoided IQ point
losses range from $2.94 million (Option 1) to $3.11 million (Options 3 and 4). Using a 7 percent discount
rate, estimates range from $0.54 million (Option 1) to $0.58 million (Options 3 and 4).
Table 5-5: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline
Regulatory Option
Number of Infants in
Scope of the Analysis per
Yearb
Total Avoided IQ Point
Losses, 2025 to 2049 in
All Infants in Scope of the
Analysis
Annualized Value of Avoided IQ Point
Losses3 (Millions 2021$)
3% Discount Rate
7% Discount Rate
Option 1
187,496
3,712
$2.94
$0.54
Option 2
187,496
3,776
$2.99
$0.55
Option 3
187,496
3,920
$3.11
$0.58
Option 4
187,496
3,923
$3.11
$0.58
a. Based on the estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings discounted to birth,
following updated Salkever (1995) values from U.S. EPA (2019e).
b. The number of infants in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the children
included in this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2022
5.5 Estimated Changes in Cancer Cases from Arsenic Exposure
Among steam electric pollutants that can contaminate fish tissue and are analyzed in the EA, arsenic is the
only confirmed carcinogen with a published dose response function (see U.S. EPA, 2010b).81 EPA used the
methodology presented in Section 3.6 of the 2015 BCA (U.S. EPA, 2015a) to estimate the number of annual
skin cancer cases associated with consumption of fish contaminated with arsenic from steam electric power
plant discharges under the baseline and the change corresponding to each regulatory option and the associated
monetary values. EPA's analysis shows negligible changes in skin cancer cases from exposure to arsenic via
81 Although other pollutants, such as cadmium, are also likely to be carcinogenic (see U.S. Department of Health and Human
Services, 2012), EPA did not identify dose-response functions to quantify the effects of changes in these other pollutants.
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consumption of self-caught fish under the regulatory options.82 Accordingly, the estimated benefits are also
negligible under all regulatory options and are not included in the total monetized benefits.
5.6 Monetary Values of Estimated Changes in Human Health Effects
Table 5-6 presents the estimated benefits under the regulatory options of changes in adverse human health
outcomes associated with the consumption of self-caught fish. Using a 3 percent discount rate, the estimated
benefits range from $2.94 million (Option 1) to $3.12 million (Option 4). Using a 7 percent discount rate, the
estimated benefits range from $0.54 million (Option 1) to $0.58 million (Options 3 and 4). Changes in
mercury exposure for children account for the majority of total monetary values from increases in adverse
health outcomes.
Table 5-6: Estimated Benefits of Changes in Human Health Outcomes Associated with Fish
Consumption under the Regulatory Options, Compared to Baseline (Millions of 2021$)
Discount Rate
Regulatory Option
Changes in Lead Exposure
for Childrenabc
Changes in Mercury
Exposure for Children313
Totalab
Option 1
$0.00
$2.94
$2.94
3%
Option 2
$0.00
$2.99
$2.99
Option 3
$0.00
$3.11
$3.11
Option 4
$0.01
$3.11
$3.12
Option 1
$0.00
$0.54
$0.54
7%
Option 2
$0.00
$0.55
$0.55
Option 3
$0.00
$0.58
$0.58
Option 4
$0.00
$0.58
$0.58
5.7 Additional Measures of Potential Changes in Human Health Effects
As noted in the introduction to this chapter, untreated pollutants in steam electric power plant discharges have
been linked to additional adverse human health effects. EPA compared immediate receiving water
concentrations to human health-based NRWQC in U.S. EPA (2020f). To provide an additional measure of the
potential health effects of the regulatory options, EPA also estimated the changes in the number of receiving
and downstream reaches with pollutant concentrations in excess of human health-based NRWQC. This
analysis compares pollutant concentrations estimated for the baseline and each analyzed regulatory option in
receiving reaches and downstream reaches to criteria established by EPA for protection of human health. EPA
compared estimated in-water concentrations of antimony, arsenic, barium, cadmium, chromium, cyanide,
copper, lead, manganese, mercury, nitrate-nitrite as N, nickel, selenium, thallium, and zinc to EPA's NRWQC
protective of human health used by states and tribes (U.S. EPA, 2018c) and to MCLs.83 Estimated pollutant
concentrations in excess of these values indicate potential risks to human health. This analysis and its findings
are not additive to the preceding analyses in this chapter, but instead represent another way of characterizing
potential health effects resulting from changes in exposure to steam electric pollutants.
82 The analysis estimated a reduction in the incidence of arsenic-related skin cancer cases of 0.01 cases between 2025 and 2049 for
all four regulatory options.
83 For pollutants that do not have NRWQC protective of human health, EPA used MCLs. These pollutants include cadmium,
chromium, lead, and mercury.
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Table 5-7 shows the results of this analysis.84 During Period 1, EPA estimates that with baseline steam
electric pollutant discharges, concentrations of steam electric pollutants exceed human health criteria for at
least one pollutant in 350 reaches based on the "consumption of water and organism" criteria, and 51 reaches
based on the "consumption of organism only" criteria nationwide. During Period 2, concentrations of steam
electric pollutants exceed human health criteria for at least one pollutant in 346 reaches based on the
"consumption of water and organism" criteria, and 51 reaches based on the "consumption of organism only"
criteria nationwide under the baseline scenario. The estimated number of reaches with exceedances of
"consumption water and organism" criteria and with exceedances of "consumption of organism only" criteria
during both Period 1 and Period 2 decreases under all regulatory options.85 For example, Option 3 eliminates
exceedances in 286 reaches (346-60) and reduces the number of exceedances in 301 reaches.
Table 5-7: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric
Pollutants
Regulatory Option
Number of Reaches with Ambient
Concentrations Exceeding Human Health
Criteria for at Least One Pollutant3
Number of Reaches with Lower Number of
Exceedances, Relative to Baselineb
Consumption of Water
+ Organism
Consumption of
Organism Only
Consumption of Water
+ Organism
Consumption of
Organism Only
Period 1 (2025-2029)
Baseline
350
51
Not applicable
Not applicable
Option 1
268
44
90
15
Option 2
267
44
91
15
Option 3
255
44
103
15
Option 4
255
44
103
15
Period 2 (2030-2049)
Baseline
346
51
Not applicable
Not applicable
Option 1
84
19
272
42
Option 2
84
17
277
47
Option 3
60
14
301
47
Option 4
60
14
301
47
a. Pollutants for which there was at least one exceedance in the baseline or regulatory options include antimony, arsenic,
chromium, cyanide, manganese, and thallium in Period 1 and arsenic, chromium, cyanide, manganese, and thallium in Period 2.
b. Pollutants for which there was at least one reach with lower number of exceedances relative to baseline include arsenic and
chromium in Period 1 and arsenic, chromium, cyanide, manganese, and thallium in Period 2.
Source: U.S. EPA Analysis, 2022
5.8 Limitations and Uncertainties
The analysis presented in this chapter does not include all possible human health effects associated with post-
technology implementation changes in pollutant discharges due to lack of data on a dose-response
relationship between ingestion rates and potential adverse health effects. Therefore, the total quantified human
84 Only reaches designated as fishable (i.e., Strahler Stream Order larger than 1) were included in the NRWQC exceedances
analysis.
85 EPA's analysis does not account for the fact that the NPDES permit for each steam electric power plant, like all NPDES permits,
is required to have limits more stringent than the technology-based limits established by an ELG, wherever necessary to protect
water quality standards. Because this analysis does not project where a permit will have more stringent limits than those required
by the ELG, it may overestimate any negative impacts to aquatic ecosystems and T&E species, including impacts that will not be
realized at all because the permits will be written to include limits as stringent as necessary to meet water quality standards as
required by the CWA.
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health effects included in this analysis represent only a subset of the potential health effects estimated to result
from the regulatory options. Section 2.1 provides a qualitative discussion of health effects omitted from the
quantitative analysis.
The methodologies and data used in the analysis of adverse health outcomes due to consumption of fish
contaminated with steam electric pollutants involve limitations and uncertainties. Table 5-8 summarizes the
limitations and uncertainties and indicates the direction of the potential bias. Additional limitations and
uncertainties associated with the environmental assessment analyses and data are discussed in the EA (see
U.S. EPA, 2023a).
Table 5-8: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
Fishers are estimated to
evenly distribute their
activity over all available
fishing sites within the 50-
mile travel distance.
Uncertain
EPA estimated that all fishers travel up to 50 miles
and distribute their visits over all fishable sites within
the area. In fact, recreational and subsistence fishers
may have preferred sites (e.g., a site located closer
to their home) that they visit more frequently. The
characteristics of these sites, notably ambient water
concentrations and fishing advisories, affects
exposure to pollutants, but EPA does not have data
to support a more detailed analysis of fishing visits.
The impact of this approach on monetary estimates
is uncertain since fewer/more fishers may be
exposed to higher/lower fish tissue concentrations
than estimated by EPA.
The exposed population is
estimated based on
households in proximity to
affected reaches and the
fraction of the general
population who fish.
Uncertain
EPA estimated the share of households that includes
fishers to be equal to the fraction of people over 16
who are fishers. This may double-count households
with more than one fisher over 16. However, the
exposed population may also include non-household
members who also consume the catch.
Fish intake rates used in
estimating exposure are
based on recommended
values for the general
consumer population.
Uncertain
The fish consumption rates used in the analysis are
based on the general consumer population, which
may understate or overstate the amount offish
consumed by fishers who may consume fish at
higher or lower rates than the general population
(e.g., Burger, 2013; U.S. EPA, 2011, 2013c)
Fish intake rates used in
estimating exposure do not
reflect potential lower fish
consumption by pregnant
women.
Overestimate
To the degree that pregnant women reduce their
consumption of self-caught fish when compared to
women in the general population, then exposure in
the baseline would be less and the proposed rule
benefits from reduced exposure to mercury
correspondingly lower.
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Table 5-8: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
100 percent of fish
consumed by recreational
fishers is self-caught.
Overestimate
The fish consumption rates used in the analysis
account for all fish sources (i.e., store-bought or self-
caught fish). Assuming that recreational fishers
consume only self-caught fish may overestimate
exposure to steam electric pollutants from fish
consumption. The degree of the overestimate is
unknown as the fraction offish consumed that is
self-caught varies significantly across different
locations and population subgroups (e.g., U.S. EPA,
2013c).
The number of subsistence
fishers was set to equal
5 percent of the total
number of fishers fishing the
affected reaches.
Uncertain
The magnitude of subsistence fishing in the United
States or individual states is not known. Using
5 percent may understate or overstate the overall
number of potentially affected subsistence fishers
(and their households) and ignores potential
variability in subsistence fishing rates across
racial/ethnic groups and different geographic
locations.
Value of an IQ point used to
quantify benefits health
effects from changes in lead
and mercury exposure
Uncertain
EPA used two alternative estimates of the value of
an IQ point in its analysis, following the methodology
in U.S. EPA (2019c; 2019d, 2020b). EPA
acknowledges recent research indicating higher IQ
point values than those calculated based on Salkever
(1995) and Lin et al. (2018). However, because the
recent research was based on either non-U.S.
populations (e.g., Gronqvist et al., 2020 ) or
unrepresentative subsets of the U.S. population
(Hollingsworth etal., 2020; Hollingsworth & Rudik,
2021),EPA continued to use IQ point values based on
Salkever (1995) and Lin etal. (2018).
There is a 0.18-point IQ loss
for each 1 ppm increase in
maternal hair mercury (i.e.,
the relationship is assumed
to be linear).
Uncertain
The exact form of the relationship between maternal
body mercury burden and IQ losses is uncertain.
Using a linear relationship may understate or
overstate the IQ losses resulting from a given change
in mercury exposure.
For the mercury- and lead-
related health impact
analyses, EPA assessed IQ
losses to be an appropriate
endpoint for quantifying
adverse cognitive and
neurological effects resulting
from childhood or in-utero
exposures to lead and
mercury (respectively).
Underestimate
IQ may not be the most sensitive endpoint.
Additionally, there are deficits in cognitive abilities
that are not reflected in IQ scores, including
increased incidence of attention-related and
problem behaviors (NTP, 2012; U.S. EPA, 2005c). To
the extent that these impacts create disadvantages
for children exposed to mercury and lead in the
absence of (or independent from) measurable IQ
losses, this analysis may underestimate the social
welfare effects of the regulatory options of changes
in lead and mercury exposure.
The IEUBK model processes
daily intake from "alternative
sources" to 2 decimal places
(Hg/day).
Underestimate
Since the fish-associated pollutant intakes are small,
some variation is missed by using this model (i.e., it
does not capture very small changes).
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5: Human Health Benefits via Fish Ingestion
Table 5-8: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
EPA did not monetize the
health effects associated
with changes in adult
exposure to lead or mercury.
Underestimate
The scientific literature suggests that exposure to
lead and mercury may have significant adverse
health effects for adults (e.g., Navas-Acien, 2021;
Aoki et a!., 2016; Chowdhury et al., 2018; Lanphear
et al., 2018). If measurable effects are occurring at
current exposure levels, excluding the effects of
increased adult exposure results in an underestimate
of benefits.
EPA did not quantify other
health effects in children
from exposure to lead or
mercury.
Underestimate
As discussed in Section 2.1, exposure to lead could
result in additional adverse health effects in children
(e.g., low birth weight and neonatal mortality from
in-utero exposure to lead, or neurological effects in
children exposed to lead after age seven) (NTP,
2012; U.S. EPA, 2013d; U.S. EPA, 2019d). Additional
neurological effects could also occur in children from
exposure to mercury after birth (Mergler et al.,
2007; CDC, 2009). If measurable effects are
occurring at current exposure levels, excluding
additional health effects of increased children
exposure results in an underestimate of benefits.
EPA did not assess combined
health risk of multiple
pollutants.
Uncertain
The combined health risk of multiple pollutants
could be greater than from a single pollutant (Evans
et al., 2020). However, quantifying cumulative risk is
challenging because a mixture of pollutants could
affect a wide range of target organs and endpoints
(ATSDR, 2004, 2009). For example, different
carcinogens found in steam electric power plant
discharges may affect different organs (e.g., arsenic
is linked to skin cancer while cadmium is linked to
kidney cancer). Other synergistic effects may
increase or lessen the risk. While there are no
existing methods to fully analyze and monetize these
effects, EPA quantified some of these effects in the
EA (U.S. EPA, 2023a).
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6: Nonmarket Benefits
6 Nonmarket Benefits from Water Quality Changes
As discussed in the EA (U.S. EPA, 2023a), heavy metals, nutrients, and other pollutants discharged by steam
electric power plants can have a wide range of effects on water resources downstream from the plants. These
environmental changes affect environmental goods and services valued by humans, including recreation;
commercial fishing; public and private property ownership; navigation; water supply and use; and existence
services such as aquatic life, wildlife, and habitat designated uses. Some environmental goods and services
(e.g., commercially caught fish) are traded in markets, and thus their value can be directly observed. Other
environmental goods and services (e.g., recreation and support of aquatic life) cannot be bought or sold
directly and thus do not have observable market values. This second type of environmental goods and
services are classified as "nonmarket/' The estimated changes in the nonmarket values of the water resources
affected by the regulatory options (hereafter nonmarket benefits) are additive to market values (e.g., avoided
costs of producing various market goods and services).
The analysis of the nonmarket value of water quality changes resulting from the regulatory options follows
the same approach EPA used in the analysis of the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b). This
approach, which is briefly summarized below, involves:
• characterizing the change in water quality under the regulatory options relative to the baseline using a
WQI and linking these changes to ecosystem services or potential uses that are valued by society (see
Section 3.4.2),
• monetizing changes in the nonmarket value of affected water resources under the regulatory options
using a meta-analysis of surface water valuation studies that provide data on the public's WTP for
water quality changes (see Section 6.1).
The analysis accounts for improvements in water quality resulting from changes in nutrient, sediment, and
toxics concentrations in reaches potentially affected by bottom ash transport water and FGD wastewater
discharges. The assessment uses the CBG as the geographic unit of analysis, assigning a radial distance of
100 miles from the CBG centroid. EPA estimates that households residing in a given CBG value water quality
changes in all modeled reaches within this range, with all unaffected reaches being viable substitutes for
affected reaches within the area around the CBG. Appendix E in U.S. EPA (2020b) describes EPA's
approach.
6.1 Estimated Total WTP for Water Quality Changes
EPA estimated economic values of water quality changes at the CBG level using results of a meta-analysis of
189 estimates of total WTP (including both use and nonuse values) for water quality improvements, provided
by 59 original studies conducted between 1981 and 2017.86 The estimated econometric model allows
calculation of total WTP for changes in a variety of environmental services affected by water quality and
valued by humans, including changes in recreational fishing opportunities, other water-based recreation, and
existence services such as aquatic life, wildlife, and habitat designated uses. The model also allows EPA to
adjust WTP values based on the core geospatial factors predicted by theory to influence WTP, including:
Although the potential limitations and challenges of benefit transfer are well established (Desvousges et al., 1987), benefit
transfers are a nearly universal component of benefit cost analyses conducted by and for government agencies. As noted by Smith
et al. (2002, p. 134), "nearly all benefit cost analyses rely on benefit transfers, whether they acknowledge it or not."
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scale (the size of affected resources or areas), market extent (the size of the market area over which WTP is
estimated), and the availability of substitutes. The meta-analysis regression is based on two models: Model 1
provides EPA's main estimate of non-market benefits, and Model 2 is used in a sensitivity analysis to develop
a range of estimates that account for uncertainty in the estimated WTP values (see Section 6.2 for Model 2
results). Appendix H provides details on how EPA used the meta-analysis to predict household WTP for each
CBG and year as well as the estimated regression equation, intercept and variable coefficients for the two
models used in this analysis. The appendix also provides names and definitions of the independent variable
and assigned values.
Based on the meta-analysis results, EPA multiplied the coefficient estimates for each variable (see Model 1
and Model 2 in Table G-3) by the variable levels calculated for each CBG or fixed at the levels indicated in
the "Assigned Value" column in Table G-3. The sum of these products represents the predicted natural log of
the WTP for a one-point improvement on the WQI (In OWTP) for a representative household in each CBG.
Equation 6-1 provides the equation used to calculate household benefits for each CBG.
Equation 6-1. HWTPYB = OWTPYiB x AWQ1B
where:
HWTPy,b = Annual household WTP in 2021$ in year 7 for households located in
the CBG (5),
OWTPy.b = WTP for a one-point improvement on the WQI for a given year (7)
and the CBG (B), estimated by the meta-analysis function and
evaluated at the midpoint of the range over which water quality is
changed,
AWQIb = Estimated annual average water quality change for the CBG (B).
To estimate WTP for water quality improvements under the regulatory options, EPA first estimated water
quality improvements for each year within Period 1 and Period 2 (see Section 3.2.1 for details) and then
applied the meta-regression model (MRM) to estimate per household WTP for water quality improvements in
a given year. Monetary values of water quality improvements are estimated for all years from 2025 through
2049. As summarized in Table 6-1, average annual household WTP estimates for the regulatory options,
based on the main estimates from Model 1, range from $0.05 under Options 1 and 2 to $0.06 under Options 3
and 4.
To estimate total WTP (TWTP) for water quality changes for each CBG, EPA multiplied the per-household
WTP values for the estimated water quality change by the number of households within each CBG in a given
year and calculated the present value (PV) of the stream of WTP over the 25 years in EPA's period of
analysis. EPA then calculated annualized total WTP values for each CBG using 3 percent and 7 percent
discount rates as shown in Equation 6-2.
Equation 6-2.
(2049 \
Y HWTPv b X HHYiB\ / ix(l + Q" \
t=45 d + i)1'-2024' ) l(i + 0""-iJ
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where:
TWTPe
HWTP
HHy,b
T
i
Y,B
Annualized total household WTP in 2021$ for households located in
the CBG (B),
Annual household WTP in 2021$ for households located in the CBG
(B) in year (Y),
the number of households residing in the CBG (B) in year (Y),
Year when benefits are realized
Discount rate (3 or 7 percent)
Duration of the analysis (25 years)87
EPA generated annual household counts for each CBG through the period of analysis based on projected
population growth following the method described in Section 1.3.6. Table 6-1 presents the main analysis
results, based on Model 1 and using 3 percent and 7 percent discount rates. The total annualized values of
water quality changes resulting from changes in toxics, nutrient and sediment discharges in these reaches
range from $2.6 million under Option 1 (7 percent discount rate) to $4.3 million under Option 4 (3 percent
discount rate).
Table 6-1: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality
Improvements under the Regulatory Options, Compared to Baseline (Main Estimates)
Regulatory Option
Number of Affected
Households (Millions)3
Average Annual WTP
Per Household
(2021$)b
Total Annualized WTP (Millions 2021$)b
3% Discount Rate
7% Discount Rate
Option 1
76.2
$0.05
$3.02
$2.64
Option 2
80.6
$0.05
$3.82
$3.32
Option 3
82.1
$0.06
$4.09
$3.56
Option 4
82.1
$0.06
$4.27
$3.73
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
Source: U.S. EPA Analysis, 2022
6.2 Sensitivity Analysis
Table 6-2 presents sensitivity analysis results produced from Model 2, including average annual household
WTP and total annualized values, for water quality improvements resulting from all regulatory options.
Average annual household WTP estimates for the regulatory options range from $0.05 under Option 1 (low
estimate) to $0.13 under Options 2, 3, and 4 (high estimate). Total annualized values range from $3.0 million
under Option 1 (low estimate, 7 percent discount rate) to $9.9 million under Option 4 (high estimate,
3 percent discount rate). The main estimates presented in Table 6-1 are closer to the low end of the sensitivity
analysis range.
87 See Section 1.3.3 for details on the period of analysis.
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Table 6-2: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Changes
under the Regulatory Options, Compared to Baseline (Sensitivity Analysis)
Regulatory Option
Number of Affected
Households
(Millions)"
Average Annual WTP
Per Household
(2021$)b
Total Annualized WTP (Millions 2021$)b
3% Discount Ratea,b
7% Discount Rate3
Low
High
Low
High
Low
High
Option 1
76.2
$0.05
$0.11
$3.50
$7.17
$3.00
$6.14
Option 2
80.6
$0.06
$0.13
$4.35
$8.92
$3.72
$7.63
Option 3
82.1
$0.06
$0.13
$4.64
$9.50
$3.97
$8.13
Option 4
82.1
$0.07
$0.13
$4.83
$9.88
$4.14
$8.48
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 2, which provides a range of estimates that account for uncertainty in the WTP estimates as a
sensitivity analysis. For the AWQI variable setting in Model 2-based sensitivity analysis, EPA used values of 20 units to develop low
estimates and 7 units to develop high estimates (see Appendix 0 for details).
Source: U.S. EPA Analysis, 2022
6.3 Limitations and Uncertainties
Table 6-3 summarizes the limitations and uncertainties in the analysis of benefits associated with changes in
surface water quality and indicates the direction of any potential bias.
Separate from this rule, EPA and the Department of the Army recently announced plans to refine methods
used to estimate wetlands benefits. The plans include peer review of how meta-analyses are applied to
estimate benefits from wetlands preservation and developing a standardized approach that increases the
reliability and transparency of the estimation methods. Specifically, the agencies stated:
"Outside of this rulemaking, the agencies plan to further refine aspects of their approach to valuing
benefits associated with preserving wetlands, including incorporating ecosystem service effects. The
agencies plan to undertake peer review on aspects of their approach including examination of
influential variables and the agencies" application of the meta-analysis." (U.S. Environmental
Protection Agency and Department of the Army, 2022)
EPA's benefits valuation for CWA regulations to date has not considered the combined effects on rivers,
streams, lakes reservoirs, wetlands, and other relevant water bodies, including interactions among quality in
these waters. Outside of this rulemaking, it is EPA's intention to explore such methodologies so more
integrated analyses of ecosystem services may be possible in the future, and EPA will follow its standards for
appropriate peer review of such future methodological updates on valuing water quality, including surface
water quality.
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Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Use of 100-mile buffer
for calculating water
quality benefits for each
CBG
Underestimate
The distance between the surveyed households and the affected
waterbodies is not well measured by any of the explanatory variables
in the MRM. EPA would expect values for water quality changes to
diminish with distance (all else equal) between the home and affected
waterbody. The choice of 100 miles is based on typical driving distance
to recreational sites (i.e., 2 hours or 100 miles). Therefore, EPA used
100 miles to approximate the distance decay effect on WTP values.
The analysis effectively assumes that people living farther than 100
miles place no value on water quality improvements for these
waterbodies despite literature that shows that while WTP tends to
decline with distance from the waterbody, people place value on the
quality of waters outside their region.
Selection of the
lnquality_ch variable
value in Model 2 for
estimating a range of
WTP values (sensitivity
analysis)
Uncertain
The value of an additional one-point improvement in WQI is expected
to decline as the magnitude of the water quality change increases. To
account for variability in WTP due to the magnitude of the valued
water quality changes, EPA estimated a range of WTP values for a one-
point improvement on the WQI using alternative settings for
lnquality_ch (AWQI= 20 and 7 units, respectively). These values were
based on the 25th and 75th percentile of water quality changes
included in the meta-data. To ensure that the benefit transfer function
satisfies the adding-up condition, this variable is treated as a
methodological (fixed) variable. The negative coefficient for
lnquality_ch implies that larger value settings produce smaller WTP
estimates for a one-point improvement, which is consistent with
economic theory; smaller value settings produce larger WTP estimates
for a one-point improvement. The selected values may bias the
estimated WTP values either upward or downward.
Potential hypothetical
bias in underlying stated
preference results
Uncertain
Following standard benefit transfer approaches, this analysis proceeds
under the assumption that each source study provides a valid,
unbiased estimate of the welfare measure under consideration (cf.
Moeltner et al., 2007; Rosenberger and Phipps, 2007). To minimize
potential hypothetical bias underlying stated preference studies
included in meta-data, EPA set independent variable values to reflect
best benefit transfer practices.
Use of different water
quality measures in the
underlying meta-data
Uncertain
The estimation of WTP may be sensitive to differences in the
presentation of water quality changes across studies in the meta-data.
Studies that did not use the WQI were mapped to the WQI, so a
comparison could be made across studies. To account for potential
effects of the use of a different water quality metric (i.e., index of
biotic integrity (IBI)) on WTP values for a one-point improvement on
the WQI, EPA used a dummy variable in the MRM (see Appendix 0 for
details). In benefit transfer applications, the IBI variable is set to zero,
which is consistent with using the WQI.
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Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Transfer error
Uncertain
Transfer error may occur when benefit estimates from a study site are
adopted to forecast the benefits of a policy site. Rosenberger and
Stanley (2006) define transfer error as the difference between the
transferred and actual, generally unknown, value. Although meta-
analyses are often more accurate compared to other types of transfer
approaches due to the data synthesis from multiple source studies
(Rosenberger and Phipps, 2007; Johnston et al., 2021), there is still a
potential for transfer errors (Shrestha et al., 2007) and no transfer
method is always superior (Johnston et al., 2021).
Omission of Great Lakes
and estuaries from
analysis of benefits from
water quality changes
Underestimate
Five out of 92 (5 percent) steam electric power plants discharge to the
Great Lakes or estuaries. Due to limitations of the water quality
models used in the analysis of the regulatory options, these
waterbodies were excluded from the analysis. This omission likely
underestimates benefits of water quality changes from the regulatory
options.
The water quality model
accounts for only a
subset of sources of
toxic pollutants
contributing to baseline
concentrations
Uncertain
The overall impact of this limitation on the estimated WTP for water
quality changes is uncertain but is expected to be small since the
estimated WTP is a function of a mid-point between the baseline and
post-technology implementation water quality. Therefore, the
difference in WTP between the baseline and post-technology
implementation would be more sensitive to the estimated water
quality changes.
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7: Threatened & Endangered Species Benefits
7 Impacts and Benefits to Threatened and Endangered Species
7.1 Introduction
T&E species are species vulnerable to future extinction or at risk of extinction in the near future, respectively.
These designations reflect low or rapidly declining population levels, loss of essential habitat, or life history
stages that are particularly vulnerable to environmental alteration or other stressors. In many cases, T&E
species are given special protection due to inherent vulnerabilities to habitat modification, disturbance, or
other impacts of human activities. This chapter examines the projected change in environmental impacts of
steam electric power plant discharges on T&E species and the estimated benefits associated with the projected
changes resulting from the regulatory options.
As described in the EA (U.S. EPA, 2023a), the untreated chemical constituents of steam electric power plant
wastestreams can pose serious threats to ecological health due to the bioaccumulative nature of many
pollutants, high concentrations, and high loadings. Pollutants such as selenium, arsenic and mercury have
been associated with fish kills, disruption of growth and reproductive cycles and behavioral and physiological
alterations in aquatic organisms. Additionally, high nutrient loads can lead to the eutrophication of
waterbodies. Eutrophication can lead to increases in the occurrence and intensity of water column
phytoplankton, including harmful algal blooms (e.g., nuisance and/or toxic species), which have been found
to cause fatal poisoning in other animals, fish, and birds. Eutrophication may also result in the loss of critical
submerged rooted aquatic plants (or macrophytes), and reduced DO levels, leading to anoxic or hypoxic
waters.
For species vulnerable to future extinction, even minor changes to growth and reproductive rates and small
levels of mortality may represent a substantial portion of annual population growth. To quantify the estimated
effects of the regulatory options compared to baseline, EPA conducted a screening analysis using changes in
projected attainment of freshwater NRWQC as an indicator. Specifically, EPA identified the reaches that are
projected to see changes in achievement of freshwater aquatic life NRWQC as a consequence of the
regulatory options, assuming no more stringent controls are established to meet applicable water quality
standards (i.e., water-quality-based effluent limits issued under Section 301(b)(1)(C)), relative to the baseline.
Using these projections, EPA then estimated the number of T&E species whose recovery could be affected
based on the species" habitat range. Because NRWQC are recommended at levels to protect aquatic
organisms, reducing the frequency at which aquatic life-based NRWQC are exceeded could translate into
reduced risk to T&E species and potential improvements in species populations.88.
In this chapter, EPA examines the current conservation status of species belonging to freshwater taxa and
identifies the extent to which the regulatory options, independent of consideration of water quality-based
controls, may benefit or adversely impact T&E species. Specifically, EPA estimated the changes in potential
impacts of steam electric power plant discharges on surface waters intersecting habitat ranges of T&E species,
to provide a quantitative, but unmonetized proxy for the benefits associated with the regulatory options.
Criteria are developed based on the 1985 Guidelines methods (U.S. EPA, 1985) and generally reflect high quality toxicity data
from at least eight different taxa groups that broadly represent aquatic organisms. To the extent that more stringent levels are
required to protect organisms in a particular location, that is addressed during the water quality standard development process for
that location.
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The analysis generally follows the approach EPA used for the analyses of the 2015 and 2020 rules (U.S. EPA,
2015a, 2020b), including updates EPA made to the methodology, assumptions, and inputs as part of the 2020
rule analysis.
7.2 Baseline Status of Freshwater Fish Species
Reviews of aquatic species' conservation status over the past three decades have documented the effect of
cumulative stressors on freshwater aquatic ecosystems, resulting in a significant decline in the biodiversity
and condition of indigenous communities (Deacon etal., 1979; J. E. Williams et al.. 1989; J. D. Williams et
al., 1993; Taylor et al., 1996; Taylor et al., 2007; Jelks et al., 2008). Overall, aquatic species may be
disproportionately imperiled relative to terrestrial species. For example, while 39 percent of freshwater and
diadromous fish species are imperiled (Jelks et al., 2008), a similar status review found that only 7 percent of
North American bird and mammal species are imperiled (Wilcove & Master, 2005). Recent studies of threats
and extinction trends in freshwater taxa also concluded that biodiversity is much more at risk in freshwater
compared to marine ecosystems (Winemiller, 2018).
Approximately 39 percent of described fish species in North America are imperiled, with 700 fish taxa
classified as vulnerable (230), threatened (190), or endangered (280) in addition to 61 taxa presumed extinct
or functionally extirpated from nature (Jelks et al., 2008). These data show that the number of T&E species
have increased by 98 percent and 179 percent when compared to similar reviews conducted by the American
Fisheries Society in 1989 (J. E. Williams etal., 1989) and 1979 (Deacon etal., 1979), respectively. Despite
recent conservation efforts, including the listing of several species under the Endangered Species Act (ESA),
only 6 percent of the fish taxa assessed in 2008 had improved in status since the 1989 inventory (Jelks et al.,
2008).
Several families of fish have high proportions of T&E species. Approximately 46 percent and 44 percent of
species within families Cyprinidae (carps and true minnows) and Percidae (darters and perches) are imperiled,
respectively. Some families with few, wide-ranging species have even higher rates of imperilment, including
the Acipenseridae (sturgeons; 88 percent) and Polyodontidae (paddlefish; 100 percent). Families with species
important to sport and commercial fisheries have imperilment levels ranging from a low of 22 percent for
Centrarchidae (sunfishes) to a high of 61 percent for Salmonidae (salmon) (Jelks et al., 2008).
7.3 T&E Species Potentially Affected by the Regulatory Options
To assess the potential effects of the regulatory options on T&E species, EPA used the U.S. FWS
Environmental Conservation Online System (ECOS) to construct a database to analyze which species have
habitats that overlap with waters projected to improve or degrade due to changes in pollutant discharge from
steam electric power plants. The database includes all animal species currently listed or proposed for listing
under the ESA (U.S. FWS, 2020d).
7.3.1 Identifying T&E Species Potentially Affected by the Regulatory Options
To estimate the effects of the regulatory options on T&E species, EPA first compiled data on habitat ranges
for all species currently listed or under consideration for listing under the ESA. EPA obtained the
geographical distribution of T&E species in geographic information system (GIS) format from ECOS (U.S.
FWS, 2020b).
EPA constructed a screening database using the spatial data on species habitat ranges and all NHD reaches
downstream from steam electric power plants. This database included all T&E species whose habitat ranges
intersect reaches immediately receiving or downstream of steam electric power plant discharges. EPA used a
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200-meter buffer on either side of each reach when estimating the intersection to account for waterbody
widths and any minor errors in habitat maps. This initial analysis identified a total of 199 T&E species.
EPA then classified these species on the basis of their vulnerability to changes in water quality for the purpose
of assessing potential impacts of the regulatory options. EPA obtained species life history data from a wide
variety of sources to assess T&E species" vulnerability to water pollution. For the purpose of this analysis,
species were classified as follows:
• Higher vulnerability - species living in aquatic habitats for several life history stages and/or species
that obtain a majority of their food from aquatic sources.
• Moderate vulnerability - species living in aquatic habitats for one life history stage and/or species that
obtain some of their food from aquatic sources.
• Lower vulnerability - species whose habitats overlap bodies of water, but whose life history traits and
food sources are terrestrial.
Table 7-1 summarize the results of this assessment. Appendix 0 lists all T&E species whose habitat ranges
intersect reaches immediately receiving or downstream of steam electric power plant discharges.
Table 7-1: Number of T&E Species with Habitat Range Intersecting Reaches Immediately Receiving
or Downstream of Steam Electric Power Plant Discharges, by Group
Species Group
Species Vulnerability
Lower
Moderate
Higher
Species Count
Amphibians
3
2
3
8
Arachnids
6
0
0
6
Birds
20
5
1
26
Clams
0
0
63
63
Crustaceans
0
2
3
5
Fishes
0
0
35
35
Insects
10
0
0
10
Mammals
15
1
1
16
Reptiles
15
1
3
19
Snails
2
0
9
11
Total
70
11
118
199
Source: U.S. EPA Analysis, 2020.
To estimate the potential impacts of the regulatory options, EPA focused the analysis on species with higher
vulnerability potentials based upon life history traits. EPA's further review of this subset of species resulted in
the removal from further analysis of those species endemic to isolated headwaters and natural springs, as
these waters are unlikely to receive steam electric power plant discharges in the scope of the proposed rule
(see Appendix 0 for details). Review of life history data for the remaining species shows pollution or water
quality issues as one of the factors influencing species decline. This suggests that water quality issues may be
important to species recovery even if not listed explicitly in species recovery plans.
7.3.2 Estimating Effects of the Rule on T&E Species
EPA used the results of the water quality model described in Chapter 3 to flag those reaches where estimated
pollutant concentrations exceed the freshwater NRWQC under the baseline or the regulatory options (see
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Section 3.4.1.1). EPA estimated exceedances for two distinct periods (2025-2029 and 2030-2049) within the
overall analysis period (2025-2049). As described in Section 3.2.1, Period 1 corresponds to the years when
the steam electric power plants would be transitioning to treatment technologies to comply with the revised
limits, whereas Period 2 reflects post-technology implementation conditions when all plants meet applicable
revised limits.
EPA then linked the water quality model outputs with the species database described in the section above to
identify potentially "affected T&E species habitats" where the reaches intersecting the habitat range of a T&E
species do not meet the NRWQC under baseline conditions but do meet the NRWQC under one or more of
the regulatory options (i.e.. potential positive benefits). EPA compared dissolved concentration estimates for
eight pollutants to the freshwater acute and chronic NRWQC values89 to assess the exceedance status of the
reaches under the baseline and each regulatory option. The first condition occurs in a subset of reaches during
Period 1, whereas the second condition is met for a subset of reaches during Period 2.
EPA's analysis indicates that thirty-six reaches intersecting habitat ranges of twenty-eight T&E species
exceed NRWQC under the baseline conditions in Period 1 and thirty-four reaches intersecting habitat ranges
of twenty-three T&E species exceed NRWQC under the baseline conditions in Period 2. In Period 1 (2025-
2029), no baseline exceedances are eliminated under Options 1 and 2, whereas under Options 3 and 4
exceedances are eliminated in three reaches, potentially benefitting five T&E fish species (Canada lynx (T),
Colorado pikeminnow (E), Razorback sucker (E), Southwestern willow flycatcher (E), and Yellow-billed
cuckoo (T)). In Period 2 (2030-2049), NRWQC exceedances are eliminated or reduced in five reaches,
potentially benefitting three species (Northern Long-Eared Bat (T), Piping Plover (E), and Topeka Shiner
(E)). Table 7-2 provides additional detail on the number of exceedances potentially affecting T&E species
vulnerable to discharges from steam electric power plants.
89 The eight pollutants are arsenic, cadmium, copper, lead, mercury, nickel, selenium, and zinc. For more information about the
aquatic life NRWQC, see Table C-7 in the SupplementalEA (U.S. EPA, 2020f).
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7: Threatened & Endangered Species Benefits
Table 7-2: Higher Vulnerability T&E Species Whose Habitat May be Affected by the Regulatory
Options Compared to Baseline
Species Name
State
Number of Reaches with NRWQC
Exceedances for at Least One Pollutant
ra
CO
Q.
O
fM
£
o
'+¦»
Q.
O
CO
c
o
'+¦»
Q.
O
c
o
'+-»
Q.
O
Period 1 (2025-2029)
Clubshell
Kentucky
1
1
1
1
1
Colorado pikeminnow
New Mexico
2
2
2
0
0
Fanshell
Kentucky/West Virginina
18
18
18
18
18
Orangefoot pimpleback (pearlymussel)
Kentucky
1
1
1
1
1
Pink mucket (pearlymussel)
Kentucky/Ohio/West Virginia
19
19
19
19
19
Razorback sucker
New Mexico
2
2
2
0
0
Ring pink (mussel)
Kentucky
1
1
1
1
1
Rough pigtoe
Kentucky
1
1
1
1
1
Sheepnose Mussel
West Virginia/Ohio
18
18
18
18
18
Snuffbox mussel
West Virginia
17
17
17
17
17
Spectaclecase (mussel)
West Virginia
17
17
17
17
17
Topeka shiner
Kansas
7
7
7
7
7
West Indian Manatee
Florida
5
5
5
5
5
Period 2 (2030-2049)
Clubshell
Kentucky
1
1
1
1
1
Fanshell
Kentucky/West Virginina
18
18
18
18
18
Orangefoot pimpleback (pearlymussel)
Kentucky
1
1
1
1
1
Pink mucket (pearlymussel)
Kentucky/Ohio/West Virginia
19
19
19
19
19
Ring pink (mussel)
Kentucky
1
1
1
1
1
Rough pigtoe
Kentucky
1
1
1
1
1
Sheepnose Mussel
West Virginia/Ohio
18
18
18
18
18
Snuffbox mussel
West Virginia
17
17
17
17
17
Spectaclecase (mussel)
West Virginia
17
17
17
17
17
Topeka shiner
Kansas
7
7
2
2
2
West Indian Manatee
Florida
5
5
5
5
5
Source: U.S. EPA Analysis, 2022
7.4 Limitations and Uncertainties
One limitation of EPA's analysis of the regulatory options" impacts on T&E species and their habitat is the
lack of data necessary to quantitively estimate population changes of T&E species and to monetize these
effects. The data required to estimate the response of T&E species populations to improved habitats are rarely
available. In addition, understanding the contribution of T&E species to ecosystem functions can be
challenging because: (1) it is often difficult to detect the location of T&E species, (2) experimental studies
including rare or threatened species are limited; and (3) ecologists studying relationships between biodiversity
and ecosystem functions typically focus on overall species diversity or estimate species contribution to
ecosystem functions based on abundance (Dee etal., 2019). Finally, much of the wildlife economic literature
focuses on recreational benefits that are not relevant for many protected species {i.e., use values) and the
existing T&E valuation studies tend to focus on species that many people consider to be "charismatic" (e.g.,
spotted owl, salmon) (L. Richardson & Loomis, 2009). Although a relatively large number of economic
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7: Threatened & Endangered Species Benefits
studies have estimated WTP for T&E protection, these studies focused on estimating WTP to avoid species
loss/extinction, reintroduction, increase in the probability of survival, or a substantial increase in species
population (Subroy etal., 2019; L. Richardson & Loomis, 2009). In addition, use of the MRMs developed by
Subroy et al. (2019) and L. Richardson and Loomis (2009) is not feasible for this analysis due to the
challenges associated with estimating T&E population changes from the proposed rule. Table 7-3 summarizes
limitations and uncertainties known to affect EPA's assessment of the impacts of the proposed rule on T&E
species. Note that the effect on benefits estimates indicated in the second column of the table refers to the
magnitude of the benefits rather than the direction (/'. e., a source of uncertainty that tends to underestimate
benefits indicates expectation for larger forgone benefits or for larger realized benefits).
Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The analysis does not
account for water quality
based effluent limits
Overestimate
This screening analysis is intended to isolate possible effects of
the regulatory options on T&E species, however, it does not
take into account the fact that the NPDES permits for each
steam electric power plant, like all NPDES permits, are required
to have limits more stringent than the technology-based limits
established by an ELG wherever necessary to protect water
quality standards. Because this analysis does not project where
a permit will have more stringent limits than those required by
the ELG, it may overestimate any negative impacts to T&E
species, including impacts that will not be realized because the
permits will be written to include limits as stringent as
necessary to meet water quality standards as required by the
CWA.
Intersection of T&E species
habitat with reaches affected
by steam electric plant
discharges is used as proxy
for exposure to steam
electric pollutants
Overestimate
EPA used the habitat range as the basis for assessing the
potential for impacts to the species from water quality
changes. This approach is reasonable given the lack of reach-
specific population data to support a national-level analysis,
but the Agency acknowledges that the habitat range of a
species does not necessarily indicate that the species is found
in individual reaches within the habitat range.
The change in T&E species
populations due to the effect
of the regulatory options is
uncertain
Uncertain
Data necessary to quantitatively estimate population changes
are unavailable. Therefore, EPA used the methodology
described in Section 7.3.1 as a screening-level analysis to
estimate whether the regulatory options could contribute to a
change in the recovery of T&E species populations.
Only those T&E species listed
as threatened or endangered
under the ESA are included
in the analysis
Underestimate
The databases used to conduct this analysis include only
species protected under the ESA. Additional species may be
considered threatened or endangered by scientific
organizations but are not protected by the ESA (e.g., the
American Fisheries Society [J. D. Williams et a!., 1993; Taylor et
al., 2007; Jelks et al., 2008]). The magnitude of the
underestimate is unknown. Although the proportion of
imperiled freshwater fish and mussel species is high (e.g., Jelks
et al., 2008; Taylor et al., 2007) the geographic distribution of
these species may or may not overlap with reaches affected by
steam electric discharges.
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Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The potential for impact to
T&E species is also present
for changes in pollutant
concentrations that don't
result in changes in NRWQC
exceedances
Underestimate
EPA's analysis quantifies changes in whether a NRWQC is
exceeded in a given reach that intersects T&E species habitat
ranges. However, changes in pollutant concentrations have the
potential to result in impacts to T&E species even where they
do not result in changes in NRWQC exceedance status. There
are also potential impacts to T&E species from changes in
pollutants for which freshwater NRWQC are not available (e.g.,
salinity).
EPA's water quality model
does not capture all sources
of pollutants with a potential
to impact aquatic T&E
species
Uncertain
EPA's water quality model focuses on toxic pollutant discharges
from steam electric power plants and certain other point
sources, but does not account for other pollution sources (e.g.,
historical contamination) or background levels. Adding these
other sources or background levels could result in additional
NRWQC exceedances under the baseline and/or regulatory
options, but it is uncertain how the regulatory options would
change the exceedance status of the intersected reaches.
Additionally, the water quality model does not capture
synergistic relationships between pollutants, which may
exacerbate adverse effects on T&E species.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
8 Air Quality-Related Benefits
The regulatory options evaluated may affect air quality through three main mechanisms: 1) changes in energy
used by steam electric power plants to operate wastewater treatment, ash handling, and other systems needed
to meet the limitations and standards under the regulatory options; 2) transportation-related emissions due to
the changes in trucking of CCR and other waste to on-site or off-site landfills; and 3) changes in the
electricity generation profile from increases in wastewater treatment costs compared to the baseline and the
resulting changes in EGU relative operating costs.
EPA estimated the climate-related benefits of changes in CO2 emissions, as well as the human health benefits
resulting from changes in particulate matter and ozone ambient exposure due to net changes in emissions of
NOx, SO2, and directly emitted fine particulate matter (PM2.5), also referred to as primary PM2.5 emissions.
8.1 Changes in Air Emissions
With respect to the third mechanism mentioned in the introduction and as discussed in the RIA, EPA used the
Integrated Planning Model (IPM) to estimate the electricity market-level effects of the proposed rule (Option
3; see Chapter 5 in RIA [U.S. EPA, 2023c]). IPM projects generation from coal to decrease in all model years
as a result of the proposed rule. Over the period of analysis, the reductions are smallest in 2028 (1.2 thousand
GWh) and highest in 2045 (11.5 thousand GWh). These changes are offset in part by an increase in
generation from natural gas, nuclear, and renewables. See details in Chapter 5 of the RIA (U.S. EPA, 2023c).
The net effects of these changes in the generation mix are reductions in air emissions that reflect differences
in EGU emissions rates for these other fuels or sources of energy, as compared to coal.
IPM outputs include estimated C02,N0x, and SO2 emissions to air from EGUs.9" EPA also used IPM outputs
to estimate EGU emissions of primary PM2.5 based on emission factors described in U.S. EPA (2020c).
Specifically, EPA estimated primary PM2.5 emissions by multiplying the generation predicted for each IPM
plant type (ultrasupercritical coal without carbon capture and storage, combined cycle, combustion turbine,
etc.) by a type-specific empirical emission factor derived from the 2016 National Emissions Inventory (NEI)
and other data sources. The emission factors reflect the fuel type (including coal rank), FGD controls, and
state emission limits for each plant type, where applicable.
Comparing emissions projected under Option 3 to those projected for the baseline provides an assessment of
the changes in air emissions resulting from changes in the profile of electricity generation under the proposed
rule.91 EPA used six of the seven IPM run years, shown in Table 8-1, to represent the period of analysis. IPM
provides outputs starting in 2028 and EPA therefore estimated no changes in air emissions from changes in
electricity generation in 2025 through 2027. The last run year (2055) falls outside of the analysis period of
2025-2049 and EPA does not include results for that year when estimating benefits.
90 EPA also estimated Hg, HC1 and PM10 emissions but does not use these estimates for the benefits analysis.
91 While EPA only ran IPM for the proposed rule (Option 3), the Agency extrapolated the benefits estimated using these IPM
outputs to options 1, 2, and 4 to provide insight on the potential air quality-related effects of the other regulatory options. See
Section 8.4 for details.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-1: IPM Run Years
IPM Run Year
Years Represented
2028
2028
2030
2029-2031
2035
2032-2037
2040
2038-2042
2045
2043-2047
2050
2048-2052
2055
2053-2059
Source: U.S. EPA, 2018b
As part of its analysis of non-water quality environmental impacts, EPA developed separate estimates of
changes in energy requirements for operating wastewater treatment and ash handling systems, and changes in
transportation needed to landfill solid waste and CCR (see TDD for details; U.S. EPA, 2023d). EPA estimated
NOx, SO2, and CO2 emissions associated with changes in energy requirements to power wastewater treatment
systems by multiplying plant-specific changes in electricity consumption by plant- or North American
Electric Reliability Corporation (NERC)-specific emission factors obtained from IPM for each run year. EPA
estimated air emissions associated with changes in transportation by multiplying the number of miles traveled
by average emission factors.
Table 8-2 and Table 8-3 respectively summarize the estimated changes in emissions associated with changes
in power requirements to operate treatment systems and with the incremental transportation of CCR and solid
waste under the regulatory options. For consistency, the tables present estimates for selected IPM model
years. EPA modeled emissions in each year based on when each plant is estimated to implement technologies
for each wastestream and any announced unit retirements. EPA estimates that changes in power requirements
and transportation will increase emissions slightly, relative to the baseline. The variations across regulatory
options reflect differences in treatment technologies and affected steam electric plants, whereas variations
across model years for a given regulatory option reflect the timing of technology implementation and
announced EGU retirements.92
Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at
Steam Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
Option 1
2028
0.016
0.012
0.013
Not estimated
2030
0.030
0.020
0.022
Not estimated
2035
0.030
0.020
0.022
Not estimated
2040
0.030
0.020
0.022
Not estimated
2045
0.030
0.020
0.022
Not estimated
2050
0.030
0.020
0.022
Not estimated
92 For the purpose of this analysis, EPA developed a time profile of air emissions changes based on plants' estimated technology
implementation years during the period of 2025 through 2029, as well as announced EGU retirements during the period of
analysis. For EGUs that retire during the analysis period, incremental power requirements and trucking associated with BA
transport water and FGD wastewater treatment cease, but those associated with CRL continue even after the unit retires.
8-2
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at
Steam Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
Option 2
2028
0.074
0.040
0.038
Not estimated
2030
0.12
0.064
0.060
Not estimated
2035
0.12
0.064
0.060
Not estimated
2040
0.12
0.064
0.060
Not estimated
2045
0.12
0.064
0.058
Not estimated
2050
0.12
0.064
0.058
Not estimated
Option 3 (Proposed Rule)
2028
0.083
0.046
0.048
Not estimated
2030
0.13
0.072
0.071
Not estimated
2035
0.13
0.072
0.070
Not estimated
2040
0.13
0.072
0.070
Not estimated
2045
0.13
0.071
0.067
Not estimated
2050
0.13
0.071
0.067
Not estimated
Option 4
2028
0.087
0.050
0.050
Not estimated
2030
0.14
0.078
0.075
Not estimated
2035
0.14
0.074
0.072
Not estimated
2040
0.13
0.073
0.072
Not estimated
2045
0.13
0.073
0.069
Not estimated
2050
0.13
0.073
0.069
Not estimated
a. Values rounded to two significant figures. Positive values indicate an increase in emissions.
Source: U.S. EPA Analysis, 2022
Table 8-3: Estimated Changes in Air Pollutant Emissions Due to Increase in Trucking at Steam
Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
Option 1
2028
0.000035
0.00010
0.00000012
Not estimated
2030
0.000090
0.00025
0.00000031
Not estimated
2035
0.000090
0.00025
0.00000031
Not estimated
2040
0.000090
0.00025
0.00000031
Not estimated
2045
0.000090
0.00025
0.00000031
Not estimated
2050
0.000090
0.00025
0.00000031
Not estimated
Option 2
2028
0.00012
0.00032
0.00000039
Not estimated
2030
0.00023
0.00065
0.00000079
Not estimated
2035
0.00023
0.00065
0.00000079
Not estimated
2040
0.00023
0.00065
0.00000079
Not estimated
2045
0.00023
0.00065
0.00000079
Not estimated
2050
0.00023
0.00065
0.00000079
Not estimated
8-3
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-3: Estimated Changes in Air Pollutant Emissions Due to Increase in Trucking at Steam
Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
Option 3 (Proposed Rule)
2028
0.0030
0.0067
0.000010
Not estimated
2030
0.0044
0.0099
0.000015
Not estimated
2035
0.0040
0.0091
0.000014
Not estimated
2040
0.0039
0.0088
0.000013
Not estimated
2045
0.0037
0.0085
0.000013
Not estimated
2050
0.0037
0.0085
0.000013
Not estimated
Option 4
2028
0.0035
0.0080
0.000012
Not estimated
2030
0.0054
0.012
0.000018
Not estimated
2035
0.0048
0.011
0.000017
Not estimated
2040
0.0046
0.011
0.000016
Not estimated
2045
0.0045
0.010
0.000015
Not estimated
2050
0.0045
0.010
0.000015
Not estimated
a. Values rounded to two significant figures. Positive values indicate an increase in emissions.
Source: U.S. EPA Analysis, 2022
Table 8-4 summarizes the estimated changes in pollutant emissions from electricity generation under the
proposed rule (i.e., Option 3).93 Projected changes in the profile of electricity generation under Option 3,
compared to the baseline, generally lead to national-level reductions in emissions for all air pollutants
modeled. The largest decline occurs in model year 2045, followed by 2035 (2050 for SO2). At the national
level, CO2 emissions decrease by 0.8 to 12 million tons, depending on the year, which is 0.1 to 1.1 percent of
corresponding baseline emissions. NOx emissions decrease by 1.9 to 7.6 thousand tons (0.6 to 2.4 percent);
SO2 emissions decrease by 1.0 to 9.3 thousand tons (0.2 to 3.9 percent); and primary PM2.5 decrease by 0.12 to
0.75 thousand tons (0.1 to 1.2 percent). The impact on emissions varies across regions and by pollutant with
emissions increasing in some and decreasing in other NERC regions, as detailed in the RIA (Table 5-4; U.S.
EPA, 2023c).
Table 8-4: Estimated Changes in Annual CO2, NOx, SO2, and Primary PM2.5 Emissions Due to
Changes in Electricity Generation Profile, Compared to Baseline
Regulatory
Option
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
2028
-0.83
-1.9
-1.0
-0.12
Option 3
(Proposed
Rule)
2030
-4.8
-3.4
-2.0
-0.20
2035
-11
-5.2
-5.9
-0.32
2040
-7.3
-3.8
-4.5
-0.19
2045
-12
-7.6
-9.3
-0.75
2050
-3.1
-2.1
-7.6
-0.13
a. Values rounded to two significant figures. Negative values indicate a reduction in emissions.
Source: U.S. EPA Analysis, 2022; See Chapter 5 in RIA for details on IPM (U.S. EPA, 2023c).
93 EPA did not run IPM for Options 1,2, and 4.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
A comparison of estimated changes in emissions across the three mechanisms (Table 8-2, Table 8-3 and Table
8-4) for the proposed rule (Option 3) shows that the largest effect on projected air emissions comes from the
change in the emissions profile of electricity generation at the market level. Table 8-5 presents the net
changes in emissions of the four pollutants compared to baseline. The next two sections quantify the climate
change and human health benefits associated with changes in emissions under the proposed rule (Option 3).
Table 8-5: Estimated Net Changes in Air Pollutant Emissions Due to Changes in Power
Requirements, Trucking, and Electricity Generation Profile, Compared to Baseline
Regulatory
Option
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
2028
-0.75
-1.9
-1.0
-0.12
Option 3
(Proposed
Rule)
2030
-4.7
-3.3
-2.0
-0.20
2035
-11
-5.1
-5.8
-0.32
2040
-7.2
-3.7
-4.4
-0.19
2045
-12
-7.5
-9.3
-0.75
2050
-3.0
-2.0
-7.6
-0.13
a. Values rounded to two significant figures. Negative values indicate a net reduction in emissions.
Source: U.S. EPA Analysis, 2022
8.2 Climate Change Benefits
8.2.1 Data and Methodology
EPA estimated the climate benefits of the net CO2 emission changes expected from this proposed rule using
the estimates of the social cost of greenhouse gases (SC-GHG)94, specifically using the social cost of carbon
(SC-CO2). The SC-CO2 is the monetary value of the net harm to society associated with a marginal increase
in CO2 emissions in a given year, or the benefit of avoiding that increase. In principle, the SC-CO2 includes
the value of all climate change impacts (both negative and positive), including (but not limited to) changes in
net agricultural productivity, human health effects, property damage from increased flood risk and natural
disasters, disruption of energy systems, risk of conflict, environmental migration, and the value of ecosystem
services. The SC-CO2 therefore reflects the societal value of reducing emissions of the gas in question 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 naturally restrain the ability of SC- CO2
estimates to include all the important physical, ecological, and economic impacts of climate change, such that
the estimates are a partial accounting of climate change impacts and will therefore, tend to be underestimates
of the marginal benefits of abatement. The EPA and other Federal agencies began regularly incorporating SC-
CO2 estimates in their benefit-cost analyses conducted under Executive Order (EO) 1286695 since 2008,
94 Estimates of the social cost of greenhouse gases are gas specific (e.g., social cost of carbon (SC-CO2), social cost of methane
(SC-CH4), social cost of nitrous oxide (SC-N2O)), but collectively they are referenced as the social cost of greenhouse gases (SC-
GHG).
95 Presidents since the 1970s have issued executive orders requiring agencies to conduct analysis of the economic consequences of
regulations as part of the rulemaking development process. EO 12866, released in 1993 and still in effect today, requires that for
all economically significant regulatory actions, an agency provide an assessment of the potential costs and benefits of the
regulatory action, and that this assessment include a quantification of benefits and costs to the extent feasible. For purposes of
this action, monetized climate benefits are presented for purposes of providing a complete benefit-cost analysis under EO 12866
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
following a Ninth Circuit Court of Appeals remand of a rule for failing to monetize the benefits of reducing
CO2 emissions in that rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a report that provides a
roadmap for how to update SC-GHG estimates used in Federal analyses going forward to ensure that they
reflect advances in the scientific literature (National Academies, 2017). The National Academies' report
recommended specific criteria for future SC-GHG updates, 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. The research community has made considerable progress in developing new data and
methods that help to advance various components of the SC-GHG estimation process in response to the
National Academies' recommendations.
In a first-day executive order (EO 13990), Protecting Public Health and the Environment and Restoring
Science to Tackle the Climate Crisis, President Biden called for a renewed focus on updating estimates of the
social cost of greenhouse gases (SC-GHG) to reflect the latest science, noting that "it is essential that agencies
capture the full benefits of reducing greenhouse gas emissions as accurately as possible." Important steps
have been taken to begin to fulfill this directive of EO 13990. In February 2021, the IWG released a technical
support document (hereinafter the "February 2021 TSD") that provided a set of IWG recommended SC-GHG
estimates while work on a more comprehensive update is underway to reflect recent scientific advances
relevant to SC-GHG estimation (IWG, 2021). In addition, as discussed further below, EPA has developed a
draft updated SC-GHG methodology within a sensitivity analysis in the regulatory impact analysis of EPA's
November 2022 supplemental proposal for oil and gas standards that is currently undergoing external peer
review and a public comment process.96
The EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's regulatory
benefit-cost analyses published since the release of the February 2021 TSD and is likewise using them in this
BCA. EPA evaluated the SC-GHG estimates in the February 2021 TSD and determined that these estimates
are appropriate for use in estimating the social benefits of GHG reductions expected to occur as a result of the
final rule and alternative standards. These SC-GHG estimates are interim values developed for use in benefit-
cost analyses until updated estimates of the impacts of climate change can be developed based on the best
available science and economics. After considering the TSD, and the issues and studies discussed therein,
EPA concludes that these estimates, while likely an underestimate, are the best currently available SC-CO2
estimates until revised estimates have been developed reflecting the latest, peer-reviewed science.
The SC-CO2 estimates presented in the February 2021 SC-GHG TSD were developed over many years, using
transparent process, peer-reviewed methodologies, the best science available at the time of that process, and
with input from the public. Specifically, in 2009, an IWG that included EPA and other executive branch
agencies and offices was established to ensure that agencies had access to the best available information when
quantifying the benefits of reducing CO2 emissions in benefit-cost analyses. The IWG published SC-CO2
estimates in 2010 that were developed from an ensemble of three widely cited integrated assessment models
(IAMs) that estimate climate damages using highly aggregated representations of climate processes and the
global economy combined into a single modeling framework. The three IAMs were run using a common set
and other relevant executive orders. The estimates of change in GHG emissions and the monetized benefits associated with those
changes play no part in the record basis for this action.
96 See https://www.epa.gov/environmental-economics/scghg
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of input assumptions in each model for future population, economic, and CO2 emissions growth, as well as
equilibrium climate sensitivity (ECS) — a measure of the globally averaged temperature response to
increased atmospheric CO2 concentrations. These estimates were updated in 2013 based on new versions of
each IAM.97 In August 2016 the IWG published estimates of the social cost of methane (SC-CH4) and nitrous
oxide (SC-N2O) using methodologies that are consistent with the methodology underlying the SC-CO2
estimates. In 2015, as part of the response to public comments received to a 2013 solicitation for comments
on the SC-CO2 estimates, the IWG announced a National Academies of Sciences, Engineering, and Medicine
review of the SC-CO2 estimates to offer advice on how to approach future updates to ensure that the estimates
continue to reflect the best available science and methodologies. In January 2017, the National Academies
released their final report, Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon
Dioxide, and recommended 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). Shortly thereafter, in
March 2017, President Trump issued EO 13783, which disbanded the IWG, withdrew the previous technical
support documents, and directed agencies to "ensure" SC-GHG estimates used in regulatory analyses "are
consistent with the guidance contained in OMB Circular A-4", "including with respect to the consideration of
domestic versus international impacts and the consideration of appropriate discount rates" (EO 13783, Section
5(c)). Benefit-cost analyses following EO 13783, including the benefit-cost analysis for the 2020 Steam
Electric Reconsideration Rule (U.S. EPA, 2020b), used SC-GHG estimates that attempted to focus on the
specific share of climate change damages in the U.S. as captured by the models (which did not reflect many
pathways by which climate impacts affect the welfare of U.S. citizens and residents) and were calculated
using two default discount rates recommended by Circular A-4 (OMB, 2003), 3 percent and 7 percent.98 All
other methodological decisions and model versions used in the SC-GHG calculations remained the same as
those used by the IWG in 2010 and 2013, respectively.
On January 20, 2021, President Biden issued EO 13990, which re-established an IWG and directed the group
to develop an update of the SC-GHG estimates that reflect the best available science and the
recommendations of National Academies (2017). In February 2021, the IWG recommended the interim use of
the most recent SC-GHG estimates developed by the IWG prior to the group being disbanded in 2017,
adjusted for inflation (IWG, 2021). As discussed in the February 2021 SC-GHG TSD, the IWG's selection of
these interim estimates reflected the immediate need to have SC-GHG estimates available for agencies to use
in regulatory benefit-cost analyses and other applications that were developed using a transparent process,
peer reviewed methodologies, and the science available at the time of that process. The February 2021 update
also recognized the limitations of the interim estimates and encouraged agencies to use their best judgment in,
for example, considering sensitivity analyses using lower discount rates. The IWG published a Federal
Register notice on May 7, 2021, soliciting comment on the February 2021 SC-GHG TSD and on how best to
97 Dynamic Integrated Climate and Economy (DICE) 2010 (Nordhaus, 2010), Climate Framework for Uncertainty, Negotiation,
and Distribution (FUND) 3.8 (Anthoff & Tol, 2013a, 2013b), and Policy Analysis of the Greenhouse Gas Effect (PAGE) 2009
(Hope, 2012).
98 EPA regulatory analyses under EO 13783 included sensitivity analyses based on global SC-GHG values and using a lower
discount rate of 2.5 percent. OMB Circular A-4 (OMB, 2003) recognizes that special considerations arise when applying discount
rates if intergenerational effects are important. In the IWG's 2015 Response to Comments, OMB—as a co-chair of the IWG—
made clear that "Circular A-4 is a living document," that "the use of 7 percent is not considered appropriate for intergenerational
discounting," and that "[t]here is wide support for this view in the academic literature, and it is recognized in Circular A-4 itself."
OMB, as part of the IWG, similarly repeatedly confirmed that "a focus on global SCC estimates in [regulatory impact analyses]
is appropriate" (IWG, 2015).
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incorporate the latest peer-reviewed scientific literature in order to develop an updated set of SC-GHG
estimates. The EPA has applied the IWG's interim SC-GHG estimates in regulatory analyses published since
the release of the February 2021 SC-GHG TSD, and is likewise using them in the benefit-cost analysis
calculations in this BCA.
As noted above, EPA participated in the IWG but has also independently evaluated the interim SC-CO2
estimates published in the February 2021 TSD and determined they are appropriate to use to estimate climate
benefits for this action. EPA and other agencies intend to undertake a fuller update of the SC- CO2 estimates
that takes into consideration the advice of the National Academies (2017) and other recent scientific literature.
EPA has also evaluated the supporting rationale of the February 2021 TSD, including the studies and
methodological issues discussed therein, and concludes that it agrees with the rationale for these estimates
presented in the TSD and summarized below. The February 2021 SC-GHG TSD provides a complete
discussion of the IWG's initial review conducted under EO 13990. In particular, the IWG found that the SC-
GHG estimates used under EO 13783 fail to reflect the full impact of GHG emissions in multiple ways. First,
the IWG concluded that those estimates fail to capture many climate impacts that can affect the welfare of
U.S. citizens and residents. Examples of affected interests include direct effects on U.S. citizens and assets
located abroad, international trade, and tourism, and spillover pathways such as economic and political
destabilization and global migration that can lead to adverse impacts on U.S. national security, public health,
and humanitarian concerns. Those impacts are better captured within global measures of the social cost of
greenhouse gases.
In addition, assessing the benefits of U.S. GHG mitigation activities requires consideration of how those
actions may affect mitigation activities by other countries, as those international mitigation actions will
provide a benefit to U.S. citizens and residents by mitigating climate impacts that affect U.S. citizens and
residents. A wide range of scientific and economic experts have emphasized the issue of reciprocity as
support for considering global damages of GHG emissions. Using a global estimate of damages in U.S.
analyses of regulatory actions allows the U.S. to continue to actively encourage other nations, including
emerging major economies, to take significant steps to reduce emissions. The only way to achieve an efficient
allocation of resources for emissions reduction on a global basis — and so benefit the U.S. and its citizens —
is for all countries to base their policies on global estimates of damages.
As a member of the IWG involved in the development of the February 2021 SC-GHG TSD, EPA agrees with
this assessment and, therefore, in this BCA EPA centers attention on a global measure of SC-CO2. This
approach is the same as that taken in EPA regulatory analyses over 2009 through 2016. A robust estimate of
climate damages only to U.S. citizens and residents that accounts for the myriad of ways that global climate
change reduces the net welfare of U.S. populations does not currently exist in the literature. As explained in
the February 2021 TSD, existing estimates are both incomplete and an underestimate of total damages that
accrue to the citizens and residents of the U.S. because they do not fully capture the regional interactions and
spillovers discussed above, nor do they include all of the important physical, ecological, and economic
impacts of climate change recognized in the climate change literature, as discussed further below. The EPA,
as a member of the IWG, will continue to review developments in the literature, including more robust
methodologies for estimating the magnitude of the various damages to U.S. populations from climate impacts
and reciprocal international mitigation activities, and explore ways to better inform the public of the full range
of carbon impacts.
Second, the IWG concluded that the use of the social rate of return on capital (7 percent under current OMB
Circular A-4 guidance) to discount the future benefits of reducing GHG emissions inappropriately
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underestimates the impacts of climate change for the purposes of estimating the SC-GHG. Consistent with the
findings of National Academies, 2017 and the economic literature, the IWG continued to conclude that the
consumption rate of interest is the theoretically appropriate discount rate in an intergenerational context
(IWG, 2010, 2013; 2016), and recommended that discount rate uncertainty and relevant aspects of
intergenerational ethical considerations be accounted for in selecting future discount rates." Furthermore, the
damage estimates developed for use in the SC-GHG are estimated in consumption-equivalent terms, and so an
application of OMB Circular A-4's guidance for regulatory analysis would then use the consumption discount
rate to calculate the SC-GHG. As a member of the IWG involved in the development of the February 2021
SC-GHG TSD, EPA agrees with this assessment and will continue to follow developments in the literature
pertaining to this issue. EPA also notes that while OMB Circular A-4, as published in 2003, recommends
using 3 percent and 7 percent discount rates as "default" values, Circular A-4 also reminds agencies that
"different regulations may call for different emphases in the analysis, depending on the nature and complexity
of the regulatory issues and the sensitivity of the benefit and cost estimates to the key assumptions." On
discounting, Circular A-4 recognizes that "special ethical considerations arise when comparing benefits and
costs across generations," and Circular A-4 acknowledges that analyses may appropriately "discount future
costs and consumption benefits... at a lower rate than for intragenerational analysis." In the 2015 Response to
Comments on the Social Cost of Carbon for Regulatory Impact Analysis, OMB, EPA, and the other IWG
members recognized that "Circular A-4 is a living document" and "the use of 7 percent is not considered
appropriate for intergenerational discounting. There is wide support for this view in the academic literature,
and it is recognized in Circular A-4 itself." Thus, EPA concludes that a 7 percent discount rate is not
appropriate to apply to value the social cost of greenhouse gases in the analysis presented in this analysis. In
this analysis, to calculate the present and annualized values of climate benefits, EPA uses the same discount
rate as the rate used to discount the value of damages from future GHG emissions, for internal consistency.
That approach to discounting follows the same approach that the February 2021 SC-GHG TSD recommends
"to ensure internal consistency—i.e., future damages from climate change using the SC-GHG at 2.5 percent
should be discounted to the base year of the analysis using the same 2.5 percent rate." EPA has also consulted
the National Academies' 2017 recommendations on how SC-GHG estimates can "be combined in RIAs with
other cost and benefits estimates that may use different discount rates." The National Academies reviewed
"several options," including "presenting all discount rate combinations of other costs and benefits with [SC-
GHG] estimates."
While the IWG works to assess how best to incorporate the latest, peer reviewed science to develop an
updated set of SC-GHG estimates, it recommends the interim estimates to be the most recent estimates
developed by the IWG prior to the group being disbanded in 2017. The estimates rely on the same models and
harmonized inputs and are calculated using a range of discount rates. As explained in the February 2021 SC-
GHG TSD, the IWG has concluded that it is appropriate for agencies to revert to the same set of four values
drawn from the SC-GHG distributions based on three discount rates as were used in regulatory analyses
between 2010 and 2016 and subject to public comment. For each discount rate, the IWG combined the
distributions across models and socioeconomic emissions scenarios (applying equal weight to each) and then
99 GHG emissions are stock pollutants, with damages associated with what has accumulated in the atmosphere over time, and they
are long lived such that subsequent damages resulting from emissions today occur over many decades or centuries depending on
the specific greenhouse gas under consideration. In calculating the SC-GHG, the stream of future damages to agriculture, human
health, and other market and non-market sectors from an additional unit of emissions are estimated in terms of reduced
consumption (or consumption equivalents). Then that stream of future damages is discounted to its present value in the year when
the additional unit of emissions was released. Given the long time horizon over which the damages are expected to occur, the
discount rate has a large influence on the present value of future damages.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
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selected a set of four values for use in benefit-cost analyses: an average value resulting from the model runs
for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a fourth value, selected as the 95th
percentile of estimates based on a 3 percent discount rate. The fourth value was included to provide
information on potentially higher-than-expected economic impacts from climate change, conditional on the
3 percent estimate of the discount rate. As explained in the February 2021 SC-GHG TSD, and EPA agrees,
this update reflects the immediate need to have an operational SC-GHG for use in regulatory benefit-cost
analyses and other applications that was developed using a transparent process, peer-reviewed methodologies,
and the science available at the time of that process. Those estimates were subject to public comment in the
context of dozens of proposed rulemakings as well as in a dedicated public comment period in 2013.
Table 8-6 presents the interim SC-CO2 estimates across all the model runs for each discount rate for emissions
occurring in 2025 to 2049. These estimates are reported in 2021 dollars but are otherwise identical to those
presented in the IWG's 2016 TSD (IWG, 2016). For purposes of capturing uncertainty around the SC-CO2
estimates in analyses, the IWG's February 2021 SC-GHG TSD emphasizes the importance of considering all
four of the SC-CO2 values. 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. EPA estimated the climate benefits of the net CO2 emission
reductions for each analysis year between 2025 and 2049 by applying the annual SC-CO2 estimates, shown in
Table 8-6, to the estimated changes in CO2 emissions in the corresponding year under the regulatory options.
EPA then calculated the present value and annualized value of climate benefits as of the expected rule
promulgation year of 2024 by discounting each year-specific value to the year 2024 using the same rate used
to calculate the corresponding SC-CO2.
Table 8-6: Interim Estimates of the Social Cost of Carbon, 2025 - 2049 (2021$/Metric Tonne CO2)
Discount Rate and Statistic
Year
5%
Average
3%
Average
2.5%
Average
3%
95th percentile
2025
$18
$59
$87
$177
2026
$18
$60
$88
$180
2027
$19
$61
$89
$184
2028
$19
$62
$91
$188
2029
$20
$63
$92
$191
2030
$20
$65
$93
$195
2031
$21
$66
$95
$199
2032
$21
$67
$96
$203
2033
$22
$68
$98
$207
2034
$23
$69
$99
$211
2035
$23
$70
$101
$215
2036
$24
$72
$102
$219
2037
$24
$73
$103
$223
2038
$25
$74
$105
$227
2039
$26
$75
$106
$231
2040
$26
$76
$108
$235
2041
$27
$78
$109
$239
2042
$28
$79
$111
$242
2043
$28
$80
$112
$246
2044
$29
$81
$113
$250
2045
$30
$82
$115
$253
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Proposed Reconsideration of the Steam Electric Power Generating ELGs 8: Air Quality-Related Benefits
Table 8-6: Interim Estimates of the Social Cost of Carbon, 2025 - 2049 (2021$/Metric Tonne CO2)
Discount Rate and Statistic
Year
5%
Average
3%
Average
2.5%
Average
3%
95th percentile
2046
O
m
¦uy
00
¦uy
$116
$257
2047
i
m
¦uy
LO
00
¦uy
$117
$261
2048
$32
ID
00
¦uy
$119
$264
2049
$32
00
¦uy
$120
$268
Note: These SC-C02 values are identical to those reported in the 2016 TSD (IWG, 2016b) and February 2021 TSD (IWG, 2021)
adjusted for inflation to 2021 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis'
(BEA) NIPA Table 1.1.9 (U.S. BEA, 2022), which are 118.895 and 92.642, respectively for 2021 and 2007. SC-C02 values are stated
in $/metric tonne C02, are rounded to the nearest dollar (1 metric tonne equals 1.102 short tons) and vary depending on the year
of C02 emissions.
Source: U.S. EPA Analysis, 2022 based on IWG, 2016)
There are a number of limitations and uncertainties associated with the SC-CO2 estimates presented in Table
8-6. Some uncertainties are captured within the analysis, while other areas of uncertainty have not yet been
quantified in way that can by modeled. Figure 8-1 presents the quantified sources of uncertainty in the form of
frequency distributions for the SC-CO2 estimates for emissions in 2030. The distribution of SC-CO2 estimates
reflect uncertainty in key model parameters such as the equilibrium climate sensitivity, as well as uncertainty
in other parameters set by the original model developers. To highlight the difference between the impact of
the discount rate and other quantified sources of uncertainty, the bars below the frequency distributions
provide a symmetric representation of quantified variability in the SC-CO2 estimates for each discount rate.
As illustrated by the figure, the assumed discount rate plays a critical role in the ultimate estimate of the SC-
CO2. This is because GHG emissions today continue to impact society far out into the future, so with a higher
discount rate, costs that accrue to future generations are weighted less, resulting in a lower estimate. As
discussed in the February 2021 SC-GHG TSD, there are other sources of uncertainty that have not yet been
quantified and are thus not reflected in these estimates.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Figure 8-1: Frequency Distribution of Interim SC-CO2 Estimates for 2030 (in 2021$ per Metric Ton
CO2)100
5% Average = $20
3% Average = $65
i
i
I..
12.5% Average = $93
3%
95th Pet.
$195
f&
IHBBHDQLJadit
~
T
I I I I
TT
T
TT
T
TT
Discount Rate
~ 5.0%
~ 3.0%
~ 2.5%
5th - 95th Percentile
of Simulations
T
TT
20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340
Social Cost of Carbon in 2030 [2021$ / metric ton C02]
The interim SC-CO2 estimates presented in Table 8-6 have a number of limitations. First, the current
scientific and economic understanding of discounting approaches suggests discount rates appropriate for
intergenerational analysis in the context of climate change are likely to be less than 3 percent, near 2 percent
or lower (IWG, 2021). Second, the IAMs used to produce these interim estimates do not include all of the
important physical, ecological, and economic impacts of climate change recognized in the climate change
literature and the science underlying their "damage functions" — /. e.. the core parts of the IAMs that map
global mean temperature changes and other physical impacts of climate change into economic (both market
and nonmarket) damages — lags behind the most recent research. For example, limitations include the
incomplete treatment of catastrophic and non-catastrophic impacts in the integrated assessment models, their
incomplete treatment of adaptation and technological change, the incomplete way in which inter-regional and
intersectoral linkages are modeled, uncertainty in the extrapolation of damages to high temperatures, and
inadequate representation of the relationship between the discount rate and uncertainty in economic growth
over long time horizons. Likewise, the socioeconomic and emissions scenarios used as inputs to the models
do not reflect new information from the last decade of scenario generation or the full range of projections.
The modeling limitations do not all work in the same direction in terms of their influence on the SC-CO2
estimates. However, the IWG has recommended that, taken together, the limitations suggest that the interim
100 Although the distributions and numbers in Figure 8-1 are based on the full set of model results (150,000 estimates for each
discount rate and gas), for display purposes the horizontal axis is truncated with 0.47 to 0.89 percent of the estimates falling
below the lowest bin displayed and 0.31 to 3.66 percent of the estimates falling above the highest bin displayed, depending on the
discount rate.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
SC-CO2 estimates used in this proposed rule likely underestimate the damages from CO2 emissions. In
particular, the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007),
which was the most current IPCC assessment available at the time when the IWG decision over the ECS input
was made, concluded that SC-CO2 estimates "very likely...underestimate the damage costs" due to omitted
impacts. Since then, the peer-reviewed literature has continued to support this conclusion, as noted in the
IPCC's Fifth Assessment report (IPCC, 2014) and other recent scientific assessments (e.g., IPCC, 2018,
2019a; 2019b); U.S. Global Change Research Program (USGCRP, 2016, 2018); and the National Academies
of Sciences, Engineering, and Medicine (National Academies, 2017, 2019). These assessments confirm and
strengthen the science, updating projections of future climate change and documenting and attributing
ongoing changes. For example, sea level rise projections from the IPCC's Fourth Assessment report ranged
from 18 to 59 centimeters by the 2090s relative to 1980-1999, while excluding any dynamic changes in ice
sheets due to the limited understanding of those processes at the time (IPCC, 2007). A decade later, the
Fourth National Climate Assessment projected a substantially larger sea level rise of 30 to 130 centimeters by
the end of the century relative to 2000, while not ruling out even more extreme outcomes (U.S. Global
Change Research Program, 2018). EPA has reviewed and considered the limitations of the models used to
estimate the interim SC-GHG estimates, and concurs with the February 2021 SC-GHG TSD's assessment
that, taken together, the limitations suggest that the interim SC-CO2 estimates likely underestimate the
damages from CO2 emissions. The February 2021 SC-GHG TSD briefly previews some of the recent
advances in the scientific and economic literature that the IWG is actively following and that could provide
guidance on, or methodologies for, addressing some of the limitations with the interim SC-CO2 estimates. The
IWG is currently working on a comprehensive update of the SC-GHG estimates taking into consideration
recommendations from the National Academies of Sciences, Engineering and Medicine, recent scientific
literature, public comments received on the February 2021 TSD and other input from experts and diverse
stakeholder groups (National Academies, 2017). While that process continues, 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. Most recently, EPA presented a draft set of updated SC-GHG estimates within a sensitivity analysis
in the regulatory impact analysis of EPA's November 2022 supplemental proposal for oil and gas standards
that that aims to incorporate recent advances in the climate science and economics literature. Specifically, the
draft updated methodology incorporates new literature and research consistent with the National Academies
near-term recommendations on socioeconomic and emissions inputs, climate modeling components,
discounting approaches, and treatment of uncertainty, and an enhanced representation of how physical
impacts of climate change translate to economic damages in the modeling framework based on the best and
readily adaptable damage functions available in the peer reviewed literature. EPA solicited public comment
on the sensitivity analysis and the accompanying draft technical report, which explains the methodology
underlying the new set of estimates, in the docket for the proposed Oil and Gas rule. EPA is also embarking
on an external peer review of this technical report. More information about this process and public comment
opportunities is available on EPA's website.101 EPA's draft technical report will be among the many technical
inputs available to the IWG as it continues its work.
8.2.2 Results
Table 8-7 presents the undiscounted annual monetized climate benefits in selected years for Option 3, the
proposed rule. Benefits are calculated using the four different estimates of the SC-CO2 from Table 8-6 (model
101 See https://www.epa.gov/environmental-economics/scghg
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3 percent discount rate).
Projected net CO2 reductions each year are multiplied by the SC-CO2 estimates for that year.
Table 8-7: Estimated Undiscounted and Total Present Value of Climate Benefits from Changes in
CO2 Emissions under the Proposed Rule by SC-CO2 Estimates, Compared to Baseline (Millions of
2021$)
Regulatory
Option
Year
3% Discount Rate
(Average)3'b
5% Discount Rate
(Average)a'b
2.5% Discount
Rate (Average)a'b
3% Discount Rate
(95th Percentile)3'b
2028
$42
$13
$61
$130
2030
$280
ID
00
¦uy
$400
$830
2035
$670
$220
$960
$2,100
Option 3
(Proposed Rule)
2040
$500
$170
$700
$1,500
2045
$890
$320
$1,200
$2,700
2049
$230
00
¦uy
$320
$720
Total present
value (2025-
2049)°
$2,000
$7,900
$12,000
$24,000
a. Values rounded to two significant figures.
b. Climate benefits are based on changes C02 emissions and are calculated using four different estimates of the SC-C02 (model
average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3 percent discount rate). The IWG
emphasized the importance and value of considering the benefits calculated using all four estimates. As discussed in the
Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG, 2021),
a consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also
warranted when discounting intergenerational impacts.
c. The total present value is estimated by mapping IPM emissions changes to corresponding years within the period of analysis
2025-2049 based on Table 8-1 and assuming no changes in air emissions from electricity generation between 2025 and 2027. For
trucking and energy use, EPA estimated changes in air emissions corresponding to the year each plant is estimated to implement
changes in technology
Source: U.S. EPA Analysis, 2022
Table 8-8 shows the annualized climate benefits associated with changes in CO2 emissions over the 2025-
2049 period under each discount rate for the proposed rule by category of emissions. EPA annualized the
climate benefits to enable consistent reporting across benefit categories (e.g., benefits from improvement in
water quality). As noted above, the IPM model run provides outputs starting in 2028. For the years 2025
through 2027, EPA assumed no change in air emissions from changes in the profile of electricity generation.
For trucking and energy use, EPA estimated changes in air emissions corresponding to the year each plant is
estimated to implement changes in technology. For each SC-CO2 estimate, EPA then calculated the present
value and annualized benefits from the perspective of 2024 by discounting each year-specific value to the
year 2024 using the same discount rate used to calculate the SC-CO2. Using the average SC-CO2 value for the
3 percent discount rate and using a 3 percent discount to annualize the benefits yields annualized benefits of
$440 million.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs 8: Air Quality-Related Benefits
Table 8-8: Estimated Total Annuali
Proposed Rule during the Period c
Estimates, Compared to Baseline
zed Climate Bent
>f 2025-2049 by C
Millions of 20213
afits from Changes in CO2 Emissions under the
ategories of Air Emissions and SC-CO2
>)
Regulatory
Option
Category of Air
Emissions
3% Discount Rate
(Average)3
5% Discount Rate
(Average)a
2.5% Discount
Rate (Average)a
3% Discount Rate
(95th Percentile)3
Option 3
(Proposed Rule)
Electricity Generation
$450
$140
$640
$1,400
Trucking
-$0.24
-$0,076
-$0.35
-$0.73
Energy use
-$7.1
-$2.1
O
1
¦uy
1
-$22
Total
$440
$140
$630
$1,300
a. Values rounded to two significant figures. Negative values indicate forgone benefits whereas positive values indicate positive
benefits.
b. Climate benefits are based on changes C02 emissions and are calculated using four different estimates of the SC-C02 (model
average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3 percent discount rate). The IWG
emphasized the importance and value of considering the benefits calculated using all four estimates. As discussed in the Technical
Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG, 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also
warranted when discounting intergenerational impacts.
Source: U.S. EPA Analysis, 2022
As discussed above, the IWG is currently working on a comprehensive update of the SC-GHG estimates
under EO 13990 taking into consideration recommendations from the National Academies of Sciences,
Engineering and Medicine, recent scientific literature, and public comments received on the February 2021
SC-GHG TSD. EPA is a member of the IWG and is participating in the IWG's review and updating process
under EO 13990.
8.3 Human Health Benefits
8.3.1 Data and Methodology
As summarized in Table 8-5, the proposed rule is estimated to influence the level of pollutants emitted in
the atmosphere that adversely affect human health, including directly emitted PM2.5, as well as SO2 and NOx,
which are both precursors to ambient PM2 5 NOx emissions are also a precursor to ambient ground-level
ozone. The change in emissions alters the ambient concentrations, which in turn leads to changes in
population exposure. EPA estimated the changes in the human health impacts associated with PM2.5 and
ozone.102
This section summarizes EPA's approach to estimating the incidence and economic value of the PM2.5 and
ozone-related benefits estimated for Option 3. The approach entails two major steps: (1) developing baseline
and Option 3 spatial fields of air quality across the U.S. using nationwide photochemical modeling and related
analyses; and (2) using these spatial fields in BenMAP-CE to quantify the benefits under Option 3 as
compared to the baseline. In this approach, EPA used IPM projections of EGU air emissions for the baseline
and Option 3 (proposed rule).
102 Ambient concentrations of both SO2 and NOx also pose health risks independent of PM2.5 and ozone, though EPA does not
quantify these impacts in this analysis (U.S. EPA, 2016b, 2017b)
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8: Air Quality-Related Benefits
8.3.1.1 Air Quality Modeling Methodology
As described in Appendix I, spatial fields of annual ozone and PM2.5 concentrations representing the baseline
and Option 3 were obtained from ozone source apportionment modeling that was performed as part of the
Regulatory Impact Analysis for the proposed Federal Implementation Plan Addressing Regional Ozone
Transport for the 2015 Ozone National Ambient Air Quality Standard (U.S. EPA, 2022c) and from PM
source apportionment modeling performed for this proposed rule. These PM2.5 and ozone spatial fields were
used as input to BenMAP-CE which, in turn, was used to quantify the benefits from this proposed rule.
EPA prepared spatial fields of air quality for the baseline and the Option 3 for two health-impact metrics:
annual mean PM2.5 and April through September seasonal average 8-hour daily maximum (MDA8) ozone
(AS-M03). The EGU emissions for the baseline and Option 3, consisting of total NOx, SO2, and primary
PM2.5 emissions summarized by year and state, were obtained from the outputs of the IPM run, as described
above and in Chapter 5 of the RIA (U.S. EPA, 2023c). As such, the spatial fields do not account for changes
in emissions associated with power requirements to operate treatment systems or with transportation. See
Section 8.3.1 regarding limitations and uncertainty associated with the analysis of air quality related benefits.
The basic methodology for determining air quality changes is the same as that used in the RIAs from multiple
previous rules (U.S. EPA, 2019g; 2020b; 2020a, 2021b; 2022c). Appendix I provides an overview of the air
quality modeling and the methodologies EPA used to develop spatial fields of seasonal ozone and annual
PM2.5 concentrations. The appendix also provides selected figures showing the geographical and temporal
distribution of air quality changes.
EPA used air quality modeling to estimate health benefits associated with changes in ozone and PM2.5
concentrations that may occur because of Option 3 of the proposed rule relative to the baseline, with the air
quality modeling baseline including emissions from all sources. Consequently, in addition to rules and
economic conditions included in IPM, the baseline for this analysis included emissions from, and rules for,
non-EGU point sources, on-road vehicles, non-road mobile equipment and marine vessels.1"3 While the air
quality modeling includes a range of pollution sources, contributions from non-EGU point sources, on-road
vehicles, non-road mobile equipment and marine vessels are held constant in this analysis, and the only
changes are those associated with the projected impacts of the proposed rule on the profile of electricity
generation and EGU emissions, as compared to the baseline. The modeled air quality changes do not include
other potential effects of the proposed rule, such as changes in power requirements to run treatment systems
or changes in CCR transportation, which were estimated separately as described in Section 8.1 and were
found to be negligible as described in section 8.4.
8.3.1.2 PM2.5 and Ozone Related Health Impacts
EPA estimated the benefits of Option 3 for the proposed rule using the open-source environmental Benefits
Mapping and Analysis Program—Community Edition (BenMAP-CE) (Sacks etal., 2018). The Estimating
PM2.5- and Ozone-Attributable Health Benefits Technical Support Document (TSD) fully describes the
Agency's approach for identifying those health endpoints to evaluate as well as quantifying their number and
value (U.S. EPA, 2023e). In the TSD, the reader can find the rationale for selecting health endpoints to
103 The air quality modeling techniques used for this analysis reflect non-EGU emissions as of 2026, so implementation or effects of
any changes in non-EGU emissions expected to occur after 2026 are not accounted for in this analysis. However, the effect of
non-EGU emissions on changes in pollution concentrations due to the final rule is likely to be small.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
quantify; the demographic, health and economic data used; modeling assumptions; and our techniques for
quantifying uncertainty.
Estimating the health benefits of reductions in PM2 5 and ozone exposure begins with estimating the change in
exposure for each individual and then estimating the change in each individual's risks for those health
outcomes affected by exposure. The dollar benefit of reducing the risk of each adverse effect is based on the
exposed individual's willingness to pay (WTP) for the risk change, assuming that each outcome is
independent of one another. The greater the magnitude of the risk reduction from a given change in
concentration, the greater the individual's WTP, all else equal. The social benefit of the change in health risks
equals the sum of the individual WTP estimates across all of the affected individuals residing in the United
States. We conduct this analysis by adapting primary research—specifically, air pollution epidemiology
studies and economic value studies—from similar contexts. This approach is sometimes referred to as
"benefits transfer." Below we describe the procedure we follow for: (1) selecting air pollution health
endpoints to quantify; (2) calculating counts of air pollution effects using a health impact function; (3)
specifying the health impact function with concentration-response parameters drawn from the epidemiological
literature.
The BenMAP-CE tool quantifies the number and value of air pollution-attributable premature deaths and
illnesses resulting from changes in PM2.5 and ozone concentrations. Table 8-9 reports the ozone and PM2.5-
related human health impacts effects EPA quantified and those the Agency did not quantify in this analysis of
Option 3 of the proposal. The list of benefit categories not quantified is not exhaustive. And, among the
effects quantified, it might not have been possible to quantify completely either the full range of human health
impacts or economic values.
Table 8-9: Human Health Effects of Ambient Ozone and PM2.5
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Premature
mortality from
exposure to
PM2.5
Adult premature mortality based on cohort study
estimates and expert elicitation estimates (age 65-99 or
age 30-99)
V
V
PM ISA
Infant mortality (age <1)
V
V
PM ISA
Morbidity from
exposure to
PM2.5
Heart attacks (age > 18)
V
V
PM ISA
Hospital admissions—cardiovascular (ages 65-99)
V
V
PM ISA
Emergency department visits— cardiovascular (age 0-99)
V
V
PM ISA
Hospital admissions—respiratory (ages 0-18 and 65-99)
V
V
PM ISA
Emergency room visits—respiratory (all ages)
V
V
PM ISA
Cardiac arrest (ages 0-99; excludes initial hospital and/or
emergency department visits)
V
V
PM ISA
Stroke (ages 65-99)
V
V
PM ISA
Asthma onset (ages 0-17)
V
V
PM ISA
Asthma symptoms/exacerbation (6-17)
V
V
PM ISA
Lung cancer (ages 30-99)
V
V
PM ISA
Allergic rhinitis (hay fever) symptoms (ages 3-17)
V
V
PM ISA
Lost work days (age 18-65)
V
V
PM ISA
Minor restricted-activity days (age 18-65)
V
V
PM ISA
Hospital admissions—Alzheimer's disease (ages 65-99)
V
V
PM ISA
Hospital admissions—Parkinson's disease (ages 65-99)
V
V
PM ISA
Other cardiovascular effects (e.g., other ages)
—
—
PM ISAb
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Proposed Reconsideration of the Steam Electric Power Generating ELGs 8: Air Quality-Related Benefits
Table 8-9: Human Health Effects of Ambient Ozone and PM2.5
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Other respiratory effects (e.g., pulmonary function, non-
asthma ER visits, non-bronchitis chronic diseases, other
ages and populations)
PM ISAb
Other nervous system effects (e.g., autism, cognitive
decline, dementia)
—
—
PM ISAb
Metabolic effects (e.g., diabetes)
—
—
PM ISAb
Reproductive and developmental effects (e.g., low birth
weight, pre-term births)
—
—
PM ISAb
Cancer, mutagenicity, and genotoxicity effects
—
—
PM ISAb
Mortality from
exposure to
ozone
Premature mortality based on short-term study
estimates (age 0-99)
V
V
Ozone ISA
Premature mortality based on long-term study estimates
(age 30-99)
V
V
Ozone ISA3
Morbidity from
exposure to
ozone
Hospital admissions—respiratory causes (ages 0-99)
V
V
Ozone ISA
Emergency department—respiratory (ages 0-99)
V
V
Ozone ISA
Asthma onset (0-17)
V
V
Ozone ISA
Asthma symptoms/exacerbation (asthmatics age 2-17)
V
V
Ozone ISA
Allergic rhinitis (hay fever) symptoms (ages 3-17)
V
V
Ozone ISA
Minor restricted-activity days (age 18-65)
V
V
Ozone ISA
School absence days (age 5-17)
V
V
Ozone ISA
Decreased outdoor worker productivity (age 18-65)
—
—
Ozone ISAb
Metabolic effects (e.g., diabetes)
—
—
Ozone ISAb
Other respiratory effects (e.g., premature aging of lungs)
—
—
Ozone ISAb
Cardiovascular and nervous system effects
—
—
Ozone ISAb
Reproductive and developmental effects
—
—
Ozone ISAbc
a. EPA assesses these benefits qualitatively due to data and resource limitations for this analysis. In other analyses EPA quantified
these effects as a sensitivity analysis.
b. EPA assesses these benefits qualitatively because of insufficient confidence in available data or methods.
c. EPA assesses these benefits qualitatively because current evidence is only suggestive of causality or there are other significant
concerns over the strength of the association.
Source: EPA Analysis, 2022
Counts of attributable effects are quantified using a health impact function, which combines information
regarding the: concentration-response relationship between air quality changes and the risk of a given adverse
outcome; population exposed to the air quality change; baseline rate of death or disease in that population;
and air pollution concentration to which the population is exposed. When used to quantify PM2.5- or ozone-
related effects, the functions combine effect estimates (i.e.. the |3 coefficients) from epidemiological studies,
which portray the relationship between a change in air quality and a health effect, such as mortality,
associated with changes in estimated PM2.5 or ozone concentrations (supplied using the IPM market model
simulations described above), population data, and baseline death rates for each county in each year. After
having quantified PM2.5- and ozone-attributable cases of premature death and illness, EPA estimated the
economic value of these cases using willingness to pay (WTP) and cost of illness (COI) measures.
EPA estimated the number of PM2 5-attributable premature deaths using effect estimates from two
epidemiology studies examining two large population cohorts: an analysis of Medicare beneficiaries (Wu et
al, 2020) and the National Health Interview Survey (NHIS) (Pope et al, 2019). For ozone-related premature
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8: Air Quality-Related Benefits
deaths, EPA uses one epidemiological study that examines the relationship between long-term exposure to
ozone and mortality (Turner el al., 2016) and two studies that examine the relationship between short-term
exposure to ozone and mortality (Katsouyanni el al.. 2009; Zanobetti & Schwartz, 2008).
Projected impacts of the proposed rule (Option 3) show both decreased and increased levels of PM2 5 and
ozone, depending on the year and location, compared to the baseline (see maps in Appendix I for details).
Some portion of the air quality and health benefits from the proposed rule occur in areas not attaining the
PM2 5 or Ozone National Ambient Air Quality Standards (NAAQS). The analysis does not account for
possible interactions between NAAQS compliance and the proposed rule, which introduces uncertainty into
the benefits (and forgone benefits) estimates. If the proposed rule increases or decreases primary PM25, SO2
and NOx emissions and consequentially PM2 5 and/or ozone concentrations, these changes may affect
compliance with existing NAAQS standards and subsequently affect the actual benefits (and forgone benefits)
of the proposed rule.
8.3.2 Results
EPA reports below the estimated number of avoided PM2 5 and ozone-related premature deaths and illnesses
in each year for Option 3, the proposed rule, relative to the baseline along with the 95% confidence interval
(see Table 8-10). The number of avoided premature deaths and illnesses under the proposed rule are
calculated from the sum of individual reduced mortality and illness risk across the population in a given year.
Table 8-11 reports the estimated economic value of avoided premature deaths and illness for each analysis
year relative to the baseline along with the 95% confidence interval.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-10: Estimated Avoided PM2.5 and Ozone-Related Premature Deaths and Illnesses by Year for Option 3 of the Proposed Rule, Compared
to Baseline (95% Confidence Interval)
Category and Basis
2028a
2030a
2035a
2040a
2045a
2050a
Avoided premature death among adultsb
PM2.5
Wu et al. (2020)
13
(11 to 14)
24
(21 to 27)
51
(45 to 57)
41
(36 to 45)
82
(72 to 91)
60
(53 to 67)
Pope III et al. (2019)
28
(20 to 35)
50
(36 to 63)
100
(74 to 130)
82
(58 to 100)
160
(120 to 210)
120
(84 to 150)
Avoided infant mortality
PM2.5
Woodruff et al., 2008
0.035
(-0.022 to 0.091)
0.050
(-0.031 to 0.13)
0.10
(-0.063 to 0.26)
0.080
(-0.050 to 0.21)
0.15
(-0.092 to 0.38)
0.099
(-0.062 to 0.25)
Ozone
(03)
Katsouyanni et al.
(2009)c d and Zanobetti
et al. (2008)d pooled
0.40
(0.16 to 0.63)
0.92
(0.37 to 1.4)
1.4
(0.55 to 2.2)
0.78
(0.32 to 1.2)
1.5
(0.61 to 2.4)
0.69
(0.28 to 1.1)
Turner et al. (2016)°
8.8
(6.1 to 11)
20
(14 to 26)
30
(21 to 39)
17
(12 to 23)
33
(23 to 43)
15
(11 to 20)
All other morbidity effects
Acute Myocardial Infarcation
0.44
(0.26 to 0.62)
0.84
(0.49 to 1.2)
1.7
(1.0 to 2.4)
1.4
(0.80 to 1.9)
2.6
(1.5 to 3.7)
2.0
(1.1 to 2.8)
Hospital admissions-
cardiovascular (PM2.5)
2.0
(1.5 to 2.6)
3.6
(2.6 to 4.5)
7.6
(5.5 to 9.6)
6.0
(4.3 to 7.6)
12
(8.6 to 15)
8.7
(6.3 to 11)
Hospital admissions-
respiratory (PM2.5)
1.3
(0.43 to 2.1)
2.3
(0.80 to 3.8)
4.7
(1.6 to 7.7)
3.9
(1.3 to 6.3)
7.2
(2.4 to 12)
5.3
(1.8 to 8.7)
Hospital admissions—
respiratoryd (03)
1.1
(-0.28 to 2.4)
2.7
(-0.71 to 6.0)
3.9
(-1.0 to 8.6)
2.2
(-0.57 to 4.9)
4.2
(-1.1 to 9.3)
1.9
(-0.50 to 4.3)
Hospital admissions-
Alzheimer's Disease (PM2.5)
6.6
(4.9 to 8.2)
12
(9.3 to 16)
27
(20 to 34)
24
(18 to 30)
44
(33 to 55)
33
(25 to 41)
Hospital admissions-
Parkinson's Disease (PM2.5)
0.83
(0.42 to 1.2)
1.6
(0.81 to 2.4)
3.3
(1.7 to 4.8)
2.5
(1.3 to 3.7)
4.9
(2.5 to 7.3)
3.6
(1.8 to 5.4)
ED visits—cardiovascular (PM2.5)
4.4
(-1.7 to 10)
7.0
(-2.7 to 16)
15
(-5.8 to 35)
12
(-4.7 to 28)
24
(-9.4 to 57)
18
(-6.9 to 42)
ED visits—respiratory (PM2.5)
9.4
(1.8 to 20)
14
(2.7 to 29)
28
(5.5 to 58)
22
(4.3 to 45)
46
(8.9 to 95)
32
(6.4 to 68)
ED visits—respiratory' (03)
23
(6.3 to 48)
46
(13 to 97)
65
(18 to 140)
33
(9.2 to 70)
69
(19 to 140)
32
(8.7 to 66)
Cardiac Arrest (PM2.5)
0.21
(-0.086 to 0.48)
0.35
(-0.14 to 0.80)
0.73
(-0.30 to 1.6)
0.57
(-0.23 to 1.3)
1.1
(-0.46 to 2.6)
0.82
(-0.33 to 1.9)
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-10: Estimated Avoided PM2.5 and Ozone-Related Premature Deaths and Illnesses by Year for Option 3 of the Proposed Rule, Compared
to Baseline (95% Confidence Interval)
Category and Basis
2028a
2030a
2035a
2040a
2045a
2050a
Stroke (PM2.5)
0.87
(0.23 to 1.5)
1.5
(0.38 to 2.5)
3.0
(0.78 to 5.1)
2.3
(0.59 to 3.9)
4.5
(1.2 to 7.7)
3.3
(0.85 to 5.6)
Lung Cancer (PM2.5)
0.96
(0.29 to 1.6)
1.6
(0.50 to 2.7)
3.5
(1.1 to 5.8)
2.8
(0.85 to 4.7)
5.7
(1.7 to 9.4)
4.1
(1.2 to 6.9)
Hay Fever/Rhinitis (PM2.5)
190
(46 to 330)
310
(74 to 530)
670
(160 to 1,200)
550
(130 to 950)
1,100
(260 to 1,900)
770
(180 to 1,300)
Hay Fever/Rhinitis8 (03)
380
(200 to 560)
800
(420 to 1,200)
1,100
(610 to 1,700)
630
(330 to 920)
1,200
(630 to 1,700)
540
(280 to 780)
Asthma Onset (PM2.5)
30
(28 to 31)
47
(45 to 49)
100
(98 to 110)
84
(80 to 87)
160
(160 to 170)
120
(110 to 120)
Asthma onset6 (O3)
66
(57 to 75)
140
(120 to 160)
200
(170 to 220)
110
(92 to 120)
200
(170 to 230)
91
(79 to 100)
Asthma symptoms- Albuterol
use (PM2.5)
4,000
(-1,900 to 9,700)
6,500
(-3,200 to 16,000)
14,000
(-6,900 to 34,000)
11,000
(-5,500 to 28,000)
22,000
(-11,000 to 55,000)
16,000
(-7,800 to 39,000)
Asthma symptoms (O3)
12,000
(-1,500 to 26,000)
26,000
(-3,200 to 54,000)
37,000
(-4,500 to 76,000)
20,000
(-2,500 to 42,000)
37,000
(-4,700 to 79,000)
17,000
(-2,100 to 35,000)
Minor restricted-activity days
(PM2.5)
8,900
(7,200 to 11,000)
14,000
(12,000 to 17,000)
30,000
(24,000 to 36,000)
25,000
(20,000 to 29,000)
49,000
(40,000 to 59,000)
36,000
(29,000 to 42,000)
Minor restricted-activity daysd f
(Ob)
6,000
(2,400 to 9,500)
12,000
(4,800 to 19,000)
17,000
(6,800 to 27,000)
9,500
(3,800 to 15,000)
19,000
(7,400 to 29,000)
8,600
(3,400 to 14,000)
Lost work days (PM2.5)
1,500
(1,300 to 1,700)
2,500
(2,100 to 2,800)
5,100
(4,300 to 5,900)
4,200
(3,500 to 4,800)
8,400
(7,000 to 9,600)
6,100
(5,100 to 7,000)
School absence days (03)
4,400
(-620 to 9,200)
9,200
(-1,300 to 19,000)
13,000
(-1,900 to 28,000)
7,200
(-1,000 to 15,000)
14,000
(-1,900 to 29,000)
6,200
(-870 to 13,000)
a. Values rounded to two significant figures. Negative values indicate forgone benefits (i.e., the number of avoided cases under the proposed rule is smaller than in the baseline). Lower
bound of confidence interval represents the 95% confidence estimate that is lower in value than the point estimate, while upper bound represents the estimate that is higher in value
than the point estimate.
b. EPA also quantified changes in premature infant mortality from exposure to PM2.5 but the estimated change was less than 1 for all years analyzed.
c. Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season.
d. Converted ozone risk estimate metric from MDA1 to MDA8.
e. Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season.
f. Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season.
g. Converted ozone risk estimate metric from DA24 to MDA8
Source: U.S. EPA Analysis, 2022
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Table 8-11: Estimated Discounted Economic Value of Avoided Ozone and PIVfe.s-Attributable Premature Mortality and Illness for Option 3 of
the Proposed Rule (95% Confidence Interval; millions of 2021$)
Year
3% Discount Rate3
7% Discount Rate3
2028
$160
($18 to $410)
and
$420
($42 to $1,100)
$140
($15 to $370)
and
$380
($36 to $1,000)
2030
$300
($35 to $780)
and
$820
($81 to $2,200)
$270
($29 to $700)
and
$730
($71 to $2,000)
2035
$640
($71 to $1,700)
and
$1,600
($160 to $4,200)
$570
($60 to $1,500)
and
$1,400
($140 to $3,800)
2040
$510
($55 to $1,300)
and
$1,200
($120 to $3,200)
$460
($48 to $1,200)
and
$1,100
($100 to $2,900)
2045
$1,100
($110 to $2,700)
and
$2,400
($240 to $6,500)
$940
($98 to $2,500)
and
$2,200
($210 to $5,900)
2050
$770
($81 to $2,000)
and
$1,700
($160 to $4,500)
$690
($71 to $1,800)
and
$1,500
($140 to $4,100)
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify that they are two separate estimates. The estimates do not represent
lower- and upper-bound estimates and should not be summed.
Source: U.S. EPA Analysis, 2022
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8: Air Quality-Related Benefits
8.4 Annualized Air Quality-Related Benefits of Regulatory Options
EPA calculated the present value (discounted to 2024) of estimated air quality-related benefits over the
analysis period of 2025-2049 and annualized these values to provide a measure that is comparable to the way
other benefit categories and social costs are reported.
Sections 0 and 8.2.1 provide benefit estimates for Option 3, the proposed rule, based on the changes in the
electricity generation profile projected in IPM. EPA mapped changes in emissions due to changes in
electricity generation for each IPM run year to individual years within the analysis period of 2025 -2049 (see
Table 8-1). Because IPM outputs are available only for 2028 onward, EPA conservatively assumed no
benefits associated with changes in the profile of electricity generation between 2025 and 2027. However,
changes in the profile of electricity generation and EGU emissions are likely to occur as steam electric power
generating plants start incurring costs to comply with the revised ELG between 2025 and 2029, and assuming
no emission reductions for the first three years of this period understates the air quality-related benefits of the
proposed rule.
For energy use and trucking, EPA estimated changes in air emissions corresponding to the year each plant is
estimated to implement changes in technology. These emissions are included in the analysis of climate change
benefits. As discussed in Section 8.3.1.1, however, the analysis of human health benefits does not account for
other changes in pollutant emissions associated with power requirements to operate wastewater treatment
systems or transport CCR or other solid waste. EPA considered adjusting the estimated benefits in proportion
to the average ratio between total air emissions of NOx and SO2 (Table 8-5) and EGU emissions associated
with changes in the electricity generation profile (Table 8-4) but concluded that such an adjustment would
have a negligible effect on the estimated human health benefit estimates given the comparably small
emissions changes associated with power requirements and trucking. Therefore, EPA is presenting unadjusted
values for the proposed rule below.
For the climate change benefits, EPA used the same discount rate used to develop SC-CO2 values. For the
human health benefits, EPA used 3 percent and 7 percent discounts.
Table 8-12: Total Annualized Air Quality-Related Benefits of Proposed Rule (Option 3), Compared to the
Baseline, 2025-2049 (Millions of 2021$)
Climate
PM2.5and
Total
Climate
PM2.5and
Total
Change
Ozone Related
Change
Ozone Related
sc-cc>2
Benefits3
Human Health
Benefits at 3%
Discount Ratea
Benefits3
Human Health
Benefits at 7%
Discount Rate
3% (Average)
$440
$1,100
$1,540
$440
$840
$1,280
5% (Average)
$140
$1,100
$1,240
$140
$840
$980
2.5% (Average)
$630
$1,100
$1,730
$630
$840
$1,470
3% (95th Percentile)
$1,300
$1,100
$2,400
$1,300
$840
$2,140
a. Values rounded to two significant figures.
b. Values calculated based on the LT mortality benefits estimates at 3 percent and 7 percent discount rates.
Source: U.S. EPA Analysis, 2022
Because EPA did not run IPM for Options 1, 2, and 4, EPA did not analyze climate and human health benefits
for Options 1, 2, and 4. To provide insight into the potential air quality-related benefits across regulatory
options, EPA estimated benefits for Options 1, 2, and 4 by scaling Option 3 benefits in proportion to the total
social costs of the respective options (see BCA Chapter 11). Specifically, EPA calculated the ratio of the
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
benefits to total social costs for Option 3, then multiplied total social costs for Options 1, 2, and 4 by this
ratio. The scaling factor provides an approximation of the benefits by assuming proportionality between air-
related benefits and total social costs.1"4 While air-related benefits are expected to be driven primarily by
changes in the profile of electricity generation (see Table 8-4 and Table 8-5) and the generation profile is
affected most directly by the incremental technology implementation costs, the effects may not be linear.
Table 8-13 summarizes the annualized air quality-related benefits of the regulatory options for the climate
change benefits estimated using the SC-CO2 at 3 percent (average) and for human health benefits discounted
using 3- and 7-percent discount rates.
Table 8-13: Total Annualized Air Quality-Related Benefits of Regulatory Options Based on
Extrapolation from Option 3, Compared to the Baseline, 2025-2049 (Millions of 2021$)
Climate
PM2.5and
Total
Climate
PM2.5and
Total
Change
Ozone
Change
Ozone
Benefits (SC-
Related
Benefits (SC-
Related
Regulatory Option
C02 3%
Human Health
C02 3%
Human Health
Average)
Benefits at 3%
Discount
Rate3
Average)
Benefits at 7%
Discount
Rate3
Option lb
$190
$500
$690
$200.0
$380
$580
Option 2b
$370
$950
$1,320
$360.0
$700
$1,060
Option 3 (Proposed rule)
$440
$1,100
$1,540
$440.0
$840
$1,280
Option 4b
$450
$1,200
$1,650
$450.0
$870
$1,320
a. Values rounded to two significant figures.
b. EPA estimated air quality-related benefits for Options 1, 2, and 4 by multiplying the total social costs for each option (see Section
11.2) by the ratio of [air quality-related benefits / total social costs] for Option 3.
social costs] for Option A
Source: U.S. EPA Analysis, 2022
8.5 Limitations and Uncertainties
Table 8-14 summarizes the limitations and uncertainties associated with the analysis of the air quality-related
benefits. The second column of the table provides a conclusion of how the limitation affects the magnitude of
the benefits estimate relative to expected actual benefits (i.e.. a source of uncertainty that has the effect of
underestimating benefits indicates an expectation that expected actual benefits are larger than the estimate).
The analysis also incorporates uncertainties associated with IPM modeling, which are discussed in Chapter 5
in the RIA (U.S. EPA, 2023c). See Appendix I for additional discussions of the uncertainty associated with
the air quality modeling methodology.
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA extrapolated Option 3
benefits to Options 1, 2 and
4.
Uncertain
EPA ran IPM only for Option 3 and used the results to
extrapolate benefits of Options 1, 2, and 4, based on the
ratios of annualized benefits and annualized social costs. Air
104 For the 2015 final rule, EPA analyzed two options using IPM and therefore had air-related benefits for both options. Using the
benefit/cost ratio of one option to estimate benefits of the other option resulted in benefits that were +7 percent than benefits
derived from the IPM outputs.
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
emissions and air quality changes are unlikely to follow
differences in social costs in a linear fashion, however, given
how marginal changes in operating costs for individual units
may affect dispatch of EGUs within the broader regional and
national electricity markets. Because benefits are dependent
on magnitude and, for human health benefits, the spatial
distribution of emissions changes, projected benefits for
Options 1, 2, and 4 are uncertain.
EPA assumed no changes in
air emissions associated
with shifts in the mix of
electricity generation in
2025-2027
Underestimate
The first IPM year is 2028. Changes in the profile of electricity
generation and EGU emissions are likely to occur as steam
electric power generating plants start incurring costs to
comply with the revised ELG between 2025 and 2029, and
assuming no emission reductions for the first three years of
this technology implementation period understates the air
quality-related benefits of the proposed rule. This is even
though the changes in air emissions predicted in IPM are
modest in 2028.
The modeled air quality
assumes a static
apportionment of EGU
sources and static emissions
from other sources.
Uncertain
The profile of EGU and other emissions sources is expected
to change over time.
The modeled air quality
surfaces used in the analysis
of human health benefits
only reflect changes in
emissions associated with
changes in the electricity
generation profile.
Uncertain
EPA developed the spatial fields based on IPM projected
emissions changes for Option 3. These projections do not
include additional changes in NOx and S02 emissions
associated with power requirements to operate wastewater
treatment systems or trucking to transport CCR and other
solid waste. While these emissions changes could affect
human health benefit estimates, such effects are expected to
be small overall given that these emissions generally
represent less than 2 percent of total NOx and S02emissions
changes.
The methodology used to
create ozone and PM2.5 Air
Quality surfaces do not
account for nonlinear
impacts of precursor
emissions changes
Uncertain
Appendix 1 provides further details on this limitation.
All fine particles, regardless
of their chemical
composition, are equally
potent in causing premature
mortality.
Uncertain
The PM ISA concluded reaffirmed the conclusion reached in
the 2009 ISA that "many PM2.5 components and sources are
associated with many health effects and that the evidence
does not indicate that any one source or component is
consistently more strongly related with health effects than
PM2.5 mass." (U.S. EPA, 2009, 2022d).
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Proposed Reconsideration of the Steam Electric Power Generating ELGs
8: Air Quality-Related Benefits
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Assumed "Cessation" lag
between the change in
PM2.5 and ozone exposures
and the total realization of
changes in long-term
mortality effects.
Uncertain
The approach distributes the incidences of premature
mortality related to PM2.5 exposures over the 20 years
following exposure based on the advice of EPA's Science
Advisory Board Health Effect Subcommittee (SAB-HES) (U.S.
EPA, 2004). This distribution is also assumed for long-term
mortality from ozone exposure. This distribution affects the
valuation of mortality benefits at different discount rates.
The actual distribution of effects over time is uncertain.
Climate changes may affect
ambient concentrations of
pollutants.
Uncertain
Estimated health benefits do not account for the influence of
future changes in the climate on ambient concentrations of
pollutants (U.S. Global Change Research Program, 2016). For
example, recent research suggests that future changes to
climate may create conditions more conducive to forming
ozone; the influence of changes in the climate on PM2.5
concentrations are less clear (Fann et al., 2015). The
estimated health benefits also do not consider the potential
for climate-induced changes in temperature to modify the
relationship between ozone and the risk of premature death
(Jhun et al., 2014; Ren, Williams, Mengersen, et al., 2008;
Ren, Williams, Morawska, etal., 2008). Modeling used to
estimate air quality changes from this proposed rule used
meteorological fields representing conditions that occurred
in 2016.
EPA did not analyze all
benefits of changes in
exposure to NOx, S02, and
other pollutants emitted by
EGUs.
Underestimate
The analysis focused on adverse health effects related to
PM2.5 and ozone levels. There are additional benefits from
changes in levels of NOx, S02 and other air pollutants emitted
by EGUs (e.g., mercury, HCI). These include health benefits
from changes in ambient N02 and S02 exposure, health
benefits from changes in mercury deposition, ecosystem
benefits associated with changes in emissions of NOx, S02,
PM, and mercury, and visibility impairment.
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9: Dredging
9 Estimated Changes in Dredging Costs
As summarized in Table 3-1, the regulatory options could result in relatively small changes in suspended
solid discharges by steam electric power plants, which could have an impact on the rate of sediment
deposition in affected reaches, including navigable waterways and reservoirs that require dredging for
maintenance.
Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the
United States" transportation network. They are prone to reduced functionality due to sediment build-up,
which can reduce the navigable depth and width of the waterway (Clark et al, 1985; M. Ribaudo, 2011). In
many cases, costly periodic dredging is necessary to keep them passable. The regulatory options could
increase or reduce costs for government and private entities responsible for maintenance of navigable
waterways by changing the need for dredging.
Reservoirs serve many functions, including water storage for drinking, irrigation, and hydropower uses, flood
control, and recreation. Streams and rivers carry sediment into reservoirs, where it can settle and build up at a
recorded average rate of 1.2 billion kilograms per reservoir every year (USGS, 2009). Sedimentation reduces
reservoir capacity (Graf et al, 2010) and the useful life of reservoirs unless measures such as dredging are
taken to reclaim capacity (Clark et al, 1985; Hargrove et al, 2010; Miranda, 2017).
9.1 Methods
In this analysis, EPA followed the same general methodology for estimating changes in costs associated with
changes in sediment depositions in navigational waterways and reservoirs that EPA used in the 2020 rule
(U.S. EPA, 2020b).1"5 The methodology utilizes information on historic dredging locations, frequency of
dredging, the amount of sediment removed, and dredging costs in conjunction with the estimated changes in
net sediment deposition (sedimentation minus erosion) in dredged waterways and reservoirs under the
regulatory options. Benefits are equal to avoided costs, calculated as the difference from historical averages in
total annualized dredging costs due to changes between the baseline and the regulatory options.
9.1.1 Estimated Changes in Navigational Dredging Costs
EPA identified 181 unique dredging jobs and 592 dredging occurrences1"6 within the affected reaches. This
corresponds to approximately 12 percent of the dredging occurrences with coordinates reported in the
Dredging Information System (U.S. Army Corps of Engineers, 2013). The recurrence interval for dredging
jobs ranged from one to 17 years across affected reaches and averaged 13.1 years. Dredging costs vary
considerably across geographic locations and dredging jobs from less than $1 per cubic yard at Sardine
105 For the 2020 rule analysis, EPA made two improvements to the methodology used in 2015. First, dredging occurrences were
considered part of a single dredging job if the latitude and longitude coordinates were identical to within two decimal places.
Second, the 10th percentile and 90th percentile of costs and sediment dredged for dredging occurrences within USACE districts
were used to fill in missing values in the Low and Eligh scenarios. EPA also made one change to the methodology used to
estimate net sediment deposition at any given location in the reach network by using the TOE4LYIELD output variable from the
SPARROW models instead of INCTOE4LYIELD. This change was implemented to be more inclusive of the upstream impacts
to affected COMIDs (INC TOZ4L YIELD excluded upstream impacts). This analysis follows the 2020 approach.
100 Dredging jobs refer to unique sites/locations defined by the U.S. Army Corps of Engineers where dredging was conducted,
whereas dredging occurrences are unique instances when dredging was conducted and may include successive dredging at the
same location.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
9: Dredging
Point1"7 in Louisiana to $485 per cubic yard at Herculaneum in St. Louis, Missouri.1"8 The median unit cost of
dredging for the entire conterminous United States is $3 per cubic yard.
Table 9-1 presents low and high estimates of dredged sediment volume and dredging costs during the period
of 2025 through 2049 in navigational waterways that may be affected by steam electric plant discharges,
based on historical averages. EPA generated low and high estimates for navigational dredging by varying the
projected future dredging occurrence, including dredging frequency and job start as well as cost of dredging
for locations that did not report location specific costs (see U.S. EPA, 2015a, Appendix K for details).
Estimated total navigational dredging costs based on historical averages range from $90.9 million to
$183.0 million per year, using a 3 percent discount rate, and from $85.2 million to $181.8 million using a
7 percent discount rate.
Table 9-1Estimated Annualized Navigational Dredging Costs at Affected Reaches Based on
Historical Averages (Millions of 2021$)
Total Sediment Dredged
(Millions Cubic Yards)
Costs at 3% Discount Rate
(Millions of 2021$ per Year)
Costs at 7% Discount Rate
(Millions of 2021$ per Year)
Low
High
Low
High
Low
High
727.7
1,320.5
$90.9
$183.0
$85.2
$181.8
Source: U.S. EPA analysis, 2022.
The difference between the estimated dredging costs using historical averages and costs resulting from the
reduction in sediment deposition under a regulatory option as compared to baseline represents the avoided
costs under the regulatory option. Table 9-2 presents estimated changes in navigational dredging costs for
four regulatory options. Using a 3 percent discount rate, benefits range from $2,900 to $4,100 under Option 1
and from $4,300 to $5,800 under Options 2, 3, and 4. Using a 7 percent discount rate, benefits range from
$2,600 to $3,900 under Option 1 and from $3,900 to $5,500 under Options 2, 3, and 4.
Table 9-2: Estimated Annualized Changes in Navigational Dredging Costs under the Regulatory
Options, Compared to Baseline
Total Reduction in Sediment
3% Discount Rate
(Millions of 2021$ per Year)3
7% Discount Rate
(Millions of 2021$ per Year)3
Regulatory
Option
Dredged (Thousands Cubic
Yards)
Low
High
Low
High
Low
High
Option 1
9.0
14.1
<$0.01
<$0.01
<$0.01
<$0.01
Option 2
12.0
17.9
<$0.01
$0.01
<$0.01
$0.01
Option 3
12.1
18.1
<$0.01
$0.01
<$0.01
$0.01
Option 4
12.1
18.2
<$0.01
$0.01
<$0.01
$0.01
a. Positive values represent cost savings.
Source: U.S. EPA analysis, 2022.
9.1.2 Estimated Changes in Reservoir Dredging Costs
EPA identified 2,612 reservoirs within the affected reaches with changes in sediment loads under at least one
of the regulatory options, corresponding to approximately one percent of the reservoirs represented in the
SPARROW models (Ator, 2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise etal.,
2019). EPA used USACE district regional estimates of average dredging costs to calculate changes in
107 The cost per cubic yard at Sardine Point is $0.12.
108 The second most expensive dredging job was $79.50 per cubic yard at the Potomac River in Virginia.
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9: Dredging
reservoir dredging costs under the regulatory options. The median cost per cubic yard ranges from $0.34 in
the Louisville USACE District (Kentucky) to $47.61 in the Rock Island USACE District (Illinois), with a
median value of $8.16 for USACE districts which contain affected reservoirs. Table 9-3 presents low and
high estimates of the projected volume of sediment to be dredged during the period of 2025 through 2049
from these reservoirs as well as estimated annualized dredging costs, based on historical averages. The
estimated annualized reservoir dredging costs based on historical averages range between $704.3 million and
$4,527.6 million using a 3 percent discount rate and $598.6 million and $4,325.2 million using a 7 percent
discount rate.
Table 9-3-: Estimated Annualized Reservoir Dredging Volume and Costs based on Historical
Averages
Total Sediment Dredged
(Millions Cubic Yards)
Costs at 3% Discount Rate
(Millions of 2021$ per Year)
Costs at 7% Discount Rate
(Millions of 2021$ per Year)
Low
High
Low
High
Low
High
6,968.5
41,810.9
$704.3
$4,527.6
$598.6
$4,325.2
Source: U.S. EPA analysis, 2022.
The difference between the estimated dredging costs using historical averages and costs resulting from the
reduction in sediment deposition under a regulatory option as compared to baseline represents the avoided
costs for that regulatory option. Table 9-4 presents avoided costs for reservoir dredging under the regulatory
options, including low and high estimates. Using a 3 percent discount rate, benefits range from $500 to $600
under Option 1, from $600 to $700 under Option 2, and from $700 to $800 under Options 3 and 4. Using a 7
percent discount rate, benefits range from $500 to $600 under Options 1 and 2 and from $600 to $700 under
Options 3 and 4.
Table 9-4: Estimated Total Annualized Changes in Reservoir Dredging Volume and Costs under the
Regulatory Options, Compared to Baseline
Regulatory
Total Reduction in Sediment
Dredged
(Thousands Cubic Yards)
Costs at 3% Discount Rate3
(Millions of 2021$ per Year)
Costs at 7% Discount Rate3
(Millions of 2021$ per Year)
Option
Low
High
Low
High
Low
High
Option 1
2.2
2.5
<$0.01
<$0.01
<$0.01
<$0.01
Option 2
2.5
2.9
<$0.01
<$0.01
<$0.01
<$0.01
Option 3
2.7
3.0
<$0.01
<$0.01
<$0.01
<$0.01
Option 4
2.7
3.0
<$0.01
<$0.01
<$0.01
<$0.01
a. Positive values represent cost savings.
Source: U.S. EPA analysis, 2022.
9.2 Limitation and Uncertainty
Table 9-5 summarizes key uncertainties and limitations in the analysis of sediment dredging benefits. A more
detailed description is provided in Appendix K of the 2015 BCA (U.S. EPA, 2015a). Note that the effect on
benefits estimates indicated in the second column of the table refers to the magnitude of the benefits rather
than the direction (i.e.. a source of uncertainty that tends to underestimate benefits indicates expectation for
larger forgone benefits or for larger realized benefits). Uncertainties and limitations associated with
SPARROW model estimates of sediment deposition are discussed in the respective regional model reports
(Ator, 2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise etal., 2019).
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9: Dredging
Table 9-5: Limitations and Uncertainties in Analysis of Changes in Dredging Costs
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The analysis scales dredging volumes
and costs in proportion to the
percent change in sediment
deposition in navigational
waterways and reservoirs.
Uncertain
EPA estimated a linear relationship between changes
in sediment deposition and dredging volumes and
costs which may not capture non-linear dynamics in
the relationships between sediment deposition and
dredging volumes and between dredging volumes and
costs.
The frequency of navigational
dredging is based on the proximity
of nearby dredging occurrences.
Uncertain
Because data in the U.S. Army Corps of Engineers
Database does not indicate whether different dredging
occurrences are part of a single dredging job, EPA
determined whether dredging occurrences are part of
a single dredging job by comparing their latitudinal and
longitudinal coordinates to two decimal places.
Changes in the precision of a job's coordinates would
affect the number of occurrences that are considered
part of the same dredging job. When precision is
changed to a single decimal place, the number of
occurrences that would be considered part of a single
dredging job increases (and vice-versa). A larger
(smaller) number of occurrences for a single dredging
job would increase (decrease) the frequency of
dredging and, as a result, total dredging costs over the
period of analysis.
The analysis of navigational
waterways includes only jobs
reported for 1998 through 2015.
Underestimate
Because some dredging jobs included in the U.S. Army
Corps of Engineers Database lack latitude and
longitude and the database does not use standardized
job names, EPA was only able to map approximately
64 percent of all recorded dredging occurrences. This
may lead to potential underestimation of historical
costs and changes in dredging costs under the
regulatory options.
The analysis of reservoir dredging is
limited to reservoirs identified on
the NHD reach network.
Underestimate
The omission of other reservoirs could understate the
magnitude of estimated historical costs and changes in
reservoir dredging benefits if there are additional
reservoirs located downstream from steam electric
power plants.
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10: Total Monetized Benefits
10 Summary of Estimated Total Monetized Benefits
Table 10-1 and Table 10-2, on the next two pages, summarize the total annualized monetized benefits using
3 percent and 7 percent discount rates, respectively.
The monetized benefits do not account for all effects of the regulatory options, including changes in certain
cancer and non-cancer health risk (e.g., effects of halogenated disinfection byproducts in drinking water,
effects of cadmium on kidney functions and bone density), impacts of pollutant load changes on T&E species
habitat, etc. See Chapter 2 for a discussion of categories of benefits EPA did not monetize. Chapter 4 through
Chapter 0 provide more detail on the estimation methodologies for each benefit category.
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10: Total Monetized Benefits
Table 10-1: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to Baseline, at 3 Percent (Millions
of 2021$)
Benefit Category
Option 1
Option 2
Option 3
Option 4
Human Health
$3.4
$12.4
$12.7
$15.8
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
$0.01
$0.01
Changes in IQ losses in children from exposure to mercury
$2.9
$3.0
$3.1
$3.1
Changes in cancer risk from disinfection by-products in drinking water
$0.5
$9.4
$9.6
$12.7
Ecological Conditions and Recreational Uses Changes
$3.0
$3.8
$4.1
$4.3
Use and nonuse values for water quality changes'5
$3.0
00
rn
¦uy
$4.1
$4.3
Market and Productivity Effects3
<$0.01
<$0.01
<$0.01
<$0.01
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
<$0.01
Air Quality-Related Effectsc
$690
$1,320
$1,540
$1,650
Climate change effects from changes in C02 emissions0
$190
$370
$440
$450
Human health effects from changes in NOx, S02, and PM2.5 emissions0
$500
$950
$1,100
$1,200
Totald
$696.4
$1,336.2
$1,556.8
$1,670.1
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air
quality-related benefits for Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
d. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2022
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10: Total Monetized Benefits
Table 10-2: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to Baseline, at 7 Percent (Millions of
2021$)
Benefit Category
Option 1
Option 2
Option 3
Option 4
Human Health
$0.8
$6.6
$6.8
$8.8
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
<$0.01
<$0.01
Changes in IQ losses in children from exposure to mercury
$0.5
$0.6
$0.6
$0.6
Changes in cancer risk from disinfection by-products in drinking water
$0.3
$6.1
$6.2
m
00
¦uy
Ecological Conditions and Recreational Uses Changes
$2.6
$3.3
$3.6
$3.7
Use and nonuse values for water quality changes'5
$2.6
$3.3
$3.6
$3.7
Market and Productivity Effects3
<$0.01
<$0.01
<$0.01
<$0.01
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
<$0.01
Air Quality-Related Effectsc
$570
$1,070
$1,280
$1,320
Climate change effects from changes in C02 emissions0
$190
$370
$440
$450
Human health effects from changes in NOx, S02, and PM2.5 emissions0
$380
$700
$840
$870
Total"
$573.5
$1,080.0
$1,290.4
$1,332.6
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air quality-
related benefits for Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
d. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2022
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11: Total Social Costs
11 Summary of Total Social Costs
This chapter discusses EPA's estimates of the costs to society under the regulatory options. Social costs
include costs incurred by both private entities and the government (e.g., in implementing the regulation). As
described further in Chapter 10 of the RIA (U.S. EPA, 2023c), EPA did not evaluate incremental baseline
costs, and associated cost savings to state governments which would no longer have to evaluate and
incorporate best professional judgment into NPDES permits under the regulatory options. Consequently, the
only category of costs used to calculate social costs are estimated technology implementation costs for steam
electric power plants.
11.1 Overview of Costs Analysis Framework
The RIA (Chapter 3) presents EPA's development of costs for the estimated 871 steam electric power plants
within the scope of the proposed rule (U.S. EPA, 2023c). These costs (pre-tax) are used as the basis of the
social cost analysis. A subset of these plants (69 to 93 plants, depending on the option) incur non-zero
incremental costs under the regulatory options, as compared to the baseline.
As described earlier in Chapter 1, EPA estimated that steam electric power plants, in the aggregate, will
implement control technologies between 2025 and 2029. For the analysis of social costs, EPA estimated a
plant- and year-explicit schedule of technology implementation cost outlays over the period of 2025 through
2049.109 This schedule accounts for retirements and repowerings by zeroing-out O&M costs to operate
treatment systems in years following unit retirement or repowering. After creating a cost-incurrence schedule
for each cost component, EPA summed the costs expected to be incurred in each year for each plant, then
aggregated these costs to estimate the total costs for each year in the analysis period. Specifically, EPA
assumed that capital costs for compliance technology equipment, installation, site preparation, construction,
and other upfront, non-annually recurring outlays associated with compliance with the regulatory options are
incurred in the modeled compliance year for each plant. Annual fixed O&M costs, including regular annual
monitoring, and annual variable O&M costs (e.g., operating labor, maintenance labor and materials,
electricity required to operate wastewater treatment systems, chemicals, combustion residual waste transport
and disposal operation and maintenance) are incurred each year. Other non-annual recurring costs are incurred
at specified intervals of 5, 6, or 10 years. See Section 3.1.2 in the RIA for details.
Following the approach used for the analyses of the 2015 and 2020 rules (U.S. EPA, 2015a, 2020b), after
technology implementation costs were assigned to the year of occurrence, the Agency adjusted these costs for
change between 2021 (the year when costs were estimated) and the year(s) of their incurrence as follows:
• All technology costs, except planning, were adjusted to their incurrence year(s) using the
Construction Cost Index (CCI) from McGraw Hill Construction and the Gross Domestic Product
(GDP) deflator index published by the U.S. Bureau of Economic Analysis (BEA).
• Planning costs were adjusted to their incurrence year(s) using the Employment Cost Index (ECI)
Bureau of Labor Statistics (BLS) and GDP deflator.
The CCI and ECI adjustment factors were developed only through the year 2031; after these years, EPA
assumed that the real change in prices is zero - that is, costs are expected to change in line with general
109 The period of analysis extends through 2049 to capture a substantive portion of the life of the wastewater treatment technology at
any steam electric power plant (20 or more years), and the last year of technology implementation (2029).
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11: Total Social Costs
inflation. EPA judges this to be a reasonable approach, given that capital expenditures will occur by 2029 and
the uncertainty of long-term future price projections.
After developing the year-explicit schedule of total costs and adjusting them for predicted real change to the
year of their incurrence, EPA calculated the present value of these cost outlays as of the anticipated rule
promulgation year by discounting the cost in each year back to 2024, using both 3 percent and 7 percent
discount rates. These discount rate values reflect guidance from the OMB regulatory analysis guidance
document, Circular A-4 (OMB, 2003). EPA calculated the constant annual equivalent value (annualized
value), again using the two values of the discount rate, 3 percent and 7 percent, over a 25-year social cost
analysis period. EPA assumed no re-installation of wastewater treatment technology during the period
covered by the social cost analysis, i.e.. upfront capital costs are incurred only once.
To assess the economic costs of the regulatory options to society, EPA relied first on the estimated costs to
steam electric power plants for the labor, equipment, material, and other economic resources needed to
comply with the regulatory options (see U.S. EPA, 2023c for details). In this analysis, the market prices for
labor, equipment, material, and other compliance resources represent the opportunity costs to society for use
of those resources in regulatory compliance. EPA assumed in its social cost analysis that the regulatory
options do not affect the aggregate quantity of electricity that will be sold to consumers and, thus, that the
rule's social cost will include no changes in consumer and producer surplus from changes in electricity sales
by the electricity industry in aggregate. Given the small impact of the regulatory options on electricity
production cost for the total industry (see RIA Chapter 5) and relatively inelastic electricity demand with
respect to price, at least in the short term (Burke and Abayasekara (2018); Bernstein and Griffin (2005)), this
approach is reasonable for the social cost analysis (for more details on the impacts of the regulatory options
on electricity production cost, see RIA Chapter 5). The social cost analysis considers costs on an as-incurred,
year-by-year basis — that is, this analysis associates each cost component to the year(s) in which they are
assumed to occur relative to the assumed rule promulgation and technology implementation years.110
Finally, as discussed in Chapter 10 of the RIA (U.S. EPA, 2023c; see Section 10.7: Paperwork Reduction Act
of 1995), the regulatory options will not result in additional administrative costs for plants to implement, and
state and federal NPDES permitting authorities to administer, the rule. As a result, the social cost analysis
focuses on the resource cost of compliance as the only direct cost incurred by society as a result of the
regulatory options.
11.2 Key Findings for Regulatory Options
Table 11-1 presents annualized incremental costs for the analyzed regulatory options, as compared to the
baseline.
110 The specific assumptions of when each cost component is incurred can be found in Chapter 3 of the RIA (U. S. EPA, 2023c).
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Table 11-1: Summary of Estimated Incremental Annualized Costs for Regulatory Options (Millions of
2021$)
Regulatory Option
Annualized Costs
3% Discount Rate
7% Discount Rate
Option 1
$88.4
$96.6
Option 2
$167.0
$180.4
Option 3
$200.3
$216.5
Option 4
$207.2
$224.1
Source: U.S. EPA Analysis, 2022.
Table 11-2 provides additional detail on the social cost calculations. The table compiles, for each regulatory
option, the assumed time profiles of technology implementation costs incurred, relative to the baseline. The
table also reports the estimated annualized values of costs at 3 percent and 7 percent discount rates (see
bottom of the table). The maximum technology implementation outlays differ across the options but are
incurred over the years 2025 through 2029, i.e., during the estimated window (defined as Period 1 in Section
3.2.1) when steam electric power plants are expected to implement wastewater treatment technologies.
Table 11-2: Time Profile of Costs to Society (Millions of 20215
>)
Year
Option 1
Option 2
Option 3
Option 4
2025
$157.9
$206.4
$256.4
$262.6
2026
$193.6
$486.2
$549.2
$560.8
2027
$172.1
$270.1
$362.9
$362.9
2028
$196.8
$415.4
$474.2
$518.5
2029
$276.0
$392.4
$471.6
$488.3
2030
$52.4
$106.4
$135.5
$141.6
2031
$54.9
$109.8
$138.4
$144.6
2032
$54.2
$109.0
$132.6
$138.1
2033
$54.1
$109.0
$132.6
$138.1
2034
$53.5
$108.4
$131.2
$136.7
2035
$54.0
$108.9
$130.6
$136.1
2036
$51.8
$106.7
$128.5
$134.0
2037
$53.5
$108.3
$130.1
$135.6
2038
$53.7
$108.6
$130.4
$135.9
2039
$52.6
$107.5
$128.2
$130.4
2040
$52.4
$107.3
$127.9
$130.2
2041
$53.3
$108.2
$128.8
$131.1
2042
$51.1
$106.0
$126.6
$128.9
2043
$52.9
$107.7
$128.4
$130.7
2044
$52.5
$107.4
$128.0
$130.3
2045
$52.4
$106.7
$126.1
$128.4
2046
$51.9
$106.3
$125.7
$127.9
2047
$52.2
$106.5
$125.9
$128.2
2048
$51.3
$105.6
$125.0
$127.3
2049
$51.4
$105.7
$125.1
$127.4
Annualized Costs, 3%
$88.4
$167.0
$200.3
$207.2
Annualized Costs, 7%
$96.6
$180.4
$216.5
$224.1
Source: U.S. EPA Analysis, 2022.
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12: Benefits and Social Costs
12 Benefits and Social Costs
This chapter compares total monetized benefits and costs for the regulatory options. Benefits and costs are
compared on two bases: (1) incrementally for each of the options analyzed as compared to the baseline and
(2) incrementally across options. The comparison of benefits and costs also satisfies the requirements of
Executive Order 12866: Regulatory Planning and Review and Executive Order 13563: Improving Regulation
and Regulatory Review (see Chapter 9 in the RIA; U.S. EPA, 2023c).
12.1 Comparison of Benefits and Costs by Option
Chapters 10 and 11 present estimates of the benefits and costs, respectively, for the regulatory options as
compared to the baseline. Table 12-1 presents EPA's estimates of benefits and costs of the regulatory options,
at 3 percent and 7 percent discount rates, and annualized over 25 years.
Table 12-1: Total Estimated Annualized Benefits and Costs by Regulatory Option and Discount
Rate, Compared to Baseline (Millions of 2021$)
Regulatory Option
Total Monetized Benefits3
Total Costs
3% Discount Rate
Option 1
$696
$88.4
Option 2
$1,336
$167.0
Option 3
$1,557
$200.3
Option 4
$1,670
$207.2
7% Discount Rate
Option 1
$573
$96.6
Option 2
$1,080
$180.4
Option 3
$1,290
$216.5
Option 4
$1,333
$224.1
a. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air quality-related benefits for
Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
Source: U.S. EPA Analysis, 2022.
12.2 Analysis of Incremental Benefits and Costs
In addition to comparing estimated benefits and costs for each regulatory option relative to the baseline, as
presented in the preceding section, EPA also estimated the benefits and costs of the options on an incremental
basis. The comparison in the preceding section addresses the simple quantitative relationship between
estimated benefits and costs for each option and determines whether costs or benefits are greater for a given
option and by how much. In contrast, incremental analysis looks at the differential relationship of benefits and
costs across options and poses a different question: as increasingly more costly options are considered, by
what amount do benefits, costs, and net benefits (i.e., benefits minus costs) change from option to option?
Incremental net benefit analysis provides insight into the net gain to society from imposing increasingly more
costly requirements.
EPA conducted the incremental net benefit analysis by calculating the change in net benefits, from option to
option, in moving from the least stringent option to successively more stringent options, where stringency is
determined based on total pollutant loads. As described in Chapter 1, the regulatory options differ in the
technology basis for different wastestreams. Thus, the difference in benefits and costs across the options
derives from the characteristics of the wastestreams controlled by an option, the relative effectiveness of the
control technology in reducing pollutant loads, the timing of control technology implementation, and the
distribution and characteristics of steam electric power plants and of the receiving reaches.
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12: Benefits and Social Costs
As reported in Table 12-2, all options have positive net annual monetized benefits, meaning benefits exceed
costs. Net annual monetized benefit estimates range from $608 million under Option 1 to $1.5 billion under
Option 4, using a 3 percent discount rate. Incremental net annual monetized benefit values are also positive
across all options, which means that the increase in benefits under the more stringent options is larger than the
increase in costs. Using a 3 percent discount rate, the incremental net annual monetized benefits of moving
from Option 1 to Option 2 is $561 million, from Option 2 to Option 3 is $187 million, and from Option 3 to
Option 4 is $106 million.
Table 12-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options, Compared
to Baseline and to Other Regulatory Options (Millions of 2021$)
Regulatory Option
Net Annual Monetized Benefits313
Incremental Net Annual Monetized
Benefit sc
3% Discount Rate
Option 1
$608
NA
Option 2
$1,169
$561.2
Option 3
$1,357
$187.3
Option 4
$1,463
$106.4
7% Discount Rate
Option 1
$477
NA
Option 2
$900
$412.7
Option 3
$1,074
$174.3
Option 4
$1,108
$34.6
NA: Not applicable for Option 1
a. Net benefits are calculated by subtracting total annualized costs from total annual monetized benefits, where both costs
and benefits are measured relative to the baseline.
b. EPA estimated the air quality-related benefits for Option 3. EPA extrapolated estimates of air quality-related benefits for
Options 1, 2, and 4 from the estimate for Option 3 that is based on IPM outputs. See Chapter 8 for details.
c. Incremental net benefits are equal to the difference between net benefits of an option and net benefits of the previous,
less stringent option.
Source: U.S. EPA Analysis, 2022.
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13: Cited References
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix A: Changes to Benefits Analysis
Appendix A Changes to Benefits Methodology since 2020 Final Rule Analysis
The table below summarizes the principal methodological changes EPA made to analyses of the benefits of
the proposed rule regulatory options, as compared to the analyses of the 2020 final rule (U.S. EPA, 2020b).
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits Category
Analysis Component
Changes to Analysis for regulatory options
[2020 final rule analysis value]
[2021 supplemental rule analysis value]
General inputs and pollutant loads
Universe of plants,
Analysis includes loadings for all coal-fired
Analysis includes updates to the steam electric
EGUs, and receiving
units operating as of 2020. The analysis
industry profile through the end of 2021,
reaches
also reflects other updates to the steam
including the timing of projected retirements
electric industry profile through the end
and refueling projects and existing treatment
of 2019, including the timing of projected
technologies. See TDD for details (U.S. EPA,
retirements and refueling projects and
2023d).
existing treatment technologies.
General pollutant
Affected reaches based on immediate
Updated immediate receiving reaches (and
loadings and
receiving reaches and flow paths in
associated downstream reaches) for selected
concentrations
medium-resolution NHD.
plants. Discharges include CRL discharge
outfalls.
SPARROW modeling of nutrient and
No change.
sediment concentrations in receiving and
downstream reaches based on the most
recent five regional SPARROW models
that use the medium-resolution NHD
stream network.
Uses the annual average loadings for two
The two analysis periods are 2025-2029 and
distinct periods during the analysis: 2021-
2030-2049.
2028 and 2029-2047, with pre-technology
implementation loads set equal to current
loads and post-retirement or repowering
loads set to zero.
Water quality index
Expresses overall water quality changes
using a seven-parameter index that
includes subindex curve parameters for
nutrients and sediment based on the
regional SPARROW models.
No change.
Population and
2019 ACS
socioeconomic
characteristics
Human health benefits from changes in exposure to halogenated disinfection byproducts in drinking water
Public water systems
Modeled changes in bromide
Modeled changes in bromide concentrations in
affected by bromide
concentrations in source water of public
source water of public water systems and total
discharges
water systems.
trihalomethane concentrations in drinking
water.
SDWIS database with
SDWIS 2020Q1 data
SDWIS 2021Q1 data
PWS network and
population served
information
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits Category
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for regulatory options
[2021 supplemental rule analysis value]
Lifetime changes in
incidence of bladder
cancer
Qualitative discussion. EPA received
public comments that further evaluation
of certain DBPs should be completed and
that the analysis at proposal should be
subjected to peer review. EPA
acknowledges that further study in this
area should be conducted, including peer
review of the model used at proposal. EPA
will continue to evaluate the scientific
data on the health impacts of DBPs.
Applied lifetime risk model to estimate changes
in bladder cancer incidence in population
served by public water systems. The modeling
approach is generally the same EPA used for
the 2019 proposed rule analysis. It is also
consistent with that in a study by Weisman et
al. (2022) which also applied the dose-response
information from Regli etal. (2015) with more
recent DBP data to estimate the potential
number of bladder cancer cases associated with
chlorination DBPs in drinking water. Weisman
et al. (2022) found that the weight of evidence
supporting causality further increased since
Regli et al., 2015.
Monetization of
changes in incidence of
bladder cancer
Because EPA did not calculate changes in
incidence of bladder cancer, the Agency
was unable to monetize this effect.
Mortality valued using VSL (U.S. EPA, 2010a).
Morbidity valued based on COI (Greco et al.,
2019).
Non-market benefits from water quality improvements
WTP for water quality
improvements
Benefits valued using a MRM
EPA added 10 new studies to the 2015 meta-
data, revised existing observations as needed to
improve consistency within the dataset, and re-
estimated the MRM (see ICF, 2022 for details).
Similar to the 2015 MRM, the model includes
spatial characteristics of the affected water
resources: size of the market, waterbody
characteristics (length and flow), availability of
substitute sites, and land use type in the
adjacent counties.
Variables characterizing the availability of
substitute sites, size of the market, and land-
use were revised based on changes in the
universe of receiving reaches and CBGs
included in the analysis.
Effects on T&E species
Categorical analysis based on designated
critical habitat overlap/proximity to
reaches with estimated changes in
NRWQC exceedances.
EPA updated the list of species included in the
analysis based on the 2020 ECOS online
database (U.S. FWS, 2020d). EPA also relied on
the habitat range of T&E species in determining
whether reaches downstream from steam
electric power plant outfalls intersect species
habitat (U.S. FWS, 2020b), rather than "critical
habitat" as the term is defined in the ESA. EPA
included all species categorized as having
higher vulnerability to water pollution in its
analysis (see Chapter 7 and Appendix H for
details). The only exception is species endemic
to springs and headwaters.
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits Category
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for regulatory options
[2021 supplemental rule analysis value]
Air quality-related effects
Emissions changes
Emissions from changes in electricity
generation profile from 2020 IPM runs.
Energy use-associated emissions were
updated to reflect emission factors
estimated using the 2020 IPM runs.
Emissions from changes in electricity
generation profile from 2022 IPM runs.
Energy use-associated emissions were updated
to reflect emission factors estimated using the
2022 IPM runs.
Air quality changes
Used the ACE modeling methodology to
estimate changes in air pollutant
concentrations.
Updated methodology to reflect the most
recent air quality surfaces.
Monetization of health
effects
Used BenMAP-CE model to estimate
associated human health benefits.
No change.
Monetization of
changes in C02
emissions
Used domestic-only SC-C02 values at 3
and 7 percent discounts.
Used global SC-C02 values at 2.5, 3 (average
and 95%), and 5 percent discounts.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix B: WQI Calculation & Subindices
Appendix B WQI Calculation and Regional Subindices
B.1 WQI Calculation
The first step in the implementation of the WQI involves obtaining water quality levels for each parameter,
and for each waterbody, under both the baseline conditions and each regulatory option. Some parameter levels
are field measurements while others are modeled values.
The second step involves transforming the parameter measurements into subindex values that express water
quality conditions on a common scale of 10 to 100. EPA used the subindex transformation curves developed
by Dunnette (1979) and Cude (2001) for the Oregon WQI for BOD, DO, and FC. For suspended sediment,
TN, and TP concentrations, EPA adapted the approach developed by Cude (2001) to account for the wide
range of natural or background nutrient and sediment concentrations that result from variability in geologic
and other region-specific conditions, and to reflect the national context of the analysis. Suspended sediment,
TN, and TP subindex curves were developed for each Level III ecoregion (Omernik & Griffith, 2014) using
pre-compliance (before the implementation of the 2020 rule) SSC and TN and TP concentrations modeled in
SPARROW at the medium-resolution NHD reach level.111 For each of the 84 Level III ecoregions intersected
by the NHD reach network, EPA derived the transformation curves by assigning a score of 100 to the 25th
percentile of the reach-level SSC level in the ecoregion (i.e., using the 25th percentile as a proxy for
"reference" concentrations), and a score of 70 to the median concentration. An exponential equation was then
fitted to the two concentration points following the approach used in Cude (2001).
For this analysis, EPA also used a toxics-specific subindex curve based on the number of NRWQC
exceedances for toxics in each waterbody. National freshwater chronic NRWQC values are available for
arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium, and zinc. See the EA for details on the
NRWQC (U.S. EPA, 2020f). To develop this subindex curve, EPA used an approach developed by the
Canadian Council of Ministers of the Environment (CCME, 2001). The CCME water quality index is based
on three attributes of water quality that relate to water quality objectives: scope (number of monitored
parameters that exceed water quality standard or toxicological benchmark); frequency (number of individual
measurements that do not meet objectives, relative to the total number of measurements for the time period of
interest) and amplitude (i.e.. amount by which measured values exceed the standards or benchmarks).
Following the CCME approach, EPA's toxics subindex considers the number of parameters with exceedances
of the relevant water quality criterion. With regards to frequency, EPA modeled long-term annual average
concentrations in ambient water, and therefore any exceedance of an NRWQC may indicate that ambient
concentrations exceed NRWQC most of the time (assumed to be 100 percent of the time). EPA did not
consider amplitude, because if the annual average concentration exceeds the chronic NRWQC then the water
is impaired for that constituent and the level of exceedance is of secondary concern. Using this approach, the
subindex curve for toxics assigns the lowest subindex score of 0 to waters where exceedances are observed
111 The SPARROW model was developed by the USGS for the regional interpretation of water-quality monitoring data. The model
relates in-stream water-quality measurements to spatially referenced characteristics of watersheds, including contaminant sources
and factors influencing terrestrial and aquatic transport. SPARROW empirically estimates the origin and fate of contaminants in
river networks and quantifies uncertainties in model predictions. More information on SPARROW can be found at
http://water.usgs.g0v/nawqa/sparr0w/FAQs/faq.html#l
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix B: WQI Calculation & Subindices
for all nine of the toxics analyzed, and a maximum score of 100 to waters where there are no exceedances.
Intermediate values are distributed evenly between 0 and 100.
Table B-l presents parameter-specific functions used for transforming water quality data into water quality
subindices for freshwater waterbodies for the six pollutants with individual subindices. Table B-2 presents the
subindex values for toxics. The equation parameters for each of the 84 ecoregion-specific SSC, TN, and TP
subindex curves are provided in the next section. The curves include threshold values below or above which
the subindex score does not change in response to changes in parameter levels. For example, improving DO
levels from 10.5 mg/L to 12 mg/L or from 2 mg/L to 3.3 mg/L would result in no change in the DO subindex
score.
Table B-1: Freshwater Water Quality Subindices
Parameter
Concentrations
Concentration
Subindex
Unit
Dissolved Oxygen (DO)
DO saturation <100%
DO
DO <3.3
mg/L
10
DO
3.3 < DO < 10.5
mg/L
-80.29+31.88xDO-1.401xDO2
DO
DO > 10.5
mg/L
100
100% < DO saturation < 275%
DO
NA
mg/L
100 x exp((DOsat - 100) x -1.197xl0"2)
275% < DO saturation
DO
NA
mg/L
10
Fecal Coliform (FC
FC
FC > 1,600
cfu/100 mL
10
FC
50 < FC < 1,600
cfu/100 mL
98 x exp((FC - 50) x -9.9178xl0"4)
FC
FC < 50
cfu/100 mL
98
Total Nitrogen (TN
a
TN
TN >TNio
mg/L
10
TN
TNioo < TN < TNio
mg/L
a x exp(TNxb); where a and b are ecoregion-
specific values
TN
TN < TNioo
mg/L
100
Total Phosphorus (TP)b
TP
TP > TPio
mg/L
10
TP
TPioo < TP < TPio
mg/L
a x exp(TPxb); where a and b are ecoregion-
specific values
TP
TP < TPioo
mg/L
100
Suspended Solids
SSC
SSC > SSCio
mg/L
10
SSC
SSCioo < SSC < SSCio
mg/L
a x exp(SSCxb); where a and b are ecoregion-
specific values
SSC
SSC < SSCioo
mg/L
100
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-1: Freshwater Water Quality Subindices
Parameter
Concentrations
Concentration
Unit
Subindex
Biochemical Oxygen Demand, 5-day (BOD)
BOD
BOD >8
mg/L
10
BOD
BOD <8
mg/L
100 xexp(BODx-0.1993)
a. TN10 and TNIOO are ecoregion-specific TN concentration values that correspond to subindex scores of 10 and 100,
respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
b. TP10 and TP100 are ecoregion-specific TP concentration values that correspond to subindex scores of 10 and 100, respectively.
Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
c. SSC10 and SSC100 are ecoregion-specific SSC concentration values that correspond to subindex scores of 10 and 100,
respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
Source: EPA analysis, 2022, based on methodology in Cude (2001).
Table B-2: Freshwater Water Quality Subindex for Toxics
Number of Toxics with NRWQC
Subindex
Exceedances
0
100.0
1
88.9
2
77.8
3
66.7
4
55.6
5
44.4
6
33.3
7
22.2
8
11.1
9
0.0
The final step in implementing the WQI involves combining the individual parameter subindices into a single
WQI value that reflects the overall water quality across the parameters. EPA calculated the overall WQI for a
given reach using a geometric mean function and assigned all WQ parameters an equal weight of 0.143 (l/7th
of the overall score). Unweighted scores for individual metrics of a WQI have previously been used in Cude
(2001), CCME, 2001, and Carruthers and Wazniak (2003).
Equation B-l presents EPA's calculation of the overall WQI score.
Equation B-1.
WQir = Uf=iQiWi
WQIr = the multiplicative water quality index (from 0 to 100) for reach r
Qi = the water quality subindex measure for parameter z
Wi = the weight of the z-th parameter (0.143)
n = the number of parameters {i.e., seven)
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix B: WQI Calculation & Subindices
B.2 Regional Subindices
The following tables provide the ecoregion-specific parameters used in estimating the suspended solids, TN,
or TP water quality subindex, as follows:
- If [WQ Parameter] < WQ Parameter 100 Subindex =100
- If WQ Parameter 100 < [WQ Parameter] < WQ Parameter 10 Subindex = a exp(b [WQ Parameter])
- If [WQ Parameter] > WQ Parameter 10 Subindex =10
Where [WQ Parameter] is the measured concentration of either suspended solids, TN, or TP and WQ
Parameter io, WQ Parameter ioo, a, and b are specified in Table B-3 for suspended solids, Table B-4 for TN,
and Table B-5 for TP.
Table B-3: Suspended Sediment Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
SSCioo
SSCio
ECOL3_01
Coast Range
140.44
-0.0069
49.5
385.0
ECOL3_02
Strait of Georgia/Puget Lowland
131.95
-0.0044
62.5
581.9
ECOL3_03
Willamette Valley
131.91
-0.0046
59.8
556.9
ECOL3_04
Cascades
108.63
-0.0080
10.4
299.7
ECOL3_05
Sierra Nevada
109.47
-0.0108
8.3
220.7
ECOL3_06
California Coastal Sage, Chaparral, and Oak
Woodlands
117.59
-0.0042
38.6
587.6
ECOL3_07
Central California Valley
105.23
-0.0012
42.0
1,940.7
ECOL3_08
Southern and Baja California Pine-Oak Mountains
122.49
-0.0062
32.8
404.8
ECOL3_09
Eastern Cascades Slopes and Foothills
110.36
-0.0053
18.6
453.5
ECOL3_10
Columbia Plateau
105.57
-0.0006
88.8
3,858.9
ECOL3_ll
Blue Mountains
118.33
-0.0026
64.2
943.1
ECOL3_12
Snake River Plain
105.49
-0.0012
45.1
1,988.9
ECOL3_13
Central Basin and Range
101.85
-0.0008
22.9
2,901.7
ECOL3_14
Mojave Basin and Range
100.33
-0.0012
2.9
1,999.7
ECOL3_15
Columbia Mountains/Northern Rockies
154.23
-0.0085
50.9
321.4
ECOL3_16
Idaho Batholith
149.46
-0.0111
36.0
242.6
ECOL3_17
Middle Rockies
102.71
-0.0057
4.7
411.9
ECOL3_18
Wyoming Basin
102.05
-0.0005
41.8
4,792.9
ECOL3_19
Wasatch and Uinta Mountains
103.18
-0.0025
12.5
929.9
ECOL3_20
Colorado Plateaus
101.57
-0.0001
111.8
16,595.3
ECOL3_21
Southern Rockies
102.90
-0.0033
8.7
712.1
ECOL3_22
Arizona/New Mexico Plateau
100.30
-0.0001
31.6
24,144.6
ECOL3_23
Arizona/New Mexico Mountains
100.62
-0.0009
6.8
2,562.6
ECOL3_24
Chihuahuan Desert
101.79
-0.0014
12.8
1,671.6
ECOL3_25
High Plains
102.70
-0.0004
66.5
5,806.3
ECOL3_26
Southwestern Tablelands
103.35
-0.0004
74.0
5,239.0
ECOL3_27
Central Great Plains
103.49
-0.0004
94.9
6,462.6
ECOL3_28
Flint Hills
111.64
-0.0012
90.3
1,979.5
ECOL3_29
Cross Timbers
106.31
-0.0017
36.9
1,425.3
4
-------
BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-3: Suspended Sediment Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
SSCioo
SSCio
ECOL3_30
Edwards Plateau
106.83
-0.0070
9.4
336.3
ECOL3_31
Southern Texas Plains/Interior Plains and Hills with
Xerophytic Shrub and Oak Forest
100.74
-0.0008
8.7
2,731.7
ECOL3_32
Texas Blackland Prairies
110.38
-0.0011
91.6
2,226.9
ECOL3_33
East Central Texas Plains
106.96
-0.0008
84.8
2,987.0
ECOL3_34
Western Gulf Coastal Plain
103.78
-0.0012
31.1
1,964.6
ECOL3_35
South Central Plains
117.84
-0.0050
32.7
491.8
ECOL3_36
Ouachita Mountains
175.85
-0.0157
36.0
182.8
ECOL3_37
Arkansas Valley
124.25
-0.0060
35.9
416.7
ECOL3_38
Boston Mountains
240.61
-0.0252
34.8
126.1
ECOL3_39
Ozark Highlands
137.77
-0.0034
95.1
778.1
ECOL3_40
Central Irregular Plains
116.98
-0.0008
193.2
3,030.6
ECOL3_41
Canadian Rockies
102.38
-0.0064
3.7
364.9
ECOL3_42
Northwestern Glaciated Plains
101.25
-0.0002
49.9
9,287.6
ECOL3_43
Northwestern Great Plains
102.30
-0.0004
50.8
5,192.4
ECOL3_44
Nebraska Sand Hills
108.78
-0.0073
11.5
327.0
ECOL3_45
Piedmont
123.28
-0.0043
48.5
582.1
ECOL3_46
Aspen Parkland/Northern Glaciated Plains
106.80
-0.0005
121.8
4,382.1
ECOL3_47
Western Corn Belt Plains
113.45
-0.0008
150.6
2,899.9
ECOL3_48
Lake Manitoba and Lake Agassiz Plain
106.32
-0.0009
66.3
2,558.1
ECOL3_49
Northern Minnesota Wetlands
104.69
-0.0047
9.7
498.9
ECOL3_50
Northern Lakes and Forests
101.64
-0.0302
0.5
76.8
ECOL3_51
North Central Hardwood Forests
101.18
-0.0063
1.9
367.1
ECOL3_52
Driftless Area
113.90
-0.0025
51.8
968.9
ECOL3_53
Southeastern Wisconsin Till Plains
107.87
-0.0015
50.0
1,569.9
ECOL3_54
Central Corn Belt Plains
126.49
-0.0018
132.9
1,434.9
ECOL3_55
Eastern Corn Belt Plains
137.96
-0.0013
238.5
1,945.4
ECOL3_56
Southern Michigan/Northern Indiana Drift Plains
104.69
-0.0049
9.4
482.9
ECOL3_57
Huron/Erie Lake Plains
110.27
-0.0022
45.0
1,105.5
ECOL3_58
Northern Appalachian and Atlantic Maritime
Highlands
105.30
-0.0220
2.3
106.9
ECOL3_59
Northeastern Coastal Zone
109.98
-0.0213
4.5
112.6
ECOL3_60
Northern Allegheny Plateau
112.39
-0.0059
19.7
408.7
ECOL3_61
Erie Drift Plain
115.53
-0.0021
69.3
1,174.2
ECOL3_62
North Central Appalachians
122.90
-0.0192
10.7
130.6
ECOL3_63
Middle Atlantic Coastal Plain
105.17
-0.0077
6.6
306.4
ECOL3_64
Northern Piedmont
124.31
-0.0048
45.0
521.0
ECOL3_65
Southeastern Plains
118.94
-0.0065
26.8
382.9
ECOL3_66
Blue Ridge
108.09
-0.0080
9.7
297.3
ECOL3_67
Ridge and Valley
115.89
-0.0049
30.1
500.8
ECOL3_68
Southwestern Appalachians
124.64
-0.0070
31.5
360.3
ECOL3_69
Central Appalachians
121.03
-0.0113
16.9
220.7
ECOL3_70
Western Allegheny Plateau
120.20
-0.0030
61.8
835.8
ECOL3_71
Interior Plateau
137.46
-0.0038
84.8
698.8
ECOL3_72
Interior River Valleys and Hills
116.26
-0.0011
135.9
2,212.1
ECOL3_73
Mississippi Alluvial Plain
105.34
-0.0008
63.4
2,866.1
ECOL3_74
Mississippi Valley Loess Plains
115.94
-0.0026
56.1
930.1
5
-------
BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix B: WQI Calculation & Subindices
Table B-3: Suspended Sediment Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
SSCioo
SSCio
ECOL3_75
Southern Coastal Plain
100.33
-0.0113
0.3
204.7
ECOL3_77
North Cascades
140.30
-0.0083
40.9
318.7
ECOL3_78
Klamath Mountains
142.69
-0.0124
28.6
213.7
ECOL3_79
Madrean Archipelago
100.41
-0.0021
1.9
1,078.2
ECOL3_80
Northern Basin and Range
102.69
-0.0010
26.5
2,319.2
ECOL3_81
Sonoran Desert
100.09
-0.0021
0.4
1,072.2
ECOL3_82
Acadian Plains and Hills
110.65
-0.0302
3.4
79.7
ECOL3_83
Eastern Great Lakes Lowlands
103.55
-0.0031
11.4
764.8
ECOL3_84
Atlantic Coastal Pine Barrens
105.25
-0.0173
3.0
135.8
ECOL3_85
California Coastal Sage, Chaparral, and Oak
Woodlands
104.56
-0.0005
95.8
5,039.6
Table B-4: TN Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TNioo
TN 10
ECOL3_01
Coast Range
117.12
-1.576
0.10
1.56
ECOL3_02
Strait of Georgia/Puget Lowland
115.02
-0.618
0.23
3.95
ECOL3_03
Willamette Valley
124.45
-0.626
0.35
4.03
ECOL3_04
Cascades
140.20
-4.890
0.07
0.54
ECOL3_05
Sierra Nevada
147.87
-5.172
0.08
0.52
ECOL3_06
California Coastal Sage, Chaparral, and Oak
Woodlands
115.62
-0.753
0.19
3.25
ECOL3_07
Central California Valley
106.36
-0.182
0.34
13.02
ECOL3_08
Southern and Baja California Pine-Oak Mountains
132.91
-1.449
0.20
1.79
ECOL3_09
Eastern Cascades Slopes and Foothills
124.23
-2.589
0.08
0.97
ECOL3_10
Columbia Plateau
107.54
-0.213
0.34
11.13
ECOL3_ll
Blue Mountains
128.88
-1.825
0.14
1.40
ECOL3_12
Snake River Plain
112.05
-0.421
0.27
5.74
ECOL3_13
Central Basin and Range
142.81
-1.582
0.23
1.68
ECOL3_14
Mojave Basin and Range
168.00
-1.527
0.34
1.85
ECOL3_15
Columbia Mountains/Northern Rockies
162.78
-6.219
0.08
0.45
ECOL3_16
Idaho Batholith
175.32
-6.599
0.09
0.43
ECOL3_17
Middle Rockies
125.63
-1.555
0.15
1.63
ECOL3_18
Wyoming Basin
133.37
-0.991
0.29
2.61
ECOL3_19
Wasatch and Uinta Mountains
182.10
-3.323
0.18
0.87
ECOL3_20
Colorado Plateaus
139.56
-1.074
0.31
2.45
ECOL3_21
Southern Rockies
125.73
-1.312
0.17
1.93
ECOL3_22
Arizona/New Mexico Plateau
164.67
-1.394
0.36
2.01
ECOL3_23
Arizona/New Mexico Mountains
196.35
-2.556
0.26
1.16
ECOL3_24
Chihuahuan Desert
178.59
-1.966
0.29
1.47
ECOL3_25
High Plains
128.76
-0.238
1.06
10.73
ECOL3_26
Southwestern Tablelands
117.79
-0.402
0.41
6.14
ECOL3_27
Central Great Plains
122.53
-0.161
1.26
15.57
ECOL3_28
Flint Hills
172.99
-0.487
1.13
5.85
ECOL3_29
Cross Timbers
127.67
-0.539
0.45
4.73
ECOL3_30
Edwards Plateau
275.43
-2.830
0.36
1.17
6
-------
BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-4: TN Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TNioo
TN 10
ECOL3_31
Southern Texas Plains/Interior Plains and Hills with
Xerophytic Shrub and Oak Forest
134.52
-1.349
0.22
1.93
ECOL3_32
Texas Blackland Prairies
140.22
-0.528
0.64
5.00
ECOL3_33
East Central Texas Plains
147.35
-0.877
0.44
3.07
ECOL3_34
Western Gulf Coastal Plain
108.99
-0.486
0.18
4.91
ECOL3_35
South Central Plains
166.55
-1.506
0.34
1.87
ECOL3_36
Ouachita Mountains
549.75
-3.223
0.53
1.24
ECOL3_37
Arkansas Valley
177.73
-0.855
0.67
3.37
ECOL3_38
Boston Mountains
280.85
-1.715
0.60
1.94
ECOL3_39
Ozark Highlands
163.12
-0.707
0.69
3.95
ECOL3_40
Central Irregular Plains
180.12
-0.386
1.53
7.50
ECOL3_41
Canadian Rockies
168.86
-4.873
0.11
0.58
ECOL3_42
Northwestern Glaciated Plains
112.01
-0.198
0.57
12.19
ECOL3_43
Northwestern Great Plains
128.64
-0.450
0.56
5.67
ECOL3_44
Nebraska Sand Hills
130.07
-0.440
0.60
5.83
ECOL3_45
Piedmont
184.09
-1.008
0.61
2.89
ECOL3_46
Aspen Parkland/Northern Glaciated Plains
131.56
-0.109
2.52
23.65
ECOL3_47
Western Corn Belt Plains
135.26
-0.101
3.00
25.87
ECOL3_48
Lake Manitoba and Lake Agassiz Plain
121.75
-0.137
1.44
18.24
ECOL3_49
Northern Minnesota Wetlands
223.00
-1.380
0.58
2.25
ECOL3_50
Northern Lakes and Forests
146.53
-1.166
0.33
2.30
ECOL3_51
North Central Hardwood Forests
119.82
-0.244
0.74
10.17
ECOL3_52
Driftless Area
143.37
-0.237
1.52
11.25
ECOL3_53
Southeastern Wisconsin Till Plains
130.76
-0.155
1.73
16.60
ECOL3_54
Central Corn Belt Plains
141.14
-0.110
3.14
24.13
ECOL3_55
Eastern Corn Belt Plains
122.49
-0.109
1.86
23.00
ECOL3_56
Southern Michigan/Northern Indiana Drift Plains
129.61
-0.236
1.10
10.86
ECOL3_57
Huron/Erie Lake Plains
118.83
-0.103
1.68
24.11
ECOL3_58
Northern Appalachian and Atlantic Maritime
Highlands
180.97
-2.805
0.21
1.03
ECOL3_59
Northeastern Coastal Zone
139.63
-1.023
0.33
2.58
ECOL3_60
Northern Allegheny Plateau
135.73
-0.742
0.41
3.52
ECOL3_61
Erie Drift Plain
174.63
-0.463
1.20
6.18
ECOL3_62
North Central Appalachians
173.28
-1.578
0.35
1.81
ECOL3_63
Middle Atlantic Coastal Plain
117.16
-0.371
0.43
6.63
ECOL3_64
Northern Piedmont
127.21
-0.327
0.74
7.78
ECOL3_65
Southeastern Plains
192.15
-1.201
0.54
2.46
ECOL3_66
Blue Ridge
276.75
-1.954
0.52
1.70
ECOL3_67
Ridge and Valley
141.88
-0.720
0.49
3.69
ECOL3_68
Southwestern Appalachians
256.93
-1.490
0.63
2.18
ECOL3_69
Central Appalachians
675.15
-3.064
0.62
1.37
ECOL3_70
Western Allegheny Plateau
340.07
-1.467
0.83
2.40
ECOL3_71
Interior Plateau
152.97
-0.594
0.72
4.59
ECOL3_72
Interior River Valleys and Hills
123.32
-0.196
1.07
12.84
ECOL3_73
Mississippi Alluvial Plain
119.35
-0.337
0.53
7.37
ECOL3_74
Mississippi Valley Loess Plains
161.09
-1.056
0.45
2.63
ECOL3_75
Southern Coastal Plain
150.19
-0.711
0.57
3.81
7
-------
BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-4: TN Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TNioo
TN 10
ECOL3_77
North Cascades
161.05
-5.800
0.08
0.48
ECOL3_78
Klamath Mountains
144.12
-5.333
0.07
0.50
ECOL3_79
Madrean Archipelago
184.29
-2.163
0.28
1.35
ECOL3_80
Northern Basin and Range
118.17
-1.049
0.16
2.36
ECOL3_81
Sonoran Desert
134.26
-1.398
0.21
1.86
ECOL3_82
Acadian Plains and Hills
153.19
-3.186
0.13
0.86
ECOL3_83
Eastern Great Lakes Lowlands
124.57
-0.396
0.55
6.37
ECOL3_84
Atlantic Coastal Pine Barrens
113.96
-0.612
0.21
3.97
ECOL3_85
California Coastal Sage, Chaparral, and Oak
Woodlands
108.05
-0.149
0.52
16.00
Table B-5: TP Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TPioo
TP io
ECOL3_01
Coast Range
120.62
-11.18
0.017
0.223
ECOL3_02
Strait of Georgia/Puget Lowland
116.41
-7.23
0.021
0.340
ECOL3_03
Willamette Valley
122.02
-4.53
0.044
0.552
ECOL3_04
Cascades
127.84
-19.74
0.012
0.129
ECOL3_05
Sierra Nevada
120.03
-31.12
0.006
0.080
ECOL3_06
California Coastal Sage, Chaparral, and Oak
Woodlands
111.64
-5.08
0.022
0.475
ECOL3_07
Central California Valley
109.69
-2.16
0.043
1.110
ECOL3_08
Southern and Baja California Pine-Oak Mountains
109.66
-5.64
0.016
0.424
ECOL3_09
Eastern Cascades Slopes and Foothills
114.91
-8.82
0.016
0.277
ECOL3_10
Columbia Plateau
106.54
-0.98
0.064
2.409
ECOL3_ll
Blue Mountains
112.26
-4.21
0.027
0.575
ECOL3_12
Snake River Plain
104.86
-1.19
0.040
1.975
ECOL3_13
Central Basin and Range
106.44
-8.32
0.007
0.284
ECOL3_14
Mojave Basin and Range
102.55
-6.82
0.004
0.341
ECOL3_15
Columbia Mountains/Northern Rockies
119.55
-26.30
0.007
0.094
ECOL3_16
Idaho Batholith
124.76
-11.69
0.019
0.216
ECOL3_17
Middle Rockies
107.73
-5.56
0.013
0.427
ECOL3_18
Wyoming Basin
106.78
-1.31
0.050
1.810
ECOL3_19
Wasatch and Uinta Mountains
109.62
-15.21
0.006
0.157
ECOL3_20
Colorado Plateaus
107.19
-4.62
0.015
0.514
ECOL3_21
Southern Rockies
110.45
-6.82
0.015
0.352
ECOL3_22
Arizona/New Mexico Plateau
103.18
-4.06
0.008
0.575
ECOL3_23
Arizona/New Mexico Mountains
104.60
-13.34
0.003
0.176
ECOL3_24
Chihuahuan Desert
109.07
-12.20
0.007
0.196
ECOL3_25
High Plains
113.62
-0.57
0.225
4.282
ECOL3_26
Southwestern Tablelands
107.60
-1.24
0.059
1.913
ECOL3_27
Central Great Plains
112.74
-0.48
0.250
5.055
ECOL3_28
Flint Hills
129.43
-1.39
0.185
1.837
ECOL3_29
Cross Timbers
108.32
-3.40
0.023
0.700
ECOL3_30
Edwards Plateau
110.37
-26.58
0.004
0.090
8
-------
BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-5: TP Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TPioo
TP 10
ECOL3_31
Southern Texas Plains/Interior Plains and Hills with
Xerophytic Shrub and Oak Forest
102.67
-7.15
0.004
0.326
ECOL3_32
Texas Blackland Prairies
112.92
-1.99
0.061
1.221
ECOL3_33
East Central Texas Plains
106.42
-2.53
0.025
0.934
ECOL3_34
Western Gulf Coastal Plain
100.87
-1.57
0.006
1.469
ECOL3_35
South Central Plains
120.39
-7.58
0.024
0.328
ECOL3_36
Ouachita Mountains
133.54
-15.66
0.018
0.165
ECOL3_37
Arkansas Valley
112.48
-2.72
0.043
0.891
ECOL3_38
Boston Mountains
131.47
-9.61
0.028
0.268
ECOL3_39
Ozark Highlands
114.84
-3.37
0.041
0.724
ECOL3_40
Central Irregular Plains
164.67
-2.20
0.227
1.274
ECOL3_41
Canadian Rockies
134.76
-33.85
0.009
0.077
ECOL3_42
Northwestern Glaciated Plains
110.26
-0.62
0.158
3.877
ECOL3_43
Northwestern Great Plains
117.40
-1.13
0.142
2.186
ECOL3_44
Nebraska Sand Hills
105.59
-1.69
0.032
1.392
ECOL3_45
Piedmont
132.98
-5.22
0.055
0.496
ECOL3_46
Aspen Parkland/Northern Glaciated Plains
128.82
-0.76
0.332
3.353
ECOL3_47
Western Corn Belt Plains
172.45
-1.54
0.355
1.854
ECOL3_48
Lake Manitoba and Lake Agassiz Plain
112.93
-0.92
0.131
2.622
ECOL3_49
Northern Minnesota Wetlands
120.81
-12.32
0.015
0.202
ECOL3_50
Northern Lakes and Forests
118.45
-14.48
0.012
0.171
ECOL3_51
North Central Hardwood Forests
111.56
-2.39
0.046
1.008
ECOL3_52
Driftless Area
139.72
-2.09
0.160
1.263
ECOL3_53
Southeastern Wisconsin Till Plains
132.83
-1.83
0.155
1.411
ECOL3_54
Central Corn Belt Plains
178.81
-2.30
0.253
1.255
ECOL3_55
Eastern Corn Belt Plains
186.94
-2.86
0.219
1.025
ECOL3_56
Southern Michigan/Northern Indiana Drift Plains
130.88
-3.90
0.069
0.659
ECOL3_57
Huron/Erie Lake Plains
142.40
-3.19
0.111
0.832
ECOL3_58
Northern Appalachian and Atlantic Maritime
Highlands
132.90
-30.01
0.009
0.086
ECOL3_59
Northeastern Coastal Zone
125.36
-13.84
0.016
0.183
ECOL3_60
Northern Allegheny Plateau
126.26
-9.88
0.024
0.257
ECOL3_61
Erie Drift Plain
134.57
-3.24
0.092
0.803
ECOL3_62
North Central Appalachians
148.98
-21.89
0.018
0.123
ECOL3_63
Middle Atlantic Coastal Plain
112.32
-4.26
0.027
0.568
ECOL3_64
Northern Piedmont
141.23
-5.01
0.069
0.528
ECOL3_65
Southeastern Plains
130.40
-7.65
0.035
0.336
ECOL3_66
Blue Ridge
117.13
-8.26
0.019
0.298
ECOL3_67
Ridge and Valley
113.75
-5.34
0.024
0.455
ECOL3_68
Southwestern Appalachians
127.64
-7.37
0.033
0.345
ECOL3_69
Central Appalachians
141.58
-19.20
0.018
0.138
ECOL3_70
Western Allegheny Plateau
154.57
-6.77
0.064
0.404
ECOL3_71
Interior Plateau
119.63
-2.12
0.085
1.172
ECOL3_72
Interior River Valleys and Hills
134.24
-1.63
0.181
1.595
ECOL3_73
Mississippi Alluvial Plain
102.40
-1.04
0.023
2.229
ECOL3_74
Mississippi Valley Loess Plains
115.53
-2.27
0.064
1.078
ECOL3_75
Southern Coastal Plain
113.24
-6.14
0.020
0.395
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix B: WQI Calculation & Subindices
Table B-5: TP Subindex Curve Parameters, by Ecoregion
ID
Ecoregion Name
a
b
TPioo
TP 10
ECOL3_77
North Cascades
118.69
-17.30
0.010
0.143
ECOL3_78
Klamath Mountains
117.21
-28.37
0.006
0.087
ECOL3_79
Madrean Archipelago
104.02
-18.29
0.002
0.128
ECOL3_80
Northern Basin and Range
103.35
-2.23
0.015
1.048
ECOL3_81
Sonoran Desert
101.23
-8.38
0.001
0.276
ECOL3_82
Acadian Plains and Hills
113.37
-25.58
0.005
0.095
ECOL3_83
Eastern Great Lakes Lowlands
114.01
-3.62
0.036
0.673
ECOL3_84
Atlantic Coastal Pine Barrens
109.88
-11.65
0.008
0.206
ECOL3_85
California Coastal Sage, Chaparral, and Oak
Woodlands
104.34
-1.37
0.031
1.717
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Appendix C Additional Details on Modeling Change in Bladder Cancer
Incidence from Change in TTHM Exposure
C.1 Details on Life Table Approach
Health Impact Function
Figure C-l shows the dependence between lifetime odds of bladder cancer and drinking water TTHM
concentration as reported by Villanueva et cil. (2004). These data were used by Regli et cil. (2015) to estimate
the log-linear relationship in Equation 4-1, which is also displayed in Figure C-l. As described in Chapter 4,
Regli et cil. (2015) showed that, while the original analysis deviated from linearity, particularly at low doses,
the overall pooled exposure-response relationship for TTHM 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 TTHM.112
Figure C-1: Estimated Relationships between Lifetime Bladder Cancer Risk and TTHM Concentrations
in Drinking Water
0 20 40 60 80 100 120
THM4, ug/l
Source: Regli et at. (2015)
EPA used the Regli et cil. (2015) relationship between the lifetime odds of bladder cancer and lifetime TTHM
exposure from drinking water to derive a set of age-specific health impact functions. A person's lifetime
TTHM exposure from drinking water by age a—denoted by xa—is defined as:
112 Regli et cil. (2015) addressed some of the limitations noted in the Hrudey etal. (2015) analysis. They suggested that the seeming
discrepancy between the slope factor derived from the pooled epidemiological data and that from animal studies was due
primarily to (1) potentially high human exposures to DBPs by the inhalation route, and (2) that trihalomethanes were acting as
proxies for other carcinogenic DBPs.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Equation C-l. xa = -JLtZo TTHMt, x0 = 0.
See Table C-lat the end of this section for definitions of all variables used in the equations in this Appendix.
Assuming a baseline exposure of za and a regulatory option exposure of xa {i.e., exposure following
implementation of a regulatory option), the relative risk (RR) of bladder cancer by age a under the option
exposure relative to the baseline exposure can be expressed as:
Equation C-2. RRfc.zJ = ¦ (is„ . - tR„ + i)
where LRa is the lifetime risk of bladder cancer within age interval [0, a] (Fay et al. 2003) under baseline
conditions.
Combining Equation C-l and Equation C-2. shows that the relative risk of bladder cancer by age a based on
Regli et al. (2015) depends only on the lifetime risk and on the magnitude of change in TTHM concentration
from baseline concentration, Axa = xa — za, but not on the baseline TTHM level:
/Q( o).e0.00427-Xfl\ —1 / O(0)-e0 00427Xa \
Equation C-3. ^Reglietal.(xa>za) _ (0(0).eo.oo427 zaJ ' \ LRa ' 0(0).eo.oo427-za — LRa + lj
_ ^-0.00427-(xa-za) . ¦ gO.00427-(xa-za) _ _|_ -Q
= e-0.00427-Axa . [LRa . e0.00427-Axa _ ^ + -Q
At the average baseline TTHM concentration level of 38.05 |a,g/L reported in Regli et al. (2015), the slope of
the Regli et al. (2015) relationship appears to be a good approximation of the slope of the piece-wise linear
relationship implied by the Villanueva et al. (2004) data. For baseline TTHM levels in the 20 |a,g/L to 60 |a,g/L
range, the Regli et al. (2015) slope is steeper than the slopes of the piece-wise linear relationship whereas for
baseline TTHM levels above 60 |a,g/L the Regli et al. (2015) slope is flatter. While this potentially has
implications for the magnitude of the health effects EPA modeled,113 the relationship based on Villanueva et
al. (2004) requires detailed information on the baseline TTHM exposure for the population of interest which
is not available.
Health Risk Model
To estimate the health effects of changes in TTHM exposure, the health risk model tracks evolution of two
populations over time —the bladder cancer-free population and the bladder cancer population. These two
populations are modeled for both the baseline annual TTHM exposure scenario and for the regulatory options
TTHM exposure scenarios. Populations in the scenarios are demographically identical but they differ in the
TTHM levels to which they are exposed. The population affected by change in bromide discharges associated
113 If the piece-wise linear relationship based on Villanueva et al. (2004) reported data had been used as the basis for health impact
function, there would have been larger effect estimates for some individuals and smaller effect estimates for others relative to the
estimates obtained using the Regli et al. (2015) linear approximation.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
with a regulatory option is assumed to be exposed to baseline TTHM levels prior to the regulatory option
implementation year (in this case 2024) and to alternative TTHM levels that reflect the impact of technology
implementation under each regulatory option starting in 2025.
To capture these effects while being consistent with the remainder of the cost-benefit framework, EPA
modeled changes in health outcomes resulting from changes in exposure between 2025 and 2049. For these
exposures, EPA modeled effects out to 2124 to capture the resultant lagged changes in lifetime bladder cancer
risk, but did not attribute changes in bromide loadings and TTHM exposures to the regulatory options beyond
2049.114
EPA tracks mortality and bladder cancer experience for a set of model populations defined by sex, location,
and age attained by 2025, which is denoted by A = 0,1,2,3,... 100. Each model population is followed from
birth (corresponding to calendar year 2025 — ^4) to age 100, using a one-year time step. Below, we first
describe the process for quantifying the evolution of model population A under the baseline TTHM exposure
assumptions. We then describe the process for quantifying the evolution of the population under the
regulatory option TTHM exposures. Finally, we describe the process for estimating the total calendar year y-
specific health benefits which aggregate estimates over all model populations (A = 0,1,2,3,... 100).
Evolution of Model Population A under Baseline TTHM Exposure
Given a model population A, for each current age a and calendar year y, the following baseline exposure
zay = ~T,i=o Baseline TTHMij3,_a+i dependent quantities are computed:
• 'c=o,a,y (za,y): The number of bladder cancer-free living individuals at the beginning of age a, in year
y;
• dc=Q,a,y(za,y): The number of deaths among bladder cancer-free individuals aged a during the year
y;
• 'c=i,a,y (za,y): The number of new bladder cancer cases among individuals aged a during the year y.
To compute each quantity above, EPA makes an assumption about the priority of events that terminate a
person's existence in the pool of bladder cancer-free living individuals. These events are general population
deaths that occur with probability115 qc=o,a and new bladder cancer diagnoses that occur with probability ya.
which is approximated by age-specific annual bladder cancer incidence rate IRa ¦ 10~5. In the model, EPA
114 This approach is equivalent to assuming that TTHM levels revert back to baseline conditions at the end of the regulatory option
costing period.
115 The model does not index the general population death rates using the calendar year, because the model relies on the most recent
static life tables.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
assumes that the new cancer diagnoses occur after general population deaths and uses the following recurrent
equations for ages a > 0:116
Equation C-4.
lc=0,a,y(za,y) = ^C=0,a-l,y-l(za-l,y-l) — ^C=0,a-l,y-l (za-l,y-l) — ^C=l,a-l,y-l(za-l,y-l)
Equation C-5. dc=0ay{zay^ —
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
In estimating additional deaths in the cancer population in the year of diagnosis (i.e., when k = 0), EPA
accounts only for cancer population deaths that are in excess of the general population deaths. As such, the
estimate of additional cancer population deaths is computed as follows:
Equation C-9. ds=SAyfi(zay) = (fls=s,a,0 ~ <7c=0,a) ¦ h=s,a,y,o{za,y)>
In years that follow the initial diagnosis year (i.e.. k > 0), EPA uses the following recurrent equations to
estimate the number of people living with bladder cancer and the annual number of deaths in the bladder
cancer population:
Equation C-10. ^-S=s,a,y,k^a,y—k) ls=s,a,y,k — l(^a,y—k) ^S=s,a,y,k— \ (.^a,y—k)i
Equation C-11. ^S=s,a,;y,fc(za,;y-fc) = in three steps. First, EPA recursively
estimates LRay{zay), the lifetime risk of bladder cancer within age interval [0, a] under the baseline
conditions:
Equation C-13. LRay(zay) = ;c_ooy_^(zoy_^)' £"=<,' lc=i.j(zj.y-A+j), a > 0 and LRihy_A(zihy_A) = 0
Second, the result of Equation C-13 is combined with the relative risk estimate RR(xa y, za y), based on Regli
etal. (2015):
Equation C-14. LRay (xa y) = RR (xay, zay)LRay (zay)
This results in a series of lifetime bladder cancer risk estimates under the option conditions. Third, EPA
computes a series of new annual bladder cancer case estimates under the option conditions as follows:
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Equation C-15. lc= \ ,a,y(xa,y ) — (^a+ l.y+ l (xa+ l,y+ l ) LRay (Xa,y)) ' 'c=0,0,;y-/l (Z0,;y-/l)
Health Effects and Benefits Attributable to Regulatory Options
To characterize the overall impact of the regulatory option in a given year y, for each model population
defined by age a in 2025, sex, and location, EPA calculates three quantities: the incremental number of new
stage s bladder cancer cases (NCA y s ). the incremental number of individuals living with stage s bladder
cancer (LCA y s), and the incremental number of excess deaths in the bladder cancer population (EDA y). The
formal definitions of each of these quantities are given below:
Equation C-16.
NCA,y,s = [0 < y — 2025 + A < 100] ¦ ^s=s,y-2025+Ay,o(zy-2025+Ay) — (s=s,y-2024+Ao(xy-2025+Ay))
Equation C-17.
Z100
[0 < y — 2025 + A + k < 100]
k=l
' (js=s,y-2025+A-k,y,k(zy-2025+A-k,y-k) ~ h=s,y-202S+A-k,y,k (xy-2025+^l-fc,y-fc))
Equation C-18.
,100
EDa
Jfc=o
Zxuu
[0 < y — 2025 + A H- k
" s 100] 2
sES
These calculations are carried out to 2125, when those aged 0 years in 2025 attain the age of 100.
Table C-1: Health Risk Model Variable Definitions
Variable
Definition
O(x)
The odds of lifetime bladder cancer incident for an individual exposed to a lifetime average TTHM
concentration in residential water supply of x (ug/L)
a
Current age or age at cancer diagnosis
xa
A person's lifetime option TTHM exposure by age a
Za
A person's lifetime baseline TTHM exposure by age a
LRa
Lifetime risk of bladder cancer within age interval [0, a) under the baseline conditions
IRa
Age-specific baseline annual bladder cancer incidence rate
RR(%ai Za)
Relative risk of bladder cancer by age a given baseline exposure za and option exposure xa
A
Age in 2025 (years)
y
Calendar year
%a, y
A person's lifetime option TTHM exposure by age a given that this age occurs in year y
za, y
A person's lifetime baseline TTHM exposure by age a given that this age occurs in yeary
lc=0,a,y(za,y)
The baseline number of bladder cancer-free living individuals at the beginning of age a given that
this age occurs in year y
dc=0,a,y (^a,y )
The baseline number of deaths among bladder cancer-free individuals at age a given that this age
occurs in yeary
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix C: Bladder Cancer Model Details
Table C-1: Health Risk Model Variable Definitions
Variable
Definition
^C=l,a,v
The baseline number of new bladder cancer cases at age a given that this age occurs in year y
Za,y)
Relative risk of bladder cancer by age a given that this age occurs in year y, baseline exposure zay
and option exposure xa y
LRa,y )
Recursive estimate of the lifetime risk of bladder cancer within age interval [0, a) under the option
conditions, given that age a occurs in year y
N ^A,y,s
The incremental number of new stage s bladder cancer cases in year y for the model population
aged A in 2025.
^^A,y,s
The incremental number of individuals living with stage s bladder cancer in year y for the model
population aged A in 2025.
EDA,y
The incremental number of excess in stage s bladder cancer population in year y for the model
population aged A in 2025.
Detailed Input Data
As noted in Section 4, EPA relied on the federal government data sources including EPA SDWIS, ACS 2019
(U.S. Census Bureau, 2019), the Surveillance, Epidemiology, and End Results (SEER) program database
(National Cancer Institute), and the Center for Disease Control (CDC) National Center for Health Statistics to
characterize sex- and age group-specific general population mortality rates and bladder cancer incidence rates
used in model simulations. All of these data are compiled by the relevant federal agencies and thus meet
federal government data quality standards. These data sources are appropriate for this analysis based on the
standards underlying their collection and publication, and their applicability to analyzing health effects of
exposure to TTHM via drinking water. Table 4-6 in Section 4 summarizes the sex- and age group-specific
share of general population mortality rates and bladder cancer incidence. Table C-2 below summarizes sex-
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix C: Bladder Cancer Model Details
and age group-specific distribution of bladder cancer cases over four analyzed stages as well as onset-specific
relative survival probability for each stage.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Table C-2: Summary of Baseline Bladder Cancer Incidence Data Used in the Model
Females
Males
Age
Incidence
Percent of Incidence in Stage
Incidence
Percent of Incidence in Stage
per100K
Localized
Regional
Distant
Un staged
per100K
Localized
Regional
Distant
Un staged
<1
-
77
4.5
14
4.5
-
66
23
11
0
1-4
-
77
4.5
14
4.5
-
66
23
11
0
5-9
-
77
4.5
14
4.5
-
66
23
11
0
10-14
-
77
4.5
14
4.5
-
66
23
11
0
15-19
-
82
8.2
5.1
4.9
0.11
90
4.8
3.1
2.5
20-24
0.17
82
8.2
5.1
4.9
0.3
90
4.8
3.1
2.5
25-29
0.26
82
8.2
5.1
4.9
0.51
90
4.8
3.1
2.5
30-34
0.5
82
8.2
5.1
4.9
1.1
90
4.8
3.1
2.5
35-39
0.89
82
8.2
5.1
4.9
2.1
90
4.8
3.1
2.5
40-44
1.5
83
8.6
6.1
2.7
4.2
85
7.4
4.9
2.5
45-49
2.9
83
8.6
6.1
2.7
8.8
85
7.4
4.9
2.5
50-54
6.6
83
8.6
6.1
2.7
19
85
7.4
4.9
2.5
55-59
11
83
8.6
6.1
2.7
38
85
7.4
4.9
2.5
60-64
18
83
8.6
6.1
2.7
67
85
7.4
4.9
2.5
65-69
29
84
7.9
5.6
2.8
114
86
6.7
4.3
2.9
70-74
43
84
7.9
5.6
2.8
176
86
6.7
4.3
2.9
75-79
58
80
7.1
5.8
6.8
245
85
6.2
4.1
5.2
80-84
71
80
7.1
5.8
6.8
315
85
6.2
4.1
5.2
85+
76
80
7.1
5.8
6.8
357
85
6.2
4.1
5.2
o
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Table C-3: Summary of Relative and Absolute Bladder Cancer Survival Used in the Model
Age at
Diagnosis
Follow-Up
Time
Females
Males
Relative Survival by Stage
(Percent)
Absolute Surv
by Stage
val (Average)
Percent)
Relative Survival by Stage
(Percent)
Absolute Survival (Average) by
Stage (Percent)
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Ages 15-39
1 year
98
79
20
90
97
79
20
90
99
85
46
100
97
83
45
98
Ages 15-39
2 years
97
58
4
83
96
57
4
83
99
67
23
97
96
65
22
95
Ages 15-39
3 years
96
47
0
80
95
46
0
79
98
60
14
95
96
58
13
92
Ages 15-39
4 years
95
39
0
80
94
39
0
79
97
58
11
91
95
56
11
89
Ages 15-39
5 years
95
32
0
80
93
32
0
79
96
56
11
91
94
54
11
89
Ages 15-39
6 years
94
28
0
80
93
27
0
79
96
56
9
91
93
54
9
89
Ages 15-39
7 years
94
28
0
80
92
27
0
79
96
56
7
91
93
54
7
88
Ages 15-39
8 years
93
28
0
80
92
27
0
78
95
56
7
91
92
54
7
88
Ages 15-39
9 years
93
28
0
80
91
27
0
78
94
52
5
91
91
51
4
88
Ages 15-39
10 years
93
28
0
80
91
27
0
78
93
52
5
85
90
50
4
82
Ages 40-64
1 year
97
73
34
84
92
69
32
80
98
78
36
85
90
72
33
78
Ages 40-64
2 years
95
53
15
81
90
50
14
76
96
57
16
79
87
52
15
72
Ages 40-64
3 years
94
45
9
77
88
42
9
72
94
48
11
75
85
43
10
67
Ages 40-64
4 years
93
40
7
76
87
37
7
70
93
43
9
73
83
38
8
65
Ages 40-64
5 years
92
37
5
74
85
34
5
69
91
40
8
71
81
35
7
63
Ages 40-64
6 years
91
36
5
74
84
33
5
68
90
38
7
68
79
33
7
60
Ages 40-64
7 years
90
34
4
73
82
31
4
66
89
37
7
66
77
32
6
57
Ages 40-64
8 years
89
32
4
71
80
29
4
64
88
36
7
64
75
30
6
54
Ages 40-64
9 years
88
31
4
70
79
28
3
63
87
35
7
61
73
29
6
51
Ages 40-64
10 years
87
31
4
70
77
27
3
62
86
34
7
61
71
28
6
51
Ages 65-74
1 year
95
67
25
72
88
62
24
66
97
74
32
81
86
66
29
72
Ages 65-74
2 years
92
48
11
67
83
44
10
61
94
55
16
75
82
48
13
65
Ages 65-74
3 years
90
38
8
63
80
34
7
57
92
47
11
72
77
39
9
60
Ages 65-74
4 years
88
34
6
60
77
30
5
52
89
42
8
69
73
34
6
56
o
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Table C-3: Summary of Relative and Absolute Bladder Cancer Survival Used in the Model
Age at
Diagnosis
Follow-Up
Time
Females
Males
Relative Survival by Stage
(Percent)
Absolute Surv
by Stage
val (Average)
Percent)
Relative Survival by Stage
(Percent)
Absolute Survival (Average) by
Stage (Percent)
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Ages 65-74
5 years
86
31
5
58
73
26
5
50
88
39
6
66
70
31
5
52
Ages 65-74
6 years
85
28
5
56
71
23
4
47
86
36
6
64
66
27
4
49
Ages 65-74
7 years
84
27
4
54
68
22
3
44
84
34
5
61
62
25
4
45
Ages 65-74
8 years
82
25
4
52
64
20
3
41
82
32
5
57
58
23
4
40
Ages 65-74
9 years
81
25
3
51
61
19
2
39
80
30
4
56
54
20
3
38
Ages 65-74
10 years
79
25
3
51
58
18
2
37
79
29
4
56
50
19
3
36
Ages 75+
1 year
86
48
17
39
44
25
9
20
92
60
22
59
45
30
11
29
Ages 75+
2 years
81
36
8
32
40
18
4
16
87
44
10
51
42
21
5
24
Ages 75+
3 years
77
30
6
27
38
15
3
13
84
38
7
45
38
17
3
21
Ages 75+
4 years
76
28
5
24
36
13
2
11
81
35
5
40
35
15
2
17
Ages 75+
5 years
73
26
4
22
33
12
2
10
79
33
5
37
33
14
2
15
Ages 75+
6 years
71
24
4
22
31
11
2
9
76
32
4
34
30
13
2
13
Ages 75+
7 years
69
22
3
20
29
9
1
8
74
29
3
31
27
11
1
11
Ages 75+
8 years
68
21
3
18
27
8
1
7
72
28
3
29
25
10
1
10
Ages 75+
9 years
66
21
2
18
25
8
1
7
70
28
3
26
22
9
1
8
Ages 75+
10 years
65
18
2
18
23
6
1
6
68
28
3
23
20
8
1
7
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
Table C-4: Summary of All-Cause and Bladder Cancer Mortality Data Used in the Model
Age
Females
Males
Rate per100K
Percent
Bladder
Cancer
Rate per100K
Percent
Bladder
Cancer
All-Cause
Bladder Cancer
All-Cause
Bladder Cancer
<1
537
-
-
646
0.01
0.00
1-4
36
-
-
44
-
-
5-9
12
-
-
15
-
-
10-14
10
-
-
12
0.01
0.07
15-19
19
-
-
34
-
-
20-24
40
0.01
0.02
112
0.01
0.01
25-29
54
0.02
0.03
142
0.02
0.01
30-34
73
0.03
0.05
159
0.05
0.03
35-39
98
0.14
0.14
185
0.19
0.10
40-44
135
0.31
0.23
229
0.52
0.23
45-49
203
0.64
0.31
323
1.40
0.42
50-54
317
1.30
0.40
508
3.10
0.61
55-59
470
2.20
0.48
784
7.10
0.91
60-64
675
4.00
0.60
1,136
12.00
1.10
65-69
987
6.50
0.66
1,593
22.00
1.40
70-74
1,533
12.00
0.77
2,304
37.00
1.60
75-79
2,481
22.00
0.87
3,577
70.00
1.90
80-84
4,171
36.00
0.85
5,770
123.00
2.10
85+
-
-
0.77
-
-
1.90
C.2 Detailed Results from Analysis
The health impact model assumes that the proposed regulatory changes begin in 2025 and end by 2049 and
thus TTHM changes are in effect during this period. After 2049, TTHM levels return to baseline levels, i.e.,
ATTHM is zero. Due to the lasting effects of changes in TTHM exposure, the benefits of the policies after
2049 were included in the final calculations for each option. Table C-5 summarizes the health impact and
valuation results in millions of 2021 dollars for each proposed regulatory option, as shown graphically and
discussed in Section 4.4.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix C: Bladder Cancer Model Details
Table C-5: Number of Adverse Health Effects Avoided Over Time Starting from 2025
Option
Evaluation period
Total"
2025-2029
2030-2039
2040-2049
2050-2059
2060-2069
2070-2079
2080-2089
2090-2099
2100-2109
2110-2119
2120-2125
Cancer morbidity cases avoidedac
Option 1
0
1
1
1
1
1
1
0
0
0
0
5
Option 2
2
20
29
13
13
12
11
7
2
0
0
110
Option 3
2
21
30
14
13
13
11
7
3
0
0
112
Option 4
3
27
39
19
18
17
15
10
3
-1
0
149
Excess cancer deaths avoidedbc
Option 1
0
0
0
0
0
0
0
0
0
0
0
2
Option 2
0
4
7
4
4
4
3
3
1
0
0
31
Option 3
0
5
8
5
4
4
3
3
1
0
0
32
Option 4
1
6
10
6
5
5
4
3
2
0
0
42
Annual value of morbidity avoided (million dollars)c
Option 1
$0.00
$0.02
$0.02
$0.01
$0.01
$0.01
$0.00
$0.00
$0.00
$0.00
$0.00
$0.07
Option 2
$0.03
$0.34
$0.42
$0.20
$0.14
$0.10
$0.07
$0.04
$0.01
$0.00
$0.00
$1.35
Option 3
$0.04
$0.35
$0.43
$0.20
$0.14
$0.10
$0.07
$0.04
$0.01
$0.00
$0.00
$1.38
Option 4
$0.07
$0.45
$0.55
$0.27
$0.20
$0.14
$0.09
$0.05
$0.02
$0.00
$0.00
$1.83
Annual value of mortality avoided (million dollars)c
Option 1
$0.12
$1.80
$2.60
$1.33
$0.84
$0.61
$0.40
$0.22
$0.08
$0.00
$0.00
$8.01
Option 2
$3.64
$41.61
$54.45
$25.82
$15.91
$11.42
$7.67
$4.49
$1.61
$0.08
-$0.02
$166.68
Option 3
$3.84
$42.57
$55.70
$26.51
$16.37
$11.75
$7.88
$4.60
$1.65
$0.08
-$0.02
$170.93
Option 4
$6.91
$54.90
$71.48
$35.36
$22.28
$15.92
$10.67
$6.20
$2.22
$0.11
-$0.03
$226.01
Notes:
a. Number of TTHM-attributable bladder cancer cases that are expected to be avoided under the policy in the calendar time period.
b. Number of excess deaths among the TTHM-attributable bladder cancer cases that are expected to be avoided under the policy in the calendar time period.
c. Number of attributable cases and deaths are rounded to the nearest digit. Values of avoided morbidity and mortality are rounded to the nearest cent. Negative values represent
increases in the number of cases/deaths and morbidity/mortality costs.
d. Total TTHM-attributable adverse health effects that are expected to be avoided between 2025 and 2125 as a result of the regulatory option changes in 2025-2049.
Source: U.S. EPA Analysis, 2022
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs
Appendix C: Bladder Cancer Model Details
C.3 Temporal Distribution of Benefits
Figure C-2 and Figure C-3 illustrate patterns of changes in benefits for the four regulatory options for the 100-
year simulation period of 2025 through 2125 based on the cumulative annual value of morbidity avoided and
the cumulative annual value of mortality, respectively (values are undiscounted). These figures show the
gradual increase in benefits for Options 2, 3, and 4 between 2025 and 2049, which continues but at a reduced
rate after 2049 until levelling off around 2107. As discussed in Section 4.4, benefits decrease during the final
decade for Options 2, 3, and 4. The magnitude of benefits associated with Option 1 are much smaller than
those of Options 2, 3, and 4.
Figure C-2: Cumulative Annual Value of Cancer Morbidity Avoided, 2025-2125 (Million 2021$
undiscounted).
£ $6
o
Source: U.S. EPA Analysis, 2022.
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BCAfor Proposed Revisions to the Steam Electric Power Generating ELGs Appendix C: Bladder Cancer Model Details
Figure C-3. Cumulative Annual Value of Mortality Avoided, 2025-2125 (Million 2021$ undiscounted).
$700
Source: U.S. EPA Analysis, 2022.
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix D: Water & Fish Tissue Concentrations
Appendix D Derivation of Ambient Water and Fish Tissue Concentrations in
Downstream Reaches
This appendix describes the methodology EPA used to estimate water and fish tissue concentrations under the
baseline and each of the regulatory options. The concentrations are used as inputs to estimate the water
quality changes and human health benefits of the regulatory options. Specifically, EPA used ambient water
toxics concentrations to derive fish tissue concentrations used to analyze human health effects from
consuming self-caught fish (see Chapter 5) and to analyze non-use benefits of water quality changes (see
Chapter 6). Nutrient and suspended solids concentrations are used to support analysis of non-use benefits
from water quality changes (see Chapter 6).
The overall modeling methodology builds on data and methods described in the EA and TDD for the
regulatory options (U.S. EPA, 2023a, 2023d). The following sections discuss calculations of the toxics
concentrations in ambient water and fish tissue and nutrient and sediment concentrations in ambient water.
D.1 Toxics
Estimating Water Concentrations in each Reach
EPA first estimated the baseline and regulatory option toxics concentrations in reaches receiving steam
electric power plant discharges and downstream reaches.
The D-FATE model (see Chapter 3) was used to estimate water concentrations. The model tracks the fate and
transport of discharged pollutants through a reach network defined based on the medium resolution NHD.119
The hydrography network represented in the D-FATE model consists of 11,515 reaches within 300 km of a
steam electric power plant, 9.3258 of which are estimated to be potentially fishable.12"
The analysis involved the following key steps for the baseline and each of the regulatory options:
• Summing plant-level loadings to the receiving reach. EPA summed the estimated plant-level
annual average loads for each unique reach receiving plant discharges from steam electric power
plants in the baseline and under the regulatory options. For a description of the approach EPA used to
identify the receiving waterbodies, see U.S. EPA, 2023a.
• Performing dilution and transport calculations. The D-FATE model calculates the concentration
of the pollutant in a given reach based on the total mass transported to the reach from upstream
sources and the EROM flows for each reach from NHDPlus v2. In the model, a plant is assumed to
119 The USGS's National Hydrology Dataset (NHD) defines a reach as a continuous piece of surface water with similar hydrologic
characteristics. In the NHD each reach is assigned a reach code; a reach may be composed of a single feature, like a lake or
isolated stream, but reaches may also be composed of a number of contiguous features. Each reach code occurs only once
throughout the nation and once assigned a reach code is permanently associated with its reach. If the reach is deleted, its reach
code is retired.
120 Reaches represented in the D-FATE model are those estimated to be potentially fishable based on type and physical
characteristics. Because the D-FATE model calculates the movement of a chemical release downstream using flow data, reaches
must have at least one downstream or upstream connecting reach and have a non-negative flow and velocity. The D-FATE model
does not calculate concentrations for certain types of reaches, such as coastlines, treatment reservoirs, and bays; the downstream
path of any chemical is assumed to stop if one of these types of reach is encountered.
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix D: Water & Fish Tissue Concentrations
release its annual load at a constant rate throughout the year. Each source-pollutant release is tracked
throughout the NHD reach network until the terminal reach.121
• Specifying concentrations in the water quality model. The D-FATE model includes background
data on estimated annual average pollutant concentrations to surface waters from facilities that
reported to the TRI in 2019. EPA added background concentrations where available to concentration
estimates from steam electric power plant dischargers.
EPA used the approach above to estimate annual average concentrations of ten toxics: arsenic, cadmium,
hexavalent chromium, copper, lead, mercury, nickel, selenium, thallium, and zinc.
Estimating Fish Tissue Concentrations in each Reach
To support analysis of the human health benefits associated with water quality improvements (see Chapter 4),
EPA estimated concentrations of arsenic, lead, and mercury in fish tissue based on the D-FATE model
outputs discussed above.
The methodology follows the same general approach described in the EA for estimating fish tissue
concentrations for receiving reaches (U.S. EPA, 2023a), but applies the calculations to the larger set of
reaches modeled using D-FATE, which include not only the receiving reaches analyzed in the EA, but also
downstream reaches. Further, the calculations use D-FATE-estimated concentrations as inputs, which account
not only for the steam electric power plant discharges, but also other major dischargers that report to TRI.
The analysis involved the following key steps for the baseline and each of the regulatory options:
1. Obtaining the relationship between water concentrations and fish tissue concentrations.
EPA used the results of the Immediate Receiving Water (IRW) model (see EA, U.S. EPA, 2023a)
to parameterize the linear relationship between water concentrations in receiving reaches and
composite fish tissue concentrations (representative of trophic levels 3 and 4 fish consumed) in
these same reaches for each of the three toxics.
2. Calculating fish tissue data for affected reaches. For reaches for which the D-FATE model
provides non-zero water concentrations (i.e.. reaches affected by steam electric power plants or
other TRI dischargers), EPA used the relationship obtained in Step 1 to calculate a preliminary
fish tissue concentration for each pollutant.
The analysis provides background toxic-specific composite fish fillet concentrations for each reach modeled
in the D-FATE model (Table D-l). Total fish tissue concentrations (D-FATE modeled concentrations plus
background concentrations) are summarized in Table D-2.
Table D-1: Background Fish Tissue Concentrations,
based on 10th percentile
Parameter
Pollutant Concentration (mg/kg)
As
0.039
Hg
0.058
Pb
0.039
Source: U.S. EPA Analysis, 2022
121 For some analyses, EPA limits the scope of reaches to 300 km (186 miles) downstream from steam electric power plant outfalls.
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BCAfor Revisions to the Steam Electric Power Generating ELGs Appendix D: Water & Fish Tissue Concentrations
Table D-2: Fish Tissue Concentrations by Regulatory Option
Regulatory
Option
Fish Fillet Concentration (mg/kg)
Arsenic
Lead
Mercury
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Period 1
Baseline
0.039
0.143
0.039
0.039
0.284
0.039
0.058
9.754
0.088
Option 1
0.039
0.090
0.039
0.039
0.284
0.039
0.058
7.044
0.075
Option 2
0.039
0.090
0.039
0.039
0.284
0.039
0.058
7.044
0.075
Option 3
0.039
0.090
0.039
0.039
0.186
0.039
0.058
7.044
0.075
Option 4
0.039
0.090
0.039
0.039
0.186
0.039
0.058
7.044
0.075
Period 2
Baseline
0.039
0.143
0.039
0.039
0.092
0.039
0.058
9.754
0.086
Option 1
0.039
0.055
0.039
0.039
0.092
0.039
0.058
1.768
0.062
Option 2
0.039
0.055
0.039
0.039
0.064
0.039
0.058
1.325
0.062
Option 3
0.039
0.055
0.039
0.039
0.040
0.039
0.058
1.325
0.062
Option 4
0.039
0.055
0.039
0.039
0.040
0.039
0.058
1.325
0.062
Source: U.S. EPA Analysis, 2022.
D.2 Nutrients and Suspended Sediment
EPA used the USGS's regional SPARROW models to estimate nutrient and sediment concentrations in
receiving and downstream reaches. The regional models used for this analysis are the five regional models
developed for the Pacific, Southwest, Midwest, Southeast, and Northeast regions for flow, total nitrogen
(TN), total phosphorus (TP), and suspended sediment (Ator, 2019; Hoos & Roland Ii, 2019; Robertson &
Saad, 2019; Wise, 2019; Wise etal., 2019). EPA adjusted the models to include a variable for steam electric
discharges using the following steps:
• Specifying a source load parameter for steam electric discharges. The regional SPARROW
models do not include an explicit explanatory variable for point sources related to industrial
dischargers (non publicly owned treatment works). EPA recalibrated the regional models by adding a
variable for steam electric loadings, initially setting all loadings for this parameter equal to zero,
assigning this new variable a calibration coefficient value of 1, and specifying zero land-to-water
delivery effects associated with this new variable.
• Appending steam electric TN, TP, and TSS loadings to regional input data. Once the regional
SPARROW models were recalibrated to include the steam electric loadings variable, EPA added the
steam electric TN, TP, and TSS122 loadings to the model input data and ran each regional model for
each pollutant to obtain catchment-level TN, TP, and SSC predictions.
For Periods 1 and 2, the SPARROW models output predicted annual average baseline and regulatory option
concentrations in each reach. EPA compared the baseline predictions to the predictions obtained for each of
the regulatory options to estimate changes in concentrations.
122 TSS loadings are converted to SSC values at this step by using location-specific relationships built into the SPARROW regional
models.
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BCAfor Revisions to the Steam Electric Power Generating ELGs Appendix E: Georeferencing of Surface Water Intakes
Appendix E Georeferencing Surface Water Intakes to the Medium-resolution
Reach Network
For the 2022 proposal analysis, EPA used the following steps to assign PWS surface water intakes to waters
represented in the medium-resolution NHD Plus version 2 dataset and identify those intakes potentially
affected by steam electric power plant discharges.
1. Identify the downstream flowpath via NHD Plus Version 2 Flowlines for all steam electric
dischargers.
2. Identify intakes within a 5-kilometer buffer of the downstream flowpath. This distance is used to
limit the set of points to be visually reviewed in the next step and provides an upper bound of the
distance between an intake and its potential associated receiving water.
3. Visually review the location of each intake within the five-kilometer buffer to determine whether
the intake is on a waterbody downstream of steam electric power plant discharges. The visual
assessment accounts for hydrographic connectivity and flow direction.
EPA then paired the intakes that were confirmed to be impacted to the closest NHD COMID based on a
simple cartesian distance.
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix F: IQ Sensitivity Analysis
Appendix F Sensitivity Analysis for IQ Point-based Human Health Effects
EPA monetized the value of an IQ point based on the methodology from Salkever (1995) but with more
recent data from the 1997 National Longitudinal Survey of Youth (U.S. EPA, 2019c). As a sensitivity
analysis of the benefits of changes in lead and mercury exposure, EPA used alternative, more conservative
estimates provided in Lin el al. (2018), which indicate that a one-point IQ reduction reduces expected lifetime
earnings by 1.39 percent, as compared to 2.63 percent based on Salkever (1995). As noted in Sections 5.3 and
5.4, values of an IQ point used in the analysis of health effects in children from lead exposure are discounted
to the third year of life to represent the midpoint of the exposed children population, and values of an IQ point
used in the analysis of health effects associated with in-utero exposure to mercury are discounted to birth.
Table F-l summarizes the estimated values of an IQ point based on Lin et al. (2018), using 3 percent and
7 percent discount rates.
Table F-1: Value of an IQ Point (2021$) based on
Expected Reductions in Lifetime Earnings
Discount Rate
Value of an IQ Point3 (2021$)
Value of an IQ point Discounted to Age 3
3 percent
$12,118
7 percent
$2,548
Value of an IQ point Discounted to Birth
3 percent
$11,089
7 percent
$2,080
a. Values are adjusted for the cost of education.
Source: U.S. EPA, 2019c and 2019d analysis of data from Lin et al. (2018)
F.1 Health Effects in Children from Changes in Lead Exposure
Table F-2 shows the benefits associated with avoided IQ losses from lead exposure via fish consumption. The
total net change in avoided IQ point losses over the entire population of children with reductions in lead
exposure ranges from 1 point to 6 points. Annualized benefits of avoided IQ losses from reductions in lead
exposure, based on the Lin et al. (2018) IQ point value, range from approximately $300 to $2,800 (3 percent
discount rate) and from approximately $100 to $600 (7 percent discount rate).
Table F-2: Estimated Benefits of Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Children 0
to 7 in Scope of the
Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049, in All
Children 0 to 7 in Scope of
the Analysis
Annualized Value of Changes in IQ Point
Losses3
(Thousands of 2021$)
3 Percent Discount
Rate
7 Percent Discount
Rate
Option 1
1,427,107
1
m
o
¦uy
1
O
¦uy
Option 2
1,427,107
2
00
o
¦uy
$0.2
Option 3
1,427,107
6
$2.8
$0.6
Option 4
1,427,107
6
$2.8
$0.6
a. Based on estimates that the loss of one IQ point results in the loss of 1.39 percent of lifetime earnings (following Lin et al. (2018)
values from U.S. EPA, 2019c).
b. The number of affected children is based on reaches analyzed across the regulatory options. Some of the children included in
this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2022
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix F: IQ Sensitivity Analysis
F.2 Heath Effects in Children from Changes in Mercury Exposure
Table F-3 shows the estimated changes in avoided IQ point losses for infants exposed to mercury in-utero and
the corresponding monetary benefits, using 3 percent and 7 percent discount rates. The total net change in
avoided IQ point losses over the entire population of infants with reductions in mercury exposure ranges from
3,712 points (Option 1) to 3,923 points (Option 4). Annualized benefits of avoided IQ losses from reductions
in mercury exposure, based on the Lin et cd. (2018) IQ point value, range from $0.3 million (7 percent
discount rate) to $1.7 million (3 percent discount rate).
Table F-3: Estimated Benefits of Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Infants in
Scope of the Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049, in All
Infants in Scope of the
Analysis
Annualized Value of Changes in IQ Point
Losses3 (Millions 2021$)
3 Percent Discount
Rate
7 Percent Discount
Rate
Option 1
187,496
3,712
$1.59
$0.28
Option 2
187,496
3,776
$1.62
$0.29
Option 3
187,496
3,920
$1.68
$0.30
Option 4
187,496
3,923
$1.69
$0.30
a. Based on estimates that the loss of one IQ point results in the loss of 1.39 percent of lifetime earnings (following Lin et al. (2018)
values from U.S. EPA, 2019c and 2019d).
b. The number of affected children is based on reaches analyzed across the regulatory options. Some of the children included in
this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2022
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix G: WTP Estimation Methodology
Appendix G Methodology for Estimating WTP for Water Quality
Changes
To estimate the nonmarket benefits of the water quality changes resulting from the regulatory options, EPA
used updated results from a meta-analysis of stated preference studies described in detail in Appendix H in the
2015 BCA (U.S. EPA, 2015a). To update results of the 2015 meta-analysis, EPA first conducted a literature
review and identified 10 new studies to augment the existing meta-data. EPA also performed quality
assurance on the meta-data, identifying revisions that improved accuracy and consistency within the meta-
data, and added or removed observations from existing studies, as appropriate. EPA then re-estimated the
MRM and made additional improvements to the model by introducing explanatory variables to account for
different survey methodologies, WTP estimation methodologies, payment mechanisms, and water quality
metrics used in some of the added studies. A memorandum titled "Revisions to the Water Quality Meta-Data
and Meta-Regression Models after the 2020 Steam Electric Analysis through December 2021" (ICF, 2022)
details changes to the meta-data and MRMs following the 2020 Steam Electric ELG analysis (U.S. EPA,
2020e), summarizes how the studies and observations included in the meta-data have changed from 2015 to
2020 to present, and compares the latest MRM results with those from 2015 (U.S. EPA, 2015a) and 2020
(U.S. EPA, 2020e).
Table G-l summarizes studies in the revised meta-data, including number of observations from each study,
state-level study location, waterbody type, geographic scope, and household WTP summary statistics. In total,
the revised meta-data includes 189 observations from 59 stated preference studies that estimated per
household WTP (use plus nonuse) for water quality changes in U.S. waterbodies. The studies address various
waterbody types including, rivers, lakes, salt ponds/marshes, and estuaries. The ten studies added to the meta-
data since 2015 are shaded in Table G-l.
Table G-1. Primary Studies Included in the Meta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
Aiken (1985)
1
CO
river/
stream and
lake
Entire state
$238.19
$238.19
$238.19
Anderson and
Edwards (1986)
1
Rl
salt pond
/marsh
Coastal salt ponds
(South Kingstown,
Charlestown, and
Narragansett)
$222.82
$222.82
$222.82
Banzhaf et al.
(2006)
2
NY
lake
Adirondack Park, New
York State
$70.86
$66.69
$75.03
Banzhaf et al.
(2016)
1
VA, WV,
TN, NC,
GA
river/
stream
Southern Appalachian
Mountains region
$18.67
$18.67
$18.67
Bockstael et al.
(1989)
2
MD, DC,
VA
estuary
Chesapeake Bay
(Baltimore-Washington
Metropolitan Area)
$137.31
$93.30
$181.32
Borisova et al.
(2008)
2
VA/WV
river/
stream
Opequon Creek
watershed
$42.54
$22.25
$62.83
Cameron and
Huppert (1989)
1
CA
estuary
San Francisco Bay
$61.07
$61.07
$61.07
Carson et al.
(1994)
2
CA
estuary
Southern California
Bight
$73.24
$50.81
$95.67
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Appendix G: WTP Estimation Methodology
Table G-1. Primary Studies Included in the Meta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
Choi and Ready
(2019)
6
PA
river/
stream
Three creek
watersheds: Spring,
Mahantango, and
Conewago
$4.56
$1.73
$10.40
Clonts and
Malone (1990)
2
AL
river/
stream
15 free-flowing rivers,
AL
$112.28
$96.56
$128.00
Collins and
Rosenberger
(2007)
1
WV
river/
stream
Cheat River Watershed
$22.43
$22.43
$22.43
Collins et al.
(2009)
1
WV
river/
stream
Deckers Creek
Watershed
$229.82
$229.82
$229.82
Corrigan (2008)
1
IA
lake
Clear Lake
$152.03
$152.03
$152.03
Croke et al.
(1986-1987)
6
IL
river/
stream
Chicago metropolitan
area river system
$90.25
$75.60
$107.18
De Zoysa (1995)
1
OH
river/
stream
Maumee River Basin
$86.53
$86.53
$86.53
Desvousges et
al. (1987)
12
PA
river/
stream
Monongahela River
basin (PA portion)
$72.98
$24.46
$169.24
Downstream
Strategies LLC
(2008)
2
PA
river/
stream
West Branch
Susquehanna River
watershed
$15.70
$13.19
$18.21
Farber and
Griner (2000)
6
PA
river/
stream
Loyalhanna Creek and
Conemaugh River
basins (western PA)
$93.91
$20.45
$183.21
Hayes et al.
(1992)
2
Rl
estuary
Upper Narragansett
Bay
$490.05
$481.71
$498.38
Herriges and
Shogren (1996)
1
IA
lake
Storm Lake watershed
$76.09
$76.09
$76.09
Hite (2002)
2
MS
river/
stream
Entire state
$74.09
$71.81
$76.36
Holland and
Johnston (2017)
6
ME
river/
stream
Merriland, Branch
Brook and Little River
Watershed
$13.90
$8.16
$21.27
Huang et al.
(1997)
2
NC
estuary
Albemarle and Pamlico
Sounds
$318.92
$314.43
$323.40
Interis and
Petrolia (2016)
10
AL/LA
estuary
Mobile Bay, AL;
Barataria-Terrebonne
estuary, LA
$87.91
$45.00
$140.47
Irvin et al.
(2007)
4
OH
river/
stream and
lake
Entire state
$26.72
$24.22
$28.64
Johnston and
Ramachandran
(2014)
3
Rl
river/
stream
Pawtuxet watershed
$14.11
$7.05
$21.16
Johnston et al.
(2002)
1
Rl
river/
stream
Wood-Pawcatuck
watershed
$48.08
$48.08
$48.08
R. J. Johnston
et al. (2017)
3
Rl
river/
stream
Pawtuxet watershed
$4.79
$2.40
$7.19
Kaoru (1993)
1
MA
salt pond
/marsh
Martha's Vineyard
$269.56
$269.56
$269.56
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Appendix G: WTP Estimation Methodology
Table G-1. Primary Studies Included in the Meta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
Lant and
Roberts (1990)
3
1 A/I L
river/
stream
Des Moines, Skunk,
English, Cedar,
Wapsipinicon, Turkey;
Illinois: Rock, Edwards,
La Moine, Sangamon,
Iroquois, and
Vermillion River basins
$177.47
$152.94
$190.26
Lant and Tobin
(1989)
9
1 A/I L
river/
stream
Edwards River,
Wapsipinicon River,
and South Skunk
drainage basins
$68.59
$50.04
$83.40
Lichtkoppler
and Blaine
(1999)
1
OH
river/
stream and
lake
Ashtabula River and
Ashtabula Harbor
$51.69
$51.69
$51.69
Lindsey (1994)
8
MD
estuary
Chesapeake Bay
$82.37
$41.18
$126.02
Lipton (2004)
1
MD
estuary
Chesapeake Bay
Watershed
$78.88
$78.88
$78.88
Londono
Cadavid and
Ando (2013)
2
IL
river/
stream
Cities of Champaign
and Urbana
$47.70
$44.30
$51.10
Loomis (1996)
1
WA
river/
stream
Elwha River
$114.75
$114.75
$114.75
Lyke (1993)
2
Wl
river/
stream and
lake
Wisconsin Great Lakes
$97.10
$73.68
$120.52
Mathews et al.
(1999)
1
MN
river/
stream
Minnesota River
$22.36
$22.36
$22.36
C. Moore et al.
(2018)
2
MD, VA,
DC, DE,
NY, PA,
WV, CT,
FL, GA,
ME, MA,
NH, NJ,
NC, Rl,
SC, VT
lake
Chesapeake Bay
Watershed
$131.21
$77.75
$184.67
N. M. Nelson et
al. (2015)
2
UT
river/
stream and
lake
Entire state
$259.70
$167.07
$352.33
Opaluch et al.
(1998)
1
NY
estuary
Peconic Estuary System
$170.73
$170.73
$170.73
Roberts and
Leitch (1997)
1
MN/SD
lake
Mud Lake
$10.30
$10.30
$10.30
Rowe et al.
(1985)
1
CO
river/
stream
Eagle River
$165.95
$165.95
$165.95
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Appendix G: WTP Estimation Methodology
Table G-1. Primary Studies Included in the Meta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
Sanders et al.
4
CO
river/
Cache la Poudre,
$198.13
$99.89
$258.99
(1990)
stream
Colorado, Conejos,
Dollores, Elk,
Encampment, Green,
Gunnison, Los Pinos,
Piedra, and Yampa
rivers
Schulze et al.
4
MT
river/
Clark Fork River Basin
$75.19
$56.62
$95.54
(1995)
stream
Shrestha and
2
FL
river/
Lake Okeechobee
$192.92
$170.12
$215.72
Alavalapati
stream and
watershed
(2004)
lake
Stumborg et al.
2
Wl
lake
Lake Mendota
$103.94
$82.28
$125.59
(2001)
Watershed
Sutherland and
1
MT
river/
Flathead River drainage
$180.05
$180.05
$180.05
Walsh (1985)
stream and
lake
system
Takatsuka
4
TN
river/
Clinch River watershed
$353.72
$224.28
$483.16
(2004)
stream
Van Houtven et
32
VA, NC,
lake
Entire state (separate
$316.16
$260.91
$374.11
al. (2014)
SC, AL,
GA, KY,
MS, TN
observations for each
state)
Wattage (1993)
2
IA
river/
stream
Bear Creek watershed
$53.68
$49.61
$57.76
Welle (1986)
4
MN
lake
Entire state
$175.44
$135.13
$227.59
Welle and
3
MN
lake
Lake Margaret and
$178.91
$13.06
$351.48
Hodgson (2011)
Sauk River Chain of
Lakes watersheds
Wey (1990)
1
Rl
salt pond
/marsh
Great Salt Pond (Block
Island)
$78.85
$78.85
$78.85
Whitehead
3
NC
river/
Neuse River watershed
$230.79
$33.93
$450.72
(2006)
stream
Whitehead and
2
NC
river/
Tar-Pamlico River
$43.08
$39.33
$46.82
Groothuis
stream
(1992)
Whitehead et
1
NC
estuary
Albermarle-Pamlico
$115.56
$115.56
$115.56
al. (1995)
estuary system
Whittington
1
TX
estuary
Galveston Bay estuary
$240.09
$240.09
$240.09
(1994)
Zhao et al.
3
Rl
river/
Pawtuxet watershed
$7.19
$3.59
$10.78
(2013)
stream and
lake
Source: U.S. EPA Analysis, 2022
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Appendix G: WTP Estimation Methodology
Similar to the 2015 MRM, the updated MRM satisfies the adding-up condition, a theoretically desirable
property.123 This condition ensures that if the model were used to estimate WTP for the cumulative water
quality change resulting from several CWA regulations, the benefits estimates would be equal to the sum of
benefits from using the model to estimate WTP for water quality changes separately for each rule (Moeltner,
2019; Newbold et al, 2018).
The meta-analysis is based on 189 observations from 59 stated preference studies, published between 1985
and 2021. The variables in the meta-data fall into four general categories:
• Study methodology and year variables characterize such features as the year in which a study was
conducted, payment vehicle and elicitation formats, and publication type. These variables are
included to explain differences in WTP across studies but are not expected to vary across benefit
transfer for different policy applications.
• Region and surveyed populations variables characterize such features as the geographical region
within the United States in which the study was conducted, the average income of respondent
households, and the representation of users and nonusers within the survey sample.
• Sampled market and affected resource variables characterize features such as the geospatial scale (or
size) of affected waterbodies, the size of the market area over which populations were sampled, as
well as land cover and the quantity of substitute waterbodies.
• Water quality (baseline and change) variables characterize baseline conditions and the extent of the
water quality change. To standardize the results across these studies, EPA expressed water quality
(baseline and change) in each study using the 100-point WQI, if they did not already employ the WQI
orWQL.
In the latest version of the MRM, EPA built upon published versions of the MRM (R. J. Johnston et al., 2017;
Johnston et al, 2019; U.S. Environmental Protection Agency, 2020b; U.S. Environmental Protection Agency,
2015a), with revisions to better account for methodological differences in the underlying studies (see ICF
(2022) for detail on changes in the meta-data and the explanatory variables used in the regression equation).
EPA also revised regional indicators to match the U.S. Census regions (U.S. Census Bureau, n.d.). To correct
for heteroskedasticity, the model is estimated using weighted least squares with observations weighted by
sample size and robust standard errors (J. P. Nelson & Kennedy, 2009). Detailed discussion of this approach
can be found in Vedogbeton and Johnston (2020). A comprehensive review of these methods is provided by
Stanley (2005).
Table G-2 provides definitions and presents descriptive statistics for variables included in the MRM, based on
the meta-data studies.
123 For a WTP function WTP (WQIo, WQI2, Yo) to satisfy the adding-up property, it must meet the simple condition that
WTP(WQIo, WQIi , Yo) + WTP(WQIi, WQI2, Yo - WTP(WQIo, WQIi, Yo)) = WTP(WQIo, WQI2, Yo) for all possible values
of baseline water quality (WQIo), potential future water quality levels (WQIi and WQI2), and baseline income (Yo).
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Appendix G: WTP Estimation Methodology
Table G-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
Dependent Variable
ln_OWTP
Natural log of WTP per unit of water quality
improvement, per household.
Natural log of
2019$
1.873
1.391
OWTPa
WTP per unit of water quality improvement,
per household.
2019$
15.931
23.595
Study Methodology and Year
OneShotVal
Binary variable indicating that the study's
survey only included one valuation question.
Binary
(Value: 0 or 1)
0.534
0.500
tax_onlyb
Binary variable indicating that the payment
mechanism used to elicit WTP is increased
taxes.
Binary
(Value: 0 or 1)
0.397
0.491
user_costb
Binary variable indicating that the payment
mechanism used to elicit WTP is increased
user costs.
Binary
(Value: 0 or 1)
0.021
0.144
RUM
Binary variable indicating that the study used a
Random Utility Model (RUM) to estimate WTP.
Binary
(Value: 0 or 1)
0.566
0.497
IBI
Binary variable indicating that the study used
the index of biotic integrity (IBI) as the water
quality metric.
Binary
(Value: 0 or 1)
0.079
0.271
Inyear
Natural log of the year in which the study was
conducted (i.e., data was collected), converted
to an index by subtracting 1980.
Natural log of
years (year
ranges from
1981 to 2017).
2.629
0.979
voluntb
Binary variable indicating that WTP was
estimated using a payment vehicle described
as voluntary as opposed to, for example,
property taxes.
Binary
(Value: 0 or 1)
0.058
0.235
non_reviewed
Binary variable indicating that the study was
not published in a peer-reviewed journal.
Binary
(Value: 0 or 1)
0.159
0.366
thesis
Binary variable indicating that the study is a
thesis.
Binary
(Value: 0 or 1)
0.079
0.271
lump_sum
Binary variable indicating that the study
provided WTP as a one-time, lump sum or
provided annual WTP values for a payment
period of five years or less. This variable
enables the benefit transfer analyst to
estimate annual WTP values by setting
lump_sum=0.
Binary
(Value: 0 or 1)
0.180
0.385
Region and Surveyed Populations
census_southc
Binary variable indicating that the affected
waters are located entirely within the South
Census region, which includes the following
states: DE, MD, DC, WV, VA, NC, SC, GA, FL, KY,
TN, MS, AL, AR, LA, OK, and TX.
Binary
(Value: 0 or 1)
0.349
0.478
census_midwestc
Binary variable indicating that the affected
waters are located entirely within the Midwest
Census region, which includes the following
states: OH, Ml, IN, IL, Wl, MN, IA, MO, ND, SD,
NE, and KS.
Binary
(Value: 0 or 1)
0.228
0.420
census_westc
Binary variable indicating that the affected
waters are located entirely within the West
Binary
(Value: 0 or 1)
0.090
0.287
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Table G-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
Census region, which includes the following
states: MT, WY, CO, NM, ID, UT, AZ, NV, WA,
OR, and CA.
non users
Binary variable indicating that the survey was
implemented over a population of nonusers
(default category for this variable is a survey of
any population that includes both users and
nonusers).
Binary
(Value: 0 or 1)
0.058
0.235
In income
Natural log of the median income (in 2019$)
for the sample area of each study based on
historical U.S. Census data. It was designed to
provide a consistent income variable given
differences in reporting of respondent income
across studies in the meta-data (i.e., mean vs.
median). Also, some studies do not report
respondent income. This variable was
estimated for all studies in the meta-data
regardless of whether the study reported
summary statistics for respondent income.
Natural log of
income (2019$)
10.946
0.160
Sampled Market and Affected Resource
swim_use
Binary variable indicating that the affected
use(s) stated in the study include swimming.
Binary
(Value: 0 or 1)
0.222
0.417
gamefish
Binary variable indicating that the affected use
stated in the study is game fishing.
Binary
(Value: 0 or 1)
0.190
0.394
ln_ar_agrd
Natural log of the proportion of the affected
resource area that is agricultural based on
National Land Cover Database (NLCD),
reflecting the nature of development in the
area surrounding the resource. The affected
resource area is defined as all counties that
intersect the affected resource(s).
Natural log of
proportion
(Proportion
Range: 0 to 1;
km2/km2)
-1.648
0.912
ln_ar_ratio
A ratio of the sampled area, in km2, relative to
the affected resource area. When not explicitly
reported in the study, the affected resource
area is measured as the total area of counties
that intersect the affected resource(s), to
create the variable ar_total_area. From here,
ln_ar_ratio = log (sa_area / ar_total_area),
where sa_area is the size of the sampled area
in km2.
Natural log of
ratio (km2/km2)
-0.594
2.408
sub_proportione
The water bodies affected by the water quality
change, as a proportion of all water bodies of
the same hydrological type in the sampled
area. The affected resource appears in both
the numerator and denominator when
calculating sub_proportion. The value can
range from 0 to 1.
Proportion
(Range: 0 to 1;
km/km)
0.351
0.401
Water Quality Baseline and Change
ln_Q
Natural log of the mid-point of the baseline
and policy water quality: Q = (l/2)( WQI-BL +
WQI-PC).
Natural log of
WQI units
3.944
0.295
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Appendix G: WTP Estimation Methodology
Table G-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
lnquality_ch
Natural log of the change in mean water
Natural log of
2.552
0.801
quality (quality_ch), specified on the WQI.
WQI units
a. Provided for informational purposes. Model uses the natural log version of the OWTP variable as the dependent variable.
b. The payment types omitted from the payment type binary variables are: (1) increased prices, (2) increased prices and/or taxes,
(3) multiple methods, (4) earmarked fund, and (5) not specified/unknown.
c. The regions omitted from the regional binary variables are the Northeast Census region (ME, NH, VT, MA, Rl, CT, NY, PA, and
NJ) and the Chesapeake Bay (studies focused on the Chesapeake Bay or Chesapeake Bay Watershed since the Chesapeake Bay
Watershed spans two Census regions).
d. In addition to the ln_ar_agr variable, EPA tested a variable forthe proportion of the affected resource area that is developed,
but it did not improve model fit.
e. The sub_proportion estimation method differs by waterbody type. For rivers, the calculation is the length of the affected river
reaches as a proportion of all reaches of the same order. For lakes and ponds, the calculation is the area of the affected
waterbody as a proportion of all water bodies of the same National Hydrography Dataset classification. For bays and estuaries,
the calculation is the shoreline length of the waterbody as a proportion of all analogous (e.g., coastal) shoreline lengths. To
account for observations where multiple waterbody types are affected, the variable sub_proportion is defined as the maximum of
separate substitute proportions for rivers, lakes, and estuaries/bays.
Source: U.S. EPA Analysis, 2022.
Using the updated meta-data, EPA developed MRMs that predict how WTP for a one-point improvement on
the WQI (hereafter, one-point WTP) depends on a variety of methodological, population, resource, and water
quality change characteristics. The estimated MRMs predict the one-point WTP values that would be
generated by a stated preference survey with a particular set of characteristics chosen to represent the water
quality changes and other specifics of the regulatory options where possible, and best practices in economic
literature (e.g., excluding outlier responses from estimating WTP). As with the 2015 meta-analysis, EPA
developed two MRMs (U.S. EPA, 2015a). Model 1 is used to provide EPA's main estimate of non-market
benefits, and Model 2 is used to develop a range of estimates to account for uncertainty in the resulting WTP
values as a sensitivity analysis. The two models differ only in how they account for the magnitude of the
water quality changes presented to respondents in the original stated preference studies:
• Model 1 assumes that individuals" one-point WTP depends on the level of water quality, but not on
the magnitude of the water quality change specified in the survey. This restriction means that the
meta-model satisfies the adding-up condition, a theoretically desirable property.
• Model 2 allows one-point WTP to depend not only on the level of water quality but also on the
magnitude of the water quality change specified in the survey. The model allows for the possibility
that one-point WTP for improving from, for example, 49 to 50 on the water quality index depends on
whether respondents were asked to value a total water quality change of 10, 20, or 50 points on a
WQI scale. This model provides a better statistical fit to the meta-data, but it satisfies the adding-up
conditions only if the same magnitude of the water quality change is considered (e.g., 10 points). To
uniquely define the demand curve and satisfy the adding-up condition using this model, EPA treats
the water quality change variable as a methodological variable and therefore must make an
assumption about the size of the water quality change that would be appropriate to use in a stated
preference survey designed to value water quality changes resulting from the regulatory options.
When the water quality change is fixed at the mean of the meta-data, the predicted WTP is very close
to the main estimate from Model 1.
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Appendix G: WTP Estimation Methodology
EPA used the two MRMs in a benefit transfer approach that follows standard methods described by Johnston
et al. (2005), Shrestha et al. (2007), and Rosenberger and Phipps (2007). Based on benefit transfer literature
(e.g., Stapler & Johnston, 2009; K.J. Boyle & Wooldridge, 2018), methodological variables are assigned
values that either reflect "best practices" associated with reducing measurement errors in primary studies or
set to their mean values over the meta-data. The literature also recommends setting variables representing
policy outcomes and policy context (/'. e., resource and population characteristics) at the levels that might be
expected from a regulation. The benefit transfer approach uses CBGs as the geographic unit of analysis.124
The transfer approach involved projecting benefits in each CBG and year, based on the following general
benefit function:
Equation G-1.
ln(OVK7Ty B) = Intercept + ^ (coefficient x (independent variable valuet)
Where
ln(OWTPr,B)
coefficient
independent
variable values
= The predicted natural log of one-point household WTP for a given year (7)
and CBG (B).
= A vector of variable coefficients from the meta-regression.
= A vector of independent variable values. Variables include baseline water
quality level ( WQI-BLt.b) and expected water quality under the regulatory
option (WQI-PCy,b) for a given year and CBG.
Here, In(OWTPr,B) is the dependent variable in the meta-analysis—the natural log of an average WTP per one
point improvement per household, in a given CBG B for water quality in a given year Y.125 The baseline water
quality level ( WQI-BLt.b) and expected water quality under the regulatory option ( WQI-PCy,b) were based on
water quality in waterbodies within a 100-mile buffer of the centroid of each CBG. A buffer of 100 miles is
consistent with Viscusi et al. (2008) and with the assumption that the majority of recreational trips would
occur within a 2-hour drive from home. Because one-point WTP is assumed to depend, according to Equation
G-1, on both baseline water quality level (WQI-BLy,b) and expected water quality under the regulatory option
(WQI-PCy.b), EPA estimated the one-point WTP for water quality changes resulting from the regulatory
options at the mid-point of the range over which water quality was changed, WQIy.b = (1/2) (WQI-BLt.b +
WQI-PCj,b).
In this analysis, EPA estimated WTP for the households in each CBG for waters within a 100-mile radius of
that CBG's centroid. EPA chose the 100 mile-radius because households are likely to be most familiar with
waterbodies and their qualities within the 100-mile distance. However, this assumption may be an
underestimate of the distance beyond which households have familiarity with and WTP for waterbodies
124 A Census Block group is a group of Census Blocks (the smallest geographic unit for the Census) in a contiguous area that never
crosses a State or county boundary. A block group typically contains a population between 600 and 3,000 individuals. There are
217,740 block groups in the 2010 Census. See http://www.census.gov/geo/maps-data/data/tallies/tractblock.html.
125 To satisfy the adding-up condition, as noted above, EPA normalized WTP values reported in the studies included in the meta-
data so that the dependent variable is WTP for a one-point improvement on the WQI.
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Appendix G: WTP Estimation Methodology
affected by steam electric power plant discharges and their quality. By focusing on a buffer around the CBG
as a unit of analysis, rather than buffers around affected waterbodies, each household is included in the
assessment exactly once, eliminating the potential for double-counting of households.126 Total national WTP
is calculated as the sum of estimated CBG-level WTP across all CBGs that have at least one affected
waterbody within 100 miles. Using this approach, EPA is unable to analyze the WTP for CBGs with no
affected waters within 100 miles. Appendix E in U.S. Environmental Protection Agency (2020b) describes
the methodology used to identify the relevant populations.
In each CBG and year, predicted WTP per household is tailored by choosing appropriate input values for the
meta-analysis parameters describing the resource(s) valued, the extent of resource changes (i.e.. WQI- PCy.b),
the scale of resource changes relative to the size of the buffer and relative to available substitutes, the
characteristics of surveyed populations (e.g., users, nonusers), and other methodological variables. For
example, EPA projected that household income (an independent variable) changes over time, resulting in
household WTP values that vary by year.
Table G-3 provides details on how EPA used the meta-analysis to predict household WTP for each CBG and
year. The table presents the estimated regression equation intercepts and variable coefficients (coefficient,) for
the two models, and the corresponding independent variables names and assigned values. The MRM allows
the Agency to forecast WTP based on assigned values for model variables that are chosen to represent a
resource change in the context of the regulatory options.
In this instance, EPA assigned six study and methodology variables, (thesis, volunt, nonreviewed, lump sum,
user cost, IB1) a value of zero. Three methodological variables (OneShotVal, taxonly, RUM) were included
with an assigned value of 1. For the study year variable (Inyear), EPA gave the variable a value of 3.6109 (or
the ln(2017-1980)), which is the maximum value in the meta-data. This value assignment reflects atime trend
interpretation of the variable. Model 2 includes an additional variable, water quality change (In quality_ch),
which allows the benefit transfer function to reflect differences in one-point WTP based on the magnitude of
changes presented to survey respondents when eliciting WTP values. To ensure that the benefit transfer
function satisfies the adding-up condition, the In quality_ch variable was treated as a demand curve shifter,
similar to the methodological control variables, and held fixed for the benefit calculations. To estimate low
and high sensitivity analysis values of WTP for water quality changes resulting from the regulatory options,
EPA estimated one-point WTP using two alternative settings of the Inqualitych variable: AWQI = 7 units
and AWQI = 20 units. These two values represent the 25th percentile and 75th percentile values of the meta-
data.
All but one of the region and surveyed population variables vary based on the characteristics of each CBG.
EPA set the variable nonusers only to zero for all CBGs because water quality changes are expected to
enhance both use and non-use values of the affected resources and thus benefit both users and nonusers (a
nonuser value of 1 implies WTP values that are representative of nonusers only, whereas the default value of
0 indicates that both users and nonusers are included in the surveyed population). For median household
income, EPA used CBG-level median household income data from the 2019 American Community Survey
(5-year data) and accounted for projected income growth over the analysis period using the methodology
described in Section 1.3.6.
126 Population double-counting issues can arise when using "distance to waterbody" to assess simultaneous improvements to many
waterbodies.
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Appendix G: WTP Estimation Methodology
The geospatial variables corresponding to the sampled market and scale of the affected resources (lnctrctgr,
ln_ar ratio, sub proportion) vary based on attributes of the CBG and attributes of the nearby affected
resources. For all options, the affected resource is based on the 9,358 NHD reaches potentially affected by
steam electric power generating plant discharges under baseline conditions. The affected resource for each
CBG is the portion of the 9,358 reaches that falls within the 100-mile buffer of the CBG. Spatial scale is held
fixed across regulatory options. The variable corresponding to the sampled market (In cir ratio) is set to the
mean value across all COMIDs within the scope of the analysis and thus does not vary across affected CBGs.
Because data on specific recreational uses of the water resources affected by the regulatory options are not
available, the recreational use variables (swimiise, game fish) are set to zero, which corresponds to
"unspecified" or "all" recreational uses in the meta-data.127 Water quality variables (O and Inqnality ch) vary
across CBGs and regulatory options based on the magnitude of the reach-length weighted average water
quality changes in resources within scope of the analysis within the 100-mile buffer of each CBG.
Table G-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
Study Methodology and Year
intercept
-2.823
-10.020
OneShotVal
0.247
0.552
1
Binary variable indicating that the study's survey only
included one valuation question. Set to one because one
valuation scenario follows best practices for generating
incentive-compatible WTP estimates (Carson et al., 2014;
Johnston, Boyle, eta!., 2017).
tax_only
-0.177
-0.478
1
Binary variable indicating that the payment mechanism used
to elicit WTP is increased taxes. Set to one because using
taxes as the payment mechanism generates incentive-
compatible WTP estimates and is inclusive of both users and
nonusers.
user_cost
-0.873
-1.199
0
Binary variable indicating that the payment mechanism used
to elicit WTP is increased user cost. Set to zero because user
cost payment mechanisms are less inclusive of nonusers
than tax-based payment mechanisms.
RUM
0.901
0.680
1
Binary variable indicating that the study used a Random
Utility Model (RUM) to estimate WTP. Set to one because
use of a RUM to estimate WTP is a standard best practice in
modern stated preference studies.
IBI
-2.355
-2.185
0
Binary variable indicating that the study used the IBI as the
water quality metric. Set to zero because the meta-
regression uses the WQI as the water quality metric, not the
IBI.
Inyear
-0.135
-0.362
ln(2017-1980)
Natural log of the year in which the study was conducted
[i.e., data were collected), converted to an index by
subtracting 1980. Set to the natural log of the maximum
value from the meta-data (ln(2017-1980)) to reflect a time
trend interpretation of the variable.
127 If a particular recreational use was not specified in the survey instrument, EPA assessed that survey
respondents were thinking of all relevant uses.
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Appendix G: WTP Estimation Methodology
Table G-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Explanation
Model 1
Model 2
Value
Binary variable indicating that WTP was estimated using a
payment vehicle described as voluntary as opposed to, for
volunt
-1.656
-1.870
0
example, property taxes. Set to zero because hypothetical
voluntary payment mechanisms are not incentive
compatible (Johnston, Boyle, et al., 2017).
Binary variable indicating that the study was not published
non_reviewed
-0.233
-0.247
0
in a peer-reviewed journal. Set to zero because studies
published in peer-reviewed journals are preferred.
Binary variable indicating that the study is a thesis or
thesis
0.431
0.580
0
dissertation. Set to zero because studies published in peer-
reviewed journals are preferred.
Binary variable indicating that the study provided WTP as a
one-time, lump sum or provided annual WTP values for a
lump_sum
0.534
0.518
0
payment period of five years or less. Set to zero to reflect
that the majority of studies from the meta-data estimated
an annual WTP, and to produce an annual WTP prediction.
Region and Surveyed Population
Binary variable indicating that the affected waters are
located entirely within the South Census region, which
census_south
0.693
0.990
Varies
includes the following states: DE, MD, DC, WV, VA, NC, SC,
GA, FL, KY, TN, MS, AL, AR, LA, OK, and TX. Set based on the
state in which the CBG is located.
Binary variable indicating that the affected waters are
located entirely within the Midwest Census region, which
census_midwest
0.667
0.945
Varies
includes the following states: OH, Ml, IN, IL, Wl, MN, IA, MO,
ND, SD, NE, and KS. Set based on the state in which the CBG
is located.
Binary variable indicating that the affected waters are
located entirely within the West Census region, which
census_west
0.393
0.400
Varies
includes the following states: MT, WY, CO, NM, ID, UT, AZ,
NV, WA, OR, and CA. Set based on the state in which the
CBG is located.
Binary variable indicating that the sampled population
included nonusers only; the alternative case includes all
nonusers
-0.283
-0.380
0
households. Set to zero to estimate the total value for water
quality changes for all households, including users and
nonusers.
Natural log of median household income values assigned
Inincome
0.478
1.199
Varies
separately for each CBG. Varies by year based on the
estimated income growth in future years.
Sampled Market and Affected Resource
swim use
0.300
0.361
0
Binary variables that identify studies in which swimming and
gamefish uses are specifically identified. Set to zero, which
gamefish
0.871
0.531
0
corresponds to all recreational uses, since data on specific
recreational uses of the reaches affected by steam electric
power plant discharges are not available.
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Appendix G: WTP Estimation Methodology
Table G-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
ln_ar_agr
-0.572
-0.654
Varies
Natural log of the proportion of the affected resource area
which is agricultural based on National Land Cover
Database, reflecting the nature of development in the area
surrounding the resource. Used Census county boundary
layers to identify counties that intersect affected resources
within the 100-mile buffer of each CBG. For intersecting
counties, calculated the fraction of total land area that is
agricultural using the National Land Cover Dataset (NLCD).
The ln_ar_agr variable was coded in the metadata to reflect
the area surrounding the affected resources.
ln_ar_ratio
-0.157
-0.153
3.675
The natural log of the ratio of the sampled area (sa_area)
relative to the affected resource area (defined as the total
area of counties that intersect the affected resource[s])
(ar_total_area). In the context of the steam electric
scenario, sa_area is set based on the total area within the
100-mile buffer from the COMIDs in scope of the analysis,
while ar_total_area is set based on the area of counties
intersecting each affected reach (COMID). ln_ar_ratio is set
to the mean value from all COMIDs within the scope of the
analysis.
sub_proportion
0.993
0.650
Varies
The size of the resources within the scope of the analysis
relative to available substitutes. Calculated as the ratio of
affected reaches miles to the total number of reach miles
within the buffer that are the same or greater than the
order(s) of the affected reaches within the buffer. Its value
can range from 0 to 1.
Water Quality
ln_Q
-0.666
-0.259
Varies
Because WTP for a one-point improvement on the WQI is
assumed to depend on both baseline water quality and
expected water quality under the regulatory option, this
variable is set to the natural log of the mid-point of the
range of water quality changes due to the regulatory
options, WQI y,b = (1/2)(WQI-BLy,b + WQI-PCy,b). Calculated
as the length-weighted average WQI score for all potentially
affected reaches within the 100-mile buffer of each CBG.
lnquality_ch
NA
-0.683
ln(7)
ln(20)
In_quality_ch was set to the natural log of AWQI=7 or
AWQI=20 for high and low estimates of one-point WTP,
respectively.
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Appendix H: T&E Species
Appendix H Identification of Threatened and Endangered Species
Potentially Affected by the Final Rule Regulatory Options
As discussed in Chapter 7, EPA identified a total of 199 T&E species whose habitat range intersects reaches
affected by steam electric power plant discharges. These species include amphibians, arachnids, birds, clams,
crustaceans, fishes, insects, mammals, reptiles, and snails. Table H-l summarizes the number of species
within each group that have habitat ranges intersecting reaches with NRWQC exceedances for at least one
pollutant under the baseline or regulatory options in Period 1 (2025-2029) or Period 2 (2030-2049). As shown
in the table, several species of amphibians, birds, clams, fishes, mammals, and reptiles have habitat ranges
overlapping reaches with baseline exceedances in Period 1. There are no additional exceedances under any of
the regulatory options, but water quality improvements under Option 3 and Option 4 reduce the number of
exceedances from the baseline conditions.
Table H-1: Number of T&E Species with Habitat Range Intersecting Reaches Downstream from Steam
Electric Power Plant Outfalls, by Species Group
Species Name
Number of Reaches with NRWQC Exceedances for at Least One Pollutant Intersecting Habitat
Ranges of T&E Species
Period 1
Period 2
a>
fM
CO
a>
fM
CO
£
£
£
£
£
£
£
£
£
£
~aj
o
o
o
o
~aj
o
o
o
o
to
a.
a.
a.
a.
to
a.
a.
a.
a.
BO
O
o
o
o
BO
O
o
o
o
Amphibians
1
1
1
1
1
1
1
1
1
1
Arachnids
0
0
0
0
0
0
0
0
0
0
Birds
6
6
6
4
4
4
4
3
3
3
Clams
9
9
9
9
9
9
9
9
9
9
Crustaceans
0
0
0
0
0
0
0
0
0
0
Fishes
3
3
3
1
1
1
1
0
0
0
Insects
0
0
0
0
0
0
0
0
0
0
Mammals
5
5
5
4
4
4
4
4
4
4
Reptiles
4
4
4
4
4
4
4
4
4
4
Snails
0
0
0
0
0
0
0
0
0
0
Total
28
28
28
23
23
23
23
21
21
21
Source: U.S. EPA Analysis, 2022
Table H-2 provides further details on the 199 T&E species whose habitat range intersects reaches affected by
steam electric power plant discharges. The table denotes, for each species, the number of reaches with at least
one reported exceedance of a NRWQC in the baseline or regulatory options in Period 1 and Period 2. The
table also includes the results of EPA's assessment of species vulnerability to water pollution. As noted in
Chapter 7, EPA classified species as follows:
• Higher vulnerability - species living in aquatic habitats for several life history stages and/or species
that obtain a majority of their food from aquatic sources.
• Moderate vulnerability - species living in aquatic habitats for one life history stage and/or species that
obtain some of their food from aquatic sources.
• Lower vulnerability - species whose habitats overlap bodies of water, but whose life history traits and
food sources are terrestrial.
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
EPA obtained species life history data from a wide variety of sources to assess T&E species vulnerability to
water pollution. These sources included U.S. DOI, 2019; Froese and Pauly, 2019; NatureServe, 2020; NOAA
Fisheries, 2020; Southwest Fisheries Science Center (SWFSC), 2019; U.S. FWS, 2019a, 2019b, 2019c,
2019d, 2019e, 2019f, 2019g, 2020a, 2020b, 2020c, 2020e, 2020f, 2020g, 2020h, 2020i, 2020j, 2020k; Upper
Colorado River Endangered Fish Recovery Program, 2020.
Section 7.3.2 discusses impacts on five higher vulnerability species whose habitat ranges intersect reaches
with estimated changes in NRWQC exceedance status under the regulatory options.
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Period 2
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Amphibians
8
Ambystoma bishopi
Moderate
0
0
0
0
0
0
0
0
0
0
Ambystoma cingulatum
Moderate
7
7
7
7
7
7
7
7
7
7
Cryptobranchus alleganiensis
bishopi
Higher
0
0
0
0
0
0
0
0
0
0
Necturus alabamensis
Higher
0
0
0
0
0
0
0
0
0
0
Phaeognathus hubrichti
Lower
0
0
0
0
0
0
0
0
0
0
Plethodon nettingi
Lower
0
0
0
0
0
0
0
0
0
0
Rana pretiosa
Higher
0
0
0
0
0
0
0
0
0
0
Rana sevosa
Lower
0
0
0
0
0
0
0
0
0
0
Arachnids
6
Cicurina baronia
Lower
0
0
0
0
0
0
0
0
0
0
Cicurina madia
Lower
0
0
0
0
0
0
0
0
0
0
Cicurina venii
Lower
0
0
0
0
0
0
0
0
0
0
Cicurina vespera
Lower
0
0
0
0
0
0
0
0
0
0
Neoleptoneta microps
Lower
0
0
0
0
0
0
0
0
0
0
Texella cokendolpheri
Lower
0
0
0
0
0
0
0
0
0
0
Birds
26
Ammodramus savannarum
florid anus
Lower
0
0
0
0
0
0
0
0
0
0
Aphelocoma coerulescens
Lower
0
0
0
0
0
0
0
0
0
0
Brachyramphus marmoratus
Moderate
0
0
0
0
0
0
0
0
0
0
Calidris canutus rufa
Lower
23
23
23
23
23
23
23
23
23
23
Campephilus principalis
Lower
0
0
0
0
0
0
0
0
0
0
Charadrius melodus
Moderate
7
7
7
7
7
7
7
2
2
2
Coccyzus americanus
Lower
0
0
0
0
0
0
0
0
0
0
Dendroica chrysoparia
Lower
0
0
0
0
0
0
0
0
0
0
Empidonax traillii extimus
Lower
0
0
0
0
0
0
0
0
0
0
Eremophila alpestris strigata
Lower
0
0
0
0
0
0
0
0
0
0
Falcofemoralis
septentrionalis
Lower
0
0
0
0
0
0
0
0
0
0
Grus americana
Moderate
0
0
0
0
0
0
0
0
0
0
Grus canadensis pulla
Moderate
0
0
0
0
0
0
0
0
0
0
Gymnogyps californianus
Lower
0
0
0
0
0
0
0
0
0
0
Laterallus jamaicensis ssp.
jamaicensis
Lower
0
0
0
0
0
0
0
0
0
0
Mycteria americana
Moderate
8
8
8
8
8
8
8
8
8
8
2
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Numenius borealis
Lower
0
0
0
0
0
0
0
0
0
0
Phoebastria (=Diomedea)
albatrus
Lower
0
0
0
0
0
0
0
0
0
0
Picoides borealis
Lower
7
7
7
7
7
7
7
7
7
7
Polyborus plancus audubonii
Lower
0
0
0
0
0
0
0
0
0
0
Rostrhamus sociabilis
plumbeus
Lower
0
0
0
0
0
0
0
0
0
0
Sterna antillarum
Higher
0
0
0
0
0
0
0
0
0
0
Sterna dougallii dougallii
Lower
0
0
0
0
0
0
0
0
0
0
Strix occidentalis lucida
Lower
0
0
0
0
0
0
0
0
0
0
Tympanuchus cupido
attwateri
Lower
0
0
0
0
0
0
0
0
0
0
Vermivora bachmanii
Moderate
0
0
0
0
0
0
0
0
0
0
Amblema neislerii
Higher
0
0
0
0
0
0
0
0
0
0
Cumberlandia monodonta
Higher
17
17
17
17
17
17
17
17
17
17
Cyprogenia stegaria
Higher
18
18
18
18
18
18
18
18
18
18
Dromus dromas
Higher
0
0
0
0
0
0
0
0
0
0
Elliptio chipolaensis
Higher
0
0
0
0
0
0
0
0
0
0
Elliptio lanceolata
Higher
0
0
0
0
0
0
0
0
0
0
Elliptio spinosa
Higher
0
0
0
0
0
0
0
0
0
0
Elliptoideus sloatianus
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma brevidens
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma capsaeformis
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma florentina
florentina
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma florentina walkeri
(=E. walkeri)
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma metastriata
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma obliquata
obliquata
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma othcaloogensis
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma torulosa
gubernaculum
Higher3
0
0
0
0
0
0
0
0
0
0
Epioblasma torulosa rangiana
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma torulosa torulosa
Higher
0
0
0
0
0
0
0
0
0
0
Epioblasma triquetra
Higher
17
17
17
17
17
17
17
17
17
17
Epioblasma turgidula
Higher
0
0
0
0
0
0
0
0
0
0
Fusconaia cor
Higher
0
0
0
0
0
0
0
0
0
0
Fusconaia cuneolus
Higher
0
0
0
0
0
0
0
0
0
0
Fusconaia masoni
Higher
0
0
0
0
0
0
0
0
0
0
Hemistena lata
Higher
0
0
0
0
0
0
0
0
0
0
Lampsilis abrupta
Higher
19
19
19
19
19
19
19
19
19
19
Lampsilis altilis
Higher
0
0
0
0
0
0
0
0
0
0
Lampsilis higginsii
Higher
0
0
0
0
0
0
0
0
0
0
Period 2
63
3
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Count
35
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Period 2
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Lampsilis perovalis
Higher
0
0
0
0
0
0
0
0
0
0
Lampsilis rafinesqueana
Higher
0
0
0
0
0
0
0
0
0
0
Lampsilis subangulata
Higher
0
0
0
0
0
0
0
0
0
0
Lampsilis virescens
Higher
0
0
0
0
0
0
0
0
0
0
Lasmigona decorata
Higher
0
0
0
0
0
0
0
0
0
0
Lemiox rimosus
Higher
0
0
0
0
0
0
0
0
0
0
Leptodea leptodon
Higher
0
0
0
0
0
0
0
0
0
0
Margaritifera hembeli
Higher
0
0
0
0
0
0
0
0
0
0
Margaritifera marrianae
Higher
0
0
0
0
0
0
0
0
0
0
Medionidus acutissimus
Higher
0
0
0
0
0
0
0
0
0
0
Medionidus parvulus
Higher
0
0
0
0
0
0
0
0
0
0
Medionidus penicillatus
Higher
0
0
0
0
0
0
0
0
0
0
Obovaria retusa
Higher
1
1
1
1
1
1
1
1
1
1
Plethobasus cicatricosus
Higher
0
0
0
0
0
0
0
0
0
0
Plethobasus cooperianus
Higher
1
1
1
1
1
1
1
1
1
1
Plethobasus cyphyus
Higher
18
18
18
18
18
18
18
18
18
18
Pleurobema clava
Higher
1
1
1
1
1
1
1
1
1
1
Pleurobema collina
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema decisum
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema furvum
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema georgianum
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema hanleyianum
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema perovatum
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema plenum
Higher
1
1
1
1
1
1
1
1
1
1
Pleurobema pyriforme
Higher
0
0
0
0
0
0
0
0
0
0
Pleurobema taitianum
Higher
0
0
0
0
0
0
0
0
0
0
Pleuronaia dolabelloides
Higher
0
0
0
0
0
0
0
0
0
0
Potamilus capax
Higher
0
0
0
0
0
0
0
0
0
0
Potamilus inflatus
Higher
0
0
0
0
0
0
0
0
0
0
Ptychobranchus greenii
Higher
0
0
0
0
0
0
0
0
0
0
Quadrula cylindrica cylindrica
Higher
0
0
0
0
0
0
0
0
0
0
Quadrula cylindrica strigillata
Higher"
0
0
0
0
0
0
0
0
0
0
Quadrula fragosa
Higher
0
0
0
0
0
0
0
0
0
0
Quadrula intermedia
Higher
0
0
0
0
0
0
0
0
0
0
Villosa fabalis
Higher"
0
0
0
0
0
0
0
0
0
0
Villosa perpurpurea
Higher
0
0
0
0
0
0
0
0
0
0
Antrolana lira
Higher
0
0
0
0
0
0
0
0
0
0
Cambarus aculabrum
Higher
0
0
0
0
0
0
0
0
0
0
Gammarus acherondytes
Moderate
0
0
0
0
0
0
0
0
0
0
Orconectes shoupic
Higher
0
0
0
0
0
0
0
0
0
0
Palaemonias alabamae
Moderate
0
0
0
0
0
0
0
0
0
0
Acipenser oxyrinchus
(=oxyrhynchus) desotoi
Higher
0
0
0
0
0
0
0
0
0
0
Amblyopsis rosae
Higher
0
0
0
0
0
0
0
0
0
0
4
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Count
10
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Chrosomus saylori
Higher"
0
0
0
0
0
0
0
0
0
0
Cottus specus
Higher"
0
0
0
0
0
0
0
0
0
0
Cyprinella caerulea
Higher
0
0
0
0
0
0
0
0
0
0
Elassoma alabama
Higher"
0
0
0
0
0
0
0
0
0
0
Erimonax monachus
Higher
0
0
0
0
0
0
0
0
0
0
Erimystax cahni
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma boschungi
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma chienense
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma etowahae
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma nianguae
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma osburni
Higher"
0
0
0
0
0
0
0
0
0
0
Etheostoma phytophilum
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma rubrum
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma scotti
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma sellare
Higher
0
0
0
0
0
0
0
0
0
0
Etheostoma trisella
Higher
0
0
0
0
0
0
0
0
0
0
Fundulus julisia
Higher"
0
0
0
0
0
0
0
0
0
0
Gila cypha
Higher
0
0
0
0
0
0
0
0
0
0
Gila elegans
Higher
0
0
0
0
0
0
0
0
0
0
Notropis cahabae
Higher
0
0
0
0
0
0
0
0
0
0
Notropis girardi
Higher
0
0
0
0
0
0
0
0
0
0
Notropis topeka (=tristis)
Higher
7
7
7
7
7
7
7
2
2
2
Noturus flavipinnis
Higher
0
0
0
0
0
0
0
0
0
0
Oncorhynchus clarkii stomias
Higher
0
0
0
0
0
0
0
0
0
0
Percina aurora
Higher
0
0
0
0
0
0
0
0
0
0
Percina rex
Higher
0
0
0
0
0
0
0
0
0
0
Percina tana si
Higher
0
0
0
0
0
0
0
0
0
0
Ptychocheilus lucius
Higher
0
0
0
0
0
0
0
0
0
0
Salvelinus confluentus
Higher
0
0
0
0
0
0
0
0
0
0
Scaphirhynchus albus
Higher
0
0
0
0
0
0
0
0
0
0
Scaphirhynchus suttkusi
Higher
0
0
0
0
0
0
0
0
0
0
Speoplatyrhinus poulsoni
Higher"
0
0
0
0
0
0
0
0
0
0
Xyrauchen texanus
Higher
0
0
0
0
0
0
0
0
0
0
Batrisodes venyivi
Lower
0
0
0
0
0
0
0
0
0
0
Bombus affinis
Lower
0
0
0
0
0
0
0
0
0
0
Cicindelidia floridana
Lower
0
0
0
0
0
0
0
0
0
0
Hesperia dacotae
Lower
0
0
0
0
0
0
0
0
0
0
Lycaeides melissa samuelis
Lower
0
0
0
0
0
0
0
0
0
0
Neonympha mitchellii
mitchellii
Lower
0
0
0
0
0
0
0
0
0
0
Nicrophorus americanus
Lower
0
0
0
0
0
0
0
0
0
0
Rhadine exilis
Lower
0
0
0
0
0
0
0
0
0
0
Rhadine infernalis
Lower
0
0
0
0
0
0
0
0
0
0
Somatochlora hineana
Higher
0
0
0
0
0
0
0
0
0
0
Period 2
5
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Period 2
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Mammals
16
Canis lupus
Lower
0
0
0
0
0
0
0
0
0
0
Corynorhinus (=Plecotus)
townsendii ingens
Lower
0
0
0
0
0
0
0
0
0
0
Corynorhinus (=Plecotus)
townsendii virginianus
Lower
0
0
0
0
0
0
0
0
0
0
Herpailurus (=Felis)
yagouaroundi cacomitli
Lower
0
0
0
0
0
0
0
0
0
0
Leopardus (=Felis) pardalis
Lower
0
0
0
0
0
0
0
0
0
0
Lynx canadensis
Lower
0
0
0
0
0
0
0
0
0
0
Mustela nigripes
Lower
0
0
0
0
0
0
0
0
0
0
Myotis grisescens
Moderate
1
1
1
1
1
1
1
1
1
1
Myotis septentrionalis
Lower
35
35
35
35
35
35
35
30
30
30
Myotis sodalis
Lower
25
25
25
25
25
25
25
25
25
25
Peromyscus polionotus
phasma
Lower
0
0
0
0
0
0
0
0
0
0
Puma (=Felis) concolor coryi
Lower
0
0
0
0
0
0
0
0
0
0
Thomomys mazama
pugetensis
Lower
0
0
0
0
0
0
0
0
0
0
Thomomys mazama tumuli
Lower
0
0
0
0
0
0
0
0
0
0
Thomomys mazama
yelmensis
Lower
0
0
0
0
0
0
0
0
0
0
Trichechus manatus
Higher
5
5
5
5
5
5
5
5
5
5
Reptiles
19
Caretta caretta
Lower
5
5
5
5
5
5
5
5
5
5
Chelonia mydas
Lower
5
5
5
5
5
5
5
5
5
5
Clemmys muhlenbergii
Moderate
0
0
0
0
0
0
0
0
0
0
Crocodylus acutus
Lower
0
0
0
0
0
0
0
0
0
0
Dermochelys coriacea
Lower
5
5
5
5
5
5
5
5
5
5
Drymarchon corais couperi
Lower
0
0
0
0
0
0
0
0
0
0
Eretmochelys imbricata
Lower
5
5
5
5
5
5
5
5
5
5
Eumeces egregius lividus
Lower
0
0
0
0
0
0
0
0
0
0
Gopherus polyphemus
Lower
0
0
0
0
0
0
0
0
0
0
Graptemys flavimaculata
Higher
0
0
0
0
0
0
0
0
0
0
Lepidochelys kempii
Lower
0
0
0
0
0
0
0
0
0
0
Neoseps reynoldsi
Lower
0
0
0
0
0
0
0
0
0
0
Pituophis melanoleucus
lodingi
Lower
0
0
0
0
0
0
0
0
0
0
Pituophis ruthveni
Lower
0
0
0
0
0
0
0
0
0
0
Pseudemys alabamensis
Higher
0
0
0
0
0
0
0
0
0
0
Sistrurus catenatus
Lower
0
0
0
0
0
0
0
0
0
0
Sternotherus depressus
Higher
0
0
0
0
0
0
0
0
0
0
Thamnophis eques megalops
Lower
0
0
0
0
0
0
0
0
0
0
Thamnophis rufipunctatus
Lower
0
0
0
0
0
0
0
0
0
0
Snails
11
Athearnia anthonyi
Higher
0
0
0
0
0
0
0
0
0
0
Campeloma decampi
Higher
0
0
0
0
0
0
0
0
0
0
6
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix H: T&E Species
Table H-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches Exceeding
NRWQC for at Least One Pollutant
Period 1
Period 2
Baseline
Option 1
Option 2
Option 3
Option 4
Baseline
Option 1
Option 2
Option 3
Option 4
Discus macclintocki
Lower
0
0
0
0
0
0
0
0
0
0
Elimia crenatella
Higher
0
0
0
0
0
0
0
0
0
0
Leptoxis foremani
Higher
0
0
0
0
0
0
0
0
0
0
Leptoxis taeniata
Higher
0
0
0
0
0
0
0
0
0
0
Lioplax cyclostomaformis
Higher
0
0
0
0
0
0
0
0
0
0
Pleurocera foremani
Higher
0
0
0
0
0
0
0
0
0
0
Pyrgulopsis ogmorhaphe
Higher
0
0
0
0
0
0
0
0
0
0
Triodopsis platysayoides
Lower
0
0
0
0
0
0
0
0
0
0
Tulotoma magnifica
Higher
0
0
0
0
0
0
0
0
0
0
a This species is presumed extinct.
b While this species is categorized as highly vulnerable to water quality changes, it is endemic to waters (headwater streams and
springs) that are not likely to receive discharges from steam electric plants or be affected by upstream discharges. EPA did not
include this species in the set of T&E species with benefits or forgone benefits as a result of the final rule.
c U.S. Fish and Wildlife Service proposed delisting this species on 11/26/2019. See notice of proposed rulemaking "Endangered and
Threatened Wildlife and Plants: Removal of the Nashville Crayfish from the Federal List of Endangered and Threatened Wildlife."
(84 FR 65098)
Source: U.S. EPA Analysis, 2022
7
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
Appendix I Methodology for Modeling Air Quality Changes for the
Proposed Rule
As noted in Chapter 8, EPA used photochemical modeling to create air quality surfaces128 that were then used
in air pollution benefits calculations of the proposed rule (i.e., Option 3). The modeling-based surfaces
captured air pollution impacts resulting from changes in electricity generation profiles due to the incremental
costs to generate electricity at plants incurring water treatment costs and did not simulate the impact of
emissions changes resulting from changes in energy use by steam electric power plants or resulting from
changes in trucking of CCR and other waste. This appendix describes the source apportionment modeling and
associated methods used to create air quality surfaces for the baseline scenario and a scenario representing
water treatment technology implementation-driven EGU profile changes for Option 3 for 7 analytic years:
2028, 2030, 2035, 2040, 2045, and 2050. EPA created air quality surfaces for the following pollutants and
metrics: annual average PM2.5; April-September average of 8-hr daily maximum (MDA8) ozone (AS-M03).
The ozone source apportionment modeling outputs are the same as those created for the Regulatory Impact
Analysis for the proposed Federal Implementation Plan Addressing Regional Ozone Transport for the 2015
Ozone National Ambient Air Quality Standard (U.S. EPA, 2022c). New PM2.5 source apportionment
modeling outputs were created using the same inputs and modeling configuration as were used for the
available ozone source apportionment modeling. The basic methodology for determining air quality changes
is the same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019g, 2020a, 2020b, 2021b,
2022c). EPA calculated baseline and Option 3 scenario EGU emissions estimates of NOx and SO2 for all
seven IPM model years from the Integrated Planning Model (IPM) (Chapter 5 of the RIA; U.S. EPA, 2020e).
EPA also used IPM outputs to estimate EGU emissions of PM25 based on emission factors described in U.S.
EPA (2020c). This appendix provides additional details on the source apportionment modeling simulations
and on the methods used to translate these emissions scenarios into air quality surfaces.
1.1 Air Quality Modeling Simulations
The air quality modeling utilized a 2016-based modeling platform which included meteorology and base year
emissions from 2016 and projected emissions for 2026.129,130 The air quality modeling included
photochemical model simulations for a 2016 base year and 2026 future year to provide hourly concentrations
of ozone and PM2.5 component species nationwide. In addition, source apportionment modeling was
performed for 2026 to quantify the contributions to ozone from NOx emissions from electric generating units
(EGUs) and to PM2.5 from NOx, SO2 and directly emitted PM2.5 emissions on a state-by-state basis. As
described below, the modeling results for 2016 and 2026, in conjunction with EGU emissions data for the
baseline and proposed rule option 3 in 2028, 2030, 2035, 3040, 2045, and 2050 were used to construct the air
quality surfaces that reflect the influence of emissions changes between the baseline and the option 3 in each
year.
128 "air quality surfaces" refers to continuous gridded spatial fields using a 12-km grid-cell resolution
129 Information on the emissions inventories used for the modeling described in Preparation of Emissions Inventories for the 2016v2
North American Emissions Modeling Platform
130 The air quality modeling performed to support the analyses in this proposed RIA can be found in the Air Quality Modeling
Technical Support Document Federal Implementation Plan Addressing Regional Ozone Transport for the 2015 Ozone National
Ambient Air Quality Standards Proposed Rulemaking
1
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
The air quality model simulations (/. e., model runs) were performed using the Comprehensive Air Quality
Model with Extensions (CAMx) version 7.1Qljl (Ramboll Environ, 2020). The nationwide modeling domain
{i.e., the geographic area included in the modeling) covers all lower 48 states plus adjacent portions of Canada
and Mexico using a horizontal grid resolution of 12 « 12 km shown in Figure 1-1. Model predictions of ozone
and PMis concentrations were compared against ambient measurements (U.S. EPA, 2022a; 2022b). Ozone
and PM2 5 model evaluations showed model performance that was adequate for applying these model
simulations for the purpose of creating air quality surfaces to estimate ozone and PM2 5 benefits.
Figure 1-1: Air Quality Modeling Domain
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The contributions to ozone and PM2 5 component species (e.g., sulfate, nitrate, ammonium, elemental carbon
(EC), organic aerosol (OA), and crustal material132) from EGU emissions in individual states were modeled
using the "source apportionment" tool. In general, source apportionment modeling quantifies the air quality
concentrations formed from individual, user-defined groups of emissions sources or "tags". These source tags
are tracked through the transport, dispersion, chemical transformation, and deposition processes within the
model to obtain hourly gridded133 contributions from the emissions in each individual tag to hourly modeled
concentrations. For this RIA we used the source apportionment contribution data to provide a means to
estimate of the effect of changes in emissions from each group of emissions sources (i.e., each tag) to changes
in ozone and PM2.5 concentrations. Specifically, we applied outputs from source apportionment modeling for
ozone and PM2 5 component species using the 2026 modeled case to obtain the contributions from EGUs
emissions in each state to ozone and PM2 5 component species concentrations in each 12 x 12 km model grid
cell nationwide. Ozone contributions were modeled using the Anthropogenic Precursor Culpability
Assessment (APCA) tool and PM2 5 contributions were modeled suing the Particulate Matter Source
Apportionment Technology (PSAT) tool (Ramboll Environ, 2020). The ozone source apportionment
modeling was performed for the period April through September to provide data for developing spatial fields
for the April through September maximum daily eight hour (MDA8) (i.e., AS-M03) average ozone
131 This CAMx simulation set the Rscale NTb dry deposition parameter to 0 which resulted in more realistic model predictions of
PM2.5 nitrate concentrations than using a default Rscale parameter of 1
133 Crustal material refers to elements that are commonly found in the earth's crust such as Aluminum, Calcium, Iron, Magnesium,
Manganese, Potassium, Silicon, Titanium and the associated oxygen atoms.
133 Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from each tag
2
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concentration exposure metric. The PM2 5 source apportionment modeling was performed for a full-year to
provide data for developing annual average PM2.5 spatial fields. Table 1-1 provides state-level 2026 EGU
emissions that were tracked for each source apportionment tag.
Table 1-1: 2026 emissions (tons) allocated to each modeled state-EGU source apportionment tag
State
Tag
Ozone Season NOx
Emissions
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
AL
6,205
9,319
1,344
2,557
AR
5,594
9,258
22,306
1,075
AZ
1,341
3,416
2,420
814
CA
6,627
16,286
249
4,810
CO
5,881
12,725
7,311
1,556
CT
1,673
3,740
845
467
DC
37
39
0
53
DE
203
320
126
119
FL
11,590
22,451
8,784
6,555
GA
3,199
5,937
1,177
2,452
IA
8,008
17,946
9,042
1,182
ID
375
705
1
185
IL
8,244
16,777
31,322
3,018
IN
11,052
36,007
34,990
6,281
KS
3,166
4,351
854
709
KY
11,894
25,207
22,940
10,476
LA
10,895
16,949
11,273
3,119
MA
2,115
4,566
839
384
MD
1,484
3,008
273
783
ME
1,233
3,063
1,147
414
Ml
11,689
22,378
31,387
3,216
MN
4,192
9,442
7,189
481
MO
10,075
34,935
105,916
3,617
MS
3,631
5,208
30
1,240
MT
3,908
8,760
3,527
1,426
NC
7,175
15,984
6,443
2,720
ND
8,053
19,276
26,188
1,265
NE
8,670
20,274
45,869
1,530
NH
224
483
159
93
NJ
1,969
4,032
915
729
NM
1,266
1,987
0
304
NV
1,577
3,017
0
901
NY
6,248
11,693
1,526
1,649
OH
9,200
27,031
46,780
4,543
OK
2,412
3,426
2
828
OR
1,122
2,145
29
455
PA
12,386
23,965
9,685
3,785
Rl
233
476
0
68
SC
3,251
7,134
6,292
2,082
SD
478
1,054
889
55
TL*
1,337
2,970
6,953
1,329
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Appendix I: Air Quality Modeling Methodology
Table 1-1: 2026 emissions (tons) allocated to each modeled state-EGU source apportionment tag
State
Tag
Ozone Season NOx
Emissions
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
TN
790
2,100
1,231
845
TX
16,548
27,164
19,169
5,027
UT
3,571
10,915
11,040
693
VA
3,607
7,270
820
1,805
VT
2
4
0
4
WA
11,78
2,532
158
384
Wl
2,097
4,304
821
1,084
WV
7,479
21,450
28,513
2,180
WY
5,026
11,036
8,725
629
* TL represents emissions occurring on tribal lands
Examples of the magnitude and spatial extent of ozone and PM2 5 contributions are provided in Figure 1-2
through Figure 1-5 for EGUs in California, Texas, Iowa, and Ohio. These figures show how the magnitude
and the spatial patterns of contributions of EGU emissions to ozone and PM2.5 component species depend on
multiple factors including the magnitude and location of emissions as wells as the atmospheric conditions that
influence the formation and transport of these pollutants. For instance, NOx emissions are a precursor to both
ozone and PM2 5 nitrate. However, ozone and nitrate form under very different types of atmospheric
conditions with ozone formation occurring in locations with ample sunlight and ambient volatile organic
compound (VOC) concentrations while nitrate formation requires colder and drier conditions and the presence
of gas-phase ammonia. California's complex terrain that tends to trap air and allow pollutant build-up
combined with warm sunny summer and cooler dry winters and sources of both ammonia and VOCs make its
atmosphere conducive to formation of both ozone and nitrate. While the magnitude of EGU NOx emissions in
Iowa and California are similar in the 2026 modeling (Table 1-1), the emissions from California lead to larger
contributions to those pollutants due to the conducive conditions in that state. Texas and Ohio both had larger
NOx emissions than California or Iowa. While maximum ozone impacts shown for Texas and Ohio EGUs are
similar order of magnitude to maximum ozone impacts from California EGUs, nitrate impacts are much
smaller in Ohio and negligible in Texas due to less conducive atmospheric conditions for nitrate formation in
those locations. California EGU SO2 emissions in the 2026 modeling are several orders of magnitude smaller
than SO2 emissions in Ohio and Texas (Table 1-1) leading to much smaller sulfate contributions from
California EGUs than from Ohio and Texas EGUs. PM2 5 organic aerosol EGU contributions in this modeling
come from primary PM2 5 emissions rather than secondary atmospheric formation. Consequently, the impacts
of EGU emissions on this pollutant tend to occur closer to the EGU sources than impacts of secondary
pollutants (ozone, nitrate, and sulfate) which have spatial patterns showing broader regional impacts. These
patterns demonstrate how the model is able capture important atmospheric processes which impact pollutant
formation and transport form emissions sources.
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Appendix I: Air Quality Modeling Methodology
Figure 1-2: Map of California EGU Tag Contributions to a) April-September Seasonal Average MDA8
Ozone (ppb) b) Annual PM2.5 Nitrate (pg/m3) c) Annual PM2.5 sulfate (pg/m3) d) Annual PM2.5 Organic
Aerosol (pg/m3)
a) Apr-Sep MDA8 03
i
f
y
J j:""-
IAr •« Mt.O at 11.11. M»« 1M» (KHIU)
c) Annual PM2 5 Sulfate
IMYW
v \
/C. f
b) Annual PM25 Nitrate
If
/
Y
d) Annual PM2 5 OA
/
O OOt-O at an • 0.299 at (J4.1M)
K-*. - 5 54231(24,12*!
Figure 1-3: Map of Texas EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone
(ppb) b) Annual PM2.5 Nitrate (pg/m3) c) Annual PM2.5 sulfate (pg/m3) d) Annual PM2.5 Organic Aerosol
(pg/m3)
a) Apr-Sep MDA8 03
J
at(LU Ma> • l.MS at(JOft,
c) Annual PM2 5 Sulfate
/
wmi
» LJ- /
# 1
A
y
b) Annual PM2 5 Nitrate
%
f
u
d) Annual PM2 5 OA
rW\
>
• •aa«X>0«c(U)t Mm»0.081 atI207.se)
»«0 atll.ll Mm * 0J4S«(218.S7)
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Figure 1-4: Map of Iowa EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone
(ppb) b) Annual PM2.5 Nitrate (pg/m3) c) Annual PM2.5 sulfate (pg/m3) d) Annual PM2.5 Organic Aerosol
(pg/m3)
a) Apr-Sep MDA8 03
ii
*JE
1 \
» \
V
Mn* CKW-0 at (Ut • 0 AOS M (I4J.IV))
c) Annual PM2 5 Sulfate
/
b) Annual PM25 Nitrate
li
-
Wn. 00«.« W(1.U Mb- 0.019« (SWUM)
d) Annual PM2 5 OA
kn • O.OOE.O «».UMm*0040« (JW.15J)
OOOE.O»t(UL Mm- 01S6.1 (2J7.1SII
Figure 1-5: Map of Ohio EGU Tag Contributions to a) April-September Seasonal Average MDA8 Ozone
(ppb) b) Annual PM2.5 Nitrate (pg/m3) c) Annual PM2.5 sulfate (pg/m3) d) Annual PM2.5 Organic Aerosol
(pg/m3)
a) Apr-Sep MDA8 03
t*
I
Jr
r
c) Annual PM2 5 Sulfate
/
, \ \
f Yl
4 y
b) Annual PM2 5 Nitrate
11
|
f
J-Jr
u
tin • 0 CBt.O KlUkMli-0 014 «t 1310.!'
d) Annual PM2 s OA
I
•OdOUM*' 0.163 at(308.134)
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Appendix I: Air Quality Modeling Methodology
1.2 Applying Modeling Outputs to Create Spatial Fields
In this section we describe the method for creating spatial fields of AS-M03 and annual average PM2.5 based
on the 2016 and 2026 modeling. The foundational data include (1) ozone and speciated PM2.5 concentrations
in each model grid cell from the 2016 and 2026 modeling, (2) ozone and speciated PM2.5 contributions in
2026 of EGUs emissions from each state in each model grid cell134, (3) 2026 emissions from EGUs that were
input to the contribution modeling, and (4) the EGU emissions for baseline and policy scenarios in each year
of analysis (2028, 2030, 2035, 2040, 2045, 2050) generated from IPM. The method to create spatial fields
applies scaling factors based on emissions changes between 2026 projections and the baseline and the control
cases to the 2026 contributions. This method is described in detail below.
Spatial fields of ozone and PM2.5 in 2026 were created based on "fusing" modeled data with measured
concentrations at air quality monitoring locations. To create the spatial fields for each future emissions
scenario these fused 2026 model fields are used in combination with 2026 state-EGU source apportionment
modeling and the EGU emissions for each scenario and analytic year135. Contributions from each state-EGU
contribution "tag" were scaled based on the ratio of emissions in the year/scenario being evaluated to the
emissions in the modeled 2026 scenario. Contributions from tags representing sources other than EGUs are
held constant at 2026 levels for each of the scenarios and year. For each scenario and year analyzed, the
scaled contributions from all sources were summed together to create a gridded surface of total modeled
ozone and PM2.5. The process is described in a step-by-step manner below starting with the methodology for
creating AS-M03 spatial fields followed by a description of the steps for creating annual PM2 5 spatial fields.
Ozone
1. Create fused spatial fields of 2026 AS-M03 incorporating information from the air quality modeling and
from ambient measured monitoring data. The enhanced Voronoi Neighbor Average (eVNA) technique
(Gold et al., 1997; US EPA, 2007; Ding et al., 2015) was applied to ozone model predictions in
conjunction with measured data to create modeled/measured fused surfaces that leverage measured
concentrations at air quality monitor locations and model predictions at locations with no monitoring data.
1.1. The AS-M03 eVNA spatial fields are created for the 2016 base year with EPA's software package,
Software for the Modeled Attainment Test - Community Edition (SMAT-CE) using 3 years of
monitoring data (2015-2017) and the 2016 modeled data.
1.2. The model-predicted spatial fields (i.e., not the eVNA fields) of AS-M03 in 2016 were paired with
the corresponding model-predicted spatial fields in 2026 to calculate the ratio of AS-M03 between
2016 and 2026 in each model grid cell.
1.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in step (1.2)
were multiplied by the corresponding eVNA spatial fields for 2016 created in step (1.1) to produce an
eVNA AS-M03 spatial field for 2026 using (Eq-1).
..... r .... . \ Modelg.futurt
eVNAgJuture - (eVNAg 2016) x Model^^
Modelgjuture Eq-1
• eVNAg juture is the eVNA concentration of AS-M03 or PM2.5 component species in grid-
134 Contributions from EGUs were modeled using projected emissions for 2026. The resulting contributions were used
to construct spatial fields in 2028, 2030, 2035, 2040, 2045, and 2050.
135 i.e., 2028,2030, 2035, 2040,2045, and 2050
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cell, g, in the future year
• eVNAg2oi6 is the eVNA concentration of AS-M03 or PM2 5 component species in grid-
cell, g, in 2016
• Modelgjuture is the CAMx modeled concentration of AS-M03 or PM2 5 component
species in grid-cell, g, in the future year
• Modelg 2016 is the CAMx modeled concentration of AS-M03 or PM2 5 component in
grid-cell, g, in 2016
2. Create spatial fields of total EGU AS-M03 contributions for each combination of scenario and analytic
year evaluated.
2.1. Use the EGU ozone season NOx emissions for the 2028 baseline and the corresponding 2026
modeled EGU ozone season emissions (Table 1-1) to calculate the ratio of 2028 baseline
emissions to 2026 modeled emissions for each EGU state contribution tag (/'. e., an ozone-
season NOx scaling factor calculated for each state)136. These scaling factors are provided in
Table 1-2.
2.2. Calculate adjusted gridded AS-M03 EGU contributions that reflect differences in state-EGU
NOx emissions between 2026 and the 2028 baseline by multiplying the ozone season NOx
scaling factors by the corresponding gridded AS-M03 ozone contributions137 from each state-
EGU tag.
2.3. Add together the adjusted AS-M03 contributions for each EGU-state tag to produce spatial
fields of adjusted EGU totals for the 2028 baseline.138
2.4. Repeat steps 2.1 through 2.3 for the 2028 option 3 policy scenario and for the baseline and
Option 3 scenarios for each additional analytic year. The scaling factors for the baseline
scenarios and the Option 3 policy scenarios are provided in Table 1-2 and Table 1-3
respectively.
3. Create a gridded spatial field of AS-M03 associated with IPM emissions for the 2028 baseline by
combining the EGU AS-M03 contributions from steps (2.3) with the corresponding contributions to AS-
M03 from all other sources. Repeat for each of the EGU contributions created in step (2.4) to create
separate gridded spatial fields for the rest of the baseline and policy scenarios for each analytic year.
136 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were tagged
especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied to any tags that tracked
less than 100 tpy emissions in the original source apportionment modeling. Any emissions changes in the low emissions state
were assigned to a nearby state as denoted in Table 1-2 through 1-9.
137 The source apportionment modeling provided separate ozone contributions for ozone formed in VOC-limited chemical regimes
(03V) and ozone formed in NOX-limited chemical regimes (03N). The emissions scaling factors are multiplied by the
corresponding 03N gridded contributions to MDA8 concentrations. Since there are no predicted changes in VOC emissions in
the control scenarios, the 03 V contributions remain unchanged.
138 The contributions from the unaltered 03V tags are added to the summed adjusted 03N EGU tags.
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Steps 2 and 3 in combination can be represented by equation 2:
AS-M03g i y = eVNAg y
T
t=l
£EGUVOC,g,t
Cg;Tot Eq-2
AS-M03g iy is the estimated fused model-obs AS-M03 for grid-cell, "g", scenario, "r'139, and year,
"v"140.
y ?
eVNAg y is the eVNA future year AS-M03 for grid-cell ""g" and year ""y" calculated using Eq-1.
Cgjot is the total modeled AS-M03 for grid-cell ""g" from all source in the 2026 source
apportionment modeling
Cg,BC is the 2026 AS-M03 modeled contribution from the modeled boundary inflow;
Cg int is the 2026 AS-M03 modeled contribution from international emissions within the modeling
Cg bio is the 2026 AS-M03 modeled contribution from biogenic emissions;
Cg, f^es is the 2026 AS-M03 modeled contribution from fires;
Cg usanthro is the total 2026 AS-M03 modeled contribution from U.S. anthropogenic sources other
than EGUs;
CEGUvoc,g,t is the 2026 AS-M03 modeled contribution from EGU emissions of VOCs from state, ""t":
CEGUNOx,g,t is the 2026 AS-M03 modeled contribution from EGU emissions of NOx from state, ""t":
4. Create fused spatial fields of 2026 annual PM2.5 component species incorporating information from the air
quality modeling and from ambient measured monitoring data. The eVNA technique was applied to PM2 5
component species model predictions in conjunction with measured data to create modeled/measured
fused surfaces that leverage measured concentrations at air quality monitor locations and model
predictions at locations with no monitoring data.
4.1. The quarterly average PM2.5 component species eVNA spatial fields are created for the 2016 base
year with EPA's SMAT-CE software package using 3 years of monitoring data (2015-2017) and the
139 Scenario "i" can represent either baseline or regulatory proposal scenario.
140 Year "y" can represent 2028, 2030, 2035, 2040, 2045, or 2050.
domain;
and
SNOx,t,i,y is the EGU NOx scaling factor for state, ""t". scenario ""i". and year, ""y".
PM2.5
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2016 modeled data.
4.2. The model-predicted spatial fields (i.e.. not the eVNA fields) of quarterly average PM2 5 component
species in 2016 were paired with the corresponding model-predicted spatial fields in 2026 to
calculate the ratio of PM2 5 component species between 2016 and 2026 in each model grid cell.
4.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in step (4.2)
were multiplied by the corresponding eVNA spatial fields for 2016 created in step (4.1) to produce an
eVNA annual average PM2 5 component species spatial field for 2026 using (Eq-1).
5. Create spatial fields of total EGU speciated PM2 5 contributions for each year/scenario evaluated.
5.1. Use the EGU annual total NOx, SO2 and PM2 5 emissions for the 2028 baseline scenario and the
corresponding 2026 modeled EGU NOx, SO2 and PM2 5 emissions to calculate the ratio of 2028
baseline emissions to 2026 modeled emissions for each EGU state contribution tag (/'. e., annual
NOx, SO2 and PM2 5 scaling factors calculated for each state)141. These scaling factors are
provided in Table 1-4 through Table 1-9.
5.2. Calculate adjusted gridded annual PM2 5 component species EGU contributions that reflect
differences in state-EGU NOx, SO2 and primary PM2 5 emissions between 2026 and the 2028
baseline by multiplying the annual NOx, SO2 and PM2 5 scaling factors by the corresponding
annual gridded PM2 5 component species contributions from each state-EGU tag142.
5.3. Add together the adjusted PM2 5 contributions of for each EGU state tag to produce spatial
fields of adjusted EGU totals for each PM2 5 component species.
5.4. Repeat steps 5.1 through 5.3 for the 2028 Option 3 scenario and for the baseline and Option 3
scenarios for each additional analytic year. The scaling factors for all PM2 5 component species
for the baseline and Option 3 scenarios are provided in Table 1-4 through Table 1-9.
6. Create gridded spatial fields of each PM2 5 component species for the 2028 baseline by combining the
EGU annual PM25 component species contributions from step (5.3) with the corresponding contributions
to annual PM2 5 component species from all other sources. Repeat for each of the EGU contributions
created in step (5.4) to create separate gridded spatial fields for the rest of the baseline and policy
scenarios and analytic years.
7. Create gridded spatial fields of total PM2 5 mass by combining the component species surfaces for sulfate,
nitrate, organic aerosol, elemental carbon and crustal material with ammonium, and particle-bound.
Ammonium and particle-bound water concentrations are calculated for each scenario based on nitrate and
sulfate concentrations along with the ammonium degree of neutralization in the base year modeling in
accordance with equations from the SMAT-CE modeling software.
Steps 5 and 6 result in Eq-3 for PM2 5 component species: sulfate, nitrate, organic aerosol, elemental carbon
and crustal material.
141 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were tagged
especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied to any tags that had less
than 100 tpy emissions in the original source apportionment modeling. Any emissions changes in the low emissions state were
assigned to a nearby state as denoted in Table 1-2 through 1-9.
142 Scaling factors for components that are formed through chemical reactions in the atmosphere were created as follows: scaling
factors for sulfate were based on relative changes in annual SO2 emissions; scaling factors for nitrate were based on relative
changes in annual NOx emissions. Scaling factors for PM2.5 components that are emitted directly from the source (OA, EC,
crustal) were based on the relative changes in annual primary PM2.5 emissions between the 2026 modeled emissions and the
baseline and the Option 3 scenarios in each year.
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PMsgiy eVNASigiy
Eq-3
CEGUs,g,t Ss,t,i,y
Cs,g,Tot
t= 1
• PMs g i y is the estimated fused model-obs PM component species "s" for grid-cell, "g", scenario,
• eVNAs g y is the eVNA future year PM component species "s" for grid-cell "g" and year "y"
calculated using Eq-1.
• Cs,g,Tot is the total modeled PM component species "s" for grid-cell "g" from all source in the 2026
source apportionment modeling
• Cs,g,BC is the 2026 PM component species "s" modeled contribution from the modeled boundary
inflow;
• Cs,g,int is the 2026 PM component species "s" modeled contribution from international emissions
within the modeling domain;
• Cs,g,bio is the 2026 PM component species "s" modeled contribution from biogenic emissions;
• Cs,g,fires is the 2026 PM component species "s" modeled contribution from fires;
• Cs,g,usanthro's the total 2026 PM component species "s" modeled contribution from U.S.
anthropogenic sources other than EGUs;
• CEGUs,g,t is the 2026 PM component species "s" modeled contribution from EGU emissions of NOx,
SO2, or primary PM2.5 from state, "t"; and
• Ss,t,i,y is the EGU scaling factor for component species "s", state, "t", scenario "i", and year, "y".
Scaling factors for nitrate are based on annual NOx emissions, scaling factors for sulfate are based on
annual SO2 emissions, scaling factors for primary PM2 5 components are based on primary PM2 5
emissions.
Selected maps showing changes in air quality concentrations between the Option 3 and the baseline are
provided later in this appendix.
143 Scenario "i" can represent either baseline or regulatory proposal scenario.
144 Year "y" can represent 2028, 2030, 2035, 2040, 2045, or 2050.
"i"143, and year,' y
",."144.
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1.3 Scaling Factors Applied to Source Apportionment Tags
Table 1-2: Ozone scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
0.92
0.94
0.73
0.86
0.71
0.73
0.73
AR
1.37
0.85
0.28
0.29
0.31
0.29
0.28
AZ
0.89
0.94
1.99
1.41
1.56
1.44
1.99
CA
0.73
0.36
0.28
0.30
0.26
0.24
0.28
CO
0.90
0.50
0.15
0.17
0.16
0.15
0.15
CT
0.70
0.69
0.77
0.70
0.75
0.79
0.77
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.36
1.38
2.06
1.88
1.93
1.97
2.06
FL
0.93
0.90
0.83
0.77
0.82
0.81
0.83
GA
1.19
1.53
0.64
1.04
0.98
0.62
0.64
IA
1.27
1.30
0.65
1.07
1.04
0.65
0.65
ID
1.22
1.21
0.52
1.06
0.74
0.53
0.52
IL
0.42
0.44
0.11
0.58
0.09
0.11
0.11
IN
1.13
1.12
0.22
0.71
0.59
0.21
0.22
KS
1.15
0.97
0.02
0.46
0.42
0.03
0.02
KY
0.91
1.02
0.20
0.55
0.28
0.20
0.20
LA
0.83
0.82
0.42
0.55
0.51
0.36
0.42
MA
1.27
1.26
1.15
1.31
1.23
1.16
1.15
MD
0.73
0.73
0.88
0.78
0.79
0.87
0.88
ME
1.79
1.32
1.30
1.33
1.33
1.30
1.30
Ml
1.00
0.71
0.34
0.34
0.32
0.33
0.34
MN
1.42
0.83
0.49
0.51
0.50
0.49
0.49
MO
1.34
1.06
0.53
0.80
0.58
0.53
0.53
MS
0.83
0.77
0.38
0.37
0.37
0.38
0.38
MT
1.01
0.97
1.00
1.02
1.01
0.97
1.00
NC
0.50
0.36
0.07
0.16
0.14
0.07
0.07
ND
1.46
1.53
0.46
0.59
0.55
0.46
0.46
NE
1.15
1.12
1.05
1.13
1.11
1.05
1.05
NH
1.13
1.17
1.16
1.13
1.13
1.16
1.16
NJ
0.97
1.00
1.16
1.02
1.07
1.09
1.16
NM
0.55
0.60
0.14
0.30
0.26
0.12
0.14
NV
0.71
1.01
0.13
0.43
0.49
0.15
0.13
NY
0.91
0.79
0.54
0.51
0.52
0.54
0.54
OH
0.82
0.73
0.66
0.89
0.90
0.64
0.66
OK
2.62
1.56
0.28
0.93
0.78
0.17
0.28
OR
0.37
0.10
0.00
0.00
0.00
0.00
0.00
PA
0.79
0.77
0.54
0.62
0.48
0.54
0.54
Rl
1.22
1.21
1.42
1.20
1.28
1.41
1.42
SC
1.30
1.01
1.46
1.24
1.32
1.51
1.46
SD
0.95
1.26
0.26
0.57
0.46
0.26
0.26
TB
1.08
1.08
0.01
0.01
0.01
0.01
0.01
TN
2.03
1.11
0.89
0.57
0.62
0.92
0.89
TX
1.09
1.10
0.65
1.02
1.11
0.63
0.65
UT
2.39
2.28
0.30
1.49
0.24
0.31
0.30
12
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
Table 1-2: Ozone scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
VA
1.10
0.79
0.55
0.88
0.69
0.54
0.55
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.74
0.85
0.80
0.77
0.82
0.80
0.80
Wl
1.28
1.33
0.61
0.81
0.91
0.61
0.61
WV
1.61
1.60
0.27
0.22
0.25
0.26
0.27
WY
1.09
1.20
0.78
0.79
0.80
0.78
0.78
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
NOx, DC emissions changes were assigned to MD and VT emissions changes were assigned to NY
Table 1-3: Ozone scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
0.92
0.97
1.02
0.87
0.71
0.73
0.73
AR
1.38
0.85
0.33
0.31
0.32
0.29
0.28
AZ
0.90
0.94
1.38
1.41
1.56
1.44
1.99
CA
0.73
0.36
0.32
0.30
0.26
0.24
0.28
CO
0.90
0.50
0.30
0.17
0.16
0.15
0.15
CT
0.70
0.69
0.66
0.70
0.75
0.79
0.77
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.36
1.36
1.38
1.80
1.92
1.96
2.06
FL
0.92
0.90
0.80
0.78
0.82
0.81
0.83
GA
1.15
1.48
1.04
1.00
0.94
0.58
0.60
IA
1.27
1.29
1.08
1.06
1.04
0.63
0.63
ID
1.21
1.21
1.08
1.06
0.74
0.53
0.52
IL
0.42
0.43
0.67
0.58
0.10
0.11
0.11
IN
1.13
1.12
0.81
0.66
0.56
0.21
0.22
KS
1.15
0.95
0.57
0.55
0.41
0.03
0.02
KY
0.94
1.01
0.60
0.50
0.26
0.20
0.20
LA
0.79
0.82
0.67
0.56
0.52
0.37
0.43
MA
1.27
1.26
1.26
1.30
1.24
1.16
1.15
MD
0.73
0.73
0.70
0.78
0.79
0.86
0.90
ME
1.67
1.20
1.23
1.21
1.21
1.19
1.19
Ml
0.99
0.71
0.67
0.34
0.32
0.33
0.34
MN
1.39
0.79
0.60
0.49
0.48
0.46
0.46
MO
1.34
1.06
0.99
0.80
0.58
0.53
0.52
MS
0.83
0.69
0.31
0.37
0.37
0.38
0.38
MT
1.01
0.97
1.01
1.02
1.01
0.97
1.00
NC
0.50
0.35
0.20
0.16
0.13
0.07
0.07
ND
1.45
1.53
0.77
0.59
0.57
0.47
0.46
NE
1.15
1.12
1.13
1.09
1.09
1.05
1.05
NH
1.13
1.17
1.11
1.13
1.13
1.16
1.16
NJ
0.97
1.00
0.96
1.02
1.04
1.09
1.17
NM
0.55
0.59
0.39
0.30
0.26
0.12
0.14
NV
0.71
1.01
0.70
0.43
0.49
0.15
0.13
13
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
Table 1-3: Ozone scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
NY
0.91
0.78
0.71
0.51
0.52
0.54
0.54
OH
0.82
0.73
0.87
0.88
0.79
0.57
0.59
OK
2.62
1.59
1.11
0.93
0.78
0.17
0.28
OR
0.37
0.10
0.08
0.00
0.00
0.00
0.00
PA
0.77
0.76
0.69
0.62
0.48
0.54
0.54
Rl
1.22
1.21
1.17
1.20
1.27
1.41
1.42
SC
1.28
0.96
1.36
1.21
1.30
1.51
1.44
SD
0.95
1.26
1.34
0.58
0.47
0.38
0.38
TB
1.08
1.08
0.01
0.01
0.01
0.01
0.01
TN
2.03
0.79
0.66
0.56
0.62
0.92
0.89
TX
1.10
1.10
1.13
1.02
1.10
0.64
0.66
UT
2.39
2.28
2.25
1.49
0.24
0.31
0.30
VA
1.10
0.83
0.70
0.88
0.69
0.54
0.55
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.73
0.83
0.91
0.76
0.81
0.78
0.79
Wl
1.27
1.33
0.84
0.81
0.89
0.61
0.61
WV
1.59
1.56
1.43
0.22
0.25
0.26
0.27
WY
1.11
1.20
1.23
0.79
0.80
0.78
0.78
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
NOx, DC emissions changes were assigned to MD and VT emissions changes were assigned to NY
Table 1-4: Nitrate scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.07
1.16
1.22
1.04
0.93
0.72
0.72
AR
1.83
0.96
0.38
0.30
0.30
0.26
0.24
AZ
1.02
0.93
1.03
1.06
1.29
1.16
1.46
CA
0.81
0.41
0.36
0.32
0.29
0.28
0.33
CO
0.84
0.42
0.34
0.20
0.18
0.17
0.18
CT
0.66
0.64
0.60
0.60
0.63
0.65
0.65
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.35
1.40
1.46
1.87
1.95
1.96
2.07
FL
0.97
0.95
0.84
0.82
0.85
0.81
0.85
GA
1.48
1.61
1.24
1.24
0.91
0.67
0.65
IA
1.27
1.28
1.07
1.02
0.93
0.53
0.53
ID
0.99
1.13
1.25
1.46
1.08
0.74
0.69
IL
0.48
0.49
0.64
0.54
0.08
0.10
0.10
IN
1.01
0.98
0.77
0.61
0.42
0.11
0.11
KS
1.88
1.44
0.79
0.54
0.45
0.04
0.03
KY
1.00
0.96
0.56
0.51
0.26
0.18
0.19
LA
0.85
0.92
0.83
0.62
0.59
0.40
0.46
MA
1.26
1.26
1.24
1.25
1.21
1.10
1.09
MD
0.78
0.78
0.77
0.85
0.82
0.89
0.90
ME
1.64
1.25
1.20
1.25
1.25
1.22
1.22
14
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
Table 1-4: Nitrate scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
Ml
1.10
0.75
0.71
0.31
0.31
0.31
0.32
MN
1.42
0.76
0.56
0.50
0.45
0.43
0.43
MO
1.31
1.10
1.04
0.86
0.67
0.36
0.36
MS
0.86
0.83
0.50
0.45
0.46
0.42
0.40
MT
1.05
1.01
1.04
1.06
1.12
1.02
1.04
NC
0.71
0.37
0.21
0.16
0.13
0.06
0.06
ND
1.47
1.45
0.71
0.51
0.44
0.40
0.40
NE
1.11
1.09
1.09
1.06
0.96
0.80
0.75
NH
2.01
2.01
1.96
1.97
1.98
2.00
2.00
NJ
0.98
1.00
0.95
0.99
1.00
1.01
1.08
NM
0.56
0.63
0.34
0.28
0.30
0.16
0.16
NV
0.67
0.93
0.61
0.50
0.52
0.18
0.18
NY
0.95
0.84
0.77
0.58
0.58
0.59
0.59
OH
0.84
0.74
0.86
0.84
0.80
0.40
0.41
OK
3.17
1.85
1.59
1.31
0.94
0.22
0.29
OR
0.49
0.27
0.17
0.00
0.00
0.00
0.00
PA
0.87
0.87
0.77
0.62
0.54
0.59
0.60
Rl
1.18
1.16
1.12
1.12
1.16
1.23
1.23
SC
1.27
1.01
1.25
1.12
1.13
1.14
1.14
SD
1.11
1.25
1.31
0.48
0.43
0.18
0.18
TB
0.93
0.93
0.00
0.00
0.00
0.00
0.00
TN
1.38
0.79
0.54
0.44
0.47
0.64
0.58
TX
1.62
1.50
1.43
1.16
1.20
0.60
0.61
UT
1.92
1.68
1.67
1.12
0.18
0.22
0.23
VA
1.25
0.85
0.78
0.91
0.73
0.62
0.62
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.85
0.99
1.18
1.10
1.19
1.11
1.14
Wl
1.45
1.50
0.89
0.83
0.77
0.52
0.52
WV
1.51
1.44
1.25
0.20
0.18
0.21
0.21
WY
1.13
1.17
1.20
0.81
0.83
0.80
0.80
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
NOx, DC emissions changes were assigned to MD and VT emissions changes were assigned to NY
Table 1-5: Nitrate scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.08
1.18
1.22
1.05
0.93
0.72
0.72
AR
1.83
0.96
0.38
0.31
0.30
0.26
0.24
AZ
1.02
0.94
1.03
1.06
1.29
1.16
1.46
CA
0.81
0.41
0.36
0.32
0.29
0.28
0.33
CO
0.84
0.42
0.34
0.20
0.18
0.17
0.18
CT
0.66
0.64
0.60
0.60
0.63
0.65
0.65
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.34
1.36
1.45
1.82
1.93
1.95
2.06
15
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BCAfor Revisions to the Steam Electric Power Generating ELGs
Appendix I: Air Quality Modeling Methodology
Table 1-5: Nitrate scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
FL
0.96
0.94
0.84
0.82
0.85
0.81
0.85
GA
1.42
1.51
1.20
1.19
0.86
0.62
0.60
IA
1.27
1.28
1.06
1.01
0.93
0.51
0.51
ID
0.99
1.13
1.25
1.46
1.08
0.74
0.69
IL
0.48
0.48
0.64
0.54
0.08
0.10
0.10
IN
1.01
0.98
0.73
0.58
0.40
0.11
0.11
KS
1.90
1.39
0.79
0.61
0.44
0.04
0.03
KY
1.01
0.95
0.53
0.48
0.25
0.19
0.19
LA
0.82
0.92
0.81
0.63
0.59
0.40
0.47
MA
1.26
1.26
1.24
1.25
1.21
1.10
1.10
MD
0.79
0.78
0.77
0.85
0.81
0.89
0.91
ME
1.53
1.14
1.09
1.14
1.14
1.11
1.11
Ml
1.09
0.75
0.70
0.31
0.31
0.31
0.32
MN
1.39
0.73
0.53
0.47
0.42
0.40
0.40
MO
1.31
1.10
1.04
0.85
0.67
0.36
0.36
MS
0.86
0.79
0.50
0.45
0.46
0.42
0.40
MT
1.05
1.01
1.04
1.06
1.12
1.02
1.04
NC
0.71
0.37
0.22
0.16
0.13
0.06
0.06
ND
1.47
1.46
0.77
0.51
0.45
0.40
0.40
NE
1.11
1.08
1.06
1.03
0.93
0.79
0.75
NH
1.78
1.78
1.72
1.74
1.74
1.76
1.76
NJ
0.98
1.00
0.95
0.99
0.99
1.01
1.09
NM
0.56
0.62
0.35
0.28
0.30
0.16
0.16
NV
0.67
0.93
0.62
0.50
0.52
0.18
0.18
NY
0.95
0.84
0.77
0.58
0.58
0.59
0.59
OH
0.84
0.73
0.82
0.83
0.63
0.36
0.37
OK
3.17
1.87
1.59
1.30
0.93
0.22
0.29
OR
0.49
0.27
0.17
0.00
0.00
0.00
0.00
PA
0.86
0.86
0.77
0.62
0.53
0.59
0.60
Rl
1.18
1.17
1.12
1.12
1.16
1.23
1.23
SC
1.25
0.97
1.23
1.10
1.11
1.13
1.12
SD
1.11
1.25
1.31
0.50
0.45
0.25
0.26
TB
0.93
0.93
0.00
0.00
0.00
0.00
0.00
TN
1.39
0.67
0.54
0.43
0.47
0.64
0.58
TX
1.63
1.51
1.42
1.15
1.20
0.60
0.61
UT
1.92
1.68
1.67
1.12
0.18
0.22
0.23
VA
1.25
0.86
0.78
0.91
0.73
0.62
0.62
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.83
0.97
1.16
1.08
1.17
1.09
1.13
Wl
1.45
1.48
0.89
0.83
0.76
0.52
0.52
WV
1.50
1.40
1.22
0.20
0.18
0.21
0.21
WY
1.13
1.17
1.20
0.81
0.83
0.80
0.80
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
NOx, DC emissions changes were assigned to MD and VT emissions changes were assigned to NY
16
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BCAfor Revisions to the Steam Electric Power Generating ELGs Appendix I: Air Quality Modeling Methodology
Table 1-6: Sulfate scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.92
1.93
2.02
2.02
2.02
2.37
1.78
AR
1.95
0.85
0.05
0.05
0.05
0.05
0.04
AZ
0.88
0.85
1.84
1.83
2.60
0.89
1.86
CA
2.42
1.56
0.42
0.40
0.49
0.49
0.50
CO
0.68
0.28
0.28
0.01
0.01
0.01
0.01
CT
0.55
0.55
0.55
0.55
0.55
0.55
0.55
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
0.73
0.73
0.73
0.73
0.73
0.73
0.73
FL
1.38
1.41
0.91
0.78
0.93
0.92
0.92
GA
4.40
5.05
1.14
1.14
0.79
0.00
0.00
IA
1.23
1.25
1.06
1.02
0.94
0.56
0.56
ID
1.00
1.00
1.00
1.00
1.00
1.00
1.00
IL
0.33
0.32
0.39
0.32
0.01
0.00
0.00
IN
1.24
1.13
0.72
0.48
0.31
0.09
0.09
KS
3.23
2.55
1.45
0.94
0.81
0.00
0.00
KY
1.15
1.17
0.40
0.37
0.15
0.07
0.08
LA
0.62
0.64
0.67
0.83
0.74
0.03
0.18
MA
0.98
0.98
0.98
0.98
0.98
0.91
0.85
MD
1.99
1.73
1.79
3.98
3.27
2.83
2.83
ME
1.14
0.88
0.88
0.89
0.89
0.88
0.88
Ml
1.12
0.43
0.43
0.01
0.01
0.01
0.01
MN
1.28
0.53
0.47
0.47
0.39
0.38
0.38
MO
1.02
0.85
0.82
0.89
0.47
0.31
0.30
MS
1.00
1.00
1.00
1.00
1.00
1.00
1.00
MT
1.36
1.15
1.20
1.20
1.37
1.15
1.20
NC
0.71
0.29
0.07
0.04
0.03
0.00
0.00
ND
1.18
1.21
0.91
0.75
0.67
0.57
0.60
NE
1.05
1.05
1.05
1.04
0.97
1.01
0.95
NH
4.35
4.25
2.46
2.45
2.45
2.45
2.45
NJ
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NM
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NV
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NY
0.98
0.98
0.98
0.98
0.98
0.98
0.98
OH
0.87
0.76
0.83
0.82
0.72
0.29
0.29
OK
1.00
1.00
1.00
1.00
1.00
1.00
1.00
OR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
PA
1.40
1.03
1.16
0.72
0.71
1.10
1.09
Rl
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.97
1.47
1.71
1.58
1.41
1.07
1.07
SD
1.17
1.33
1.33
0.48
0.44
0.17
0.17
TB
0.98
0.98
0.00
0.00
0.00
0.00
0.00
TN
2.19
0.64
0.00
0.00
0.00
0.00
0.00
TX
3.96
3.06
2.38
2.09
2.56
1.20
1.27
UT
1.27
1.28
1.36
0.77
0.33
0.33
0.33
17
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Appendix I: Air Quality Modeling Methodology
Table 1-6: Sulfate scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
VA
1.22
0.93
0.88
1.10
0.98
0.80
0.80
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.79
0.72
2.02
0.34
0.16
0.16
0.16
Wl
2.92
2.98
1.78
1.70
1.63
0.61
0.61
WV
1.49
1.38
1.31
0.17
0.14
0.04
0.04
WY
1.01
1.11
1.16
0.66
0.66
0.63
0.63
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
S02, the following emissions change assignments were applied: DC -> MD, ID -> MT, MS -> AL, NV -> UT, NM
-> AZ, OK -> TX, OR -> WA, Rl -> CT, VT -> NY
Table 1-7: Sulfate scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.92
1.94
2.02
2.02
2.02
2.37
1.78
AR
1.96
0.85
0.05
0.05
0.05
0.05
0.04
AZ
1.00
0.85
1.84
1.82
2.62
0.89
1.86
CA
2.42
1.56
0.42
0.40
0.49
0.49
0.50
CO
0.68
0.28
0.28
0.01
0.01
0.01
0.01
CT
0.55
0.55
0.55
0.55
0.55
0.55
0.55
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
0.73
0.73
0.73
0.73
0.73
0.73
0.73
FL
1.26
1.35
0.83
0.76
0.93
0.92
0.92
GA
4.26
4.73
1.14
1.14
0.79
0.00
0.00
IA
1.23
1.24
1.05
1.02
0.94
0.56
0.56
ID
1.00
1.00
1.00
1.00
1.00
1.00
1.00
IL
0.33
0.32
0.39
0.31
0.01
0.00
0.00
IN
1.24
1.14
0.68
0.44
0.33
0.09
0.09
KS
3.30
2.43
1.45
1.12
0.81
0.00
0.00
KY
1.20
1.15
0.40
0.34
0.13
0.08
0.08
LA
0.47
0.64
0.67
0.81
0.72
0.03
0.18
MA
0.98
0.98
0.98
0.98
0.98
0.91
0.86
MD
1.99
1.73
1.79
3.98
3.27
2.83
3.07
ME
1.14
0.88
0.88
0.89
0.89
0.88
0.88
Ml
1.12
0.43
0.43
0.01
0.01
0.01
0.01
MN
1.28
0.53
0.47
0.47
0.39
0.38
0.38
MO
1.02
0.85
0.82
0.89
0.47
0.30
0.30
MS
1.00
1.00
1.00
1.00
1.00
1.00
1.00
MT
1.36
1.15
1.20
1.20
1.37
1.15
1.20
NC
0.71
0.28
0.08
0.04
0.04
0.00
0.00
ND
1.20
1.24
0.92
0.75
0.69
0.58
0.60
NE
1.05
1.05
1.04
1.03
0.95
1.01
0.95
NH
3.66
3.56
2.21
2.21
2.21
2.21
2.21
NJ
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NM
1.00
1.00
1.00
1.00
1.00
1.00
1.00
18
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Appendix I: Air Quality Modeling Methodology
Table 1-7: Sulfate scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
NV
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NY
0.98
0.98
0.98
0.98
0.98
0.98
0.98
OH
0.88
0.75
0.82
0.80
0.63
0.17
0.27
OK
1.00
1.00
1.00
1.00
1.00
1.00
1.00
OR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
PA
1.40
1.01
1.15
0.72
0.71
1.09
1.09
Rl
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.97
1.46
1.72
1.64
1.22
1.07
1.07
SD
1.17
1.33
1.33
0.50
0.46
0.25
0.25
TB
0.98
0.98
0.00
0.00
0.00
0.00
0.00
TN
2.19
0.40
0.00
0.00
0.00
0.00
0.00
TX
3.97
3.13
2.36
2.03
2.39
1.13
1.29
UT
1.27
1.28
1.36
0.77
0.33
0.33
0.33
VA
1.22
0.94
0.88
1.10
0.98
0.80
0.80
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.79
0.72
2.02
0.34
0.16
0.16
0.16
Wl
2.91
2.93
1.78
1.70
1.60
0.61
0.61
WV
1.47
1.34
1.22
0.17
0.14
0.04
0.04
WY
1.01
1.11
1.16
0.66
0.66
0.63
0.63
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
S02, the following emissions change assignments were applied: DC -> MD, ID -> MT, MS -> AL, NV -> UT, NM
-> AZ, OK -> TX, OR -> WA, Rl -> CT, VT -> NY
Table 1-8: Primary PM2.5 scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.06
1.09
1.15
1.13
1.10
0.94
0.94
AR
1.61
1.04
0.89
0.70
0.69
0.62
0.61
AZ
1.09
1.02
1.29
1.35
1.64
1.83
2.27
CA
0.85
0.59
0.52
0.47
0.39
0.41
0.51
CO
0.69
0.64
0.59
0.57
0.51
0.49
0.54
CT
0.70
0.68
0.54
0.56
0.63
0.78
0.77
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.34
1.40
1.46
1.68
1.75
1.73
1.95
FL
0.97
1.00
0.96
0.97
1.04
0.93
0.95
GA
0.87
0.90
0.87
0.93
0.95
0.87
0.84
IA
1.43
1.46
1.23
1.15
1.02
0.64
0.64
ID
1.80
2.15
2.45
2.84
1.98
1.15
1.04
IL
0.48
0.50
0.56
0.49
0.17
0.26
0.26
IN
0.85
0.85
0.69
0.53
0.44
0.29
0.29
KS
1.15
0.84
0.25
0.19
0.16
0.08
0.06
KY
0.18
0.17
0.14
0.14
0.14
0.12
0.12
LA
0.94
0.99
1.01
0.96
0.90
0.80
0.82
MA
1.08
1.07
1.00
0.97
0.91
0.82
0.82
19
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Appendix I: Air Quality Modeling Methodology
Table 1-8: Primary PM2.5 scaling factors for EGU tags in the baseline scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
MD
0.63
0.66
0.69
0.77
0.74
0.90
0.92
ME
1.34
1.29
1.26
1.27
1.27
1.29
1.29
Ml
0.89
0.68
0.69
0.49
0.51
0.57
0.60
MN
1.77
0.77
0.53
0.45
0.42
0.40
0.39
MO
0.98
0.82
0.77
0.53
0.31
0.21
0.22
MS
1.13
1.18
1.15
1.03
1.03
0.92
0.86
MT
0.97
0.96
0.99
1.02
1.08
0.99
0.99
NC
0.90
0.51
0.42
0.32
0.22
0.12
0.12
ND
2.01
1.93
1.21
1.02
0.88
0.81
0.81
NE
0.40
0.38
0.38
0.36
0.32
0.25
0.25
NH
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NJ
1.18
1.26
1.13
1.26
1.23
1.26
1.57
NM
0.84
0.89
1.08
0.98
1.10
0.60
0.64
NV
0.68
0.78
0.83
0.84
0.93
0.26
0.28
NY
1.18
1.00
0.85
0.61
0.63
0.68
0.70
OH
0.77
0.74
0.88
0.95
0.95
0.69
0.70
OK
1.87
1.15
1.05
0.74
0.43
0.23
0.28
OR
0.68
0.32
0.20
0.04
0.04
0.04
0.04
PA
1.14
1.14
1.18
1.11
0.96
1.03
1.05
Rl
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.08
1.09
1.29
1.20
1.16
1.06
1.07
SD
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TB
1.56
1.31
0.01
0.02
0.01
0.02
0.02
TN
1.17
0.62
0.63
0.58
0.64
0.88
0.80
TX
1.48
1.48
1.63
1.33
1.41
0.92
0.91
UT
1.40
1.44
1.42
1.23
1.13
1.42
1.57
VA
0.84
0.71
0.61
0.86
0.61
0.36
0.36
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
1.23
1.24
1.88
1.90
1.97
1.92
1.93
Wl
0.68
0.72
0.68
0.62
0.54
0.45
0.46
WV
1.67
1.55
1.36
0.45
0.39
0.61
0.63
WY
1.32
1.49
1.61
1.07
1.28
1.16
1.17
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
primary PM2.5, the following emissions change assignments were applied: DC -> MD, NH -> ME, Rl -> CT, SD
-> ND, VT -> NY
20
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BCAfor Revisions to the Steam Electric Power Generating ELGs Appendix I: Air Quality Modeling Methodology
Table 1-9: Primary PM2.5 scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
AL
1.07
1.11
1.15
1.13
1.10
0.94
0.94
AR
1.62
1.04
0.89
0.70
0.69
0.62
0.61
AZ
1.09
1.02
1.29
1.35
1.65
1.83
2.27
CA
0.85
0.59
0.52
0.47
0.39
0.41
0.51
CO
0.69
0.64
0.59
0.57
0.51
0.49
0.54
CT
0.70
0.68
0.54
0.56
0.63
0.78
0.77
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.35
1.39
1.45
1.65
1.75
1.74
1.96
FL
0.97
1.00
0.96
0.98
1.04
0.93
0.95
GA
0.87
0.90
0.87
0.93
0.95
0.87
0.84
IA
1.43
1.46
1.23
1.14
1.01
0.63
0.63
ID
1.79
2.15
2.44
2.84
1.98
1.15
1.04
IL
0.48
0.50
0.56
0.49
0.17
0.26
0.26
IN
0.85
0.85
0.67
0.52
0.44
0.28
0.29
KS
1.17
0.76
0.25
0.20
0.16
0.07
0.06
KY
0.18
0.17
0.14
0.14
0.15
0.13
0.12
LA
0.92
0.99
1.00
0.96
0.90
0.80
0.82
MA
1.08
1.07
1.00
0.97
0.91
0.82
0.82
MD
0.64
0.68
0.69
0.77
0.74
0.90
0.93
ME
1.32
1.26
1.23
1.24
1.24
1.26
1.26
Ml
0.89
0.68
0.69
0.49
0.51
0.57
0.60
MN
1.76
0.76
0.53
0.45
0.42
0.39
0.40
MO
0.98
0.82
0.77
0.53
0.31
0.21
0.22
MS
1.13
1.18
1.14
1.02
1.03
0.92
0.86
MT
0.97
0.96
0.99
1.02
1.08
0.99
0.99
NC
0.90
0.51
0.42
0.32
0.22
0.12
0.12
ND
1.99
1.94
1.25
1.02
0.89
0.82
0.81
NE
0.40
0.38
0.35
0.34
0.30
0.25
0.25
NH
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NJ
1.18
1.26
1.13
1.25
1.21
1.25
1.58
NM
0.84
0.89
1.08
0.98
1.10
0.60
0.64
NV
0.68
0.78
0.83
0.84
0.93
0.26
0.28
NY
1.18
1.00
0.85
0.61
0.63
0.68
0.70
OH
0.77
0.74
0.87
0.94
0.83
0.67
0.68
OK
1.87
1.15
1.05
0.74
0.43
0.23
0.28
OR
0.67
0.32
0.20
0.04
0.04
0.04
0.04
PA
1.14
1.13
1.18
1.11
0.95
1.03
1.05
Rl
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.08
1.08
1.29
1.20
1.16
1.07
1.07
SD
1.00
1.00
1.00
1.00
1.00
1.00
1.00
21
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Appendix I: Air Quality Modeling Methodology
Table 1-9: Primary PM2.5 scaling factors for EGU tags in the option 3 scenario
State Tag
2028
2030
2035
2040
2045
2050
2055
TB
1.56
1.31
0.01
0.02
0.01
0.02
0.02
TN
1.17
0.59
0.63
0.58
0.63
0.88
0.80
TX
1.48
1.49
1.62
1.32
1.38
0.91
0.91
UT
1.40
1.44
1.41
1.23
1.13
1.42
1.57
VA
0.84
0.72
0.60
0.86
0.61
0.36
0.36
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
1.23
1.24
1.88
1.90
1.97
1.92
1.93
Wl
0.68
0.72
0.68
0.62
0.54
0.45
0.46
WV
1.66
1.51
1.35
0.45
0.39
0.61
0.62
WY
1.34
1.49
1.61
1.07
1.27
1.16
1.17
*TB = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original
source apportionment modeling. Any emissions changes in that state were assigned to a nearby state. For
primary PM2.5, the following emissions change assignments were applied: DC -> MD, NH -> ME, Rl -> CT, SD
-> ND, VT -> NY
1.4 Air Quality Surface Results
The spatial fields of baseline AS-M03 and Annual Average PM25 in 2028 are presented in Figure 1-6 and 1-7,
respectively. It is important to recognize that ozone is a secondary pollutant, meaning that it is formed
through chemical reactions of precursor emissions in the atmosphere. As a result of the time necessary for
precursors to mix in the atmosphere and for these reactions to occur, ozone can either be highest at the
location of the precursor emissions or peak at some distance downwind of those emissions sources. The
spatial gradients of ozone depend on a multitude of factors including the spatial patterns of NOx and VOC
emissions and the meteorological conditions on a particular day. Thus, on any individual day, high ozone
concentrations may be found in narrow plumes downwind of specific point sources, may appear as urban
outflow with large concentrations downwind of urban source locations or may have a more regional signal.
However, in general, because the AS-M03 metric is based on the average of concentrations over more than
180 days in the spring and summer, the resulting spatial fields are rather smooth without sharp gradients,
compared to what might be expected when looking at the spatial patterns of MDA8 ozone concentrations on
specific high ozone episode days. PM2 5 is made up of both primary and secondary components. Secondary
PM2 5 species sulfate and nitrate often demonstrate regional signals without large local gradients while
primary PM2 5 components often have heterogenous spatial patterns with larger gradients near emissions
sources. Both secondary and primary PM2 5 contribute to the spatial patterns shown in Figure 1-7 as
demonstrated by the extensive areas of elevated concentrations over much of the Eastern US which have large
secondary components and hotspots in urban areas which are impacted by primary PM emissions.
Figure 1-6 through Figure 1-13 present the model-predicted changes in the AS-M03 between the baseline and
Option 3 for 2028, 2030, 2035, 2040, 2045, and 2050 calculated as Option 3 minus the baseline. Figures 1-14
to 1-19 present the model-predicted changes in annual average PM2 5 between the baseline and Option 3 for
2028, 2030, 2035, 2040, 2045, and 2050 calculated as Option 3 minus the baseline. The spatial patterns
shown in the figures are a result of (1) of the spatial distribution of EGU sources that are predicted to have
changes in emissions and (2) of the physical or chemical processing that the model simulates in the
atmosphere. While SO2, NOx and primary PM2 5 emissions changes all contributed to the PM2 5 changes
depicted in Figures 1-14 through 1-19, the PM2 5 component species with the larger changes was sulfate and
22
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consequently the SO2 emissions changes have the largest impact on predicted changes in PM2 5 concentrations
through sulfate, ammonium and particle-bound water impacts. The spatial fields used to create these maps
serve as an input to the benefits analysis.
Figure 1-6: Map of AS-M03 in the 2028 Baseline
159 233 318
Min = 24.372 at (396,11), Max = 70.626 at (48,99)
Figure 1-7: Map of Annual Average PM2.5 in the 2028 Baseline
247
80 159 239 318
Min = 1.548 at (127,138), Max = 85.661 at (164,15)
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Figure 1-8: Map of Change in Apr-September MDA8 Ozone (ppb): 2028 Option 3 - Baseline
159 239 318
Min = -0.088 at (235,57), Max = 0.062 at (287,150)
Figure 1-9: Map of Change in Apr-September MDA8 Ozone (ppb): 2030 Option 3 - Baseline
159 239 313
Min = -0.130 at (265,50), Max = 0.048 at (278,81)
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Figure 1-10: Map of Change in Apr-September MDA8 Ozone (ppb): 2035 Option 3 - Baseline
247
159 239 318
Min = -0.172 at (310,131), Max = 0.061 at (174,204)
Figure 1-11: Map of Change in Apr-September MDA8 Ozone (ppb): 2040 Option 3 - Baseline
n
159 239 318
Min = -0.130 at (287,130), Max = 0.080 at (219,121)
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Figure 1-14: Map of Change in Annual Mean PM2.5 (ng/m3): 2028 Option 3 - Baseline
1 80 159 239 318 397
Min = -0.032 at (325,33), Max = 5.26E-3 at (262,112)
Figure 1-15: Map of Change in Annual Mean PM2.5 (ug/m3): 2030 Option 3 - Baseline
1 80 153 239 318 397
Min = -0.036 at (325,74), Max = 8.14E-3 at (354,136)
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Figure 1-16: Map of Change in Annual Mean PM2.5 (ng/m3): 2035 Option 3 - Baseline
1 80 159 239 318 397
Min = -0.021 at (325,33), Max = 4.99E-3 at (175,201)
Figure 1-17: Map of Change in Annual Mean PM2.5 (|ig/m3): 2040 Option 3 - Baseline
1 80 159 239 318 397
Min = -0.014 at (268,124), Max = 5.46E-3 at (332,98)
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Figure 1-18: Map of Change in Annual Mean PM2.5 (ng/m3): 2045 Option 3 - Baseline
1 80 159 239 318 397
Min = -0.050 at (316,155), Max = 2.43E-3 at (171,204)
Figure 1-19: Map of Change in Annual Mean PM2.5 (ug/m3): 2050 Option 3 - Baseline
1 80 159 239 318 397
Min = -0.032 at (308,134), Max = 1.56E-3 at (175,201)
J.5 Uncertainties and Limitations of the Air Quality Methodology
One limitation of the scaling methodology for creating ozone and PM2 5 surfaces associated with the baseline
or Option 3 scenarios described above is that the methodology treats air quality changes from the tagged
sources as linear and additive. It therefore does not account for nonlinear atmospheric chemistry and does not
account for interactions between emissions of different pollutants and between emissions from different
tagged sources. The method applied in this analysis is consistent with how air quality estimations have been
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made in several prior regulatory analyses (U.S. EPA, 2012, 2019h, 2020d). We note that air quality is
calculated in the same manner for the baseline and for Option 3, so any uncertainties associated with these
assumptions is propagated through results for both the baseline and Option 3 scenarios in the same manner. In
addition, emissions changes between baseline and Option 3 are relatively small compared to modeled 2026
emissions that form the basis of the source apportionment approach described in this appendix. Previous
studies have shown that air pollutant concentrations generally respond linearly to small emissions changes of
up to 30 percent (D. Cohan & Napelenok, 2011;D. S. Cohan et al., 2005; Dunker et al., 2002; Koo etal.,
2007; Napelenok et al., 2006; Zavala et al., 2009). A second limitation is that the source apportionment
contributions are informed by the spatial and temporal distribution of the emissions from each source tag as
they occur in the 2026 modeled case. Thus, the contribution modeling results do not allow us to consider the
effects of any changes to spatial distribution of EGU emissions within a state between the 2026 modeled case
and the baseline and Option 3 scenarios analyzed in this RIA. Finally, the 2026 CAMx-modeled
concentrations themselves have some uncertainty. While all models have some level of inherent uncertainty in
their formulation and inputs, the base-year 2016 model outputs have been evaluated against ambient
measurements and have been shown to adequately reproduce spatially and temporally varying concentrations
(U.S. EPA, 2022a, 2022b).
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